WorldWideScience

Sample records for satellite cloud imagery

  1. Automatic Mosaicking of Satellite Imagery Considering the Clouds

    Science.gov (United States)

    Kang, Yifei; Pan, Li; Chen, Qi; Zhang, Tong; Zhang, Shasha; Liu, Zhang

    2016-06-01

    With the rapid development of high resolution remote sensing for earth observation technology, satellite imagery is widely used in the fields of resource investigation, environment protection, and agricultural research. Image mosaicking is an important part of satellite imagery production. However, the existence of clouds leads to lots of disadvantages for automatic image mosaicking, mainly in two aspects: 1) Image blurring may be caused during the process of image dodging, 2) Cloudy areas may be passed through by automatically generated seamlines. To address these problems, an automatic mosaicking method is proposed for cloudy satellite imagery in this paper. Firstly, modified Otsu thresholding and morphological processing are employed to extract cloudy areas and obtain the percentage of cloud cover. Then, cloud detection results are used to optimize the process of dodging and mosaicking. Thus, the mosaic image can be combined with more clear-sky areas instead of cloudy areas. Besides, clear-sky areas will be clear and distortionless. The Chinese GF-1 wide-field-of-view orthoimages are employed as experimental data. The performance of the proposed approach is evaluated in four aspects: the effect of cloud detection, the sharpness of clear-sky areas, the rationality of seamlines and efficiency. The evaluation results demonstrated that the mosaic image obtained by our method has fewer clouds, better internal color consistency and better visual clarity compared with that obtained by traditional method. The time consumed by the proposed method for 17 scenes of GF-1 orthoimages is within 4 hours on a desktop computer. The efficiency can meet the general production requirements for massive satellite imagery.

  2. Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

    Science.gov (United States)

    Logan, T. L.; Huning, J. R.; Glackin, D. L.

    1983-01-01

    The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

  3. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

    Science.gov (United States)

    Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

    2016-12-16

    Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

  4. Landsat 7 ETM/1G satellite imagery - Hawaiian Islands cloud-free mosaics

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Cloud-free Landsat satellite imagery mosaics of the islands of the main 8 Hawaiian Islands (Hawaii, Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau)....

  5. Landsat 7 ETM/1G satellite imagery - Hawaiian Islands cloud-free mosaics

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Cloud-free Landsat satellite imagery mosaics of the islands of the main 8 Hawaiian Islands (Hawaii, Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau). Landsat...

  6. Physical effect of cloud seeding revealed by NOAA satellite imagery

    Institute of Scientific and Technical Information of China (English)

    YU Xing; DAI Jin; LEI Hengchi; XU Xiaohong; FAN Peng; CHEN Zhengqi; DUAN Changhui; WANG Yong

    2005-01-01

    From 0615 to 0749 UTC, 14 March 2000, a precipitation enhancement operation with AgI using an aircraft was conducted at the middle part of Shaanxi Province, China. 80 min after cloud seeding (0735 UTC), NOAA-14 satellite data showed a vivid zigzag cloud track on the satellite image. Its length is 301 km, and its average and maximum width are 8.3 and 11 km. The cloud track is very similar in shape with, but different in position and width from that of cloud seeding line. In order to determine that the cloud track is indeed caused by cloud seeding, a three-dimensional numerical model of transport and diffusion of seeding material is used to simulate the shape of seeding material concentration distribution, the turning points, width and length of seeding line. The simulated results are compared with the features of cloud track at 0735 UTC. Every segment of the cloud track is consistent with the transport and diffusion of every segment of seeding line. The transport position, length, width and the variation trend of seeding line agree with those of cloud track. All suggest that the cloud track is the direct physical reflection of cloud seeding effect on the cloud top, which can respond to the transport and diffusion of seeding material. For this study case, the main effecting duration for every segment of seeding line is from 20 to 80 min, the time for each segment of seeding line diffusing to the maximum width is from about 50 to 70 min. This time is obtained from the appearing and disappearing time, width variation of the cloud track segments and simulated results. Also, the comparisons demonstrate that the numerical model of transport and diffusion can simulate the main characteristics of transport and diffusion of seeding material, and the simulating results are sound and trustworthy.

  7. Cloud detection method for Chinese moderate high resolution satellite imagery (Conference Presentation)

    Science.gov (United States)

    Zhong, Bo; Chen, Wuhan; Wu, Shanlong; Liu, Qinhuo

    2016-10-01

    Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors don't have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called OCM (Object oriented Cloud and cloud-shadow Matching method) is presented in this paper. It firstly modified the Automatic Cloud Cover Assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold setting produces different cloud map. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Secondly, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Thirdly, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. OCM method was tested using almost 200 HJ-1/CCD images across China and the overall accuracy of cloud detection is close to 90%.

  8. Do clouds save the great barrier reef? satellite imagery elucidates the cloud-SST relationship at the local scale.

    Directory of Open Access Journals (Sweden)

    Susannah M Leahy

    Full Text Available Evidence of global climate change and rising sea surface temperatures (SSTs is now well documented in the scientific literature. With corals already living close to their thermal maxima, increases in SSTs are of great concern for the survival of coral reefs. Cloud feedback processes may have the potential to constrain SSTs, serving to enforce an "ocean thermostat" and promoting the survival of coral reefs. In this study, it was hypothesized that cloud cover can affect summer SSTs in the tropics. Detailed direct and lagged relationships between cloud cover and SST across the central Great Barrier Reef (GBR shelf were investigated using data from satellite imagery and in situ temperature and light loggers during two relatively hot summers (2005 and 2006 and two relatively cool summers (2007 and 2008. Across all study summers and shelf positions, SSTs exhibited distinct drops during periods of high cloud cover, and conversely, SST increases during periods of low cloud cover, with a three-day temporal lag between a change in cloud cover and a subsequent change in SST. Cloud cover alone was responsible for up to 32.1% of the variation in SSTs three days later. The relationship was strongest in both El Niño (2005 and La Niña (2008 study summers and at the inner-shelf position in those summers. SST effects on subsequent cloud cover were weaker and more variable among study summers, with rising SSTs explaining up to 21.6% of the increase in cloud cover three days later. This work quantifies the often observed cloud cooling effect on coral reefs. It highlights the importance of incorporating local-scale processes into bleaching forecasting models, and encourages the use of remote sensing imagery to value-add to coral bleaching field studies and to more accurately predict risks to coral reefs.

  9. Do clouds save the great barrier reef? satellite imagery elucidates the cloud-SST relationship at the local scale.

    Science.gov (United States)

    Leahy, Susannah M; Kingsford, Michael J; Steinberg, Craig R

    2013-01-01

    Evidence of global climate change and rising sea surface temperatures (SSTs) is now well documented in the scientific literature. With corals already living close to their thermal maxima, increases in SSTs are of great concern for the survival of coral reefs. Cloud feedback processes may have the potential to constrain SSTs, serving to enforce an "ocean thermostat" and promoting the survival of coral reefs. In this study, it was hypothesized that cloud cover can affect summer SSTs in the tropics. Detailed direct and lagged relationships between cloud cover and SST across the central Great Barrier Reef (GBR) shelf were investigated using data from satellite imagery and in situ temperature and light loggers during two relatively hot summers (2005 and 2006) and two relatively cool summers (2007 and 2008). Across all study summers and shelf positions, SSTs exhibited distinct drops during periods of high cloud cover, and conversely, SST increases during periods of low cloud cover, with a three-day temporal lag between a change in cloud cover and a subsequent change in SST. Cloud cover alone was responsible for up to 32.1% of the variation in SSTs three days later. The relationship was strongest in both El Niño (2005) and La Niña (2008) study summers and at the inner-shelf position in those summers. SST effects on subsequent cloud cover were weaker and more variable among study summers, with rising SSTs explaining up to 21.6% of the increase in cloud cover three days later. This work quantifies the often observed cloud cooling effect on coral reefs. It highlights the importance of incorporating local-scale processes into bleaching forecasting models, and encourages the use of remote sensing imagery to value-add to coral bleaching field studies and to more accurately predict risks to coral reefs.

  10. Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering

    Science.gov (United States)

    Berendes, Todd A.; Mecikalski, John R.; MacKenzie, Wayne M.; Bedka, Kristopher M.; Nair, U. S.

    2008-10-01

    This paper describes a statistical clustering approach toward the classification of cloud types within meteorological satellite imagery, specifically, visible and infrared data. The method is based on the Standard Deviation Limited Adaptive Clustering (SDLAC) procedure, which has been used to classify a variety of features within both polar orbiting and geostationary imagery, including land cover, volcanic ash, dust, and clouds of various types. In this study, the focus is on classifying cumulus clouds of various types (e.g., "fair weather, "towering, and newly glaciated cumulus, in addition to cumulonimbus). The SDLAC algorithm is demonstrated by showing examples using Geostationary Operational Environmental Satellite (GOES) 12, Meteosat Second Generation's (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and the Moderate Resolution Infrared Spectrometer (MODIS). Results indicate that the method performs well, classifying cumulus similarly between MODIS, SEVIRI, and GOES, despite the obvious channel and resolution differences between these three sensors. The SDLAC methodology has been used in several research activities related to convective weather forecasting, which offers some proof of concept for its value.

  11. The Matsu Wheel: A Cloud-based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

    OpenAIRE

    Patterson, Maria T.; Anderson, Nikolas; Bennett, Collin; Bruggemann, Jacob; Grossman, Robert; Handy, Matthew; Ly, Vuong; Mandl, Dan; Pederson, Shane; Pivarski, Jim; Powell, Ray; Spring, Jonathan; Wells, Walt

    2016-01-01

    Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery. A particular focus is the development of applications for detecting fires and floods to help support natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStac...

  12. Feature extraction and classification of clouds in high resolution panchromatic satellite imagery

    Science.gov (United States)

    Sharghi, Elan

    The development of sophisticated remote sensing sensors is rapidly increasing, and the vast amount of satellite imagery collected is too much to be analyzed manually by a human image analyst. It has become necessary for a tool to be developed to automate the job of an image analyst. This tool would need to intelligently detect and classify objects of interest through computer vision algorithms. Existing software called the Rapid Image Exploitation Resource (RAPIER®) was designed by engineers at Space and Naval Warfare Systems Center Pacific (SSC PAC) to perform exactly this function. This software automatically searches for anomalies in the ocean and reports the detections as a possible ship object. However, if the image contains a high percentage of cloud coverage, a high number of false positives are triggered by the clouds. The focus of this thesis is to explore various feature extraction and classification methods to accurately distinguish clouds from ship objects. An examination of a texture analysis method, line detection using the Hough transform, and edge detection using wavelets are explored as possible feature extraction methods. The features are then supplied to a K-Nearest Neighbors (KNN) or Support Vector Machine (SVM) classifier. Parameter options for these classifiers are explored and the optimal parameters are determined.

  13. The Matsu Wheel: A Cloud-based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

    CERN Document Server

    Patterson, Maria T; Bennett, Collin; Bruggemann, Jacob; Grossman, Robert; Handy, Matthew; Ly, Vuong; Mandl, Dan; Pederson, Shane; Pivarski, Jim; Powell, Ray; Spring, Jonathan; Wells, Walt

    2016-01-01

    Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery. A particular focus is the development of applications for detecting fires and floods to help support natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce, Storm and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most of the data, in a database or data repository. We also descri...

  14. Verification of sectoral cloud motion based direct normal irradiance nowcasting from satellite imagery

    Science.gov (United States)

    Schroedter-Homscheidt, Marion; Gesell, Gerhard

    2016-05-01

    The successful integration of solar electricity from photovoltaics or concentrating solar power plants into the existing electricity supply requires an electricity production forecast for 48 hours, while any improved surface irradiance forecast over the next upcoming hours is relevant for an optimized operation of the power plant. While numerical weather prediction has been widely assessed and is in commercial use, the short-term nowcasting is still a major field of development. European Commission's FP7 DNICast project is especially focusing on this task and this paper reports about parts of DNICast results. A nowcasting scheme based on Meteosat Second Generation cloud imagery and cloud movement tracking has been developed for Southern Spain as part of a solar production forecasting tool (CSP-FoSyS). It avoids the well-known, but not really satisfying standard cloud motion vector approach by using a sectoral approach and asking the question at which time any cloud structure will affect the power plant. It distinguishes between thin cirrus clouds and other clouds, which typically occur in different heights in the atmosphere and move in different directions. Also, their optical properties are very different - especially for the calculation of direct normal irradiances as required by concentrating solar power plants. Results for Southern Spain show a positive impact of up to 8 hours depending of the time of the day and a RMSD reduction of up to 10% in hourly DNI irradiation compared to day ahead forecasts. This paper presents the verification of this scheme at other locations in Europe and Northern Africa (BSRN and EnerMENA stations) with different cloud conditions. Especially for Jordan and Tunisia as the most relevant countries for CSP in this station list, we also find a positive impact of up to 8 hours.

  15. The Matsu Wheel: A Cloud-Based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

    Science.gov (United States)

    Patterson, Maria T.; Anderson, Nicholas; Bennett, Collin; Bruggemann, Jacob; Grossman, Robert L.; Handy, Matthew; Ly, Vuong; Mandl, Daniel J.; Pederson, Shane; Pivarski, James; hide

    2016-01-01

    Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for cloud-based processing of Earth satellite imagery with practical applications to aid in natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework allows batches of analytics, scanning for new data, to be applied to data as it flows in. In the Matsu Wheel, the data only need to be accessed and preprocessed once, regardless of the number or types of analytics, which can easily be slotted into the existing framework. The Matsu Wheel system provides a significantly more efficient use of computational resources over alternative methods when the data are large, have high-volume throughput, may require heavy preprocessing, and are typically used for many types of analysis. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The result products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for

  16. 7 CFR 611.22 - Availability of satellite imagery.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Availability of satellite imagery. 611.22 Section 611... § 611.22 Availability of satellite imagery. Cloud-free maps of the United States based on imagery received from a satellite are prepared and released to the pubic by NRCS. The maps offer the first image...

  17. On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

    Directory of Open Access Journals (Sweden)

    K.-G. Karlsson

    2013-02-01

    Full Text Available A method for detailed evaluation of a new satellite-derived global 28-yr cloud and radiation climatology (Climate Monitoring SAF Cloud, Albedo and Radiation dataset from AVHRR data, named CLARA-A1 from polar orbiting NOAA and Metop satellites is presented. The method combines 1 km and 5 km resolution cloud datasets from the CALIPSO-CALIOP cloud lidar for estimating cloud detection limitations and the accuracy of cloud top height estimations.

    Cloud detection is shown to work efficiently for clouds with optical thicknesses above 0.30 except for at twilight conditions when this value increases to 0.45. Some misclassifications generating erroneous clouds over land surfaces in semi-arid regions in the sub-tropical and tropical regions are revealed. In addition, a substantial fraction of all clouds remains undetected in the Polar regions during the polar winter season due to the lack of or an inverted temperature contrast between Earth surfaces and clouds.

    Subsequent cloud top height evaluation took into account the derived information about the cloud detection limits. It was shown that this has fundamental importance for the achieved results. An overall bias of −274 m was achieved compared to a bias of −2762 m if no measures were taken to compensate for cloud detection limitations. Despite this improvement it was concluded that high-level clouds still suffer from substantial height underestimations while the opposite is true for low-level (boundary layer clouds.

    The validation method and the specifically collected satellite dataset with optimal matching in time and space are suggested for a wider use in the future for evaluation of other cloud retrieval methods based on passive satellite imagery.

  18. Normalization of satellite imagery

    Science.gov (United States)

    Kim, Hongsuk H.; Elman, Gregory C.

    1990-01-01

    Sets of Thematic Mapper (TM) imagery taken over the Washington, DC metropolitan area during the months of November, March and May were converted into a form of ground reflectance imagery. This conversion was accomplished by adjusting the incident sunlight and view angles and by applying a pixel-by-pixel correction for atmospheric effects. Seasonal color changes of the area can be better observed when such normalization is applied to space imagery taken in time series. In normalized imagery, the grey scale depicts variations in surface reflectance and tonal signature of multi-band color imagery can be directly interpreted for quantitative information of the target.

  19. Thematic mapping from satellite imagery

    CERN Document Server

    Denègre, J

    2013-01-01

    Thematic Mapping from Satellite Imagery: A Guidebook discusses methods in producing maps using satellite images. The book is comprised of five chapters; each chapter covers one stage of the process. Chapter 1 tackles the satellite remote sensing imaging and its cartographic significance. Chapter 2 discusses the production processes for extracting information from satellite data. The next chapter covers the methods for combining satellite-derived information with that obtained from conventional sources. Chapter 4 deals with design and semiology for cartographic representation, and Chapter 5 pre

  20. Cloud-based Web Services for Near-Real-Time Web access to NPP Satellite Imagery and other Data

    Science.gov (United States)

    Evans, J. D.; Valente, E. G.

    2010-12-01

    We are building a scalable, cloud computing-based infrastructure for Web access to near-real-time data products synthesized from the U.S. National Polar-Orbiting Environmental Satellite System (NPOESS) Preparatory Project (NPP) and other geospatial and meteorological data. Given recent and ongoing changes in the the NPP and NPOESS programs (now Joint Polar Satellite System), the need for timely delivery of NPP data is urgent. We propose an alternative to a traditional, centralized ground segment, using distributed Direct Broadcast facilities linked to industry-standard Web services by a streamlined processing chain running in a scalable cloud computing environment. Our processing chain, currently implemented on Amazon.com's Elastic Compute Cloud (EC2), retrieves raw data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and synthesizes data products such as Sea-Surface Temperature, Vegetation Indices, etc. The cloud computing approach lets us grow and shrink computing resources to meet large and rapid fluctuations (twice daily) in both end-user demand and data availability from polar-orbiting sensors. Early prototypes have delivered various data products to end-users with latencies between 6 and 32 minutes. We have begun to replicate machine instances in the cloud, so as to reduce latency and maintain near-real time data access regardless of increased data input rates or user demand -- all at quite moderate monthly costs. Our service-based approach (in which users invoke software processes on a Web-accessible server) facilitates access into datasets of arbitrary size and resolution, and allows users to request and receive tailored and composite (e.g., false-color multiband) products on demand. To facilitate broad impact and adoption of our technology, we have emphasized open, industry-standard software interfaces and open source software. Through our work, we envision the widespread establishment of similar, derived, or interoperable systems for

  1. ERTS-A satellite imagery

    Science.gov (United States)

    Colvocoresses, Alden P.

    1970-01-01

    The first satellite designed to survey the Earth's resources is scheduled to be launched in 1972. This satellite, known as ERTS-A, will telemeter frames of imagery each covering 100-nautical-mile squares of the Earth. Except for the internal anomalies in the sensor system, the imagery, after being properly scaled, rectified, and controlled, may be considered an orthographic view of the Earth and used as a planimetric photomap. The accuracy of this photomap will be limited, principally by the geometric fidelity of the sensor system rather than by external effects, such as relief displacement, which restrict the direct cartographic use of the conventional aerial photograph. ERST-A is not designed as a topographic mapping satellite but does have real potential' for thematic mapping particularly in areas now covered by topographic maps.

  2. User Validation of VIIRS Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Don Hillger

    2015-12-01

    Full Text Available Visible/Infrared Imaging Radiometer Suite (VIIRS Imagery from the Suomi National Polar-orbiting Partnership (S-NPP satellite is the finest spatial resolution (375 m multi-spectral imagery of any operational meteorological satellite to date. The Imagery environmental data record (EDR has been designated as a Key Performance Parameter (KPP for VIIRS, meaning that its performance is vital to the success of a series of Joint Polar Satellite System (JPSS satellites that will carry this instrument. Because VIIRS covers the high-latitude and Polar Regions especially well via overlapping swaths from adjacent orbits, the Alaska theatre in particular benefits from VIIRS more than lower-latitude regions. While there are no requirements that specifically address the quality of the EDR Imagery aside from the VIIRS SDR performance requirements, the value of VIIRS Imagery to operational users is an important consideration in the Cal/Val process. As such, engaging a wide diversity of users constitutes a vital part of the Imagery validation strategy. The best possible image quality is of utmost importance. This paper summarizes the Imagery Cal/Val Team’s quality assessment in this context. Since users are a vital component to the validation of VIIRS Imagery, specific examples of VIIRS imagery applied to operational needs are presented as an integral part of the post-checkout Imagery validation.

  3. Automatic Cloud and Shadow Detection in Optical Satellite Imagery Without Using Thermal Bands—Application to Suomi NPP VIIRS Images over Fennoscandia

    Directory of Open Access Journals (Sweden)

    Eija Parmes

    2017-08-01

    Full Text Available In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite’s VIIRS (Visible Infrared Imaging Radiometer Suite satellite images. The proposed cloud and shadow detection method has two distinct features when compared to many other methods. First, the method does not use the thermal bands and can thus be applied to other sensors which do not contain thermal channels, such as Sentinel-2 data. Secondly, the method uses the ratio between blue and green reflectance to detect shadows. Seven hundred and forty-seven VIIRS images over Fennoscandia from August 2014 to April 2016 were processed to train and develop the method. Twenty four points from every tenth of the images were used in accuracy assessment. These 1752 points were interpreted visually to cloud, cloud shadow and clear classes, then compared to the output of the cloud and shadow detection. The comparison on VIIRS images showed 94.2% correct detection rates and 11.1% false alarms for clouds, and respectively 36.1% and 82.7% for shadows. The results on cloud detection were similar to state-of-the-art methods. Shadows showed correctly on the northern edge of the clouds, but many shadows were wrongly assigned to other classes in some cases (e.g., to water class on lake and forest boundary, or with shadows over cloud. This may be due to the low spatial resolution of VIIRS images, where shadows are only a few pixels wide and contain lots of mixed pixels.

  4. Proposed Use of the NASA Ames Nebula Cloud Computing Platform for Numerical Weather Prediction and the Distribution of High Resolution Satellite Imagery

    Science.gov (United States)

    Limaye, Ashutosh S.; Molthan, Andrew L.; Srikishen, Jayanthi

    2010-01-01

    The development of the Nebula Cloud Computing Platform at NASA Ames Research Center provides an open-source solution for the deployment of scalable computing and storage capabilities relevant to the execution of real-time weather forecasts and the distribution of high resolution satellite data to the operational weather community. Two projects at Marshall Space Flight Center may benefit from use of the Nebula system. The NASA Short-term Prediction Research and Transition (SPoRT) Center facilitates the use of unique NASA satellite data and research capabilities in the operational weather community by providing datasets relevant to numerical weather prediction, and satellite data sets useful in weather analysis. SERVIR provides satellite data products for decision support, emphasizing environmental threats such as wildfires, floods, landslides, and other hazards, with interests in numerical weather prediction in support of disaster response. The Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS) has been configured for Nebula cloud computing use via the creation of a disk image and deployment of repeated instances. Given the available infrastructure within Nebula and the "infrastructure as a service" concept, the system appears well-suited for the rapid deployment of additional forecast models over different domains, in response to real-time research applications or disaster response. Future investigations into Nebula capabilities will focus on the development of a web mapping server and load balancing configuration to support the distribution of high resolution satellite data sets to users within the National Weather Service and international partners of SERVIR.

  5. Motion/imagery secure cloud enterprise architecture analysis

    Science.gov (United States)

    DeLay, John L.

    2012-06-01

    Cloud computing with storage virtualization and new service-oriented architectures brings a new perspective to the aspect of a distributed motion imagery and persistent surveillance enterprise. Our existing research is focused mainly on content management, distributed analytics, WAN distributed cloud networking performance issues of cloud based technologies. The potential of leveraging cloud based technologies for hosting motion imagery, imagery and analytics workflows for DOD and security applications is relatively unexplored. This paper will examine technologies for managing, storing, processing and disseminating motion imagery and imagery within a distributed network environment. Finally, we propose areas for future research in the area of distributed cloud content management enterprises.

  6. Automatic detection of ship tracks in ATSR-2 satellite imagery

    Directory of Open Access Journals (Sweden)

    E. Campmany

    2009-03-01

    Full Text Available Ships modify cloud microphysics by adding cloud condensation nuclei (CCN to a developing or existing cloud. These create lines of larger reflectance in cloud fields that are observed in satellite imagery. An algorithm has been developed to automate the detection of ship tracks in Along Track Scanning Radiometer 2 (ATSR-2 imagery. The scheme has been integrated into the Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE processing chain. The algorithm firstly identifies intensity ridgelets in clouds which have the potential to be part of a ship track. This identification is done by comparing each pixel with its surrounding ones. If the intensity of three adjacent pixels is greater than the intensity of their neighbours, then it is classified as a ridgelet. These ridgelets are then connected together, according to a set of connectivity rules, to form tracks which are classed as ship tracks if they are long enough. The algorithm has been applied to two years of ATSR-2 data. Ship tracks are most frequently seen off the west coast of California, and the Atlantic coast of both West Africa and South-Western Europe. The global distribution of ship tracks shows strong seasonality, little inter-annual variability and a similar spatial pattern to the distribution of ship emissions.

  7. International Satellite Cloud Climatology Project (ISCCP)

    Data.gov (United States)

    National Aeronautics and Space Administration — International Satellite Cloud Climatology Project (ISCCP) focuses on the distribution and variation of cloud radiative properties to improve the understanding of the...

  8. Commercial satellite imagery comes of age

    Energy Technology Data Exchange (ETDEWEB)

    Jasani, Bhupendra [King' s College, London (United Kingdom). Dept. of War Studies

    2003-05-01

    In the absence of on-site inspections until recently, in the Seventh Quarterly Report to the United Nations Security Council, the Executive Director of the UN Monitoring, Verification and Inspection Commission (UNMOVIC) stated that the imagery acquired over Iraq, which UNMOVIC is receiving through a commercial satellite supplier is continuously, being analysed. Not only this but the report hopes that 'Member States will continue to provide it with imagery from their own assets as such assistance provided to date has proven very valuable' Even after the on-site inspections have begun, satellite imagery over Iraq continues, for example, to be used for inspection planning purposes. This indicates that commercial satellite imagery might finally be used on a routine basis. As the findings by the UNMOVIC are not made public, this paper examines a number of images acquired over Baghdad from different commercial satellite sources and at different times to determine what could be concluded about Iraq's nuclear and chemical weapon activities in the region.

  9. Detection of ship tracks in ATSR2 satellite imagery

    Directory of Open Access Journals (Sweden)

    E. Campmany

    2008-08-01

    Full Text Available Ships modify cloud microphysics by adding cloud condensation nuclei (CCN to a developing or existing cloud. These create lines of larger reflectance in cloud fields that are observed in satellite imagery. Ship tracks are most frequently seen off the west coast of California, and the Atlantic coast of both west Africa and south-western Europe. In order to automate their detection within the Along Track Scanning Radiometer 2 (ATSR2 data set an algorithm was developed and integrated with the Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE processing chain. The algorithm firstly identifies intensity ridgelets in clouds which have the potential to be part of a ship track. This identification is done by comparing each pixel with its surrounding ones. If the intensity of three adjacent pixels is greater than the intensity of its neighbours, then it is classified as a ridgelet. These ridgelets are then connected together, according to a set of connectivity rules, to form tracks which are classed as ship tracks if they are long enough. The algorithm has been applied to two years of ATSR2 data. A month of results have been compared with other satellite datasets to validate the algorithm. There is a high ratio of false detections. Nevertheless the global distribution of ship tracks shows a similar pattern to the ship emissions distribution.

  10. Vertical Accuracy Comparison of Digital Elevation Model from LIDAR and Multitemporal Satellite Imagery

    Science.gov (United States)

    Octariady, J.; Hikmat, A.; Widyaningrum, E.; Mayasari, R.; Fajari, M. K.

    2017-05-01

    Digital elevation model serves to illustrate the appearance of the earth's surface. DEM can be produced from a wide variety of data sources including from radar data, LiDAR data, and stereo satellite imagery. Making the LiDAR DEM conducted using point cloud data from LiDAR sensor. Making a DEM from stereo satellite imagery can be done using same temporal or multitemporal stereo satellite imagery. How much the accuracy of DEM generated from multitemporal stereo stellite imagery and LiDAR data is not known with certainty. The study was conducted using LiDAR DEM data and multitemporal stereo satellite imagery DEM. Multitemporal stereo satellite imagery generated semi-automatically by using 3 scene stereo satellite imagery with acquisition 2013-2014. The high value given each of DEM serve as the basis for calculating high accuracy DEM respectively. The results showed the high value differences in the fraction of the meter between LiDAR DEM and multitemporal stereo satellite imagery DEM.

  11. A review study of cloud classification using satellite imagery%基于卫星云图的云分类研究

    Institute of Scientific and Technical Information of China (English)

    刘扬; 王彬; 韩雷

    2011-01-01

    Radiation features and the distribution of kinds of clouds have significant influences on atmospheric radiation balance and climate. Therefore, how to classify clouds in satellite images is an important application as well as study focus in remote sensing field. The article introduces not only some methods of feature selection and extraction, but also unsupervised,supervised and neural network classifiers, which always used in cloud classification studies. Then it reviews studies of cloud classification over the past decades. Evaluation methods are discussed briefly with classification results in some studies.%各种类型云的辐射特性以及分布情况,对大气收支平衡以及天气气候都有重大影响,对云进行正确分类是遥感领域的重要应用和研究热点.文章基于对卫星云图进行自动准确识别和分类研究的前提,通过介绍几种特征提取和选择方法,以及介绍无监督、有监督和神经网络3类云分类研究常用分类方法,对国内外近几十年来所做的卫星云图分类研究进行综述介绍.并简要介绍了云分类结果的评价方法,对分类研究的结果进行定性讨论.

  12. Intercomparison of satellite-derived cloud analyses for the Arctic Ocean in spring and summer

    Science.gov (United States)

    Mcguffie, K.; Barry, R. G.; Schweiger, A.; Newell, J.; Robinson, D. A.

    1988-01-01

    Several methods of deriving Arctic cloud information, primarily from satellite imagery, have been intercompared. The comparisons help in establishing what cloud information is most readily determined in polar regions from satellite data analysis. The analyses for spring-summer conditions show broad agreement, but subjective errors affecting some geographical areas and cloud types are apparent. The results suggest that visible and thermal infrared data may be insufficient for adequate cloud mapping over some Arctic surfaces.

  13. Crop classification using temporal stacks of multispectral satellite imagery

    Science.gov (United States)

    Moody, Daniela I.; Brumby, Steven P.; Chartrand, Rick; Keisler, Ryan; Longbotham, Nathan; Mertes, Carly; Skillman, Samuel W.; Warren, Michael S.

    2017-05-01

    The increase in performance, availability, and coverage of multispectral satellite sensor constellations has led to a drastic increase in data volume and data rate. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. The data analysis capability, however, has lagged behind storage and compute developments, and has traditionally focused on individual scene processing. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and can scale with the high-rate and dimensionality of imagery being collected. We investigate and compare the performance of pixel-level crop identification using tree-based classifiers and its dependence on both temporal and spectral features. Classification performance is assessed using as ground-truth Cropland Data Layer (CDL) crop masks generated by the US Department of Agriculture (USDA). The CDL maps contain 30m spatial resolution, pixel-level labels for around 200 categories of land cover, but are however only available post-growing season. The analysis focuses on McCook county in South Dakota and shows crop classification using a temporal stack of Landsat 8 (L8) imagery over the growing season, from April through October. Specifically, we consider the temporal L8 stack depth, as well as different normalized band difference indices, and evaluate their contribution to crop identification. We also show an extension of our algorithm to map corn and soy crops in the state of Mato Grosso, Brazil.

  14. Thunderstorm-associated cloud motions as computed from 5-minute SMS pictures. [Synchronous Meteorological Satellite

    Science.gov (United States)

    Tecson, J. J.; Umenhofer, T. A.; Fujita, T. T.

    1977-01-01

    The five-minute rapid-scan imagery from the Synchronous Meteorological Satellite is employed to study cloud motions associated with the Omaha tornado of May 6, 1975. Cloud-motion vectors derived from automated and man-machine interactive systems provide an account of the mesoscale phenomena. In addition to the geostationary satellite data, aerial photography obtained during a cloud-truth mission is used in the severe storm investigation. For tracking overland cumuli with short half-lives, a three-minute scan interval appears necessary for the satellite imagery.

  15. Photogrammetric Processing Using ZY-3 Satellite Imagery

    Science.gov (United States)

    Kornus, W.; Magariños, A.; Pla, M.; Soler, E.; Perez, F.

    2015-03-01

    This paper evaluates the stereoscopic capacities of the Chinese sensor ZiYuan-3 (ZY-3) for the generation of photogrammetric products. The satellite was launched on January 9, 2012 and carries three high-resolution panchromatic cameras viewing in forward (22º), nadir (0º) and backward direction (-22º) and an infrared multi-spectral scanner (IRMSS), which is slightly looking forward (6º). The ground sampling distance (GSD) is 2.1m for the nadir image, 3.5m for the two oblique stereo images and 5.8m for the multispectral image. The evaluated ZY-3 imagery consists of a full set of threefold-stereo and a multi-spectral image covering an area of ca. 50km x 50km north-west of Barcelona, Spain. The complete photogrammetric processing chain was executed including image orientation, the generation of a digital surface model (DSM), radiometric image correction, pansharpening, orthoimage generation and digital stereo plotting. All 4 images are oriented by estimating affine transformation parameters between observed and nominal RPC (rational polynomial coefficients) image positions of 17 ground control points (GCP) and a subsequent calculation of refined RPC. From 10 independent check points RMS errors of 2.2m, 2.0m and 2.7m in X, Y and H are obtained. Subsequently, a DSM of 5m grid spacing is generated fully automatically. A comparison with the Lidar data results in an overall DSM accuracy of approximately 3m. In moderate and flat terrain higher accuracies in the order of 2.5m and better are achieved. In a next step orthoimages from the high resolution nadir image and the multispectral image are generated using the refined RPC geometry and the DSM. After radiometric corrections a fused high resolution colour orthoimage with 2.1m pixel size is created using an adaptive HSL method. The pansharpen process is performed after the individual geocorrection due to the different viewing angles between the two images. In a detailed analysis of the colour orthoimage artifacts are

  16. An interactive system for analysis of global cloud imagery

    Science.gov (United States)

    Woodberry, Karen; Tanaka, Ken; Hendon, Harry; Salby, Murry

    1991-01-01

    Synoptic images of the global cloud pattern composited from six contemporaneous satellites provide an unprecedented view of the global cloud field. Having horizontal resolution of about 0.5 deg and temporal resolution of 3 h, the global cloud imagery (GCI) resolves most of the variability of organized convection, including several harmonics of the diurnal cycle. Although the GCI has these attractive features, the dense and 3D nature of that data make it a formidable volume of information to treat in a practical and efficient manner. An interactive image-analysis system (IAS) has been developed to investigate the space-time variability of global cloud behavior. In the IAS, data, hardware, and software are integrated into a single system providing a variety of space-time covariance analyses in a menu-driven format. Owing to its customized architecture and certain homogeneous properties of the GCI, the IAS calculates such quantities effectively. Many covariance statistics are derived from 3D data with interactive speed, allowing the user to interrogate the archive iteratively in a single session. The 3D nature of those analyses and the speed with which they are performed distinguish the IAS from conventional image processing of 2D data.

  17. High Resolution Imagery and Three-line Array Imagery Automatic Registration for China’s TH-1 Satellite Imagery

    OpenAIRE

    2014-01-01

    An automatic image registration method of high resolution (HR) imagery and three-line array imagery for China’s TH-1 mapping satellite is invented. The 2m resolution HR imagery is normalized to 5m resolution three-line array imagery firstly. Then using precise point prediction model (P3M) matching method, thousands of correspondent points can be matched. Based on these matched points, feature points collected on HR imagery can be converted onto three-line array imagery automatically. Conseque...

  18. The Three-Dimensional Spatial Structure of Cirrus Clouds Determined from Lidar Satellite Observations

    Science.gov (United States)

    Eloranta, E. W.; Wylie, D.; Wolf, W.

    1996-01-01

    Simultaneous imagery from the University of Wisconsin Volume Imaging Lidar (VIL) and meteorological satellites were used to quantify the spatial structure of cirrus clouds with 60 m resolution. This data was used to determine the spatial distributions of cloud base altitude, cloud top altitude, and mid-cloud altitude. Two dimensional auto-correlation functions describing the mean shape of cirrus clouds were computed. Because cirrus clouds seldom have distinct edges, these correlation functions are derived as a function of a threshold value which defines the cloud edge.

  19. Quantitative Cloud Analysis using Meteorological Satellites

    NARCIS (Netherlands)

    Feijt, A.J.

    2000-01-01

    This thesis is about observations of clouds from satellite and ground based instruments. The aim is to reconstruct the three dimensional cloud distributions. This information is used both in climate research and operational meteorological applications. In climate research, cloud observations provide

  20. Radiometric Correction of Multitemporal Satellite Imagery

    Directory of Open Access Journals (Sweden)

    S. G. Biday,

    2010-01-01

    Full Text Available Problem statement: Repeated observation of a given area over time yields potential for many forms of change detection analysis. These repeated observations are confounded in terms of radiometric consistency due to changes in sensor calibration over time, differences in illumination, observation angles and variation in atmospheric effects. Also major problem with satellite images is that regions below clouds are not covered by sensor. Cloud detection, removal and data prediction in cloudy region is essential for image interpretation. Approach: This study demonstrated applicability of empirical relative radiometric normalization methods to a set of multitemporal cloudy images acquired by Resourcesat-1 LISS III sensor. Objective of this study was to detect and remove cloud cover and normalize an image radiometrically. Cloud detection was achieved by using Average Brightness Threshold (ABT algorithm. The detected cloud removed and replaced with data from another images of the same area. We proposed a new method in which cloudy pixels are replaced with predicted pixel values obtained by regression. After cloud removal, the proposed normalization method was applied to reduce the radiometric influence caused by non surface factors. This process identified landscape elements whose reflectance values are nearly constant over time, i.e., the subset of non-changing pixels are identified using frequency based correlation technique. Further, we proposed another method of radiometric correction in frequency domain, Pseudo-Invariant Feature regression and this process removed landscape elements such as vegetation whose reflectance values are not constant over time. It takes advantage of vegetation being typically high frequency area, can be removed by low pass filter. Results: The quality of radiometric normalization is statistically assessed by R2 value and Root Mean Square Error (RMSE between each pair of analogous band. Further we verified that difference

  1. Quantifying River Widths of North America from Satellite Imagery

    Science.gov (United States)

    Allen, G. H.; Pavelsky, T.; Miller, Z.

    2013-12-01

    River width is a fundamental predictor variable in many hydrologic, geomorphic, and biogeochemical models, yet current large-scale models rely on theoretical hydraulic geometry relationships that do not fully capture natural variability in river form. Here we present the first high-resolution dataset of long-term mean width of North American rivers wider than 30 m. The dataset contains 7.93 million georeferenced width measurements derived from Landsat TM and ETM+ imagery that were acquired when rivers were most likely to be at mean discharge. We built the dataset by developing an automated procedure that selects and downloads raw imagery, creates cloud-free normalized difference water index images, histogram balances and mosaics them together, and produces a water mask using a dynamic water-land threshold technique. We then visually inspected and corrected the mask for errors and used RivWidth software to calculate river width at each river centerline pixel. We validated our dataset using >1000 United States Geological Survey and Water Survey of Canada in situ gauge station measurements. Error analysis shows a robust relationship between the remotely sensed widths and in situ gauge measurements with an r 2 = 0.86 (Spearman's = 0.81) and a mean absolute error of 27.5 m. We find that North American river widths lie on logarithmic frequency curve with some notable exceptions at widths SWOT) satellite mission.

  2. Using satellite imagery for crime mapping in South Africa.

    CSIR Research Space (South Africa)

    Schmitz, Peter MU

    2002-12-01

    Full Text Available . Increasingly, technologies such as digital orthophotographs, high-resolution satellite imagery and the global positioning system (GPS) are being used for these areas to provide base mapping and application data for geographical information systems (GIS...

  3. Potentials of satellite imagery for monitoring arctic goose productivity

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This paper reports upon the exciting possibility that satellite imagery may now provide feasible means for grossly monitoring arctic habitat conditions in a timely...

  4. Andean terraced hills (a use of satellite imagery)

    CERN Document Server

    Sparavigna, Amelia Carolina

    2010-01-01

    The aim of this paper is in stimulating the use of satellite imagery, in particular the free service of Google Maps, to investigate the distribution of the agricultural technique of terraced hills in Andean countries, near Titicaca Lake. In fact, satellite maps can give a clear view of the overall surface modified by human work, being then a precious help for on-site archaeological researches and for historical analysis. Satellite imagery is also able to give the distribution of burial and worship places. The paper discusses some examples near the Titicaca Lake.

  5. Identifying true satellites of the Magellanic Clouds

    Science.gov (United States)

    Sales, Laura V.; Navarro, Julio F.; Kallivayalil, Nitya; Frenk, Carlos S.

    2017-02-01

    The hierarchical nature of ΛCDM suggests that the Magellanic Clouds must have been surrounded by a number of satellites before their infall into the Milky Way halo. Many of those satellites should still be in close proximity to the Clouds, but some could have dispersed ahead/behind the Clouds along their Galactic orbit. Either way, prior association with the Clouds constrains the present-day positions and velocities of candidate Magellanic satellites: they must lie close to the nearly polar orbital plane of the Magellanic Stream, and their distances and radial velocities must follow the latitude dependence expected for a tidal stream with the Clouds near pericentre. We use a cosmological numerical simulation of the disruption of a massive sub-halo in a Milky Way-sized ΛCDM halo to test whether any of the 20 dwarfs recently discovered in the Dark Energy Survey, the Survey of the MAgellanic Stellar History, Pan-STARRS, and ATLAS surveys are truly associated with the Clouds. Of the six systems with kinematic data, only Hor 1 has distance and radial velocities consistent with a Magellanic origin. Of the remaining dwarfs, six (Hor 2, Eri 3, Ret 3, Tuc 4, Tuc 5, and Phx 2) have positions and distances consistent with a Magellanic origin, but kinematic data are needed to substantiate that possibility. Conclusive evidence for association would require proper motions to constrain the orbital angular momentum direction, which, for true Magellanic satellites, must be similar to that of the Clouds. We use this result to predict radial velocities and proper motions for all new dwarfs, assuming that they were Magellanic satellites. Our results are relatively insensitive to the assumption of first or second pericentre for the Clouds.

  6. Harnessing Satellite Imageries in Feature Extraction Using Google Earth Pro

    Science.gov (United States)

    Fernandez, Sim Joseph; Milano, Alan

    2016-07-01

    Climate change has been a long-time concern worldwide. Impending flooding, for one, is among its unwanted consequences. The Phil-LiDAR 1 project of the Department of Science and Technology (DOST), Republic of the Philippines, has developed an early warning system in regards to flood hazards. The project utilizes the use of remote sensing technologies in determining the lives in probable dire danger by mapping and attributing building features using LiDAR dataset and satellite imageries. A free mapping software named Google Earth Pro (GEP) is used to load these satellite imageries as base maps. Geotagging of building features has been done so far with the use of handheld Global Positioning System (GPS). Alternatively, mapping and attribution of building features using GEP saves a substantial amount of resources such as manpower, time and budget. Accuracy-wise, geotagging by GEP is dependent on either the satellite imageries or orthophotograph images of half-meter resolution obtained during LiDAR acquisition and not on the GPS of three-meter accuracy. The attributed building features are overlain to the flood hazard map of Phil-LiDAR 1 in order to determine the exposed population. The building features as obtained from satellite imageries may not only be used in flood exposure assessment but may also be used in assessing other hazards and a number of other uses. Several other features may also be extracted from the satellite imageries.

  7. Mapping Urban Tree Canopy Cover Using Fused Airborne LIDAR and Satellite Imagery Data

    Science.gov (United States)

    Parmehr, Ebadat G.; Amati, Marco; Fraser, Clive S.

    2016-06-01

    Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.

  8. Cloud geometry from high-resolution airborne solar spectral imagery

    Science.gov (United States)

    Zinner, Tobias; Schwarz, Ulrich; Kölling, Tobias; Höppler, Lucas; Mayer, Bernhard

    2017-04-01

    The spatial distribution of clouds is the most fundamental cloud characteristic. Before successive methods can provide any additional microphysical insight, the cloud geometry has to be identified. The cloud spatial distribution itself has an important effect on the radiation budget and its variability over a cloudy scene and can this way feed back on cloud dynamics. In addition to the impact on the cloud radiative effect, orientation of the cloud surface has an decisive effect on remote sensing of microphysical parameters of inhomogeneous clouds with passive sensors. It is found that knowledge of cloud geometry significantly reduces retrieval uncertainties. With the latter motivation in mind, we will present the derivation of cloud geometry from passive observations of solar radiation reflected by clouds. observations collected during the German HALO aircraft campaigns ACRIDICON in Brazil 2014 for cloud sides as well as nadir observations during the North Atlantic NARVAL-2 and NAWDEX 2016 campaigns are used. Measurements of spectral radiation around the oxygen-A band from the hyperspectral imager specMACS as well as stereographic data collected by a video camera are used. In the spectral method distance between sensor and cloud is derived using the fact that an increase in absorption path length is reflected by a deepening of the oxygen absorption band around 762 nm. Sensitivity of the depth of this absorption band to other parameters like the surface albedo, aerosol content or cloud density (LWC or extinction) is investigated and the related uncertainty is quantified. For validation, results of the spectral method are compared to results from stereographic methods based on visible imagery collected at the same time.

  9. Identifying true satellites of the Magellanic Clouds

    CERN Document Server

    Sales, Laura V; Kallivayalil, Nitya; Frenk, Carlos S

    2016-01-01

    The hierarchical nature of LCDM suggests that the Magellanic Clouds must have been surrounded by a number of satellites before their infall into the Milky Way. Many of those satellites should still be in close proximity to the Clouds, but some could have dispersed ahead/behind the Clouds along their Galactic orbit. Either way, prior association with the Clouds results in strong restrictions on the present-day positions and velocities of candidate Magellanic satellites: they must lie close to the nearly-polar orbital plane of the Magellanic stream, and their distances and radial velocities must follow the latitude dependence expected for a tidal stream with the Clouds at pericenter. We use a cosmological numerical simulation of the disruption of a massive subhalo in a Milky Way-sized LCDM halo to test whether any of the 20 dwarfs recently-discovered in the DES, SMASH, Pan-STARRS, and ATLAS surveys are truly associated with the Clouds. Of the 6 systems with kinematic data, only Hydra II and Hor 1 have distances...

  10. On RPC Model of Satellite Imagery

    Institute of Scientific and Technical Information of China (English)

    ZHANG Guo; YUAN Xiuxiao

    2006-01-01

    The RPC model has recently raised considerable interest in the photogrammetry and remote sensing community. The RPC is a generalized sensor model that is capable of achieving high approximation accuracy. Unfortunately, the computation of the parameters of RPC model is subject to the initial of the parameter in all available literature. An algorithm for computation of parameters of RPC model without initial value is presented and tested on SPOT-5, CBERS-2, ERS-1 imageries. RPC model is suitable for both push-broom and SAR imagery.

  11. Mapping cultivable land from satellite imagery with clustering algorithms

    Science.gov (United States)

    Arango, R. B.; Campos, A. M.; Combarro, E. F.; Canas, E. R.; Díaz, I.

    2016-07-01

    Open data satellite imagery provides valuable data for the planning and decision-making processes related with environmental domains. Specifically, agriculture uses remote sensing in a wide range of services, ranging from monitoring the health of the crops to forecasting the spread of crop diseases. In particular, this paper focuses on a methodology for the automatic delimitation of cultivable land by means of machine learning algorithms and satellite data. The method uses a partition clustering algorithm called Partitioning Around Medoids and considers the quality of the clusters obtained for each satellite band in order to evaluate which one better identifies cultivable land. The proposed method was tested with vineyards using as input the spectral and thermal bands of the Landsat 8 satellite. The experimental results show the great potential of this method for cultivable land monitoring from remote-sensed multispectral imagery.

  12. Get Close to Glaciers with Satellite Imagery.

    Science.gov (United States)

    Hall, Dorothy K.

    1986-01-01

    Discusses the use of remote sensing from satellites to monitor glaciers. Discusses efforts to use remote sensing satellites of the Landsat series for examining the global distribution, mass, balance, movements, and dynamics of the world's glaciers. Includes several Landsat images of various glaciers. (TW)

  13. Parameterization of Vegetation Aerodynamic Roughness of Natural Regions Satellite Imagery

    Science.gov (United States)

    Jasinski, Michael F.; Crago, Richard; Stewart, Pamela

    1998-01-01

    Parameterizations of the frontal area index and canopy area index of natural or randomly distributed plants are developed, and applied to the estimation of local aerodynamic roughness using satellite imagery. The formulas are expressed in terms of the subpixel fractional vegetation cover and one non-dimensional geometric parameter that characterizes the plant's shape. Geometrically similar plants and Poisson distributed plant centers are assumed. An appropriate averaging technique to extend satellite pixel-scale estimates to larger scales is provided. The parameterization is applied to the estimation of aerodynamic roughness using satellite imagery for a 2.3 sq km coniferous portion of the Landes Forest near Lubbon, France, during the 1986 HAPEX-Mobilhy Experiment. The canopy area index is estimated first for each pixel in the scene based on previous estimates of fractional cover obtained using Landsat Thematic Mapper imagery. Next, the results are incorporated into Raupach's (1992, 1994) analytical formulas for momentum roughness and zero-plane displacement height. The estimates compare reasonably well to reference values determined from measurements taken during the experiment and to published literature values. The approach offers the potential for estimating regionally variable, vegetation aerodynamic roughness lengths over natural regions using satellite imagery when there exists only limited knowledge of the vegetated surface.

  14. Identifying hydro resources with enhanced satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Cambridge, M.; Moore, J.M.; Canas, A.

    1986-10-01

    Landsat multi-spectral scanner (MSS) imagery in photographic form was used, in conjunction with available topographic and geologic maps, during the preliminary planning studies of potential dam sites in the Ulu Jelai river basin, Peninsular Malaysia. The Imperial College (London, UK) prototype analog image processing system was used to provide colour composite and edge-enhanced images for drainage, geological fold, fault and joint trace (tectonic fabric) mapping and to provide information on rock type distribution at scales between 1:250,000 and 1:50,000. Remotely sensed space imagery, enhanced by analog (optical) techniques, is a cheap, convenient and useful supplement to existing geologic and topographic maps for preliminary regional site assessment in tropical terrain.

  15. Detecting long-duration cloud contamination in hyper-temporal NDVI imagery

    NARCIS (Netherlands)

    Ali, A.; de Bie, C.A.J.M.; Skidmore, A.K.

    2013-01-01

    Cloud contamination impacts on the quality of hyper-temporal NDVI imagery and its subsequent interpretation. Short-duration cloud impacts are easily removed by using quality flags and an upper envelope filter, but long-duration cloud contamination of NDVI imagery remains. In this paper, an approach

  16. Recommended satellite imagery capabilities for disaster management

    Science.gov (United States)

    Richards, P. B.; Robinove, C. J.; Wiesnet, D. R.; Salomonson, V. V.; Maxwell, M. S.

    1982-01-01

    This study explores the role that satellite imaging systems might play in obtaining information needed in the management of natural and manmade disasters. Information requirements which might conceivably be met by satellite were identified for over twenty disasters. These requirements covered pre-disaster mitigation and preparedness activities, disaster response activities, and post-disaster recovery activities. The essential imaging satellite characteristics needed to meet most of the information requirements are 30 meter (or finer) spatial resolution, frequency of observations of one week or less, data delivery times of one day or less, and stereo, synoptic all-weather coverage of large areas in the visible, near infrared, thermal infrared and microwave bands. Of the current and planned satellite systems investigated for possible application to disaster management, Landsat-D and SPOT appear to have the greatest potential during disaster mitigation and preparedness activities, but all satellites studied have serious deficiencies during response and recovery activities. Several strawman concepts are presented for a satellite system optimized to support all disaster management activities.

  17. Verification of Cloud Analyses used to Support Overhead Imagery Collection

    Science.gov (United States)

    2012-03-01

    Geostationary Operational Environmental Satellite LWIR Longwave Infrared METEOSAT European Space Agency’s Meteorological Satellite xx METAR...observations undergo a four level process to be merged into a global cloud analysis. Level one is data calibration , level two classifies each pixel...night) and hot scenes (e.g., clear skies over a warm surface during daytime), the single-beam Zmin varies by ± 1 dB. Cloudsat’s calibration as of

  18. Satellite discrimination of snow/cloud surfaces

    Science.gov (United States)

    Crane, R. G.; Anderson, M. R.

    1984-01-01

    Differentiation between cloud cover and snow surfaces using remotely sensed data is complicated by the similarity of their radiative temperatures, and also by their similar reflectances at visible wavelengths. A method of cloud analysis over snow-covered regions is presented, using 1.51-1.63 micron data from an experimental sensor on board a U.S. Air Force Defense Meteorological Satellite Program platform. At these wavelengths, snow appears relatively 'black' while clouds are highly reflective. The spatial structure of the 1.51-1.63 micron reflectivity fields over a continuous snow surface are examined. Plots of mean reflectance against coefficients of variation for 4 x 4 pixel areas reveals a cluster of points have low reflectivity and low variability, corresponding to snow-covered (cloud free) areas, and a similar cluster with high reflectances corresponding to 100 per cent cloud cover. For the case of a single layered cloud, the radiances associated with partially filled fields of view are also inferred.

  19. Current and Future Applications of Multispectral (RGB) Satellite Imagery for Weather Analysis and Forecasting Applications

    Science.gov (United States)

    Molthan, Andrew L.; Fuell, Kevin K.; LaFontaine, Frank; McGrath, Kevin; Smith, Matt

    2013-01-01

    Current and future satellite sensors provide remotely sensed quantities from a variety of wavelengths ranging from the visible to the passive microwave, from both geostationary and low ]Earth orbits. The NASA Short ]term Prediction Research and Transition (SPoRT) Center has a long history of providing multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA fs Terra and Aqua satellites in support of NWS forecast office activities. Products from MODIS have recently been extended to include a broader suite of multispectral imagery similar to those developed by EUMETSAT, based upon the spectral channels available from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard METEOSAT ]9. This broader suite includes products that discriminate between air mass types associated with synoptic ]scale features, assists in the identification of dust, and improves upon paired channel difference detection of fog and low cloud events. Future instruments will continue the availability of these products and also expand upon current capabilities. The Advanced Baseline Imager (ABI) on GOES ]R will improve the spectral, spatial, and temporal resolution of our current geostationary capabilities, and the recent launch of the Suomi National Polar ]Orbiting Partnership (S ]NPP) carries instruments such as the Visible Infrared Imager Radiometer Suite (VIIRS), the Cross ]track Infrared Sounder (CrIS), and the Advanced Technology Microwave Sounder (ATMS), which have unrivaled spectral and spatial resolution, as precursors to the JPSS era (i.e., the next generation of polar orbiting satellites. New applications from VIIRS extend multispectral composites available from MODIS and SEVIRI while adding new capabilities through incorporation of additional CrIS channels or information from the Near Constant Contrast or gDay ]Night Band h, which provides moonlit reflectance from clouds and detection of fires or city lights. This presentation will

  20. Calibration and Navigation of INSAT Infrared Imagery Using a Novel Cloud Clearing Approach

    Science.gov (United States)

    Datta, S.; Molnar, G. I.

    2002-05-01

    The Indian Ocean and surrounding landmasses are important regions of study for understanding the summer monsoon in Southeast Asia and monitoring clouds over the Tropics. For over two decades, the Indian National Satellite (INSAT) series is observing clouds in infrared (IR) and visible channels over that region from a geostationary orbit. Properly analyzed INSAT data could provide some very useful information over the Indian Ocean region. The raw images, received and archived at NASA are not properly navigated or calibrated. The current paper presents the effort to navigate and calibrate raw IR INSAT imagery. The navigation is performed using coastline-matching technique. The navigation software takes into account the tilted geometry of INSAT orbit and produces navigation for each 1024x1024 pixels. The accuracy of navigation at present is about +/-30 km. The rather large uncertainty is partly due to lack of information about the time of image and exact scanning strategy of the sensor. The IR calibration makes use of Sea Surface Temperature (SST) data that have been operationally retrieved using blended satellite and in situ observations by NOAA. We will show that this dedicated data series can be used effectively to calibrate IR window channel INSAT satellite radiances. This must be achieved through regression techniques, because not even the channel filter responses are published to allow for accurate modeling the satellite-observed radiances through high spectral resolution radiative transfer calculations. In order to lessen biases associated with cloud contamination, we make use of a novel, cloud contamination insensitive clear-sky brightness temperature (CBT) -retrieval approach. We intercompare our CBTs with the NOAA analyses on the NOAA SST retrieval grid scale thus assuring a large enough area to allow for clear pixels. On the other hand, to avoid the potential biases associated with SST diurnal cycle undersampling by the NOAA blended SST analyses, we also

  1. Updating Maps Using High Resolution Satellite Imagery

    Science.gov (United States)

    Alrajhi, Muhamad; Shahzad Janjua, Khurram; Afroz Khan, Mohammad; Alobeid, Abdalla

    2016-06-01

    Kingdom of Saudi Arabia is one of the most dynamic countries of the world. We have witnessed a very rapid urban development's which are altering Kingdom's landscape on daily basis. In recent years a substantial increase in urban populations is observed which results in the formation of large cities. Considering this fast paced growth, it has become necessary to monitor these changes, in consideration with challenges faced by aerial photography projects. It has been observed that data obtained through aerial photography has a lifecycle of 5-years because of delay caused by extreme weather conditions and dust storms which acts as hindrances or barriers during aerial imagery acquisition, which has increased the costs of aerial survey projects. All of these circumstances require that we must consider some alternatives that can provide us easy and better ways of image acquisition in short span of time for achieving reliable accuracy and cost effectiveness. The approach of this study is to conduct an extensive comparison between different resolutions of data sets which include: Orthophoto of (10 cm) GSD, Stereo images of (50 cm) GSD and Stereo images of (1 m) GSD, for map updating. Different approaches have been applied for digitizing buildings, roads, tracks, airport, roof level changes, filling stations, buildings under construction, property boundaries, mosques buildings and parking places.

  2. Hazing Iran: Satellite Imagery, Human Rights, and City as Camp

    OpenAIRE

    Zhang, Amy

    2014-01-01

    As perhaps most obviously evidenced in the political maneuverings that led up to the second Gulf War in 2003, the use of satellite imagery to document spatial terrain is often, and almost instantly, politicized. In the two images presented here, this politicization takes on a two-way relationship and is open to contrasting and inevitably dualistic readings. One way of describing their relationship is as "Target" and "Aftermath". On the left, we have an image of the nuclear plant near the Iran...

  3. Visualization techniques for data mining of Latur district satellite imagery

    OpenAIRE

    Kodge, B. G.; Hiremath, P. S.

    2011-01-01

    This study presents a new visualization tool for classification of satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and related uncertainty. Visual Data Mining provides added value to image classifications as the user can be involved in the classification process providing increased confidence in and understanding of the results. In this study, we present a prototype visualization tool for visual dat...

  4. IAEA Safeguards: Cost/benefit analysis of commercial satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Andersson, Christer [SSC Satellitbild AB, Kiruna (Sweden)

    1999-03-01

    A major milestone in the efforts to strengthen the Safeguards System was reached in May 1997 when the Board of Governors approved a `Model Protocol Additional to Safeguards Agreements`. The Protocol provides the legal basis necessary to enhance the Agency`s ability to detect undeclared nuclear material and activities by using information available from open sources to complement the declarations made by Member States. Commercially available high-resolution satellite data has emerged as one potential complementary open information source to support the traditional and extended Safeguard activities of IAEA. This document constitutes a first report from SSC Satellitbild giving the Agency tentative and initial estimates of the potential cost and time-savings possible with the new proposed technology. The initial cost/benefit simulation will be further finalised in the following `Implementation Blueprint` study. The general foundation and starting point for the cost/benefit calculation is to simulate a new efficient and relatively small `imagery unit` within the IAEA, capable of performing advanced image processing as a tool for various safeguards tasks. The image processing capacity is suggested to be task- and interpretation-oriented. The study was performed over a period of 1,5 weeks in late 1998, and is based upon interviews of IAEA staff, reviews of existing IAEA documentation as well as from SSC Satellitbild`s long-standing experience of satellite imagery and field missions. The cost/benefit analysis is based on a spreadsheet simulation of five potential applications of commercial satellite imagery: Reference information; Confirmation of Agency acquired and Member State supplied data; Change detection and on-going monitoring; Assessing open source information available to the Agency; Detecting undeclared activities and undeclared sites. The study confirms that the proposed concept of a relatively small `imagery unit` using high-resolution data will be a sound and

  5. Satellite Imagery Assisted Road-Based Visual Navigation System

    Science.gov (United States)

    Volkova, A.; Gibbens, P. W.

    2016-06-01

    There is a growing demand for unmanned aerial systems as autonomous surveillance, exploration and remote sensing solutions. Among the key concerns for robust operation of these systems is the need to reliably navigate the environment without reliance on global navigation satellite system (GNSS). This is of particular concern in Defence circles, but is also a major safety issue for commercial operations. In these circumstances, the aircraft needs to navigate relying only on information from on-board passive sensors such as digital cameras. An autonomous feature-based visual system presented in this work offers a novel integral approach to the modelling and registration of visual features that responds to the specific needs of the navigation system. It detects visual features from Google Earth* build a feature database. The same algorithm then detects features in an on-board cameras video stream. On one level this serves to localise the vehicle relative to the environment using Simultaneous Localisation and Mapping (SLAM). On a second level it correlates them with the database to localise the vehicle with respect to the inertial frame. The performance of the presented visual navigation system was compared using the satellite imagery from different years. Based on comparison results, an analysis of the effects of seasonal, structural and qualitative changes of the imagery source on the performance of the navigation algorithm is presented. * The algorithm is independent of the source of satellite imagery and another provider can be used

  6. Evaluating large scale orthophotos derived from high resolution satellite imagery

    Science.gov (United States)

    Ioannou, Maria Teresa; Georgopoulos, Andreas

    2013-08-01

    For the purposes of a research project, for the compilation of the archaeological and environmental digital map of the island of Antiparos, the production of updated large scale orthophotos was required. Hence suitable stereoscopic high resolution satellite imagery was acquired. Two Geoeye-1 stereopairs were enough to cover this small island of the Cyclades complex in the central Aegean. For the orientation of the two stereopairs numerous ground control points were determined using GPS observations. Some of them would also serve as check points. The images were processed using commercial stereophotogrammetric software suitable to process satellite stereoscopic imagery. The results of the orientations are evaluated and the digital terrain model was produced using automated and manual procedures. The DTM was checked both internally and externally with comparison to other available DTMs. In this paper the procedures for producing the desired orthophotography are critically presented and the final result is compared and evaluated for its accuracy, completeness and efficiency. The final product is also compared against the orthophotography produced by Ktimatologio S.A. using aerial images in 2007. The orthophotography produced has been evaluated metrically using the available check points, while qualitative evaluation has also been performed. The results are presented and a critical approach for the usability of satellite imagery for the production of large scale orthophotos is attempted.

