WorldWideScience

Sample records for remote sensing imagery

  1. JEarth | Analytical Remote Sensing Imagery Application for Researchers and Practitioners

    Science.gov (United States)

    Prashad, L.; Christensen, P. R.; Anwar, S.; Dickenshied, S.; Engle, E.; Noss, D.

    2009-12-01

    The ASU 100 Cities Project and the ASU Mars Space Flight Facility (MSFF) present JEarth, a set of analytical Geographic Information System (GIS) tools for viewing and processing Earth-based remote sensing imagery and vectors, including high-resolution and hyperspectral imagery such as TIMS and MASTER. JEarth is useful for a wide range of researchers and practitioners who need to access, view, and analyze remote sensing imagery. JEarth stems from existing MSFF applications: the Java application JMars (Java Mission-planning and Analysis for Remote Sensing) for viewing and analyzing remote sensing imagery and THMPROC, a web-based, interactive tool for processing imagery to create band combinations, stretches, and other imagery products. JEarth users can run the application on their desktops by installing Java-based open source software on Windows, Mac, or Linux operating systems.

  2. Crowdsourcing earthquake damage assessment using remote sensing imagery

    Directory of Open Access Journals (Sweden)

    Stuart Gill

    2011-06-01

    Full Text Available This paper describes the evolution of recent work on using crowdsourced analysis of remote sensing imagery, particularly high-resolution aerial imagery, to provide rapid, reliable assessments of damage caused by earthquakes and potentially other disasters. The initial effort examined online imagery taken after the 2008 Wenchuan, China, earthquake. A more recent response to the 2010 Haiti earthquake led to the formation of an international consortium: the Global Earth Observation Catastrophe Assessment Network (GEO-CAN. The success of GEO-CAN in contributing to the official damage assessments made by the Government of Haiti, the United Nations, and the World Bank led to further development of a web-based interface. A current initiative in Christchurch, New Zealand, is underway where remote sensing experts are analyzing satellite imagery, geotechnical engineers are marking liquefaction areas, and structural engineers are identifying building damage. The current site includes online training to improve the accuracy of the assessments and make it possible for even novice users to contribute to the crowdsourced solution. The paper discusses lessons learned from these initiatives and presents a way forward for using crowdsourced remote sensing as a tool for rapid assessment of damage caused by natural disasters around the world.

  3. Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data

    Directory of Open Access Journals (Sweden)

    Wei Tu

    2018-01-01

    Full Text Available Portraying urban functional zones provides useful insights into understanding complex urban systems and establishing rational urban planning. Although several studies have confirmed the efficacy of remote sensing imagery in urban studies, coupling remote sensing and new human sensing data like mobile phone positioning data to identify urban functional zones has still not been investigated. In this study, a new framework integrating remote sensing imagery and mobile phone positioning data was developed to analyze urban functional zones with landscape and human activity metrics. Landscapes metrics were calculated based on land cover from remote sensing images. Human activities were extracted from massive mobile phone positioning data. By integrating them, urban functional zones (urban center, sub-center, suburbs, urban buffer, transit region and ecological area were identified by a hierarchical clustering. Finally, gradient analysis in three typical transects was conducted to investigate the pattern of landscapes and human activities. Taking Shenzhen, China, as an example, the conducted experiment shows that the pattern of landscapes and human activities in the urban functional zones in Shenzhen does not totally conform to the classical urban theories. It demonstrates that the fusion of remote sensing imagery and human sensing data can characterize the complex urban spatial structure in Shenzhen well. Urban functional zones have the potential to act as bridges between the urban structure, human activity and urban planning policy, providing scientific support for rational urban planning and sustainable urban development policymaking.

  4. Information mining in remote sensing imagery

    Science.gov (United States)

    Li, Jiang

    The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and

  5. Researching on the process of remote sensing video imagery

    Science.gov (United States)

    Wang, He-rao; Zheng, Xin-qi; Sun, Yi-bo; Jia, Zong-ren; Wang, He-zhan

    Unmanned air vehicle remotely-sensed imagery on the low-altitude has the advantages of higher revolution, easy-shooting, real-time accessing, etc. It's been widely used in mapping , target identification, and other fields in recent years. However, because of conditional limitation, the video images are unstable, the targets move fast, and the shooting background is complex, etc., thus it is difficult to process the video images in this situation. In other fields, especially in the field of computer vision, the researches on video images are more extensive., which is very helpful for processing the remotely-sensed imagery on the low-altitude. Based on this, this paper analyzes and summarizes amounts of video image processing achievement in different fields, including research purposes, data sources, and the pros and cons of technology. Meantime, this paper explores the technology methods more suitable for low-altitude video image processing of remote sensing.