  7. Processing Satellite Imagery To Detect Waste Tire Piles

    Science.gov (United States)

    Skiles, Joseph; Schmidt, Cynthia; Wuinlan, Becky; Huybrechts, Catherine

    2007-01-01

    A methodology for processing commercially available satellite spectral imagery has been developed to enable identification and mapping of waste tire piles in California. The California Integrated Waste Management Board initiated the project and provided funding for the method s development. The methodology includes the use of a combination of previously commercially available image-processing and georeferencing software used to develop a model that specifically distinguishes between tire piles and other objects. The methodology reduces the time that must be spent to initially survey a region for tire sites, thereby increasing inspectors and managers time available for remediation of the sites. Remediation is needed because millions of used tires are discarded every year, waste tire piles pose fire hazards, and mosquitoes often breed in water trapped in tires. It should be possible to adapt the methodology to regions outside California by modifying some of the algorithms implemented in the software to account for geographic differences in spectral characteristics associated with terrain and climate. The task of identifying tire piles in satellite imagery is uniquely challenging because of their low reflectance levels: Tires tend to be spectrally confused with shadows and deep water, both of which reflect little light to satellite-borne imaging systems. In this methodology, the challenge is met, in part, by use of software that implements the Tire Identification from Reflectance (TIRe) model. The development of the TIRe model included incorporation of lessons learned in previous research on the detection and mapping of tire piles by use of manual/ visual and/or computational analysis of aerial and satellite imagery. The TIRe model is a computational model for identifying tire piles and discriminating between tire piles and other objects. The input to the TIRe model is the georeferenced but otherwise raw satellite spectral images of a geographic region to be surveyed

  8. Automated Generation of the Alaska Coastline Using High-Resolution Satellite Imagery

    Science.gov (United States)

    Roth, G.; Porter, C. C.; Cloutier, M. D.; Clementz, M. E.; Reim, C.; Morin, P. J.

    2015-12-01

    Previous campaigns to map Alaska's coast at high resolution have relied on airborne, marine, or ground-based surveying and manual digitization. The coarse temporal resolution, inability to scale geographically, and high cost of field data acquisition in these campaigns is inadequate for the scale and speed of recent coastal change in Alaska. Here, we leverage the Polar Geospatial Center (PGC) archive of DigitalGlobe, Inc. satellite imagery to produce a state-wide coastline at 2 meter resolution. We first select multispectral imagery based on time and quality criteria. We then extract the near-infrared (NIR) band from each processed image, and classify each pixel as water or land with a pre-determined NIR threshold value. Processing continues with vectorizing the water-land boundary, removing extraneous data, and attaching metadata. Final coastline raster and vector products maintain the original accuracy of the orthorectified satellite data, which is often within the local tidal range. The repeat frequency of coastline production can range from 1 month to 3 years, depending on factors such as satellite capacity, cloud cover, and floating ice. Shadows from trees or structures complicate the output and merit further data cleaning. The PGC's imagery archive, unique expertise, and computing resources enabled us to map the Alaskan coastline in a few months. The DigitalGlobe archive allows us to update this coastline as new imagery is acquired, and facilitates baseline data for studies of coastal change and improvement of topographic datasets. Our results are not simply a one-time coastline, but rather a system for producing multi-temporal, automated coastlines. Workflows and tools produced with this project can be freely distributed and utilized globally. Researchers and government agencies must now consider how they can incorporate and quality-control this high-frequency, high-resolution data to meet their mapping standards and research objectives.

  9. Using Satellite Imagery to Study Landslides

    Science.gov (United States)

    Reif, S. L.; Bluth, G. J.; Rose, W. I.; Matias, O.; Wolf, R.

    2003-04-01

    Much of the world's population currently lives under the threat of volcanic hazards in the secondary form of debris movements such as landslides and lahars. Remote sensing is becoming a useful tool for hazard studies, yet many hazard-prone areas do not utilize this important resource. In this project, we intend to use common remote sensing techniques to study characteristics of landslides and lahars in order to predict hazard zones. Fuego Volcano in Guatemala is a steep sided volcano with a history of large eruptive events, including the well-studied 1974 eruption, that have extruded a large amount of material onto the upper reaches of the Fuego watersheds. Eruption processes have been a primary focus of studies; however, remobilization during the rainy season of the erupted material is hazardous to the local population and agriculture (Vallance et al. 2001, USGS Open-File Report 01-431). A study of the way material moves down Fuego and to the extent that it moves is needed to help properly mitigate the potential hazards. We propose an in-depth remote sensing survey to map the hazard-prone areas. The study will consist of processing 20 years (15 cloud-free images) of Landsat TM and ETM+ data to look at changes in landforms and vegetation. Vegetation indices will be calculated to locate areas devoid of vegetation and a masking process will be used to measure the area of these zones. These area changes will be related to field measurements to create GIS layers denoting geometry changes in the channels around Fuego. These changes will be loaded into a GIS, along with regional climate data, DEMs, hydrologic data, infrastructure, and information about the known volcanic activity recorded in the area by the local volcanologists. Modeling of lahars using LAHARZ and climate data will also be done to determine an estimate of the amount of material moved and to what distances it can be transported. A field survey undertaken in January 2003 acquired GPS ground truth data of

  10. Approximate Approaches to Geometric Corrections of High Resolution Satellite Imagery

    Institute of Scientific and Technical Information of China (English)

    SHI Wenzhong; Ahmed Shaker

    2004-01-01

    The exploitation of different non-rigorous mathematical models as opposed to the satellite rigorous models is discussed for geometric corrections and topographic/thematic maps production of high-resolution satellite imagery (HRSI). Furthermore, this paper focuses on the effects of the number of GCPs and the terrain elevation difference within the area covered by the images on the obtained ground points accuracy. From the research, it is obviously found that non-rigorous orientation and triangulation models can be used successfully in most cases for 2D rectification and 3D ground points determination without a camera model or the satellite ephemeris data. In addition, the accuracy up to the sub-pixel level in plane and about one pixel in elevation can be achieved with a modest number of GCPs.

  11. Volumetric Forest Change Detection Through Vhr Satellite Imagery

    Science.gov (United States)

    Akca, Devrim; Stylianidis, Efstratios; Smagas, Konstantinos; Hofer, Martin; Poli, Daniela; Gruen, Armin; Sanchez Martin, Victor; Altan, Orhan; Walli, Andreas; Jimeno, Elisa; Garcia, Alejandro

    2016-06-01

    Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView - 3, SPOT - 5 HRS, SPOT - 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in

  12. Direct determination of surface albedos from satellite imagery

    Science.gov (United States)

    Mekler, Y.; Joseph, J. H.

    1983-01-01

    An empirical method to measure the spectral surface albedo of surfaces from Landsat imagery is presented and analyzed. The empiricism in the method is due only to the fact that three parameters of the solution must be determined for each spectral photograph of an image on the basis of independently known albedos at three points. The approach is otherwise based on exact solutions of the radiative transfer equation for upwelling intensity. Application of the method allows the routine construction of spectral albedo maps from satelite imagery, without requiring detailed knowledge of the atmospheric aerosol content, as long as the optical depth is less than 0.75, and of the calibration of the satellite sensor.

  13. Sugarcane Land Classification with Satellite Imagery using Logistic Regression Model

    Science.gov (United States)

    Henry, F.; Herwindiati, D. E.; Mulyono, S.; Hendryli, J.

    2017-03-01

    This paper discusses the classification of sugarcane plantation area from Landsat-8 satellite imagery. The classification process uses binary logistic regression method with time series data of normalized difference vegetation index as input. The process is divided into two steps: training and classification. The purpose of training step is to identify the best parameter of the regression model using gradient descent algorithm. The best fit of the model can be utilized to classify sugarcane and non-sugarcane area. The experiment shows high accuracy and successfully maps the sugarcane plantation area which obtained best result of Cohen’s Kappa value 0.7833 (strong) with 89.167% accuracy.

  14. Classification of Clouds and Deep Convection from GEOS-5 Using Satellite Observations

    Science.gov (United States)

    Putman, William; Suarez, Max

    2010-01-01

    With the increased resolution of global atmospheric models and the push toward global cloud resolving models, the resemblance of model output to satellite observations has become strikingly similar. As we progress with our adaptation of the Goddard Earth Observing System Model, Version 5 (GEOS-5) as a high resolution cloud system resolving model, evaluation of cloud properties and deep convection require in-depth analysis beyond a visual comparison. Outgoing long-wave radiation (OLR) provides a sufficient comparison with infrared (IR) satellite imagery to isolate areas of deep convection. We have adopted a binning technique to generate a series of histograms for OLR which classify the presence and fraction of clear sky versus deep convection in the tropics that can be compared with a similar analyses of IR imagery from composite Geostationary Operational Environmental Satellite (GOES) observations. We will present initial results that have been used to evaluate the amount of deep convective parameterization required within the model as we move toward cloud system resolving resolutions of 10- to 1-km globally.

  15. Probabilistic approach to cloud and snow detection on AVHRR imagery

    Science.gov (United States)

    Musial, J. P.; Hüsler, F.; Sütterlin, M.; Neuhaus, C.; Wunderle, S.

    2013-09-01

    The derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. The information about a confidence level of provided physical quantities is required to construct an error budget of higher level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data the common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher level products and may alter their usability. Within this scope a novel Probabilistic Cloud Mask (PCM) algorithm suited for the 1×1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on a decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve the probability estimates from the pre-computed Look Up Tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of thresholds. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the Invariant Coordinate System (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the PPSv2012 and MOD35 collection 6 cloud masks, SYNOP weather

  16. Probabilistic approach to cloud and snow detection on AVHRR imagery

    Directory of Open Access Journals (Sweden)

    J. P. Musial

    2013-09-01

    Full Text Available The derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. The information about a confidence level of provided physical quantities is required to construct an error budget of higher level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data the common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher level products and may alter their usability. Within this scope a novel Probabilistic Cloud Mask (PCM algorithm suited for the 1×1 km Advanced Very High Resolution Radiometer (AVHRR data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on a decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve the probability estimates from the pre-computed Look Up Tables (LUTs. Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of thresholds. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the Invariant Coordinate System (ICS transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the PPSv2012 and MOD35 collection 6 cloud masks

  17. Combining satellite imagery and machine learning to predict poverty.

    Science.gov (United States)

    Jean, Neal; Burke, Marshall; Xie, Michael; Davis, W Matthew; Lobell, David B; Ermon, Stefano

    2016-08-19

    Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries--Nigeria, Tanzania, Uganda, Malawi, and Rwanda--we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains.

  18. Daytime Low Stratiform Cloud Detection on AVHRR Imagery

    Directory of Open Access Journals (Sweden)

    Jan Pawel Musial

    2014-06-01

    Full Text Available The near-real time retrieval of low stratiform cloud (LSC coverage is of vital interest for such disciplines as meteorology, transport safety, economy and air quality. Within this scope, a novel methodology is proposed which provides the LSC occurrence probability estimates for a satellite scene. The algorithm is suited for the 1 × 1 km Advanced Very High Resolution Radiometer (AVHRR data and was trained and validated against collocated SYNOP observations. Utilisation of these two combined data sources requires a formulation of constraints in order to discriminate cases where the LSC is overlaid by higher clouds. The LSC classification process is based on six features which are first converted to the integer form by step functions and combined by means of bitwise operations. Consequently, a set of values reflecting a unique combination of those features is derived which is further employed to extract the LSC occurrence probability estimates from the precomputed look-up vectors (LUV. Although the validation analyses confirmed good performance of the algorithm, some inevitable misclassification with other optically thick clouds were reported. Moreover, the comparison against Polar Platform System (PPS cloud-type product revealed superior classification accuracy. From the temporal perspective, the acquired results reported a presence of diurnal and annual LSC probability cycles over Europe.

  19. A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land

    Directory of Open Access Journals (Sweden)

    Feng Gao

    2012-10-01

    Full Text Available Thermal infrared (TIR imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to shortwave band pixel resolutions, which are often fine enough for field-scale applications. A classic thermal sharpening technique, TsHARP, uses a relationship between land surface temperature (LST and Normalized Difference Vegetation Index (NDVI developed empirically at the TIR pixel resolution and applied at the NDVI pixel resolution. However, recent studies show that unique relationships between temperature and NDVI may only exist for a limited class of landscapes, with mostly green vegetation and homogeneous air and soil conditions. To extend application of thermal sharpening to more complex conditions, a new data mining sharpener (DMS technique is developed. The DMS approach builds regression trees between TIR band brightness temperatures and shortwave spectral reflectances based on intrinsic sample characteristics. A comparison of sharpening techniques applied over a rainfed agricultural area in central Iowa, an irrigated agricultural region in the Texas High Plains, and a heterogeneous naturally vegetated landscape in Alaska indicates that the DMS outperformed TsHARP in all cases. The artificial box-like patterns in LST generated by the TsHARP approach are greatly reduced using the DMS scheme, especially for areas containing irrigated crops, water bodies, thin clouds or terrain. While the DMS technique can provide fine resolution TIR imagery, there are limits to the sharpening ratios that can be reasonably implemented. Consequently, sharpening techniques cannot replace actual thermal band imagery at fine resolutions or missions that

  20. Measurement of sea ice and icebergs topography using satellite imagery

    Science.gov (United States)

    Zakharov, I.; Power, D.; Prasad, S.

    2016-12-01

    Sea ice topography represents geospatial information on the three-dimensional geometrical attributes of the ice surface including height and shape of various ice features. The features interest consist of deformed (pressure ridges, rubbles and hummocks) and level sea ice as well as glacial ice. Sea ice topography is important for scientific research and climate studies because it helps characterise ice volume and thickness and it influences the near-surface atmospheric transport by impacting the drag coefficients. It also represents critical information to marine operational applications, such as ships navigation and risks assessment for offshore infrastructures. The several methods were used to measure sea ice topography from a single satellite image as well as multiple images. The techniques based on the single image, acquired by optical or synthetic aperture radar (SAR) satellites, derive the height and shape information from shadow and shading. Optical stereo images acquired by very high resolution (0.5 m) satellites were used to extract highly detailed digital elevation model (DEM). SAR imagery allowed extraction of DEM using stereo-radargrammetry and interferometry. The images from optical satellites WorldView, Pleiades, GeoEye, Spot, and Landsat-8 were used to measure topography of sea ice deformation features and glacial ice including icebergs and ice islands. These features were mapped in regions of the Central Arctic, Baffin Bay and the coast of Greenland. SAR imagery including interferometric TanDEM-X data and full polarimetric Radarsat-2 were used to extract ridge frequency and measure spatial parameters of glacial features. The accuracy was evaluated by comparison of the results from different methods demonstrating their strengths and limitations. Ridge height and frequency were also compared with the high resolution results from the Los Alamos sea ice model (CICE), regionally implemented for Baffin Bay and the Labrador Sea.

  1. Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data

    OpenAIRE

    Nam, Christine C. W.; Quaas, Johannes

    2015-01-01

    Observations from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat satellites are used to evaluate clouds and precipitation in the ECHAM5 general circulation model. Active lidar and radar instruments on board CALIPSO and CloudSat allow the vertical distribution of clouds and their optical properties to be studied on a global scale. To evaluate the clouds modeled by ECHAM5 with CALIPSO and CloudSat, the lidar and radar satellite simulators of the Cloud ...

  2. Progress of research to identify rotating thunderstorms using satellite imagery

    Science.gov (United States)

    Anderson, Charles E.

    1988-01-01

    The possibility of detecting potentially tornadic thunderstorm cells from geosynchronous satelite imagery is determined. During the life of the contract, we examined eight tornado outbreak cases which had a total of 124 individual thunderstorm cells, 37 of which were tornadic.These 37 cells produced a total of 119 tornadoes. The outflow characteristics of all the cells were measured. Through the use of a 2-D flow field model, we were able to simulate the downstream developmemt of an anvil cloud plume which was emitted by the storm updraft at or near the tropopause. We used two parameters to characterize the anvil plume behavior: its speed of downstream propagation (U max) and the clockwise deviation of the centerline of the anvil plume from the storm relative ambient wind at the anvil plume outflow level (MDA). U max was the maximum U-component of the anvil wind parameter required to successfully maintain an envelope of translating particles at the tip of the expanding anvil cloud. MDA was the measured deviation angle acquired from McIDAS, between the storm relative ambient wind direction and the storm relative anvil plume outflow direction; tha latter being manipulated by controlling a tangential wind component to force the envelope of particles to maintain their position of surrounding the expanding outflow cloud.

  3. Using satellite imagery to assess the influence of urban development on the impacts of extreme rainfall

    DEFF Research Database (Denmark)

    Kaspersen, Per Skougaard; Drews, Martin; Madsen, Henrik;

    We investigate the applicability of medium resolution Landsat satellite imagery for mapping temporal changes in urban land cover for direct use in urban flood models. The overarching aim is to provide accurate and cost- and resource-efficient quantification of temporal changes in risk towards...... the impacts of pluvial flooding. Initial results show that satellite imagery may have considerable potential in this respect....

  4. Spectrally Enhanced Cloud Objects—A generalized framework for automated detection of volcanic ash and dust clouds using passive satellite measurements: 1. Multispectral analysis

    Science.gov (United States)

    Pavolonis, Michael J.; Sieglaff, Justin; Cintineo, John

    2015-08-01

    While satellites are a proven resource for detecting and tracking volcanic ash and dust clouds, existing algorithms for automatically detecting volcanic ash and dust either exhibit poor overall skill or can only be applied to a limited number of sensors and/or geographic regions. As such, existing techniques are not optimized for use in real-time applications like volcanic eruption alerting and data assimilation. In an effort to significantly improve upon existing capabilities, the Spectrally Enhanced Cloud Objects (SECO) algorithm was developed. The SECO algorithm utilizes a combination of radiative transfer theory, a statistical model, and image processing techniques to identify volcanic ash and dust clouds in satellite imagery with a very low false alarm rate. This fully automated technique is globally applicable (day and night) and can be adapted to a wide range of low earth orbit and geostationary satellite sensors or even combinations of satellite sensors. The SECO algorithm consists of four primary components: conversion of satellite measurements into robust spectral metrics, application of a Bayesian method to estimate the probability that a given satellite pixel contains volcanic ash and/or dust, construction of cloud objects, and the selection of cloud objects deemed to have the physical attributes consistent with volcanic ash and/or dust clouds. The first two components of the SECO algorithm are described in this paper, while the final two components are described in a companion paper.

  5. Satellite retrieval of cloud condensation nuclei concentrations by using clouds as CCN chambers

    OpenAIRE

    2016-01-01

    Quantifying the aerosol/cloud-mediated radiative effect at a global scale requires simultaneous satellite retrievals of cloud condensation nuclei (CCN) concentrations and cloud base updraft velocities (Wb). Hitherto, the inability to do so has been a major cause of high uncertainty regarding anthropogenic aerosol/cloud-mediated radiative forcing. This can be addressed by the emerging capability of estimating CCN and Wb of boundary layer convective clouds from an operational polar orbiting wea...

  6. Fast Simulators for Satellite Cloud Optical Centroid Pressure Retrievals, 1. Evaluation of OMI Cloud Retrievals

    Science.gov (United States)

    Joiner, J.; Vasilkov, A.; Gupta, P.; Bhartia, P. K.; Veefkind, P.; Sneep, M.; de Haan, J.; Polonsky, I.; Spurr, R.

    2012-01-01

    The cloud Optical Centroid Pressure (OCP), also known as the effective cloud pressure, is a satellite-derived parameter that is commonly used in trace-gas retrievals to account for the effects of clouds on near-infrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosol. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals from the Ozone Monitoring Instrument (OMI) with estimates based on collocated cloud extinction profiles from a combination of CloudS at radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, low altitude clouds missed by CloudSat, and the fact that CloudSat only observes a relatively small fraction of an OMI field-of-view.

  7. Surface Characteristics of Green Island Wakes from Satellite Imagery

    Science.gov (United States)

    Cheng, Kai-Ho; Hsu, Po-Chun; Ho, Chung-Ru

    2017-04-01

    Characteristics of an island wake induced by the Kuroshio Current flows pass by Green Island, a small island 40 km off southeast of Taiwan is investigated by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The MODIS sea surface temperature (SST) and chlorophyll-a (chl-a) imagery is produced at 250-meter resolution from 2014 to 2015 using the SeaDAS software package which is developed by the National Aeronautics and Space Administration. The wake occurrence is 59% observed from SST images during the data span. The average cooling area is 190 km2, but the area is significantly changed with wind directions. The wake area is increased during southerly winds and is reduced during northerly winds. Besides, the average cooling SST was about 2.1 oC between the front and rear island. Comparing the temperature difference between the wake and its left side, the difference is 1.96 oC. In addition, the wakes have 1 3 times higher than normal in chlorophyll concentration. The results indicate the island mass effect makes the surface water of Green island wake colder and chl-a higher.

  8. Spatial Cloud Detection and Retrieval System for Satellite Images

    Directory of Open Access Journals (Sweden)

    Ayman Nasr

    2013-01-01

    Full Text Available In last the decade we witnessed a large increase in data generated by earth observing satellites. Hence, intelligent processing of the huge amount of data received by hundreds of earth receiving stations, with specific satellite image oriented approaches, presents itself as a pressing need. One of the most important steps in earlier stages of satellite image processing is cloud detection. Satellite images having a large percentage of cloud cannot be used in further analysis. While there are many approaches that deal with different semantic meaning, there are rarely approaches that deal specifically with cloud detection and retrieval. In this paper we introduce a novel approach that spatially detect and retrieve clouds in satellite images using their unique properties .Our approach is developed as spatial cloud detection and retrieval system (SCDRS that introduce a complete framework for specific semantic retrieval system. It uses a Query by polygon (QBP paradigm for the content of interest instead of using the more conventional rectangular query by image approach. First, we extract features from the satellite images using multiple tile sizes using spatial and textural properties of cloud regions. Second, we retrieve our tiles using a parametric statistical approach within a multilevel refinement process. Our approach has been experimentally validated against the conventional ones yielding enhanced precision and recall rates in the same time it gives more precise detection of cloud coverage regions.

  9. Efficient Algorithm for Railway Tracks Detection Using Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Ali Javed

    2012-10-01

    Full Text Available Satellite imagery can produce maps including roads, railway tracks, buildings, bridges, oceans, lakes, rivers, etc. In developed countries like USA, Canada, Australia, Europe, images produced by Google map are of high resolution and good quality. On the other hand, mostly images of the third world countries like Pakistan, Asian and African countries are of poor quality and not clearly visible. Similarly railway tracks of these countries are hardly visible in Google map. We have developed an efficient algorithm for railway track detection from a low quality image of Google map. This would lead to detect damaged railway track, railway crossings and help to schedule/divert locomotive movements in order to avoid catastrophe.

  10. Estimation of Satellite Orientation from Space Surveillance Imagery Measured with an Adaptive Optics Telescope

    Science.gov (United States)

    1996-12-01

    SATELLITE ORIENTATION FROM SPACE SURVEILLANCE IMAGERY MEASURED WITH AN ADAPTIVE OPTICS TELESCOPE THESIS Gregory E. Wood Lieutenant, USAF AFIT/GSO/ENP...the official policy or position of the Department of Defense or the U. S. Government. AFIT/GSO/ENP/96D-02 ESTIMATION OF SATELLITE ORIENTATION FROM...surveillance operations. xii ESTIMATION OF SATELLITE ORIENTATION FROM SPACE SURVEILLANCE IMAGERY MEASURED WITH AN ADAPTIVE OPTICS TELESCOPE

  11. Fast simulators for satellite cloud optical centroid pressure retrievals; evaluation of OMI cloud retrievals

    Directory of Open Access Journals (Sweden)

    J. Joiner

    2012-03-01

    Full Text Available The cloud Optical Centroid Pressure (OCP is a satellite-derived parameter that is commonly used in trace-gas retrievals to account for the effects of clouds on near-infrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosols. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals, from the Ozone Monitoring Instrument (OMI, with estimates based on collocated cloud extinction profiles from a combination of CloudSat radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, cases of low clouds obscurred by ground-clutter in CloudSat observations and by opaque high clouds in CALIPSO lidar observations, and the fact that CloudSat/CALIPSO only observes a relatively small fraction of an OMI field-of-view.

  12. DEVELOPMENT OF IMPROVED TECHNIQUES FOR SATELLITE REMOTE SENSING OF CLOUDS AND RADIATION USING ARM DATA, FINAL REPORT

    Energy Technology Data Exchange (ETDEWEB)

    Minnis, Patrick [NASA Langley Research Center, Hampton, VA

    2013-06-28

    During the period, March 1997 – February 2006, the Principal Investigator and his research team co-authored 47 peer-reviewed papers and presented, at least, 138 papers at conferences, meetings, and workshops that were supported either in whole or in part by this agreement. We developed a state-of-the-art satellite cloud processing system that generates cloud properties over the Atmospheric Radiation (ARM) surface sites and surrounding domains in near-real time and outputs the results on the world wide web in image and digital formats. When the products are quality controlled, they are sent to the ARM archive for further dissemination. These products and raw satellite images can be accessed at http://cloudsgate2.larc.nasa.gov/cgi-bin/site/showdoc?docid=4&cmd=field-experiment-homepage&exp=ARM and are used by many in the ARM science community. The algorithms used in this system to generate cloud properties were validated and improved by the research conducted under this agreement. The team supported, at least, 11 ARM-related or supported field experiments by providing near-real time satellite imagery, cloud products, model results, and interactive analyses for mission planning, execution, and post-experiment scientific analyses. Comparisons of cloud properties derived from satellite, aircraft, and surface measurements were used to evaluate uncertainties in the cloud properties. Multiple-angle satellite retrievals were used to determine the influence of cloud structural and microphysical properties on the exiting radiation field.

  13. Satellite Regional Cloud Climatology over the Great Lakes

    Directory of Open Access Journals (Sweden)

    Steven A. Ackerman

    2013-11-01

    Full Text Available Thirty-one years of imager data from polar orbiting satellites are composited to produce a satellite climate data set of cloud amount for the Great Lakes region. A trend analysis indicates a slight decreasing trend in cloud cover over the region during this time period. The trend is significant and largest (~2% per decade over the water bodies. A strong seasonal cycle of cloud cover is observed over both land and water surfaces. Winter cloud amounts are greater over the water bodies than land due to heat and moisture flux into the atmosphere. Late spring through early autumn cloud amounts are lower over the water bodies than land due to stabilization of the boundary layer by relatively cooler lake waters. The influence of the lakes on cloud cover also extends beyond their shores, affecting cloud cover and properties far down wind. Cloud amount composited by wind direction demonstrate that the increasing cloud amounts downwind of the lakes is greatest during autumn and winter. Cold air flows over relatively warm lakes in autumn and winter generate wind parallel convective cloud bands. The cloud properties of these wind parallel cloud bands over the lakes during winter are presented.

  14. Satellite retrieval of cloud condensation nuclei concentrations by using clouds as CCN chambers

    Science.gov (United States)

    Rosenfeld, Daniel; Zheng, Youtong; Hashimshoni, Eyal; Pöhlker, Mira L.; Jefferson, Anne; Pöhlker, Christopher; Yu, Xing; Zhu, Yannian; Liu, Guihua; Yue, Zhiguo; Fischman, Baruch; Li, Zhanqing; Giguzin, David; Goren, Tom; Artaxo, Paulo; Pöschl, Ulrich

    2016-01-01

    Quantifying the aerosol/cloud-mediated radiative effect at a global scale requires simultaneous satellite retrievals of cloud condensation nuclei (CCN) concentrations and cloud base updraft velocities (Wb). Hitherto, the inability to do so has been a major cause of high uncertainty regarding anthropogenic aerosol/cloud-mediated radiative forcing. This can be addressed by the emerging capability of estimating CCN and Wb of boundary layer convective clouds from an operational polar orbiting weather satellite. Our methodology uses such clouds as an effective analog for CCN chambers. The cloud base supersaturation (S) is determined by Wb and the satellite-retrieved cloud base drop concentrations (Ndb), which is the same as CCN(S). Validation against ground-based CCN instruments at Oklahoma, at Manaus, and onboard a ship in the northeast Pacific showed a retrieval accuracy of ±25% to ±30% for individual satellite overpasses. The methodology is presently limited to boundary layer not raining convective clouds of at least 1 km depth that are not obscured by upper layer clouds, including semitransparent cirrus. The limitation for small solar backscattering angles of <25° restricts the satellite coverage to ∼25% of the world area in a single day. PMID:26944081

  15. Detection of Tropical Overshooting Cloud Tops Using Himawari-8 Imagery

    Directory of Open Access Journals (Sweden)

    Miae Kim

    2017-07-01

    Full Text Available Abstract: Overshooting convective cloud Top (OT-accompanied clouds can cause severe weather conditions, such as lightning, strong winds, and heavy rainfall. The distribution and behavior of OTs can affect regional and global climate systems. In this paper, we propose a new approach for OT detection by using machine learning methods with multiple infrared images and their derived features. Himawari-8 satellite images were used as the main input data, and binary detection (OT or nonOT with class probability was the output of the machine learning models. Three machine learning techniques—random forest (RF, extremely randomized trees (ERT, and logistic regression (LR—were used to develop OT classification models to distinguish OT from non-OT. The hindcast validation over the Southeast Asia and West Pacific regions showed that RF performed best, resulting in a mean probabilities of detection (POD of 77.06% and a mean false alarm ratio (FAR of 36.13%. Brightness temperature at 11.2 μm (Tb11 and its standard deviation (STD in a 3 × 3 window size were identified as the most contributing variables for discriminating OT and nonOT classes. The proposed machine learning-based OT detection algorithms produced promising results comparable to or even better than the existing approaches, which are the infrared window (IRW-texture and water vapor (WV minus IRW brightness temperature difference (BTD methods.

  16. Diagnosing Aircraft Icing Potential from Satellite Cloud Retrievals

    Science.gov (United States)

    Smith, William L., Jr.; Minnis, Patrick; Fleeger, Cecilia; Spangenberg, Douglas

    2013-01-01

    The threat for aircraft icing in clouds is a significant hazard that routinely impacts aviation operations. Accurate diagnoses and forecasts of aircraft icing conditions requires identifying the location and vertical distribution of clouds with super-cooled liquid water (SLW) droplets, as well as the characteristics of the droplet size distribution. Traditional forecasting methods rely on guidance from numerical models and conventional observations, neither of which currently resolve cloud properties adequately on the optimal scales needed for aviation. Satellite imagers provide measurements over large areas with high spatial resolution that can be interpreted to identify the locations and characteristics of clouds, including features associated with adverse weather and storms. This paper describes new techniques for interpreting cloud products derived from satellite data to infer the flight icing threat to aircraft. For unobscured low clouds, the icing threat is determined using empirical relationships developed from correlations between satellite imager retrievals of liquid water path and droplet size with icing conditions reported by pilots (PIREPS). For deep ice over water cloud systems, ice and liquid water content (IWC and LWC) profiles are derived by using the imager cloud properties to constrain climatological information on cloud vertical structure and water phase obtained apriori from radar and lidar observations, and from cloud model analyses. Retrievals of the SLW content embedded within overlapping clouds are mapped to the icing threat using guidance from an airfoil modeling study. Compared to PIREPS and ground-based icing remote sensing datasets, the satellite icing detection and intensity accuracies are approximately 90% and 70%, respectively, and found to be similar for both low level and deep ice over water cloud systems. The satellite-derived icing boundaries capture the reported altitudes over 90% of the time. Satellite analyses corresponding to

  17. An ASIFT-Based Local Registration Method for Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Xiangjun Wang

    2015-05-01

    Full Text Available Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion. Finally, the parameters of the homography matrix were optimized by the Levenberg–Marquardt (LM algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang’E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE and better distribution of feature points.

  18. The development of a land use inventory for regional planning using satellite imagery

    Science.gov (United States)

    Hessling, A. H.; Mara, T. G.

    1975-01-01

    Water quality planning in Ohio, Kentucky, and Indiana is reviewed in terms of use of land use data and satellite imagery. A land use inventory applicable to water quality planning and developed through computer processing of LANDSAT-1 imagery is described.

  19. Casa Grande Ruins National Monument Vegetation Mapping Project - Quickbird Satellite Imagery

    Data.gov (United States)

    National Park Service, Department of the Interior — This imagery was acquired on December 3, 2007 by DigitalGlobe, Inc.'s Quickbird satellite. Its 4 multispectral bands (blue, green, red, near infrared), together with...

  20. Estimated Depth Maps of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Estimated shallow-water, depth maps were produced using rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the...

  1. Estimated Depth Maps of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Estimated shallow-water, depth maps were produced using rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the...

  2. Nearshore Benthic Habitats of Timor-Leste Derived from WorldView-2 Satellite Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Benthic habitat classes were derived for nearshore waters around Timor-Leste from WorldView-2 satellite imagery. Habitat classes include different combinations of...

  3. An Automatic Cloud Detection Method for ZY-3 Satellite

    Directory of Open Access Journals (Sweden)

    CHEN Zhenwei

    2015-03-01

    Full Text Available Automatic cloud detection for optical satellite remote sensing images is a significant step in the production system of satellite products. For the browse images cataloged by ZY-3 satellite, the tree discriminate structure is adopted to carry out cloud detection. The image was divided into sub-images and their features were extracted to perform classification between clouds and grounds. However, due to the high complexity of clouds and surfaces and the low resolution of browse images, the traditional classification algorithms based on image features are of great limitations. In view of the problem, a prior enhancement processing to original sub-images before classification was put forward in this paper to widen the texture difference between clouds and surfaces. Afterwards, with the secondary moment and first difference of the images, the feature vectors were extended in multi-scale space, and then the cloud proportion in the image was estimated through comprehensive analysis. The presented cloud detection algorithm has already been applied to the ZY-3 application system project, and the practical experiment results indicate that this algorithm is capable of promoting the accuracy of cloud detection significantly.

  4. Comparison of different cloud types from surface and satellite cloud classification products over China

    Science.gov (United States)

    Wang, Minyan; Zeng, Le; Wang, Shengjie; Gu, Junxia; Yang, Runzhi

    2016-04-01

    Different cloud types usually have different cloud dynamic process and micro-physical characteristics, and the relative cloud radiation forcing effects vary much. In recent years, the focus of cloud classification is the algorithm development, as well as the analysis on total cloud amount, high/middle/low cloud amount. While, research on the different cloud types (like cirrus, stratus, and cumulonimbus) is not enough. In this research, we use multi-resources cloud classification products including FY-2, Cloudsat and surface observation to obtain the temporal-spatial distribution characteristics and evolvement of different cloud types in different regions of China, analyze the quantitative difference of multi-source products and the reasons. According to the temporal and spatial scales of cloud, and temporal-spatial representation of cloud classification products based on CloudSat, etc, the scaling is necessary to explore in temporal-spatial matching/validation research. This research have important scientific significances on understanding the regional characteristics of different cloud types in China, improving the remote sensing retrieve algorithms on cloud classification, temporal-spatial matching/validation techniques of satellite data, and cloud vertical structure parameterized methods in numerical models.

  5. Use of satellite imagery to map and monitor vegetation in New Zealand

    OpenAIRE

    Stephens, P. R.; Dymond, J. R.; Brown, L J

    1995-01-01

    研究概要:Land resource and environmental decision makers require quantitative information on the spatial distribution of vegetation types and their condition, and changes in these over time. Such vegetation mapping and monitoring is often required to be undertaken quickly. Remotely-sensed satellite imagery, in conjunction with other data sources, have been used to satisfy this need. This paper describes the uses of satellite imagery by reference to three regional mapping projects in New Zealand. ...

  6. APPLICABILITY EVALUATION OF OBJECT DETECTION METHOD TO SATELLITE AND AERIAL IMAGERIES

    Directory of Open Access Journals (Sweden)

    K. Kamiya

    2016-06-01

    Full Text Available Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ object detection before object recognition can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images.

  7. Applicability Evaluation of Object Detection Method to Satellite and Aerial Imageries

    Science.gov (United States)

    Kamiya, K.; Fuse, T.; Takahashi, M.

    2016-06-01

    Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ object detection before object recognition can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING) method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images.

  8. Microphysical Effects of Cloud Seeding in Supercooled Stratiform Clouds Observed from NOAA Satellite

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Based on the satellite retrieval methodology, the spectral characteristics and cloud microphysical properties were analyzed that included brightness temperatures of Channels 4 and 5, and their brightness temperature difference (BTD), the particle effective radius of seeded cloud track caused by an operational cloud seeding and the microphysical effects of cloud seeding were revealed by the comparisons of their differences inside and outside the seeded track. The cloud track was actually a cloud channel reaching 1.5-km deep and 14-km wide lasting for more than 80 min. The effective radius of ambient clouds was 10-15μm, while that within the cloud track ranged from 15 to 26 μm. The ambient clouds were composed of supercooled droplets, and the composition of the cloud within the seeding track was ice. With respect to the rather stable reflectance of two ambient sides around the track, the visible spectral reflectance in the cloud track varied at least 10%, and reached a maximum of 35%, the reflectance of 3.7 μm in the seeded track relatively decreased at least 10%. As cloud seeding advanced, the width and depth were gradually increased. Simultaneously the cloud top temperature within the track became progressively warmer with respect to the ambient clouds,and the maximum temperature differences reached 4.2 and 3.9℃ at the first seeding position for Channels 4 and 5. In addition, the BTD in the track also increased steadily to a maximum of 1.4℃, compared with 0.2-0.4℃ of the ambient clouds. The evidence that the seeded cloud became thinner comes from the visible image showing a channel, the warming of the cloud tops, and the increase of BTD in the seeded track.The seeded cloud became thinner mainly because the cloud top descended and it lost water to precipitation throughout its depth. For this cloud seeding case, the glaciation became apparent at cloud tops about 22min after seeding. The formation of a cloud track in the supercooled stratiform clouds was

  9. Defense Meteorological Satellite Program (DMSP)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Defense Meteorological Satellite Program (DMSP) satellites collect visible and infrared cloud imagery as well as monitoring the atmospheric, oceanographic,...

  10. Three attempts of earthquake prediction with satellite cloud images

    Directory of Open Access Journals (Sweden)

    G. Guangmeng

    2013-01-01

    Full Text Available Thermal anomalies detected from satellite data are widely reported. Nearly all the anomalies are reported after the quake. Here we report three earthquake predictions in Italy and Iran according to satellite cloud anomalies. These cloud anomalies usually show a linear pattern, stay there for hours and do not move with winds. According to these anomalies, we can give a rough estimation about impending earthquake activities. All the estimated dates and magnitudes are in good agreement with the earthquake facts, and the only unsatisfactory point is that the distance error is 100–300 km. Because the cloud anomaly is long, we can not reduce the distance error further. A possible way is to combine geophysical data and satellite data together to estimate the epicenter and this will increase the prediction accuracy.

  11. Routine Ocean Monitoring With Synthetic Aperture Radar Imagery Obtained From the Alaska Satellite Facility

    Science.gov (United States)

    Pichel, W. G.; Clemente-Colon, P.; Li, X.; Friedman, K.; Monaldo, F.; Thompson, D.; Wackerman, C.; Scott, C.; Jackson, C.; Beal, R.; McGuire, J.; Nicoll, J.

    2006-12-01

    The Alaska Satellite Facility (ASF) has been processing synthetic aperture radar (SAR) data for research and for near-real-time applications demonstrations since shortly after the launch of the European Space Agency's ERS-1 satellite in 1991. The long coastline of Alaska, the vast extent of ocean adjacent to Alaska, a scarcity of in-situ observations, and the persistence of cloud cover all contribute to the need for all-weather ocean observations in the Alaska region. Extensive experience with SAR product processing algorithms and SAR data analysis techniques, and a growing sophistication on the part of SAR data and product users have amply demonstrated the value of SAR instruments in providing this all-weather ocean observation capability. The National Oceanic and Atmospheric Administration (NOAA) has been conducting a near-real-time applications demonstration of SAR ocean and hydrologic products in Alaska since September 1999. This Alaska SAR Demonstration (AKDEMO) has shown the value of SAR-derived, high-resolution (sub kilometer) ocean surface winds to coastal weather forecasting and the understanding of coastal wind phenomena such as gap winds, barrier jets, vortex streets, and lee waves. Vessel positions and ice information derived from SAR imagery have been used for management of fisheries, protection of the fishing fleet, enforcement of fisheries regulations, and protection of endangered marine mammals. Other ocean measurements, with potentially valuable applications, include measurement of wave state (significant wave height, dominant wave direction and wavelength, and wave spectra), mapping of oil spills, and detection of shallow-water bathymetric features. In addition to the AKDEMO, ASF-processed SAR imagery is being used: (1) in the Gulf of Mexico for hurricane wind studies, and post-hurricane oil-spill and oil-platform analyses (the latter employing ship-detection algorithms for detection of changes in oil-platform locations); (2) in the North Pacific

  12. Demonstrative potential of multitemporal satellite imagery in documenting urban dynamics: generalisation from the Bucharest city case

    Science.gov (United States)

    Aldea, Mihaela; Petrescu, Florian; Parlow, Eberhard; Iacoboaea, Cristina; Luca, Oana; Sercaianu, Mihai; Gaman, Florian

    2016-08-01

    The main objective of this paper is to demonstrate the potential of multitemporal satellite imagery to be processed and used in documenting urban changes that took place over time, with limited resources involved and taking advantage of the opportunity to be able to use the satellite imagery available as open data. The possibilities to analyse and compare the written literature regarding the chronological evolution of a city with the patterns of Land Use/Land Cover obtained from the processing of satellite remotely sensed images of the respective scenery were investigated based upon a case study of a selected city. The extent of the prospects of using remote sensing based methods and multitemporal satellite imagery is also expressed as a result of this investigation.

  13. A review of uses of satellite imagery in monitoring mangrove forests

    Science.gov (United States)

    Rhyma Purnamasayangsukasih, P.; Norizah, K.; Ismail, Adnan A. M.; Shamsudin, I.

    2016-06-01

    Satellite image could provide much information of earth surfaces in a large scale in a short time, thus saving time. With the evolution and development of sensors providing satellite image, resolution of object captured enhanced with advance image processing techniques. In forestry, satellite image has been widely used for resources management, planning, monitoring, predicting, etc. However, the uses of satellite image are reported to be moderate and sometimes poor for mangrove forests due to homogenous species existed in salty and inundation areas. Many researches had been carried out to improve the uses of satellite imagery of either optical or radar data for mangrove forests. This paper reviews the uses of satellite imagery data in mangrove with the main focus of the literature related to mangroves monitoring.

  14. High-resolution satellite imagery is an important yet underutilized resource in conservation biology.

    Science.gov (United States)

    Boyle, Sarah A; Kennedy, Christina M; Torres, Julio; Colman, Karen; Pérez-Estigarribia, Pastor E; de la Sancha, Noé U

    2014-01-01

    Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.

  15. High-Resolution Satellite Imagery Is an Important yet Underutilized Resource in Conservation Biology

    Science.gov (United States)

    Boyle, Sarah A.; Kennedy, Christina M.; Torres, Julio; Colman, Karen; Pérez-Estigarribia, Pastor E.; de la Sancha, Noé U.

    2014-01-01

    Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making. PMID:24466287

  16. Evaluating cloud precipitation efficiency with satellite retrievals of water isotopologues

    Science.gov (United States)

    Bailey, A.; Noone, D. C.; Wood, R.

    2015-12-01

    The efficiency with which clouds precipitate is believed to influence climate by modifying cloud lifetime and, ultimately, cloud amount. Aerosols can influence this linkage by reducing the effective radii of cloud droplets and suppressing precipitation. This relationship, however, is not unidirectional. Cloud precipitation efficiency can also regulate particle concentrations, since precipitation effectively scavenges aerosols from the atmosphere. One challenge in studying how aerosols, clouds, and precipitation processes interrelate is that observational constraints are difficult to attain. This work evaluates the ability of isotope ratios in water vapor to quantify cloud precipitation efficiency across the tropical and subtropical oceans. Theory suggests isotope ratios will record the precipitation efficiency of a convective plume, since heavier isotopologues precipitate preferentially; and a recent analysis of in situ measurements from the Mauna Loa Observatory (MLO, Hawaii, USA) verifies this to be the case. The challenge now lies in understanding whether satellite retrievals of isotope ratios in water vapor are sensitive enough to track precipitation efficiency globally. To answer this question, vertical profiles of the D/H ratio derived from NASA's Tropospheric Emission Spectrometer (TES) are first compared with the MLO in situ measurements. A qualitative match indicates the satellite retrievals can distinguish high from low precipitation efficiency convection. To expand the analysis geographically, TES profiles between 40°S and 40°N are compared with estimates of precipitation efficiency derived from the Tropical Rainfall Measuring Mission (TRMM) and ECMWF's ERA-Interim. Retrievals are binned by lower-tropospheric humidity and by vertical velocity in order to minimize large-scale thermodynamical influences. Co-located cloud retrievals provide the context necessary to evaluate the utility of these new estimates in elucidating cloud feedbacks on climate.

  17. Satellite Imagery Cadastral Features Extractions using Image Processing Algorithms: A Viable Option for Cadastral Science

    Directory of Open Access Journals (Sweden)

    Usman Babawuro

    2012-07-01

    Full Text Available Satellite images are used for feature extraction among other functions. They are used to extract linear features, like roads, etc. These linear features extractions are important operations in computer vision. Computer vision has varied applications in photogrammetric, hydrographic, cartographic and remote sensing tasks. The extraction of linear features or boundaries defining the extents of lands, land covers features are equally important in Cadastral Surveying. Cadastral Surveying is the cornerstone of any Cadastral System. A two dimensional cadastral plan is a model which represents both the cadastral and geometrical information of a two dimensional labeled Image. This paper aims at using and widening the concepts of high resolution Satellite imagery data for extracting representations of cadastral boundaries using image processing algorithms, hence minimizing the human interventions. The Satellite imagery is firstly rectified hence establishing the satellite imagery in the correct orientation and spatial location for further analysis. We, then employ the much available Satellite imagery to extract the relevant cadastral features using computer vision and image processing algorithms. We evaluate the potential of using high resolution Satellite imagery to achieve Cadastral goals of boundary detection and extraction of farmlands using image processing algorithms. This method proves effective as it minimizes the human demerits associated with the Cadastral surveying method, hence providing another perspective of achieving cadastral goals as emphasized by the UN cadastral vision. Finally, as Cadastral science continues to look to the future, this research aimed at the analysis and getting insights into the characteristics and potential role of computer vision algorithms using high resolution satellite imagery for better digital Cadastre that would provide improved socio economic development.

  18. Evidence for climate change in the satellite cloud record

    Science.gov (United States)

    Norris, Joel R.; Allen, Robert J.; Evan, Amato T.; Zelinka, Mark D.; O'Dell, Christopher W.; Klein, Stephen A.

    2016-08-01

    Clouds substantially affect Earth’s energy budget by reflecting solar radiation back to space and by restricting emission of thermal radiation to space. They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and observational datasets over what cloud changes have occurred during recent decades and will occur in response to global warming. This is because observational systems originally designed for monitoring weather have lacked sufficient stability to detect cloud changes reliably over decades unless they have been corrected to remove artefacts. Here we show that several independent, empirically corrected satellite records exhibit large-scale patterns of cloud change between the 1980s and the 2000s that are similar to those produced by model simulations of climate with recent historical external radiative forcing. Observed and simulated cloud change patterns are consistent with poleward retreat of mid-latitude storm tracks, expansion of subtropical dry zones, and increasing height of the highest cloud tops at all latitudes. The primary drivers of these cloud changes appear to be increasing greenhouse gas concentrations and a recovery from volcanic radiative cooling. These results indicate that the cloud changes most consistently predicted by global climate models are currently occurring in nature.

  19. APPLYING SATELLITE IMAGERY TO TRIAGE ASSESSMENT OF ECOSYSTEM HEALTH

    Science.gov (United States)

    Considerable evidence documents that certain changes in vegetation and soils result in irreversibly degraded rangeland ecosystems. We used Advanced Very High Resolution Radiometer (AVHRR)imagery to develop calibration patterns of change in the Normalized Difference Vegetation Ind...

  20. A thermodynamic geography: night-time satellite imagery as a proxy measure of emergy.

    Science.gov (United States)

    Coscieme, Luca; Pulselli, Federico M; Bastianoni, Simone; Elvidge, Christopher D; Anderson, Sharolyn; Sutton, Paul C

    2014-11-01

    Night-time satellite imagery enables the measurement, visualization, and mapping of energy consumption in an area. In this paper, an index of the "sum of lights" as observed by night-time satellite imagery within national boundaries is compared with the emergy of the nations. Emergy is a measure of the solar energy equivalent used, directly or indirectly, to support the processes that characterize the economic activity in a country. Emergy has renewable and non-renewable components. Our results show that the non-renewable component of national emergy use is positively correlated with night-time satellite imagery. This relationship can be used to produce emergy density maps which enable the incorporation of spatially explicit representations of emergy in geographic information systems. The region of Abruzzo (Italy) is used to demonstrate this relationship as a spatially disaggregate case.

  1. Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution

    Directory of Open Access Journals (Sweden)

    C. K. Carbajal Henken

    2015-03-01

    Full Text Available This work presents a study on the sensitivity of two satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity is exploited by relating the difference in the retrieved cloud heights to cloud vertical extent. The two cloud height retrievals, performed within the Freie Universität Berlin AATSR MERIS Cloud (FAME-C algorithm, are based on independent measurements and different retrieval techniques. First, cloud top temperature (CTT is retrieved from Advanced Along Track Scanning Radiometer (AATSR measurements in the thermal infrared. Second, cloud top pressure (CTP is retrieved from Medium Resolution Imaging Spectrometer (MERIS measurements in the oxygen-A absorption band. Both CTT and CTP are converted to cloud top height (CTH using atmospheric profiles from a numerical weather prediction model. A sensitivity study using radiative transfer simulations in the near-infrared and thermal infrared were performed to demonstrate the larger impact of the assumed cloud vertical extinction profile on MERIS than on AATSR top-of-atmosphere measurements. The difference in retrieved CTH (ΔCTH from AATSR and MERIS are related to cloud vertical extent (CVE as observed by ground-based lidar and radar at three ARM sites. To increase the impact of the cloud vertical extinction profile on the MERIS-CTP retrievals, single-layer and geometrically thin clouds are assumed in the forward model. The results of the comparison to the ground-based observations were separated into single-layer and multi-layer cloud cases. Analogous to previous findings, the MERIS-CTP retrievals appear to be close to pressure levels in the middle of the cloud. Assuming a linear relationship, the ΔCTH multiplied by 2.5 gives an estimate on the CVE for single-layer clouds. The relationship is weaker for multi-layer clouds. Due to large variations of cloud vertical extinction profiles occurring in nature, a quantitative estimate of the cloud vertical extent

  2. CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    K.-Y. Lee

    2016-06-01

    Full Text Available Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012 uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+ and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate

  3. Cloud Detection of Optical Satellite Images Using Support Vector Machine

    Science.gov (United States)

    Lee, Kuan-Yi; Lin, Chao-Hung

    2016-06-01

    Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection

  4. Comparison of TOMS and AVHRR imagery of volcanic clouds

    Science.gov (United States)

    Rose, William I.

    1994-01-01

    This project consisted of merging volcanic cloud data from initial studies from TOMS and AVHRR sensors. Because of the different data acquisition platforms and processing algorithms and formats, the combination of the two data required special consideration. Once the registration technique was perfected, several eruptions with data from both sensors were studied. The conclusion showed that, from the initial work, the positions of the volcanic clouds as detected by the two sensors are very similar. Because the AVHRR algorithm is now capable of retrieving masses of ash and sulfate aerosol, the capability of comparing data from the two sensors now allows changes in SO2, H2SO4, and silicated ash in drifting clouds to be described. The results of this study culminated in several presentations, and a master's thesis, which in turn was converted into a paper submitted to the Journal of Geophysical Research. Abstracts of two presentations are attached.

  5. The Potential Uses of Commercial Satellite Imagery in the Middle East

    Energy Technology Data Exchange (ETDEWEB)

    Vannoni, M.G.

    1999-06-08

    It became clear during the workshop that the applicability of commercial satellite imagery to the verification of future regional arms control agreements is limited at this time. Non-traditional security topics such as environmental protection, natural resource management, and the development of infrastructure offer the more promising applications for commercial satellite imagery in the short-term. Many problems and opportunities in these topics are regional, or at least multilateral, in nature. A further advantage is that, unlike arms control and nonproliferation applications, cooperative use of imagery in these topics can be done independently of the formal Middle East Peace Process. The value of commercial satellite imagery to regional arms control and nonproliferation, however, will increase during the next three years as new, more capable satellite systems are launched. Aerial imagery, such as that used in the Open Skies Treaty, can also make significant contributions to both traditional and non-traditional security applications but has the disadvantage of requiring access to national airspace and potentially higher cost. There was general consensus that commercial satellite imagery is under-utilized in the Middle East and resources for remote sensing, both human and institutional, are limited. This relative scarcity, however, provides a natural motivation for collaboration in non-traditional security topics. Collaborations between scientists, businesses, universities, and non-governmental organizations can work at the grass-roots level and yield contributions to confidence building as well as scientific and economic results. Joint analysis projects would benefit the region as well as establish precedents for cooperation.

  6. Satellite Imagery Measures of the Astronomically Aligned Megaliths at Nabta Playa

    Science.gov (United States)

    Brophy, T. G.; Rosen, P. A.

    2003-12-01

    Astronomically aligned megalithic structures described in field reports (Wendorf, F. and Malville, J.M., The Megalith Alignments, pp.489-502 in Holocene Settlement of the Egyptian Sahara, Vol.I, 2001.) are identified in newly acquired georectified 60 cm panchromatic satellite imagery of Nabta Playa, southern Egypt. The satellite images allow refinement, often significant, of the reported locations of the megaliths. The report that the primary megalithic alignment was constructed to point to the bright star Sirius, circa 4,820 BC, is reconsidered in light of the satellite data, new field data, radiocarbon, lithostratigraphic and geochronologic data, and the playa sedimentation history. Other possible archaeoastronomical interpretations are considered for that alignment, including the three stars of Orion's Belt circa 6,270 BC that are also implicated in the small Nabta Playa `calendar circle'. Other new features apparent in the satellite imagery are also considered.

  7. Speckle filtering in satellite SAR change detection imagery

    NARCIS (Netherlands)

    Dekker, R.J.

    1998-01-01

    Repeat-pass Synthetic Aperture Radar (SAR) imagery is useful for change detection. A disadvantage of SAR is the system-inherent speckle noise. This can be reduced by filtering. Various filter types and methods are described in the literature, but not one fits the speckle noise in change detection

  8. Vegetation extraction from high-resolution satellite imagery using the Normalized Difference Vegetation Index (NDVI)

    Science.gov (United States)

    AlShamsi, Meera R.

    2016-10-01

    Over the past years, there has been various urban development all over the UAE. Dubai is one of the cities that experienced rapid growth in both development and population. That growth can have a negative effect on the surrounding environment. Hence, there has been a necessity to protect the environment from these fast pace changes. One of the major impacts this growth can have is on vegetation. As technology is evolving day by day, there is a possibility to monitor changes that are happening on different areas in the world using satellite imagery. The data from these imageries can be utilized to identify vegetation in different areas of an image through a process called vegetation detection. Being able to detect and monitor vegetation is very beneficial for municipal planning and management, and environment authorities. Through this, analysts can monitor vegetation growth in various areas and analyze these changes. By utilizing satellite imagery with the necessary data, different types of vegetation can be studied and analyzed, such as parks, farms, and artificial grass in sports fields. In this paper, vegetation features are detected and extracted through SAFIY system (i.e. the Smart Application for Feature extraction and 3D modeling using high resolution satellite ImagerY) by using high-resolution satellite imagery from DubaiSat-2 and DEIMOS-2 satellites, which provide panchromatic images of 1m resolution and spectral bands (red, green, blue and near infrared) of 4m resolution. SAFIY system is a joint collaboration between MBRSC and DEIMOS Space UK. It uses image-processing algorithms to extract different features (roads, water, vegetation, and buildings) to generate vector maps data. The process to extract green areas (vegetation) utilize spectral information (such as, the red and near infrared bands) from the satellite images. These detected vegetation features will be extracted as vector data in SAFIY system and can be updated and edited by end-users, such as

  9. Satellite observations of cloud regime development: the role of aerosol processes

    OpenAIRE

    E. Gryspeerdt; Stier, P.; D. G. Partridge

    2013-01-01

    Many different interactions between aerosols and clouds have been postulated based on correlations between satellite retrieved aerosol and cloud properties. Previous studies highlighted the importance of meteorological covariability to the observed correlations. In this work, we make use of multiple temporally-spaced satellite retrievals to observe the development of cloud regimes. The observation of cloud regime development allows us to account for the influences of cloud fraction (C...

  10. Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements

    OpenAIRE

    Zhang, M.; Lin, W.; Klein, S.; J. Bacmeister; Bony, S.; Cederwall, R.; Del Genio, A; Hack, J.; Loeb, N.; Lohmann, U.; P. Minnis; Musat, I.; Pincus, R; Stier, P.; Suarez, M.

    2005-01-01

    To assess the current status of climate models in simulating clouds, basic cloud climatologies from ten atmospheric general circulation models are compared with satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) and the Clouds and Earth's Radiant Energy System (CERES) program. An ISCCP simulator is employed in all models to facilitate the comparison. Models simulated a four-fold difference in high-top clouds. There are also, however, large uncertainties ...

  11. The pan-sharpening of satellite and UAV imagery for agricultural applications

    Science.gov (United States)

    Jenerowicz, Agnieszka; Woroszkiewicz, Malgorzata

    2016-10-01

    Remote sensing techniques are widely used in many different areas of interest, i.e. urban studies, environmental studies, agriculture, etc., due to fact that they provide rapid, accurate and information over large areas with optimal time, spatial and spectral resolutions. Agricultural management is one of the most common application of remote sensing methods nowadays. Monitoring of agricultural sites and creating information regarding spatial distribution and characteristics of crops are important tasks to provide data for precision agriculture, crop management and registries of agricultural lands. For monitoring of cultivated areas many different types of remote sensing data can be used- most popular are multispectral satellites imagery. Such data allow for generating land use and land cover maps, based on various methods of image processing and remote sensing methods. This paper presents fusion of satellite and unnamed aerial vehicle (UAV) imagery for agricultural applications, especially for distinguishing crop types. Authors in their article presented chosen data fusion methods for satellite images and data obtained from low altitudes. Moreover the authors described pan- sharpening approaches and applied chosen pan- sharpening methods for multiresolution image fusion of satellite and UAV imagery. For such purpose, satellite images from Landsat- 8 OLI sensor and data collected within various UAV flights (with mounted RGB camera) were used. In this article, the authors not only had shown the potential of fusion of satellite and UAV images, but also presented the application of pan- sharpening in crop identification and management.

  12. Reconstruction of an infrared band of meteorological satellite imagery with abductive networks

    Science.gov (United States)

    Singer, Harvey A.; Cockayne, John E.; Versteegen, Peter L.

    1995-01-01

    As the current fleet of meteorological satellites age, the accuracy of the imagery sensed on a spectral channel of the image scanning system is continually and progressively degraded by noise. In time, that data may even become unusable. We describe a novel approach to the reconstruction of the noisy satellite imagery according to empirical functional relationships that tie the spectral channels together. Abductive networks are applied to automatically learn the empirical functional relationships between the data sensed on the other spectral channels to calculate the data that should have been sensed on the corrupted channel. Using imagery unaffected by noise, it is demonstrated that abductive networks correctly predict the noise-free observed data.

  13. Measurements of Cumulonimbus Clouds using quantitative satellite and radar data

    Science.gov (United States)

    Negri, A. J.; Reynolds, D. W.; Maddox, R. A.

    1977-01-01

    Results are reported for a preliminary study of SMS-2 digital brightness and IR data obtained at frequent 5-7.5 min intervals. The clouds studied were over the Central and Great Plains in midlatitudes and thus were typical of an environment much different from that of the tropical oceans. The satellite data are compared to radar data for both a severe weather event and weak thundershower activity of the type which might be a target for weather modification efforts. The relative importance of short time interval satellite data is shown for both cases, and possible relationships between the two types of data are presented. It is concluded that (1) using a threshold technique for visible reflected brightness, precipitating vs. nonprecipitating clouds can be discriminated; (2) brightness is well related to cloud size and shape; and (3) satellite-derived growth rates may be a significant parameter to be used in determining storm severity, especially if rapid time sequence data are used during the development phase of the storm.

  14. Identifying Hail Signatures in Satellite Imagery from the 9-10 August 2011 Severe Weather Event

    Science.gov (United States)

    Dryden, Rachel L.; Molthan, Andrew L.; Cole, Tony A.; Bell, Jordan R.

    2014-01-01

    Hail scars are identifiable in MODIS satellite imagery based on NDVI change, which was dominantly negative. Hail damage spatially correlates with SPC hail reports and MESH. This study developed a proxy for quantifying crop loss at varying thresholds to address the gap between SPC damage estimates and insurance payouts.

  15. Estimation of Vegetation Aerodynamic Roughness of Natural Regions Using Frontal Area Density Determined from Satellite Imagery

    Science.gov (United States)

    Jasinski, Michael F.; Crago, Richard

    1994-01-01

    Parameterizations of the frontal area index and canopy area index of natural or randomly distributed plants are developed, and applied to the estimation of local aerodynamic roughness using satellite imagery. The formulas are expressed in terms of the subpixel fractional vegetation cover and one non-dimensional geometric parameter that characterizes the plant's shape. Geometrically similar plants and Poisson distributed plant centers are assumed. An appropriate averaging technique to extend satellite pixel-scale estimates to larger scales is provided. ne parameterization is applied to the estimation of aerodynamic roughness using satellite imagery for a 2.3 sq km coniferous portion of the Landes Forest near Lubbon, France, during the 1986 HAPEX-Mobilhy Experiment. The canopy area index is estimated first for each pixel in the scene based on previous estimates of fractional cover obtained using Landsat Thematic Mapper imagery. Next, the results are incorporated into Raupach's (1992, 1994) analytical formulas for momentum roughness and zero-plane displacement height. The estimates compare reasonably well to reference values determined from measurements taken during the experiment and to published literature values. The approach offers the potential for estimating regionally variable, vegetation aerodynamic roughness lengths over natural regions using satellite imagery when there exists only limited knowledge of the vegetated surface.

  16. Detection of ZY-3 Satellite Platform Jitter Using Multi-spectral Imagery

    Directory of Open Access Journals (Sweden)

    ZHU Ying

    2015-04-01

    Full Text Available Satellite platform jitter is one of the factors that affect the quality of high resolution imagery, which can cause image blur and internal distortion. Taking ZiYuan-3 (ZY-3 multi-spectral camera as a prototype, this paper proposes a satellite platform jitter detection method by utilizing multi-spectral imagery. First, imaging characteristics of multispectral camera and the main factors affecting band-to-band registration error are introduced. Then the regularity of registration error caused by platform jitter is analyzed by theoretical derivation and simulation. Meanwhile, the platform jitter detection method based on high accuracy dense points matching is presented. Finally, the experiments were conducted by using ZY-3 multi-spectral imagery captured in different time. The result indicates that ZY-3 has a periodic platform jitter about 0.6 Hz in the imaging period of test data, and the jitter amplitude across track is greater than that along track, which causes periodic band-to-band registration error with the same frequency. The result shows the possibility of the improvement in geometric processing accuracy for ZY-3 imagery products and provides an important reference for satellite platform jitter source analysis and satellite platform design optimization.

  17. A data mining approach for sharpening satellite thermal imagery over land

    Science.gov (United States)

    Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes which are at significant...

  18. Cloud detection using Meteosat imagery and numerical weather prediction model data

    CERN Document Server

    Feijt, A; Van der Veen, S

    2000-01-01

    The cloud detection algorithm of the Royal Netherlands Meteorological Institute (KNMI) Meteosat Cloud Detection and Characterization KNMI (Metclock) scheme is introduced. The algorithm analyzes the Meteosat infrared and visual channel measurements over an area from about 25 degrees W to 25 degrees E and from 35 degrees to 70 degrees N, encompassing Europe and a small part of northern Africa. The scheme utilizes surface temperatures from a numerical weather prediction model. Synoptic observations are used to adjust the model surface temperatures to represent satellite brightness temperatures for cloud-free conditions. The measured reflected sunlight is analyzed using a minimum reflectivity atlas. Comparison of cloud detection results with synoptic observations of cloud cover at about 800 synoptic stations over land and 50 over sea were made on a 3-h basis for 1997. In total, two million synoptic observations were used to evaluate the detection method. Of the reported cloud cover, Metclock detected 89% during d...

  19. Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution

    Science.gov (United States)

    Carbajal Henken, C. K.; Doppler, L.; Lindstrot, R.; Preusker, R.; Fischer, J.

    2015-08-01

    This work presents a study on the sensitivity of two satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity is exploited by relating the difference in the retrieved cloud heights to cloud vertical extent. The two cloud height retrievals, performed within the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) algorithm, are based on independent measurements and different retrieval techniques. First, cloud-top temperature (CTT) is retrieved from Advanced Along Track Scanning Radiometer (AATSR) measurements in the thermal infrared. Second, cloud-top pressure (CTP) is retrieved from Medium Resolution Imaging Spectrometer (MERIS) measurements in the oxygen-A absorption band and a nearby window channel. Both CTT and CTP are converted to cloud-top height (CTH) using atmospheric profiles from a numerical weather prediction model. First, a sensitivity study using radiative transfer simulations in the near-infrared and thermal infrared was performed to demonstrate, in a quantitative manner, the larger impact of the assumed cloud vertical extinction profile, described in terms of shape and vertical extent, on MERIS than on AATSR top-of-atmosphere measurements. Consequently, cloud vertical extinction profiles will have a larger influence on the MERIS than on the AATSR cloud height retrievals for most cloud types. Second, the difference in retrieved CTH (ΔCTH) from AATSR and MERIS are related to cloud vertical extent (CVE), as observed by ground-based lidar and radar at three ARM sites. To increase the impact of the cloud vertical extinction profile on the MERIS-CTP retrievals, single-layer and geometrically thin clouds are assumed in the forward model. Similarly to previous findings, the MERIS-CTP retrievals appear to be close to pressure levels in the middle of the cloud. Assuming a linear relationship, the ΔCTH multiplied by 2.5 gives an estimate on the CVE for single-layer clouds. The relationship is stronger for single

  20. Fully automated procedure for ship detection using optical satellite imagery

    Science.gov (United States)

    Corbane, C.; Pecoul, E.; Demagistri, L.; Petit, M.

    2009-01-01

    Ship detection from remote sensing imagery is a crucial application for maritime security which includes among others traffic surveillance, protection against illegal fisheries, oil discharge control and sea pollution monitoring. In the framework of a European integrated project GMES-Security/LIMES, we developed an operational ship detection algorithm using high spatial resolution optical imagery to complement existing regulations, in particular the fishing control system. The automatic detection model is based on statistical methods, mathematical morphology and other signal processing techniques such as the wavelet analysis and Radon transform. This paper presents current progress made on the detection model and describes the prototype designed to classify small targets. The prototype was tested on panchromatic SPOT 5 imagery taking into account the environmental and fishing context in French Guiana. In terms of automatic detection of small ship targets, the proposed algorithm performs well. Its advantages are manifold: it is simple and robust, but most of all, it is efficient and fast, which is a crucial point in performance evaluation of advanced ship detection strategies.

  1. Normalization and calibration of geostationary satellite radiances for the International Satellite Cloud Climatology Project

    Science.gov (United States)

    Desormeaux, Yves; Rossow, William B.; Brest, Christopher L.; Campbell, G. G.

    1993-01-01

    Procedures are described for normalizing the radiometric calibration of image radiances obtained from geostationary weather satellites that contributed data to the International Satellite Cloud Climatology Project. The key step is comparison of coincident and collocated measurements made by each satellite and the concurrent AVHRR on the 'afternoon' NOAA polar-orbiting weather satellite at the same viewing geometry. The results of this comparison allow transfer of the AVHRR absolute calibration, which has been established over the whole series, to the radiometers on the geostationary satellites. Results are given for Meteosat-2, 3, and 4, for GOES-5, 6, and 7, for GMS-2, 3, and 4 and for Insat-1B. The relative stability of the calibrations of these radiance data is estimated to be within +/- 3 percent; the uncertainty of the absolute calibrations is estimated to be less than 10 percent. The remaining uncertainties are at least two times smaller than for the original radiance data.

  2. Mapping snow depth in open alpine terrain from stereo satellite imagery

    Science.gov (United States)

    Marti, R.; Gascoin, S.; Berthier, E.; de Pinel, M.; Houet, T.; Laffly, D.

    2016-07-01

    To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available.

  3. Current Usage and Future Prospects of Multispectral (RGB) Satellite Imagery in Support of NWS Forecast Offices and National Centers

    Science.gov (United States)

    Molthan, Andrew L.; Fuell, Kevin K.; Knaff, John; Lee, Thomas

    2012-01-01

    Current and future satellite sensors provide remotely sensed quantities from a variety of wavelengths ranging from the visible to the passive microwave, from both geostationary and low-Earth orbits. The NASA Short-term Prediction Research and Transition (SPoRT) Center has a long history of providing multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA s Terra and Aqua satellites in support of NWS forecast office activities. Products from MODIS have recently been extended to include a broader suite of multispectral imagery similar to those developed by EUMETSAT, based upon the spectral channel s available from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard METEOSAT-9. This broader suite includes products that discriminate between air mass types associated with synoptic-scale features, assists in the identification of dust, and improves upon paired channel difference detection of fog and low cloud events. Similarly, researchers at NOAA/NESDIS and CIRA have developed air mass discrimination capabilities using channels available from the current GOES Sounders. Other applications of multispectral composites include combinations of high and low frequency, horizontal and vertically polarized passive microwave brightness temperatures to discriminate tropical cyclone structures and other synoptic-scale features. Many of these capabilities have been transitioned for evaluation and operational use at NWS Weather Forecast Offices and National Centers through collaborations with SPoRT and CIRA. Future instruments will continue the availability of these products and also expand upon current capabilities. The Advanced Baseline Imager (ABI) on GOES-R will improve the spectral, spatial, and temporal resolution of our current geostationary capabilities, and the recent launch of the Suomi National Polar-Orbiting Partnership (S-NPP) carries instruments such as the Visible Infrared Imager Radiometer Suite (VIIRS), the Cross

  4. Phase 2 Final Report. IAEA Safeguards: Implementation blueprint of commercial satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Andersson, Christer [SSC Satellitbild AB, Solna (Sweden)

    2000-01-01

    This document - IAEA Safeguards: Implementation Blueprint of Commercial Satellite Imagery - constitutes the second report from SSC Satellitbild giving a structured view and solid guidelines on how to proceed with a conceivable implementation of satellite imagery to support Safeguards activities of the Agency. This Phase 2 report presents a large number of concrete recommendations regarding suggested management issues, work organisation, imagery purchasing and team building. The study has also resulted in several lists of actions and preliminary project plans with GANT schedules concerning training, hardware and software, as well as for the initial pilot studies. In both the Phase 1 and Phase 2 studies it is confirmed that the proposed concept of a relatively small Imagery Unit using high-resolution data will be a sound and feasible undertaking. Such a unit capable of performing advanced image processing as a tool for various safeguard tasks will give the Agency an effective instrument for reference, monitoring, verification, and detection of declared and undeclared activities. The total cost for implementing commercial satellite imagery at the Department for Safeguards, as simulated in these studies, is approximately MUSD 1,5 per year. This cost is founded on an activity scenario with a staff of 4 experts working in an IAEA Imagery Unit with a workload of three dossiers or issues per week. The imagery unit is built around an advanced PC image processing system capable of handling several hundreds of pre-processed images per year. Alternatively a Reduced Scenario with a staff of 3 would need a budget of approximately MUSD 0,9 per year, whereas an Enhanced Imagery Unit including 5 experts and a considerably enlarged capacity would cost MUSD 1,7 per year. The Imagery Unit should be organised so it clearly reflects the objectives and role as set by the Member States and the management of the Agency. We recommend the Imagery Unit to be organised into four main work

  5. Validation of Satellite-Based Objective Overshooting Cloud-Top Detection Methods Using CloudSat Cloud Profiling Radar Observations

    Science.gov (United States)

    Bedka, Kristopher M.; Dworak, Richard; Brunner, Jason; Feltz, Wayne

    2012-01-01

    Two satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11-mm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD.0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions.

  6. Visualization and unsupervised classification of changes in multispectral satellite imagery

    DEFF Research Database (Denmark)

    Canty, Morton J.; Nielsen, Allan Aasbjerg

    2006-01-01

    The statistical techniques of multivariate alteration detection, minimum/maximum autocorrelation factors transformation, expectation maximization and probabilistic label relaxation are combined in a unified scheme to visualize and to classify changes in multispectral satellite data. The methods...

  7. Unsupervised classification of changes in multispectral satellite imagery

    DEFF Research Database (Denmark)

    Canty, Morton J.; Nielsen, Allan Aasbjerg

    2004-01-01

    The statistical techniques of multivariate alteration detection, maximum autocorrelation factor transformation, expectation maximization, fuzzy maximum likelihood estimation and probabilistic label relaxation are combined in a unified scheme to classify changes in multispectral satellite data...

  8. Visualization and unsupervised classification of changes in multispectral satellite imagery

    DEFF Research Database (Denmark)

    Canty, Morton J.; Nielsen, Allan Aasbjerg

    2006-01-01

    The statistical techniques of multivariate alteration detection, minimum/maximum autocorrelation factors transformation, expectation maximization and probabilistic label relaxation are combined in a unified scheme to visualize and to classify changes in multispectral satellite data. The methods...

  9. Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Zhenfeng Shao

    2017-03-01

    Full Text Available Cloud detection of remote sensing imagery is quite challenging due to the influence of complicated underlying surfaces and the variety of cloud types. Currently, most of the methods mainly rely on prior knowledge to extract features artificially for cloud detection. However, these features may not be able to accurately represent the cloud characteristics under complex environment. In this paper, we adopt an innovative model named Fuzzy Autoencode Model (FAEM to integrate the feature learning ability of stacked autoencode networks and the detection ability of fuzzy function for highly accurate cloud detection on remote sensing imagery. Our proposed method begins by selecting and fusing spectral, texture, and structure information. Thereafter, the proposed technique established a FAEM to learn the deep discriminative features from a great deal of selected information. Finally, the learned features are mapped to the corresponding cloud density map with a fuzzy function. To demonstrate the effectiveness of the proposed method, 172 Landsat ETM+ images and 25 GF-1 images with different spatial resolutions are used in this paper. For the convenience of accuracy assessment, ground truth data are manually outlined. Results show that the average RER (ratio of right rate and error rate on Landsat images is greater than 29, while the average RER of Support Vector Machine (SVM is 21.8 and Random Forest (RF is 23. The results on GF-1 images exhibit similar performance as Landsat images with the average RER of 25.9, which is much higher than the results of SVM and RF. Compared to traditional methods, our technique has attained higher average cloud detection accuracy for either different spatial resolutions or various land surfaces.

  10. Cloud cover estimation: Use of GOES imagery in development of cloud cover data base for insolation assessment

    Science.gov (United States)

    Huning, J. R.; Logan, T. L.; Smith, J. H.

    1982-01-01

    The potential of using digital satellite data to establish a cloud cover data base for the United States, one that would provide detailed information on the temporal and spatial variability of cloud development are studied. Key elements include: (1) interfacing GOES data from the University of Wisconsin Meteorological Data Facility with the Jet Propulsion Laboratory's VICAR image processing system and IBIS geographic information system; (2) creation of a registered multitemporal GOES data base; (3) development of a simple normalization model to compensate for sun angle; (4) creation of a variable size georeference grid that provides detailed cloud information in selected areas and summarized information in other areas; and (5) development of a cloud/shadow model which details the percentage of each grid cell that is cloud and shadow covered, and the percentage of cloud or shadow opacity. In addition, comparison of model calculations of insolation with measured values at selected test sites was accomplished, as well as development of preliminary requirements for a large scale data base of cloud cover statistics.

  11. Epipolar Resampling of Cross-Track Pushbroom Satellite Imagery Using the Rigorous Sensor Model.

    Science.gov (United States)

    Jannati, Mojtaba; Valadan Zoej, Mohammad Javad; Mokhtarzade, Mehdi

    2017-01-11

    Epipolar resampling aims to eliminate the vertical parallax of stereo images. Due to the dynamic nature of the exterior orientation parameters of linear pushbroom satellite imagery and the complexity of reconstructing the epipolar geometry using rigorous sensor models, so far, no epipolar resampling approach has been proposed based on these models. In this paper for the first time it is shown that the orientation of the instantaneous baseline (IB) of conjugate image points (CIPs) in the linear pushbroom satellite imagery can be modeled with high precision in terms of the rows- and the columns-number of CIPs. Taking advantage of this feature, a novel approach is then presented for epipolar resampling of cross-track linear pushbroom satellite imagery. The proposed method is based on the rigorous sensor model. As the instantaneous position of sensors remains fixed, the digital elevation model of the area of interest is not required in the resampling process. Experimental results obtained from two pairs of SPOT and one pair of RapidEye stereo imagery with different terrain conditions shows that the proposed epipolar resampling approach benefits from a superior accuracy, as the remained vertical parallaxes of all CIPs in the normalized images are close to zero.

  12. Epipolar Resampling of Cross-Track Pushbroom Satellite Imagery Using the Rigorous Sensor Model

    Directory of Open Access Journals (Sweden)

    Mojtaba Jannati

    2017-01-01

    Full Text Available Epipolar resampling aims to eliminate the vertical parallax of stereo images. Due to the dynamic nature of the exterior orientation parameters of linear pushbroom satellite imagery and the complexity of reconstructing the epipolar geometry using rigorous sensor models, so far, no epipolar resampling approach has been proposed based on these models. In this paper for the first time it is shown that the orientation of the instantaneous baseline (IB of conjugate image points (CIPs in the linear pushbroom satellite imagery can be modeled with high precision in terms of the rows- and the columns-number of CIPs. Taking advantage of this feature, a novel approach is then presented for epipolar resampling of cross-track linear pushbroom satellite imagery. The proposed method is based on the rigorous sensor model. As the instantaneous position of sensors remains fixed, the digital elevation model of the area of interest is not required in the resampling process. Experimental results obtained from two pairs of SPOT and one pair of RapidEye stereo imagery with different terrain conditions shows that the proposed epipolar resampling approach benefits from a superior accuracy, as the remained vertical parallaxes of all CIPs in the normalized images are close to zero.

  13. Multipath sparse coding for scene classification in very high resolution satellite imagery

    Science.gov (United States)

    Fan, Jiayuan; Tan, Hui Li; Lu, Shijian

    2015-10-01

    With the rapid development of various satellite sensors, automatic and advanced scene classification technique is urgently needed to process a huge amount of satellite image data. Recently, a few of research works start to implant the sparse coding for feature learning in aerial scene classification. However, these previous research works use the single-layer sparse coding in their system and their performances are highly related with multiple low-level features, such as scale-invariant feature transform (SIFT) and saliency. Motivated by the importance of feature learning through multiple layers, we propose a new unsupervised feature learning approach for scene classification on very high resolution satellite imagery. The proposed unsupervised feature learning utilizes multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex satellite scene images. In addition, the dense low-level features are extracted from the raw satellite data by using different image patches with varying size at different layers, and this approach is not limited to a particularly designed feature descriptors compared with the other related works. The proposed technique has been evaluated on two challenging high-resolution datasets, including the UC Merced dataset containing 21 different aerial scene categories with a 1 foot resolution and the Singapore dataset containing 5 land-use categories with a 0.5m spatial resolution. Experimental results show that it outperforms the state-of-the-art that uses the single-layer sparse coding. The major contributions of this proposed technique include (1) a new unsupervised feature learning approach to generate feature representation for very high-resolution satellite imagery, (2) the first multipath sparse coding that is used for scene classification in very high-resolution satellite imagery, (3) a simple low-level feature descriptor instead of many particularly designed low-level descriptor

  14. Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

    Science.gov (United States)

    Shelestov, Andrii; Lavreniuk, Mykola; Kussul, Nataliia; Novikov, Alexei; Skakun, Sergii

    2017-02-01

    Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory ( 28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

  15. Study of the Nevada Test Site using Landsat satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Zimmerman, P.D. [Georgetown Univ., Washington, DC (United States). Center for Strategic and International Studies

    1993-07-01

    In the period covered by the purchase order CSIS has obtained one Landsat image and determined that two images previously supplied to the principal investigator under a subcontract with George Washington University were inherently defective. We have negotiated with EOSAT over the reprocessing of those scenes and anticipate final delivery within the next few weeks. A critical early purchase during the subcontract period was of an EXABYTE tape drive, Adaptec SCSI interface, and the appropriate software with which to read Landsat images at CSIS. This gives us the capability of reading and manipulating imagery in house without reliance on outside services which have not proven satisfactory. In addition to obtaining imagery for the study, we have also performed considerable analytic work on the newly and previously purchased images. A technique developed under an earlier subcontract for identifying underground nuclear tests at Pahute Mesa has been significantly refined, and similar techniques were applied to the summit of Rainier Mesa and to the Yucca Flats area. An entirely new technique for enhancing the spectral signatures of different regions of NTS was recently developed, and appears to have great promise of success.

  16. Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

    Science.gov (United States)

    Kit, Oleksandr; Lüdeke, Matthias

    2013-09-01

    This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

  17. Satellite orientation and position for geometric correction of scanner imagery.

    Science.gov (United States)

    Salamonowicz, P.H.

    1986-01-01

    The USGS Mini Image Processing System currently relies on a polynomial method for geometric correction of Landsat multispectral scanner (MSS) data. A large number of ground control points are required because polynomials do not model the sources of error. In order to reduce the number of necessary points, a set of mathematical equations modeling the Landsat satellite motions and MSS scanner has been derived and programmed. A best fit to the equations is obtained by using a least-squares technique that permits computation of the satellite orientation and position parameters based on only a few control points.-from Author

  18. The Application of the Technology of 3D Satellite Cloud Imaging in Virtual Reality Simulation

    Directory of Open Access Journals (Sweden)

    Xiao-fang Xie

    2007-05-01

    Full Text Available Using satellite cloud images to simulate clouds is one of the new visual simulation technologies in Virtual Reality (VR. Taking the original data of satellite cloud images as the source, this paper depicts specifically the technology of 3D satellite cloud imaging through the transforming of coordinates and projection, creating a DEM (Digital Elevation Model of cloud imaging and 3D simulation. A Mercator projection was introduced to create a cloud image DEM, while solutions for geodetic problems were introduced to calculate distances, and the outer-trajectory science of rockets was introduced to obtain the elevation of clouds. For demonstration, we report on a computer program to simulate the 3D satellite cloud images.

  19. Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV Imagery

    Directory of Open Access Journals (Sweden)

    Arko Lucieer

    2012-05-01

    Full Text Available Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud ( < 1–3 cm point spacing is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from 50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion can be monitored.

  20. a Detailed Study about Digital Surface Model Generation Using High Resolution Satellite Stereo Imagery

    Science.gov (United States)

    Gong, K.; Fritsch, D.

    2016-06-01

    Photogrammetry is currently in a process of renaissance, caused by the development of dense stereo matching algorithms to provide very dense Digital Surface Models (DSMs). Moreover, satellite sensors have improved to provide sub-meter or even better Ground Sampling Distances (GSD) in recent years. Therefore, the generation of DSM from spaceborne stereo imagery becomes a vivid research area. This paper presents a comprehensive study about the DSM generation of high resolution satellite data and proposes several methods to implement the approach. The bias-compensated Rational Polynomial Coefficients (RPCs) Bundle Block Adjustment is applied to image orientation and the rectification of stereo scenes is realized based on the Project-Trajectory-Based Epipolarity (PTE) Model. Very dense DSMs are generated from WorldView-2 satellite stereo imagery using the dense image matching module of the C/C++ library LibTsgm. We carry out various tests to evaluate the quality of generated DSMs regarding robustness and precision. The results have verified that the presented pipeline of DSM generation from high resolution satellite imagery is applicable, reliable and very promising.

  1. Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT

    Directory of Open Access Journals (Sweden)

    I. M. Lensky

    2008-03-01

    Full Text Available A methodology for representing much of the physical information content of the METEOSAT Second Generation (MSG geostationary satellite using red-green-blue (RGB composites of the computed physical values of the picture elements is presented. The physical values are the solar reflectance in the solar channels and brightness temperature in the thermal channels. The main RGB compositions are (1 "Day Natural Colors", presenting vegetation in green, bare surface in brown, sea surface in black, water clouds as white, ice as magenta; (2 "Day Microphysical", presenting cloud microstructure using the solar reflectance component of the 3.9 μm, visible and thermal IR channels; (3 "Night Microphysical", also presenting clouds microstructure using the brightness temperature differences between 10.8 and 3.9 μm; (4 "Day and Night", using only thermal channels for presenting surface and cloud properties, desert dust and volcanic emissions; (5 "Air Mass", presenting mid and upper tropospheric features using thermal water vapor and ozone channels. The scientific basis for these rendering schemes is provided, with examples for the applications. The expanding use of these rendering schemes requires their proper documentation and setting as standards, which is the main objective of this publication.

  2. Satellite observations of cloud regime development: the role of aerosol processes

    OpenAIRE

    E. Gryspeerdt; Stier, P.; D. G. Partridge

    2014-01-01

    Many different interactions between aerosols and clouds have been postulated, based on correlations between satellite retrieved aerosol and cloud properties. Previous studies highlighted the importance of meteorological covariations to the observed correlations. In this work, we make use of multiple temporally-spaced satellite retrievals to observe the development of cloud regimes. The observation of cloud regime development allows us to account for the influences of clo...

  3. A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery

    Directory of Open Access Journals (Sweden)

    Kaveh Shahi

    2015-06-01

    Full Text Available This research develops a spectral index to automatically extract asphalt road networks named road extraction index (REI. This index uses WorldView-2 (WV-2 imagery, which has high spatial resolution and is multispectral. To determine the best bands for WV-2, field spectral data using a field spectroradiometer were collected. These data were then analyzed statistically. The bands were selected through the methodology of stepwise discriminant analysis. The appropriate WV-2 bands were distinguished from one another as per significant wavelengths. The proposed index is based on this classification. By applying REI to WV-2 imagery, we can extract asphalt roads accurately. Results demonstrate that REI is automated, transferable, and efficient in asphalt road extraction from high-resolution satellite imagery.

  4. Cloud and Thermodynamic Parameters Retrieved from Satellite Ultraspectral Infrared Measurements

    Science.gov (United States)

    Zhou, Daniel K.; Smith, William L.; Larar, Allen M.; Liu, Xu; Taylor, Jonathan P.; Schluessel, Peter; Strow, L. Larrabee; Mango, Stephen A.

    2008-01-01

    Atmospheric-thermodynamic parameters and surface properties are basic meteorological parameters for weather forecasting. A physical geophysical parameter retrieval scheme dealing with cloudy and cloud-free radiance observed with satellite ultraspectral infrared sounders has been developed and applied to the Infrared Atmospheric Sounding Interferometer (IASI) and the Atmospheric InfraRed Sounder (AIRS). The retrieved parameters presented herein are from radiance data gathered during the Joint Airborne IASI Validation Experiment (JAIVEx). JAIVEx provided intensive aircraft observations obtained from airborne Fourier Transform Spectrometer (FTS) systems, in-situ measurements, and dedicated dropsonde and radiosonde measurements for the validation of the IASI products. Here, IASI atmospheric profile retrievals are compared with those obtained from dedicated dropsondes, radiosondes, and the airborne FTS system. The IASI examples presented here demonstrate the ability to retrieve fine-scale horizontal features with high vertical resolution from satellite ultraspectral sounder radiance spectra.

  5. The Application Achievements And Perspective Of CBERS Series Satellite Imagery

    Institute of Scientific and Technical Information of China (English)

    Li Xingchao; Qi Xueyong; Lu Yilin

    2009-01-01

    @@ Since the first China-Brazil Earth Resources Satellite (CBERS-1),launched in 1999,the CBERS data has been applied in many fields extensively.Remarkable social and economic benefits have been achieved.This article presents the application achievements during the past nine years,and gives a perspective for the future.All these applications demonstrate that the CBERS data has been an important data source for resources investigation and monitoring.

  6. Image Dodging Algorithm for GF-1 Satellite WFV Imagery

    Directory of Open Access Journals (Sweden)

    HAN Jie

    2016-12-01

    Full Text Available Image dodging method is one of the important processes that determines whether the mosaicking image can be used for remote sensing quantitative application. GF-1 satellite is the first satellite in CHEOS (Chinese high-resolution earth observation system. WFV multispectral sensor is one of the instruments onboard GF-1 satellite which consist of four cameras to mosaic imaging. According to the characteristics of WFV sensor, this paper proposes an image dodging algorithm based on cross/inter-radiometric calibration method. First, the traditional cross calibration method is applied to obtain the calibration coefficients of one WFV camera. Then statistical analysis and simulation methods are adopted to build the correlation models of DN and TOA (top of atmosphere radiances between adjacent cameras. The proposed method can not only accomplish the radiation performance transfer, but also can fulfill the image dodging. The experimental results show the cross/inter-radiometric calibration coefficients in this paper can effectively eliminate the radiation inconsistency problem of the adjacent camera image which realizes the image dodging. So our proposed dodging method can provide an important reference for other similar sensor in future.

  7. Detecting inter-annual variability in the phenological characteristics of southern Africa’s vegetation using satellite imagery

    CSIR Research Space (South Africa)

    Wessels, Konrad J

    2011-01-01

    Full Text Available Vegetation phenology refers to the timing of seasonal biological events (for example, bud burst, leaf unfolding, vegetation growth and leaf senescence) and biotic and abiotic forces that control these. Daily, coarse-resolution satellite imagery...

  8. A geographic investigation of hazards, disasters and recovery using satellite imagery

    Science.gov (United States)

    Keys-Mathews, Lisa D.

    Disaster recovery is depicted on the landscape by change through time. Given the classic uses of remote sensing for detecting change, this dissertation assessed the applicability of remote sensing image analysis to the study of long-term recovery from disasters. Because recovery is complex and dynamic a framework was that established that divided the recovery landscape into three components: the built, relief, and natural environments. Four study sites were selected for this research representing three types of hazard events (earthquakes, tsunami and hurricane), three climatic environments (tropical, dry and humid subtropical), and four cultures (Iran, Indonesia, Peru and the United States). The four disasters occurred between 2001 and 2005 with each a catastrophic event. To begin the research, a list of diagnostic features of recovery was created through field observations, reconnaissance reports, descriptions of disaster recovery case studies, and current literature. These features were then documented in the satellite imagery as examples of their portrayal on the landscape. Second, elements of each environment (built, relief, and natural) were explored through application of digital image processing techniques including: principal components analysis, texture analysis, normalized differenced vegetation index, and digital image classification. Each of these techniques was applied to the imagery with the final results being a digital analysis through time. Finally, the analysis was integrated to determine if differential recovery was visible through the analysis of satellite imagery. This neighborhood scale investigation compared satellite imagery findings to a rapid visual assessment in Gulfport and synthesized the findings toward an understanding of differential recovery. This dissertation determined that satellite imagery and remote sensing techniques supported by fieldwork are appropriate and valuable tools in the study of disaster recovery. Features and

  9. Identifying Hail Signatures in Satellite Imagery from the 9-10 August 2011 Severe Weather Event

    Science.gov (United States)

    Dryden, Rachel L.; Molthan, Andrew L.; Cole, Tony A.; Bell, Jordan

    2014-01-01

    Severe thunderstorms can produce large hail that causes property damage, livestock fatalities, and crop failure. However, detailed storm surveys of hail damage conducted by the National Weather Service (NWS) are not required. Current gaps also exist between Storm Prediction Center (SPC) hail damage estimates and crop-insurance payouts. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites can be used to support NWS damage assessments, particularly to crops during the growing season. The two-day severe weather event across western Nebraska and central Kansas during 9-10 August 2011 offers a case study for investigating hail damage signatures by examining changes in Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery. By analyzing hail damage swaths in satellite imagery, potential economic losses due to crop damage can be quantified and further improve the estimation of weather impacts on agriculture without significantly increasing manpower requirements.

  10. Needs for registration and rectification of satellite imagery for land use and land cover and hydrologic applications

    Science.gov (United States)

    Gaydos, L.

    1982-01-01

    The use of satellite imagery and data for registration of land use, land cover and hydrology was discussed. Maps and aggregations are made from existing the data in concert with other data in a geographic information system. Basic needs for registration and rectification of satellite imagery related to specifying, reformatting, and overlaying the data are noted. It is found that the data are sufficient for users who must expand much effort in registering data.

  11. Geospatial Information from Satellite Imagery for Geovisualisation of Smart Cities in India

    Science.gov (United States)

    Mohan, M.

    2016-06-01

    In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

  12. Commercial Imagery Satellite Threat: How Can U.S. Forces Protect Themselves?

    Science.gov (United States)

    2006-05-31

    10 Joint Chiefs of Staff, Joint Warfare of the Armed Forces of the United States, Appendix B. 11 Laurence Nardon, "The Dilemma of...January 2004]. Nardon, Laurence . "The Dilemma of Satellite Imagery Control." Military Technology, July 2002, 37-46. National Defense Panel...34 Fact Sheet. Washington DC: White House, 13 May 2003. <http://www.au.af.mil/au/awc/awcgate/space/ 2003remotesensing.htm> [3 December 2003]. Rees

  13. GEOSPATIAL INFORMATION FROM SATELLITE IMAGERY FOR GEOVISUALISATION OF SMART CITIES IN INDIA

    Directory of Open Access Journals (Sweden)

    M. Mohan

    2016-06-01

    Full Text Available In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

  14. Estimating Monthly Rainfall from Geostationary Satellite Imagery Over Amazonia, Brazil.

    Science.gov (United States)

    Cutrim, Elen Maria Camara

    The infrared regression and the grid-history satellite rainfall estimating techniques were utilized to estimate monthly rainfall in Amazonia during one month of the rainy season (March, 1980) and one month of the dry season (September, 1980). The estimates were based on 3-hourly SMS-II infrared and visible images. Three sets of coefficients for the grid history method (Marajo, Arabian Sea, and GATE) were used to estimate rainfall. The estimated rain was compared with gauge measurements over the region. The infrared regression technique overestimated by a factor of 1.5. The Marajo coefficients yielded the best estimate, especially for eastern Amazonia. In the wet month Marajo coefficients overestimated rain by 10% and in the dry month by 70%. The Arabian Sea coefficients overestimated rain and the GATE coefficients slightly underestimated rain for Amazonia. Two maps of monthly rainfall over Amazonia were constructed for March and September, 1980, combining the ground station and satellite inferred rainfall of the grid history method using the Marajo coefficients. The satellite observations and ground data were mutually compatible and were contourable on these final, composite maps. Monthly rainfall was found to be much more inhomogeneous than previously reported. In March there was a belt of high precipitation trending southwest, with higher values and sharpest gradients in the coastal area. The upper Amazon was also an area of high precipitation, both north and south of the equator. In Roraima rainfall decreased drastically to the north. In September, the area of highest precipitation was the northwestern part of Amazonas State (northern hemisphere). Rainfall elsewhere was very localized and in northeastern Amazonia varied from 0 to 150 mm. Even though the grid history method presented better results for estimating rainfall over Amazonia, the IR model could be utilized more efficiently and economically on an operational basis if the calibration were properly made

  15. Modelling tick abundance using machine learning techniques and satellite imagery

    DEFF Research Database (Denmark)

    Kjær, Lene Jung; Korslund, L.; Kjelland, V.

    satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

  16. SOME ASPECTS OF SATELLITE IMAGERY INTEGRATION FROM EROS B AND LANDSAT 8

    Directory of Open Access Journals (Sweden)

    A. Fryskowska

    2016-06-01

    Full Text Available The Landsat 8 satellite which was launched in 2013 is a next generation of the Landsat remote sensing satellites series. It is equipped with two new sensors: the Operational Land Imager (OLI and the Thermal Infrared Sensor (TIRS. What distinguishes this satellite from the previous is four new bands (coastal aerosol, cirrus and two thermal infrared TIRS bands. Similar to its antecedent, Landsat 8 records electromagnetic radiation in a panchromatic band at a range of 0.5‐0.9 μm with a spatial resolution equal to 15 m. In the paper, multispectral imagery integration capabilities of Landsat 8 with data from the new high resolution panchromatic EROS B satellite are analyzed. The range of panchromatic band for EROS B is 0.4‐0.9 μm and spatial resolution is 0.7 m. Research relied on improving the spatial resolution of natural color band combinations (bands: 4,3,2 and of desired false color band composition of Landsat 8 satellite imagery. For this purpose, six algorithms have been tested: Brovey’s, Mulitplicative, PCA, IHS, Ehler's, HPF. On the basis of the visual assessment, it was concluded that the best results of multispectral and panchromatic image integration, regardless land cover, are obtained for the multiplicative method. These conclusions were confirmed by statistical analysis using correlation coefficient, ERGAS and R-RMSE indicators.

  17. ANALYSIS OF AMBIENT FIELDS AND SATELLITE IMAGERY CHARACTERISTICS OF EFFECT OF BAY OF BENGAL STORMS ON LOW-LATITUDE PLATEAU

    Institute of Scientific and Technical Information of China (English)

    XU Mei-ling; ZHANG Xiu-nian; YANG Su-yu

    2007-01-01

    Based on the composite analysis method, 12 rainstorms triggered by Bay of Bengal storms(shortened as B-storms hereafter) across the whole province of Yunnan were studied, and some interesting results of rain and circulation characteristics influenced by the storms were obtained for low-latitude plateau.Usually, when a rainstorm weather occurs in low-latitude plateau, the B-storm center locates in the central,east or north parts of the Bay of Bengal. At the same time, the subtropical high ridge moves to 15°N - 20°Nand the west ridge point moves to the Indo-china Peninsula from the South China Sea and the low-latitude plateau is controlled by southwest air streams coming from the front of the trough and the periphery of the subtropical high. The southwest low-level jet stream from the east side of the bay storm has great effect on heavy rains. On the one hand, the southwest low-level jet stream is playing the role of transporting water vapor and energy. On the other hand, the southwest low-level jet stream is helpful to keep essential dynamical condition. From the analysis of the satellite cloud imagery, it is found that mesoscale convection cloud clusters will keep growing and moving into the low-latitude plateau to cause heavy rains when a storm forms in the Bay of Bengal.

  18. Forests through the Eye of a Satellite: Understanding regional forest-cover dynamics using Landsat Imagery

    Science.gov (United States)

    Baumann, Matthias

    Forests are changing at an alarming pace worldwide. Forests are an important provider of ecosystem services that contribute to human wellbeing, including the provision of timber and non-timber products, habitat for biodiversity, recreation amenities. Most prominently, forests serve as a sink for atmospheric carbon dioxide that ultimately helps to mitigate changes in the global climate. It is thus important to understand where, how and why forests change worldwide. My dissertation provides answers to these questions. The overarching goal of my dissertation is to improve our understanding of regional forest-cover dynamics by analyzing Landsat satellite imagery. I answer where forests change following drastic socio-economic shocks by using the breakdown of the Soviet Union as a natural experiment. My dissertation provides innovative algorithms to answer why forests change---because of human activities or because of natural events such as storms. Finally, I will show how dynamic forests are within one year by providing ways to characterize green-leaf phenology from satellite imagery. With my findings I directly contribute to a better understanding of the processes on the Earth's surface and I highlight the importance of satellite imagery to learn about regional and local forest-cover dynamics.

  19. Polar bears from space: assessing satellite imagery as a tool to track Arctic wildlife.

    Directory of Open Access Journals (Sweden)

    Seth Stapleton

    Full Text Available Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark-recapture models. This estimate (N: 94; 95% Confidence Interval: 92-105 was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (N: 102; 95% CI: 69-152. Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics.

  20. Plastic and Glass Greenhouses Detection and Delineation from WORLDVIEW-2 Satellite Imagery

    Science.gov (United States)

    Koc-San, D.; Sonmez, N. K.

    2016-06-01

    Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML), Random Forest (RF), and Support Vector Machines (SVM) classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.

  1. Quantitative comparisons of satellite observations and cloud models

    Science.gov (United States)

    Wang, Fang

    Microwave radiation interacts directly with precipitating particles and can therefore be used to compare microphysical properties found in models with those found in nature. Lower frequencies (minimization procedures but produce different CWP and RWP. The similarity in Tb can be attributed to comparable Total Water Path (TWP) between the two retrievals while the disagreement in the microphysics is caused by their different degrees of constraint of the cloud/rain ratio by the observations. This situation occurs frequently and takes up 46.9% in the one month 1D-Var retrievals examined. To attain better constrained cloud/rain ratios and improved retrieval quality, this study suggests the implementation of higher microwave frequency channels in the 1D-Var algorithm. Cloud Resolving Models (CRMs) offer an important pathway to interpret satellite observations of microphysical properties of storms. High frequency microwave brightness temperatures (Tbs) respond to precipitating-sized ice particles and can, therefore, be compared with simulated Tbs at the same frequencies. By clustering the Tb vectors at these frequencies, the scene can be classified into distinct microphysical regimes, in other words, cloud types. The properties for each cloud type in the simulated scene are compared to those in the observation scene to identify the discrepancies in microphysics within that cloud type. A convective storm over the Amazon observed by the Tropical Rainfall Measuring Mission (TRMM) is simulated using the Regional Atmospheric Modeling System (RAMS) in a semi-ideal setting, and four regimes are defined within the scene using cluster analysis: the 'clear sky/thin cirrus' cluster, the 'cloudy' cluster, the 'stratiform anvil' cluster and the 'convective' cluster. The relationship between Tb difference of 37 and 85 GHz and Tb at 85 GHz is found to contain important information of microphysical properties such as hydrometeor species and size distributions. Cluster

  2. Towards 3D Matching of Point Clouds Derived from Oblique and Nadir Airborne Imagery

    Science.gov (United States)

    Zhang, Ming

    Because of the low-expense high-efficient image collection process and the rich 3D and texture information presented in the images, a combined use of 2D airborne nadir and oblique images to reconstruct 3D geometric scene has a promising market for future commercial usage like urban planning or first responders. The methodology introduced in this thesis provides a feasible way towards fully automated 3D city modeling from oblique and nadir airborne imagery. In this thesis, the difficulty of matching 2D images with large disparity is avoided by grouping the images first and applying the 3D registration afterward. The procedure starts with the extraction of point clouds using a modified version of the RIT 3D Extraction Workflow. Then the point clouds are refined by noise removal and surface smoothing processes. Since the point clouds extracted from different image groups use independent coordinate systems, there are translation, rotation and scale differences existing. To figure out these differences, 3D keypoints and their features are extracted. For each pair of point clouds, an initial alignment and a more accurate registration are applied in succession. The final transform matrix presents the parameters describing the translation, rotation and scale requirements. The methodology presented in the thesis has been shown to behave well for test data. The robustness of this method is discussed by adding artificial noise to the test data. For Pictometry oblique aerial imagery, the initial alignment provides a rough alignment result, which contains a larger offset compared to that of test data because of the low quality of the point clouds themselves, but it can be further refined through the final optimization. The accuracy of the final registration result is evaluated by comparing it to the result obtained from manual selection of matched points. Using the method introduced, point clouds extracted from different image groups could be combined with each other to build a

  3. Phase 2 Final Report. IAEA Safeguards: Implementation blueprint of commercial satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Andersson, Christer [SSC Satellitbild AB, Solna (Sweden)

    2000-01-01

    This document - IAEA Safeguards: Implementation Blueprint of Commercial Satellite Imagery - constitutes the second report from SSC Satellitbild giving a structured view and solid guidelines on how to proceed with a conceivable implementation of satellite imagery to support Safeguards activities of the Agency. This Phase 2 report presents a large number of concrete recommendations regarding suggested management issues, work organisation, imagery purchasing and team building. The study has also resulted in several lists of actions and preliminary project plans with GANT schedules concerning training, hardware and software, as well as for the initial pilot studies. In both the Phase 1 and Phase 2 studies it is confirmed that the proposed concept of a relatively small Imagery Unit using high-resolution data will be a sound and feasible undertaking. Such a unit capable of performing advanced image processing as a tool for various safeguard tasks will give the Agency an effective instrument for reference, monitoring, verification, and detection of declared and undeclared activities. The total cost for implementing commercial satellite imagery at the Department for Safeguards, as simulated in these studies, is approximately MUSD 1,5 per year. This cost is founded on an activity scenario with a staff of 4 experts working in an IAEA Imagery Unit with a workload of three dossiers or issues per week. The imagery unit is built around an advanced PC image processing system capable of handling several hundreds of pre-processed images per year. Alternatively a Reduced Scenario with a staff of 3 would need a budget of approximately MUSD 0,9 per year, whereas an Enhanced Imagery Unit including 5 experts and a considerably enlarged capacity would cost MUSD 1,7 per year. The Imagery Unit should be organised so it clearly reflects the objectives and role as set by the Member States and the management of the Agency. We recommend the Imagery Unit to be organised into four main work

  4. RPC Stereo Processor (rsp) - a Software Package for Digital Surface Model and Orthophoto Generation from Satellite Stereo Imagery

    Science.gov (United States)

    Qin, R.

    2016-06-01

    Large-scale Digital Surface Models (DSM) are very useful for many geoscience and urban applications. Recently developed dense image matching methods have popularized the use of image-based very high resolution DSM. Many commercial/public tools that implement matching methods are available for perspective images, but there are rare handy tools for satellite stereo images. In this paper, a software package, RPC (rational polynomial coefficient) stereo processor (RSP), is introduced for this purpose. RSP implements a full pipeline of DSM and orthophoto generation based on RPC modelled satellite imagery (level 1+), including level 2 rectification, geo-referencing, point cloud generation, pan-sharpen, DSM resampling and ortho-rectification. A modified hierarchical semi-global matching method is used as the current matching strategy. Due to its high memory efficiency and optimized implementation, RSP can be used in normal PC to produce large format DSM and orthophotos. This tool was developed for internal use, and may be acquired by researchers for academic and non-commercial purpose to promote the 3D remote sensing applications.

  5. Performance Evaluation of Data Compression Systems Applied to Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Lilian N. Faria

    2012-01-01

    Full Text Available Onboard image compression systems reduce the data storage and downlink bandwidth requirements in space missions. This paper presents an overview and evaluation of some compression algorithms suitable for remote sensing applications. Prediction-based compression systems, such as DPCM and JPEG-LS, and transform-based compression systems, such as CCSDS-IDC and JPEG-XR, were tested over twenty multispectral (5-band images from CCD optical sensor of the CBERS-2B satellite. Performance evaluation of these algorithms was conducted using both quantitative rate-distortion measurements and subjective image quality analysis. The PSNR, MSSIM, and compression ratio results plotted in charts and the SSIM maps are used for comparison of quantitative performance. Broadly speaking, the lossless JPEG-LS outperforms other lossless compression schemes, and, for lossy compression, JPEG-XR can provide lower bit rate and better tradeoff between compression ratio and image quality.

  6. Using high-resolution satellite imagery to assess populations of animals in the Antarctic

    Science.gov (United States)

    LaRue, Michelle Ann

    The Southern Ocean is one of the most rapidly-changing ecosystems on the planet due to the effects of climate change and commercial fishing for ecologically-important krill and fish. It is imperative that populations of indicator species, such as penguins and seals, be monitored at regional- to global scales to decouple the effects of climate and anthropogenic changes for appropriate ecosystem-based management of the Southern Ocean. Remotely monitoring populations through high-resolution satellite imagery is currently the only feasible way to gain information about population trends of penguins and seals in Antarctica. In my first chapter, I review the literature where high-resolution satellite imagery has been used to assess populations of animals in polar regions. Building on this literature, my second chapter focuses on estimating changes in abundance in the Weddell seal population in Erebus Bay. I found a strong correlation between ground and satellite counts, and this finding provides an alternate method for assessing populations of Weddell seals in areas where less is known about population status. My third chapter explores how size of the guano stain of Adelie penguins can be used to predict population size. Using high-resolution imagery and ground counts, I built a model to estimate the breeding population of Adelie penguins using a supervised classification to estimate guano size. These results suggest that the size of guano stain is an accurate predictor of population size, and can be applied to estimate remote Adelie penguin colonies. In my fourth chapter, I use air photos, satellite imagery, climate and mark-resight data to determine that climate change has positively impacted the population of Adelie penguins at Beaufort Island through a habitat release that ultimately affected the dynamics within the southern Ross Sea metapopulation. Finally, for my fifth chapter I combined the literature with observations from aerial surveys and satellite imagery to

  7. Automated Rock Detection and Shape Analysis from Mars Rover Imagery and 3D Point Cloud Data

    Institute of Scientific and Technical Information of China (English)

    Kaichang Di; Zongyu Yue; Zhaoqin Liu; Shuliang Wang

    2013-01-01

    A new object-oriented method has been developed for the extraction of Mars rocks from Mars rover data.It is based on a combination of Mars rover imagery and 3D point cloud data.First,Navcam or Pancam images taken by the Mars rovers are segmented into homogeneous objects with a mean-shift algorithm.Then,the objects in the segmented images are classified into small rock candidates,rock shadows,and large objects.Rock shadows and large objects are considered as the regions within which large rocks may exist.In these regions,large rock candidates are extracted through ground-plane fitting with the 3D point cloud data.Small and large rock candidates are combined and postprocessed to obtain the final rock extraction results.The shape properties of the rocks (angularity,circularity,width,height,and width-height ratio) have been calculated for subsequent geological studies.

  8. Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

    Directory of Open Access Journals (Sweden)

    Nahuel R. Peralta

    2016-10-01

    Full Text Available A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington of Kansas (total of 457 ha. Three basic tests were conducted on the data: (1 spatial dependence on each of the yield and vegetation indices (VIs using Moran’s I test; (2 model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS and spatial econometric (SPL models; and (3 model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG was tested positive and statistically significant for most of the fields (p < 0.05, except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02 was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to

  9. River-ice and sea-ice velocity fields from near-simultaneous satellite imagery

    Science.gov (United States)

    Kaeaeb, A.; Leprince, S.; Prowse, T. D.; Beltaos, S.; Lamare, M.; Abrams, M.

    2013-12-01

    Satellite stereo and satellites that follow each other on similar orbits within short time periods produce near-simultaneous space imagery, a kind of data that is little exploited. In this study, we track river-ice and sea-ice motion over time periods of tens of seconds to several minutes, which is the typical time lag between the two or more images of such near-simultaneous acquisition constellations. Using this novel approach, we measure and visualize for the first time the almost complete two-dimensional minute-scale velocity fields over several thousand square-kilometers of sea ice cover or over up to several hundred kilometers long river reaches. We present the types of near-simultaneous imagery and constellations suitable for the measurements and discuss application examples, using a range of high and medium resolution imagery such as from ASTER, ALOS PRISM, Ikonos, WorldView-2, Landsat and EO-1. The river ice velocities obtained provide new insights into ice dynamics, river flow and river morphology, in particular during ice breakup. River-ice breakup and the associated downstream transport of ice debris is often the most important hydrological event of the year, producing flood levels that commonly exceed those for the open-water period and dramatic consequences for river infrastructure and ecology. We also estimate river discharge from ice/water surface velocities using near-simultaneous satellite imagery. Our results for sea ice complement velocity fields typically obtained over time-scales of days and can thus contribute to better understanding of a number of processes involved in sea ice drift, such as wind impact, tidal currents and interaction of ice floes with each other and with obstacles.

  10. Rice yield forecasting models using satellite imagery in Egypt

    Directory of Open Access Journals (Sweden)

    N.A. Noureldin

    2013-06-01

    Full Text Available Ability to make yield prediction before harvest using satellite remote sensing is important in many aspects of agricultural decision-making. In this study, canopy reflectance band and different band ratios in form of vegetation indices (VI with leaf area index (LAI were used to generate remotely sensed pre-harvest empirical rice yield prediction models. LAI measurements, spectral data derived from two SPOT data acquired on August 24, 2008 and August 23, 2009 and observed rice yield were used as main inputs for rice yield modeling. Each remotely sensed factor was used separately and in combination with LAI to generate the models. The results showed that green spectral band, middle infra-red spectral band and green vegetation index (GVI did not show sufficient capability as rice yield estimators while other inputs such as red spectral band, near infrared spectral band and vegetation indices that are algebraic ratios from these two spectral bands when used separately or in combined with leaf area index (LAI produced high accurate rice yield estimation models. The validation process was carried out using two statistical tests; standard error of estimate and the correlation coefficient between modeled and predicted yield. The validation results indicated that using normalized difference vegetation index (NDVI combined with leaf area index (LAI produced the model with highest accuracy and stability during the two rice seasons. The generated models are applicable 90 days after planting in any similar environmental conditions and agricultural practices.

  11. Man-made objects cuing in satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Skurikhin, Alexei N [Los Alamos National Laboratory

    2009-01-01

    We present a multi-scale framework for man-made structures cuing in satellite image regions. The approach is based on a hierarchical image segmentation followed by structural analysis. A hierarchical segmentation produces an image pyramid that contains a stack of irregular image partitions, represented as polygonized pixel patches, of successively reduced levels of detail (LOOs). We are jumping off from the over-segmented image represented by polygons attributed with spectral and texture information. The image is represented as a proximity graph with vertices corresponding to the polygons and edges reflecting polygon relations. This is followed by the iterative graph contraction based on Boruvka's Minimum Spanning Tree (MST) construction algorithm. The graph contractions merge the patches based on their pairwise spectral and texture differences. Concurrently with the construction of the irregular image pyramid, structural analysis is done on the agglomerated patches. Man-made object cuing is based on the analysis of shape properties of the constructed patches and their spatial relations. The presented framework can be used as pre-scanning tool for wide area monitoring to quickly guide the further analysis to regions of interest.

  12. Environmental monitoring of El Hierro Island submarine volcano, by combining low and high resolution satellite imagery

    Science.gov (United States)

    Eugenio, F.; Martin, J.; Marcello, J.; Fraile-Nuez, E.

    2014-06-01

    El Hierro Island, located at the Canary Islands Archipelago in the Atlantic coast of North Africa, has been rocked by thousands of tremors and earthquakes since July 2011. Finally, an underwater volcanic eruption started 300 m below sea level on October 10, 2011. Since then, regular multidisciplinary monitoring has been carried out in order to quantify the environmental impacts caused by the submarine eruption. Thanks to this natural tracer release, multisensorial satellite imagery obtained from MODIS and MERIS sensors have been processed to monitor the volcano activity and to provide information on the concentration of biological, chemical and physical marine parameters. Specifically, low resolution satellite estimations of optimal diffuse attenuation coefficient (Kd) and chlorophyll-a (Chl-a) concentration under these abnormal conditions have been assessed. These remote sensing data have played a fundamental role during field campaigns guiding the oceanographic vessel to the appropriate sampling areas. In addition, to analyze El Hierro submarine volcano area, WorldView-2 high resolution satellite spectral bands were atmospherically and deglinted processed prior to obtain a high-resolution optimal diffuse attenuation coefficient model. This novel algorithm was developed using a matchup data set with MERIS and MODIS data, in situ transmittances measurements and a seawater radiative transfer model. Multisensor and multitemporal imagery processed from satellite remote sensing sensors have demonstrated to be a powerful tool for monitoring the submarine volcanic activities, such as discolored seawater, floating material and volcanic plume, having shown the capabilities to improve the understanding of submarine volcanic processes.

  13. Satellite Imagery Measures of the Astronomically Aligned Megalithis at Nabta Playa.

    Science.gov (United States)

    Brophy, T.; Rosen, P.

    The Nabta Playa megalithic complex consists of two types of features: first are the large stones, many of them shaped, placed on or in the sediments of an ancient seasonal lake bed that is now hyper-ariad, second are large sculpted bedrocks features underneath the sediments and associated with the surface megaliths (Wendorf et al. 1992). The astronomically aligned surface megalithic structures described in field reports (Wendorf and Malville, 2001) are identified in recent georectified 60cm panchromatic satellite imagery of Nabta Playa, Southern Egypt. The satellite images allow refinement, often significant of the reported locations of the megaliths (Malville et al 1998, and Wendorf and Malville 2001). The report that a primary megalithic alignment was constructed to point to the bright star Sirius, circa 4820BC, is reconsidered in light of the satellite data, new field, data, radiocarbon, lithostratigraphic and geochronological data, and the playa sedimentation history. Other possible archaeoastronomical interpretations are considered for that alignment, including the three star asterism (of Alnitak, Alniham and Mintaka) circa 6270BC that is also implicated in the small Nebta Playa "calendar circle". Signatures of other possible features apparent in the satellite imagery and a recent field study are also considered. Only a small number of the subsurface bedrock sculptures have been excavated. We recommend the use of ground penetrating imaging methods to illuminate the known but not yet excavated subsurface features. The problem of determining the astronomical intent of the builders of the megalithic structures is approached by considering the complex of features as a whole.

  14. Optimizing statistical classification accuracy of satellite remotely sensed imagery for supporting fast flood hydrological analysis

    Science.gov (United States)

    Alexakis, Dimitrios; Agapiou, Athos; Hadjimitsis, Diofantos; Retalis, Adrianos

    2012-06-01

    The aim of this study is to improve classification results of multispectral satellite imagery for supporting flood risk assessment analysis in a catchment area in Cyprus. For this purpose, precipitation and ground spectroradiometric data have been collected and analyzed with innovative statistical analysis methods. Samples of regolith and construction material were in situ collected and examined in the spectroscopy laboratory for their spectral response under consecutive different conditions of humidity. Moreover, reflectance values were extracted from the same targets using Landsat TM/ETM+ images, for drought and humid time periods, using archived meteorological data. The comparison of the results showed that spectral responses for all the specimens were less correlated in cases of substantial humidity, both in laboratory and satellite images. These results were validated with the application of different classification algorithms (ISODATA, maximum likelihood, object based, maximum entropy) to satellite images acquired during time period when precipitation phenomena had been recorded.

  15. Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery

    Science.gov (United States)

    Musial, J. P.; Hüsler, F.; Sütterlin, M.; Neuhaus, C.; Wunderle, S.

    2014-03-01

    Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging

  16. Analysis on the Utility of Satellite Imagery for Detection of Agricultural Facility

    Science.gov (United States)

    Kang, J.-M.; Baek, S.-H.; Jung, K.-Y.

    2012-07-01

    Now that the agricultural facilities are being increase owing to development of technology and diversification of agriculture and the ratio of garden crops that are imported a lot and the crops cultivated in facilities are raised in Korea, the number of vinyl greenhouses is tending upward. So, it is important to grasp the distribution of vinyl greenhouses as much as that of rice fields, dry fields and orchards, but it is difficult to collect the information of wide areas economically and correctly. Remote sensing using satellite imagery is able to obtain data of wide area at the same time, quickly and cost-effectively collect, monitor and analyze information from every object on earth. In this study, in order to analyze the utilization of satellite imagery at detection of agricultural facility, image classification was performed about the agricultural facility, vinyl greenhouse using Formosat-2 satellite imagery. The training set of sea, vegetation, building, bare ground and vinyl greenhouse was set to monitor the agricultural facilities of the object area and the training set for the vinyl greenhouses that are main monitoring object was classified and set again into 3 types according the spectral characteristics. The image classification using 4 kinds of supervise classification methods applied by the same training set were carried out to grasp the image classification method which is effective for monitoring agricultural facilities. And, in order to minimize the misclassification appeared in the classification using the spectral information, the accuracy of classification was intended to be raised by adding texture information. The results of classification were analyzed regarding the accuracy comparing with that of naked-eyed detection. As the results of classification, the method of Mahalanobis distance was shown as more efficient than other methods and the accuracy of classification was higher when adding texture information. Hence the more effective

  17. Swords into Ploughshares: Archaeological Applications of CORONA Satellite Imagery in the Near East

    Directory of Open Access Journals (Sweden)

    Jesse Casana

    2012-09-01

    Full Text Available Since their declassification in 1995, CORONA satellite images collected by the United States military from 1960-1972 have proved to be an invaluable resource in the archaeology of the Near East. Because CORONA images pre-date the widespread construction of reservoirs, urban expansion, and agricultural intensification the region has undergone in recent decades, these high-resolution, stereo images preserve a picture of archaeological sites and landscapes that have often been destroyed or obscured by modern development. Despite its widely recognised value, the application of CORONA imagery in archaeological research has remained limited to a small group of specialists, largely because of the challenges involved in correcting spatial distortions produced by the satellites' unusual panoramic cameras. This article presents results of an effort to develop new methods of efficiently orthorectifying CORONA imagery and to use these methods to produce geographically corrected images across the Near East, now freely available through an online database. Following an overview of our methods, we present examples of how recent development has affected the archaeological record, new discoveries that analysis of our CORONA imagery database has already made possible, and emerging applications of CORONA including stereo analysis and DEM extraction.

  18. Surface flow structure of the Gulf Stream from composite imagery and satellite-tracked drifters

    Directory of Open Access Journals (Sweden)

    C. P. Mullen

    1994-01-01

    Full Text Available A unique set of coutemporaneous satellite-tracked drifters and five-day composite Advanced Very High Resolution Radionmeter (AVHRR satellite imagery of the North Atlantic has been analyzed to examine the surface flow structure of the Gulf Stream. The study region was divided into two sections, greater than 37° N and less than 37° N, in order to answer the question of geographic variability. Fractal and spectral analyses methods were applied to the data. Fractal analysis of the Lagrangian trajectories showed a fractal dimension of 1.21 + 0.02 with a scaling range of 83 - 343 km. The fractal dimension of the temperature fronts of the composite imagery is similar for the two regions with D = 1.11 + 0.01 over a scaling range of 4 - 44 km. Spectral analysis also reports a fairly consistent value for the spectral slope and its scaling range. Therefore, we conclude there is no geographic variability in the data set. A suitable scaling range for this contemporaneous data set is 80 - 200 km which is consistent with the expected physical conditions in the region. Finally, we address the idea of using five-day composite imagery to infer the surface flow of the Gulf Stream. Close analyses of the composite thermal fronts and the Lagrangian drifter trajectories show that the former is not a good indicator of the latter.

  19. PlumeSat: A Micro-Satellite Based Plume Imagery Collection Experiment

    Energy Technology Data Exchange (ETDEWEB)

    Ledebuhr, A.G.; Ng, L.C.

    2002-06-30

    This paper describes a technical approach to cost-effectively collect plume imagery of boosting targets using a novel micro-satellite based platform operating in low earth orbit (LEO). The plume collection Micro-satellite or PlueSat for short, will be capable of carrying an array of multi-spectral (UV through LWIR) passive and active (Imaging LADAR) sensors and maneuvering with a lateral divert propulsion system to different observation altitudes (100 to 300 km) and different closing geometries to achieve a range of aspect angles (15 to 60 degrees) in order to simulate a variety of boost phase intercept missions. The PlumeSat will be a cost effective platform to collect boost phase plume imagery from within 1 to 10 km ranges, resulting in 0.1 to 1 meter resolution imagery of a variety of potential target missiles with a goal of demonstrating reliable plume-to-hardbody handover algorithms for future boost phase intercept missions. Once deployed on orbit, the PlumeSat would perform a series phenomenology collection experiments until expends its on-board propellants. The baseline PlumeSat concept is sized to provide from 5 to 7 separate fly by data collects of boosting targets. The total number of data collects will depend on the orbital basing altitude and the accuracy in delivering the boosting target vehicle to the nominal PlumeSat fly-by volume.