  6. On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery.

    Science.gov (United States)

    Qi, Baogui; Shi, Hao; Zhuang, Yin; Chen, He; Chen, Liang

    2018-04-25

    With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

  7. On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

    Science.gov (United States)

    Qi, Baogui; Zhuang, Yin; Chen, He; Chen, Liang

    2018-01-01

    With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited. PMID:29693585

  8. Monitoring Nuclear Facilities Using Satellite Imagery and Associated Remote Sensing Techniques

    International Nuclear Information System (INIS)

    Lafitte, Marc; Robin, Jean‑Philippe

    2015-01-01

    The mission of the European Union Satellite Centre (SatCen) is “to support the decision making and actions of the European Union in the field of the CFSP and in particular the CSDP, including European Union crisis management missions and operations, by providing, at the request of the Council or the European Union High Representative, products and services resulting from the exploitation of relevant space assets and collateral data, including satellite and aerial imagery, and related services”. The SatCen Non‑Proliferation Team, part of the SatCen Operations Division, is responsible for the analysis of installations that are involved, or could be involved, in the preparation or acquisition of capabilities intended to divert the production of nuclear material for military purposes and, in particular, regarding the spread of Weapons of Mass destruction and their means of delivery. For the last four decades, satellite imagery and associated remote sensing and geospatial techniques have increasingly expanded their capabilities. The unprecedented Very High Resolution (VHR) data currently available, the improved spectral capabilities, the increasing number of sensors and ever increasing computing capacity, has opened up a wide range of new perspectives for remote sensing applications. Concurrently, the availability of open source information (OSINF), has increased exponentially through the medium of the internet. This range of new capabilities for sensors and associated remote sensing techniques have strengthened the SatCen analysis capabilities for the monitoring of suspected proliferation installations for the detection of undeclared nuclear facilities, processes and activities. The combination of these remote sensing techniques, imagery analysis, open source investigation and their integration into Geographic Information Systems (GIS), undoubtedly improve the efficiency and comprehensive analysis capability provided by the SatCen to the EU stake‑holders. The

  9. Advanced Remote Sensing Research

    Science.gov (United States)

    Slonecker, Terrence; Jones, John W.; Price, Susan D.; Hogan, Dianna

    2008-01-01

    'Remote sensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotely sensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remote sensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).

  10. Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

    Science.gov (United States)

    Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

    2018-04-01

    Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

  11. Reduction of Topographic Effect for Curve Number Estimated from Remotely Sensed Imagery

    Science.gov (United States)

    Zhang, Wen-Yan; Lin, Chao-Yuan

    2016-04-01

    The Soil Conservation Service Curve Number (SCS-CN) method is commonly used in hydrology to estimate direct runoff volume. The CN is the empirical parameter which corresponding to land use/land cover, hydrologic soil group and antecedent soil moisture condition. In large watersheds with complex topography, satellite remote sensing is the appropriate approach to acquire the land use change information. However, the topographic effect have been usually found in the remotely sensed imageries and resulted in land use classification. This research selected summer and winter scenes of Landsat-5 TM during 2008 to classified land use in Chen-You-Lan Watershed, Taiwan. The b-correction, the empirical topographic correction method, was applied to Landsat-5 TM data. Land use were categorized using K-mean classification into 4 groups i.e. forest, grassland, agriculture and river. Accuracy assessment of image classification was performed with national land use map. The results showed that after topographic correction, the overall accuracy of classification was increased from 68.0% to 74.5%. The average CN estimated from remotely sensed imagery decreased from 48.69 to 45.35 where the average CN estimated from national LULC map was 44.11. Therefore, the topographic correction method was recommended to normalize the topographic effect from the satellite remote sensing data before estimating the CN.

  12. Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

    Directory of Open Access Journals (Sweden)

    Yongyang Xu

    2018-01-01

    Full Text Available Very high resolution (VHR remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.

  13. USGS Provision of Near Real Time Remotely Sensed Imagery for Emergency Response

    Science.gov (United States)

    Jones, B. K.

    2014-12-01

    The use of remotely sensed imagery in the aftermath of a disaster can have an important impact on the effectiveness of the response for many types of disasters such as floods, earthquakes, volcanic eruptions, landslides, and other natural or human-induced disasters. Ideally, responders in areas that are commonly affected by disasters would have access to archived remote sensing imagery plus the ability to easily obtain the new post event data products. The cost of obtaining and storing the data and the lack of trained professionals who can process the data into a mapping product oftentimes prevent this from happening. USGS Emergency Operations provides remote sensing and geospatial support to emergency managers by providing access to satellite images from numerous domestic and international space agencies including those affiliated with the International Charter Space and Major Disasters and their space-based assets and by hosting and distributing thousands of near real time event related images and map products through the Hazards Data Distribution System (HDDS). These data may include digital elevation models, hydrographic models, base satellite images, vector data layers such as roads, aerial photographs, and other pre and post disaster data. These layers are incorporated into a Web-based browser and data delivery service, the Hazards Data Distribution System (HDDS). The HDDS can be made accessible either to the general public or to specific response agencies. The HDDS concept anticipates customer requirements and provides rapid delivery of data and services. This presentation will provide an overview of remotely sensed imagery that is currently available to support emergency response operations and examples of products that have been created for past events that have provided near real time situational awareness for responding agencies.