  20. Satellite Hyperspectral Imagery to Support Tick-Borne Infectious Diseases Surveillance.

    Directory of Open Access Journals (Sweden)

    Gina Polo

    Full Text Available This study proposed the use of satellite hyperspectral imagery to support tick-borne infectious diseases surveillance based on monitoring the variation in amplifier hosts food sources. To verify this strategy, we used the data of the human rickettsiosis occurrences in southeastern Brazil, region in which the emergence of this disease is associated with the rising capybara population. Spatio-temporal analysis based on Monte Carlo simulations was used to identify risk areas of human rickettsiosis and hyperspectral moderate-resolution imagery was used to identify the increment and expansion of sugarcane crops, main food source of capybaras. In general, a pixel abundance associated with increment of sugarcane crops was detected in risk areas of human rickettsiosis. Thus, the hypothesis that there is a spatio-temporal relationship between the occurrence of human rickettsiosis and the sugarcane crops increment was verified. Therefore, due to the difficulty of monitoring locally the distribution of infectious agents, vectors and animal host's, satellite hyperspectral imagery can be used as a complementary tool for the surveillance of tick-borne infectious diseases and potentially of other vector-borne diseases.

  1. Statistical Analyses of Satellite Cloud Object Data From CERES. Part 4; Boundary-layer Cloud Objects During 1998 El Nino

    Science.gov (United States)

    Xu, Kuan-Man; Wong, Takmeng; Wielicki, Bruce A.; Parker, Lindsay

    2006-01-01

    Three boundary-layer cloud object types, stratus, stratocumulus and cumulus, that occurred over the Pacific Ocean during January-August 1998, are identified from the CERES (Clouds and the Earth s Radiant Energy System) single scanner footprint (SSF) data from the TRMM (Tropical Rainfall Measuring Mission) satellite. This study emphasizes the differences and similarities in the characteristics of each cloud-object type between the tropical and subtropical regions and among different size categories and among small geographic areas. Both the frequencies of occurrence and statistical distributions of cloud physical properties are analyzed. In terms of frequencies of occurrence, stratocumulus clouds dominate the entire boundary layer cloud population in all regions and among all size categories. Stratus clouds are more prevalent in the subtropics and near the coastal regions, while cumulus clouds are relatively prevalent over open ocean and the equatorial regions, particularly, within the small size categories. The largest size category of stratus cloud objects occurs more frequently in the subtropics than in the tropics and has much larger average size than its cumulus and stratocumulus counterparts. Each of the three cloud object types exhibits small differences in statistical distributions of cloud optical depth, liquid water path, TOA albedo and perhaps cloud-top height, but large differences in those of cloud-top temperature and OLR between the tropics and subtropics. Differences in the sea surface temperature (SST) distributions between the tropics and subtropics influence some of the cloud macrophysical properties, but cloud microphysical properties and albedo for each cloud object type are likely determined by (local) boundary-layer dynamics and structures. Systematic variations of cloud optical depth, TOA albedo, cloud-top height, OLR and SST with cloud object sizes are pronounced for the stratocumulus and stratus types, which are related to systematic

  2. Delineating Tree Types in a Complex Tropical Forest Setting Using High Resolution Multispectral Satellite Imagery

    Science.gov (United States)

    Cross, M.

    2016-12-01

    An improved process for the identification of tree types from satellite imagery for tropical forests is needed for more accurate assessments of the impact of forests on the global climate. La Selva Biological Station in Costa Rica was the tropical forest area selected for this particular study. WorldView-3 imagery was utilized because of its high spatial, spectral and radiometric resolution, its availability, and its potential to differentiate species in a complex forest setting. The first-step was to establish confidence in the high spatial and high radiometric resolution imagery from WorldView-3 in delineating tree types within a complex forest setting. In achieving this goal, ASD field spectrometer data were collected of specific tree species to establish solid ground control within the study site. The spectrometer data were collected from the top of each specific tree canopy utilizing established towers located at La Selva Biological Station so as to match the near-nadir view of the WorldView-3 imagery. The ASD data was processed utilizing the spectral response functions for each of the WorldView-3 bands to convert the ASD data into a band specific reflectivity. This allowed direct comparison of the ASD spectrometer reflectance data to the WorldView-3 multispectral imagery. The WorldView-3 imagery was processed to surface reflectance using two standard atmospheric correction procedures and the proprietary DigitalGlobe Atmospheric Compensation (AComp) product. The most accurate correction process was identified through comparison to the spectrometer data collected. A series of statistical measures were then utilized to access the accuracy of the processed imagery and which imagery bands are best suited for tree type identification. From this analysis, a segmentation/classification process was performed to identify individual tree type locations within the study area. It is envisioned the results of this study will improve traditional forest classification

  3. Scheduling satellite imagery acquisition for sequential assimilation of water level observation into flood modelling

    Science.gov (United States)

    García-Pintado, Javier; Neal, Jeff C.; Mason, David C.; Dance, Sarah L.; Bates, Paul D.

    2013-04-01

    Satellite-based imagery has proved useful for obtaining information on water levels in flood events. Microwave frequencies are generally more useful for flood detection than visible-band sensors because of its all-weather day-night capability. Specifically, the future SWOT mission, with Ka-band interferometry, will be able to provide direct Water Level Observations (WLOs), and current and future Synthetic Aperture Radar (SAR) sensors can provide information of flood extent, which, when intersected with a Digital Elevation Model (DEM) of the floodplain, provides indirect WLOs. By either means, satellite-based WLOs can be assimilated into a hydrodynamic model to decrease forecast uncertainty and further to estimate river discharge into the flooded domain. Operational scenarios can even make a combined use of imagery from different uncoordinated missions to sequentially estimate river discharge. Thus, with an increasing number of operational satellites with WLO capability, information on the relationship between satellite first visit, revisit times, and forecast performance is required to optimise the operational scheduling of satellite imagery. By using an Ensemble Transform Kalman Filter (ETKF) and a synthetic analysis with the 2D hydrodynamic model LISFLOOD-FP based on a real flooding case affecting an urban area (summer 2007, Tewkesbury, Southwest UK), we evaluate the sensitivity of the forecast performance to visit parameters. As an example, we use different scenarios of revisit times and observational errors expected from the current COSMO-Skymed (CSK) constellation, with X-band SAR. We emulate a generic hydrologic-hydrodynamic modelling cascade by imposing a bias and spatiotemporal correlations to the inflow error ensemble into the hydrodynamic domain. First, in agreement with previous research, estimation and correction for this bias leads to a clear improvement in keeping the forecast on track. Second, imagery obtained early in the flood is shown to have a

  4. AN EFFICIENT APPROACH FOR EXTRACTION OF LINEAR FEATURES FROM HIGH RESOLUTION INDIAN SATELLITE IMAGERIES

    Directory of Open Access Journals (Sweden)

    DK Bhattacharyya

    2010-07-01

    Full Text Available This paper presents an Object oriented feature extraction approach in order to classify the linear features like drainage, roads etc. from high resolution Indian satellite imageries. It starts with the multiresolution segmentations of image objects for optimal separation and representation of image regions or objects. Fuzzy membership functions were defined for a selected set of image object parameters such as mean, ratio, shape index, area etc. for representation of required image objects. Experiment was carried out for both panchromatic (CARTOSAT-I and multispectral (IRSP6 LISS IV Indiansatellite imageries. Experimental results show that the extractionof linear features can be achieved in a satisfactory level throughproper segmentation and appropriate definition & representationof key parameters of image objects.

  5. Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

    Directory of Open Access Journals (Sweden)

    Dimitris G. Stavrakoudis

    2014-07-01

    Full Text Available This study investigates the effectiveness of combining multispectral very high resolution (VHR and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM. The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

  6. On land-use modeling: A treatise of satellite imagery data and misclassification error

    Science.gov (United States)

    Sandler, Austin M.

    Recent availability of satellite-based land-use data sets, including data sets with contiguous spatial coverage over large areas, relatively long temporal coverage, and fine-scale land cover classifications, is providing new opportunities for land-use research. However, care must be used when working with these datasets due to misclassification error, which causes inconsistent parameter estimates in the discrete choice models typically used to model land-use. I therefore adapt the empirical correction methods developed for other contexts (e.g., epidemiology) so that they can be applied to land-use modeling. I then use a Monte Carlo simulation, and an empirical application using actual satellite imagery data from the Northern Great Plains, to compare the results of a traditional model ignoring misclassification to those from models accounting for misclassification. Results from both the simulation and application indicate that ignoring misclassification will lead to biased results. Even seemingly insignificant levels of misclassification error (e.g., 1%) result in biased parameter estimates, which alter marginal effects enough to affect policy inference. At the levels of misclassification typical in current satellite imagery datasets (e.g., as high as 35%), ignoring misclassification can lead to systematically erroneous land-use probabilities and substantially biased marginal effects. The correction methods I propose, however, generate consistent parameter estimates and therefore consistent estimates of marginal effects and predicted land-use probabilities.

  7. Calibration of Numerical Model for Shoreline Change Prediction Using Satellite Imagery Data

    Directory of Open Access Journals (Sweden)

    Sigit Sutikno

    2015-12-01

    Full Text Available This paper presents a method for calibration of numerical model for shoreline change prediction using satellite imagery data in muddy beach. Tanjung Motong beach, a muddy beach that is suffered high abrasion in Rangsang Island, Riau province, Indonesia was picked as study area. The primary numerical modeling tool used in this research was GENESIS (GENEralized Model for Simulating Shoreline change, which has been successfully applied in many case studies of shoreline change phenomena on a sandy beach.The model was calibrated using two extracted coastlines satellite imagery data, such as Landsat-5 TM and Landsat-8 OLI/TIRS. The extracted coastline data were analyzed by using DSAS (Digital Shoreline Analysis System tool to get the rate of shoreline change from 1990 to 2014. The main purpose of the calibration process was to find out the appropriate value for K 1 and K coefficients so that the predicted shoreline change had an acceptable correlation with the output of the satellite data processing. The result of this research showed that the shoreline change prediction had a good correlation with the historical evidence data in Tanjung Motong coast. It means that the GENESIS tool is not only applicable for shoreline prediction in sandy beach but also in muddy beach.

  8. Integrating satellite imagery with simulation modeling to improve burn severity mapping.

    Science.gov (United States)

    Karau, Eva C; Sikkink, Pamela G; Keane, Robert E; Dillon, Gregory K

    2014-07-01

    Both satellite imagery and spatial fire effects models are valuable tools for generating burn severity maps that are useful to fire scientists and resource managers. The purpose of this study was to test a new mapping approach that integrates imagery and modeling to create more accurate burn severity maps. We developed and assessed a statistical model that combines the Relative differenced Normalized Burn Ratio, a satellite image-based change detection procedure commonly used to map burn severity, with output from the Fire Hazard and Risk Model, a simulation model that estimates fire effects at a landscape scale. Using 285 Composite Burn Index (CBI) plots in Washington and Montana as ground reference, we found that an integrated model explained more variability in CBI (R (2) = 0.47) and had lower mean squared error (MSE = 0.28) than image (R (2) = 0.42 and MSE = 0.30) or simulation-based models (R (2) = 0.07 and MSE = 0.49) alone. Overall map accuracy was also highest for maps created with the Integrated Model (63 %). We suspect that Simulation Model performance would greatly improve with higher quality and more accurate spatial input data. Results of this study indicate the potential benefit of combining satellite image-based methods with a fire effects simulation model to create improved burn severity maps.

  9. Method for validating cloud mask obtained from satellite measurements using ground-based sky camera.

    Science.gov (United States)

    Letu, Husi; Nagao, Takashi M; Nakajima, Takashi Y; Matsumae, Yoshiaki

    2014-11-01

    Error propagation in Earth's atmospheric, oceanic, and land surface parameters of the satellite products caused by misclassification of the cloud mask is a critical issue for improving the accuracy of satellite products. Thus, characterizing the accuracy of the cloud mask is important for investigating the influence of the cloud mask on satellite products. In this study, we proposed a method for validating multiwavelength satellite data derived cloud masks using ground-based sky camera (GSC) data. First, a cloud cover algorithm for GSC data has been developed using sky index and bright index. Then, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived cloud masks by two cloud-screening algorithms (i.e., MOD35 and CLAUDIA) were validated using the GSC cloud mask. The results indicate that MOD35 is likely to classify ambiguous pixels as "cloudy," whereas CLAUDIA is likely to classify them as "clear." Furthermore, the influence of error propagations caused by misclassification of the MOD35 and CLAUDIA cloud masks on MODIS derived reflectance, brightness temperature, and normalized difference vegetation index (NDVI) in clear and cloudy pixels was investigated using sky camera data. It shows that the influence of the error propagation by the MOD35 cloud mask on the MODIS derived monthly mean reflectance, brightness temperature, and NDVI for clear pixels is significantly smaller than for the CLAUDIA cloud mask; the influence of the error propagation by the CLAUDIA cloud mask on MODIS derived monthly mean cloud products for cloudy pixels is significantly smaller than that by the MOD35 cloud mask.

  10. Geometric Quality Assessment of Bundle Block Adjusted Mulit- Sensor Satellite Imageries

    Science.gov (United States)

    Ghosh, S.; Bhawani Kumar, P. S.; Radhadevi, P. V.; Srinivas, V.; Saibaba, J.; Varadan, G.

    2014-11-01

    The integration of multi-sensor earth observation data belonging to same area has become one of the most important input for resource mapping and management. Geometric error and fidelity between adjacent scenes affects large-area digital mosaic if the images/ scenes are processed independently. A block triangulation approach "Bundle Block Adjustment (BBA)" system has been developed at ADRIN for combined processing of multi-sensor, multi-resolution satellite imagery to achieve better geometric continuity. In this paper we present the evaluation results of BBA software along with performance assessment and operational use of products thus generated. The application evaluation deals with functional aspects of block-adjustment of satellite imagery consisting of data from multiple sources, i.e. AWiFs, LISS-3, LISS-4 and Cartosat-1 in various combinations as single block. It has provision for automatic generation of GCPs and tie-points using image metafile/ Rational Polynomial Coefficient's (RPC's) and ortho/ merged/ mosaicked products generation. The study is carried out with datasets covering different terrain types (ranging from high mountainous area, moderately undulating terrain, coastal plain, agriculture fields, urban area and water-body) across Indian subcontinent with varying block sizes and spatial reference systems. Geometric accuracy assessment is carried out to figure out error propagation at scene based ortho/ merged products as well as block level. The experimental results confirm that pixel tagging, geometric fidelity and feature continuity across adjacent scenes as well as for multiple sensors reduced to a great extent, due to the high redundancy. The results demonstrate that it is one of the most affective geometric corrections for generating large area digital mosaic over High mountainous terrain using high resolution good swath satellite imagery, like Cartosat-1, with minimum human intervention.

  11. UNSUPERVISED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGERY BY SELF-ORGANIZING NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    ÁRPÁD BARSI

    2010-06-01

    Full Text Available The current paper discusses the importance of the modern high resolution satellite imagery. The acquired high amount of data must be processed by an efficient way, where the used Kohonen-type self-organizing map has been proven as a suitable tool. The paper gives an introduction to this interesting method. The tests have shown that the multispectral image information can be taken after a resampling step as neural network inputs, and then the derived network weights are able to evaluate the whole image with acceptable thematic accuracy.

  12. Environmental waste site characterization utilizing aerial photographs and satellite imagery: Three sites in New Mexico, USA

    Energy Technology Data Exchange (ETDEWEB)

    Van Eeckhout, E.; Pope, P.; Becker, N.; Wells, B. [Los Alamos National Lab., NM (United States); Lewis, A.; David, N. [Environmental Research Inst. of Michigan, Santa Fe, NM (United States)

    1996-04-01

    The proper handling and characterization of past hazardous waste sites is becoming more and more important as world population extends into areas previously deemed undesirable. Historical photographs, past records, current aerial satellite imagery can play an important role in characterizing these sites. These data provide clear insight into defining problem areas which can be surface samples for further detail. Three such areas are discussed in this paper: (1) nuclear wastes buried in trenches at Los Alamos National Laboratory, (2) surface dumping at one site at Los Alamos National Laboratory, and (3) the historical development of a municipal landfill near Las Cruces, New Mexico.

  13. The high resolution topographic evolution of an active retrogressive thaw slump compiled from a decade of photography, ground surveys, laser scans and satellite imagery

    Science.gov (United States)

    Crosby, B. T.; Barnhart, T. B.; Rowland, J. C.

    2015-12-01

    Remote sensing imagery has enables the temporal reconstruction of thermal erosion features including lakes, shorelines and hillslope failures in remote Arctic locations, yet these planar data limit analysis to lines and areas. This study explores the application of varying techniques to reconstruct the three dimensional evolution of a single thermal erosion feature using a mixture of opportunistic oblique photos, ground surveys and satellite imagery. At the Selawik River retrogressive thaw slump in northwest Alaska, a bush plane collected oblique aerial photos when the feature was first discovered in 2004 and in subsequent years. These images were recently processed via Structure from Motion to generate georeferenced point clouds for the years prior to the initiation of our research. High resolution ground surveys in 2007, 2009 and 2010 were completed using robotic total station. Terrestrial laser scans (TLS) were collected in the summers of 2011 and 2012. Analysis of stereo satellite imagery from 2012 and 2015 enable continued monitoring of the feature after ground campaigns ended. As accurate coregistraion between point clouds is vital to topographic change detection, all prior and subsequent datasets were georeferenced to stable features observed in the 2012 TLS scan. Though this coregistration introduces uncertainty into each image, the magnitudes of uncertainty are significantly smaller than the topographic changes detected. Upslope retreat of the slump headwall generally decreases over time as the slump floor progresses from a highly dissected gully topography to a low relief, earthflow dominated depositional plane. The decreasing slope of the slump floor diminishes transport capacity, resulting in the progressive burial of the slump headwall, thus decreasing headwall retreat rates. This self-regulation of slump size based on feature relief and transport capacity suggests a capacity to predict the maximum size a given feature can expand to before

  14. Comparison Between Computer Simulation of Transport and Diffusion of Cloud Seeding Material Within Stratiform Cloud and the NOAA-14 Satellite Cloud Track

    Institute of Scientific and Technical Information of China (English)

    YU Xing; DAI Jin; LEI Hengchi; FAN Peng

    2005-01-01

    A precipitation enhancement operation using an aircraft was conducted from 1415 to 1549 LST 14 March 2000 in Shaanxi Province. The NOAA-14 satellite data received at 1535 LST soon after the cloud seeding shows that a vivid cloud track appears on the satellite image. The length, average width and maximum width of the cloud track are 301 km, 8.3 and 11 km, respectively. Using a three-dimensional numerical model of transport and diffusion of seeding material within stratiform clouds, the spatial concentration distribution characteristics of seeding material at different times, especially at the satellite receiving time,are simulated. The model results at the satellite receiving time are compared with the features of the cloud track. The transported position of the cloud seeding material coincides with the position of the track. The width, shape and extent of diffusion of the cloud seeding material are similar to that of the cloud track.The spatial variation of width is consistent with that of the track. The simulated length of each segment of the seeding line accords with the length of every segment of the track. Each segment of the cloud track corresponds to the transport and diffusion of each segment of the seeding line. These results suggest that the cloud track is the direct physical reflection of cloud seeding at the cloud top. The comparison demonstrates that the numerical model of transport and diffusion can simulate the main characteristics of transport and diffusion of seeding material, and the simulated results are sound and trustworthy. The area, volume, width, depth, and lateral diffusive rate corresponding to concentrations 1, 4, and 10 L-1are simulated in order to understand the variations of influencing range.

  15. Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery

    Directory of Open Access Journals (Sweden)

    Nobuoto Nojima

    2010-09-01

    Full Text Available For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR loaded on the Advanced Land Observing Satellite (ALOS satellite, a model combining the usage of satellite synthetic aperture radar (SAR imagery and Japan Meteorological Agency (JMA-scale seismic intensity is proposed. In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper rebuilds a likelihood function for severe damage ratio, on the basis of dataset from Japanese Earth Resource Satellite-1 (JERS-1/SAR (L-band SAR images observed during the 1995 Kobe earthquake and its detailed ground truth data. The model which integrates the fragility functions of building damage in terms of seismic intensity and the proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 earthquake in Pisco, Peru. The accuracy of the proposed damage estimation model is examined by comparing the results of the analyses with field investigations and/or interpretation of high-resolution satellite images.

  16. Cloud Detection with MATLAB

    OpenAIRE

    P. Shrivastava

    2013-01-01

    The accurate detection of clouds in satellite imagery is important in research and operational applications. Cloud cover influences the distribution of solar radiation reaching the ground where it is absorbed. Resulting fluxes of sensible and latent heat are critical to the accurate characterization of boundary layer behavior and mesoscale circulations that often lead to convective development. Therefore the spatial and temporal variation in cloud cover can greatly affect regional an...

  17. Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing

    Directory of Open Access Journals (Sweden)

    M. Joseph Hughes

    2014-05-01

    Full Text Available The use of Landsat data to answer ecological questions is greatly increased by the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, SPARCS: Spatial Procedures for Automated Removal of Cloud and Shadow. The method uses a neural network approach to determine cloud, cloud shadow, water, snow/ice and clear sky classification memberships of each pixel in a Landsat scene. It then applies a series of spatial procedures to resolve pixels with ambiguous membership by using information, such as the membership values of neighboring pixels and an estimate of cloud shadow locations from cloud and solar geometry. In a comparison with FMask, a high-quality cloud and cloud shadow classification algorithm currently available, SPARCS performs favorably, with substantially lower omission errors for cloud shadow (8.0% and 3.2%, only slightly higher omission errors for clouds (0.9% and 1.3%, respectively and fewer errors of commission (2.6% and 0.3%. Additionally, SPARCS provides a measure of uncertainty in its classification that can be exploited by other algorithms that require clear sky pixels. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of a method for vegetation change detection.

  18. Automated detection of cloud and cloud-shadow in single-date Landsat imagery using neural networks and spatial post-processing

    Energy Technology Data Exchange (ETDEWEB)

    Hughes, Michael J. [University of Tennessee, Knoxville (UTK); Hayes, Daniel J [ORNL

    2014-01-01

    Use of Landsat data to answer ecological questions is contingent on the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, \\textsc{sparcs}: Spacial Procedures for Automated Removal of Cloud and Shadow. The method uses neural networks to determine cloud, cloud-shadow, water, snow/ice, and clear-sky membership of each pixel in a Landsat scene, and then applies a set of procedures to enforce spatial rules. In a comparison to FMask, a high-quality cloud and cloud-shadow classification algorithm currently available, \\textsc{sparcs} performs favorably, with similar omission errors for clouds (0.8% and 0.9%, respectively), substantially lower omission error for cloud-shadow (8.3% and 1.1%), and fewer errors of commission (7.8% and 5.0%). Additionally, textsc{sparcs} provides a measure of uncertainty in its classification that can be exploited by other processes that use the cloud and cloud-shadow detection. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of algorithms detecting vegetation change.

  19. Global, Persistent, Real-time Multi-sensor Automated Satellite Image Analysis and Crop Forecasting in Commercial Cloud

    Science.gov (United States)

    Brumby, S. P.; Warren, M. S.; Keisler, R.; Chartrand, R.; Skillman, S.; Franco, E.; Kontgis, C.; Moody, D.; Kelton, T.; Mathis, M.

    2016-12-01

    Cloud computing, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of space and time granularity never before feasible. Multi-decadal Earth remote sensing datasets at the petabyte scale (8×10^15 bits) are now available in commercial cloud, and new satellite constellations will generate daily global coverage at a few meters per pixel. Public and commercial satellite observations now provide a wide range of sensor modalities, from traditional visible/infrared to dual-polarity synthetic aperture radar (SAR). This provides the opportunity to build a continuously updated map of the world supporting the academic community and decision-makers in government, finanace and industry. We report on work demonstrating country-scale agricultural forecasting, and global-scale land cover/land, use mapping using a range of public and commercial satellite imagery. We describe processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work combining this imagery with time-series SAR collected by ESA Sentinel 1. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields and detect threats to food security (e.g., flooding, drought). The software platform and analysis methodology also support monitoring water resources, forests and other general

  20. Direct measurements of laser light aberration from the ARTEMIS geostationary satellite through thin clouds

    CERN Document Server

    Kuzkov, Volodymyr; Sodnik, Zoran

    2015-01-01

    A precise ground based telescope system was developed for laser communication experiments with the geostationary satellite ARTEMIS of ESA. Precise tracking of the satellite was realized by using time resolved coordinates of the satellite. During the experiments, the time propagation of laser signal from the satellite and the point-ahead angle for the laser beam were calculated. Some laser experiments though thin clouds were performed. A splitting of some images of the laser beam from the satellite along declination and right ascension coordinates of telescope could be observed through thin clouds. The splitting along the declination coordinate may be interpreted as refraction in the atmosphere. The splitting along the right ascension coordinate is equivalent to the calculated point-ahead angle for the satellite. We find out that a small part of laser beam was observed ahead of the velocity vector in the point where the satellite would be after the laser light from the satellite reaches the telescope. These re...

  1. Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

    Directory of Open Access Journals (Sweden)

    C. A. Poulsen

    2012-08-01

    Full Text Available Clouds play an important role in balancing the Earth's radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase.

    The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick

  2. Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

    Directory of Open Access Journals (Sweden)

    Jane Southworth

    2010-12-01

    Full Text Available Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure.

  3. Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

    Science.gov (United States)

    Rand, Robert S.; Khuon, Timothy; Truslow, Eric

    2016-07-01

    A proposed framework using spectral and spatial information is introduced for neural net multisensor data fusion. This consists of a set of independent-sensor neural nets, one for each sensor (type of data), coupled to a fusion net. The neural net of each sensor is trained from a representative data set of the particular sensor to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. During the initial processing, three-dimensional (3-D) point cloud data (PCD) are segmented using a multidimensional mean-shift algorithm into clustered objects. Concurrently, multiband spectral imagery data (multispectral or hyperspectral) are spectrally segmented by the stochastic expectation-maximization into a cluster map containing (spectral-based) pixel classes. For the proposed sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The success of the approach in utilizing sensor synergism for an enhanced classification is demonstrated for the specific case of classifying hyperspectral imagery and PCD extracted from LIDAR, obtained from an airborne data collection over the campus of University of Southern Mississippi, Gulfport, Mississippi.

  4. Inferring urban household socio-economic conditions in Mafikeng, South Africa, using high spatial resolution satellite imagery

    Directory of Open Access Journals (Sweden)

    Christopher Munyati

    2014-01-01

    Full Text Available Updated household socio-economic information is necessary for planning the delivery of municipal services, particularly for cities in third world countries. The repetitive coverage of satellite imagery provides a possibility for sourcing and frequently updating information on household socio-economic conditions in urban landscapes. This paper examines the potential use of satellite imagery in inferring urban household socio-economic variables, using two high-resolution images of 2001 and 2010. Manual image interpretation was employed in deducing selected socio-economic variables that are utilised in census enumerations in South Africa, at four suburbs in Mafikeng. Of the three socio-economic variables that were examined (type of main dwelling, toilet facilities, and energy source for cooking, type of dwelling could more readily be deduced from the high-resolution imagery. Identified change in number of formal and informal houses indicated potential of satellite imagery in monitoring third world setting urban sprawl and the associated growth in informal settlements due to migration, among other factors. Satellite imagery appears useful as a supplementary source of socio-economic data to municipal authorities, for periods between regular censuses.

  5. Identification of lake trout Salvelinus namaycush spawning habitat in northern Lake Huron using high-resolution satellite imagery

    Science.gov (United States)

    Grimm, Amanda G.; Brooks, Colin N.; Binder, Thomas R.; Riley, Stephen C.; Farha, Steve A.; Shuchman, Robert A.; Krueger, Charles C.

    2016-01-01

    The availability and quality of spawning habitat may limit lake trout recovery in the Great Lakes, but little is known about the location and characteristics of current spawning habitats. Current methods used to identify lake trout spawning locations are time- and labor-intensive and spatially limited. Due to the observation that some lake trout spawning sites are relatively clean of overlaying algae compared to areas not used for spawning, we suspected that spawning sites could be identified using satellite imagery. Satellite imagery collected just before and after the spawning season in 2013 was used to assess whether lake trout spawning habitat could be identified based on its spectral characteristics. Results indicated that Pléiades high-resolution multispectral satellite imagery can be successfully used to estimate algal coverage of substrates and temporal changes in algal coverage, and that models developed from processed imagery can be used to identify potential lake trout spawning sites based on comparison of sites where lake trout eggs were and were not observed after spawning. Satellite imagery is a potential new tool for identifying lake trout spawning habitat at large scales in shallow nearshore areas of the Great Lakes.

  6. Satellite imagery and airborne geophysics for geologic mapping of the Edembo area, Eastern Hoggar (Algerian Sahara)

    Science.gov (United States)

    Lamri, Takfarinas; Djemaï, Safouane; Hamoudi, Mohamed; Zoheir, Basem; Bendaoud, Abderrahmane; Ouzegane, Khadidja; Amara, Massinissa

    2016-03-01

    Satellite imagery combined with airborne geophysical data and field observations were employed for new geologic mapping of the Edembo area in the Eastern Hoggar (Tuareg Shield, Sahara). Multi-spectral band fusion, filtering, and transformation techniques, i.e., band combination, band-rationing and principal component analysis of ETM+ and ASTER data are used for better spectral discrimination of the different rocks units. A thematic map assessed by field data and available geologic information is compiled by supervised classification of satellite data with high overall accuracy (>90%). The automated extraction technique efficiently aided the detection of the structural lineaments, i.e., faults, shear zones, and joints. Airborne magnetic and Gamma-ray spectrometry data showed the pervasiveness of the large structures beneath the Paleozoic sedimentary cover and aeolian sands. The aeroradiometric K-range is used for discrimination of the high-K granitoids of Djanet from the peralumineous granites of Edembo, and to verify the Silurian sediments with their high K-bearing minerals. The new geological map is considered to be a high resolution improvement on all pre-existing maps of this hardly accessible area in the Tuareg Shield. Integration of the airborne geophysical and space-borne imagery data can hence provide a rapid means of geologically mapping areas hitherto poorly known or difficult to access.

  7. Ship detection in satellite imagery using rank-order greyscale hit-or-miss transforms

    Energy Technology Data Exchange (ETDEWEB)

    Harvey, Neal R [Los Alamos National Laboratory; Porter, Reid B [Los Alamos National Laboratory; Theiler, James [Los Alamos National Laboratory

    2010-01-01

    Ship detection from satellite imagery is something that has great utility in various communities. Knowing where ships are and their types provides useful intelligence information. However, detecting and recognizing ships is a difficult problem. Existing techniques suffer from too many false-alarms. We describe approaches we have taken in trying to build ship detection algorithms that have reduced false alarms. Our approach uses a version of the grayscale morphological Hit-or-Miss transform. While this is well known and used in its standard form, we use a version in which we use a rank-order selection for the dilation and erosion parts of the transform, instead of the standard maximum and minimum operators. This provides some slack in the fitting that the algorithm employs and provides a method for tuning the algorithm's performance for particular detection problems. We describe our algorithms, show the effect of the rank-order parameter on the algorithm's performance and illustrate the use of this approach for real ship detection problems with panchromatic satellite imagery.

  8. Assessing Glacial Lake Outburst Flood Hazard in the Nepal Himalayas using Satellite Imagery and Hydraulic Models

    Science.gov (United States)

    Rounce, D.; McKinney, D. C.

    2015-12-01

    The last half century has witnessed considerable glacier melt that has led to the formation of large glacial lakes. These glacial lakes typically form behind terminal moraines comprising loose boulders, debris, and soil, which are susceptible to fail and cause a glacial lake outburst flood (GLOF). These lakes also act as a heat sink that accelerates glacier melt and in many cases is accompanied by rapid areal expansion. As these glacial lakes continue to grow, their hazard also increases due to the increase in potential flood volume and the lakes' proximity to triggering events such as avalanches and landslides. Despite the large threat these lakes may pose to downstream communities, there are few detailed studies that combine satellite imagery with hydraulic models to present a holistic understanding of the GLOF hazard. The aim of this work is to assess the GLOF hazard of glacial lakes in Nepal using a holistic approach based on a combination of satellite imagery and hydraulic models. Imja Lake will be the primary focus of the modeling efforts, but the methods will be developed in a manner that is transferable to other potentially dangerous glacial lakes in Nepal.

  9. Wind class sampling of satellite SAR imagery for offshore wind resource mapping

    DEFF Research Database (Denmark)

    Badger, Merete; Badger, Jake; Nielsen, Morten

    2010-01-01

    High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical-dynamical down......High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical......-dynamical downscaling of large-scale wind conditions using a set of wind classes that describe representative wind situations. One or more SAR scenes are then selected to represent each wind class and the classes are weighted according to their frequency of occurrence. The wind class methodology was originally...... developed for mesoscale modeling of wind resources. Its performance in connection with sampling of SAR scenes is tested against two sets of random SAR samples and meteorological observations at three sites in the North Sea during 2005–08. Predictions of the mean wind speed and the Weibull scale parameter...

  10. Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

    Directory of Open Access Journals (Sweden)

    Xavier Monteys

    2015-10-01

    Full Text Available The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management.

  11. THE IMPACT OF SHADOWS IN THE RECENT INDIAN REMOTE SENSING SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    Mrs. G.Devi

    2011-08-01

    Full Text Available Remote sensing technology is emerging as a strong tool to extract information about the earth resources from the satellite imagery. However, shadow in fine resolution imagery affects this information. The fine resolution images from recent Indian Remote Sensing (IRS satellites are compared for the pixel values in shadow and non-shadow areas using histogram occupy large shadow area compared to Cartosat-1 of resolution 2.5m. The solar elevation angle is 41degree for which long shadows are formed in case of Cartosat-2 images. The solarelevation angle is 59 degree for which short shadows are formed in case of Cartosat-1 images. The shadows in an image are a function of the solar elevation angle, azimuth angle and spatial resolution etc. The fine resolution image (Cartosat-2 building and their shadow pixel values are analysed by bimodal histogram splitting technique. The shadow boundaries are extracted. Finally Gamma filtering applied and with the Gaussian enhancement technique the shadows are eliminated from Cartosat-2 image. The building shadow under objectcan be identified in this method. The main application in shadow elimination is used for urban map preparation and the object oriented classification.

  12. Proximity graph analysis for linear networks extraction from high-resolution satellite imagery

    Science.gov (United States)

    Skourikhine, Alexei N.

    2006-05-01

    Reliable and accurate methods for detection and extraction of linear network, such as road networks, in satellite imagery are essential to many applications. We present an approach to the road network extraction from high-resolution satellite imagery that is based on proximity graph analysis. We are jumping off from the classification provided by existing spectral and textural classification tools, which produce a set of candidate road patches. Then, constrained Delaunay triangulation and Chordal Axis transform are used to extract centerline characterization of the delineated candidate road patches. We refine produced center lines to reduce noise influence on patch boundaries, resulting in a smaller set of robust center lines authentically representing their road patches. Refined center lines are triangulated using constrained Delaunay triangulation (CDT) algorithm to generate a sub-optimal mesh of interconnections among them. The generated triangle edges connecting different center lines are used for spatial analysis of the center lines relations. A subset of the Delaunay tessellation grid contains the Euclidian Minimum Spanning Tree (EMST) that provides an approximation of road network. The approach can be generalized to the multi-criteria MST and multi-criteria shortest path algorithms to integrate other factors important for road network extraction, in addition to proximity relations considered by standard EMST.

  13. Vehicle detection in WorldView-2 satellite imagery based on Gaussian modeling and contextual learning

    Science.gov (United States)

    Shen, Bichuan; Chen, Chi-Hau; Marchisio, Giovanni B.

    2012-06-01

    In this paper, we aim to study the detection of vehicles from WorldView-2 satellite imagery. For this purpose, accurate modeling of vehicle features and signatures and efficient learning of vehicle hypotheses are critical. We present a joint Gaussian and maximum likelihood based modeling and machine learning approach using SVM and neural network algorithms to describe the local appearance densities and classify vehicles from non-vehicle buildings, objects, and backgrounds. Vehicle hypotheses are fitted by elliptical Gaussians and the bottom-up features are grouped by Gabor orientation filtering based on multi-scale analysis and distance transform. Global contextual information such as road networks and vehicle distributions can be used to enhance the recognition. In consideration of the problem complexity the practical vehicle detection task faces due to dense and overlapping vehicle distributions, partial occlusion and clutters by building, shadows, and trees, we employ a spectral clustering strategy jointly combined with bootstrapped learning to estimate the parameters of centroid, orientation, and extents for local densities. We demonstrate a high detection rate 94.8%,with a missing rate 5.2% and a false alarm rate 5.3% on the WorldView-2 satellite imagery. Experimental results show that our method is quite effective to model and detect vehicles.

  14. Coral Reef environment reconstruction using small drones, new generation photogrammetry algorithms and satellite imagery

    Science.gov (United States)

    Elisa, Casella; Rovere, Alessio; Harris, Daniel; Parravicini, Valeriano

    2016-04-01

    Surveys based on Remotely Piloted Aircraft Systems (RPAS), together with new-generation Structure from Motion (SfM) and Multi-View Stereo (MVS) reconstruction algorithms have been employed to reconstruct the shallow bathymetry of the inner lagoon of a coral reef in Moorea, French Polinesia. This technique has already been used with a high rate of success on coastal environments (e.g. sandy beaches and rocky shorelines) reaching accuracy of the final Digital Elevation Model in the order of few centimeters. The application of such techniques to reconstruct shallow underwater environments is, though, still little reported. We then used the bathymetric dataset obtained from aerial pictures as ground-truth for relative bathymetry obtained from satellite imagery (WorldView-2) of a larger area within the same study site. The first results of our work suggest that RPAS coupled with SfM and MVS algorithms can be used to reconstruct shallow water environments with favorable weather conditions, and can be employed to ground-truth to satellite imagery.

  15. Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations

    Directory of Open Access Journals (Sweden)

    Checchi Francesco

    2013-01-01

    Full Text Available Abstract Background Estimating the size of forcibly displaced populations is key to documenting their plight and allocating sufficient resources to their assistance, but is often not done, particularly during the acute phase of displacement, due to methodological challenges and inaccessibility. In this study, we explored the potential use of very high resolution satellite imagery to remotely estimate forcibly displaced populations. Methods Our method consisted of multiplying (i manual counts of assumed residential structures on a satellite image and (ii estimates of the mean number of people per structure (structure occupancy obtained from publicly available reports. We computed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons’ camps and two urban neighbourhoods with a mixture of residents and displaced ranging in population from 1,969 to 90,547, and compared these to “gold standard” reference population figures from census or other robust methods. Results Structure counts by independent analysts were reasonably consistent. Between one and 11 occupancy reports were available per site and most of these reported people per household rather than per structure. The imagery-based method had a precision relative to reference population figures of Conclusions In settings with clearly distinguishable individual structures, the remote, imagery-based method had reasonable accuracy for the purposes of rapid estimation, was simple and quick to implement, and would likely perform better in more current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern of roofs and multi-level buildings. Based on these results, we discuss possible ways forward for the method’s development.

  16. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS

    Science.gov (United States)

    Wang, Wencai; Sheng, Lifang; Dong, Xu; Qu, Wenjun; Sun, Jilin; Jin, Hongchun; Logan, Timothy

    2017-02-01

    Dust aerosol effect on the retrievals of dusty cloud top height (DCTH) are analyzed over Northwest China using cloud products from MODerate Resolution Imaging Spectroradiometer (MODIS) on Aqua, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and CloudSat for the Spring season of March-May over the years 2007-2011. An excellent agreement is found between CloudSat and CALIPSO derived DCTHs for all cloud types, suggesting that the effect of dust aerosols plays a small role in DCTHs determination for lidar and radar measurements. However, the presence of dust aerosols greatly affects the retrievals of DCTHs for MODIS compared with pure clouds and the active sensors derived results. The differences of DCTHs retrieving from CloudSat and MODIS range from -2.30 to 6.8 km. Likewise, the differences of DCTHs retrieving from CALIPSO and MODIS range from -2.66 to 6.78 km. In addition, the results show that the differences in DCTHs for active and passive sensors are dependent on cloud type. On the whole, dust aerosols have the largest effect on cloud top heights (CTH) retrieved of nimbostratus (Ns), followed by altocumulus (Ac) and altostratus (As), the last is cirrus (Ci) over Northwest China. Our results also indicate that the accuracy of MODIS-derived retrievals reduces accompanied with a decrease of height.

  17. Application of satellite imagery to monitoring human rights abuse of vulnerable communities, with minimal risk to relief staff

    Science.gov (United States)

    Lavers, C.; Bishop, C.; Hawkins, O.; Grealey, E.; Cox, C.; Thomas, D.; Trimel, S.

    2009-07-01

    Space imagery offers remote surveillance of ethnic people groups at risk of human rights abuse. We highlight work in alleged violations in Burma and Sudan, using satellite imagery for verification with Amnesty International. We consider how imaging may effectively support small to medium-sized Non Governmental Organisations and charities, e.g. HART, working in dangerous zones on the ground. Satellite based sensing applications are now at a sufficiently mature stage for moderate Governmental funding levels to help prevent human rights abuse, rather than the greater cost of rebuilding communities and healing sectarian divisions after abuse has taken place.

  18. Evaluating the Cloud Cover Forecast of NCEP Global Forecast System with Satellite Observation

    CERN Document Server

    Ye, Quanzhi

    2011-01-01

    To assess the quality of daily cloud cover forecast generated by the operational global numeric model, the NCEP Global Forecast System (GFS), we compose a large sample with outputs from GFS model and satellite observations from the International Satellite Cloud Climatology Project (ISCCP) in the period of July 2004 to June 2008, to conduct a quantitative and systematic assessment of the performance of a cloud model that covers a relatively long range of time, basic cloud types, and in a global view. The evaluation has revealed the goodness of the model forecast, which further illustrates our completeness on understanding cloud generation mechanism. To quantity the result, we found a remarkably high correlation between the model forecasts and the satellite observations over the entire globe, with mean forecast error less than 15% in most areas. Considering a forecast within 30% difference to the observation to be a "good" one, we find that the probability for the GFS model to make good forecasts varies between...

  19. Cloud parameters using Ground Based Remote Sensing Systems and Satellites over urban coastal area

    Science.gov (United States)

    Han, Z. T.; Gross, B.; Moshary, F.; Wu, Y.; Ahmed, S. A.

    2013-12-01

    Determining cloud radiative and microphysical properties are very important as a means to assess their effect on earths energy balance. While MODIS and GOES have been used for estimating cloud properties, assessing cloud properties directly has been difficult due the lack of consistent ground based sensor measurements except in such established places such as the ARM site in Oklahoma. However, it is known that significant aerosol seeding from urban and/or maritime sources can modify cloud properties such as effective radius and cloud optical depth and therefore evaluation of satellite retrievals in such a unique area offers novel opportunities to assess the potential of satellite retrievals to distinguish these mechanisms In our study, we used a multi-filter rotating shadow band radiometer (MFRSR) and micro wave radiometer (MWR) to retrieve the cloud optical depth and cloud droplets effective radius . In particular, we make a statistical study during summer 2013 where water phase clouds dominate and assess the accuracy of both MODIS and GOES satellite cloud products including LWP, COD and Reff. Most importantly, we assess performance against satellite observing geometries. Much like previous studies at the ARM site, we observe significant biases in the effective radius when the solar zenith angle is too large. In addition, we show that biases are also sensitive to the LWP limiting such measurement s in assessing potential aerosol-cloud signatures Finally, we discuss preliminary aerosol-cloud interactions from our ground system where local lidar is used to assess aerosols below clouds and explore the Aerosol Cloud Index.

  20. A procedure for semi-Automatic Orthophoto Generation from High Resolution Satellite Imagery

    Science.gov (United States)

    Alrajhi, M. N.; Jacobsen, K.; Heipke, C.

    2013-10-01

    The General Directorate of Surveying and Mapping (GDSM), under the Ministry of Municipal and Rural Affairs (MOMRA) is responsible for the production and dissemination of accurate geospatial data for all the metropolitan cities, towns and rural settlements in the Kingdom of Saudi Arabia. GDSM maintains digital geospatial databases that support the production of conventional line and orthophoto maps at scales ranging from 1:1,000 to 1:20,000. The current procedures for the acquisition of new aerial imagery cover a long time cycle of three or more years. Consequently, the availability of recently acquired High Resolution Satellite Imagery (HRSI) presents an attractive alternative image data source for rapid response to updated geospatial data needs. The direct sensor orientation of HRSI is not accurate enough requiring ground control points (GCP). A field survey of GCP is time consuming and costly. Seeking an alternative approach, a research study has recently been completed to use existing image and data base information instead of traditional ground control for the orthoprojection of HRSI in order to automate and speed up as much as possible the whole process. Based on a series of practical experiments, the ability for automated matching of aerial and satellite images by using the Speeded-Up Robust Features (SURF) algorithm is demonstrated to be useful for this task. Practical results from matching with SURF validate the ability for multi-scale, multi-sensor and multi-season matching of aerial and satellite images. The matched tie points are then used to transform the satellite orthophoto to the aerial orthophoto through a 2D affine coordinate transformation. GeoEye-1 and IKONOS imagery, when geo-referenced through SURF-based matching and transformed meet the MOMRA Map Accuracy Standards for 1:10,000 and 1:20,000 scale. However, a similarly processed SPOT-5 image does not meet these standards. This research has led to the development of a simple and efficient tool

  1. Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery

    Science.gov (United States)

    Garrity, Steven R.; Allen, Craig D.; Brumby, Steven P.; Gangodagamage, Chandana; McDowell, Nate G.; Cai, D. Michael

    2013-01-01

    Widespread tree mortality events have recently been observed in several biomes. To effectively quantify the severity and extent of these events, tools that allow for rapid assessment at the landscape scale are required. Past studies using high spatial resolution satellite imagery have primarily focused on detecting green, red, and gray tree canopies during and shortly after tree damage or mortality has occurred. However, detecting trees in various stages of death is not always possible due to limited availability of archived satellite imagery. Here we assess the capability of high spatial resolution satellite imagery for tree mortality detection in a southwestern U.S. mixed species woodland using archived satellite images acquired prior to mortality and well after dead trees had dropped their leaves. We developed a multistep classification approach that uses: supervised masking of non-tree image elements; bi-temporal (pre- and post-mortality) differencing of normalized difference vegetation index (NDVI) and red:green ratio (RGI); and unsupervised multivariate clustering of pixels into live and dead tree classes using a Gaussian mixture model. Classification accuracies were improved in a final step by tuning the rules of pixel classification using the posterior probabilities of class membership obtained from the Gaussian mixture model. Classifications were produced for two images acquired post-mortality with overall accuracies of 97.9% and 98.5%, respectively. Classified images were combined with land cover data to characterize the spatiotemporal characteristics of tree mortality across areas with differences in tree species composition. We found that 38% of tree crown area was lost during the drought period between 2002 and 2006. The majority of tree mortality during this period was concentrated in piñon-juniper (Pinus edulis-Juniperus monosperma) woodlands. An additional 20% of the tree canopy died or was removed between 2006 and 2011, primarily in areas

  2. Fast terrain modelling for hydrogeological risk mapping and emergency management: the contribution of high-resolution satellite SAR imagery

    Directory of Open Access Journals (Sweden)

    A. Nascetti

    2015-07-01

    Full Text Available Geomatic tools fast terrain modelling play a relevant role in hydrogeological risk mapping and emergency management. Given their complete independence from logistic constraints on the ground (as for airborne data collection, illumination (daylight, and weather (clouds conditions, synthetic aperture radar (SAR satellite systems may provide important contributions in terms of digital surface models (DSMs and digital elevation models (DEMs. For this work we focused on the potential of high-resolution SAR satellite imagery for DSM generation using an interferometric (InSAR technique and using a revitalized radargrammetric stereomapping approach. The goal of this work was just methodological. Our goal was to illustrate both the fundamental advantages and drawbacks of the radargrammetric approach with respect to the InSAR technique for DSM generation, and to outline their possible joint role in hydrogeological risk mapping and emergency management. Here, it is worth mentioning that radargrammetry procedures are independent of image coherence (unlike the interferometric approach and phase unwrapping, as well as of parsimony (only a few images are necessary. Therefore, a short time is required for image collection (from tens of minutes to a few hours, thanks to the independence from illumination and weather. The most relevant obstacles of the technique are speckle and the lack of texture impact on image matching, as well as the well-known deformations of SAR imagery (layover and foreshortening, which may produce remarkable difficulties with complex morphologies and that must be accounted for during acquisition planning. Here, we discuss results obtained with InSAR and radargrammetry applied to a COSMO-SkyMed SpotLight triplet (two stereopairs suited for radargrammetry and InSAR, sharing one common image acquired over suburbs of San Francisco (United States, which are characterized by mixed morphology and land cover. We mainly focused on urban areas and

  3. Cloud cover diurnal cycles in satellite data and regional climate model simulations

    Energy Technology Data Exchange (ETDEWEB)

    Pfeifroth, Uwe; Ahrens, Bodo [Frankfurt Univ., Frankfurt am Main (Germany). Inst. for Atmospheric and Environmental Sciences; Hollmann, Rainer [Deutscher Wetterdienst, Offenbach (Germany)

    2012-12-15

    The amount and diurnal cycle of cloud cover play an important role in the energy and water cycle of the earth-atmosphere system and influence the radiation budget of the earth. Due to its importance and the challenging nature of its quantification, cloud cover is considered the biggest uncertainty factor in climate modeling. There is a clear need for reliable cloud datasets suitable for climate model evaluation studies. This study analyzes two datasets of cloud cover and its diurnal cycle derived from satellite observations by the International Satellite Cloud Climatology Project (ISCCP) and by EUMETSAT's Satellite Application Facility on Climate Monitoring (CM SAF) in Africa and Europe. Two regions, Europe and the subtropical southern Atlantic Ocean, were identified as offering distinct cloud cover diurnal cycles reasonably observed by both satellite datasets. In these regions, simulations by the regional climate model COSMO-CLM (CCLM) were evaluated in terms of cloud cover and its diurnal cycle during the time period of 1990 to 2007. Results show that the satellite derived cloud diurnal cycles largely agree, while discrepancies occur under extreme conditions like in the Sahara region. The CCLM is able to simulate the diurnal cycle observed consistently in the two satellite datasets in the South-Atlantic ocean, but not in Europe. CCLM misses the afternoon maximum cloud cover in Summer in Europe, which implies deficiencies in the parameterization of convection and in the treatment of surface-atmosphere interactions. The simulation of the diurnal cycle of the more stratiform cloud cover over the subtropical Atlantic was satisfactory in CCLM. (orig.)

  4. 3D modeling of building indoor spaces and closed doors from imagery and point clouds.

    Science.gov (United States)

    Díaz-Vilariño, Lucía; Khoshelham, Kourosh; Martínez-Sánchez, Joaquín; Arias, Pedro

    2015-02-03

    3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.

  5. 3D Modeling of Building Indoor Spaces and Closed Doors from Imagery and Point Clouds

    Directory of Open Access Journals (Sweden)

    Lucía Díaz-Vilariño

    2015-02-01

    Full Text Available 3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.

  6. Object Based Image Analysis Combining High Spatial Resolution Imagery and Laser Point Clouds for Urban Land Cover

    Science.gov (United States)

    Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

    2016-06-01

    With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high

  7. Estimating seasonal variations in cloud droplet number concentration over the boreal forest from satellite observations

    Directory of Open Access Journals (Sweden)

    R. H. H. Janssen

    2011-08-01

    Full Text Available Seasonal variations in cloud droplet number concentration (NCD in low-level stratiform clouds over the boreal forest are estimated from MODIS observations of cloud optical and microphysical properties, using a sub-adiabatic cloud model to interpret vertical profiles of cloud properties. An uncertainty analysis of the cloud model is included to reveal the main sensitivities of the cloud model. We compared the seasonal cycle in NCD, obtained using 9 yr of satellite data, to surface concentrations of potential cloud activating aerosols, measured at the SMEAR II station at Hyytiälä in Finland. The results show that NCD and cloud condensation nuclei (CCN concentrations have no clear correlation at seasonal time scale. The fraction of aerosols that actually activate as cloud droplet decreases sharply with increasing aerosol concentrations. Furthermore, information on the stability of the atmosphere shows that low NCD is linked to stable atmospheric conditions. Combining these findings leads to the conclusion that cloud droplet activation for the studied clouds over the boreal forest is limited by convection. Our results suggest that it is important to take the strength of convection into account when studying the influence of aerosols from the boreal forest on cloud formation, although they do not rule out the possibility that aerosols from the boreal forest affect other types of clouds with a closer coupling to the surface.

  8. Observations of Three-Dimensional Radiative Effects that Influence Satellite Retrievals of Cloud Properties

    Science.gov (United States)

    Varnai, Tamas; Marshak, Alexander; Lau, William K. M. (Technical Monitor)

    2001-01-01

    This paper examines three-dimensional (3D) radiative effects, which arise from horizontal radiative interactions between areas that have different cloud properties. Earlier studies have argued that these effects can cause significant uncertainties in current satellite retrievals of cloud properties, because the retrievals rely on one-dimensional (1D) theory and do not consider the effects of horizontal changes in cloud properties. This study addresses two questions: which retrieved cloud properties are influenced by 3D radiative effects, and where 3D effects tend to occur? The influence of 3D effects is detected from the wayside illumination and shadowing make clouds appear asymmetric: Areas appear brighter if the cloud top surface is tilted toward, rather than away from, the Sun. The analysis of 30 images by the Moderate Resolution Imaging Spectroradiometer (MODIS) reveals that retrievals of cloud optical thickness and cloud water content are most influenced by 3D effects, whereas retrievals of cloud particle size are much less affected. The results also indicate that while 3D effects are strongest at cloud edges, cloud top variability in cloud interiors, even in overcast regions, also produces considerable 3D effects. Finally, significant 3D effects are found in a wide variety of situations, ranging from thin clouds to thick ones and from low clouds to high ones.

  9. The Role of Satellite Imagery to Improve Pastureland Estimates in South America

    Science.gov (United States)

    Graesser, J.

    2015-12-01

    Agriculture has changed substantially across the globe over the past half century. While much work has been done to improve spatial-temporal estimates of agricultural changes, we still know more about the extent of row-crop agriculture than livestock-grazed land. The gap between cropland and pastureland estimates exists largely because it is challenging to characterize natural versus grazed grasslands from a remote sensing perspective. However, the impasse of pastureland estimates is set to break, with an increasing number of spaceborne sensors and freely available satellite data. The Landsat satellite archive in particular provides researchers with immense amounts of data to improve pastureland information. Here we focus on South America, where pastureland expansion has been scrutinized for the past few decades. We explore the challenges of estimating pastureland using temporal Landsat imagery and focus on key agricultural countries, regions, and ecosystems. We focus on the suggested shift of pastureland from the Argentine Pampas to northern Argentina, and the mixing of small-scale and large-scale ranching in eastern Paraguay and how it could impact the Chaco forest to the west. Further, the Beni Savannahs of northern Bolivia and the Colombian Llanos—both grassland and savannah regions historically used for livestock grazing—have been hinted at as future areas for cropland expansion. There are certainly environmental concerns with pastureland expansion into forests; but what are the environmental implications when well-managed pasture systems are converted to intensive soybean or palm oil plantation? Tropical, grazed grasslands are important habitats for biodiversity, and pasturelands can mitigate soil erosion when well managed. Thus, we must improve estimates of grazed land before we can make informed policy and conservation decisions. This talk presents insights into pastureland estimates in South America and discusses the feasibility to improve current

  10. Identifying clouds over the Pierre Auger Observatory using infrared satellite data

    NARCIS (Netherlands)

    Abreu, P.; Aglietta, M.; Ahlers, M.; Ahn, E. J.; Albuquerque, I. F. M.; Allekotte, I.; Allen, J.; Allison, P.; Almela, A.; Alvarez Castillo, J.; Alvarez-Muniz, J.; Alves Batista, R.; Ambrosio, M.; Aminaei, A.; Anchordoqui, L.; Andringa, S.; Anticic, T.; Aramo, C.; Arqueros, F.; Asorey, H.; Assis, P.; Aublin, J.; Ave, M.; Avenier, M.; Avila, G.; Badescu, A. M.; Barber, K. B.; Barbosa, A. F.; Bardenet, R.; Baughman, B.; Baeuml, J.; Baus, C.; Beatty, J. J.; Becker, K. H.; Belletoile, A.; Bellidol, J. A.; BenZvi, S.; Berat, C.; Bertou, X.; Biermann, P. L.; Billoir, P.; Blanco, F.; Blanco, M.; Bleve, C.; Bluemer, H.; Bohacova, M.; Boncioli, D.; Bonifazi, C.; Bonino, R.; Borodai, N.; Brack, J.; Brancus, I.; Brogueira, P.; Brown, W. C.; Buchholz, P.; Bueno, A.; Buroker, L.; Burton, R. E.; Buscemi, M.; Caballero-Mora, K. S.; Caccianiga, B.; Caccianiga, L.; Caramete, L.; Caruso, R.; Castellina, A.; Cataldi, G.; Cazon, L.; Cester, R.; Cheng, S. H.; Chiavassa, A.; Chinellato, J. A.; Chirinos, J.; Chudoba, J.; Cilmo, M.; Clay, R. W.; Cocciolo, G.; Colalillo, R.; Collica, L.; Coluccia, M. R.; Conceicao, R.; Contreras, F.; Cook, H.; Cooper, M. J.; Coutu, S.; Covault, C. E.; Criss, A.; Cronin, J.; Curutiu, A.; Dallier, R.; Daniel, B.; Dasso, S.; Daumiller, K.; Dawson, B. R.; de Almeida, R. M.; De Domenico, M.; de Jong, S. J.; De La Vega, G.; de Mello, W. J. M.; de Mello Neto, J. R. T.; De Mitri, I.; de Souza, V.; de Vries, K. D.; del Peral, L.; Deligny, O.; Dembinski, H.; Dhital, N.; Di Giulio, C.; Diaz, J. C.; Diaz Castro, M. L.; Diep, P. N.; Diogo, F.; Dobrigkeit, C.; Docters, W.; D'Olivo, J. C.; Dong, P. N.; Dorofeev, A.; dos Anjos, J. C.; Dova, M. T.; D'Urso, D.; Ebr, J.; Engel, R.; Erdmann, M.; Escobar, C. O.; Espadanal, J.; Etchegoyen, A.; San Luis, P. Facal; Falcke, H.; Fang, K.; Farrar, G.; Fauth, A. C.; Fazzini, N.; Ferguson, A. P.; Fick, B.; Figueira, J. M.; Filevich, A.; Filipcic, A.; Fliescher, S.; Fox, B. D.; Fracchiolla, C. E.; Fraenkel, E. D.; Fratu, O.; Froehlich, U.; Fuchs, B.; Gaior, R.; Gamarra, R. F.; Gambetta, S.; Garcia, B.; Garcia Roca, S. T.; Garcia-Gamez, D.; Garcia-Pinto, D.; Garilli, G.; Gascon Bravo, A.; Gemmeke, H.; Ghia, P. L.; Giller, M.; Gitto, J.; Glaser, C.; Glass, H.; Golup, G.; Gomez Albarracin, F.; Gomez Berisso, M.; Gomez Vitale, P. F.; Goncalves, P.; Gonzalez, J. G.; Gookin, B.; Gorgi, A.; Gorham, P.; Gouffon, P.; Grebe, S.; Griffith, N.; Grillo, A. F.; Grubb, T. D.; Guardincerri, Y.; Guarino, F.; Guedes, G. P.; Hansen, P.; Harari, D.; Harrison, T. A.; Harton, J. L.; Haungs, A.; Hebbeker, T.; Heck, D.; Herve, A. E.; Hill, G. C.; Hojvat, C.; Hollon, N.; Holmes, V. C.; Homola, P.; Horandel, J. R.; Horvath, P.; Hrabovsky, M.; Huber, D.; Huege, T.; Insolia, A.; Jansen, S.; Jarne, C.; Jiraskova, S.; Josebachuili, M.; Kadija, K.; Kampert, K. H.; Karhan, P.; Kasper, P.; Katkov, I.; Kegl, B.; Keilhauer, B.; Keivani, A.; Kelley, J. L.; Kemp, E.; Kieckhafer, R. M.; Klages, H. O.; Kleifges, M.; Kleinfeller, J.; Knapp, J.; Krause, R.; Krohm, N.; Kroemer, O.; Kruppke-Hansen, D.; Kuempel, D.; Kulbartz, J. K.; Kunka, N.; La Rosa, G.; LaHurd, D.; Latronico, L.; Lauer, R.; Lauscher, M.; Lautridou, P.; Le Coz, S.; Leao, M. S. A. B.; Lebrun, D.; Lebrun, P.; Leigui de Oliveira, M. A.; Letessier-Selvon, A.; Lhenry-Yvon, I.; Link, K.; Lopez, R.; Lopez Agueera, A.; Louedec, K.; Lozano Bahilo, J.; Lu, L.; Lucero, A.; Ludwig, M.; Lyberis, H.; Maccarone, M. C.; Macolino, C.; Malacari, M.; Maldera, S.; Maller, J.; Mandat, D.; Mantsch, P.; Mariazzi, A. G.; Marin, J.; Marin, V.; Maris, I. C.; Marquez Falcon, H. R.; Marsella, G.; Martello, D.; Martinez, H.; Martinez Bravo, O.; Martraire, D.; Masias Meza, J. J.; Mathes, H. J.; Matthews, J.; Matthews, J. A. J.; Matthiae, G.; Maurel, D.; Maurizio, D.; Mayotte, E.; Mazur, P. O.; Medina-Tanco, G.; Melissas, M.; Melo, D.; Menichetti, E.; Menshikov, A.; Messina, S.; Meyhandan, R.; Micanovic, S.; Micheletti, M. I.; Middendorf, L.; Minaya, I. A.; Mirarrionti, L.; Mitrica, B.; Molina-Bueno, L.; Mollerach, S.; Monasor, M.; Ragaigne, D. Monnier; Montanet, F.; Morales, B.; Morello, C.; Moreno, J. C.; Mostafa, M.; Moura, C. A.; Muller, M. A.; Mueller, G.; Muenchmeyer, M.; Mussa, R.; Navarra, G.; Navarro, J. L.; Navas, S.; Necesal, P.; Nellen, L.; Nelles, A.; Neuser, T. J.; Nhung, P. T.; Niechciol, M.; Niemietz, L.; Nierstenhoefer, N.; Niggemann, T.; Nitz, D.; Nosek, D.; Nozka, L.; Oehlschlaeger, J.; Olinto, A.; Oliveira, M.; Ortiz, M.; Pacheco, N.; Pakk Selmi-Dei, D.; Palatka, M.; Pallotta, J.; Palmieri, N.; Parente, G.; Parra, A.; Pastor, S.; Paul, T.; Pech, M.; Pekala, J.; Pelayo, R.; Pepe, I. M.; Perrone, L.; Pesce, R.; Petermann, E.; Petrera, S.; Petrolini, A.; Petrov, Y.; Pfendner, C.; Piegaia, R.; Pierog, T.; Pieroni, P.; Pimenta, M.; Pirronello, V.; Platino, M.; Plum, M.; Ponce, V. H.; Pontz, M.; Porcelli, A.; Privitera, P.; Prouza, M.; Quel, E. J.; Querchfeld, S.; Rautenberg, J.; Ravel, O.; Ravignani, D.; Revenu, B.; Ridky, J.; Riggi, S.; Risse, M.; Ristori, P.; Rivera, H.; Rizi, V.; Roberts, J.; Rodrigues de Carvalho, W.; Rodriguez Cabo, I.; Fernandez, G. Rodriguez; Rodriguez Martino, J.; Rodriguez Rojo, J.; Rodriguez-Frias, M. D.; Ros, G.; Rosado, J.; Rossler, T.; Roth, M.; Rouille-d'Orfeuil, B.; Roulet, E.; Rovero, A. C.; Ruehle, C.; Saffi, S. J.; Saftoiu, A.; Salamida, F.; Salazar, H.; Greus, F. Salesa; Salina, G.; Sanchez, F.; Santo, C. E.; Santos, E.; Santos, E. M.; Sarazin, F.; Sarkar, B.; Sato, R.; Scharf, N.; Scherini, V.; Schieler, H.; Schiffer, P.; Schmidt, A.; Scholten, O.; Schoorlemmer, H.; Schovancova, J.; Schovanek, P.; Schroeder, F. G.; Schulz, J.; Schuster, D.; Sciutto, Si.; Scuderi, M.; Segreto, A.; Settimo, M.; Shadkam, A.; Shellard, R. C.; Sidelnik, I.; Sigl, G.; Sima, O.; Smialkowski, A.; Smida, R.; Snow, G. R.; Sommers, P.; Sorokin, J.; Spinka, H.; Squartini, R.; Srivastava, Y. N.; Stanic, S.; Stapleton, J.; Stasielak, J.; Stephan, M.; Straub, M.; Stutz, A.; Suarez, F.; Suomijaervi, T.; Supanitsky, A. D.; Susa, T.; Sutherland, M. S.; Swain, J.; Szadkowski, Z.; Szuba, M.; Tapia, A.; Tartare, M.; Tascau, O.; Tcaciuc, R.; Thao, N. T.; Thomas, D.; Tiffenberg, J.; Timmermans, C.; Tkaczyk, W.; Todero Peixoto, C. J.; Toma, G.; Tomankova, L.; Tome, B.; Tonachini, A.; Torralba Elipe, G.; Machado, D. Torres; Travnicek, P.; Tridapalli, D. B.; Trovato, E.; Tueros, M.; Ulrich, R.; Unger, M.; Urban, M.; Valdes Galicia, J. F.; Valino, I.; Valore, L.; van Aar, G.; van den Berg, A. M.; van Velzen, S.; van Vliet, A.; Varela, E.; Vargas Cardenas, B.; Varner, G.; Vazquez, J. R.; Vazquez, R. A.; Veberic, D.; Verzi, V.; Vicha, J.; Videla, M.; Villasenor, L.; Wahlberg, H.; Wahrlich, P.; Wainberg, O.; Walz, D.; Watson, A. A.; Weber, M.; Weidenhaupt, K.; Weindl, A.; Werner, F.; Westerhoff, S.; Whelan, B. J.; Widom, A.; Wieczorek, G.; Wiencke, L.; Wilczynska, B.; Wilczynski, H.; Will, M.; Williams, C.; Winchen, T.; Wundheiler, B.; Yamamoto, T.; Yapici, T.; Younk, P.; Yuan, G.; Yushkov, A.; Zamorano Garcia, B.; Zas, E.; Zavrtanik, D.; Zavrtanik, M.; Zaw, I.; Zepeda, A.; Zhou, J.; Zhu, Y.; Zimbres Silva, M.; Ziolkowski, M.; Martin, L.