  14. Space-Derived Imagery and a Commercial Remote Sensing Industry: Impossible Dream or Inevitable Reality?

    Science.gov (United States)

    Murray, Felsher

    Landsat-1 was launched in 1972 as a research satellite. Many of us viewed this satellite as a precursor to remote sensing "commercialization." Indeed since that time, the birth, growth and maturation of a remote sensing "industry" has been an ongoing objective for much of the U.S. private sector engaged in space and ground-segment activities related to the acquisition, analysis, and dissemination of imagery. In September 1999 a U.S. commercial entity, Space Imaging, Inc. launched its 1-meter pan/4-meter multispectral IKONOS sensor. DigitalGlobe, Inc. (nee EarthWatch, Inc.) matched this feat in October 2001. Thus, a full 30 years later, we are finally on the brink of building a true remote sensing information industry based on the global availability of competitively-priced space- derived imagery of the Earth. The upcoming availability of similar imagery from non-U.S. sources as ImageSat and U.S. sources as ORBIMAGE will only strengthen that reality. However, a remote sensing industry can only grow by allowing these entities (in times of peace) unencumbered access to a world market. And that market continues to expand -- up 11% in 2001, with gross revenues of U.S. commercial remote sensing firms alone reaching 2.44 billion, according to a joint NASA/ASPRS industry survey. However, the 30-year gap between the research-labeled Landsat-1 and our current commercial successes was not technology-driven. That lacuna was purely political -- driven by valid concerns related to national security. Although the world's governments have cooperated thoroughly and completely in areas related to satellite telecommunications, cooperation in space-derived image information is still today done cautiously and on a case-by-case basis -- and then only for science- based undertakings. It is still a fact that, except for the United States, all other Earth-imaging satellites/sensors flying today are owned, operated, and their products disseminated, by national governments -- and not private

  15. Copyright protection of remote sensing imagery by means of digital watermarking

    Science.gov (United States)

    Barni, Mauro; Bartolini, Franco; Cappellini, Vito; Magli, Enrico; Olmo, Gabriella; Zanini, R.

    2001-12-01

    The demand for remote sensing data has increased dramatically mainly due to the large number of possible applications capable to exploit remotely sensed data and images. As in many other fields, along with the increase of market potential and product diffusion, the need arises for some sort of protection of the image products from unauthorized use. Such a need is a very crucial one even because the Internet and other public/private networks have become preferred and effective means of data exchange. An important issue arising when dealing with digital image distribution is copyright protection. Such a problem has been largely addressed by resorting to watermarking technology. Before applying watermarking techniques developed for multimedia applications to remote sensing applications, it is important that the requirements imposed by remote sensing imagery are carefully analyzed to investigate whether they are compatible with existing watermarking techniques. On the basis of these motivations, the contribution of this work is twofold: (1) assessment of the requirements imposed by the characteristics of remotely sensed images on watermark-based copyright protection; (2) discussion of a case study where the performance of two popular, state-of-the-art watermarking techniques are evaluated by the light of the requirements at the previous point.

  16. Redefining nondiscriminatory access to remote sensing imagery and its impact on global transparency

    Science.gov (United States)

    Aten, Michelle L.

    2003-04-01

    Global transparency is founded on the Open Skies philosophy and its precept of non-discriminatory access. Global transparency implies that anyone can have anytime, anyplace access to a wide-array of remotely sensed imagery. The custom of non-discriminatory access requires that datasets of interest must be affordable, usable, and obtainable in a timely fashion devoid of political, economic or technical obstacles. Thus, an assessment of the correlation between the availability of satellite imagery and changes in governmental policies, pricing fluctuations of data, and advances in technology is critical to assessing the viability of global transparency. The Open Skies philosophy was originally proposed at the 1955 Geneva Summit to advocate mutually beneficial aerial reconnaissance missions over the USSR and the US as a verification tool for arms control and non-proliferation agreements. However, due to Cold War tensions, this philosophy and the custom of non-discriminatory were not widely adopted in the civilian remote sensing community until the commissioning of the Landsat Program in 1972. Since this time, commercial high-resolution satellites have drastically changed the circumstances on which the fundamental tenets of this philosophy are based. Since the successful launch of the first of this satellite class, the IKONOS satellite, high-resolution imagery is now available to non-US governments and an unlimited set of non-state actors. As more advanced capabilities are added to the growing assortment of remote sensing satellites, the reality of global transparency will rapidly evolve. This assessment includes an overview of historical precedents and a brief explanation of relevant US policy decisions that define non-discriminatory access with respect to US government and US based corporate assets. It also presents the dynamics of the political, economic, and technical barriers that may dictate or influence the remote sensing community's access to satellite data. In

  17. An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification

    Directory of Open Access Journals (Sweden)

    Yingchang Xiu

    2017-11-01

    Full Text Available Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA and a boosting naïve Bayesian tree (NBTree, is proposed. First, feature extraction was carried out with PCA. The feature set was randomly split into several disjoint subsets; then, PCA was applied to each subset, and new training data for linear extracted features based on original training data were obtained. These steps were repeated several times. Second, based on the new training data, a boosting naïve Bayesian tree was constructed as the base classifier, which aims to achieve lower prediction error than a decision tree in the original rotation forest. At the classification phase, the improved rotation forest has two-layer voting. It first obtains several predictions through weighted voting in a boosting naïve Bayesian tree; then, the first-layer vote predicts by majority to obtain the final result. To examine the classification performance, the improved rotation forest was applied to multi-feature remote-sensing images, including MODIS Enhanced Vegetation Index (EVI imagery time series, MODIS Surface Reflectance products and ancillary data in Shandong Province for 2013. The EVI imagery time series was preprocessed using harmonic analysis of time series (HANTS to reduce the noise effects. The overall accuracy of the final classification result was 89.17%, and the Kappa coefficient was 0.71, which outperforms the original rotation forest and other classifier ensemble results, as well as the NASA land cover product. However, this new algorithm requires more computational time, meaning the efficiency needs to be further improved. Generally, the improved rotation forest has a potential advantage in

  18. Estimation of the distribution of Tabebuia guayacan (Bignoniaceae) using high-resolution remote sensing imagery.

    Science.gov (United States)

    Sánchez-Azofeifa, Arturo; Rivard, Benoit; Wright, Joseph; Feng, Ji-Lu; Li, Peijun; Chong, Mei Mei; Bohlman, Stephanie A

    2011-01-01

    Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments.

  19. The use of remote sensing imagery for environmental land use and flood hazard mapping

    Science.gov (United States)

    Mouat, D. A.; Miller, D. A.; Foster, K. E.

    1976-01-01

    Flood hazard maps have been constructed for Graham, Yuma, and Yavapai Counties in Arizona using remote sensing techniques. Watershed maps of priority areas were selected on the basis of their interest to the county planning staff and represented areas of imminent or ongoing development and those known to be subject to inundation by storm runoff. Landsat color infrared imagery at scales of 1:1,000,000, 1:500,000, and 1:250,000 was used together with high-altitude aerial photography at scales of 1:120,000 and 1:60,000 to determine drainage patterns and erosional features, soil type, and the extent and type of ground cover. The satellite imagery was used in the form of 70 mm chips for enhancement in a color additive viewer and in all available enlargement modes. Field checking served as the main backup to the interpretations. Areas with high susceptibility to flooding were determined with a high level of confidence from the remotely sensed imagery.

  20. Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Rui Guo

    2018-03-01

    Full Text Available Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification.

  1. Remote sensing of ocean currents using ERTS imagery

    Science.gov (United States)

    Maul, G. A.

    1973-01-01

    Major ocean currents such as the Loop Current in the eastern Gulf of Mexico have surface manifestations which can be exploited for remote sensing. Surface chlorophyll-a concentrations, which contribute to the shift in color from blue to green in the open sea, were found to have high spatial variability; significantly lower concentrations were observed in the current. The cyclonic edge of the current is an accumulation zone which causes a peak in chlorophyll concentration. The dynamics also cause surface concentrations of algae, which have a high reflectance in the near infrared. Combining these observations gives rise to an edge effect which can show up as a bright lineation on multispectral imagery delimiting the current's boundary under certain environmental conditions. When high seas introduce bubbles, white caps, and foam, the reflectance is dominated by scattering rather than absorption. This has been detected in ERTS imagery and used for current location.

  2. PARALLEL AND ADAPTIVE UNIFORM-DISTRIBUTED REGISTRATION METHOD FOR CHANG’E-1 LUNAR REMOTE SENSED IMAGERY

    Directory of Open Access Journals (Sweden)

    X. Ning

    2012-08-01

    To resolve the above-mentioned registration difficulties, a parallel and adaptive uniform-distributed registration method for CE-1 lunar remote sensed imagery is proposed in this paper. Based on 6 pairs of randomly selected images, both the standard SIFT algorithm and the parallel and adaptive uniform-distributed registration method were executed, the versatility and effectiveness were assessed. The experimental results indicate that: by applying the parallel and adaptive uniform-distributed registration method, the efficiency of CE-1 lunar remote sensed imagery registration were increased dramatically. Therefore, the proposed method in the paper could acquire uniform-distributed registration results more effectively, the registration difficulties including difficult to obtain results, time-consuming, non-uniform distribution could be successfully solved.