    2013-01-01

    We describe a new method of identifying night-time clouds over the Pierre Auger Observatory using infrared data from the Imager instruments on the GOES-12 and GOES-13 satellites. We compare cloud. identifications resulting from our method to those obtained by the Central Laser Facility of the Auger

  11. Heavy rainfall prediction applying satellite-based cloud data assimilation over land

    Science.gov (United States)

    Seto, Rie; Koike, Toshio; Rasmy, Mohamed

    2016-08-01

    To optimize flood management, it is crucial to determine whether rain will fall within a river basin. This requires very fine precision in prediction of rainfall areas. Cloud data assimilation has great potential to improve the prediction of precipitation area because it can directly obtain information on locations of rain systems. Clouds can be observed globally by satellite-based microwave remote sensing. Microwave observation also includes information of latent heat and water vapor associated with cloud amount, which enables the assimilation of not only cloud itself but also the cloud-affected atmosphere. However, it is difficult to observe clouds over land using satellite microwave remote sensing, because their emissivity is much lower than that of the land surface. To overcome this challenge, we need appropriate representation of heterogeneous land emissivity. We developed a coupled atmosphere and land data assimilation system with the Weather Research and Forecasting Model (CALDAS-WRF), which can assimilate soil moisture, vertically integrated cloud water content over land, and heat and moisture within clouds simultaneously. We applied this system to heavy rain events in Japan. Results show that the system effectively assimilated cloud signals and produced very accurate cloud and precipitation distributions. The system also accurately formed a consistent atmospheric field around the cloud. Precipitation intensity was also substantially improved by appropriately representing the local atmospheric field. Furthermore, combination of the method and operationally analyzed dynamical and moisture fields improved prediction of precipitation duration. The results demonstrate the method's promise in dramatically improving predictions of heavy rain and consequent flooding.

  12. Mapping of land cover in northern California with simulated hyperspectral satellite imagery

    Science.gov (United States)

    Clark, Matthew L.; Kilham, Nina E.

    2016-09-01

    Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Analysis of hyperspectral, or imaging spectrometer, imagery has shown an impressive capacity to map a wide range of natural and anthropogenic land cover. Applications have been mostly with single-date imagery from relatively small spatial extents. Future hyperspectral satellites will provide imagery at greater spatial and temporal scales, and there is a need to assess techniques for mapping land cover with these data. Here we used simulated multi-temporal HyspIRI satellite imagery over a 30,000 km2 area in the San Francisco Bay Area, California to assess its capabilities for mapping classes defined by the international Land Cover Classification System (LCCS). We employed a mapping methodology and analysis framework that is applicable to regional and global scales. We used the Random Forests classifier with three sets of predictor variables (reflectance, MNF, hyperspectral metrics), two temporal resolutions (summer, spring-summer-fall), two sample scales (pixel, polygon) and two levels of classification complexity (12, 20 classes). Hyperspectral metrics provided a 16.4-21.8% and 3.1-6.7% increase in overall accuracy relative to MNF and reflectance bands, respectively, depending on pixel or polygon scales of analysis. Multi-temporal metrics improved overall accuracy by 0.9-3.1% over summer metrics, yet increases were only significant at the pixel scale of analysis. Overall accuracy at pixel scales was 72.2% (Kappa 0.70) with three seasons of metrics. Anthropogenic and homogenous natural vegetation classes had relatively high confidence and producer and user accuracies were over 70%; in comparison, woodland and forest classes had considerable confusion. We next focused on plant functional types with relatively pure spectra by removing open-canopy shrublands

  13. Total cloud cover from satellite observations and climate models

    Directory of Open Access Journals (Sweden)

    P. Probst

    2010-09-01

    Full Text Available Global and zonal monthly means of cloud cover fraction for total cloudiness (CF from the ISCCP D2 dataset are compared to same quantity produced by the 20th century simulations of 21 climate models from the World Climate Research Programme's (WCRP's Coupled Model Intercomparison Project phase 3 (CMIP3 multi-model dataset archived by the Program for Climate Model Diagnosis and Intercomparison (PCMDI. The comparison spans the time frame from January 1984 to December 1999 and the global and zonal average of CF are studied. The restriction to total cloudiness depends on the output of some models that does not include the 3D cloud structure. It is shown that the global mean of CF for the PCMDI/CMIP3 models, averaged over the whole period, exhibits a considerable variance and generally underestimates the ISCCP value. Very large discrepancies among models, and between models and observations, are found in the polar areas, where both models and satellite observations are less reliable, and especially near Antarctica. For this reason the zonal analysis is focused over the 60° S–60° N latitudinal belt, which includes the tropical area and mid latitudes. The two hemispheres are analyzed separately to show the variation of the amplitude of the seasonal cycle. Most models overestimate the yearly averaged values of CF over all of the analysed areas, while differences emerge in their ability to capture the amplitude of the seasonal cycle. The models represent, in a qualitatively correct way, the magnitude and the weak sign of the seasonal cycle over the whole geographical domain, but overestimate the strength of the signal in the tropical areas and at mid-latitudes, when taken separately. The interannual variability of the two yearly averages and of the amplitude of the seasonal cycle is greatly underestimated by all models in each area analysed. This work shows that the climate models have an heterogeneous behaviour in simulating the CF over

  14. Estimating Uncertainties in Bio-Optical Products Derived from Satellite Ocean Color Imagery Using an Ensemble Approach

    Science.gov (United States)

    2011-01-01

    We propose a methodology to quantify errors and produce uncertainty maps for satellite-derived ocean color bio -optical products using ensemble...retrievals of bio -optical properties from satellite ocean color imagery are related to a variety of factors, including sensor calibration, atmospheric...correction, and the bio -optical inversion algorithms. Errors propagate, amplify, and intertwine along the processing path, so it is important to

  15. Using Information From Prior Satellite Scans to Improve Cloud Detection Near the Day-Night Terminator

    Science.gov (United States)

    Yost, Christopher R.; Minnis, Patrick; Trepte, Qing Z.; Palikonda, Rabindra; Ayers, Jeffrey K.; Spangenberg, Doulas A.

    2012-01-01

    With geostationary satellite data it is possible to have a continuous record of diurnal cycles of cloud properties for a large portion of the globe. Daytime cloud property retrieval algorithms are typically superior to nighttime algorithms because daytime methods utilize measurements of reflected solar radiation. However, reflected solar radiation is difficult to accurately model for high solar zenith angles where the amount of incident radiation is small. Clear and cloudy scenes can exhibit very small differences in reflected radiation and threshold-based cloud detection methods have more difficulty setting the proper thresholds for accurate cloud detection. Because top-of-atmosphere radiances are typically more accurately modeled outside the terminator region, information from previous scans can help guide cloud detection near the terminator. This paper presents an algorithm that uses cloud fraction and clear and cloudy infrared brightness temperatures from previous satellite scan times to improve the performance of a threshold-based cloud mask near the terminator. Comparisons of daytime, nighttime, and terminator cloud fraction derived from Geostationary Operational Environmental Satellite (GOES) radiance measurements show that the algorithm greatly reduces the number of false cloud detections and smoothes the transition from the daytime to the nighttime clod detection algorithm. Comparisons with the Geoscience Laser Altimeter System (GLAS) data show that using this algorithm decreases the number of false detections by approximately 20 percentage points.

  16. Evaluation of cloud base height measurements from ceilometer CL31 and MODIS satellite over Ahmedabad, India

    Directory of Open Access Journals (Sweden)

    S. Sharma

    2015-11-01

    Full Text Available Clouds play a tangible role in the Earth's atmosphere and in particular, the cloud base height (CBH which is linked to cloud type is one of the important characteristic to describe the influence of clouds on the environment. In present study, CBH observations from ceilometer CL31 have been extensively studied during May 2013 to January 2015 over Ahmedabad (23.03° N, 72.54° E, India. A detail comparison has been performed with the use of ground-based CBH measurements from ceilometer CL31 and CBH retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer onboard Aqua and Terra satellite. Some interesting features of cloud dynamics viz. strong downdraft and updraft have been observed over Ahmedabad which revealed different cloud characteristics during monsoon and post-monsoon periods. CBH shows seasonal variation during Indian summer monsoon and post-monsoon period. Results indicate that ceilometer is one of the excellent instruments to precisely detect low and mid-level clouds and MODIS satellite provides accurate retrieval of high-level clouds over this region. The CBH algorithm used for MODIS satellite is also able to capture the low-level clouds.

  17. Building high-performance system for processing a daily large volume of Chinese satellites imagery

    Science.gov (United States)

    Deng, Huawu; Huang, Shicun; Wang, Qi; Pan, Zhiqiang; Xin, Yubin

    2014-10-01

    The number of Earth observation satellites from China increases dramatically recently and those satellites are acquiring a large volume of imagery daily. As the main portal of image processing and distribution from those Chinese satellites, the China Centre for Resources Satellite Data and Application (CRESDA) has been working with PCI Geomatics during the last three years to solve two issues in this regard: processing the large volume of data (about 1,500 scenes or 1 TB per day) in a timely manner and generating geometrically accurate orthorectified products. After three-year research and development, a high performance system has been built and successfully delivered. The high performance system has a service oriented architecture and can be deployed to a cluster of computers that may be configured with high end computing power. The high performance is gained through, first, making image processing algorithms into parallel computing by using high performance graphic processing unit (GPU) cards and multiple cores from multiple CPUs, and, second, distributing processing tasks to a cluster of computing nodes. While achieving up to thirty (and even more) times faster in performance compared with the traditional practice, a particular methodology was developed to improve the geometric accuracy of images acquired from Chinese satellites (including HJ-1 A/B, ZY-1-02C, ZY-3, GF-1, etc.). The methodology consists of fully automatic collection of dense ground control points (GCP) from various resources and then application of those points to improve the photogrammetric model of the images. The delivered system is up running at CRESDA for pre-operational production and has been and is generating good return on investment by eliminating a great amount of manual labor and increasing more than ten times of data throughput daily with fewer operators. Future work, such as development of more performance-optimized algorithms, robust image matching methods and application

  18. A method for retrieving clouds with satellite infrared radiances using the particle filter

    Science.gov (United States)

    Xu, Dongmei; Auligné, Thomas; Descombes, Gaël; Snyder, Chris

    2016-11-01

    Ensemble-based techniques have been widely utilized in estimating uncertainties in various problems of interest in geophysical applications. A new cloud retrieval method is proposed based on the particle filter (PF) by using ensembles of cloud information in the framework of Gridpoint Statistical Interpolation (GSI) system. The PF cloud retrieval method is compared with the Multivariate Minimum Residual (MMR) method that was previously established and verified. Cloud retrieval experiments involving a variety of cloudy types are conducted with the PF and MMR methods with measurements of infrared radiances on multi-sensors onboard both geostationary and polar satellites, respectively. It is found that the retrieved cloud masks with both methods are consistent with other independent cloud products. MMR is prone to producing ambiguous small-fraction clouds, while PF detects clearer cloud signals, yielding closer heights of cloud top and cloud base to other references. More collections of small-fraction particles are able to effectively estimate the semi-transparent high clouds. It is found that radiances with high spectral resolutions contribute to quantitative cloud top and cloud base retrievals. In addition, a different way of resolving the filtering problem over each model grid is tested to better aggregate the weights with all available sensors considered, which is proven to be less constrained by the ordering of sensors. Compared to the MMR method, the PF method is overall more computationally efficient, and the cost of the model grid-based PF method scales more directly with the number of computing nodes.

  19. Assessing Usefulness of High-Resolution Satellite Imagery (HRSI) for Re-Survey of Cadastral Maps

    Science.gov (United States)

    Rao, S. S.; Sharma, J. R.; Rajashekar, S. S.; Rao, D. S. P.; Arepalli, A.; Arora, V.; Kuldeep; Singh, R. P.; Kanaparthi, M.

    2014-11-01

    The Government of India has initiated "National Land Records Modernization Programme (NLRMP)" with emphasis to modernize management of land records, minimize scope of land/property disputes, enhance transparency in the land records maintenance system, and facilitate moving eventually towards guaranteed conclusive titles to immovable properties in the country. One of the major components of the programme is survey/re-survey and updating of all survey and settlement records including creation of original cadastral records wherever necessary. The use of ETS/GPS, Aerial or High Resolution Satellite Images (HRSI) and hybrid method of images are suggested for re-survey in the guidelines. The emerging new satellite technologies enabling earth observation at a spatial resolution of 1.0m or 0.5m or even 0.41m have brought revolutionary changes in the field of cadastral survey. The highresolution satellite imagery (HRSI) is showing its usefulness for cadastral surveys in terms of clear identification of parcel boundaries and other cultural features due to which traditional cadastre and land registration systems have been undergoing major changes worldwide. In the present research study, cadastral maps are derived from ETS/GPS, HRSI of 1.0m and 0.5m and used for comparison. The differences in areas, perimeter and position of parcels derived from HRSI are compared vis-a-vis ETS/GPS boundaries. An assessment has been made on the usefulness of HRSI for re-survey of cadastral maps vis-a-vis conventional ground survey.

  20. Stratified estimation of forest area using satellite imagery, inventory data, and the k-nearest neighbors technique

    Science.gov (United States)

    Ronald E. McRoberts; Mark D. Nelson; Daniel G. Wendt

    2002-01-01

    For two large study areas in Minnesota, USA, stratified estimation using classified Landsat Thematic Mapper satellite imagery as the basis for stratification was used to estimate forest area. Measurements of forest inventory plots obtained for a 12-month period in 1998 and 1999 were used as the source of data for within-stratum estimates. These measurements further...

  1. Estimation of Reservoir Discharges from Lake Nasser and Roseires Reservoir in the Nile Basin Using Satellite Altimetry and Imagery Data

    NARCIS (Netherlands)

    Muala, E.; Mohamed, Y.A.; Duan, Z.; Van der Zaag, P.

    2014-01-01

    This paper presents the feasibility of estimating discharges from Roseires Reservoir (Sudan) for the period from 2002 to 2010 and Aswan High Dam/Lake Nasser (Egypt) for the periods 1999–2002 and 2005–2009 using satellite altimetry and imagery with limited in situ data. Discharges were computed using

  2. Onboard Detection of Snow, Ice, Clouds, and Other Processes

    Data.gov (United States)

    National Aeronautics and Space Administration — The detection of clouds within a satellite image is essential for retrieving surface geophysical parameters from optical and thermal imagery. Even a small percentage...

  3. Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Hasituya

    2017-03-01

    Full Text Available In recent years, the area of plastic-mulched farmland (PMF has undergone rapid growth and raised remarkable environmental problems. Therefore, mapping the PMF plays a crucial role in agricultural production, environmental protection and resource management. However, appropriate data selection criteria are currently lacking. Thus, this study was carried out in two main plastic-mulching practice regions, Jizhou and Guyuan, to look for an appropriate spatial scale for mapping PMF with remote sensing. The average local variance (ALV function was used to obtain the appropriate spatial scale for mapping PMF based on the GaoFen-1 (GF-1 satellite imagery. Afterwards, in order to validate the effectiveness of the selected method and to interpret the relationship between the appropriate spatial scale derived from the ALV and the spatial scale with the highest classification accuracy, we classified the imagery with varying spatial resolution by the Support Vector Machine (SVM algorithm using the spectral features, textural features and the combined spectral and textural features respectively. The results indicated that the appropriate spatial scales from the ALV lie between 8 m and 20 m for mapping the PMF both in Jizhou and Guyuan. However, there is a proportional relation: the spatial scale with the highest classification accuracy is at the 1/2 location of the appropriate spatial scale generated from the ALV in Jizhou and at the 2/3 location of the appropriate spatial scale generated from the ALV in Guyuan. Therefore, the ALV method for quantitatively selecting the appropriate spatial scale for mapping PMF with remote sensing imagery has theoretical and practical significance.

  4. Inversion Technique for Estimating Emissions of Volcanic Ash from Satellite Imagery

    Science.gov (United States)

    Pelley, Rachel; Cooke, Michael; Manning, Alistair; Thomson, David; Witham, Claire; Hort, Matthew

    2014-05-01

    When using dispersion models such as NAME (Numerical Atmospheric-dispersion Modelling Environment) to predict the dispersion of volcanic ash, a source term defining the mass release rate of ash is required. Inversion modelling using observations of the ash plume provides a method of estimating the source term for use in NAME. Our inversion technique makes use of satellite retrievals, calculated using data from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument on-board the MSG (Meteosat Second Generation) satellite, as the ash observations. InTEM (Inversion Technique for Emission Modelling) is the UK Met Office's inversion modelling system. Recently the capability to estimate time and height varying source terms has been implemented and applied to volcanic ash. InTEM uses a probabilistic approach to fit NAME model concentrations to satellite retrievals. This is achieved by applying Bayes Theorem to give a cost function for the source term. Source term profiles with lower costs generate model concentrations that better fit the satellite retrievals. InTEM uses the global optimisation technique, simulated annealing, to find the minimum of the cost function. The use of a probabilistic approach allows the uncertainty in the satellite retrievals to be incorporated into the inversion technique. InTEM makes use of satellite retrievals of both ash column loadings and of cloud free regions. We present a system that allows InTEM to be used during an eruption. The system is automated and can produce source term updates up to four times a day. To allow automation hourly satellite retrievals of ash are routinely produced using conservative detection limits. The conservative detection limits provide good detection of the ash plume while limiting the number of false alarms. Regions which are flagged as ash contaminated or free from cloud (both meteorological and ash) are used in the InTEM system. This approach is shown to improve the concentrations in the

  5. Clouds and the Earth's Radiant Energy System (CERES) Visualization Single Satellite Footprint (SSF) Plot Generator

    Science.gov (United States)

    Barsi, Julia A.

    1995-01-01

    The first Clouds and the Earth's Radiant Energy System (CERES) instrument will be launched in 1997 to collect data on the Earth's radiation budget. The data retrieved from the satellite will be processed through twelve subsystems. The Single Satellite Footprint (SSF) plot generator software was written to assist scientists in the early stages of CERES data analysis, producing two-dimensional plots of the footprint radiation and cloud data generated by one of the subsystems. Until the satellite is launched, however, software developers need verification tools to check their code. This plot generator will aid programmers by geolocating algorithm result on a global map.

  6. Monitoring and characterizing natural hazards with satellite InSAR imagery

    Science.gov (United States)

    Lu, Zhong; Zhang, Jixian; Zhang, Yonghong; Dzurisin, Daniel

    2010-01-01

    Interferometric synthetic aperture radar (InSAR) provides an all-weather imaging capability for measuring ground-surface deformation and inferring changes in land surface characteristics. InSAR enables scientists to monitor and characterize hazards posed by volcanic, seismic, and hydrogeologic processes, by landslides and wildfires, and by human activities such as mining and fluid extraction or injection. Measuring how a volcano’s surface deforms before, during, and after eruptions provides essential information about magma dynamics and a basis for mitigating volcanic hazards. Measuring spatial and temporal patterns of surface deformation in seismically active regions is extraordinarily useful for understanding rupture dynamics and estimating seismic risks. Measuring how landslides develop and activate is a prerequisite to minimizing associated hazards. Mapping surface subsidence or uplift related to extraction or injection of fluids during exploitation of groundwater aquifers or petroleum reservoirs provides fundamental data on aquifer or reservoir properties and improves our ability to mitigate undesired consequences. Monitoring dynamic water-level changes in wetlands improves hydrological modeling predictions and the assessment of future flood impacts. In addition, InSAR imagery can provide near-real-time estimates of fire scar extents and fire severity for wildfire management and control. All-weather satellite radar imagery is critical for studying various natural processes and is playing an increasingly important role in understanding and forecasting natural hazards.

  7. Geometric Potential of Pléiades 1A Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Postelniak Andrii

    2014-10-01

    Full Text Available In this paper, the geometrical characteristics of Pléiades 1A satellite imagery (both single and stereo are analysed. At first the process of digital surface model (DSM extraction from a Pléiades 1A stereo pair is described and analysed. After that geometric an accuracy of imagery, orthorectified using the extracted DSM and using the SRTM (Shuttle radar topographic mission was analysed. The Pléiades 1A stereo pair was acquired on October 22, 2012 from the same orbital pass over an urban zone (Kiev, Ukraine. The study area is heterogeneous: there are both built-up and flat areas. The iImage orientation, DSM extraction and orthorectified images generation were performed using the PCI Geomatica 2013 software. The results showed that a strong, positive correlation between reference-derived elevations and DSM-derived elevations can be observed, and the orthorectified image accuracy, generated using that DSM, approximately equal to 1 m can be achieved using a bias compensation sensor model. Different sensor models were used for orthorectification using the SRTM. In this case, the geometric accuracy is а function of a chosen sensor model and a number of ground control points (GCP.

  8. The Northeast Greenland Sirius Water Polynya dynamics and variability inferred from satellite imagery

    DEFF Research Database (Denmark)

    Pedersen, Jørn Bjarke Torp; Kaufmann, Laura Hauch; Kroon, Aart

    2010-01-01

    ’, and examines its spatial and temporal dynamics by analysis of recent satellite imagery, modelled meteorological data and historical data covering the last decade. The dominating mechanisms to form and sustain the polynya are inferred and the persistence and inter-annual variability of the phenomenon...... and summer regimes in the seasonal evolution of the polynya. During the winter regime, both the size of and the ice cover within the polynya varies significantly on a temporal and spatial scale. Intermittent wind-driven openings of the polynya alternate with periods of increasing ice cover. Some of the most...... persistent areas of open water in the polynya coincide with locations where significant concentrations of spring and summer settlements from the Thule Inuit culture (AD 1400-1850) are observed, indicating a connection between the presence of the polynya and the Thule Inuit living in the area in prehistoric...

  9. The Northeast Greenland Sirius Water Polynya dynamics and variability inferred from satellite imagery

    DEFF Research Database (Denmark)

    Pedersen, Jørn Bjarke Torp; Kaufmann, Laura Hauch; Kroon, Aart

    2010-01-01

    ’, and examines its spatial and temporal dynamics by analysis of recent satellite imagery, modelled meteorological data and historical data covering the last decade. The dominating mechanisms to form and sustain the polynya are inferred and the persistence and inter-annual variability of the phenomenon...... are estimated. The polynya formation is predominantly governed by mechanical forcing caused by northerly gales, and it is classified as a wind-driven shelf water polynya. A marked seasonal difference in the surface wind field, together with the obvious seasonal cycle in insolation, creates distinct winter...... and summer regimes in the seasonal evolution of the polynya. During the winter regime, both the size of and the ice cover within the polynya varies significantly on a temporal and spatial scale. Intermittent wind-driven openings of the polynya alternate with periods of increasing ice cover. Some of the most...

  10. Advancing Coastal Climate Adaptation in Denmark by Land Subsidence Mapping using Sentinel-1 Satellite Imagery

    DEFF Research Database (Denmark)

    Sørensen, Carlo Sass; Broge, Niels H.; Mølgaard, Mads R.

    2016-01-01

    There are still large uncertainties in projections of climate change and sea level rise. Here, land subsidence is an additional factor that may adversely affect the vulnerability towards floods in low-lying coastal communities. The presented study performs an initial assessment of subsidence...... mapping using Sentinel-1 satellite imagery and leveling at two coastal locations in Denmark. Within both investigated areas current subsidence rates of 5-10 millimeters per year are found. This subsidence is related to the local geology, and challenges and potentials in bringing land subsidence mapping...... and geology into climate adaptation are discussed in relation to perspectives of a national subsidence monitoring system partly based on the findings from the two coastal locations. The current lack of subsidence data and a fragmentation of geotechnical information are considered as hindrances to optimal...

  11. Coastal erosion and accretion in Pak Phanang, Thailand by GIS analysis of maps and satellite imagery

    Directory of Open Access Journals (Sweden)

    Sayedur Rahman Chowdhury

    2013-12-01

    Full Text Available Coastal erosion and accretion in Pak Phanang of southern Thailand between 1973 and 2003 was measured using multi-temporal topographic maps and Landsat satellite imageries. Within a GIS environment landward and seaward movements of shoreline was estimated by a transect-based analysis, and amounts of land accretion and erosion were estimated by a parcel-based geoprocessing. The whole longitudinal extent of the 58 kilometer coast was classified based on the erosion and accretion trends during this period using agglomerative hierarchical clustering approach. Erosion and accretion were found variable over time and space, and periodic reversal of status was also noticed in many places. Estimates of erosion were evaluated against field-survey based data, and found reasonably accurate where the rates were relatively great. Smoothing of shoreline datasets was found desirable as its impacts on the estimates remained within tolerable limits.

  12. A fast radiative transfer method for the simulation of visible satellite imagery

    Science.gov (United States)

    Scheck, Leonhard; Frèrebeau, Pascal; Buras-Schnell, Robert; Mayer, Bernhard

    2016-05-01

    A computationally efficient radiative transfer method for the simulation of visible satellite images is presented. The top of atmosphere reflectance is approximated by a function depending on vertically integrated optical depths and effective particle sizes for water and ice clouds, the surface albedo, the sun and satellite zenith angles and the scattering angle. A look-up table (LUT) for this reflectance function is generated by means of the discrete ordinate method (DISORT). For a constant scattering angle the reflectance is a relatively smooth and symmetric function of the two zenith angles, which can be well approximated by the lowest-order terms of a 2D Fourier series. By storing only the lowest Fourier coefficients and adopting a non-equidistant grid for the scattering angle, the LUT is reduced to a size of 21 MB per satellite channel. The computation of the top of atmosphere reflectance requires only the calculation of the cloud parameters from the model state and the evaluation and interpolation of the reflectance function using the compressed LUT and is thus orders of magnitude faster than DISORT. The accuracy of the method is tested by generating synthetic satellite images for the 0.6 μm and 0.8 μm channels of the SEVIRI instrument for operational COSMO-DE model forecasts from the German Weather Service (DWD) and comparing them to DISORT results. For a test period in June the root mean squared absolute reflectance error is about 10-2 and the mean relative reflectance error is less than 2% for both channels. For scattering angles larger than 170 ° the rapid variation of reflectance with the particle size related to the backscatter glory reduces the accuracy and the errors increase by a factor of 3-4. Speed and accuracy of the new method are sufficient for operational data assimilation and high-resolution model verification applications.

  13. Improvements to Lunar BRDF-Corrected Nighttime Satellite Imagery: Uses and Applications

    Science.gov (United States)

    Cole, T.; Molthan, A.; Schultz, L. A.; Roman, M. O.; Wanik, D. W.

    2016-12-01

    Observations made by the VIIRS day/night band (DNB) provide daily, nighttime measurements to monitor Earth surface processes. However, these observations are impacted by variations in reflected solar radiation on the moon's surface. As the moon transitions from new to full phase, increasing radiance is reflected to the Earth's surface and contributes additional reflected moonlight from clouds and land surface, in addition to emissions from other light sources observed by the DNB. The introduction of a bi-directional reflectance distribution function (BRDF) algorithm serves to remove these lunar variations and normalize observed radiances. Provided by the Terrestrial Information Systems Laboratory at Goddard Space Flight Center, a 1 km gridded lunar BRDF-corrected DNB product and VIIRS cloud mask can be used for a multitude of nighttime applications without influence from the moon. Such applications include the detection of power outages following severe weather events using pre- and post-event DNB imagery, as well as the identification of boat features to curtail illegal fishing practices. This presentation will provide context on the importance of the lunar BRDF correction algorithm and explore the aforementioned uses of this improved DNB product for applied science applications.

  14. Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

    Directory of Open Access Journals (Sweden)

    C. A. Poulsen

    2011-04-01

    Full Text Available Clouds play an important role in balancing the Earth's radiation budget. Clouds reflect sunlight which cools the Earth, and also trap infrared radiation in the same manner as greenhouse gases. Changes in cloud cover and cloud properties over time can have important consequences for climate. The Intergovernmental Panel for Climate Change (IPCC has identified current gaps in the understanding of clouds and related climate feedback processes as a leading cause of uncertainty in forecasting climate change. In this paper we present an algorithm that uses optimal estimation to retrieve cloud parameters from satellite multi-spectral imager data, in particular the Along-Track Scanning Radiometers ATSR-2 and AATSR. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase. Importantly, the technique also provides estimated errors along with the retrieved values and quantifies the consistency between retrieval representation of cloud and satellite radiances. This should enable the effective use of the products for comparison with climate models or for exploitation via data assimilation. The technique is evaluated by performing retrieval simulations for a variety of simulated single layer and multi-layer conditions. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed. This algorithm has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation project to produce a 14 year consistent record for climate research (Sayer et al., 2010.

  15. Evaluation of GISS SCM Simulated Cloud and Radiative Properties Using Both Surface and Satellite Observations

    Science.gov (United States)

    Kennedy, A. D.; Dong, X.; Xi, B.; Del Genio, A.; Wolf, A.; Minnis, P.; Khaiyer, M.; Doelling, D.; Nordeen, M.; Keyes, D.

    2009-05-01

    To evaluate the GISS SCM simulated cloud fractions, three years of surface and GOES satellite data have been collected at DOE ARM Southern Great Plains (SGP) site during 1999-2001. The GOES derived total and high cloud fractions from both 0.5° and 2.5° grid boxes are in excellent agreement with surface observations, suggesting that the ARM point observations can represent large areal observations. Compared to the ARM radar-lidar observed cloud fractions, the SCM simulated most mid-level clouds, overestimated low clouds, and underestimated total and high clouds with additional missed during the summer season. Further studies have revealed that the model simulated cloud fractions are strongly dependent on the large-scale synoptic pattern and its associated variables such as vertical motion and relative humidity. Because a significant amount of clouds over ARM SGP occur during synoptically quiescent conditions, the model has issues producing enough high cloud cover. This work suggests that alterations need to be made to the stratiform cloud scheme to better represent the sub-grid scale cloud variability in this case. The model simulated radiation budget is also evaluated with two years of collocated ARM surface radiation and CERES and GOES TOA radiation over the SGP site during March 2000-Dec. 2001. For this comparison, the model simulated surface and TOA radiation budgets agree well with surface and satellite observations (˜10 W m-2). Model simulated cloud optical depth, however, is about an order of magnitude higher than CERES/GOES retrievals, which may explain why the radiation budget is reasonable and yet total cloud fraction has a negative bias compared to observations. Further study is warranted to better understand how this impacts cloud radiative forcing.

  16. Glacier Fluctuations in the Western Himalaya: Multi-temporal Assessment Using Multi-sensor Satellite Imagery

    Science.gov (United States)

    Bishop, M. P.; Shroder, J. F.

    2004-12-01

    Alpine glaciers are retreating and downwasting in many mountain environments. Systematic and quantitative assessments are sorely needed, as regional mass-balance trends are not known, and many glaciers may disappear before we can study them and assess glacier sensitivity to climate forcing. This urgency dictates remote sensing and GIS-based studies to provide baseline information and estimates of mass balance. In the Western Himalaya there is a paucity of quantitative information on glacier fluctuations and meltwater contributions to rising sea level. As part of the Global Land Ice Measurements from Space (GLIMS) project, we conducted several glacier change-detection studies to assess ice fluctuations on selected glaciers. We compared SPOT imagery from the 1990's to ASTER satellite imagery from the 2000-2004 time period. Ground photography and satellite image analysis using artificial neural networks were used to compare glacier characteristics. Results indicate that some glaciers have retreated, while others exhibit very similar terminus positions to past positions, but have downwasted. Glacier retreat and downwasting have resulted in the disconnection of tributary glaciers to valley glaciers in the Hindu Kush and Nanga Parbat Himalaya. In addition, there are increases in meltwater production on some glaciers, as revealed by surging and variation in the frequency and size of supraglacial lakes. These results identify increased hazard potential in many areas, and suggest negative mass balance for some glaciers. Quantitative results from remote sensing studies, however, should be carefully interpreted, as climate, glacier, lithosphere interactions that dictate glacier fluctuations are not adequately accounted for in image-based analyses of supraglacial conditions. The integration of quantitative remote sensing/GIS information into numerical ice flow/mass balance models is required to obtain better estimates of mass balance and glacier sensitivity to climate forcing.

  17. Monitoring of oil pollution in the Arabian Gulf based on medium resolution satellite imagery

    Science.gov (United States)

    Zhao, J.; Ghedira, H.

    2013-12-01

    A large number of inland and offshore oil fields are located in the Arabian Gulf where about 25% of the world's oil is produced by the countries surrounding the Arabian Gulf region. Almost all of this oil production is shipped by sea worldwide through the Strait of Hormuz making the region vulnerable to environmental and ecological threats that might arise from accidental or intentional oil spills. Remote sensing technologies have the unique capability to detect and monitor oil pollutions over large temporal and spatial scales. Synoptic satellite imaging can date back to 1972 when Landsat-1 was launched. Landsat satellite missions provide long time series of imagery with a spatial resolution of 30 m. MODIS sensors onboard NASA's Terra and Aqua satellites provide a wide and frequent coverage at medium spatial resolution, i.e. 250 m and 500, twice a day. In this study, the capability of medium resolution MODIS and Landsat data in detecting and monitoring oil pollutions in the Arabian Gulf was tested. Oil spills and slicks show negative or positive contrasts in satellite derived RGB images compared with surrounding clean waters depending on the solar/viewing geometry, oil thickness and evolution, etc. Oil-contaminated areas show different spectral characteristics compared with surrounding waters. Rayleigh-corrected reflectance at the seven medium resolution bands of MODIS is lower in oil affected areas. This is caused by high light absorption of oil slicks. 30-m Landsat image indicated the occurrence of oil spill on May 26 2000 in the Arabian Gulf. The oil spill showed positive contrast and lower temperature than surrounding areas. Floating algae index (FAI) images are also used to detect oil pollution. Oil-contaminated areas were found to have lower FAI values. To track the movement of oil slicks found on October 21 2007, ocean circulations from a HYCOM model were examined and demonstrated that the oil slicks were advected toward the coastal areas of United Arab

  18. Using Satellite Imagery to Identify Tornado Damage Tracks and Recovery from the April 27, 2011 Severe Weather Outbreak

    Science.gov (United States)

    Cole, Tony A.; Molthan, Andrew L.; Bell, Jordan R.

    2014-01-01

    Emergency response to natural disasters requires coordination between multiple local, state, and federal agencies. Single, relatively weak tornado events may require comparatively simple response efforts; but larger "outbreak" events with multiple strong, long-track tornadoes can benefit from additional tools to help expedite these efforts. Meteorologists from NOAA's National Weather Service conduct field surveys to map tornado tracks, assess damage, and determine the tornado intensity following each event. Moderate and high resolution satellite imagery can support these surveys by providing a high-level view of the affected areas. Satellite imagery could then be used to target areas for immediate survey or to corroborate the results of the survey after it is completed. In this study, the feasibility of using satellite imagery to identify tornado damage tracks was determined by comparing the characteristics of tracks observed from low-earth orbit to tracks assessed during the official NWS storm survey process. Of the 68 NWS confirmed centerlines, 24 tracks (35.3%) could be distinguished from other surface features using satellite imagery. Within each EF category, 0% of EF-0, 3% of EF-1, 50% of EF-2, 77.7% of EF-3, 87.5% of EF-4 and 100% of EF-5 tornadoes were detected. It was shown that satellite data can be used to identify tornado damage tracks in MODIS and ASTER NDVI imagery, where damage to vegetation creates a sharp drop in values though the minimum EF-category which can be detected is dependent upon the type of sensor used and underlying vegetation. Near-real time data from moderate resolution sensors compare favorably to field surveys after the event and suggest that the data can provide some value in the assessment process.

  19. Quantifying Above-Cloud Aerosols through Integrating Multi-Sensor Measurements from A-Train Satellites

    Science.gov (United States)

    Zhang, Yan

    2012-01-01

    Quantifying above-cloud aerosols can help improve the assessment of aerosol intercontinental transport and climate impacts. Large-scale measurements of aerosol above low-level clouds had been generally unexplored until very recently when CALIPSO lidar started to acquire aerosol and cloud profiles in June 2006. Despite CALIPSO s unique capability of measuring above-cloud aerosol optical depth (AOD), such observations are substantially limited in spatial coverage because of the lidar s near-zero swath. We developed an approach that integrates measurements from A-Train satellite sensors (including CALIPSO lidar, OMI, and MODIS) to extend CALIPSO above-cloud AOD observations to substantially larger areas. We first examine relationships between collocated CALIPSO above-cloud AOD and OMI absorbing aerosol index (AI, a qualitative measure of AOD for elevated dust and smoke aerosol) as a function of MODIS cloud optical depth (COD) by using 8-month data in the Saharan dust outflow and southwest African smoke outflow regions. The analysis shows that for a given cloud albedo, above-cloud AOD correlates positively with AI in a linear manner. We then apply the derived relationships with MODIS COD and OMI AI measurements to derive above-cloud AOD over the whole outflow regions. In this talk, we will present spatial and day-to-day variations of the above-cloud AOD and the estimated direct radiative forcing by the above-cloud aerosols.

  20. Identifying clouds over the Pierre Auger Observatory using infrared satellite data

    Science.gov (United States)

    Abreu, P.; Aglietta, M.; Ahlers, M.; Ahn, E. J.; Albuquerque, I. F. M.; Allekotte, I.; Allen, J.; Allison, P.; Almela, A.; Alvarez Castillo, J.; Alvarez-Muñiz, J.; Alves Batista, R.; Ambrosio, M.; Aminaei, A.; Anchordoqui, L.; Andringa, S.; Antičić, T.; Aramo, C.; Arqueros, F.; Asorey, H.; Assis, P.; Aublin, J.; Ave, M.; Avenier, M.; Avila, G.; Badescu, A. M.; Barber, K. B.; Barbosa, A. F.; Bardenet, R.; Baughman, B.; Bäuml, J.; Baus, C.; Beatty, J. J.; Becker, K. H.; Bellétoile, A.; Bellido, J. A.; BenZvi, S.; Berat, C.; Bertou, X.; Biermann, P. L.; Billoir, P.; Blanco, F.; Blanco, M.; Bleve, C.; Blümer, H.; Boháčová, M.; Boncioli, D.; Bonifazi, C.; Bonino, R.; Borodai, N.; Brack, J.; Brancus, I.; Brogueira, P.; Brown, W. C.; Buchholz, P.; Bueno, A.; Buroker, L.; Burton, R. E.; Buscemi, M.; Caballero-Mora, K. S.; Caccianiga, B.; Caccianiga, L.; Caramete, L.; Caruso, R.; Castellina, A.; Cataldi, G.; Cazon, L.; Cester, R.; Cheng, S. H.; Chiavassa, A.; Chinellato, J. A.; Chirinos, J.; Chudoba, J.; Cilmo, M.; Clay, R. W.; Cocciolo, G.; Colalillo, R.; Collica, L.; Coluccia, M. R.; Conceição, R.; Contreras, F.; Cook, H.; Cooper, M. J.; Coutu, S.; Covault, C. E.; Criss, A.; Cronin, J.; Curutiu, A.; Dallier, R.; Daniel, B.; Dasso, S.; Daumiller, K.; Dawson, B. R.; de Almeida, R. M.; De Domenico, M.; de Jong, S. J.; De La Vega, G.; de Mello, W. J. M.; de Mello Neto, J. R. T.; De Mitri, I.; de Souza, V.; de Vries, K. D.; del Peral, L.; Deligny, O.; Dembinski, H.; Dhital, N.; Di Giulio, C.; Diaz, J. C.; Díaz Castro, M. L.; Diep, P. N.; Diogo, F.; Dobrigkeit, C.; Docters, W.; D'Olivo, J. C.; Dong, P. N.; Dorofeev, A.; dos Anjos, J. C.; Dova, M. T.; D'Urso, D.; Ebr, J.; Engel, R.; Erdmann, M.; Escobar, C. O.; Espadanal, J.; Etchegoyen, A.; Facal San Luis, P.; Falcke, H.; Fang, K.; Farrar, G.; Fauth, A. C.; Fazzini, N.; Ferguson, A. P.; Fick, B.; Figueira, J. M.; Filevich, A.; Filipčič, A.; Fliescher, S.; Fox, B. D.; Fracchiolla, C. E.; Fraenkel, E. D.; Fratu, O.; Fröhlich, U.; Fuchs, B.; Gaior, R.; Gamarra, R. F.; Gambetta, S.; García, B.; Garcia Roca, S. T.; Garcia-Gamez, D.; Garcia-Pinto, D.; Garilli, G.; Gascon Bravo, A.; Gemmeke, H.; Ghia, P. L.; Giller, M.; Gitto, J.; Glaser, C.; Glass, H.; Golup, G.; Gomez Albarracin, F.; Gómez Berisso, M.; Gómez Vitale, P. F.; Gonçalves, P.; Gonzalez, J. G.; Gookin, B.; Gorgi, A.; Gorham, P.; Gouffon, P.; Grebe, S.; Griffith, N.; Grillo, A. F.; Grubb, T. D.; Guardincerri, Y.; Guarino, F.; Guedes, G. P.; Hansen, P.; Harari, D.; Harrison, T. A.; Harton, J. L.; Haungs, A.; Hebbeker, T.; Heck, D.; Herve, A. E.; Hill, G. C.; Hojvat, C.; Hollon, N.; Holmes, V. C.; Homola, P.; Hörandel, J. R.; Horvath, P.; Hrabovský, M.; Huber, D.; Huege, T.; Insolia, A.; Jansen, S.; Jarne, C.; Jiraskova, S.; Josebachuili, M.; Kadija, K.; Kampert, K. H.; Karhan, P.; Kasper, P.; Katkov, I.; Kégl, B.; Keilhauer, B.; Keivani, A.; Kelley, J. L.; Kemp, E.; Kieckhafer, R. M.; Klages, H. O.; Kleifges, M.; Kleinfeller, J.; Knapp, J.; Krause, R.; Krohm, N.; Krömer, O.; Kruppke-Hansen, D.; Kuempel, D.; Kulbartz, J. K.; Kunka, N.; La Rosa, G.; LaHurd, D.; Latronico, L.; Lauer, R.; Lauscher, M.; Lautridou, P.; Le Coz, S.; Leão, M. S. A. B.; Lebrun, D.; Lebrun, P.; Leigui de Oliveira, M. A.; Letessier-Selvon, A.; Lhenry-Yvon, I.; Link, K.; López, R.; Lopez Agüera, A.; Louedec, K.; Lozano Bahilo, J.; Lu, L.; Lucero, A.; Ludwig, M.; Lyberis, H.; Maccarone, M. C.; Macolino, C.; Malacari, M.; Maldera, S.; Maller, J.; Mandat, D.; Mantsch, P.; Mariazzi, A. G.; Marin, J.; Marin, V.; Mariş, I. C.; Marquez Falcon, H. R.; Marsella, G.; Martello, D.; Martin, L.; Martinez, H.; Martínez Bravo, O.; Martraire, D.; Masías Meza, J. J.; Mathes, H. J.; Matthews, J.; Matthews, J. A. J.; Matthiae, G.; Maurel, D.; Maurizio, D.; Mayotte, E.; Mazur, P. O.; Medina-Tanco, G.; Melissas, M.; Melo, D.; Menichetti, E.; Menshikov, A.; Messina, S.; Meyhandan, R.; Mićanović, S.; Micheletti, M. I.; Middendorf, L.; Minaya, I. A.; Miramonti, L.; Mitrica, B.; Molina-Bueno, L.; Mollerach, S.; Monasor, M.; Monnier Ragaigne, D.; Montanet, F.; Morales, B.; Morello, C.; Moreno, J. C.; Mostafá, M.; Moura, C. A.; Muller, M. A.; Müller, G.; Münchmeyer, M.; Mussa, R.; Navarra, G.; Navarro, J. L.; Navas, S.; Necesal, P.; Nellen, L.; Nelles, A.; Neuser, J.; Nhung, P. T.; Niechciol, M.; Niemietz, L.; Nierstenhoefer, N.; Niggemann, T.; Nitz, D.; Nosek, D.; Nožka, L.; Oehlschläger, J.; Olinto, A.; Oliveira, M.; Ortiz, M.; Pacheco, N.; Pakk Selmi-Dei, D.; Palatka, M.; Pallotta, J.; Palmieri, N.; Parente, G.; Parra, A.; Pastor, S.; Paul, T.; Pech, M.; Peķala, J.; Pelayo, R.; Pepe, I. M.; Perrone, L.; Pesce, R.; Petermann, E.; Petrera, S.; Petrolini, A.; Petrov, Y.; Pfendner, C.; Piegaia, R.; Pierog, T.; Pieroni, P.; Pimenta, M.; Pirronello, V.; Platino, M.; Plum, M.; Ponce, V. H.; Pontz, M.; Porcelli, A.; Privitera, P.; Prouza, M.; Quel, E. J.; Querchfeld, S.; Rautenberg, J.; Ravel, O.; Ravignani, D.; Revenu, B.; Ridky, J.; Riggi, S.; Risse, M.; Ristori, P.; Rivera, H.; Rizi, V.; Roberts, J.; Rodrigues de Carvalho, W.; Rodriguez Cabo, I.; Rodriguez Fernandez, G.; Rodriguez Martino, J.; Rodriguez Rojo, J.; Rodríguez-Frías, M. D.; Ros, G.; Rosado, J.; Rossler, T.; Roth, M.; Rouillé-d'Orfeuil, B.; Roulet, E.; Rovero, A. C.; Rühle, C.; Saffi, S. J.; Saftoiu, A.; Salamida, F.; Salazar, H.; Salesa Greus, F.; Salina, G.; Sánchez, F.; Santo, C. E.; Santos, E.; Santos, E. M.; Sarazin, F.; Sarkar, B.; Sato, R.; Scharf, N.; Scherini, V.; Schieler, H.; Schiffer, P.; Schmidt, A.; Scholten, O.; Schoorlemmer, H.; Schovancova, J.; Schovánek, P.; Schröder, F. G.; Schulz, J.; Schuster, D.; Sciutto, S. J.; Scuderi, M.; Segreto, A.; Settimo, M.; Shadkam, A.; Shellard, R. C.; Sidelnik, I.; Sigl, G.; Sima, O.; Śmiałkowski, A.; Šmída, R.; Snow, G. R.; Sommers, P.; Sorokin, J.; Spinka, H.; Squartini, R.; Srivastava, Y. N.; Stanič, S.; Stapleton, J.; Stasielak, J.; Stephan, M.; Straub, M.; Stutz, A.; Suarez, F.; Suomijärvi, T.; Supanitsky, A. D.; Šuša, T.; Sutherland, M. S.; Swain, J.; Szadkowski, Z.; Szuba, M.; Tapia, A.; Tartare, M.; Taşcău, O.; Tcaciuc, R.; Thao, N. T.; Thomas, D.; Tiffenberg, J.; Timmermans, C.; Tkaczyk, W.; Todero Peixoto, C. J.; Toma, G.; Tomankova, L.; Tomé, B.; Tonachini, A.; Torralba Elipe, G.; Torres Machado, D.; Travnicek, P.; Tridapalli, D. B.; Trovato, E.; Tueros, M.; Ulrich, R.; Unger, M.; Urban, M.; Valdés Galicia, J. F.; Valiño, I.; Valore, L.; van Aar, G.; van den Berg, A. M.; van Velzen, S.; van Vliet, A.; Varela, E.; Vargas Cárdenas, B.; Varner, G.; Vázquez, J. R.; Vázquez, R. A.; Veberič, D.; Verzi, V.; Vicha, J.; Videla, M.; Villaseñor, L.; Wahlberg, H.; Wahrlich, P.; Wainberg, O.; Walz, D.; Watson, A. A.; Weber, M.; Weidenhaupt, K.; Weindl, A.; Werner, F.; Westerhoff, S.; Whelan, B. J.; Widom, A.; Wieczorek, G.; Wiencke, L.; Wilczyńska, B.; Wilczyński, H.; Will, M.; Williams, C.; Winchen, T.; Wundheiler, B.; Yamamoto, T.; Yapici, T.; Younk, P.; Yuan, G.; Yushkov, A.; Zamorano Garcia, B.; Zas, E.; Zavrtanik, D.; Zavrtanik, M.; Zaw, I.; Zepeda, A.; Zhou, J.; Zhu, Y.; Zimbres Silva, M.; Ziolkowski, M.

    2013-12-01

    We describe a new method of identifying night-time clouds over the Pierre Auger Observatory using infrared data from the Imager instruments on the GOES-12 and GOES-13 satellites. We compare cloud identifications resulting from our method to those obtained by the Central Laser Facility of the Auger Observatory. Using our new method we can now develop cloud probability maps for the 3000 km2 of the Pierre Auger Observatory twice per hour with a spatial resolution of ˜2.4 km by ˜5.5 km. Our method could also be applied to monitor cloud cover for other ground-based observatories and for space-based observatories.

  1. Identifying clouds over the Pierre Auger Observatory using infrared satellite data

    Energy Technology Data Exchange (ETDEWEB)

    Abreu, Pedro; et al.,

    2013-12-01

    We describe a new method of identifying night-time clouds over the Pierre Auger Observatory using infrared data from the Imager instruments on the GOES-12 and GOES-13 satellites. We compare cloud identifications resulting from our method to those obtained by the Central Laser Facility of the Auger Observatory. Using our new method we can now develop cloud probability maps for the 3000 km^2 of the Pierre Auger Observatory twice per hour with a spatial resolution of ~2.4 km by ~5.5 km. Our method could also be applied to monitor cloud cover for other ground-based observatories and for space-based observatories.

  2. NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE): Changing patterns in the use of NRT satellite imagery

    Science.gov (United States)

    Davies, D.; Michael, K.; Schmaltz, J. E.; Harrison, S.; Ding, F.; Durbin, P. B.; Boller, R. A.; Cechini, M. F.; Rinsland, P. L.; Ye, G.; Mauoka, E.

    2015-12-01

    NASA's Land, Atmosphere Near real-time Capability for EOS (Earth Observing System) (LANCE) provides data and imagery approximately 3 hours from satellite observation, to monitor natural events globally and to meet the needs of the near real-time (NRT) applications community. This article describes LANCE, and how the use of NRT data and imagery has evolved. Since 2010 there has been a four-fold increase in both the volume of data and the number of files downloaded. Over the last year there has been a marked shift in the way in which users are accessing NRT imagery; users are gravitating towards Worldview and the Global Imagery Browse Services (GIBS) and away from MODIS Rapid Response, in part due to the increased exposure through social media. In turn this is leading to a broader range of users viewing NASA NRT imagery. This article also describes new, and planned, product enhancements to LANCE. Over the last year, LANCE has expanded to support NRT products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Multi-angle Imaging SpectroRadiometer (MISR). LANCE elements are also planning to ingest and process NRT data from the Visible Infrared Imager Radiometer Suite (VIIRS), and the advanced Ozone Mapping and Profiler Suite (OMPS) instruments onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite in the near future.

  3. Improved modeling of cloudy-sky actinic flux using satellite cloud retrievals

    Science.gov (United States)

    Ryu, Young-Hee; Hodzic, Alma; Descombes, Gael; Hall, Samuel; Minnis, Patrick; Spangenberg, Douglas; Ullmann, Kirk; Madronich, Sasha

    2017-02-01

    Clouds play a critical role in modulating tropospheric radiation and thus photochemistry. We develop a methodology for calculating the vertical distribution of tropospheric ultraviolet (300-420 nm) actinic fluxes using satellite cloud retrievals and a radiative transfer model. We demonstrate that our approach can accurately reproduce airborne-measured actinic fluxes from the 2013 Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign as a case study. The results show that the actinic flux is reduced below moderately thick clouds with increasing cloud optical depth and can be enhanced by a factor of 2 above clouds. Inside clouds, the actinic flux can be enhanced by up to 2.4 times in the upper part of clouds or reduced up to 10 times in the lower parts of clouds. Our study suggests that the use of satellite-derived actinic fluxes as input to chemistry-transport models can improve the accuracy of photochemistry calculations.

  4. Sherlock Holmes' or Don Quixote`s certainty? Interpretations of cropmarks on satellite imageries in archaeological investigation

    Science.gov (United States)

    Wilgocka, Aleksandra; RÄ czkowski, Włodzimierz; Kostyrko, Mikołaj; Ruciński, Dominik

    2016-08-01

    Years of experience in air-photo interpretations provide us to conclusion that we know what we are looking at, we know why we can see cropmarks, we even can estimate, when are the best opportunities to observe them. But even today cropmarks may be a subject of misinterpretation or wishful thinking. The same problems appear when working with aerial photographs, satellite imageries, ALS, geophysics, etc. In the paper we present several case studies based on data acquired for and within ArchEO - archaeological applications of Earth Observation techniques project to discuss complexity and consequences of archaeological interpretations. While testing usefulness of satellite imagery in Poland on various types of sites, cropmarks were the most frequent indicators of past landscapes as well as archaeological and natural features. Hence, new archaeological sites have been discovered mainly thanks to cropmarks. This situation has given us an opportunity to test not only satellite imageries as a source of data but also confront them with results of other non-invasive methods of data acquisition. When working with variety of data we have met several issues which raised problems of interpretation. Consequently, questions related to the cognitive value of remote sensing data appear and should be discussed. What do the data represent? To what extent the imageries, cropmarks or other visualizations represent the past? How should we deal with ambiguity of data? What can we learn from pitfalls in the interpretation of cropmarks, soilmarks etc. to share more Sherlock's methodology rather than run around Don Quixote's delusions?

  5. Galactic cosmic ray and El Nino Southern Oscillation trends in International Satellite Cloud Climatology Project D2 low-cloud properties

    DEFF Research Database (Denmark)

    Marsh, N.; Svensmark, Henrik

    2003-01-01

    -cloud properties over the period July 1983 to August 1994 suggests that low clouds are statistically related to two processes, (1) GCR and (2) El Nino-Southern Oscillation (ENSO), with GCR explaining a greater percentage of the total variance. Areas where satellites have an unobstructed view of low cloud possess...

  6. Observing Red Tide Algal Blooms From Satellite Ocean Color Imagery: West Florida Shelf

    Science.gov (United States)

    Krueger, E. T.; Jose, F.

    2016-12-01

    Harmful algal blooms (HABs) from Karenia brevis occur along the west Florida shelf (WFS) almost every year, producing a brevetoxin that is harmful to birds, fish, marine mammals, shellfish, and humans. These HABs are commonly known as "red tide" from the reddish discoloration in the water, but color can vary from yellow to deep brown depending on other parameters. Ocean color data is a viable tool for monitoring the outbreak and persistence of these ecological phenomena. Also, the spatial extend of this outbreak could be evaluated effectively from satellite imagery. Chlorophyll (Chl) and sea surface temperature (SST) data from four satellites during the period from 2010 to 2013 were analyzed, and compared the monthly composite data with in situ observation on K. brevis cell counts collected by the Florida Fish and Wildlife Conservation Commission (FWC). Remote sensing data were extracted from the NASA Ocean Color data servers and were processed using WimSoft, a Windows-based remote sensing data analysis program. Based on the comparison of data from 26 transects from the WFS, which were extended from nearshore to 400 km offshore, highest Chl concentrations were observed in the sector from St. Petersburg to Sanibel Island. FWC data also showed that highest K. brevis cell counts were concentrated in this region during the 2011 to 2012 period. Additionally, a high Chl concentration was observed for the Big Bend region, particularly during the spring and early summer. The inter-annual variability of Chl, SST, and red tide occurrence are also discussed in this study.

  7. Does the Data Resolution/origin Matter? Satellite, Airborne and Uav Imagery to Tackle Plant Invasions

    Science.gov (United States)

    Müllerová, Jana; Brůna, Josef; Dvořák, Petr; Bartaloš, Tomáš; Vítková, Michaela

    2016-06-01

    Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.

  8. Epipolar resampling of linear pushbroom satellite imagery by a new epipolarity model

    Science.gov (United States)

    Wang, Mi; Hu, Fen; Li, Jonathan

    This paper presents a practical epipolarity model for high-resolution linear pushbroom satellite images acquired in either along-track or cross-track mode, based on the projection reference plane in object space. A new method for epipolar resampling of satellite stereo imagery based on this model is then developed. In this method, the pixel-to-pixel relationship between the original image and the generated epipolar image is established directly by the geometric sensor model. The approximate epipolar images are generated in a manner similar to digital image rectification. In addition, by arranging the approximate epipolar lines on the defined projection reference plane, a stereoscopic model with consistent ground sampling distance and parallel to the object space is thus available, which is more convenient for three-dimensional measurement and interpretation. The results obtained from SPOT5, IKONOS, IRS-P5, and QuickBird stereo images indicate that the generated epipolar images all achieve high accuracy. Moreover, the vertical parallaxes at check points are at sub-pixel level, thus proving the feasibility, correctness, and applicability of the method.

  9. Multiple Satellite Observations of Cloud Cover in Extratropical Cyclones

    Science.gov (United States)

    Naud, Catherine M.; Booth, James F.; Posselt, Derek J.; van den Heever, Susan C.

    2013-01-01

    Using cloud observations from NASA Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, and CloudSat-CALIPSO, composites of cloud fraction in southern and northern hemisphere extratropical cyclones are obtained for cold and warm seasons between 2006 and 2010, to assess differences between these three data sets, and between summer and winter cyclones. In both hemispheres and seasons, over the open ocean, the cyclone-centered cloud fraction composites agree within 5% across the three data sets, but behind the cold fronts, or over sea ice and land, the differences are much larger. To supplement the data set comparison and learn more about the cyclones, we also examine the differences in cloud fraction between cold and warm season for each data set. The difference in cloud fraction between cold and warm season southern hemisphere cyclones is small for all three data sets, but of the same order of magnitude as the differences between the data sets. The cold-warm season contrast in northern hemisphere cyclone cloud fractions is similar for all three data sets: in the warm sector, the cold season cloud fractions are lower close to the low, but larger on the equator edge than their warm season counterparts. This seasonal contrast in cloud fraction within the cyclones warm sector seems to be related to the seasonal differences in moisture flux within the cyclones. Our analysis suggests that the three different data sets can all be used confidently when studying the warm sector and warm frontal zone of extratropical cyclones but caution should be exerted when studying clouds in the cold sector.

  10. On the Quality of Point-Clouds Derived from Sfm-Photogrammetry Applied to UAS Imagery

    Science.gov (United States)

    Carbonneau, P.; James, T.