  3. Operational Use of Remote Sensing within USDA

    Science.gov (United States)

    Bethel, Glenn R.

    2007-01-01

    A viewgraph presentation of remote sensing imagery within the USDA is shown. USDA Aerial Photography, Digital Sensors, Hurricane imagery, Remote Sensing Sources, Satellites used by Foreign Agricultural Service, Landsat Acquisitions, and Aerial Acquisitions are also shown.

  4. Optical remote sensing

    CERN Document Server

    Prasad, Saurabh; Chanussot, Jocelyn

    2011-01-01

    Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data: challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, patter

  5. Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

    Science.gov (United States)

    Beck, J. M.; Bridges, S.; Collins, C.; Rushing, J.; Graves, S. J.

    2017-12-01

    Researchers at the Information Technology and Systems Center at the University of Alabama in Huntsville are using Deep Learning with Convolutional Neural Networks (CNNs) to develop a method for enhancing the spatial resolutions of moderate resolution (10-60m) multispectral satellite imagery. This enhancement will effectively match the resolutions of imagery from multiple sensors to provide increased global temporal-spatial coverage for a variety of Earth science products. Our research is centered on using Deep Learning for automatically generating transformations for increasing the spatial resolution of remotely sensed images with different spatial, spectral, and temporal resolutions. One of the most important steps in using images from multiple sensors is to transform the different image layers into the same spatial resolution, preferably the highest spatial resolution, without compromising the spectral information. Recent advances in Deep Learning have shown that CNNs can be used to effectively and efficiently upscale or enhance the spatial resolution of multispectral images with the use of an auxiliary data source such as a high spatial resolution panchromatic image. In contrast, we are using both the spatial and spectral details inherent in low spatial resolution multispectral images for image enhancement without the use of a panchromatic image. This presentation will discuss how this technology will benefit many Earth Science applications that use remotely sensed images with moderate spatial resolutions.

  6. Toward interactive search in remote sensing imagery

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Do [Los Alamos National Laboratory; Harvey, Neal [Los Alamos National Laboratory; Theile, James [Los Alamos National Laboratory

    2010-01-01

    To move from data to information in almost all science and defense applications requires a human-in-the-loop to validate information products, resolve inconsistencies, and account for incomplete and potentially deceptive sources of information. This is a key motivation for visual analytics which aims to develop techniques that complement and empower human users. By contrast, the vast majority of algorithms developed in machine learning aim to replace human users in data exploitation. In this paper we describe a recently introduced machine learning problem, called rare category detection, which may be a better match to visual analytic environments. We describe a new design criteria for this problem, and present comparisons to existing techniques with both synthetic and real-world datasets. We conclude by describing an application in broad-area search of remote sensing imagery.

  7. Toward interactive search in remote sensing imagery

    Science.gov (United States)

    Porter, Reid; Hush, Don; Harvey, Neal; Theiler, James

    2010-04-01

    To move from data to information in almost all science and defense applications requires a human-in-the-loop to validate information products, resolve inconsistencies, and account for incomplete and potentially deceptive sources of information. This is a key motivation for visual analytics which aims to develop techniques that complement and empower human users. By contrast, the vast majority of algorithms developed in machine learning aim to replace human users in data exploitation. In this paper we describe a recently introduced machine learning problem, called rare category detection, which may be a better match to visual analytic environments. We describe a new design criteria for this problem, and present comparisons to existing techniques with both synthetic and real-world datasets. We conclude by describing an application in broad-area search of remote sensing imagery.

  8. A Neutral-Network-Fusion Architecture for Automatic Extraction of Oceanographic Features from Satellite Remote Sensing Imagery

    National Research Council Canada - National Science Library

    Askari, Farid

    1999-01-01

    This report describes an approach for automatic feature detection from fusion of remote sensing imagery using a combination of neural network architecture and the Dempster-Shafer (DS) theory of evidence...

  9. Considerations and techniques for incorporating remotely sensed imagery into the land resource management process.

    Science.gov (United States)

    Brooner, W. G.; Nichols, D. A.

    1972-01-01

    Development of a scheme for utilizing remote sensing technology in an operational program for regional land use planning and land resource management program applications. The scheme utilizes remote sensing imagery as one of several potential inputs to derive desired and necessary data, and considers several alternative approaches to the expansion and/or reduction and analysis of data, using automated data handling techniques. Within this scheme is a five-stage program development which includes: (1) preliminary coordination, (2) interpretation and encoding, (3) creation of data base files, (4) data analysis and generation of desired products, and (5) applications.