    2014-12-01

    Structure from Motion photogrammetry (SfM-photogrammetry) recently appeared in environmental sciences as an impressive tool allowing for the creation of topographic data from unstructured imagery. Several authors have tested the performance of SfM-photogrammetry vs that of TLS or dGPS. Whilst the initial results were very promising, there is currently a growing awareness that systematic deformations occur in DEMs and point-clouds derived from SfM-photogrammetry. Notably, some authors have identified a systematic doming manifest as an increasing error vs distance to the model centre. Simulation studies have confirmed that this error is due to errors in the calibration of camera distortions. This work aims to further investigate these effects in the presence of real data. We start with a dataset of 220 images acquired from a sUAS. After obtaining an initial self-calibration of the camera lens with Agisoft Photoscan, our method consists in applying systematic perturbations to 2 key lens parameters: Focal length and the k1 distortion parameter. For each perturbation, a point-cloud was produced and compared to LiDAR data. After deriving the mean and standard deviation of the error residuals (ɛ), a 2nd order polynomial surface was fitted to the errors point-cloud and the peak ɛ defined as the mathematical extrema of this surface. The results are presented in figure 1. This figure shows that lens perturbations can induce a range of errors with systematic behaviours. Peak ɛ is primarily controlled by K1 with a secondary control exerted by the focal length. These results allow us to state that: To limit the peak ɛ to 10cm, the K1 parameter must be calibrated to within 0.00025 and the focal length to within 2.5 pixels (≈10 µm). This level of calibration accuracy can only be achieved with proper design of image acquisition and control network geometry. Our main point is therefore that SfM is not a bypass to a rigorous and well-informed photogrammetric approach. Users

  11. An analytic method of space debris cloud evolution and its collision evaluation for constellation satellites

    Science.gov (United States)

    Zhang, Binbin; Wang, Zhaokui; Zhang, Yulin

    2016-09-01

    When a debris cloud is formed in the neighborhood of a constellation, the constellation satellites will face a serious threat of collision. In order to evaluate the collision probability in a long time scale, first we build an analytic model to describe the evolution process of the debris cloud. Under the perturbations of atmospheric drag, nonspherical gravity field, etc., results of numerical simulation indicate that after the breakup of an object, the distribution of debris cloud will evolve into a relatively stable band. Based on the stable distribution characteristic of the debris cloud, fragments are divided into several groups according their orbital heights and area-mass ratios. For each debris group, the dynamics of the distribution process under the perturbation of atmosphere drag is described by a partial differential equation (PDE). Solutions of those PDEs are obtained. And the distribution of the debris cloud can be easily propagated over long time scales. Applying this analytic model, the collision probability between a debris cloud and the Globalstar satellites is analyzed and computed. Results show that the collision probability is nearly 10,000 times of the average collision probability in the near Earth environment. Moreover, as the band distribution of the space debris cloud is stable, the collisional risk on constellation satellites will last for quite a long time.

  12. gProcess and ESIP Platforms for Satellite Imagery Processing over the Grid

    Science.gov (United States)

    Bacu, Victor; Gorgan, Dorian; Rodila, Denisa; Pop, Florin; Neagu, Gabriel; Petcu, Dana

    2010-05-01

    The Environment oriented Satellite Data Processing Platform (ESIP) is developed through the SEE-GRID-SCI (SEE-GRID eInfrastructure for regional eScience) co-funded by the European Commission through FP7 [1]. The gProcess Platform [2] is a set of tools and services supporting the development and the execution over the Grid of the workflow based processing, and particularly the satelite imagery processing. The ESIP [3], [4] is build on top of the gProcess platform by adding a set of satellite image processing software modules and meteorological algorithms. The satellite images can reveal and supply important information on earth surface parameters, climate data, pollution level, weather conditions that can be used in different research areas. Generally, the processing algorithms of the satellite images can be decomposed in a set of modules that forms a graph representation of the processing workflow. Two types of workflows can be defined in the gProcess platform: abstract workflow (PDG - Process Description Graph), in which the user defines conceptually the algorithm, and instantiated workflow (iPDG - instantiated PDG), which is the mapping of the PDG pattern on particular satellite image and meteorological data [5]. The gProcess platform allows the definition of complex workflows by combining data resources, operators, services and sub-graphs. The gProcess platform is developed for the gLite middleware that is available in EGEE and SEE-GRID infrastructures [6]. gProcess exposes the specific functionality through web services [7]. The Editor Web Service retrieves information on available resources that are used to develop complex workflows (available operators, sub-graphs, services, supported resources, etc.). The Manager Web Service deals with resources management (uploading new resources such as workflows, operators, services, data, etc.) and in addition retrieves information on workflows. The Executor Web Service manages the execution of the instantiated workflows

  13. Seeing is believing I: The use of thermal sensing from satellite imagery to predict crop yield

    Science.gov (United States)

    B, Potgieter A.; D, Rodriguez; B, Power; J, Mclean; P, Davis

    2014-02-01

    Volatility in crop production has been part of the Australian environment since cropping began with the arrival of the first European settlers. Climate variability is the main factor affecting crop production at national, state and local scales. At field level spatial patterns on yield production are also determined by spatially changing soil properties in interaction with seasonal climate conditions and weather patterns at critical stages in the crop development. Here we used a combination of field level weather records, canopy characteristics, and satellite information to determine the spatial performance of a large field of wheat. The main objective of this research is to determine the ability of remote sensing technologies to capture yield losses due to water stress at the canopy level. The yield, canopy characteristics (i.e. canopy temperature and ground cover) and seasonal conditions of a field of wheat (~1400ha) (-29.402° South and 149.508°, New South Wales, Australia) were continuously monitored during the winter of 2011. Weather and crop variables were continuously monitored by installing three automatic weather stations in a transect covering different positions and soils in the landscape. Weather variables included rainfall, minimum and maximum temperatures and relative humidity, and crop characteristics included ground cover and canopy temperature. Satellite imagery Landsat TM 5 and 7 was collected at five different stages in the crop cycle. Weather variables and crop characteristics were used to calculate a crop stress index (CSI) at point and field scale (39 fields). Field data was used to validate a spatial satellite image derived index. Spatial yield data was downloaded from the harvester at the different locations in the field. We used the thermal band (land surface temperature, LST) and enhanced vegetation index (EVI) bands from the MODIS (250 m for visible bands and 1km for thermal band) and a derived EVI from Landsat TM 7 (25 m for visible and

  14. An improved technique for global daily sunshine duration estimation using satellite imagery

    Institute of Scientific and Technical Information of China (English)

    Muhammad Ali SHAMIM; Renji REMESAN; Da-wei HAN; Naeem EJAZ; Ayub ELAHI

    2012-01-01

    This paper presents an improved model for global sunshine duration estimation.The methodology incorporates geostationary satellite images by including snow cover information,sun and satellite angles and a trend correction factor for seasons,for the determination of cloud cover index.The effectiveness of the proposed methodology has been tested using Meteosat geostationary satellite images in the visible band with a temporal resolution of 1 h and spatial resolution of 2.5 km×2.5 km,for the Brue Catchment in the southwest of England.Validation results show a significant improvement in the estimation of global sunshine duration by the proposed method as compared to its predecessor (R2 is improved from 0.68 to 0.83,root mean squared error (RMSE) from 2.37 h/d to 1.19 h/d and the mean biased error (MBE) from 0.21 h/d to 0.08 h/d).Further studies are needed to test this method in other parts of the world with different climate and geographical conditions.

  15. Visual attention based detection of signs of anthropogenic activities in satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Skurikhin, Alexei N [Los Alamos National Laboratory

    2010-10-13

    With increasing deployment of satellite imaging systems, only a small fraction of collected data can be subject to expert scrutiny. We present and evaluate a two-tier approach to broad area search for signs of anthropogenic activities in high-resolution commercial satellite imagery. The method filters image information using semantically oriented interest points by combining Harris corner detection and spatial pyramid matching. The idea is that anthropogenic structures, such as rooftop outlines, fence corners, road junctions, are locally arranged in specific angular relations to each other. They are often oriented at approximately right angles to each other (which is known as rectilinearity relation). Detecting the rectilinearity provides an opportunity to highlight regions most likely to contain anthropogenic activity. This is followed by supervised classification of regions surrounding the detected corner points as man-made vs. natural scenes. We consider, in particular, a search for anthropogenic activities in uncluttered areas. In this paper, we proposed and evaluated a two-tier approach to broad area search for signs of anthropogenic activities. Results from experiments on high-resolution ({approx}0.6m) commercial satellite image data showed the potential applicability of this approach and its ability of achieving both high precision and recall rates. The main advantage of combining corner-based cueing with general object recognition is that the incorporation of domain specific knowledge even in its more general form, such as presence of comers, provides a useful cue to narrow the focus of search for signs of anthropogenic activities. Combination of comer based cueing with spatial pyramid matching addressed the issue of comer categorization. An important practical issue for further research is optimizing the balance between false positive and false negative rates. While the results presented in the paper are encouraging, the problem of an automated broad area

  16. The RISCO RapidIce Viewer: An application for monitoring the polar ice sheets with multi-resolution, multi-temporal, multi-sensor satellite imagery

    Science.gov (United States)

    Herried, B.; Porter, C. C.; Morin, P. J.; Howat, I. M.

    2013-12-01

    The Rapid Ice Sheet Change Observatory (RISCO) is a NASA-funded, inter-organizational collaboration created to provide a systematic framework for gathering, processing, analyzing, and distributing consistent satellite imagery of polar ice sheet change for Antarctica and Greenland. RISCO gathers observations over areas of rapid change and makes them easily accessible to investigators, media, and the general public. As opposed to existing data centers, which are structured to archive and distribute diverse types of raw data to end users with the specialized software and skills to analyze them, RISCO distributes processed georeferenced raster image data products in JPEG and GeoTIFF formats, making them immediately viewable in a browser-based application. Currently, the archive includes 16 sensors including: MODIS Terra, MODIS Aqua, MODIS Terra Bands 3-6-7, Landsat MSS, Landsat TM, Landsat ETM+, Landsat 8 OLI, EO-1, SPOT, ASTER VNIR, Operation IceBridge ATM and LVIS, and commercial satellites such as WorldView-1, WorldView-2, QuickBird-2, GeoEye-1 and IKONOS. The RISCO RapidIce Viewer is a lightweight JavaScript application that provides an interface to viewing and downloading the satellite imagery from predefined areas-of-interest (or 'subsets'), which are normally between 10,000 and 20,000 sq km. Users select a subset (from a map or drop-down) and the archive of individual granules is loaded in a thumbnail grid, sorted chronologically (newest first). For each thumbnail, users can choose to view a larger preview JPG, download a GeoTIFF, or be redirected back to the original data center to see the original imagery or view metadata. There are several options for filtering displayed including by sensor, by date range, by month, or by cloud cover. Last, users can select multiple images to play back as an animation. The RapidIce Viewer is an easy-to-use, software independent application for researchers to quickly monitor daily changes in ice sheets or download historical

  17. Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction

    Science.gov (United States)

    Wu, Teng; Hu, Xiangyun; Zhang, Yong; Zhang, Lulin; Tao, Pengjie; Lu, Luping

    2016-11-01

    The automatic extraction of terrain from high-resolution satellite optical images is very difficult under cloudy conditions. Therefore, accurate cloud detection is necessary to fully use the cloud-free parts of images for terrain extraction. This paper addresses automated cloud detection by introducing an image matching based method under a stereo vision framework, and the optimization usage of non-cloudy areas in stereo matching and the generation of digital surface models (DSMs). Given that clouds are often separated from the terrain surface, cloudy areas are extracted by integrating dense matching DSM, worldwide digital elevation model (DEM) (i.e., shuttle radar topography mission (SRTM)) and gray information from the images. This process consists of the following steps: an image based DSM is firstly generated through a multiple primitive multi-image matcher. Once it is aligned with the reference DEM based on common features, places with significant height differences between the DSM and the DEM will suggest the potential cloud covers. Detecting cloud at these places in the images then enables precise cloud delineation. In the final step, elevations of the reference DEM within the cloud covers are assigned to the corresponding region of the DSM to generate a cloud-free DEM. The proposed approach is evaluated with the panchromatic images of the Tianhui satellite and has been successfully used in its daily operation. The cloud detection accuracy for images without snow is as high as 95%. Experimental results demonstrate that the proposed method can significantly improve the usage of the cloudy panchromatic satellite images for terrain extraction.

  18. LANDSAT imagery: Description of products available from the CSIR Satellite Remote Sensing Centre

    Science.gov (United States)

    1982-01-01

    An overview of the LANDSAT system is provided along with information to assist prospective users in establishing whether imagery for their areas of interest is available and how to obtain such imagery. Spectral bands, spatial resolution, and digital data are explained as well as worldwide reference system indexing and the identification number assigned to images. The sizes and scales of standard black and white imagery and of false color composite imagery are listed. The format is given for computer compatible tapes and standard enhanced imagery is described. Other information available to users include LANDSAT index maps, catalogs of available imagery, a schedule of overpass dates, and a list of product prices.

  19. 4-D Cloud Water Content Fields Derived from Operational Satellite Data

    Science.gov (United States)

    Smith, William L., Jr.; Minnis, Patrick

    2010-01-01

    In order to improve operational safety and efficiency, the transportation industry, including aviation, has an urgent need for accurate diagnoses and predictions of clouds and associated weather conditions. Adverse weather accounts for 70% of all air traffic delays within the U.S. National Airspace System. The Federal Aviation Administration has determined that as much as two thirds of weather-related delays are potentially avoidable with better weather information and roughly 20% of all aviation accidents are weather related. Thus, it is recognized that an important factor in meeting the goals of the Next Generation Transportation System (NexGen) vision is the improved integration of weather information. The concept of a 4-D weather cube is being developed to address that need by integrating observed and forecasted weather information into a shared 4-D database, providing an integrated and nationally consistent weather picture for a variety of users and to support operational decision support systems. Weather analyses and forecasts derived using Numerical Weather Prediction (NWP) models are a critical tool that forecasters rely on for guidance and also an important element in current and future decision support systems. For example, the Rapid Update Cycle (RUC) and the recently implemented Rapid Refresh (RR) Weather Research and Forecast (WRF) models provide high frequency forecasts and are key elements of the FAA Aviation Weather Research Program. Because clouds play a crucial role in the dynamics and thermodynamics of the atmosphere, they must be adequately accounted for in NWP models. The RUC, for example, cycles at full resolution five cloud microphysical species (cloud water, cloud ice, rain, snow, and graupel) and has the capability of updating these fields from observations. In order to improve the models initial state and subsequent forecasts, cloud top altitude (or temperature, T(sub c)) derived from operational satellite data, surface observations of

  20. Atmospheric Correction Performance of Hyperspectral Airborne Imagery over a Small Eutrophic Lake under Changing Cloud Cover

    Directory of Open Access Journals (Sweden)

    Lauri Markelin

    2016-12-01

    Full Text Available Atmospheric correction of remotely sensed imagery of inland water bodies is essential to interpret water-leaving radiance signals and for the accurate retrieval of water quality variables. Atmospheric correction is particularly challenging over inhomogeneous water bodies surrounded by comparatively bright land surface. We present results of AisaFENIX airborne hyperspectral imagery collected over a small inland water body under changing cloud cover, presenting challenging but common conditions for atmospheric correction. This is the first evaluation of the performance of the FENIX sensor over water bodies. ATCOR4, which is not specifically designed for atmospheric correction over water and does not make any assumptions on water type, was used to obtain atmospherically corrected reflectance values, which were compared to in situ water-leaving reflectance collected at six stations. Three different atmospheric correction strategies in ATCOR4 was tested. The strategy using fully image-derived and spatially varying atmospheric parameters produced a reflectance accuracy of ±0.002, i.e., a difference of less than 15% compared to the in situ reference reflectance. Amplitude and shape of the remotely sensed reflectance spectra were in general accordance with the in situ data. The spectral angle was better than 4.1° for the best cases, in the spectral range of 450–750 nm. The retrieval of chlorophyll-a (Chl-a concentration using a popular semi-analytical band ratio algorithm for turbid inland waters gave an accuracy of ~16% or 4.4 mg/m3 compared to retrieval of Chl-a from reflectance measured in situ. Using fixed ATCOR4 processing parameters for whole images improved Chl-a retrieval results from ~6 mg/m3 difference to reference to approximately 2 mg/m3. We conclude that the AisaFENIX sensor, in combination with ATCOR4 in image-driven parametrization, can be successfully used for inland water quality observations. This implies that the need for in situ

  1. Contribution of MODIS satellite imagery in modelling the flooding patterns of the coastal wetlands of the Tana River, Kenya

    Science.gov (United States)

    Leauthaud, C.; Duvail, S.; Belaud, G.; Albergel, J.; Moussa, R.; Grunberger, O.

    2012-04-01

    In sub-Saharan Africa, much of the arid and semi-arid lands are used by pastoralist groups as seasonal grazing zones. In such a context, wetlands are a vital resource as they act as retreat zones during the dry seasons when water and fodder resources are scarce. At a larger scale, wetlands also render numerous services including groundwater recharge, water quality improvement and climate regulation. As regular floods are the underlying factor determining the healthiness of wetland ecosystems, it is important to understand their dynamics for a better water resource management at the catchment scale in the context of increased water abstraction and hydroelectric infrastructure development. Yet, this is challenging in many places because of scarce or poor quality data and a often difficult access to the zone. In tropical or coastal areas, frequent cloud cover can also limit the use of remote sensing data. The MODIS instruments on board the Terra and Aqua satellites offer high temporal resolution images at a moderate spatial resolution in the visible and infrared spectrum. In particular the MOD09A1 and MYD09A1 500m, 8-day synthesis products select the best possible observation for each 8-day period thus decreasing poor quality pixels due to cloud cover in an image while retaining a high frequency coverage. Here we assess their potential use to monitor floods in the Tana River Delta (TRD), Kenya. In this study, all 8-day synthesis products from 2001 to 2011 were screened and selected for low cloud cover. The total flooded surface was then extracted from each image using the Normalized Difference Moisture Index (Xu, 2006) to obtain time-series inundation maps from 2002 onward. In a third step, the images were used, combined with river-flow data, to analyse the hydrological system of the area. The maximal extent, start and end inundation dates were determined for the major floods of the past decade. There were major differences in these characteristics for medium to large

  2. Satellite cloud and precipitation property retrievals for climate monitoring and hydrological applications

    Science.gov (United States)

    Wolters, E. L. A.

    2012-03-01

    This thesis presents the retrieval, evaluation, and application of cloud physical property datasets (cloud phase, cloud particle effective radius, and precipitation occurrence and intensity) obtained from Spinning Enhanced Visible and Infrared Imager (SEVIRI) reflectance measurements using the Cloud Physical Properties (CPP) retrieval algorithm. In Chapter 3 it is shown that the CPP cloud-phase retrieval algorithm has sufficient accuracy (West Africa. During the afternoon, precipitation occurrence frequency over dry soils becomes significantly higher than over wet soils, whereas for precipitation intensity no significant difference is discerned. The study demonstrates that the combination of satellite-based soil moisture and precipitation observations can be helpful in improving the understanding of the land surface-precipitation interaction over tropical areas. The thesis concludes with a number of recommendations on future algorithm improvements and potential research applications. For both cloud phase and precipitation properties, extension of the algorithm to include nighttime observations would be desirable to enable detailed studies on the full diurnal cycle. Further, the SEVIRI High-Resolution Visible (HRV) channel could be incorporated to correct retrieved cloud physical properties for broken and inhomogeneous cloud cases. Finally, the accurate cloud phase and precipitation datasets combined with the high SEVIRI spatial and temporal sampling resolution enables possibilities for detailed research on climate monitoring, nowcasting applications, evaluation of cloud schemes in climate models, studies on land surface-precipitation interactions (with a special focus on the diurnal cycle), and assimilation of the datasets in weather and climate models

  3. Retrieval Using Texture Features in High Resolution Multi-spectral Satellite Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Newsam, S D; Kamath, C

    2004-01-22

    Texture features have long been used in remote sensing applications to represent and retrieve image regions similar to a query region. Various representations of texture have been proposed based on the Fourier power spectrum, spatial co-occurrence, wavelets, Gabor filters, etc. These representations vary in their computational complexity and their suitability for representing different region types. Much of the work done thus far has focused on panchromatic imagery at low to moderate spatial resolutions, such as images from Landsat 1-7 which have a resolution of 15-30 m/pixel, and from SPOT 1-5 which have a resolution of 2.5-20 m/pixel. However, it is not clear which texture representation works best for the new classes of high resolution panchromatic (60-100 cm/pixel) and multi-spectral (4 bands for red, green, blue, and near infra-red at 2.4-4 m/pixel) imagery. It is also not clear how the different spectral bands should be combined. In this paper, we investigate the retrieval performance of several different texture representations using multi-spectral satellite images from IKONOS. A query-by-example framework, along with a manually chosen ground truth dataset, allows different combinations of texture representations and spectral bands to be compared. We focus on the specific problem of retrieving inhabited regions from images of urban and rural scenes. Preliminary results show that (1) the use of all spectral bands improves the retrieval performance, and (2) co-occurrence, wavelet and Gabor texture features perform comparably.

  4. High-spatial resolution multispectral and panchromatic satellite imagery for mapping perennial desert plants

    Science.gov (United States)

    Alsharrah, Saad A.; Bruce, David A.; Bouabid, Rachid; Somenahalli, Sekhar; Corcoran, Paul A.

    2015-10-01

    The use of remote sensing techniques to extract vegetation cover information for the assessment and monitoring of land degradation in arid environments has gained increased interest in recent years. However, such a task can be challenging, especially for medium-spatial resolution satellite sensors, due to soil background effects and the distribution and structure of perennial desert vegetation. In this study, we utilised Pleiades high-spatial resolution, multispectral (2m) and panchromatic (0.5m) imagery and focused on mapping small shrubs and low-lying trees using three classification techniques: 1) vegetation indices (VI) threshold analysis, 2) pre-built object-oriented image analysis (OBIA), and 3) a developed vegetation shadow model (VSM). We evaluated the success of each approach using a root of the sum of the squares (RSS) metric, which incorporated field data as control and three error metrics relating to commission, omission, and percent cover. Results showed that optimum VI performers returned good vegetation cover estimates at certain thresholds, but failed to accurately map the distribution of the desert plants. Using the pre-built IMAGINE Objective OBIA approach, we improved the vegetation distribution mapping accuracy, but this came at the cost of over classification, similar to results of lowering VI thresholds. We further introduced the VSM which takes into account shadow for further refining vegetation cover classification derived from VI. The results showed significant improvements in vegetation cover and distribution accuracy compared to the other techniques. We argue that the VSM approach using high-spatial resolution imagery provides a more accurate representation of desert landscape vegetation and should be considered in assessments of desertification.

  5. High-resolution multispectral satellite imagery for extracting bathymetric information of Antarctic shallow lakes

    Science.gov (United States)

    Jawak, Shridhar D.; Luis, Alvarinho J.

    2016-05-01

    High-resolution pansharpened images from WorldView-2 were used for bathymetric mapping around Larsemann Hills and Schirmacher oasis, east Antarctica. We digitized the lake features in which all the lakes from both the study areas were manually extracted. In order to extract the bathymetry values from multispectral imagery we used two different models: (a) Stumpf model and (b) Lyzenga model. Multiband image combinations were used to improve the results of bathymetric information extraction. The derived depths were validated against the in-situ measurements and root mean square error (RMSE) was computed. We also quantified the error between in-situ and satellite-estimated lake depth values. Our results indicated a high correlation (R = 0.60 0.80) between estimated depth and in-situ depth measurements, with RMSE ranging from 0.10 to 1.30 m. This study suggests that the coastal blue band in the WV-2 imagery could retrieve accurate bathymetry information compared to other bands. To test the effect of size and dimension of lake on bathymetry retrieval, we distributed all the lakes on the basis of size and depth (reference data), as some of the lakes were open, some were semi frozen and others were completely frozen. Several tests were performed on open lakes on the basis of size and depth. Based on depth, very shallow lakes provided better correlation (≈ 0.89) compared to shallow (≈ 0.67) and deep lakes (≈ 0.48). Based on size, large lakes yielded better correlation in comparison to medium and small lakes.

  6. Study of Ice Crystal Orientation in Cirrus Clouds based on Satellite Polarized Radiance Measurements

    OpenAIRE

    Noel, Vincent; Chepfer, Hélène

    2004-01-01

    International audience; The goal of this paper is to retrieve information about ice particle orientation in cirrus clouds. This is achieved by comparing simulations of sunlight reflection on a cirrus cloud with measurements of polarized radiances from the spaceborne instrument Polarization and Directionality of the Earth's Reflectance (POLDER-1) on Advanced Earth Observing Satellite-1 (ADEOS-1). Results show that horizontal orientation of cr ystals can be spotted by the presence of a local ma...

  7. A quantitative method for estimating cloud cover over tropical cyclones from satellite data

    OpenAIRE

    BALOGUN, E. E.

    2011-01-01

    A photometric method for quantifying cloud cover over tropical cyclones as observed from satellite photographs is presented. Two gridded photographs of tropical cyclones are analyzed by this method. On each photograph, nine concentric circles are drawn. The observed or reported centre of the cyclones is used as the centre for each set of concentric circles. Photometric estimates of cloud cover are made along the nine concentric circles. The principle of harmonic analysis is applied to the cl...

  8. Adjusting thresholds of satellite-based convective initiation interest fields based on the cloud environment

    Science.gov (United States)

    Jewett, Christopher P.; Mecikalski, John R.

    2013-11-01

    The Time-Space Exchangeability (TSE) concept states that similar characteristics of a given property are closely related statistically for objects or features within close proximity. In this exercise, the objects considered are growing cumulus clouds, and the data sets to be considered in a statistical sense are geostationary satellite infrared (IR) fields that help describe cloud growth rates, cloud top heights, and whether cloud tops contain significant amounts of frozen hydrometeors. In this exercise, the TSE concept is applied to alter otherwise static thresholds of IR fields of interest used within a satellite-based convective initiation (CI) nowcasting algorithm. The convective environment in which the clouds develop dictate growth rate and precipitation processes, and cumuli growing within similar mesoscale environments should have similar growth characteristics. Using environmental information provided by regional statistics of the interest fields, the thresholds are examined for adjustment toward improving the accuracy of 0-1 h CI nowcasts. Growing cumulus clouds are observed within a CI algorithm through IR fields for many 1000 s of cumulus cloud objects, from which statistics are generated on mesoscales. Initial results show a reduction in the number of false alarms of ~50%, yet at the cost of eliminating approximately ~20% of the correct CI forecasts. For comparison, static thresholds (i.e., with the same threshold values applied across the entire satellite domain) within the CI algorithm often produce a relatively high probability of detection, with false alarms being a significant problem. In addition to increased algorithm performance, a benefit of using a method like TSE is that a variety of unknown variables that influence cumulus cloud growth can be accounted for without need for explicit near-cloud observations that can be difficult to obtain.

  9. Geo-spatial distribution of cloud cover and influence of cloud induced attenuation and noise temperature on satellite signal propagation over Nigeria

    Science.gov (United States)

    Ojo, Joseph Sunday

    2017-05-01

    The study of the influence of cloud cover on satellite propagation links is becoming more demanding due to the requirement of larger bandwidth for different satellite applications. Cloud attenuation is one of the major factors to consider for optimum performance of Ka/V and other higher frequency bands. In this paper, the geo-spatial distribution of cloud coverage over some chosen stations in Nigeria has been considered. The substantial scale spatial dispersion of cloud cover based on synoptic meteorological data and the possible impact on satellite communication links at higher frequency bands was also investigated. The investigation was based on 5 years (2008-2012) achieved cloud cover data collected by the Nigerian Meteorological Agency (NIMET) Federal Ministry of Aviation, Oshodi Lagos over four synoptic hours of the day covering day and night. The performances of satellite signals as they traverse through the cloud and cloud noise temperature at different seasons and over different hours of days at Ku/W-bands frequency are also examined. The overall result shows that the additional total atmospheric noise temperature due to the clear air effect and the noise temperature from the cloud reduces the signal-to-noise ratio of the satellite receiver systems, leading to more signal loss and if not adequately taken care of may lead to significant outage. The present results will be useful for Earth-space link budgeting, especially for the proposed multi-sensors communication satellite systems in Nigeria.

  10. Land and Water Interface of Louisiana from 2002 Landsat Thematic Mapper Satellite Imagery, Geographic NAD83, LOSCO (2005) [landwater_interface_la_03ac_LOSCO_2002

    Data.gov (United States)

    Louisiana Geographic Information Center — These are polygon and raster data sets derived from 2002 Landsat Thematic Mapper Satellite Imagery that indicates areas of land and areas of water in Louisiana. The...

  11. Land and Water Interface of Louisiana from 2002 Landsat Thematic Mapper Satellite Imagery, Geographic NAD83, LOSCO (2004) [landwater_interface_la_25ac_LOSCO_2002

    Data.gov (United States)

    Louisiana Geographic Information Center — These are polygon and raster data sets derived from 2002 Landsat Thematic Mapper Satellite Imagery that indicates areas of land and areas of water in Louisiana. The...

  12. Detailed Maps Depicting the Shallow-Water Benthic Habitats of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Detailed, shallow-water coral reef ecosystem maps were generated by rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations...

  13. Detailed Maps Depicting the Shallow-Water Benthic Habitats of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Detailed, shallow-water coral reef ecosystem maps were generated by rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations...

  14. Land and Water Interface of Louisiana from 2002 Landsat Thematic Mapper Satellite Imagery, Geographic NAD83, LOSCO (2005) [landwater_interface_la_05ac_LOSCO_2002

    Data.gov (United States)

    Louisiana Geographic Information Center — These are polygon and raster data sets derived from 2002 Landsat Thematic Mapper Satellite Imagery that indicates areas of land and areas of water in Louisiana. The...

  15. Phenological dynamics of arctic tundra vegetation and its implications on satellite imagery interpretation

    Science.gov (United States)

    Juutinen, Sari; Aurela, Mika; Mikola, Juha; Räsänen, Aleksi; Virtanen, Tarmo

    2016-04-01

    Remote sensing is a key methodology when monitoring the responses of arctic ecosystems to climatic warming. The short growing season and rapid vegetation development, however, set demands to the timing of image acquisition in the arctic. We used multispectral very high spatial resolution satellite images to study the effect of vegetation phenology on the spectral reflectance and image interpretation in the low arctic tundra in coastal Siberia (Tiksi, 71°35'39"N, 128°53'17"E). The study site mainly consists of peatlands, tussock, dwarf shrub, and grass tundra, and stony areas with some lichen and shrub patches. We tested the hypotheses that (1) plant phenology is responsive to the interannual weather variation and (2) the phenological state of vegetation has an impact on satellite image interpretation and the ability to distinguish between the plant communities. We used an empirical transfer function with temperature sums as drivers to reconstruct daily leaf area index (LAI) for the different plant communities for years 2005, and 2010-2014 based on measured LAI development in summer 2014. Satellite images, taken during growing seasons, were acquired for two years having late and early spring, and short and long growing season, respectively. LAI dynamics showed considerable interannual variation due to weather variation, and particularly the relative contribution of graminoid dominated communities was sensitive to these phenology shifts. We have also analyzed the differences in the reflectance values between the two satellite images taking account the LAI dynamics. These results will increase our understanding of the pitfalls that may arise from the timing of image acquisition when interpreting the vegetation structure in a heterogeneous tundra landscape. Very high spatial resolution multispectral images are available at reasonable cost, but not in high temporal resolution, which may lead to compromises when matching ground truth and the imagery. On the other hand

  16. Multi-decadal record of ice dynamics on Daugaard Jensen Gletscher, East Greenland, from satellite imagery and terrestrial measurements

    DEFF Research Database (Denmark)

    Stearns, L.A.; Hamilton, G.S.; Reeh, Niels

    2005-01-01

    The history of ice velocity and calving front position of Daugaard Jensen Gletscher, a large outlet glacier in East Greenland, is reconstructed from field measurements, aerial photography and satellite imagery for the period 1950-2001. The calving terminus of the glacier has remained in approxima......The history of ice velocity and calving front position of Daugaard Jensen Gletscher, a large outlet glacier in East Greenland, is reconstructed from field measurements, aerial photography and satellite imagery for the period 1950-2001. The calving terminus of the glacier has remained...... vs snow accumulation in the interior catchment show that Daugaard Jensen Gletscher has a small negative mass balance. This result is consistent with other mass-balance estimates for the inland region of the glacier....

  17. Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

    Directory of Open Access Journals (Sweden)

    CAO Yungang

    2016-10-01

    Full Text Available A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon.

  18. Cirrus cloud-temperature interactions over a tropical station, Gadanki from lidar and satellite observations

    Energy Technology Data Exchange (ETDEWEB)

    S, Motty G, E-mail: mottygs@gmail.com; Satyanarayana, M., E-mail: mottygs@gmail.com; Krishnakumar, V., E-mail: mottygs@gmail.com; Dhaman, Reji k., E-mail: mottygs@gmail.com [Department of Optoelectronics, University of Kerala, Kariavattom, Trivandrum-695 581, Kerala (India)

    2014-10-15

    The cirrus clouds play an important role in the radiation budget of the earth's atmospheric system and are important to characterize their vertical structure and optical properties. LIDAR measurements are obtained from the tropical station Gadanki (13.5{sup 0} N, 79.2{sup 0} E), India, and meteorological indicators derived from Radiosonde data. Most of the cirrus clouds are observed near to the tropopause, which substantiates the strength of the tropical convective processes. The height and temperature dependencies of cloud height, optical depth, and depolarization ratio were investigated. Cirrus observations made using CALIPSO satellite are compared with lidar data for systematic statistical study of cirrus climatology.

  19. Using Satellite Imagery to Monitor the Major Lakes; Case Study Lake Hamun

    Science.gov (United States)

    Norouzi, H.; Islam, R.; Bah, A.; AghaKouchak, A.

    2015-12-01

    Proper lakes function can ease the impact of floods and drought especially in arid and semi-arid regions. They are important environmentally and can directly affect human lives. Better understanding of the effect of climate change and human-driven changes on lakes would provide invaluable information for policy-makers and local people. As part of a comprehensive study, we aim to monitor the land-cover/ land-use changes in the world's major lakes using satellite observations. As a case study, Hamun Lake which is a pluvial Lake, also known as shallow Lake, located on the south-east of Iran and adjacent to Afghanistan, and Pakistan borders is investigated. The Lake is the main source of resources (agriculture, fishing and hunting) for the people around it and politically important in the region since it is shared among three different countries. The purpose of the research is to find the Lake's area from 1972 to 2015 and to see if any drought or water resources management has affected the lake. Analyzing satellites imagery from Landsat shows that the area of the Lake changes seasonally and intra-annually. Significant seasonal effects are found in 1975,1977, 1987, 1993, 1996, 1998, 2000, 2009 and 2011, as well as, substantial amount of shallow water is found throughout the years. The precipitation records as well as drought historical records are studied for the lake's basin. Meteorological studies suggest that the drought, decrease of rainfalls in the province and the improper management of the Lake have caused environmental, economic and geographical consequences. The results reveal that lake has experienced at least two prolong dryings since 1972 which drought cannot solely be blamed as main forcing factor.Proper lakes function can ease the impact of floods and drought especially in arid and semi-arid regions. They are important environmentally and can directly affect human lives. Better understanding of the effect of climate change and human-driven changes on lakes

  20. Subsurface Dynamical Properties of Variable Features Seen in Satellite IR Imagery off Point Sur and Their Acoustic Significance.

    Science.gov (United States)

    1980-06-01

    profile as in Fig. 87 except 60 foot source at 50 Hz -------------------------------. 205 90 Graph showing the difference between clima - tological PL...graph as in Fig. 90 except 300 foot source ---------------------------------------- 207 92 Graph showing the difference between clima - tological PL and... temporal scales, to make observations on the subsurface structure of features observed by satellite imagery, to investigate the diurnal variation of the sea

  1. Combining Satellite Ocean Color Imagery and Circulation Modeling to Forecast Bio-Optical Properties: Comparison of Models and Advection Schemes

    Science.gov (United States)

    2008-10-01

    Remote sensing of ocean color provides synoptic surface ocean bio -optical properties but is limited to real-time or climatological applications. Many...this, we couple satellite imagery with numerical circulation models to provide short-term (24-48 hr) forecasts of bio -optical properties. These are...physical processes control the bio -optical distribution patterns. We compare optical forecast results from three Navy models and two advection

  2. Exploring Land use and Land cover change in the mining areas of Wa East District, Ghana using Satellite Imagery

    Science.gov (United States)

    Basommi, Prosper Laari; Guan, Qingfeng; Cheng, Dandan

    2015-11-01

    Satellite imagery has been widely used to monitor the extent of environmental change in both mine and post mine areas. This study uses Remote sensing and Geographical Information System techniques for the assessment of land use/land cover dynamics of mine related areas in Wa East District of Ghana. Landsat satellite imageries of three different time periods, i.e., 1991, 2000 and 2014 were used to quantify the land use/cover changes in the area. Supervised Classification using Maximum Likelihood Technique in ERDAS was utilized. The images were categorized into five different classes: Open Savannah, Closed Savannah, Bare Areas, Settlement and Water. Image differencing method of change detection was used to investigate the changes. Normalized Differential Vegetative Index valueswere used to correlate the state of healthy vegetation. The image differencing showed a positive correlation to the changes in the Land use and Land cover classes. NDVI values reduced from 0.48 to 0.11. The land use change matrix also showed conversion of savannah areas into bare ground and settlement. Open and close savannah reduced from 50.80% to 36.5% and 27.80% to 22.67% respectively whiles bare land and settlement increased. Overall accuracy of classified 2014 image and kappa statistics was 83.20% and 0.761 respectively. The study revealed the declining nature of the vegetation and the significance of using satellite imagery. A higher resolution satellite Imagery is however needed to satisfactorily delineate mine areas from other bare areas in such Savannah zones.

  3. Constraining cloud lifetime effects of aerosols using A-Train satellite observations

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Minghuai; Ghan, Steven J.; Liu, Xiaohong; Ecuyer, Tristan L.; Zhang, Kai; Morrison, H.; Ovchinnikov, Mikhail; Easter, Richard C.; Marchand, Roger; Chand, Duli; Qian, Yun; Penner, Joyce E.

    2012-08-15

    Aerosol indirect effects have remained the largest uncertainty in estimates of the radiative forcing of past and future climate change. Observational constraints on cloud lifetime effects are particularly challenging since it is difficult to separate aerosol effects from meteorological influences. Here we use three global climate models, including a multi-scale aerosol-climate model PNNL-MMF, to show that the dependence of the probability of precipitation on aerosol loading, termed the precipitation frequency susceptibility (S{sub pop}), is a good measure of the liquid water path response to aerosol perturbation ({lambda}), as both Spop and {lambda} strongly depend on the magnitude of autoconversion, a model representation of precipitation formation via collisions among cloud droplets. This provides a method to use satellite observations to constrain cloud lifetime effects in global climate models. S{sub pop} in marine clouds estimated from CloudSat, MODIS and AMSR-E observations is substantially lower than that from global climate models and suggests a liquid water path increase of less than 5% from doubled cloud condensation nuclei concentrations. This implies a substantially smaller impact on shortwave cloud radiative forcing (SWCF) over ocean due to aerosol indirect effects than simulated by current global climate models (a reduction by one-third for one of the conventional aerosol-climate models). Further work is needed to quantify the uncertainties in satellite-derived estimates of S{sub pop} and to examine S{sub pop} in high-resolution models.

  4. Evaluating the Impact of Smoke Particle Absorption on Passive Satellite Cloud Optical Depth Retrievals

    Science.gov (United States)

    Alfaro-Contreras, R.; Zhang, J.; Reid, J. S.; Campbell, J. R.

    2013-12-01

    Absorbing aerosol particles, when lifted above clouds, can perturb top-of-atmosphere radiation radiances measured by passive satellite sensors through the absorption of reflected solar energy. This scenario, if not properly screened, impacts cloud physical retrievals, like cloud optical depth (COD), conducted using radiances/channels in the visible spectrum. We describe observations of smoke particle presence above cloud off the southwest coast of Africa, using spatially and temporally collocated Aqua Moderate Resolution Imaging Spectroradiometer (AQUA MODIS), Ozone Monitoring Instrument (OMI) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements. Results from this study indicate that above cloud aerosol episodes happen rather frequent in the smoke outflow region during the Northern Hemisphere summer where above cloud aerosol plumes introduce a significant bias to MODIS COD retrievals in the visible spectrum. This suggests that individual COD retrievals as well as COD climatology from MODIS can be affected over the smoke outflow region by above cloud aerosol contamination and thus showing the need to account for the presence of above cloud absorbing aerosols in the MODIS visible COD retrievals.

  5. Alpine cloud climatology using long-term NOAA-AVHRR satellite data

    Energy Technology Data Exchange (ETDEWEB)

    Kaestner, M.; Kriebel, K.T.

    2000-07-01

    Three different climates have been identified by our evaluation of AVHRR (advanced very high resolution radiometer) data using APOLLO (AVHRR processing scheme over land, clouds and ocean) for a five-years cloud climatology of the Alpine region. The cloud cover data from four layers were spatially averaged in boxes of 15 km by 14 km. The study area only comprises 540 km by 560 km, but contains regions with moderate, Alpine and Mediterranean climate. Data from the period July 1989 until December 1996 have been considered. The temporal resolution is one scene per day, the early afternoon pass, yielding monthly means of satellite derived cloud coverages 5% to 10% above the daily mean compared to conventional surface observation. At nonvegetated sites the cloudiness is sometimes significantly overestimated. Averaging high resolution cloud data seems to be superior to low resolution measurements of cloud properties and averaging is favourable in topographical homogeneous regions only. The annual course of cloud cover reveals typical regional features as foehn or temporal singularities as the so-called Christmas thaw. The cloud cover maps in spatially high resolution show local luff/lee features which outline the orography. Less cloud cover is found over the Alps than over the forelands in winter, an accumulation of thick cirrus is found over the High Alps and an accumulation of thin cirrus north of the Alps. (orig.)

  6. Monitoring of Conservation Tillage and Tillage Intensity by Ground and Satellite Imagery

    Directory of Open Access Journals (Sweden)

    M.A Rostami

    2014-09-01

    Full Text Available Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect and photo comparison methods are tedious and time-consuming. The present study was devoted to investigate accurate methods for monitoring residue management and tillage practices. The satellite imagery technique was used as a rapid and spatially explicit method for delineating crop residue coverage and as an estimator of conservation tillage adoption and intensity. The potential of multispectral high-spatial resolution WorldView-2 local data was evaluated using the total of eleven satellite spectral indices and Linear Spectral Unmixing Analysis (LSUA. The total of ninety locations was selected for this study and for each location the residue coverage was measured by the image processing method and recorded as ground control. The output of indices and LSUA method were individually correlated to the control and the relevant R2 was calculated. Results indicated that crop residue cover was related to IPVI, RVI1, RVI2 and GNDVI spectral indices and satisfactory correlations were established (0.74 - 0.81. The crop residue coverage estimated from the LSUA approach was found to be correlated with the ground residue data (0.75. Two effective indices named as Infrared Percentage Vegetation Index (IPVI and Ratio Vegetation Index (RVI with maximum R2 were considered for classification of tillage intensity. Results indicated that the classification accuracy with IPVI and RVI indices in different conditions varied from 78-100 percent and therefore in good agreement with ground measurement, observations and field records.

  7. Distributions and radiative forcings of various cloud types based on active and passive satellite datasets – Part 1: Geographical distributions and overlap of cloud types

    Directory of Open Access Journals (Sweden)

    J. Li

    2014-04-01

    Full Text Available Based on four year' 2B-CLDCLASS-Lidar (Radar-Lidar cloud classification product from CloudSat, we analyze the geographical distributions of different cloud types and their co-occurrence frequency across different seasons, moreover, utilize the vertical distributions of cloud type to further evaluate the cloud overlap assumptions. The statistical results show that more high clouds, altocumulus, stratocumulus or stratus and cumulus are identified in the Radar-Lidar cloud classification product compared to previous results from Radar-only cloud classification (2B-CLDCLASS product from CloudSat. In particularly, high clouds and cumulus cloud fractions increased by factors 2.5 and 4–7, respectively. The new results are in more reasonable agreement with other datasets (typically the International Satellite Cloud Climatology Project (ISCCP and surface observer reports. Among the cloud types, altostratus and altocumulus are more popular over the arid/semi-arid land areas of the Northern and Southern Hemispheres, respectively. These features weren't observed by using the ISCCP D1 dataset. For co-occurrence of cloud types, high cloud, altostratus, altocumulus and cumulus are much more likely to co-exist with other cloud types. However, stratus/stratocumulus, nimbostratus and convective clouds are much more likely to exhibit individual features. After considering the co-occurrence of cloud types, the cloud fraction based on the random overlap assumption is underestimated over the vast ocean except in the west-central Pacific Ocean warm pool. Obvious overestimations are mainly occurring over land areas in the tropics and subtropics. The investigation therefore indicates that incorporate co-occurrence information of cloud types based on Radar-Lidar cloud classification into the overlap assumption schemes used in the current GCMs possible be able to provide an better predictions for vertically projected total cloud fraction.

  8. Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information

    Directory of Open Access Journals (Sweden)

    Curt H. Davis

    2005-08-01

    Full Text Available High-resolution satellite imagery provides an important new data source for building extraction. We demonstrate an integrated strategy for identifying buildings in 1-meter resolution satellite imagery of urban areas. Buildings are extracted using structural, contextual, and spectral information. First, a series of geodesic opening and closing operations are used to build a differential morphological profile (DMP that provides image structural information. Building hypotheses are generated and verified through shape analysis applied to the DMP. Second, shadows are extracted using the DMP to provide reliable contextual information to hypothesize position and size of adjacent buildings. Seed building rectangles are verified and grown on a finely segmented image. Next, bright buildings are extracted using spectral information. The extraction results from the different information sources are combined after independent extraction. Performance evaluation of the building extraction on an urban test site using IKONOS satellite imagery of the City of Columbia, Missouri, is reported. With the combination of structural, contextual, and spectral information, 72.7% of the building areas are extracted with a quality percentage 58.8%.

  9. Reducing Errors in Satellite Simulated Views of Clouds with an Improved Parameterization of Unresolved Scales

    Science.gov (United States)

    Hillman, B. R.; Marchand, R.; Ackerman, T. P.

    2016-12-01

    Satellite instrument simulators have emerged as a means to reduce errors in model evaluation by producing simulated or psuedo-retrievals from model fields, which account for limitations in the satellite retrieval process. Because of the mismatch in resolved scales between satellite retrievals and large-scale models, model cloud fields must first be downscaled to scales consistent with satellite retrievals. This downscaling is analogous to that required for model radiative transfer calculations. The assumption is often made in both model radiative transfer codes and satellite simulators that the unresolved clouds follow maximum-random overlap with horizontally homogeneous cloud condensate amounts. We examine errors in simulated MISR and CloudSat retrievals that arise due to these assumptions by applying the MISR and CloudSat simulators to cloud resolving model (CRM) output generated by the Super-parameterized Community Atmosphere Model (SP-CAM). Errors are quantified by comparing simulated retrievals performed directly on the CRM fields with those simulated by first averaging the CRM fields to approximately 2-degree resolution, applying a "subcolumn generator" to regenerate psuedo-resolved cloud and precipitation condensate fields, and then applying the MISR and CloudSat simulators on the regenerated condensate fields. We show that errors due to both assumptions of maximum-random overlap and homogeneous condensate are significant (relative to uncertainties in the observations and other simulator limitations). The treatment of precipitation is particularly problematic for CloudSat-simulated radar reflectivity. We introduce an improved subcolumn generator for use with the simulators, and show that these errors can be greatly reduced by replacing the maximum-random overlap assumption with the more realistic generalized overlap and incorporating a simple parameterization of subgrid-scale cloud and precipitation condensate heterogeneity. Sandia National Laboratories is a

  10. Demarcation of Prime Farmland Protection Areas around a Metropolis Based on High-Resolution Satellite Imagery

    Science.gov (United States)

    Xia, Nan; Wang, Yajun; Xu, Hao; Sun, Yuefan; Yuan, Yi; Cheng, Liang; Jiang, Penghui; Li, Manchun

    2016-12-01

    Prime farmland (PF) is defined as high-quality farmland and a prime farmland protection area (PFPA, including related roads, waters and facilities) is a region designated for the special protection of PF. However, rapid urbanization in China has led to a tremendous farmland loss and to the degradation of farmland quality. Based on remote sensing and geographic information system technology, this study developed a semiautomatic procedure for designating PFPAs using high-resolution satellite imagery (HRSI), which involved object-based image analysis, farmland composite evaluation, and spatial analysis. It was found that the HRSIs can provide elaborate land-use information, and the PFPA demarcation showed strong correlation with the farmland area and patch distance. For the benefit of spatial planning and management, different demarcation rules should be applied for suburban and exurban areas around a metropolis. Finally, the overall accuracy of HRSI classification was about 80% for the study area, and high-quality farmlands from evaluation results were selected as PFs. About 95% of the PFs were demarcated within the PFPAs. The results of this study will be useful for PFPA planning and the methods outlined could help in the automatic designation of PFPAs from the perspective of the spatial science.

  11. Urban thermal environment and its biophysical parameters derived from satellite remote sensing imagery

    Science.gov (United States)

    Zoran, Maria A.; Savastru, Roxana S.; Savastru, Dan M.; Tautan, Marina N.; Baschir, Laurentiu V.

    2013-10-01

    In frame of global warming, the field of urbanization and urban thermal environment are important issues among scientists all over the world. This paper investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Bucharest metropolitan area of Romania based on satellite remote sensing imagery Landsat TM/ETM+, time series MODIS Terra/Aqua data and IKONOS acquired during 1990 - 2012 period. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also retrieved from thermal infrared band of Landsat TM/ETM+, from MODIS Terra/Aqua datasets. Based on these parameters, the urban growth, urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. Results indicated that the metropolitan area ratio of impervious surface in Bucharest increased significantly during two decades investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

  12. Monitoring the Urban Growth of Dhaka (bangladesh) by Satellite Imagery in Flooding Risk Management Perspective

    Science.gov (United States)

    Bitelli, G.; Franci, F.; Mandanici, E.

    2013-01-01

    There is large consensus that demographic changes, the lack of appropriate environmental policies and sprawling urbanization result in high vulnerability and exposure to the natural disasters. This work reports some experiences of using multispectral satellite imagery to produce landuse/cover maps for the Dhaka city, the capital of Bangladesh, which is subject to frequent flooding events.The activity was conducted in collaboration with the non-profit organization ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The Landsat images acquired in 2000, 2002 and 2009 were used to evaluate the urban growth in order to support risk assessment studies; to identify areas routinely flooded during the monsoon season, the image of October 2009 (the most critical month for the effects of rain) was compared with two images acquired in January and February 2010. The analysis between 2000 and 2009 was able to quantify a very rapid growth of the metropolis, with an increase in built-up areas from 75 to 111 km2. The analysis highlights also a sharp rise of Bare soil class, likely related to the construction of embankments for the creation of new building space; consequently a decrease of cultivated land was observed. In particular, these artificial islands have been invading flooding areas. The change detection procedure also showed that the flooding in October 2009 affected about 20% (115 out of 591 km2) of the entire study area; furthermore these areas became wetlands and farmland over the next three/four months.

  13. Flood mapping using VHR satellite imagery: a comparison between different classification approaches

    Science.gov (United States)

    Franci, Francesca; Boccardo, Piero; Mandanici, Emanuele; Roveri, Elena; Bitelli, Gabriele

    2016-10-01

    Various regions in Europe have suffered from severe flooding over the last decades. Flood disasters often have a broad extent and a high frequency. They are considered the most devastating natural hazards because of the tremendous fatalities, injuries, property damages, economic and social disruption that they cause. In this context, Earth Observation techniques have become a key tool for flood risk and damage assessment. In particular, remote sensing facilitates flood surveying, providing valuable information, e.g. flood occurrence, intensity and progress of flood inundation, spurs and embankments affected/threatened. The present work aims to investigate the use of Very High Resolution satellite imagery for mapping flood-affected areas. The case study is the November 2013 flood event which occurred in Sardinia region (Italy), affecting a total of 2,700 people and killing 18 persons. The investigated zone extends for 28 km2 along the Posada river, from the Maccheronis dam to the mouth in the Tyrrhenian sea. A post-event SPOT6 image was processed by means of different classification methods, in order to produce the flood map of the analysed area. The unsupervised classification algorithm ISODATA was tested. A pixel-based supervised technique was applied using the Maximum Likelihood algorithm; moreover, the SPOT 6 image was processed by means of object-oriented approaches. The produced flood maps were compared among each other and with an independent data source, in order to evaluate the performance of each method, also in terms of time demand.

  14. Effective System for Automatic Bundle Block Adjustment and Ortho Image Generation from Multi Sensor Satellite Imagery

    Science.gov (United States)

    Akilan, A.; Nagasubramanian, V.; Chaudhry, A.; Reddy, D. Rajesh; Sudheer Reddy, D.; Usha Devi, R.; Tirupati, T.; Radhadevi, P. V.; Varadan, G.

    2014-11-01

    Block Adjustment is a technique for large area mapping for images obtained from different remote sensingsatellites.The challenge in this process is to handle huge number of satellite imageries from different sources with different resolution and accuracies at the system level. This paper explains a system with various tools and techniques to effectively handle the end-to-end chain in large area mapping and production with good level of automation and the provisions for intuitive analysis of final results in 3D and 2D environment. In addition, the interface for using open source ortho and DEM references viz., ETM, SRTM etc. and displaying ESRI shapes for the image foot-prints are explained. Rigorous theory, mathematical modelling, workflow automation and sophisticated software engineering tools are included to ensure high photogrammetric accuracy and productivity. Major building blocks like Georeferencing, Geo-capturing and Geo-Modelling tools included in the block adjustment solution are explained in this paper. To provide optimal bundle block adjustment solution with high precision results, the system has been optimized in many stages to exploit the full utilization of hardware resources. The robustness of the system is ensured by handling failure in automatic procedure and saving the process state in every stage for subsequent restoration from the point of interruption. The results obtained from various stages of the system are presented in the paper.

  15. Glacier changes in the Karakoram region mapped by multi-mission satellite imagery

    Directory of Open Access Journals (Sweden)

    M. Rankl

    2013-08-01

    Full Text Available Glaciers in the Karakoram region are known to show stable and advancing terminus positions or surging behavior, which contrasts the worldwide retreat of many mountain glaciers. The present study uses Landsat imagery to derive an updated and extended glacier inventory. Surging and advancing glaciers and their annual termini position changes are mapped in addition. Out of 1334 glaciers, 134 show advancing or surging behavior, with a marked increase since 2000. The length distribution of surging glaciers differs significantly from non-surging glaciers. More than 50% of the advancing/surging glaciers are shorter than 10 km. Besides a regional spatial coverage of ice dynamics, high-resolution SAR data allows to investigate very small and comparably fast flowing glaciers (up to 1.8 m day−1. Such data enables mapping of temporal changes of ice dynamics of individual small surging or advancing glaciers. In a further case study, glacier volume changes of three glaciers around Braldu Glacier are quantified during a surge event comparing digital elevation models from the Shuttle Radar Topography Mission (SRTM and the new TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X Mission. We recommend regular acquisitions of high resolution (bi-static SAR satellite data and further exploitation of the archives in order to generate an improved database for monitoring changes, and to at least partially compensate for the lack of in-situ and long-term climatological measurements in the Karakoram region.

  16. Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

    Science.gov (United States)

    Husak, G.J.; Marshall, M. T.; Michaelsen, J.; Pedreros, Diego; Funk, Christopher C.; Galu, G.

    2008-01-01

    Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

  17. Detection of facilities in satellite imagery using semi-supervized image classification and auxiliary contextual observables

    Science.gov (United States)

    Harvey, Neal R.; Ruggiero, C.; Pawley, N. H.; MacDonald, B.; Oyer, A.; Balick, L.; Brumby, S. P.

    2009-05-01

    Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem that human analysts try to solve by applying world knowledge. Often there are known observables that can be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary observables may provide sufficient context for detection by a machine learning algorithm. We describe an approach for automatic detection of facilities that uses an automated feature extraction algorithm to extract auxiliary observables, and a semi-supervised assisted target recognition algorithm to then identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image data of Albuquerque, New Mexico. We use Los Alamos National Laboratory's Genie Pro automated feature extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as parking lots, large buildings, sports fields and residential areas and then combine these features using Genie Pro's assisted target recognition algorithm to learn a classifier that finds schools in the image data.

  18. Determination of mangrove change in Matang Mangrove Forest using multi temporal satellite imageries

    Science.gov (United States)

    Ibrahim, N. A.; Mustapha, M. A.; Lihan, T.; Ghaffar, M. A.

    2013-11-01

    Mangrove protects shorelines from damaging storm and hurricane winds, waves, and floods. Mangroves also help prevent erosion by stabilizing sediments with their tangled root systems. They maintain water quality and clarity, filtering pollutants and trapping sediments originating from land. However, mangrove has been reported to be threatened by land conversion for other activities. In this study, land use and land cover changes in Matang Mangrove Forest during the past 18 years (1993 to 2011) were determined using multi-temporal satellite imageries by Landsat TM and RapidEye. In this study, classification of land use and land cover approach was performed using the maximum likelihood classifier (MCL) method along with vegetation index differencing (NDVI) technique. Data obtained was evaluated through Kappa coefficient calculation for accuracy and results revealed that the classification accuracy was 81.25% with Kappa Statistics of 0.78. The results indicated changes in mangrove forest area to water body with 2,490.6 ha, aquaculture with 890.7 ha, horticulture with 1,646.1 ha, palm oil areas with 1,959.2 ha, dry land forest with 2,906.7 ha and urban settlement area with 224.1 ha. Combinations of these approaches were useful for change detection and for indication of the nature of these changes.

  19. GPU-based normalized cuts for road extraction using satellite imagery

    Indian Academy of Sciences (India)

    J Senthilnath; S Sindhu; S N Omkar

    2014-12-01

    This paper presents a GPU implementation of normalized cuts for road extraction problem using panchromatic satellite imagery. The roads have been extracted in three stages namely pre-processing, image segmentation and post-processing. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, vegetation, and fallow regions). The road regions are then extracted using the normalized cuts algorithm. Normalized cuts algorithm is a graph-based partitioning approach whose focus lies in extracting the global impression (perceptual grouping) of an image rather than local features. For the segmented image, post-processing is carried out using morphological operations – erosion and dilation. Finally, the road extracted image is overlaid on the original image. Here, a GPGPU (General Purpose Graphical Processing Unit) approach has been adopted to implement the same algorithm on the GPU for fast processing. A performance comparison of this proposed GPU implementation of normalized cuts algorithm with the earlier algorithm (CPU implementation) is presented. From the results, we conclude that the computational improvement in terms of time as the size of image increases for the proposed GPU implementation of normalized cuts. Also, a qualitative and quantitative assessment of the segmentation results has been projected.

  20. Detection of facilities in satellite imagery using semi-supervised image classification and auxiliary contextual observables

    Energy Technology Data Exchange (ETDEWEB)

    Harvey, Neal R [Los Alamos National Laboratory; Ruggiero, Christy E [Los Alamos National Laboratory; Pawley, Norma H [Los Alamos National Laboratory; Brumby, Steven P [Los Alamos National Laboratory; Macdonald, Brian [Los Alamos National Laboratory; Balick, Lee [Los Alamos National Laboratory; Oyer, Alden [Los Alamos National Laboratory

    2009-01-01

    Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem that human analysts try to solve by applying world knowledge. Often there are known observables that can be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary observables may provide sufficient context for detection by a machine learning algorithm. We describe an approach for automatic detection of facilities that uses an automated feature extraction algorithm to extract auxiliary observables, and a semi-supervised assisted target recognition algorithm to then identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image data of Albuquerque, New Mexico. We use Los Alamos National Laboratory's Genie Pro automated feature extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as parking lots, large buildings, sports fields and residential areas and then combine these features using Genie Pro's assisted target recognition algorithm to learn a classifier that finds schools in the image data.

  1. Object-oriented industrial solid waste identification using HJ satellite imagery: a case study of phosphogypsum

    Science.gov (United States)

    Fu, Zhuo; Shen, Wenming; Xiao, Rulin; Xiong, Wencheng; Shi, Yuanli; Chen, Baisong

    2012-10-01

    The increasing volume of industrial solid wastes presents a critical problem for the global environment. In the detection and monitoring of these industrial solid wastes, the traditional field methods are generally expensive and time consuming. With the advantages of quick observations taken at a large area, remote sensing provides an effective means for detecting and monitoring the industrial solid wastes in a large scale. In this paper, we employ an object-oriented method for detecting the industrial solid waste from HJ satellite imagery. We select phosphogypsum which is a typical industrial solid waste as our target. Our study area is located in Fuquan in Guizhou province of China. The object oriented method we adopted consists of the following steps: 1) Multiresolution segmentation method is adopted to segment the remote sensing images for obtaining the object-based images. 2) Build the feature knowledge set of the object types. 3) Detect the industrial solid wastes based on the object-oriented decision tree rule set. We analyze the heterogeneity in features of different objects. According to the feature heterogeneity, an object-oriented decision tree rule set is then built for aiding the identification of industrial solid waste. Then, based on this decision tree rule set, the industrial solid waste can be identified automatically from remote sensing images. Finally, the identified results are validated using ground survey data. Experiments and results indicate that the object-oriented method provides an effective method for detecting industrial solid wastes.

  2. Seasonally-managed wetland footprint delineation using Landsat ETM+ satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Quinn, Nigel W. T. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Epshtein, Olga [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Arizona State Univ., Tempe, AZ (United States). School of Sustainable Engineering and the Built Environment

    2014-01-09

    One major challenge in water resource management is the estimation of evapotranspiration losses from seasonally managed wetlands. Quantifying these losses is complicated by the dynamic nature of the wetlands' areal footprint during the periods of flood-up and drawdown. In this paper, we present a data-lean solution to this problem using an example application in the San Joaquin Basin, California. Through analysis of high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery, we develop a metric to better capture the extent of total flooded wetland area. The procedure is validated using year-long, continuously-logged field datasets for two wetlands within the study area. The proposed classification which uses a Landsat ETM + Band 5 (mid-IR wavelength) to Band 2 (visible green wavelength) ratio improves estimates by 30–50% relative to previous wetland delineation studies. Finally, requiring modest ancillary data, the study results provide a practical and efficient option for wetland management in data-sparse regions or un-gauged watersheds.

  3. Correcting bias in the rational polynomial coefficients of satellite imagery using thin-plate smoothing splines

    Science.gov (United States)

    Shen, Xiang; Liu, Bin; Li, Qing-Quan

    2017-03-01

    The Rational Function Model (RFM) has proven to be a viable alternative to the rigorous sensor models used for geo-processing of high-resolution satellite imagery. Because of various errors in the satellite ephemeris and instrument calibration, the Rational Polynomial Coefficients (RPCs) supplied by image vendors are often not sufficiently accurate, and there is therefore a clear need to correct the systematic biases in order to meet the requirements of high-precision topographic mapping. In this paper, we propose a new RPC bias-correction method using the thin-plate spline modeling technique. Benefiting from its excellent performance and high flexibility in data fitting, the thin-plate spline model has the potential to remove complex distortions in vendor-provided RPCs, such as the errors caused by short-period orbital perturbations. The performance of the new method was evaluated by using Ziyuan-3 satellite images and was compared against the recently developed least-squares collocation approach, as well as the classical affine-transformation and quadratic-polynomial based methods. The results show that the accuracies of the thin-plate spline and the least-squares collocation approaches were better than the other two methods, which indicates that strong non-rigid deformations exist in the test data because they cannot be adequately modeled by simple polynomial-based methods. The performance of the thin-plate spline method was close to that of the least-squares collocation approach when only a few Ground Control Points (GCPs) were used, and it improved more rapidly with an increase in the number of redundant observations. In the test scenario using 21 GCPs (some of them located at the four corners of the scene), the correction residuals of the thin-plate spline method were about 36%, 37%, and 19% smaller than those of the affine transformation method, the quadratic polynomial method, and the least-squares collocation algorithm, respectively, which demonstrates

  4. A robust method for removal of glint effects from satellite ocean colour imagery

    Directory of Open Access Journals (Sweden)

    R. K. Singh

    2014-12-01

    Full Text Available Removal of the glint effects from satellite imagery for accurate retrieval of water-leaving radiances is a complicated problem since its contribution in the measured signal is dependent on many factors such as viewing geometry, sun elevation and azimuth, illumination conditions, wind speed and direction, and the water refractive index. To simplify the situation, existing glint correction models describe the extent of the glint-contaminated region and its contribution to the radiance essentially as a function of the wind speed and sea surface slope that often lead to a tremendous loss of information with a considerable scientific and financial impact. Even with the glint-tilting capability of modern sensors, glint contamination is severe on the satellite-derived ocean colour products in the equatorial and sub-tropical regions. To rescue a significant portion of data presently discarded as "glint contaminated" and improving the accuracy of water-leaving radiances in the glint contaminated regions, we developed a glint correction algorithm which is dependent only on the satellite derived Rayleigh Corrected Radiance and absorption by clear waters. The new algorithm is capable of achieving meaningful retrievals of ocean radiances from the glint-contaminated pixels unless saturated by strong glint in any of the wavebands. It takes into consideration the combination of the background absorption of radiance by water and the spectral glint function, to accurately minimize the glint contamination effects and produce robust ocean colour products. The new algorithm is implemented along with an aerosol correction method and its performance is demonstrated for many MODIS-Aqua images over the Arabian Sea, one of the regions that are heavily affected by sunglint due to their geographical location. The results with and without sunglint correction are compared indicating major improvements in the derived products with sunglint correction. When compared to the

  5. The retrieval of cloud microphysical properties using satellite measurements and an in situ database

    Directory of Open Access Journals (Sweden)

    Christophe Poix

    Full Text Available By combining AVHRR data from the NOAA satellites with information from a database of in situ measurements, large-scale maps can be generated of the microphysical parameters most immediately significant for the modelling of global circulation and climate. From the satellite data, the clouds can be classified into cumuliform, stratiform and cirrus classes and then into further sub-classes by cloud top temperature. At the same time a database of in situ measurements made by research aircraft is classified into the same sub-classes and a statistical analysis is used to derive relationships between the sub-classes and the cloud microphysical properties. These two analyses are then linked to give estimates of the microphysical properties of the satellite observed clouds. Examples are given of the application of this technique to derive maps of the probability of occurrence of precipitating clouds and of precipitating water content derived from a case study within the International Cirrus Experiment (ICE held in 1989 over the North Sea.

  6. A cloud detection scheme for the Chinese Carbon Dioxide Observation Satellite (TANSAT)

    Science.gov (United States)

    Wang, Xi; Guo, Zheng; Huang, Yipeng; Fan, Hongjie; Li, Wanbiao

    2017-01-01

    Cloud detection is an essential preprocessing step for retrieving carbon dioxide from satellite observations of reflected sunlight. During the pre-launch study of the Chinese Carbon Dioxide Observation Satellite (TANSAT), a cloud-screening scheme was presented for the Cloud and Aerosol Polarization Imager (CAPI), which only performs measurements in five channels located in the visible to near-infrared regions of the spectrum. The scheme for CAPI, based on previous cloudscreening algorithms, defines a method to regroup individual threshold tests for each pixel in a scene according to the derived clear confidence level. This scheme is proven to be more effective for sensors with few channels. The work relies upon the radiance data from the Visible and Infrared Radiometer (VIRR) onboard the Chinese FengYun-3A Polar-orbiting Meteorological Satellite (FY-3A), which uses four wavebands similar to that of CAPI and can serve as a proxy for its measurements. The scheme has been applied to a number of the VIRR scenes over four target areas (desert, snow, ocean, forest) for all seasons. To assess the screening results, comparisons against the cloud-screening product from MODIS are made. The evaluation suggests that the proposed scheme inherits the advantages of schemes described in previous publications and shows improved cloud-screening results. A seasonal analysis reveals that this scheme provides better performance during warmer seasons, except for observations over oceans, where results are much better in colder seasons.

  7. The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics

    Directory of Open Access Journals (Sweden)

    Jędrzej S. Bojanowski

    2014-12-01

    Full Text Available Cloud property data sets derived from passive sensors onboard the polar orbiting satellites (such as the NOAA’s Advanced Very High Resolution Radiometer have global coverage and now span a climatological time period. Synoptic surface observations (SYNOP are often used to characterize the accuracy of satellite-based cloud cover. Infrequent overpasses of polar orbiting satellites combined with the 3- or 6-h SYNOP frequency lead to collocation time differences of up to 3 h. The associated collocation error degrades the cloud cover performance statistics such as the Hanssen-Kuiper’s discriminant (HK by up to 45%. Limiting the time difference to 10 min, on the other hand, introduces a sampling error due to a lower number of corresponding satellite and SYNOP observations. This error depends on both the length of the validated time series and the SYNOP frequency. The trade-off between collocation and sampling error call for an optimum collocation time difference. It however depends on cloud cover characteristics and SYNOP frequency, and cannot be generalized. Instead, a method is presented to reconstruct the unbiased (true HK from HK affected by the collocation differences, which significantly (t-test p < 0.01 improves the validation results.

  8. A comparison of cloud layers from ground and satellite active remote sensing at the Southern Great Plains ARM site

    Science.gov (United States)

    Zhang, Jinqiang; Xia, Xiang'ao; Chen, Hongbin

    2017-03-01

    Using the data collected over the Southern Great Plains ARM site from 2006 to 2010, the surface Active Remote Sensing of Cloud (ARSCL) and CloudSat-CALIPSO satellite (CC) retrievals of total cloud and six specified cloud types [low, mid-low (ML), high-mid-low (HML), mid, high-mid (HM) and high] were compared in terms of cloud fraction (CF), cloud-base height (CBH), cloud-top height (CTH) and cloud thickness (CT), on different temporal scales, to identify their respective advantages and limitations. Good agreement between the two methods was exhibited in the total CF. However, large discrepancies were found between the cloud distributions of the two methods at a high (240-m) vertical grid spacing. Compared to the satellites, ARSCL retrievals detected more boundary layer clouds, while they underestimated high clouds. In terms of the six specific cloud types, more low- and mid-level clouds but less HML- and high-level clouds were detected by ARSCL than by CC. In contrast, the ARSCL retrievals of ML- and HM-level clouds agreed more closely with the estimations from the CC product. Lower CBHs tended to be reported by the surface data for low-, ML- and HML-level clouds; however, higher CTHs were often recorded by the satellite product for HML-, HM- and high-level clouds. The mean CTs for low- and ML-level cloud were similar between the two products; however, the mean CTs for HML-, mid-, HM- and high-level clouds from ARSCL were smaller than those from CC.

  9. A long-term satellite study of aerosol effects on convective clouds in Nordic background air

    Directory of Open Access Journals (Sweden)

    M. K. Sporre

    2013-05-01

    Full Text Available Aerosol-cloud interactions constitute a~major uncertainty in future climate predictions. This study combines 10 yr of ground-based aerosol particle measurements from 2 Nordic background stations (Vavihill and Hyytiälä with MODIS (Moderate Resolution Imaging Spectroradiometer satellite data of convective clouds. The merged data are used to examine the indirect aerosol effects on convective clouds over the Nordic countries. From the satellite scenes, vertical profiles of cloud droplet effective radius (re are created by plotting re against cloud top temperature. The profiles have been divided according to aerosol loading but also modeled meteorological parameters from the ECMWF (European Centre for Medium-Range Forecasts. Furthermore, weather radar data from the BALTEX (Baltic Sea Experiment and ground based precipitation measurements from several ground-based meteorological measurement stations have been investigated to determine whether aerosols affect precipitation intensity and amount. Higher aerosol number concentrations result in smaller re throughout the entire profiles at both stations. Profiles associated with no or little precipitation have smaller droplets than those associated with more precipitation. Furthermore, an increase in aerosol loadings results in a suppression of precipitation rates, when the vertical extent of the clouds has been taken into account. Clouds with greater vertical extent have the highest precipitation rates and are most sensitive to aerosol perturbations. Nevertheless, meteorological parameters such as the vertical extent of the clouds, the atmospheric instability and the relative humidity in the lower atmosphere affect the amount of precipitation that reaches the ground more than the aerosols do. The combination of these ground-based and remote sensing datasets provides a unique long-term study of the effects of aerosols on convective clouds over the Nordic countries.

  10. Evaluating Cloud and Precipitation Processes in Numerical Models using Current and Potential Future Satellite Missions

    Science.gov (United States)

    van den Heever, S. C.; Tao, W. K.; Skofronick Jackson, G.; Tanelli, S.; L'Ecuyer, T. S.; Petersen, W. A.; Kummerow, C. D.

    2015-12-01

    Cloud, aerosol and precipitation processes play a fundamental role in the water and energy cycle. It is critical to accurately represent these microphysical processes in numerical models if we are to better predict cloud and precipitation properties on weather through climate timescales. Much has been learned about cloud properties and precipitation characteristics from NASA satellite missions such as TRMM, CloudSat, and more recently GPM. Furthermore, data from these missions have been successfully utilized in evaluating the microphysical schemes in cloud-resolving models (CRMs) and global models. However, there are still many uncertainties associated with these microphysics schemes. These uncertainties can be attributed, at least in part, to the fact that microphysical processes cannot be directly observed or measured, but instead have to be inferred from those cloud properties that can be measured. Evaluation of microphysical parameterizations are becoming increasingly important as enhanced computational capabilities are facilitating the use of more sophisticated schemes in CRMs, and as future global models are being run on what has traditionally been regarded as cloud-resolving scales using CRM microphysical schemes. In this talk we will demonstrate how TRMM, CloudSat and GPM data have been used to evaluate different aspects of current CRM microphysical schemes, providing examples of where these approaches have been successful. We will also highlight CRM microphysical processes that have not been well evaluated and suggest approaches for addressing such issues. Finally, we will introduce a potential NASA satellite mission, the Cloud and Precipitation Processes Mission (CAPPM), which would facilitate the development and evaluation of different microphysical-dynamical feedbacks in numerical models.

  11. Preliminary study on direct assimilation of cloud-affected satellite microwave brightness temperatures

    Science.gov (United States)

    Zhang, Sibo; Guan, Li

    2017-02-01

    Direct assimilation of cloud-affected microwave brightness temperatures from AMSU-A into the GSI three-dimensional variational (3D-Var) assimilation system is preliminarily studied in this paper. A combination of cloud microphysics parameters retrieved by the 1D-Var algorithm (including vertical profiles of cloud liquid water content, ice water content, and rain water content) and atmospheric state parameters from objective analysis fields of an NWP model are used as background fields. Three cloud microphysics parameters (cloud liquid water content, ice water content, and rain water content) are applied to the control variable. Typhoon Halong (2014) is selected as an example. The results show that direct assimilation of cloud-affected AMSU-A observations can effectively adjust the structure of large-scale temperature, humidity and wind analysis fields due to the assimilation of more AMSU-A observations in typhoon cloudy areas, especially typhoon spiral cloud belts. These adjustments, with temperatures increasing and humidities decreasing in the movement direction of the typhoon, bring the forecasted typhoon moving direction closer to its real path. The assimilation of cloud-affected satellite microwave brightness temperatures can provide better analysis fields that are more similar to the actual situation. Furthermore, typhoon prediction accuracy is improved using these assimilation analysis fields as the initial forecast fields in NWP models.

  12. Himalayan glaciers: understanding contrasting patterns of glacier behavior using multi-temporal satellite imagery

    Science.gov (United States)

    Racoviteanu, A.

    2014-12-01

    High rates of glacier retreat for the last decades are often reported, and believed to be induced by 20th century climate changes. However, regional glacier fluctuations are complex, and depend on a combination of climate and local topography. Furthermore, in ares such as the Hindu-Kush Himalaya, there are concerns about warming, decreasing monsoon precipitation and their impact on local glacier regimes. Currently, the challenge is in understanding the magnitude of feedbacks between large-scale climate forcing and small-scale glacier behavior. Spatio-temporal patterns of glacier distribution are still llimited in some areas of the high Hindu-Kush Himalaya, but multi-temporal satellite imagery has helped fill spatial and temporal gaps in regional glacier parameters in the last decade. Here I present a synopsis of the behavior of glaciers across the Himalaya, following a west to east gradient. In particular, I focus on spatial patterns of glacier parameters in the eastern Himalaya, which I investigate at multi-spatial scales using remote sensing data from declassified Corona, ASTER, Landsat ETM+, Quickbird and Worldview2 sensors. I also present the use of high-resolution imagery, including texture and thermal analysis for mapping glacier features at small scale, which are particularly useful in understanding surface trends of debris-covered glaciers, which are prevalent in the Himalaya. I compare and contrast spatial patterns of glacier area and élévation changes in the monsoon-influenced eastern Himalaya (the Everest region in the Nepal Himalaya and Sikkim in the Indian Himalaya) with other observations from the dry western Indian Himalaya (Ladakh and Lahul-Spiti), both field measurements and remote sensing-based. In the eastern Himalaya, results point to glacier area change of -0.24 % ± 0.08% per year from the 1960's to the 2006's, with a higher rate of retreat in the last decade (-0.43% /yr). Debris-covered glacier tongues show thinning trends of -30.8 m± 39 m

  13. Quantifying the Value of Satellite Imagery in Agriculture and other Sectors

    Science.gov (United States)

    Brown, M. E.; Abbott, P. C.; Escobar, V. M.

    2013-12-01

    This study focused on quantifying the commercial value of satellite remote sensing for agriculture. Commercial value from satellite imagery arises when improved information leads to better economic decisions. We identified five areas of application of remote sensing to agriculture where there is this potential: crop management (precision agriculture), insurance, real estate assessment, crop forecasting, and environmental monitoring. These applications can be divided between public information (crop forecasting) and those that may generate private commercial value (crop management), with both public and private information dimensions in some categories. Public information applications of remote sensing have been more successful in the past, and are likely to generate more economic value in the future. It was found that several issues have limited realization of the potential to generate private value from remote sensing in agriculture. The scale of use is small to the high cost of acquiring and interpreting large images has limited the cost effectiveness to individual farmers. Insurance, environmental monitoring, and crop management services by cooperatives or consultants may be cases overcoming this limitation. The greatest opportunities for potential commercial value from agriculture are probably in the crop forecasting area, especially where agricultural statistics services are not as well developed, since public market information benefits a broad range of economic actors, not limited to countries where forecasts are made. We estimate here the value from components of USDA's World Agricultural Supply and Demand Estimates (WASDE) forecasts for corn, indicating potential value increasing in the range of 60 to 240 million if improved satellite based information enhances those forecasts. The research was conducted by agricultural economists at Purdue University, and will be the basis for further evaluation of the use of satellite data within the NASA Carbon

  14. Automated satellite cloud analysis: a multispectral approach to the problem of snow/cloud discrimination

    OpenAIRE

    Allen, Robert C. Jr.

    1987-01-01

    Approved for public release; distribution is unlimited An algorithm is developed and evaluated for discriminating among clouds, snow cover and clear land. The multispectral technique uses daytime images of AVHRR channels 1 (0.63^m). 3 (3.7jim) and 4 (11.0[im). Reflectance is derived for channel 3 by using the channel 4 emission temperature to estimate and remove the channel 3 thermal emission. Separation of clouds from snow and land is based primarily on this derived channel...

  15. Seasonal variability of cloud optical depth over northwestern China derived from CERES/MODIS satellite measurements

    Institute of Scientific and Technical Information of China (English)

    Yonghang Chen; Hongtao Bai; Jianping Huang; Hua Zhang; Jinming Ge; Xiaodan Guan; Xiaoqin Mao

    2008-01-01

    The seasonal variability of cloud optical depth over northwestern China derived from Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Edition 1B data from July 2002 to June 2004 is presented. The regions of interest are those with Asia monsoon influence, the Tianshan and Qilian Mountains, and the Taklimakan Desert. The results show that the instantaneous measurements presented here are much higher than the previous results derived from International Satellite Cloud Climatology Project (ISCCP) D2 monthly mean data. Generally the measurements of cloud optical depth are the highest in summer and the lowest in winter, however, Taklimakan Desert has the lowest measurements in autumn. The regional variation is quite significant over northwestern China.

  16. Impact of Atmospheric Attenuations Time Resolutions in Solar Radiation Derived from Satellite Imagery

    Science.gov (United States)

    Cony, Marco; Liria, Juan; Weisenberg, Ralf; Serrano, Enrique

    2014-05-01

    Accurate knowledge of solar irradiance components at the earth surface is of highly interest in many scientific and technology branches concerning meteorology, climate, agriculture and solar energy applications. In the specific case of solar energy systems the solar resource analysis with accuracy is a first step in every project since it is a required data for design, power output estimations, systems simulations and risk assessments. Solar radiation measurement availability is increasing both in spatial density and in historical archiving. However, it is still quite limited and most of the situations cannot make use of a long term ground database of high quality since solar irradiance is not generally measured where users need data. Satellite-derived solar radiation estimations are a powerful and valuable tool for solar resource assessment studies that have achieved a relatively high maturity due to years of developments and improvements. However, several sources of uncertainty are still present in satellite-derived methods. In particular, the strong influence of atmospheric attenuation information as input to the method is one of the main topics of improvement. Since solar radiation attenuation by atmospheric aerosols, and water vapor in a second place, is, after clouds, the second most important factor determining solar radiation, and particularly direct normal irradiance, the accurate knowledge of aerosol optical depth and water vapor content is relevant in the final output of satellite-derived methods. This present work, two different datasets we are used for extract atmospheric attenuation information. On the one hand the monthly mean values of the Linke turbidity factor from Meteotest database, which are twelve unique values of the Linke turbidity worldwide with a spatial resolution of 1/12º. On the other hand, daily values of AOD (Aerosol Optical Depth) at 550 nm, Angstrom alpha exponent and water vapor column were taken from a gridded database that

  17. Prototype of the Mexican spatial data infrastructure for climate raster models and satellite imagery (“VISTA-C”)

    Science.gov (United States)

    Couturier, S.; Osorno Covarrubias, J.; Magaña Rueda, V.; Martínez Zazueta, I.; Vázquez Cruz, G.

    2017-01-01

    In the face of climatic uncertainty and its impacts on agriculture yields, there is a growing need for public institutions of subtropical countries to access as reliable as possible meteorological models and transmit a representation of their results in an effective way to stakeholders in agriculture. In many of these countries however, broad climatic regions and point-based statistics remain the core of these representations. The use of satellite imagery is largely limited to visual assessment, although it could serve as complementary data to meteorological raster models and the basis for spatially consistent quantitative impact assessments of meteorological events. In view of this situation in Mexico, a project developed by the Institute of Geography at UNAM university, and promoted by the National Institute of Geography and Statistics, consisted in the development of a climate monitoring system, which includes three main features: 1) a modular array storage system containing NOAA and GOES satellite imagery acquired though a receiving station (ERISA), 2) a climate modeling squeme based on successive error corrections of climate raster maps and associated models using the above mentioned imagery, and 3) an online, dynamic geovisualization of the results of the models. We discuss the implemented technologies and illustrate the VISTA-C prototype which has been released.

  18. Estimation of hydraulic conductivity of a coastal aquifer using satellite imagery

    Science.gov (United States)

    Rebolledo-Vieyra, M.; Iglesias-Prieto, R.; Marino-Tapia, I.

    2012-12-01

    The northern Yucatan Peninsula is characterized by a young and dynamic karstic system that yields very high secondary porosity and permeability. However, we have little, if none, knowledge about the hydraulic conductivity and the amount of groundwater being discharged in to ocean. Here we present and estimation of the hydraulic conductivity and quantity of groundwater being discharged by the northern Yucatan Peninsula coastal aquifer into the Gulf of Mexico, using the Sea Surface Temperature (SST) Images offshore the Yucatan coast, where we have detected a thermal anomaly that appears few hours after heavy rainfall in northern Yucatan. We associated these thermal anomalies of the SST to the groundwater being discharged into the ocean. To test our hypothesis we conducted a review of extreme rainfall events in the last 10 years; in parallel we used data from pressure and flow direction gauges installed in a known submarine groundwater discharge (SGD) to estimate the hydraulic conductivity and the quantity of groundwater being discharged. The satellite imagery and the rainfall data, allowed us to estimate the time lag between the rainfall and the SGD beginning, along with the hydraulic data from the gauges we have estimated the hydrogeological parameters of the coastal aquifer. This data is very important to contribute to the understanding the hydrogeological setting of the Yucatan coastal aquifer and its implications of the impact of human activities on the water quality. July 29th, 2005, NOAA's Sea Surface Temperature (SST) image of the Gulf of Mexico taken a week after hurricane Emily (2005). A thermal low is present offshore northern Yucatan.

  19. Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach

    Directory of Open Access Journals (Sweden)

    Charles R. Lane

    2014-12-01

    Full Text Available Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2 for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA. We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85 for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.

  20. EXTRACTING URBAN MORPHOLOGY FOR ATMOSPHERIC MODELING FROM MULTISPECTRAL AND SAR SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    S. Wittke

    2017-05-01

    Full Text Available This paper presents an approach designed to derive an urban morphology map from satellite data while aiming to minimize the cost of data and user interference. The approach will help to provide updates to the current morphological databases around the world. The proposed urban morphology maps consist of two layers: 1 Digital Elevation Model (DEM and 2 land cover map. Sentinel-2 data was used to create a land cover map, which was realized through image classification using optical range indices calculated from image data. For the purpose of atmospheric modeling, the most important classes are water and vegetation areas. The rest of the area includes bare soil and built-up areas among others, and they were merged into one class in the end. The classification result was validated with ground truth data collected both from field measurements and aerial imagery. The overall classification accuracy for the three classes is 91 %. TanDEM-X data was processed into two DEMs with different grid sizes using interferometric SAR processing. The resulting DEM has a RMSE of 3.2 meters compared to a high resolution DEM, which was estimated through 20 control points in flat areas. Comparing the derived DEM with the ground truth DEM from airborne LIDAR data, it can be seen that the street canyons, that are of high importance for urban atmospheric modeling are not detectable in the TanDEM-X DEM. However, the derived DEM is suitable for a class of urban atmospheric models. Based on the numerical modeling needs for regional atmospheric pollutant dispersion studies, the generated files enable the extraction of relevant parametrizations, such as Urban Canopy Parameters (UCP.

  1. Validation of the morphological compositing method for ZY-3 satellite imagery

    Science.gov (United States)

    Feng, Shuna; Zhao, Yindi

    2014-11-01

    Each scene of image generated from earth observation satellites can only cover a certain area. When one scene cannot cover a user's area of interest, two or more scenes are needed to be registered and combined into a single image, and this composition process is referred to as image mosaicking. The key issue in image composition is to decide where to place the seam line in overlapping region. The optimal seam line which joins several scenes of images covered the entire study area, is usually determined by texture and other characteristics of the overlap region for seamless quality. Recently, a morphological image compositing algorithm was proposed which is able to automatically delineate seam lines along salient image structures. And this algorithm uses the ideas of marker-controlled segmentation for image mosaicking and divides the overlap region into a determined number of areas. The resulting seam lines of the morphological image compositing algorithm cut along high gradient regions which are object edges in initial images. However, the morphological compositing method only applied to delineate the invisible seam line to the human eyes based on Landsat ETM+ data which is the representation of medium resolution data. In this paper, we test the validation of the morphological compositing method to generate visually pleasing seam line for image mosaic without changing the image radiometry and feasibility to handle two adjacent scenes simultaneously on high spatial resolution imagery by using ZY-3 multispectral image data. The focus of this paper is developing a quantitative evaluation measure which is usually formulated as the sum of morphological gradient of the image mosaic along the seam line divided by the length of the seam to quantitatively estimate the `quality' of the automatically delineate seam line.

  2. From Satellite Imagery to Peatland Vegetation Diversity: How Reliable Are Habitat Maps?

    Directory of Open Access Journals (Sweden)

    Monique F. Poulin

    2002-12-01

    Full Text Available Although satellite imagery is becoming a basic component of the work of ecologists and conservationists, its potential and reliability are still relatively unknown for a large number of ecosystems. Using Landsat 7/ETM+ (Enhanced Thematic Mapper Plus data, we tested the accuracy of two types of supervised classifications for mapping 13 peatland habitats in southern Quebec, Canada. Before classifying peatland habitats, we applied a mask procedure that revealed 629 peatlands covering a total of 18,103 ha; 26% of them were larger than 20 ha. We applied both a simple maximum likelihood (ML function and a weighted maximum likelihood (WML function that took into account the proportion of each habitat class within each peatland when classifying the habitats on the image. By validating 626 Global Positioning System locations within 92 peatlands, we showed that both classification procedures provided an accurate representation of the 13 peatland habitat classes. For all habitat classes except lawn with pools, the predominant classified habitat within 45 m of the center of the validation location was of the same type as the one observed in the field. There were differences in the performance of the two classification procedures: ML was a better tool for mapping rare habitats, whereas WML favored the most common habitats. Based on ordinations, peatland habitat classes were as effective as environmental variables such as humidity indicators and water chemistry components at explaining the distribution of plant species and performed 1.6 times better when it came to accounting for vegetation structure patterns. Peatland habitats with pools had the most distinct plant assemblages, and the habitats dominated by herbs were moderately distinct from those characterized by ericaceous shrubs. Habitats dominated by herbs were the most variable in terms of plant species assemblages. Because peatlands are economically valuable wetlands, the maps resulting from the new

  3. Quantifying ice loss in the eastern Himalayas since 1974 using declassified spy satellite imagery

    Science.gov (United States)

    Maurer, Joshua M.; Rupper, Summer B.; Schaefer, Joerg M.

    2016-09-01

    Himalayan glaciers are important natural resources and climate indicators for densely populated regions in Asia. Remote sensing methods are vital for evaluating glacier response to changing climate over the vast and rugged Himalayan region, yet many platforms capable of glacier mass balance quantification are somewhat temporally limited due to typical glacier response times. We here rely on declassified spy satellite imagery and ASTER data to quantify surface lowering, ice volume change, and geodetic mass balance during 1974-2006 for glaciers in the eastern Himalayas, centered on the Bhutan-China border. The wide range of glacier types allows for the first mass balance comparison between clean, debris, and lake-terminating (calving) glaciers in the region. Measured glaciers show significant ice loss, with an estimated mean annual geodetic mass balance of -0.13 ± 0.06 m w.e. yr-1 (meters of water equivalent per year) for 10 clean-ice glaciers, -0.19 ± 0.11 m w.e. yr-1 for 5 debris-covered glaciers, -0.28 ± 0.10 m w.e. yr-1 for 6 calving glaciers, and -0.17 ± 0.05 m w.e. yr-1 for all glaciers combined. Contrasting hypsometries along with melt pond, ice cliff, and englacial conduit mechanisms result in statistically similar mass balance values for both clean-ice and debris-covered glacier groups. Calving glaciers comprise 18 % (66 km2) of the glacierized area yet have contributed 30 % (-0.7 km3) to the total ice volume loss, highlighting the growing relevance of proglacial lake formation and associated calving for the future ice mass budget of the Himalayas as the number and size of glacial lakes increase.

  4. Covariance Between Arctic Sea Ice and Clouds Within Atmospheric State Regimes at the Satellite Footprint Level

    Science.gov (United States)

    Taylor, Patrick C.; Kato, Seiji; Xu, Kuan-Man; Cai, Ming

    2015-01-01

    Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and mid-tropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.

  5. Object-based approach to national land cover mapping using HJ satellite imagery

    Science.gov (United States)

    Zhang, Lei; Li, Xiaosong; Yuan, Quanzhi; Liu, Yu

    2014-01-01

    To meet the carbon storage estimate in ecosystems for a national carbon strategy, we introduce a consistent database of China land cover. The Chinese Huan Jing (HJ) satellite is proven efficient in the cloud-free acquisition of seasonal image series in a monsoon region and in vegetation identification for mesoscale land cover mapping. Thirty-eight classes of level II land cover are generated based on the Land Cover Classification System of the United Nations Food and Agriculture Organization that follows a standard and quantitative definition. Twenty-four layers of derivative spectral, environmental, and spatial features compose the classification database. Object-based approach characterizing additional nonspectral features is conducted through mapping, and multiscale segmentations are applied on object boundary match to target real-world conditions. This method sufficiently employs spatial information, in addition to spectral characteristics, to improve classification accuracy. The algorithm of hierarchical classification is employed to follow step-by-step procedures that effectively control classification quality. This algorithm divides the dual structures of universal and local trees. Consistent universal trees suitable to most regions are performed first, followed by local trees that depend on specific features of nine climate stratifications. The independent validation indicates the overall accuracy reaches 86%.

  6. Validation of VIIRS Cloud Base Heights at Night Using Ground and Satellite Measurements over Alaska

    Science.gov (United States)

    NOH, Y. J.; Miller, S. D.; Seaman, C.; Forsythe, J. M.; Brummer, R.; Lindsey, D. T.; Walther, A.; Heidinger, A. K.; Li, Y.

    2016-12-01

    Knowledge of Cloud Base Height (CBH) is critical to describing cloud radiative feedbacks in numerical models and is of practical significance to aviation communities. We have developed a new CBH algorithm constrained by Cloud Top Height (CTH) and Cloud Water Path (CWP) by performing a statistical analysis of A-Train satellite data. It includes an extinction-based method for thin cirrus. In the algorithm, cloud geometric thickness is derived with upstream CTH and CWP input and subtracted from CTH to generate the topmost layer CBH. The CBH information is a key parameter for an improved Cloud Cover/Layers product. The algorithm has been applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP spacecraft. Nighttime cloud optical properties for CWP are retrieved from the nighttime lunar cloud optical and microphysical properties (NLCOMP) algorithm based on a lunar reflectance model for the VIIRS Day/Night Band (DNB) measuring nighttime visible light such as moonlight. The DNB has innovative capabilities to fill the polar winter and nighttime gap of cloud observations which has been an important shortfall from conventional radiometers. The CBH products have been intensively evaluated against CloudSat data. The results showed the new algorithm yields significantly improved performance over the original VIIRS CBH algorithm. However, since CloudSat is now operational during daytime only due to a battery anomaly, the nighttime performance has not been fully assessed. This presentation will show our approach to assess the performance of the CBH algorithm at night. VIIRS CBHs are retrieved over the Alaska region from October 2015 to April 2016 using the Clouds from AVHRR Extended (CLAVR-x) processing system. Ground-based measurements from ceilometer and micropulse lidar at the Atmospheric Radiation Measurement (ARM) site on the North Slope of Alaska are used for the analysis. Local weather conditions are checked using temperature and precipitation

  7. Combining structure-from-motion derived point clouds from satellites and unmanned aircraft systems images with ground-truth data to create high-resolution digital elevation models

    Science.gov (United States)

    Palaseanu, M.; Thatcher, C.; Danielson, J.; Gesch, D. B.; Poppenga, S.; Kottermair, M.; Jalandoni, A.; Carlson, E.

    2016-12-01

    Coastal topographic and bathymetric (topobathymetric) data with high spatial resolution (1-meter or better) and high vertical accuracy are needed to assess the vulnerability of Pacific Islands to climate change impacts, including sea level rise. According to the Intergovernmental Panel on Climate Change reports, low-lying atolls in the Pacific Ocean are extremely vulnerable to king tide events, storm surge, tsunamis, and sea-level rise. The lack of coastal topobathymetric data has been identified as a critical data gap for climate vulnerability and adaptation efforts in the Republic of the Marshall Islands (RMI). For Majuro Atoll, home to the largest city of RMI, the only elevation dataset currently available is the Shuttle Radar Topography Mission data which has a 30-meter spatial resolution and 16-meter vertical accuracy (expressed as linear error at 90%). To generate high-resolution digital elevation models (DEMs) in the RMI, elevation information and photographic imagery have been collected from field surveys using GNSS/total station and unmanned aerial vehicles for Structure-from-Motion (SfM) point cloud generation. Digital Globe WorldView II imagery was processed to create SfM point clouds to fill in gaps in the point cloud derived from the higher resolution UAS photos. The combined point cloud data is filtered and classified to bare-earth and georeferenced using the GNSS data acquired on roads and along survey transects perpendicular to the coast. A total station was used to collect elevation data under tree canopies where heavy vegetation cover blocked the view of GNSS satellites. A subset of the GPS / total station data was set aside for error assessment of the resulting DEM.

  8. Aerosol-Cloud Interactions Over the North Pacific Ocean: an Integrated Assessment Using Aircraft, Satellites and a Global Model

    Science.gov (United States)

    Wilcox, E. M.; Mauger, G.; Lariviere, O.; Roberts, G.; Ramanathan, V.; Ming, Y.

    2004-12-01

    Interactions between aerosols and the cloud systems of the North Pacific Ocean were observed by aircraft during the Cloud Indirect Forcing Experiment (CIFEX) in April 2004. The CIFEX project seeks to determine the impact of aerosol indirect effects on the radiative forcing of highly reflective North Pacific clouds under the influence of aerosols traveling across the ocean basin from Asia. Toward this end, CIFEX aircraft observations from the Northeast Pacific of aerosol and cloud microphysics are blended with coincident satellite observations of cloud properties from MODIS and cloud radiative forcing from CERES. The satellite observations are then compared with global model simulations of aerosol indirect forcing over the entire North Pacific basin. During April 2004 the U. Wyoming King Air research aircraft sampled aerosol and cloud microphysical parameters including aerosol and cloud particle sizes and concentrations, cloud liquid water amounts, and cloud structure using the Wyoming Cloud Radar. A range of clean and polluted conditions were observed by the aircraft during the period, in addition to two major Asian dust storm events. CN concentrations below stratus clouds varied from 25 to 300 cm-3. A variety of cloud systems were sampled as well, ranging from shallow stratus and stratocumulus clouds to mixed-phase precipitating cumulus. Under pristine conditions, many shallow clouds were observed to be drizzling, suggesting that Northern Pacific Ocean cloud systems may be highly susceptible to the influence of aerosols. Clouds in this region are responsible for a large cooling of the ocean surface. The magnitude of shortwave cloud radiative cooling exceeded -80 W m-2 over much of the North Pacific during the experiment. Stratus cloud drop concentrations varied from 25 to 150 cm-3 and are correlated with the concentration of accumulation mode aerosols below cloud base. Mean cloud albedos vary from 0.3 to 0.5 for these same clouds, and MODIS observations of cloud

  9. Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery

    Science.gov (United States)

    Wang, Shifeng; So, Emily; Smith, Pete

    2015-04-01

    Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting tents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81% in this study. Further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as the extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics.

  10. Detection of supercooled liquid water-topped mixed-phase clouds >from shortwave-infrared satellite observations

    Science.gov (United States)

    NOH, Y. J.; Miller, S. D.; Heidinger, A. K.

    2015-12-01

    Many studies have demonstrated the utility of multispectral information from satellite passive radiometers for detecting and retrieving the properties of cloud globally, which conventionally utilizes shortwave- and thermal-infrared bands. However, the satellite-derived cloud information comes mainly from cloud top or represents a vertically integrated property. This can produce a large bias in determining cloud phase characteristics, in particular for mixed-phase clouds which are often observed to have supercooled liquid water at cloud top but a predominantly ice phase residing below. The current satellite retrieval algorithms may report these clouds simply as supercooled liquid without any further information regarding the presence of a sub-cloud-top ice phase. More accurate characterization of these clouds is very important for climate models and aviation applications. In this study, we present a physical basis and preliminary results for the algorithm development of supercooled liquid-topped mixed-phase cloud detection using satellite radiometer observations. The detection algorithm is based on differential absorption properties between liquid and ice particles in the shortwave-infrared bands. Solar reflectance data in narrow bands at 1.6 μm and 2.25 μm are used to optically probe below clouds for distinction between supercooled liquid-topped clouds with and without an underlying mixed phase component. Varying solar/sensor geometry and cloud optical properties are also considered. The spectral band combination utilized for the algorithm is currently available on Suomi NPP Visible/Infrared Imaging Radiometer Suite (VIIRS), Himawari-8 Advanced Himawari Imager (AHI), and the future GOES-R Advance Baseline Imager (ABI). When tested on simulated cloud fields from WRF model and synthetic ABI data, favorable results were shown with reasonable threat scores (0.6-0.8) and false alarm rates (0.1-0.2). An ARM/NSA case study applied to VIIRS data also indicated promising

  11. Evaluation of the shortwave cloud radiative effect over the ocean by use of ship and satellite observations

    Directory of Open Access Journals (Sweden)

    T. Hanschmann

    2012-12-01

    Full Text Available In this study the shortwave cloud radiative effect (SWCRE over ocean calculated by the ECHAM 5 climate model is evaluated for the cloud property input derived from ship based measurements and satellite based estimates and compared to ship based radiation measurements. The ship observations yield cloud fraction, liquid water path from a microwave radiometer, cloud bottom height as well as temperature and humidity profiles from radiosonde ascents. Level-2 products of the Satellite Application Facility on Climate Monitoring (CM~SAF from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI have been used to characterize clouds. Within a closure study six different experiments have been defined to find the optimal set of measurements to calculate downward shortwave radiation (DSR and the SWCRE from the model, and their results have been evaluated under seven different synoptic situations. Four of these experiments are defined to investigate the advantage of including the satellite-based cloud droplet effective radius as additional cloud property. The modeled SWCRE based on satellite retrieved cloud properties has a comparable accuracy to the modeled SWCRE based on ship data. For several cases, an improvement through introducing the satellite-based estimate of effective radius as additional information to the ship based data was found. Due to their different measuring characteristics, however, each dataset shows best results for different atmospheric conditions.

  12. Estimating Carbon STOCK Changes of Mangrove Forests Using Satellite Imagery and Airborne LiDAR Data in the South Sumatra State, Indonesia

    Science.gov (United States)

    Maeda, Y.; Fukushima, A.; Imai, Y.; Tanahashi, Y.; Nakama, E.; Ohta, S.; Kawazoe, K.; Akune, N.

    2016-06-01

    The purposes of this study were 1) to estimate the biomass in the mangrove forests using satellite imagery and airborne LiDAR data, and 2) to estimate the amount of carbon stock changes using biomass estimated. The study area is located in the coastal area of the South Sumatra state, Indonesia. This area is approximately 66,500 ha with mostly flat land features. In this study, the following procedures were carried out: (1) Classification of types of tree species using Satellite imagery in the study area, (2) Development of correlation equations between spatial volume based on LiDAR data and biomass stock based on field survey for each types of tree species, and estimation of total biomass stock and carbon stock using the equation, and (3) Estimation of carbon stock change using Chronological Satellite Imageries. The result showed the biomass and the amount of carbon stock changes can be estimated with high accuracy, by combining the spatial volume based on airborne LiDAR data with the tree species classification based on satellite imagery. Quantitative biomass monitoring is in demand for projects related to REDD+ in developing countries, and this study showed that combining airborne LiDAR data with satellite imagery is one of the effective methods of monitoring for REDD+ projects.

  13. ESTIMATING CARBON STOCK CHANGES OF MANGROVE FORESTS USING SATELLITE IMAGERY AND AIRBORNE LiDAR DATA IN THE SOUTH SUMATRA STATE, INDONESIA

    Directory of Open Access Journals (Sweden)

    Y. Maeda

    2016-06-01

    Full Text Available The purposes of this study were 1 to estimate the biomass in the mangrove forests using satellite imagery and airborne LiDAR data, and 2 to estimate the amount of carbon stock changes using biomass estimated. The study area is located in the coastal area of the South Sumatra state, Indonesia. This area is approximately 66,500 ha with mostly flat land features. In this study, the following procedures were carried out: (1 Classification of types of tree species using Satellite imagery in the study area, (2 Development of correlation equations between spatial volume based on LiDAR data and biomass stock based on field survey for each types of tree species, and estimation of total biomass stock and carbon stock using the equation, and (3 Estimation of carbon stock change using Chronological Satellite Imageries. The result showed the biomass and the amount of carbon stock changes can be estimated with high accuracy, by combining the spatial volume based on airborne LiDAR data with the tree species classification based on satellite imagery. Quantitative biomass monitoring is in demand for projects related to REDD+ in developing countries, and this study showed that combining airborne LiDAR data with satellite imagery is one of the effective methods of monitoring for REDD+ projects.

  14. Probing orographic controls in the Himalayas during the monsoon using satellite imagery

    Directory of Open Access Journals (Sweden)

    A. P. Barros

    2004-01-01

    Full Text Available The linkages between the space-time variability of observed clouds, rainfall, large-circulation patterns and topography in northern India and the Himalayas were investigated using remote sensing data. The research purpose was to test the hypothesis that cloudiness patterns are dynamic tracers of rainstorms, and therefore their temporal and spatial evolution can be used as a proxy of the spatial and temporal organization of precipitation and precipitation processes in the Himalayan range during the monsoon. The results suggest that the space-time distribution of precipitation, the spatial variability of the diurnal cycle of convective activity, and the terrain (landform and altitudinal gradients are intertwined at spatial scales ranging from the order of a few kms (1–5km up to the continental-scale. Furthermore, this relationship is equally strong in the time domain with respect to the onset and intra-seasonal variability of the monsoon. Infrared and microwave imagery of cloud fields were analyzed to characterize the spatial and temporal evolution of mesoscale convective weather systems and short-lived convection in Northern India, the Himalayan range, and in the Tibetan Plateau during three monsoon seasons (1999, 2000 and 2001. The life cycle of convective systems suggests landform and orographic controls consistent with a convergence zone constrained to the valley of the Ganges and the Himalayan range, bounded in the west by the Aravalli range and the Garhwal mountains and in the East by the Khasi Hills and the Bay of Bengal, which we call the Northern India Convergence Zone (NICZ. The NICZ exhibits strong night-time activity along the south-facing slopes of the Himalayan range, which is characterized by the development of short-lived convection (1–3h aligned with protruding ridges between 1:00 and 3:00 AM. The intra-annual and inter-annual variability of convective activity in the NICZ were assessed with respect to large-scale synoptic

  15. CLASSIFIER FUSION OF HIGH-RESOLUTION OPTICAL AND SYNTHETIC APERTURE RADAR (SAR SATELLITE IMAGERY FOR CLASSIFICATION IN URBAN AREA

    Directory of Open Access Journals (Sweden)

    T. Alipour Fard

    2014-10-01

    Full Text Available This study concerned with fusion of synthetic aperture radar and optical satellite imagery. Due to the difference in the underlying sensor technology, data from synthetic aperture radar (SAR and optical sensors refer to different properties of the observed scene and it is believed that when they are fused together, they complement each other to improve the performance of a particular application. In this paper, two category of features are generate and six classifier fusion operators implemented and evaluated. Implementation results show significant improvement in the classification accuracy.

  16. Measuring snow cover using satellite imagery during 1973 and 1974 melt season: North Santiam, Boise, and Upper Snake Basins, phase 1. [LANDSAT satellites, imaging techniques

    Science.gov (United States)

    Wiegman, E. J.; Evans, W. E.; Hadfield, R.

    1975-01-01

    Measurements are examined of snow coverage during the snow-melt season in 1973 and 1974 from LANDSAT imagery for the three Columbia River Subbasins. Satellite derived snow cover inventories for the three test basins were obtained as an alternative to inventories performed with the current operational practice of using small aircraft flights over selected snow fields. The accuracy and precision versus cost for several different interactive image analysis procedures was investigated using a display device, the Electronic Satellite Image Analysis Console. Single-band radiance thresholding was the principal technique employed in the snow detection, although this technique was supplemented by an editing procedure involving reference to hand-generated elevation contours. For each data and view measured, a binary thematic map or "mask" depicting the snow cover was generated by a combination of objective and subjective procedures. Photographs of data analysis equipment (displays) are shown.

  17. Ice Cloud Backscatter Study and Comparison with CALIPSO and MODIS Satellite Data

    Science.gov (United States)

    Ding, Jiachen; Yang, Ping; Holz, Robert E.; Platnick, Steven; Meyer, Kerry G.; Vaughan, Mark A.; Hu, Yongxiang; King, Michael D.

    2016-01-01

    An invariant imbedding T-matrix (II-TM) method is used to calculate the single-scattering properties of 8-column aggregate ice crystals. The II-TM based backscatter values are compared with those calculated by the improved geometric-optics method (IGOM) to refine the backscattering properties of the ice cloud radiative model used in the MODIS Collection 6 cloud optical property product. The integrated attenuated backscatter-to-cloud optical depth (IAB-ICOD) relation is derived from simulations using a CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite) lidar simulator based on a Monte Carlo radiative transfer model. By comparing the simulation results and co-located CALIPSO and MODIS (Moderate Resolution Imaging Spectroradiometer) observations, the non-uniform zonal distribution of ice clouds over ocean is characterized in terms of a mixture of smooth and rough ice particles. The percentage of the smooth particles is approximately 6 percent and 9 percent for tropical and mid-latitude ice clouds, respectively.

  18. Spatial and Temporal Variability of Satellite-Derived Cloud and Surface Characteristics During FIRE-ACE

    Science.gov (United States)

    Maslanik, J. A.; Key, J.; Fowler, C. W.; Nguyen, T.; Wang, X.a

    2000-01-01

    Advanced very high resolution radiometer (AVHRR) products calculated for the western Arctic for April-July 1998 are used to investigate spatial, temporal, and regional patterns and variability in energy budget parameters associated with ocean- ice-atmosphere interactions over the Arctic Ocean during the Surface Heat Budget of the Arctic Ocean (SHEBA) project and the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment - Arctic Cloud Experiment (FIRE-ACE). The AVHRR-derived parameters include cloud fraction, clear-sky and all-sky skin temperature and broadband albedo, upwelling and downwelling shortwave and longwave radiation, cloud top pressure and temperature, and cloud optical depth. The remotely sensed products generally agree well with field observations at the SHEBA site, which in turn is shown to be representative of a surrounding region comparable in size to a climate-model grid cell. Time series of products for other locations in the western Arctic illustrate the magnitude of spatial variability during the study period and provide spatial and temporal detail useful for studying regional processes. The data illustrate the progression of reduction in cloud cover, albedo decrease, and the considerable heating of the open ocean associated with the anomalous decrease in sea ice cover in the eastern Beaufort Sea that began in late spring. Above-freezing temperatures are also recorded within the ice pack, suggesting warming of the open water areas within the ice cover.

  19. Satellite Cloud Assimilation in the Weather Research & Forecasting (WRF) Model and its Impact on Air Quality Simulations

    Science.gov (United States)

    Pour Biazar, Arastoo; White, Andrew; McNider, Richard; Khan, Maudood; Dornblaser, Bright; Wu, Yuling

    2017-04-01

    Clouds have a significant role in air quality simulations as they modulate biogenic hydrocarbon emissions and photolysis rates, impact boundary-layer development, lead to deep vertical mixing of pollutants and precursors, and induce aqueous phase chemistry. Unfortunately, numerical meteorological models still have difficulty in creating clouds in the right place and time compared to observed clouds. This is especially the case when synoptic-scale forcing is weak, as often is the case during air pollution episodes in the Southeast United States. Thus, poor representation of clouds impacts the photochemical model's ability in simulating the air quality. However, since satellites provide the best observational platform for defining the formation and location of clouds, satellite observations can be of great value in retrospective simulations. Here, we present results from a recent activity in which the Geostationary Operational Environmental Satellite (GOES) derived cloud fields are assimilated within Weather Research and Forecasting (WRF) model to improve simulated clouds. The assimilation technique dynamically support cloud formation/dissipation within WRF based on GOES observations. The technique uses observations to identify model cloud errors, estimates a target vertical velocity and moisture to create/remove clouds, and adjust the flow field accordingly. The technique was implemented and tested in WRF for a month-long simulation during August 2006, and was tested in an air quality simulation over the period of August-September 2013 (NASA's Discover-AQ field campaign). The cloud assimilation on the average improved model cloud simulation by 15%. The cloud correction not only improved the spatial and temporal distribution of clouds, it also improved boundary layer temperature, humidity, and wind speed. These improvements in meteorological fields directly impacted the air quality simulations and altered trace gas concentrations. For air quality simulations, WRF

  20. Study of time-lapse processing for dynamic hydrologic conditions. [electronic satellite image analysis console for Earth Resources Technology Satellites imagery

    Science.gov (United States)

    Serebreny, S. M.; Evans, W. E.; Wiegman, E. J.

    1974-01-01

    The usefulness of dynamic display techniques in exploiting the repetitive nature of ERTS imagery was investigated. A specially designed Electronic Satellite Image Analysis Console (ESIAC) was developed and employed to process data for seven ERTS principal investigators studying dynamic hydrological conditions for diverse applications. These applications include measurement of snowfield extent and sediment plumes from estuary discharge, Playa Lake inventory, and monitoring of phreatophyte and other vegetation changes. The ESIAC provides facilities for storing registered image sequences in a magnetic video disc memory for subsequent recall, enhancement, and animated display in monochrome or color. The most unique feature of the system is the capability to time lapse the imagery and analytic displays of the imagery. Data products included quantitative measurements of distances and areas, binary thematic maps based on monospectral or multispectral decisions, radiance profiles, and movie loops. Applications of animation for uses other than creating time-lapse sequences are identified. Input to the ESIAC can be either digital or via photographic transparencies.

  1. Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

    Science.gov (United States)

    Champion, Nicolas

    2016-06-01

    Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pl

  2. Vectorized Shoreline of Guam, Derived from IKONOS Satellite Imagery, 2000 through 2003

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping...

  3. Derived bathymetry from WorldView-2 satellite imagery of nearshore benthic habitats

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Methods used were adapted from a "cookbook" of instructions developed by Kyle Hogref for using IKONOS imagery data to derive seafloor elevations in optically clear...

  4. Vectorized Shoreline of Guam, Derived from IKONOS Satellite Imagery, 2000 through 2003

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping...

  5. Influence of 3D Radiative Effects on Satellite Retrievals of Cloud Properties

    Science.gov (United States)

    Varnai, Tamas; Marshak, Alexander; Einaudi, Franco (Technical Monitor)

    2001-01-01

    When cloud properties are retrieved from satellite observations, the calculations apply 1D theory to the 3D world: they only consider vertical structures and ignore horizontal cloud variability. This presentation discusses how big the resulting errors can be in the operational retrievals of cloud optical thickness. A new technique was developed to estimate the magnitude of potential errors by analyzing the spatial patterns of visible and infrared images. The proposed technique was used to set error bars for optical depths retrieved from new MODIS measurements. Initial results indicate that the 1 km resolution retrievals are subject to abundant uncertainties. Averaging over 50 by 50 km areas reduces the errors, but does not remove them completely; even in the relatively simple case of high sun (30 degree zenith angle), about a fifth of the examined areas had biases larger than ten percent. As expected, errors increase substantially for more oblique illumination.

  6. CHASER: An Innovative Satellite Mission Concept to Measure the Effects of Aerosols on Clouds and Climate

    Science.gov (United States)

    Renno, N.; Williams, E.; Rosenfeld, D.; Fischer, D.; Fischer, J.; Kremic, T.; Agrawal, A.; Andreae, M.; Bierbaum, R.; Blakeslee, R.; Boerner, A.; Bowles, N.; Christian, H.; Dunion, J.; Horvath, A.; Huang, X.; Khain, A.; Kinne, S.; Lemos, M.-C.; Penner, J.

    2012-04-01

    The formation of cloud droplets on aerosol particles, technically known as the activation of cloud condensation nuclei (CCN), is the fundamental process driving the interactions of aerosols with clouds and precipitation. Knowledge of these interactions is foundational to our understanding of weather and climate. The Intergovernmental Panel on Climate Change (IPCC) and the Decadal Survey (NRC 2007) indicate that the uncertainty in how clouds adjust to aerosol perturbations dominates the uncertainty in the overall quantification of the radiative forcing attributable to human activities. The Clouds, Hazards, and Aerosols Survey for Earth Researchers (CHASER) mission concept responds to the IPCC and Decadal Survey concerns by studying the activation of CCN and their interactions with clouds and storms. CHASER proposes to revolutionize our understanding of the interactions of aerosols with clouds by making the first global measurements of the fundamental physical entity linking them: activated cloud condensation nuclei. The CHASER mission was conceptualized to measure all quantities necessary for determining the interactions of aerosols with clouds and storms. Measurements by current satellites allow the determination of crude profiles of cloud particle size but not of the activated CCN that seed them. CHASER uses a new technique (Freud et al. 2011; Rosenfeld et al. 2012) and high-heritage instruments to produce the first global maps of activated CCN and the properties of the clouds associated with them. CHASER measures the CCN concentration and cloud thermodynamic forcing simultaneously, allowing their effects to be distinguished. Changes in the behavior of a group of weather systems in which only one of the quantities varies (a partial derivative of the intensity with the desirable quantity) allow the determination of each effect statistically. The high uncertainties of current climate predictions limit their much-needed use in decision-making. CHASER mitigates this

  7. Multi-decadal record of ice dynamics on Daugaard Jensen Gletscher, East Greenland, from satellite imagery and terrestrial measurements

    DEFF Research Database (Denmark)

    Stearns, L.A.; Hamilton, G.S.; Reeh, Niels

    2005-01-01

    The history of ice velocity and calving front position of Daugaard Jensen Gletscher, a large outlet glacier in East Greenland, is reconstructed from field measurements, aerial photography and satellite imagery for the period 1950-2001. The calving terminus of the glacier has remained...... in approximately the same position over the past similar to 50 years. There is no evidence of a change in ice motion between 1968 and 2001, based on a comparison of velocities derived from terrestrial surveying and feature tracking using sequential satellite images. Estimates of flux near the entrance to the fjord...... vs snow accumulation in the interior catchment show that Daugaard Jensen Gletscher has a small negative mass balance. This result is consistent with other mass-balance estimates for the inland region of the glacier....

  8. Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012

    Directory of Open Access Journals (Sweden)

    Jihye Ahn

    2014-06-01

    Full Text Available Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to achieve better predictions by incorporating techniques that can deal with temporal variation of the relationships between sea ice concentration and climate factors. In this paper, we describe the statistical approaches by ordinary least squares (OLS regression and a time-series method for modeling sea ice concentration using satellite imagery and climate reanalysis data for the Barents and Kara Seas during 1979–2012. The OLS regression model could summarize the overall climatological characteristics in the relationships between sea ice concentration and climate variables. We also introduced autoregressive integrated moving average (ARIMA models because the sea ice concentration is such a long-range dataset that the relationships may not be explained by a single equation of the OLS regression. Temporally varying relationships between sea ice concentration and the climate factors such as skin temperature, sea surface temperature, total column liquid water, total column water vapor, instantaneous moisture flux, and low cloud cover were modeled by the ARIMA method, which considerably improved the prediction accuracies. Our method may also be worth consideration when forecasting future sea ice concentration by using the climate data provided by general circulation models (GCM.

  9. Summit-to-sea mapping and change detection using satellite imagery: tools for conservation and management of coral reefs.

    Science.gov (United States)

    Shapiro, A C; Rohmann, S O

    2005-05-01

    Continuous summit-to-sea maps showing both land features and shallow-water coral reefs have been completed in Puerto Rico and the U.S. Virgin Islands, using circa 2000 Landsat 7 Enhanced Thematic Mapper (ETM+) Imagery. Continuous land/sea terrain was mapped by merging Digital Elevation Models (DEM) with satellite-derived bathymetry. Benthic habitat characterizations were created by unsupervised classifications of Landsat imagery clustered using field data, and produced maps with an estimated overall accuracy of>75% (Tau coefficient >0.65). These were merged with Geocover-LC (land use/land cover) data to create continuous land/ sea cover maps. Image pairs from different dates were analyzed using Principle Components Analysis (PCA) in order to detect areas of change in the marine environment over two different time intervals: 2000 to 2001, and 1991 to 2003. This activity demonstrates the capabilities of Landsat imagery to produce continuous summit-to-sea maps, as well as detect certain changes in the shallow-water marine environment, providing a valuable tool for efficient coastal zone monitoring and effective management and conservation.

  10. Spatio-Temporal Analysis of Urban Heat Island and Urban Metabolism by Satellite Imagery over the Phoenix Metropolitan Area

    Science.gov (United States)

    Zhao, Q.; Zhan, S.; Kuai, X.; Zhan, Q.

    2015-12-01

    The goal of this research is to combine DMSP-OLS nighttime light data with Landsat imagery and use spatio-temporal analysis methods to evaluate the relationships between urbanization processes and temperature variation in Phoenix metropolitan area. The urbanization process is a combination of both land use change within the existing urban environment as well as urban sprawl that enlarges the urban area through the transformation of rural areas to urban structures. These transformations modify the overall urban climate environment, resulting in higher nighttime temperatures in urban areas compared to the surrounding rural environment. This is a well-known and well-studied phenomenon referred to as the urban heat island effect (UHI). What is unknown is the direct relationship between the urbanization process and the mechanisms of the UHI. To better understand this interaction, this research focuses on using nighttime light satellite imagery to delineate and detect urban extent changes and utilizing existing land use/land cover map or newly classified imagery from Landsat to analyze the internal urban land use variations. These data are combined with summer and winter land surface temperature data extracted from Landsat. We developed a time series of these combined data for Phoenix, AZ from 1992 to 2013 to analyze the relationships among land use change, land surface temperature and urban growth.

  11. 3D high resolution tracking of ice flow using mutli-temporal stereo satellite imagery, Franz Josef Glacier, New Zealand

    Science.gov (United States)

    Leprince, S.; Lin, J.; Ayoub, F.; Herman, F.; Avouac, J.

    2013-12-01

    We present the latest capabilities added to the Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) software, which aim at analyzing time-series of stereoscopic imagery to document 3D variations of the ground surface. We review the processing chain and present the new and improved modules for satellite pushbroom imagery, in particular the N-image bundle block adjustment to jointly optimize the viewing geometry of multiple acquisitions, the improved multi-scale image matching based on Semi-Global Matching (SGM) to extract high resolution topography, and the triangulation of multi-temporal disparity maps to derive 3D ground motion. In particular, processes are optimized to run on a cluster computing environment. This new suite of algorithms is applied to the study of Worldview stereo imagery above the Franz Josef, Fox, and Tasman Glaciers, New Zealand, acquired on 01/30/2013, 02/09/2013, and 02/28/2013. We derive high resolution (1m post-spacing) maps of ice flow in three dimensions, where ice velocities of up to 4 m/day are recorded. Images were collected in early summer during a dry and sunny period, which followed two weeks of unsettled weather with several heavy rainfall events across the Southern Alps. The 3D tracking of ice flow highlights the surface response of the glaciers to changes in effective pressure at the ice-bedrock interface due to heavy rainfall, at an unprecedented spatial resolution.

  12. A radiation closure study of Arctic stratus cloud microphysical properties using the collocated satellite-surface data and Fu-Liou radiative transfer model

    Science.gov (United States)

    Dong, Xiquan; Xi, Baike; Qiu, Shaoyue; Minnis, Patrick; Sun-Mack, Sunny; Rose, Fred

    2016-09-01

    Retrievals of cloud microphysical properties based on passive satellite imagery are especially difficult over snow-covered surfaces because of the bright and cold surface. To help quantify their uncertainties, single-layered overcast liquid-phase Arctic stratus cloud microphysical properties retrieved by using the Clouds and the Earth's Radiant Energy System Edition 2 and Edition 4 (CERES Ed2 and Ed4) algorithms are compared with ground-based retrievals at the Atmospheric Radiation Measurement North Slope of Alaska (ARM NSA) site at Barrow, AK, during the period from March 2000 to December 2006. A total of 206 and 140 snow-free cases (Rsfc ≤ 0.3), and 108 and 106 snow cases (Rsfc > 0.3), respectively, were selected from Terra and Aqua satellite passes over the ARM NSA site. The CERES Ed4 and Ed2 optical depth (τ) and liquid water path (LWP) retrievals from both Terra and Aqua are almost identical and have excellent agreement with ARM retrievals under snow-free and snow conditions. In order to reach a radiation closure study for both the surface and top of atmosphere (TOA) radiation budgets, the ARM precision spectral pyranometer-measured surface albedos were adjusted (63.6% and 80% of the ARM surface albedos for snow-free and snow cases, respectively) to account for the water and land components of the domain of 30 km × 30 km. Most of the radiative transfer model calculated SW↓sfc and SW↑TOA fluxes by using ARM and CERES cloud retrievals and the domain mean albedos as input agree with the ARM and CERES flux observations within 10 W m-2 for both snow-free and snow conditions. Sensitivity studies show that the ARM LWP and re retrievals are less dependent on solar zenith angle (SZA), but all retrieved optical depths increase with SZA.