  10. An efficient cloud detection method for high resolution remote sensing panchromatic imagery

    Science.gov (United States)

    Li, Chaowei; Lin, Zaiping; Deng, Xinpu

    2018-04-01

    In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

  11. Object-oriented spatial-temporal association rules mining on ocean remote sensing imagery

    International Nuclear Information System (INIS)

    Xue, C J; Dong, Q; Ma, W X

    2014-01-01

    Using the long term marine remote sensing imagery, we develop an object-oriented spatial-temporal association rules mining framework to explore the association rules mining among marine environmental elements. Within the framework, two key issues are addressed. They are how to effectively deal with the related lattices and how to reduce the related dimensions? To deal with the first key issues, this paper develops an object-oriented method for abstracting marine sensitive objects from raster pixels and for representing them with a quadruple. To deal with the second key issues, by embedding the mutual information theory, we construct the direct association pattern tree to reduce the related elements at the first step, and then the Apriori algorithm is used to discover the spatio-temporal associated rules. Finally, Pacific Ocean is taken as a research area and multi- marine remote sensing imagery in recent three decades is used as a case study. The results show that the object-oriented spatio-temporal association rules mining can acquire the associated relationships not only among marine environmental elements in same region, also among the different regions. In addition, the information from association rules mining is much more expressive and informative in space and time than traditional spatio-temporal analysis

  12. Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

    Science.gov (United States)

    Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

    2016-09-01

    Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

  13. 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.

  14. Teachers as Learners Examine Land-Use Change in the Local Environment Using Remote Sensing Imagery

    Science.gov (United States)

    Klagges, Hope; Harbor, Jon; Shepardson, Daniel; Bell, Cheryl; Meyer, Jason; Burgess, Willie; Leuenberger, Ted

    2002-01-01

    In environmental science education, learners are exposed to earth phenomena that occur across a wide range of spatial and temporal scales. However, it is challenging for learners to grasp the significance of spatial and temporal change because they have limited perspectives of the Earth. Within the scientific community, remotely sensed imagery is…

  15. Use of Openly Available Satellite Images for Remote Sensing Education

    Science.gov (United States)

    Wang, C.-K.

    2011-09-01

    With the advent of Google Earth, Google Maps, and Microsoft Bing Maps, high resolution satellite imagery are becoming more easily accessible than ever. It have been the case that the college students may already have wealth experiences with the high resolution satellite imagery by using these software and web services prior to any formal remote sensing education. It is obvious that the remote sensing education should be adjusted to the fact that the audience are already the customers of remote sensing products (through the use of the above mentioned services). This paper reports the use of openly available satellite imagery in an introductory-level remote sensing course in the Department of Geomatics of National Cheng Kung University as a term project. From the experience learned from the fall of 2009 and 2010, it shows that this term project has effectively aroused the students' enthusiastic toward Remote Sensing.

  16. Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning

    Directory of Open Access Journals (Sweden)

    Yang Chen

    2018-05-01

    Full Text Available Accurate information on urban surface water is important for assessing the role it plays in urban ecosystem services in the context of human survival and climate change. The precise extraction of urban water bodies from images is of great significance for urban planning and socioeconomic development. In this paper, a novel deep-learning architecture is proposed for the extraction of urban water bodies from high-resolution remote sensing (HRRS imagery. First, an adaptive simple linear iterative clustering algorithm is applied for segmentation of the remote-sensing image into high-quality superpixels. Then, a new convolutional neural network (CNN architecture is designed that can extract useful high-level features of water bodies from input data in a complex urban background and mark the superpixel as one of two classes: an including water or no-water pixel. Finally, a high-resolution image of water-extracted superpixels is generated. Experimental results show that the proposed method achieved higher accuracy for water extraction from the high-resolution remote-sensing images than traditional approaches, and the average overall accuracy is 99.14%.

  17. Remote sensing for wind power potential: a prospector's handbook

    Energy Technology Data Exchange (ETDEWEB)

    Wade, J.E.; Maule, P.A.; Bodvarsson, G.; Rosenfeld, C.L.; Woolley, S.G.; McClenahan, M.R.