  13. Ship and satellite observations over the ocean for verification of the shortwave cloud radiative effect in climate models

    Directory of Open Access Journals (Sweden)

    T. Hanschmann

    2012-07-01

    Full Text Available In this study the accuracy of the radiative transfer scheme of the ECHAM-5 climate model for reproducing the shortwave cloud radiative effect (SWCRE at the sea surface has been investigated. A characterization of both the observed state of the atmosphere and the surface radiation budget from ship and satellite is used for this purpose. The ship observations yield cloud fraction, liquid water path from a microwave radiometer, cloud bottom height as well as temperature and humidity profiles from radiosonde ascents. Level-2 products of the Satellite Application Facility on Climate Monitoring (CM-SAF from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI have been used to characterize clouds. Within a closure study six different experiments have been defined to find the optimal set of measurements to calculate downward shortwave radiation (DSR and the SWCRE from the model, and their results have been evaluated under seven different synoptic situations. Four of these experiments are defined to investigate the advantage of including the satellite-based cloud droplet effective radius as additional cloud property. The modeled SWCRE based on satellite retrieved cloud properties has a comparable accuracy to the modeled SWCRE based on ship data. For several cases, an improvement through introducing the satellite-based estimate of effective radius as additional information to the ship based data was found. Due to their different measuring characteristics, however, each dataset shows best results for different atmospheric conditions.

  14. Remote Estimation of Greenland Ice Sheet Supraglacial River Discharge using GIS Modeling and WorldView-2 Satellite Imagery

    Science.gov (United States)

    Chu, V. W.; Smith, L. C.; Yang, K.; Gleason, C. J.; Rennermalm, A. K.; Pitcher, L. H.; Legleiter, C. J.; Forster, R. R.

    2014-12-01

    Increasing surface melting on the Greenland ice sheet and rising sea level have heightened the need for understanding the complex pathways transporting meltwater from the ice sheet surface to the ice edge and the ocean. Satellite images show supraglacial rivers abundantly covering the western ablation zone throughout the melt season, transporting large volumes of meltwater into moulins and to the ice edge, yet these rivers remain poorly studied. Here, a GIS modeling framework is developed to estimate supraglacial river discharge by spatially adapting Manning's equation for use with remotely sensed imagery and is applied to supraglacial rivers on the Greenland Ice Sheet. This framework incorporates high-resolution visible/near-infrared WorldView-2 (WV2) satellite imagery, the Greenland Ice Mapping Project (GIMP) DEM, and a field-calibrated WV2 river bathymetry retrieval algorithm and channel roughness parameter. Orthogonal cross-sections are simulated along river centerlines to extract cross-sectional discharge using Manning's equation for open channel flow. A total of 1,629,502 reach-averaged points were retrieved over 465 river networks of western Greenland in 2012, including attributes of width, depth, velocity, slope, wetted perimeter, hydraulic radius, and discharge. This work provides a method for producing spatially extensive, high-resolution estimates of supraglacial meltwater flux in river networks and into the ice sheet.

  15. Extracting impervious surfaces from multi-source satellite imagery based on unified conceptual model by decision tree algorithm

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Extraction of impervious surfaces is one of the necessary processes in urban change detection.This paper derived a unified conceptual model (UCM) from the vegetation-impervious surface-soil (VIS) model to make the extraction more effective and accurate.UCM uses the decision tree algorithm with indices of spectrum and texture,etc.In this model,we found both dependent and independent indices for multi-source satellite imagery according to their similarity and dissimilarity.The purpose of the indices is to remove the other land-use and land-cover types (e.g.,vegetation and soil) from the imagery,and delineate the impervious surfaces as the result.UCM has the same steps conducted by decision tree algorithm.The Landsat-5 TM image (30 m) and the Satellite Probatoire d’Observation de la Terre (SPOT-4) image (20 m) from Chaoyang District (Beijing) in 2007 were used in this paper.The results show that the overall accuracy in Landsat-5 TM image is 88%,while 86.75% in SPOT-4 image.It is an appropriate method to meet the demand of urban change detection.

  16. Satellite geological and geophysical remote sensing of Iceland: Preliminary results from analysis of MSS imagery

    Science.gov (United States)

    Williams, R. S., Jr.; Boedvarsson, A.; Fridriksson, S.; Palmason, G.; Rist, S.; Sigtryggsson, H.; Thorarinsson, S.; Thorsteinsson, I.

    1973-01-01

    A binational, multidisciplinary research effort in Iceland is directed at an analysis of MSS imagery from ERTS-1 to study a variety of geologic, hydrologic, oceanographic, and agricultural phenomena. A preliminary evaluation of available MSS imagery of Iceland has yielded several significant results - some of which may have direct importance to the Icelandic economy. Initial findings can be summarized as follows: (1) recent lava flows can be delineated from older flows at Askja and Hekla; (2) MSS imagery from ERTS-1 and VHRR visible and infrared imagery from NOAA-2 recorded the vocanic eruption on Heimaey, Vestmann Islands; (3) coastline changes, particularly changes in the position of bars and beaches along the south coast are mappable; and (4) areas covered with new and residual snow can be mapped, and the appearance of newly fallen snow on ERTS-1, MSS band 7 appears dark where it is melting. ERTS-1 imagery provides a means of updating various types of maps of Iceland and will permit the compilation of special maps specifically aimed at those dynamic environmental phenomena which impact on the Icelandic economy.

  17. Study on the volcanic ash cloud with Feng Yun-3 meteorological satellite data

    Science.gov (United States)

    Gong, Cai-lan T.; Jiang, Shan; Hu, Yong; Meng, Peng

    2013-09-01

    Volcano eruption can produce a mass of volcanic ash floating in the air for a long period, which will seriously threaten the aerial planes safety, and cause the air pollution, it could do harm to people's living environment and their health. Take the Iceland Eyjafjallajokull volcano as an example which erupted in April to May 2010, the volcano ash cloud were derived with the visible and infrared scanning radiometer of FengYun-3(FY-3 VIRR) meteorological satellite data. The medium wave infrared (MWIR) and the thermal infrared split windows (THIR-SW) data were used separately. the MODIS THIR-SW data were also be used to retrieve ash cloud to test the results derived from FY-3 VIRR data. It showed that the MWIR was more applicable for the ash cloud retrieving than the THIR-SW with FY-3 VIRR data, and the threshold value should be adjusted to around negative 1 rather than 0 for VIRR THIR-SW data. And the threshold should be adjusted with the THIR-SW of FY-3. The ash cloud radiation and bright temperature(BT), spatial distribution characteristics were also analyzed quantitatively with the two channels data. The study could provide parameters for the prediction of volcanic ash cloud dispersion simulate. When the real temperature of lava flow were high enough, the sensor will show a false bright temperature, how to retrieve the real temperature of the higher lava flow is a problem need to be studied in the future.

  18. Global Cloud Detection and Distribution with Night Time using Satellite Infrared Data

    Science.gov (United States)

    Kadosaki, G.; Yamanouchi, T.; Hirasawa, N.

    2007-12-01

    Knowledge of the current climate system is necessary to clearly estimate large-scale global warming and abnormal weather in the future. Net radiation is one of the main factors that influence a climate system. The earth, which is covered by cloud of dozens of surface giving it a high albedo, reflects a large part of solar radiation. In addition, during nights, when the earth's radiation increases, the earth acts as a radiator. There is no doubt that clouds are closely related to the radiation balance. Satellite data analysis is the most useful method to understand cloud climatology. The targets are to establish an algorithm to detect clouds for night term of the earth, and to get to know more about global cloud distribution with night term. Brightness temperature difference of split window channels is used in this method. We decided three thresholds which have some slopes are used in the case of over land, open sea, and snow or ice surface including sea ice, respectively. We examined on some sensors which has difference response function in itself plat home, GLI/ADEOS2, AVHRR/NOAA, MODIS/Terra and Aqua.

  19. A method for cloud detection and opacity classification based on ground based sky imagery

    Directory of Open Access Journals (Sweden)

    M. S. Ghonima

    2012-07-01

    Full Text Available Digital images of the sky obtained using a total sky imager (TSI are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR to the RBR of a clear sky library (CSL generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

  20. A method for cloud detection and opacity classification based on ground based sky imagery

    Directory of Open Access Journals (Sweden)

    M. S. Ghonima

    2012-11-01

    Full Text Available Digital images of the sky obtained using a total sky imager (TSI are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR to the RBR of a clear sky library (CSL generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

  1. Using satellite-derived optical thickness to assess the influence of clouds on terrestrial carbon uptake

    Science.gov (United States)

    Cheng, S. J.; Steiner, A. L.; Hollinger, D. Y.; Bohrer, G.; Nadelhoffer, K. J.

    2016-07-01

    Clouds scatter direct solar radiation, generating diffuse radiation and altering the ratio of direct to diffuse light. If diffuse light increases plant canopy CO2 uptake, clouds may indirectly influence climate by altering the terrestrial carbon cycle. However, past research primarily uses proxies or qualitative categories of clouds to connect the effect of diffuse light on CO2 uptake to sky conditions. We mechanistically link and quantify effects of cloud optical thickness (τc) to surface light and plant canopy CO2 uptake by comparing satellite retrievals of τc to ground-based measurements of diffuse and total photosynthetically active radiation (PAR; 400-700 nm) and gross primary production (GPP) in forests and croplands. Overall, total PAR decreased with τc, while diffuse PAR increased until an average τc of 6.8 and decreased with larger τc. When diffuse PAR increased with τc, 7-24% of variation in diffuse PAR was explained by τc. Light-use efficiency (LUE) in this range increased 0.001-0.002 per unit increase in τc. Although τc explained 10-20% of the variation in LUE, there was no significant relationship between τc and GPP (p > 0.05) when diffuse PAR increased. We conclude that diffuse PAR increases under a narrow range of optically thin clouds and the dominant effect of clouds is to reduce total plant-available PAR. This decrease in total PAR offsets the increase in LUE under increasing diffuse PAR, providing evidence that changes within this range of low cloud optical thickness are unlikely to alter the magnitude of terrestrial CO2 fluxes.

  2. Satellite and Surface Data Synergy for Developing a 3D Cloud Structure and Properties Characterization Over the ARM SGP. Stage 1: Cloud Amounts, Optical Depths, and Cloud Heights Reconciliation

    Science.gov (United States)

    Genkova, I.; Long, C. N.; Heck, P. W.; Minnis, P.

    2003-01-01

    One of the primary Atmospheric Radiation Measurement (ARM) Program objectives is to obtain measurements applicable to the development of models for better understanding of radiative processes in the atmosphere. We address this goal by building a three-dimensional (3D) characterization of the cloud structure and properties over the ARM Southern Great Plains (SGP). We take the approach of juxtaposing the cloud properties as retrieved from independent satellite and ground-based retrievals, and looking at the statistics of the cloud field properties. Once these retrievals are well understood, they will be used to populate the 3D characterization database. As a first step we determine the relationship between surface fractional sky cover and satellite viewing angle dependent cloud fraction (CF). We elaborate on the agreement intercomparing optical depth (OD) datasets from satellite and ground using available retrieval algorithms with relation to the CF, cloud height, multi-layer cloud presence, and solar zenith angle (SZA). For the SGP Central Facility, where output from the active remote sensing cloud layer (ARSCL) valueadded product (VAP) is available, we study the uncertainty of satellite estimated cloud heights and evaluate the impact of this uncertainty for radiative studies.

  3. Cloud Masking and Surface Temperature Distribution in the Polar Regions Using AVHRR and other Satellite Data

    Science.gov (United States)

    Comiso, Joey C.

    1995-01-01

    Surface temperature is one of the key variables associated with weather and climate. Accurate measurements of surface air temperatures are routinely made in meteorological stations around the world. Also, satellite data have been used to produce synoptic global temperature distributions. However, not much attention has been paid on temperature distributions in the polar regions. In the polar regions, the number of stations is very sparse. Because of adverse weather conditions and general inaccessibility, surface field measurements are also limited. Furthermore, accurate retrievals from satellite data in the region have been difficult to make because of persistent cloudiness and ambiguities in the discrimination of clouds from snow or ice. Surface temperature observations are required in the polar regions for air-sea-ice interaction studies, especially in the calculation of heat, salinity, and humidity fluxes. They are also useful in identifying areas of melt or meltponding within the sea ice pack and the ice sheets and in the calculation of emissivities of these surfaces. Moreover, the polar regions are unique in that they are the sites of temperature extremes, the location of which is difficult to identify without a global monitoring system. Furthermore, the regions may provide an early signal to a potential climate change because such signal is expected to be amplified in the region due to feedback effects. In cloud free areas, the thermal channels from infrared systems provide surface temperatures at relatively good accuracies. Previous capabilities include the use of the Temperature Humidity Infrared Radiometer (THIR) onboard the Nimbus-7 satellite which was launched in 1978. Current capabilities include the use of the Advance Very High Resolution Radiometer (AVHRR) aboard NOAA satellites. Together, these two systems cover a span of 16 years of thermal infrared data. Techniques for retrieving surface temperatures with these sensors in the polar regions have

  4. A Satellite Survey of Cloud Cover and Water Vapor in the Southwestern USA and Northern Mexico

    Science.gov (United States)

    Carrasco, E.; Avila, R.; Erasmus, A.; Djorgovski, S. G.; Walker, A. R.; Blum, R.

    2017-03-01

    Cloud cover and water vapor conditions in the southwestern USA and northern Mexico were surveyed as a preparatory work for the Thirty Meter Telescope (TMT) in situ site testing program. Although the telescope site is already selected, the TMT site testing team decided to make public these results for its usefulness for the community. Using 58 months of meteorological satellite observations between 1993 July and 1999 September, different atmospheric parameters were quantified from data of the 10.7 μm and of 6.7 μm windows. In particular, cloud cover and water vapor conditions were identified in preferred areas. As a result of the aerial analysis, 15 sites of existing and potential telescope were selected, compared, and ranked in terms of their observing quality. The clearest sites are located along the spine of the Baja peninsula and into southern California on mountain peaks above the temperature inversion layer. A steep gradient of cloudiness was observed along the coast where coastal cloud and fog are trapped below the inversion layer. Moving from west to east over the continent, a significant increase in cloudiness was observed. The analysis shows that San Pedro Mártir, San Gorgonio Mountain and San Jacinto Peak have the largest fraction of clear sky conditions (∼74%). The site with the optimal combination of clear skies and low precipitable water vapor is Boundary Peak, Nevada. An approach based in satellite data provided a reliable method for sites comparison.

  5. Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

    Directory of Open Access Journals (Sweden)

    U. Amato

    2014-06-01

    Full Text Available We introduce a classification method (Cumulative Discriminant Analysis of the Discriminant Analysis type to discriminate between cloudy and clear sky satellite observations in the thermal infrared. The tool is intended for the high spectral resolution infrared sounder (IRS planned for the geostationary METEOSAT (Meteorological Satellite Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer data as a proxy. The Cumulative Discriminant Analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A Principal Component Analysis prior step is also introduced which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer and SEVIRI (Spinning Enhanced Visible and Infrared Imager imagers. The agreement with these independent cloud masks is generally well above 80%, except at high latitudes in their winter seasons.

  6. Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

    Science.gov (United States)

    Amato, U.; Lavanant, L.; Liuzzi, G.; Masiello, G.; Serio, C.; Stuhlmann, R.; Tjemkes, S. A.

    2014-10-01

    We introduce a classification method (cumulative discriminant analysis) of the discriminant analysis type to discriminate between cloudy and clear-sky satellite observations in the thermal infrared. The tool is intended for the high-spectral-resolution infrared sounder (IRS) planned for the geostationary METEOSAT (Meteorological Satellite) Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer) data as a proxy. The cumulative discriminant analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A principal component analysis prior step is also introduced, which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer) and SEVIRI (Spinning Enhanced Visible and Infrared Imager) imagers. The agreement with these independent cloud masks is generally well above 80 %, except at high latitudes in the winter seasons.

  7. An Alternative Approach for Registration of High-Resolution Satellite Optical Imagery and ICESat Laser Altimetry Data

    Directory of Open Access Journals (Sweden)

    Shijie Liu

    2016-11-01

    Full Text Available Satellite optical images and altimetry data are two major data sources used in Antarctic research. The integration use of these two datasets is expected to provide more accurate and higher quality products, during which data registration is the first issue that needs to be solved. This paper presents an alternative approach for the registration of high-resolution satellite optical images and ICESat (Ice, Cloud, and land Elevation Satellite laser altimetry data. Due to the sparse distribution characteristic of the ICESat laser point data, it is difficult and even impossible to find same-type conjugate features between ICESat data and satellite optical images. The method is implemented in a direct way to correct the point-to-line inconsistency in image space through 2D transformation between the projected terrain feature points and the corresponding 2D image lines, which is simpler than discrepancy correction in object space that requires stereo images for 3D model construction, and easier than the indirect way of image orientation correction via photogrammetric bundle adjustment. The correction parameters are further incorporated into imaging model through RPCs (Rational Polynomial Coefficients generation/regeneration for the convenience of photogrammetric applications. The experimental results by using the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer images and ZY-3 (Ziyuan-3 satellite images for registration with ICESat data showed that sub-pixel level registration accuracies were achieved after registration, which have validated the feasibility and effectiveness of the presented approach.

  8. Automatic Radiometric Normalization of Multitemporal Satellite Imagery with the Iteratively Re-weighted MAD Transformation

    DEFF Research Database (Denmark)

    Canty, Morton John; Nielsen, Allan Aasbjerg

    2008-01-01

    A recently proposed method for automatic radiometric normalization of multi- and hyper-spectral imagery based on the invariance property of the Multivariate Alteration Detection (MAD) transformation and orthogonal linear regression is extended by using an iterative re-weighting scheme involving no...

  9. The scientific basis for a satellite mission to retrieve CCN concentrations and their impacts on convective clouds

    Directory of Open Access Journals (Sweden)

    D. Rosenfeld

    2012-02-01

    Full Text Available The cloud -mediated radiative forcing is widely recognized as the main source of uncertainty in our knowledge of the anthropogenic climate forcing and in our understanding of climate sensitivity. Current outstanding challenges are (1 global measurements of cloud condensation nuclei (CCN in the cloudy boundary layer from space, and, (2 disentangling the effects of aerosols from the thermodynamic and meteorological effects on the clouds. Here we present a new concept for a way to overcome these two challenges, using relatively simple passive satellite measurements in the visible and IR. The idea is to use the clouds themselves as natural CCN chambers by retrieving simultaneously the number of activated aerosols at cloud base, Na, and the cloud base updraft speed. The Na is obtained by analyzing the distribution of cloud drop effective radius in convective elements as a function distance above cloud base. The cloud base updraft velocities are estimated by double stereoscopic viewing and tracking of the evolution of cloud surface features just above cloud base. In order to resolve the vertical dimension of the clouds, the field of view will be 100 m for the microphysical retrievals, and 50 m for the stereoscopic measurements. The viewing geometry will be 30 degrees off nadir eastward, with the Sun in the back at 30 degrees off zenith westward, which requires a Sun synchronous orbit at 14:00 LST. Having measured simultaneously the thermodynamic environment, the vertical motions of the clouds, their microstructure and the CCN concentration will allow separating the dynamic from the CCN effects. This concept is being applied in the proposed satellite mission named Clouds, Hazards and Aerosols Survey for Earth Researchers (CHASER.

  10. The scientific basis for a satellite mission to retrieve CCN concentrations and their impacts on convective clouds

    Directory of Open Access Journals (Sweden)

    D. Rosenfeld

    2012-08-01

    Full Text Available The cloud-mediated aerosol radiative forcing is widely recognized as the main source of uncertainty in our knowledge of the anthropogenic forcing on climate. The current challenges for improving our understanding are (1 global measurements of cloud condensation nuclei (CCN in the cloudy boundary layer from space, and (2 disentangling the effects of aerosols from the thermodynamic and meteorological effects on the clouds. Here, we present a new conceptual framework to help us overcome these two challenges, using relatively simple passive satellite measurements in the visible and infared (IR. The idea is to use the clouds themselves as natural CCN chambers by retrieving simultaneously the number of activated aerosols at cloud base, Na, and the cloud base updraft speed. The Na is obtained by analyzing the distribution of cloud drop effective radius in convective elements as a function of distance above cloud base. The cloud base updraft velocities are estimated by double stereoscopic viewing and tracking of the evolution of cloud surface features just above cloud base. In order to resolve the vertical dimension of the clouds, the field of view will be 100 m for the microphysical retrievals, and 50 m for the stereoscopic measurements. The viewing geometry will be eastward and 30 degrees off nadir, with the Sun in the back at 30 degrees off zenith westward, requiring a Sun-synchronous orbit at 14 LST. Measuring simultaneously the thermodynamic environment, the vertical motions of the clouds, their microstructure and the CCN concentration will allow separating the dynamics from the CCN effects. This concept is being applied in the proposed satellite mission named Clouds, Hazards and Aerosols Survey for Earth Researchers (CHASER.

  11. An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV Imagery, Based on Structure from Motion (SfM Point Clouds

    Directory of Open Access Journals (Sweden)

    Christopher Watson

    2012-05-01

    Full Text Available Unmanned Aerial Vehicles (UAVs are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65–120 cm whilst the GCP technique achieves an accuracy of approximately 10–15 cm.

  12. Revisiting the iris effect of tropical cirrus clouds with TRMM and A-Train satellite data

    Science.gov (United States)

    Choi, Yong-Sang; Kim, WonMoo; Yeh, Sang-Wook; Masunaga, Hirohiko; Kwon, Min-Jae; Jo, Hyun-Su; Huang, Lei

    2017-06-01

    Just as the iris of human eye controls the light influx (iris effect), tropical anvil cirrus clouds may regulate the Earth's surface warming by controlling outgoing longwave radiation. This study examines this possible effect with monthly satellite observations such as Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer cirrus fraction, and Clouds and the Earth's Radiant Energy System top-of-the-atmosphere radiative fluxes averaged over different tropical domains from March 2000 to October 2014. To confirm that high-level cirrus is relevant to this study, Cloud-Aerosol Lidar with Orthogonal Polarization high cloud observations were also analyzed from June 2006 to December 2015. Our analysis revealed that the increase in sea surface temperature in the tropical western Pacific tends to concentrate convective cloud systems. This concentration effect very likely induces the significant reduction of both stratiform rain rate and cirrus fraction, without appreciable change in the convective rain rate. This reduction of stratiform rain rate and cirrus fraction cannot be found over its subregion or the tropical eastern Pacific, where the concentration effect of anvil cirrus is weak. Consistently, over the tropical western Pacific, the higher ratio of convective rain rate to total rain rate (i.e., precipitation efficiency) significantly correlates with warmer sea surface temperature and lower cirrus fraction. The reduced cirrus eventually increased outgoing longwave radiation to a greater degree than absorbed solar radiation. Finally, the negative relationship between precipitation efficiency and cirrus fraction tends to correspond to a low global equilibrium climate sensitivity in the models in the Coupled Model Intercomparison Project Phase 5. This suggests that tropical anvil cirrus clouds exert a negative climate feedback in strong association with precipitation efficiency.

  13. Use of A-train satellite observations (CALIPSO-PARASOL) to evaluate tropical cloud properties in the LMDZ5 GCM

    Science.gov (United States)

    Konsta, D.; Dufresne, J.-L.; Chepfer, H.; Idelkadi, A.; Cesana, G.

    2016-08-01

    The evaluation of key cloud properties such as cloud cover, vertical profile and optical depth as well as the analysis of their intercorrelation lead to greater confidence in climate change projections. In addition, the comparison between observations and parameterizations of clouds in climate models is improved by using collocated and instantaneous data of cloud properties. Simultaneous and independent observations of the cloud cover and its three-dimensional structure at high spatial and temporal resolutions are made possible by the new space-borne multi-instruments observations collected with the A-train. The cloud cover and its vertical structure observed by CALIPSO and the visible directional reflectance (a surrogate for the cloud optical depth) observed by PARASOL, are used to evaluate the representation of cloudiness in two versions of the atmospheric component of the IPSL-CM5 climate model (LMDZ5). A model-to-satellite approach, applying the CFMIP Observation Simulation Package (COSP), is used to allow a quantitative comparison between model results and observations. The representation of clouds in the two model versions is first evaluated using monthly mean data. This classical approach reveals biases of different magnitudes in the two model versions. These biases consist of (1) an underestimation of cloud cover associated to an overestimation of cloud optical depth, (2) an underestimation of low- and mid-level tropical clouds and (3) an overestimation of high clouds. The difference in the magnitude of these biases between the two model versions clearly highlights the improvement of the amount of boundary layer clouds, the improvement of the properties of high-level clouds, and the improvement of the simulated mid-level clouds in the tropics in LMDZ5B compared to LMDZ5A, due to the new convective, boundary layer, and cloud parametrizations implemented in LMDZ5B. The correlation between instantaneous cloud properties allows for a process-oriented evaluation

  14. Application of High Resolution Satellite Imagery to Characterize Individual-Based Environmental Heterogeneity in a Wild Blue Tit Population

    Directory of Open Access Journals (Sweden)

    Marta Szulkin

    2015-10-01

    Full Text Available Environmental heterogeneity in space and time plays a key role in influencing trait variability in animals, and can be particularly relevant to animal phenology. Until recently, the use of remotely sensed imagery in understanding animal variation was limited to analyses at the population level, largely because of a lack of high-resolution data that would allow inference at the individual level. We evaluated the potential of SPOT 4 (Take 5 satellite imagery data (with observations every fifth day at 20 m resolution and equivalent to acquisition parameters of Sentinel-2 in animal ecology research. We focused on blue tit Cyanistes caeruleus reproduction in a study site containing 227 nestboxes scattered in a Mediterranean forest dominated by deciduous downy oaks Quercus pubescens with a secondary cover of evergreen holm oaks Quercus ilex. We observed high congruence between ground data collected in a 50 m radius around each nestbox and NDVI values averaged across a 5 by 5 pixel grid centered around each nestbox of the study site. The number of deciduous and evergreen oaks around nestboxes explained up to 66% of variance in nestbox-centered, SPOT-derived NDVI values. We also found highly equivalent patterns of spatial autocorrelation for both ground- and satellite-derived indexes of environmental heterogeneity. For deciduous and evergreen oaks, the derived NDVI signal was highly distinctive in winter and early spring. June NDVI values for deciduous and evergreen oaks were higher by 58% and 8% relative to February values, respectively. The number of evergreen oaks was positively associated with later timing of breeding in blue tits. SPOT-derived, Sentinel-2 like imagery thus provided highly reliable, ground-validated information on habitat heterogeneity of direct relevance to a long-term field study of a free-living passerine bird. Given that the logistical demands of gathering ground data often limit our understanding of variation in animal

  15. The users, uses, and value of Landsat and other moderate-resolution satellite imagery in the United States-Executive report

    Science.gov (United States)

    Miller, Holly M.; Sexton, Natalie R.; Koontz, Lynne; Loomis, John; Koontz, Stephen R.; Hermans, Caroline

    2011-01-01

    Moderate-resolution imagery (MRI), such as that provided by the Landsat satellites, provides unique spatial information for use by many people both within and outside of the United States (U.S.). However, exactly who these users are, how they use the imagery, and the value and benefits derived from the information are, to a large extent, unknown. To explore these issues, social scientists at the USGS Fort Collins Science Center conducted a study of U.S.-based MRI users from 2008 through 2010 in two parts: 1) a user identification and 2) a user survey. The objectives for this study were to: 1) identify and classify U.S.-based users of this imagery; 2) better understand how and why MRI, and specifically Landsat, is being used; and 3) qualitatively and quantitatively measure the value and societal benefits of MRI (focusing on Landsat specifically). The results of the survey revealed that respondents from multiple sectors use Landsat imagery in many different ways, as demonstrated by the breadth of project locations and scales, as well as application areas. The value of Landsat imagery to these users was demonstrated by the high importance placed on the imagery, the numerous benefits received from projects using Landsat imagery, the negative impacts if Landsat imagery was no longer available, and the substantial willingness to pay for replacement imagery in the event of a data gap. The survey collected information from users who are both part of and apart from the known user community. The diversity of the sample delivered results that provide a baseline of knowledge about the users, uses, and value of Landsat imagery. While the results supply a wealth of information on their own, they can also be built upon through further research to generate a more complete picture of the population of Landsat users as a whole.

  16. Cloud Effects in Hyperspectral Imagery from First-Principles Scene Simulations

    Science.gov (United States)

    2009-01-01

    ran overnight on a Beowulf PC cluster with 40 processes. The simulated hyperspectral data cube was atmospherically corrected using the first...other, parallel computing on a Beowulf cluster yields dramatic speed improvements. To generate cloudy MCScene images, realistic three-dimensional cloud

  17. Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-oriented multi-variable model

    Institute of Scientific and Technical Information of China (English)

    Li-gang MA; Jin-song DENG; Huai YANG; Yang HONG; Ke WANG

    2015-01-01

    The Chinese ZY-1 02C satellite is one of the most advanced high-resolution earth observation systems designed for terrestrial resource monitoring. Its capability for comprehensive landscape classification, especially in urban areas, has been under constant study. In view of the limited spectral resolution of the ZY-1 02C satellite (three bands), and the complexity and hetero-geneity across urban environments, we attempt to test its performance of urban landscape classification by combining a multi-variable model with an object-oriented approach. The multiple variables including spectral reflection, texture, spatial autocorre-lation, impervious surface fraction, vegetation, and geometry indexes were first calculated and selected using forward stepwise linear discriminant analysis and applied in the following object-oriented classification process. Comprehensive accuracy as-sessment which adopts traditional error matrices with stratified random samples and polygon area consistency (PAC) indexes was then conducted to examine the real area agreement between a classified polygon and its references. Results indicated an overall classification accuracy of 92.63%and a kappa statistic of 0.9124. Furthermore, the proposed PAC index showed that more than 82%of all polygons were correctly classified. Misclassification occurred mostly between residential area and barren/farmland. The presented method and the Chinese ZY-1 02C satellite imagery are robust and effective for urban landscape classification.

  18. Reference crop evapotranspiration derived from geo-stationary satellite imagery: a case study for the Fogera flood plain, NW-Ethiopia and the Jordan Valley, Jordan

    NARCIS (Netherlands)

    Bruin, de H.A.R.; Trigo, I.F.; Jitan, M.A.; Enku, N.T.; Tol, van der C.; Gieske, A.S.M.

    2010-01-01

    First results are shown of a project aiming to estimate daily values of reference crop evapotranspiration ET0 from geo-stationary satellite imagery. In particular, for Woreta, a site in the Ethiopian highland at an elevation of about 1800 m, we tested a radiation-temperature based approximate formul

  19. Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations

    Science.gov (United States)

    We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop si...

  20. Reference crop evapotranspiration derived from geo-stationary satellite imagery: a case study for the Fogera flood plain, NW-Ethiopia and the Jordan Valley, Jordan

    NARCIS (Netherlands)

    Bruin, de H.A.R.; Trigo, I.F.; Jitan, M.A.; Enku, N.T.; Tol, van der C.; Gieske, A.S.M.

    2010-01-01

    First results are shown of a project aiming to estimate daily values of reference crop evapotranspiration ET0 from geo-stationary satellite imagery. In particular, for Woreta, a site in the Ethiopian highland at an elevation of about 1800 m, we tested a radiation-temperature based approximate

  1. Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations

    Science.gov (United States)

    We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop si...

  2. Analysis and forecast experiments incorporating satellite soundings and cloud and water vapor drift wind information

    Science.gov (United States)

    Goodman, Brian M.; Diak, George R.; Mills, Graham A.

    1986-01-01

    A system for assimilating conventional meteorological data and satellite-derived data in order to produce four-dimensional gridded data sets of the primary atmospheric variables used for updating limited area forecast models is described. The basic principles of a data assimilation scheme as proposed by Lorenc (1984) are discussed. The design of the system and its incremental assimilation cycles are schematically presented. The assimilation system was tested using radiosonde, buoy, VAS temperature, dew point, gradient wind data, cloud drift, and water vapor motion data. The rms vector errors for the data are analyzed.

  3. Satellite Cloud Data Validation through MAGIC Ground Observation and the S'COOL Project: Scientific Benefits grounded in Citizen Science

    Science.gov (United States)

    Crecelius, S.; Chambers, L. H.; Lewis, P. M.; Rogerson, T.

    2013-12-01

    The Students' Cloud Observation On-Line (S'COOL) Project was launched in 1997 as the Formal Education and Public Outreach arm of the Clouds and the Earth's Radiant Energy System (CERES) Mission. ROVER, the Citizen Scientist area of S'COOL, started in 2007 and allows participants to make 'roving' observations from any location as opposed to a fixed, registered classroom. The S'COOL Project aids the CERES Mission in trying to answer the research question: 'What is the Effect of Clouds on the Earth's Climate'. Participants from all 50 states, most U.S. Territories, and 63 countries have reported more than 100,500 observations to the S'COOL Project over the past 16 years. The Project is supported by an intuitive website that provides curriculum support and guidance through the observation steps; 1) Request satellite overpass schedule, 2) Observe clouds, and 3) Report cloud observations. The S'COOL Website also hosts a robust database housing all participants' observations as well as the matching satellite data. While the S'COOL observation parameters are based on the data collected by 5 satellite missions, ground observations provide a unique perspective to data validation. Specifically, low to mid level clouds can be obscured by overcast high-level clouds, or difficult to observe from a satellite's perspective due to surface cover or albedo. In these cases, ground observations play an important role in filling the data gaps and providing a better, global picture of our atmosphere and clouds. S'COOL participants, operating within the boundary layer, have an advantage when observing low-level clouds that affect the area we live in, regional weather patterns, and climate change. S'COOL's long-term data set provides a valuable resource to the scientific community in improving the "poorly characterized and poorly represented [clouds] in climate and weather prediction models'. The MAGIC Team contacted S'COOL in early 2012 about making cloud observations as part of the MAGIC

  4. Identifying and locating land irrigated by center-pivot irrigation systems using satellite imagery

    Science.gov (United States)

    Hoffman, R. O.

    1980-01-01

    A methodology for using Landsat imagery for the identification and location of land irrigated by center-pivot irrigation systems is presented. The procedure involves the use of sets of Landsat band 5 imagery taken separated in time by about three weeks during the irrigation season, a zoom transfer scope and mylar base maps to record the locations of center pivots. Further computer processing of the data has been used to obtain plots of center-pivot irrigation systems and tables indicating the distribution and growth of systems by county for the state of Nebraska, and has been found to be in 95% agreement with current high-altitude IR photography. The information obtainable can be used for models of ground-water aquifers or resource planning.

  5. USING OF THE MULTITEMPORAL THERMAL INFRARED SATELLITE IMAGERY FOR NATURAL AREAS MAPPING (CASE OF MENDELEEV VOLCANO

    Directory of Open Access Journals (Sweden)

    M. Y. Grishchenko

    2014-01-01

    Full Text Available In the paper authors examine the mountain group of Mendeleev volcano situated on the Kunashir island, Kuril archipelago, Russia. Ground observations were led to examine the vegetation cover of the area as well as its typical landscapes. The other type of used data is Landsat imagery. Images were combined into multitemporal thermal infrared and multispectral pictures, which were classified to reveal the heterogeneity of the study area. Ground observations and comparison of the classification results with landscape map derive that the multitemporal thermal infrared image classification result describes better the vegetation cover structure of the area and particularity of its typical landscapes distribution. It leads to the proposition that miltitemporal thermal infrared imagery can be used to refine landscape and vegetation cover contours. 

  6. Integrated snow and avalanche monitoring syatem for Indian Himalaya using multi-temporal satellite imagery and ancillary data

    Science.gov (United States)

    Sharma, S. S.; Mani, Sneh; Mathur, P.

    The variations in the local climate, environment and altitude as well as fast snow cover build up and rapid changes in snow characteristics with passage of winter are major contributing factors to make snow avalanches as one of the threatening problems in the North West Himalaya. For sustainable development of these mountainous areas, a number of multi-purpose projects are being planned. In recent times, the danger of natural and man-made hazards is increasing and the availability of water is fluctuating; and thus, making the project implementation difficult. To overcome these difficulties to a great extent, an integrated monitoring system is required for short term as well as long term assessment of snowcover variation and avalanche hazard. In order to monitor the spatial extent of snow cover, satellite data can be employed on an operational basis. Spectral settings as well as the temporal and spatial resolution make time series NOAA-AVHHR and MODIS sensor data well suited for operational snow cover monitoring at regional or continental scale; Indian Remote Sensing Satellite (IRS) LISS, WiFS and AWiFS sensor data suitable for studies at larger scale; and microwave data for extraction of snow wetness information.. In the present paper, an attempt is made to study the trends of changes in snow characteristics and related avalanche phenomenon using time series multi-temporal, multi-resolution satellite data with respect to different ranges in Western Himalaya, namely Pir Panjal range, Great Himalaya range, Zanskar range, Ladakh range and Great Karakoram range. The operational processing of these data included geocoding, calibration, terrain normalization, classification, statistical post classification and derivation of snow cover statistics. The calibration and normalization of imageries allowed the application of physically based classification thresholds possible for albedo, brightness temperature and the Normalized Difference Snow Index (NDSI) parameters

  7. Integrated snow and avalanche monitoring system for Indian Himalaya using multi-temporal satellite imagery and ancillary data

    Science.gov (United States)

    Sharma, S. S.; Mani, Sneh; Mathur, P.

    The variations in the local climate, environment and altitude as well as fast snow cover build up and rapid changes in snow characteristics with passage of winter are major contributing factors to make snow avalanches as one of the threatening problems in the North West Himalaya. For sustainable development of these mountainous areas, a number of multi-purpose projects are being planned. In recent times, the danger of natural and man-made hazards is increasing and the availability of water is fluctuating; and thus, making the project implementation difficult. To overcome these difficulties to a great extent, an integrated monitoring system is required for short term as well as long term assessment of snowcover variation and avalanche hazard. In order to monitor the spatial extent of snow cover, satellite data can be employed on an operational basis. Spectral settings as well as the temporal and spatial resolution make time series NOAA-AVHHR and MODIS sensor data well suited for operational snow cover monitoring at regional or continental scale; Indian Remote Sensing Satellite (IRS) LISS, WiFS and AWiFS sensor data suitable for studies at larger scale; and microwave data for extraction of snow wetness information.. In the present paper, an attempt is made to study the trends of changes in snow characteristics and related avalanche phenomenon using time series multi-temporal, multi-resolution satellite data with respect to different ranges in Western Himalaya, namely Pir Panjal range, Great Himalaya range, Zanskar range, Ladakh range and Great Karakoram range. The operational processing of these data included geocoding, calibration, terrain normalization, classification, statistical post classification and derivation of snow cover statistics. The calibration and normalization of imageries allowed the application of physically based classification thresholds possible for albedo, brightness temperature and the Normalized Difference Snow Index (NDSI) parameters

  8. Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

    Directory of Open Access Journals (Sweden)

    Shasha Jiang

    2016-05-01

    Full Text Available Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

  9. Geospatial mapping of Antarctic coastal oasis using geographic object-based image analysis and high resolution satellite imagery

    Science.gov (United States)

    Jawak, Shridhar D.; Luis, Alvarinho J.

    2016-04-01

    An accurate spatial mapping and characterization of land cover features in cryospheric regions is an essential procedure for many geoscientific studies. A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (OBIA) to extract cryospheric geospatial information from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for OBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, east Antarctica. Multilevel segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features with respect to scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify land mass, man-made features, snow/ice, and water bodies. We focus on water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and OBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≍97%. In conclusion, our results suggest that OBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geospatial information.

  10. Geographic Object-based Image Analysis for Developing Cryospheric Surface Mapping Application using Remotely Sensed High-Resolution Satellite Imagery

    Science.gov (United States)

    Jawak, S. D.; Luis, A. J.

    2015-12-01

    A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (GEOBIA) to extract cryospheric geoinformation from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for GEOBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, Antarctica. Multi-level segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features w.r.t scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify landmass, man-made features, snow/ice, and water bodies. A specific attention was paid to water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and GEOBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≈97%. In conclusion, the results suggest that GEOBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geoinformation.

  11. Aerial Photography and Imagery, Ortho-Corrected, This data set contains imagery from the National Agriculture Imagery Program (NAIP). NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. NAIP imagery may contain as much as 10% cloud cover per tile. This fil, Published in 2005, 1:63360 (1in=1mile) scale, University of Georgia.

    Data.gov (United States)

    NSGIC Education | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2005. This data set contains imagery from the National Agriculture Imagery Program (NAIP). NAIP...

  12. "Cloud Slicing" : A New Technique to Derive Tropospheric Ozone Profile Information from Satellite Measurements

    Science.gov (United States)

    Ziemke, J. R.; Chandra, S.; Bhartia, P. K.; Einaudi, Franco (Technical Monitor)

    2000-01-01

    A new technique denoted cloud slicing has been developed for estimating tropospheric ozone profile information. All previous methods using satellite data were only capable of estimating the total column of ozone in the troposphere. Cloud slicing takes advantage of the opaque property of water vapor clouds to ultraviolet wavelength radiation. Measurements of above-cloud column ozone from the Nimbus 7 total ozone mapping spectrometer (TOMS) instrument are combined together with Nimbus 7 temperature humidity and infrared radiometer (THIR) cloud-top pressure data to derive ozone column amounts in the upper troposphere. In this study tropical TOMS and THIR data for the period 1979-1984 are analyzed. By combining total tropospheric column ozone (denoted TCO) measurements from the convective cloud differential (CCD) method with 100-400 hPa upper tropospheric column ozone amounts from cloud slicing, it is possible to estimate 400-1000 hPa lower tropospheric column ozone and evaluate its spatial and temporal variability. Results for both the upper and lower tropical troposphere show a year-round zonal wavenumber 1 pattern in column ozone with largest amounts in the Atlantic region (up to approx. 15 DU in the 100-400 hPa pressure band and approx. 25-30 DU in the 400-1000 hPa pressure band). Upper tropospheric ozone derived from cloud slicing shows maximum column amounts in the Atlantic region in the June-August and September-November seasons which is similar to the seasonal variability of CCD derived TCO in the region. For the lower troposphere, largest column amounts occur in the September-November season over Brazil in South America and also southern Africa. Localized increases in the tropics in lower tropospheric ozone are found over the northern region of South America around August and off the west coast of equatorial Africa in the March-May season. Time series analysis for several regions in South America and Africa show an anomalous increase in ozone in the lower

  13. Remote Sensing of Clouds using Satellites, Lidars, CLF/XLF and IR Cameras at the Pierre Auger Observatory

    Directory of Open Access Journals (Sweden)

    Chirinos J.

    2015-01-01

    Full Text Available Clouds in the field of view of the fluorescence detectors affect the detection of the extensive air showers. Several remote sensing techniques are used to detect night-time clouds over the 3000 km2 of the Pierre Auger Observatory. Four lidars at the fluorescence detector sites are performing different patterns of scans of the surrounding sky detecting clouds. Two laser facilities (CLF and XLF are shooting into the sky delivering cloud cover above them every 15 minutes. Four IR cameras detect the presence of clouds within the FOV of the fluorescence detectors every 5 minutes. A method using GOES-12 and GOES-13 satellites identifies night-time clouds twice per hour with a spatial resolution of 2.4 km by 5.5 km over the Observatory. We upload all this information into several databases to be used for the reconstruction of cosmic ray events and to find exotic events.

  14. Remote Sensing of Clouds using Satellites, Lidars, CLF/XLF and IR Cameras at the Pierre Auger Observatory

    Science.gov (United States)

    Chirinos, J.

    2015-12-01

    Clouds in the field of view of the fluorescence detectors affect the detection of the extensive air showers. Several remote sensing techniques are used to detect night-time clouds over the 3000 km2 of the Pierre Auger Observatory. Four lidars at the fluorescence detector sites are performing different patterns of scans of the surrounding sky detecting clouds. Two laser facilities (CLF and XLF) are shooting into the sky delivering cloud cover above them every 15 minutes. Four IR cameras detect the presence of clouds within the FOV of the fluorescence detectors every 5 minutes. A method using GOES-12 and GOES-13 satellites identifies night-time clouds twice per hour with a spatial resolution of 2.4 km by 5.5 km over the Observatory. We upload all this information into several databases to be used for the reconstruction of cosmic ray events and to find exotic events.

  15. Diabatic initialization for improvement in the tropical analysis of divergence and moisture using satellite radiometric imagery data

    Science.gov (United States)

    Kasahara, Akira; Mizzi, Arthur P.; Donner, Leo J.

    1994-05-01

    To improve the quality of horizontal divergence and moisture analyses in the tropics, a diabatic initialization scheme is developed to incorporate information on convective activity and the proxy data of precipitation obtained from satellite radiometric imagery data. The tropical precipitation rates are estimated by developing a relationship between the pentad precipitation data of the Global Precipitation Climatology Project with daily outgoing longwave radiation data. The tropical belt from 35°S to 25°N (for January 1988) is divided into 3 parts: convective, convective fringe, and downward-motion (clear-air) areas. In the convective region, the algorithm adjusts the horizontal divergence and humidity fields such that a version of the Kuo cumulus parameterization will yield the precipitation rates closest to the proxy data. The temperature in the planetary boundary layer is also adjusted, if necessary, to ensure the initiation of cumulus convection. In the downward-motion region, the divergence field is adjusted to yield descending motion expected from the thermodynamic balance between radiative cooling and adiabatic warming. In the convective fringe region, where convective criteria are not met, the divergence field is adjusted only to satisfy the global conservation of divergence. The humidity field is left intact in both the downward-motion and convective fringe regions. This adjustment scheme will ameliorate problems associated with spinup of precipitation in a numerical prediction model with the same cumulus parameterization as used in the initialization. This initialization scheme may be used as a method of quality control for first-guess fields in four-dimensional data assimilation by means of satellite radiometric imagery data.

  16. Use of multispectral satellite imagery and hyperspectral endmember libraries for urban land cover mapping at the metropolitan scale

    Science.gov (United States)

    Priem, Frederik; Okujeni, Akpona; van der Linden, Sebastian; Canters, Frank

    2016-10-01

    The value of characteristic reflectance features for mapping urban materials has been demonstrated in many experiments with airborne imaging spectrometry. Analysis of larger areas requires satellite-based multispectral imagery, which typically lacks the spatial and spectral detail of airborne data. Consequently the need arises to develop mapping methods that exploit the complementary strengths of both data sources. In this paper a workflow for sub-pixel quantification of Vegetation-Impervious-Soil urban land cover is presented, using medium resolution multispectral satellite imagery, hyperspectral endmember libraries and Support Vector Regression. A Landsat 8 Operational Land Imager surface reflectance image covering the greater metropolitan area of Brussels is selected for mapping. Two spectral libraries developed for the cities of Brussels and Berlin based on airborne hyperspectral APEX and HyMap data are used. First the combined endmember library is resampled to match the spectral response of the Landsat sensor. The library is then optimized to avoid spectral redundancy and confusion. Subsequently the spectra of the endmember library are synthetically mixed to produce training data for unmixing. Mapping is carried out using Support Vector Regression models trained with spectra selected through stratified sampling of the mixed library. Validation on building block level (mean size = 46.8 Landsat pixels) yields an overall good fit between reference data and estimation with Mean Absolute Errors of 0.06, 0.06 and 0.08 for vegetation, impervious and soil respectively. Findings of this work may contribute to the use of universal spectral libraries for regional scale land cover fraction mapping using regression approaches.

  17. Assessing the population coverage of a health demographic surveillance system using satellite imagery and crowd-sourcing.

    Science.gov (United States)

    Di Pasquale, Aurelio; McCann, Robert S; Maire, Nicolas

    2017-01-01

    Remotely sensed data can serve as an independent source of information about the location of residential structures in areas under demographic and health surveillance. We report on results obtained combining satellite imagery, imported from Bing, with location data routinely collected using the built-in GPS sensors of tablet computers, to assess completeness of population coverage in a Health and Demographic Surveillance System in Malawi. The Majete Malaria Project Health and Demographic Surveillance System, in Malawi, started in 2014 to support a project with the aim of studying the reduction of malaria using an integrated control approach by rolling out insecticide treated nets and improved case management supplemented with house improvement and larval source management. In order to support the monitoring of the trial a Health and Demographic Surveillance System was established in the area that surrounds the Majete Wildlife Reserve (1600 km2), using the OpenHDS data system. We compared house locations obtained using GPS recordings on mobile devices during the demographic surveillance census round with those acquired from satellite imagery. Volunteers were recruited through the crowdcrafting.org platform to identify building structures on the images, which enabled the compilation of a database with coordinates of potential residences. For every building identified on these satellite images by the volunteers (11,046 buildings identified of which 3424 (ca. 30%) were part of the censused area), we calculated the distance to the nearest house enumerated on the ground by fieldworkers during the census round of the HDSS. A random sample of buildings (85 structures) identified on satellite images without a nearby location enrolled in the census were visited by a fieldworker to determine how many were missed during the baseline census survey, if any were missed. The findings from this ground-truthing effort suggest that a high population coverage was achieved in the

  18. Estimation of Reservoir Discharges from Lake Nasser and Roseires Reservoir in the Nile Basin Using Satellite Altimetry and Imagery Data

    Directory of Open Access Journals (Sweden)

    Eric Muala

    2014-08-01

    Full Text Available This paper presents the feasibility of estimating discharges from Roseires Reservoir (Sudan for the period from 2002 to 2010 and Aswan High Dam/Lake Nasser (Egypt for the periods 1999–2002 and 2005–2009 using satellite altimetry and imagery with limited in situ data. Discharges were computed using the water balance of the reservoirs. Rainfall and evaporation data were obtained from public domain data sources. In situ measurements of inflow and outflow (for validation were obtained, as well. The other water balance components, such as the water level and surface area, for derivation of the change of storage volume were derived from satellite measurements. Water levels were obtained from Hydroweb for Roseires Reservoir and Hydroweb and Global Reservoir and Lake Monitor (GRLM for Lake Nasser. Water surface areas were derived from Landsat TM/ETM+ images using the Normalized Difference Water Index (NDWI. The water volume variations were estimated by integrating the area-level relationship of each reservoir. For Roseires Reservoir, the water levels from Hydroweb agreed well with in situ water levels (RMSE = 0.92 m; R2 = 0.96. Good agreement with in situ measurements were also obtained for estimated water volume (RMSE = 23%; R2 = 0.94 and computed discharge (RMSE = 18%; R2 = 0.98. The accuracy of the computed discharge was considered acceptable for typical reservoir operation applications. For Lake Nasser, the altimetry water levels also agreed well with in situ levels, both for Hydroweb (RMSE = 0.72 m; R2 = 0.81 and GRLM (RMSE = 0.62 m; R2 = 0.96 data. Similar agreements were also observed for the estimated water volumes (RMSE = 10%–15%. However, the estimated discharge from satellite data agreed poorly with observed discharge, Hydroweb (RMSE = 70%; R2 = 0.09 and GRLM (RMSE = 139%; R2 = 0.36. The error could be attributed to the high sensitivity of discharge to errors in storage volume because of the immense reservoir compared to inflow

  19. An Accuracy Assessment of Georeferenced Point Clouds Produced via Multi-View Stereo Techniques Applied to Imagery Acquired via Unmanned Aerial Vehicle

    Science.gov (United States)

    Harwin, S.; Lucieer, A.

    2012-08-01

    Low-cost Unmanned Aerial Vehicles (UAVs) are becoming viable environmental remote sensing tools. Sensor and battery technology is expanding the data capture opportunities. The UAV, as a close range remote sensing platform, can capture high resolution photography on-demand. This imagery can be used to produce dense point clouds using multi-view stereopsis techniques (MVS) combining computer vision and photogrammetry. This study examines point clouds produced using MVS techniques applied to UAV and terrestrial photography. A multi-rotor micro UAV acquired aerial imagery from a altitude of approximately 30-40 m. The point clouds produced are extremely dense (study area, a 70 m section of sheltered coastline in southeast Tasmania. Areas with little surface texture were not well captured, similarly, areas with complex geometry such as grass tussocks and woody scrub were not well mapped. The process fails to penetrate vegetation, but extracts very detailed terrain in unvegetated areas. Initially the point clouds are in an arbitrary coordinate system and need to be georeferenced. A Helmert transformation is applied based on matching ground control points (GCPs) identified in the point clouds to GCPs surveying with differential GPS. These point clouds can be used, alongside laser scanning and more traditional techniques, to provide very detailed and precise representations of a range of landscapes at key moments. There are many potential applications for the UAV-MVS technique, including coastal erosion and accretion monitoring, mine surveying and other environmental monitoring applications. For the generated point clouds to be used in spatial applications they need to be converted to surface models that reduce dataset size without loosing too much detail. Triangulated meshes are one option, another is Poisson Surface Reconstruction. This latter option makes use of point normal data and produces a surface representation at greater detail than previously obtainable. This

  20. Using high-resolution satellite imagery to engage students in classroom experiences which meld research, the nature of science, and inquiry-based instruction

    Science.gov (United States)

    Pennycook, J.; LaRue, M.; Herried, B.; Morin, P. J.

    2013-12-01

    Recognizing the need to bridge the gap between scientific research and the classroom, we have developed an exciting activity which engages students in grades 5-12 using high-resolution satellite imagery to observe Weddell seal populations in Antarctica. Going beyond the scope of the textbook, students experience the challenge researchers face in counting and monitoring animal populations in the field. The activity is presented in a non-expert, non-technical exercise enriched for students, with background information, tutorials, and satellite imagery included. Teachers instruct their class in how to use satellite imagery analysis techniques to collect data on seal populations in the McMurdo Sound region of the Ross Sea, Antarctica. Students participate in this inquiry-based, open-ended exercise to evaluate changes in the seal population within and between seasons. The activity meets the New Generation Science Standards (NGSS) through inquiry-based, real-world application and supports seven Performance Expectations (PE) for grade 5-12. In addition, it offers students a glimpse into the work of a field biologist, promoting interest in entering the STEM career pipeline. As every new Antarctica season unfolds, new imagery will be uploaded to the website allowing each year of students to add their counts to a growing long-term dataset for the classroom. The activity files provide 1) a tutorial in how to use the images to count the populations, 2) background information about Weddell seals in the McMurdo Sound region of the Ross Sea for the students and the teachers, and 3) collections of satellite imagery for spatial and temporal analysis of population fluctuations. Teachers can find all activity files to conduct the activity, including student instructions, on the Polar Geospatial Center's website (http://z.umn.edu/seals). Satellite image, Big Razorback Island, Antarctica Weddell seals,Tent Island, Antarctica

  1. Study of Multi-Scale Cloud Processes Over the Tropical Western Pacific Using Cloud-Resolving Models Constrained by Satellite Data

    Energy Technology Data Exchange (ETDEWEB)

    Dudhia, Jimy

    2013-03-12

    Clouds in the tropical western Pacific are an integral part of the large scale environment. An improved understanding of the multi-scale structure of clouds and their interactions with the environment is critical to the ARM (Atmospheric Radiation Measurement) program for developing and evaluating cloud parameterizations, understanding the consequences of model biases, and providing a context for interpreting the observational data collected over the ARM Tropical Western Pacific (TWP) sites. Three-dimensional cloud resolving models (CRMs) are powerful tools for developing and evaluating cloud parameterizations. However, a significant challenge in using CRMs in the TWP is that the region lacks conventional data, so large uncertainty exists in defining the large-scale environment for clouds. This project links several aspects of the ARM program, from measurements to providing improved analyses, and from cloud-resolving modeling to climate-scale modeling and parameterization development, with the overall objective to improve the representations of clouds in climate models and to simulate and quantify resolved cloud effects on the large-scale environment. Our objectives will be achieved through a series of tasks focusing on the use of the Weather Research and Forecasting (WRF) model and ARM data. Our approach includes: -- Perform assimilation of COSMIC GPS radio occultation and other satellites products using the WRF Ensemble Kalman Filter assimilation system to represent the tropical large-scale environment at 36 km grid resolution. This high-resolution analysis can be used by the community to derive forcing products for single-column models or cloud-resolving models. -- Perform cloud-resolving simulations using WRF and its nesting capabilities, driven by the improved regional analysis and evaluate the simulations against ARM datasets such as from TWP-ICE to optimize the microphysics parameters for this region. A cirrus study (Mace and co-authors) already exists for

  2. An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Xiao Ling

    2016-08-01

    Full Text Available This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC, which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3, Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

  3. Use and Assessment of Multi-Spectral Satellite Imagery in NWS Operational Forecasting Environments

    Science.gov (United States)

    Molthan, Andrew; Fuell, Kevin; Stano, Geoffrey; McGrath, Kevin; Schultz, Lori; LeRoy, Anita

    2015-01-01

    NOAA's Satellite Proving Grounds have established partnerships between product developers and NWS WFOs for the evaluation of new capabilities from the GOES-R and JPSS satellite systems. SPoRT has partnered with various WFOs to evaluate multispectral (RGB) products from MODIS, VIIRS and Himawari/AHI to prepare for GOES-R/ABI. Assisted through partnerships with GINA, UW/CIMSS, NOAA, and NASA Direct Broadcast capabilities.

  4. Mapping coastal & estuarine vegetation using VHR satellite imagery in St Lucia

    CSIR Research Space (South Africa)

    Lück-Vogel, Melanie

    2015-04-01

    Full Text Available DAR-derived DSM raster products 1m grid from unthinned point cloud 2m grid from 25cm contours 5m grid from unthinned point cloud Increasing loss of detail 13 Example: WV2-based classification results WV-2 image 9 Apr 2010 14 Example: WV2-based classification... Cape Town, 22 April 2015 2Landsat TM 30m 400m Coastal remote sensing RapidEye 5m WorldView-2 2m Upcoming new MS sensors: more bands, more bits, more spatial detail Coast has long been neglected in South Africa because of small scale pattern...

  5. Predicting lake trophic state by relating Secchi-disk transparency measurements to Landsat-satellite imagery for Michigan inland lakes, 2003-05 and 2007-08

    Science.gov (United States)

    Fuller, L.M.; Jodoin, R.S.; Minnerick, R.J.

    2011-01-01

    Inland lakes are an important economic and environmental resource for Michigan. The U.S. Geological Survey and the Michigan Department of Natural Resources and Environment have been cooperatively monitoring the quality of selected lakes in Michigan through the Lake Water Quality Assessment program. Sampling for this program began in 2001; by 2010, 730 of Michigan’s 11,000 inland lakes are expected to have been sampled once. Volunteers coordinated by the Michigan Department of Natural Resources and Environment began sampling lakes in 1974 and continue to sample (in 2010) approximately 250 inland lakes each year through the Michigan Cooperative Lakes Monitoring Program. Despite these sampling efforts, it still is impossible to physically collect measurements for all Michigan inland lakes; however, Landsat-satellite imagery has been used successfully in Minnesota, Wisconsin, Michigan, and elsewhere to predict the trophic state of unsampled inland lakes greater than 20 acres by producing regression equations relating in-place Secchi-disk measurements to Landsat bands. This study tested three alternatives to methods previously used in Michigan to improve results for predicted statewide Trophic State Index (TSI) computed from Secchi-disk transparency (TSI (SDT)). The alternative methods were used on 14 Landsat-satellite scenes with statewide TSI (SDT) for two time periods (2003– 05 and 2007–08). Specifically, the methods were (1) satellitedata processing techniques to remove areas affected by clouds, cloud shadows, haze, shoreline, and dense vegetation for inland lakes greater than 20 acres in Michigan; (2) comparison of the previous method for producing a single open-water predicted TSI (SDT) value (which was based on an area of interest (AOI) and lake-average approach) to an alternative Gethist method for identifying open-water areas in inland lakes (which follows the initial satellite-data processing and targets the darkest pixels, representing the deepest water

  6. Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering

    Science.gov (United States)

    DeWitt, Jessica D.; Warner, Timothy A.; Chirico, Pete; Bergstresser, Sarah

    2017-01-01

    For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.

  7. Building damage assessment after the earthquake in Haiti using two postevent satellite stereo imagery and DSMs

    DEFF Research Database (Denmark)

    Tian, Jiaojiao; Nielsen, Allan Aasbjerg; Reinartz, Peter

    2015-01-01

    In this article, a novel after-disaster building damage monitoring method is presented. This method combines the multispectral imagery and digital surface models (DSMs) from stereo matching of two dates to obtain three kinds of changes: collapsed buildings, newly built buildings and temporary...... shelters. The proposed method contains three basic steps. The first step is to focus on the DSMs and orthorectified images preparation. The second step is to segment the panchromatic images in obtaining small homogeneous regions. In the last step, a rule-based classification is built on the change...

  8. A Comparative Accuracy Analysis of Classification Methods in Determination of Cultivated Lands with Spot 5 Satellite Imagery

    Science.gov (United States)

    kaya, S.; Alganci, U.; Sertel, E.; Ustundag, B.

    2013-12-01

    A Comparative Accuracy Analysis of Classification Methods in Determination of Cultivated Lands with Spot 5 Satellite Imagery Ugur ALGANCI1, Sinasi KAYA1,2, Elif SERTEL1,2,Berk USTUNDAG3 1 ITU, Center for Satellite Communication and Remote Sensing, 34469, Maslak-Istanbul,Turkey 2 ITU, Department of Geomatics, 34469, Maslak-Istanbul, Turkey 3 ITU, Agricultural and Environmental Informatics Research Center,34469, Maslak-Istanbul,Turkey alganci@itu.edu.tr, kayasina@itu.edu.tr, sertele@itu.edu.tr, berk@berk.tc ABSTRACT Cultivated land determination and their area estimation are important tasks for agricultural management. Derived information is mostly used in agricultural policies and precision agriculture, in specifically; yield estimation, irrigation and fertilization management and farmers declaration verification etc. The use of satellite image in crop type identification and area estimate is common for two decades due to its capability of monitoring large areas, rapid data acquisition and spectral response to crop properties. With launch of high and very high spatial resolution optical satellites in the last decade, such kind of analysis have gained importance as they provide information at big scale. With increasing spatial resolution of satellite images, image classification methods to derive the information form them have become important with increase of the spectral heterogeneity within land objects. In this research, pixel based classification with maximum likelihood algorithm and object based classification with nearest neighbor algorithm were applied to 2012 dated 2.5 m resolution SPOT 5 satellite images in order to investigate the accuracy of these methods in determination of cotton and corn planted lands and their area estimation. Study area was selected in Sanliurfa Province located on Southeastern Turkey that contributes to Turkey's agricultural production in a major way. Classification results were compared in terms of crop type identification using

  9. Mapping sub-antarctic cushion plants using random forests to combine ver