    1983-02-01

    Remote sensing can aid in identifying and locating indicators of wind power potential from the terrestrial, marine, and atmospheric environments (i.e.: wind-deformed trees, white caps, and areas of thermal flux). It is not considered as a tool for determining wind power potential. A wide variety of remotely sensed evidence is described in terms of the scale at which evidence of wind power can be identified, and the appropriate remote sensors for finding such evidence. Remote sensing can be used for regional area prospecting using small-scale imagery. The information from such small-scale imagery is most often qualitative, and if it is transitory, examination of a number of images to verify presistence of the feature may be required. However, this evidence will allow rapid screening of a large area. Medium-scale imagery provides a better picture of the evidence obtained from small-scale imagery. At this level it is best to use existing imagery. Criteria relating to land use, accessibility, and proximity of candidate sites to nearby transmission lines can also be effectively evaluated from medium-scale imagery. Large-scale imagery provides the most quantitative evidence of the strength of wind. Wind-deformed trees can be identified at a large number of sites using only a few hours in locally chartered aircraft. A handheld 35mm camera can adequately document any evidence of wind. Three case studies that employ remote sensing prospecting techniques are described. Based on remotely sensed evidence, the wind power potential in three geographically and climatically diverse areas of the United States is estimated, and the estimates are compared to actual wind data in those regions. In addition, the cost of each survey is discussed. The results indicate that remote sensing for wind power potential is a quick, cost effective, and fairly reliable method for screening large areas for wind power potential.

  18. Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

    Directory of Open Access Journals (Sweden)

    Yuanxin Jia

    2018-03-01

    Full Text Available Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI and mobile phone positioning data (MPPD for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.

  19. Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    ZHENG Zhuo

    2018-05-01

    Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

  20. 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.

  1. Remote sensing image fusion

    CERN Document Server

    Alparone, Luciano; Baronti, Stefano; Garzelli, Andrea

    2015-01-01

    A synthesis of more than ten years of experience, Remote Sensing Image Fusion covers methods specifically designed for remote sensing imagery. The authors supply a comprehensive classification system and rigorous mathematical description of advanced and state-of-the-art methods for pansharpening of multispectral images, fusion of hyperspectral and panchromatic images, and fusion of data from heterogeneous sensors such as optical and synthetic aperture radar (SAR) images and integration of thermal and visible/near-infrared images. They also explore new trends of signal/image processing, such as

  2. Introduction to remote sensing

    CERN Document Server

    Campbell, James B

    2012-01-01

    A leading text for undergraduate- and graduate-level courses, this book introduces widely used forms of remote sensing imagery and their applications in plant sciences, hydrology, earth sciences, and land use analysis. The text provides comprehensive coverage of principal topics and serves as a framework for organizing the vast amount of remote sensing information available on the Web. Including case studies and review questions, the book's four sections and 21 chapters are carefully designed as independent units that instructors can select from as needed for their courses. Illustrations in

  3. International Conference on Remote Sensing Applications for Archaeological Research and World Heritage Conservation

    Science.gov (United States)

    2002-01-01

    Contents include the following: Monitoring the Ancient Countryside: Remote Sensing and GIS at the Chora of Chersonesos (Crimea, Ukraine). Integration of Remote Sensing and GIS for Management Decision Support in the Pendjari Biosphere Reserve (Republic of Benin). Monitoring of deforestation invasion in natural reserves of northern Madagascar based on space imagery. Cartography of Kahuzi-Biega National Park. Cartography and Land Use Change of World Heritage Areas and the Benefits of Remote Sensing and GIS for Conservation. Assessing and Monitoring Vegetation in Nabq Protected Area, South Sinai, Egypt, using combine approach of Satellite Imagery and Land Surveys. Evaluation of forage resources in semi-arid savannah environments with satellite imagery: contribution to the management of a protected area (Nakuru National Park) in Kenya. SOGHA, the Surveillance of Gorilla Habitat in World Heritage sites using Space Technologies. Application of Remote Sensing to monitor the Mont-Saint-Michel Bay (France). Application of Remote Sensing & GIS for the Conservation of Natural and Cultural Heritage Sites of the Southern Province of Sri Lanka. Social and Environmental monitoring of a UNESCO Biosphere Reserve: Case Study over the Vosges du Nord and Pfalzerwald Parks using Corona and Spot Imagery. Satellite Remote Sensing as tool to Monitor Indian Reservation in the Brazilian Amazonia. Remote Sensing and GIS Technology for Monitoring UNESCO World Heritage Sites - A Pilot Project. Urban Green Spaces: Modern Heritage. Monitoring of the technical condition of the St. Sophia Cathedral and related monastic buildings in Kiev with Space Applications, geo-positioning systems and GIS tools. The Murghab delta palaeochannel Reconstruction on the Basis of Remote Sensing from Space. Acquisition, Registration and Application of IKONOS Space Imagery for the cultural World Heritage site at Mew, Turkmenistan. Remote Sensing and VR applications for the reconstruction of archaeological landscapes

  4. Object-based vegetation classification with high resolution remote sensing imagery

    Science.gov (United States)

    Yu, Qian

    Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions

  5. Preface: Remote Sensing in Coastal Environments

    Directory of Open Access Journals (Sweden)

    Deepak R. Mishra

    2016-08-01

    Full Text Available The Special Issue (SI on “Remote Sensing in Coastal Environments” presents a wide range of articles focusing on a variety of remote sensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp habitats. The SI is comprised of twenty-one papers, covering a broad range of research topics that employ remote sensing imagery, models, and techniques to monitor water quality, vegetation, habitat suitability, and geomorphology in the coastal zone. This preface provides a brief summary of each article published in the SI.

  6. Remote RemoteRemoteRemote sensing potential for sensing ...

    African Journals Online (AJOL)

    Remote RemoteRemoteRemote sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing p. A Ngie, F Ahmed, K Abutaleb ...

  7. Best practices in Remote Sensing for REDD+

    DEFF Research Database (Denmark)

    Dons, Klaus; Grogan, Kenneth

    2012-01-01

    due to steep terrain, • phenological gradients across natural, agricultural and forestry ecosystems including plantations and • the need to serve the REDD-specific context of deforestation and forest degradation across spatial and temporal scales make remote sensing based approaches particularly...... be expected from remote sensing imagery and the provided information shall help to better anticipate problems that will be encountered when acquiring, analyzing and interpreting remote sensing data. Beyond remote sensing, it may be a good point of departure for a large group of scientists with a diverse...... and governance, and deforestation and forest degradation processes. The second part summarizes the available literature on remote sensing based good practices for REDD. It largely draws from the documents of the Intergovernmental Panel on Climate Change (IPCC), the United Nations Framework Convention on Climate...

  8. Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA (Final)

    Science.gov (United States)

    EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result o...

  9. Automated Verification of Spatial Resolution in Remotely Sensed Imagery

    Science.gov (United States)

    Davis, Bruce; Ryan, Robert; Holekamp, Kara; Vaughn, Ronald

    2011-01-01

    Image spatial resolution characteristics can vary widely among sources. In the case of aerial-based imaging systems, the image spatial resolution characteristics can even vary between acquisitions. In these systems, aircraft altitude, speed, and sensor look angle all affect image spatial resolution. Image spatial resolution needs to be verified with estimators that include the ground sample distance (GSD), the modulation transfer function (MTF), and the relative edge response (RER), all of which are key components of image quality, along with signal-to-noise ratio (SNR) and dynamic range. Knowledge of spatial resolution parameters is important to determine if features of interest are distinguishable in imagery or associated products, and to develop image restoration algorithms. An automated Spatial Resolution Verification Tool (SRVT) was developed to rapidly determine the spatial resolution characteristics of remotely sensed aerial and satellite imagery. Most current methods for assessing spatial resolution characteristics of imagery rely on pre-deployed engineered targets and are performed only at selected times within preselected scenes. The SRVT addresses these insufficiencies by finding uniform, high-contrast edges from urban scenes and then using these edges to determine standard estimators of spatial resolution, such as the MTF and the RER. The SRVT was developed using the MATLAB programming language and environment. This automated software algorithm assesses every image in an acquired data set, using edges found within each image, and in many cases eliminating the need for dedicated edge targets. The SRVT automatically identifies high-contrast, uniform edges and calculates the MTF and RER of each image, and when possible, within sections of an image, so that the variation of spatial resolution characteristics across the image can be analyzed. The automated algorithm is capable of quickly verifying the spatial resolution quality of all images within a data

  10. Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

    Science.gov (United States)

    Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

    2016-09-01

    classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.

  11. Remote sensing applications for the dam industry

    Energy Technology Data Exchange (ETDEWEB)

    Pryse-Phillips, A.; Woolgar, R. [Hatch Ltd., St. John' s, NL (Canada); Puestow, T.; Warren, S. [Memorial Univ. of Newfoundland, St. John' s, NL (Canada). C-Core; Rogers, K. [Nalcor Energy, St. John' s, NL (Canada); Khan, A. [Government of Newfoundland and Labrador, St. Johns, NL (Canada)

    2009-07-01

    There has been an increase in the earth observation missions providing satellite imagery for operational monitoring applications. This technique has been found to be especially useful for the surveillance of large, remote areas, which is challenging to achieve in a cost-effective manner by conventional field-based or aerial means. This paper discussed the utility of satellite-based monitoring for different applications relevant to hydrology and water resources management. Emphasis was placed on the monitoring of river ice covers in near, real-time and water resources management. The paper first outlined river ice monitoring using remote sensing on the Lower Churchill River. The benefits of remote sensing over traditional survey methods for the dam industry was then outlined. Satellite image acquisition and interpretation for the Churchill River was then presented. Several images were offered. Watershed physiographic characterization using remote sensing was also described. It was concluded that satellite imagery proved to be a useful tool to develop physiographic characteristics when conducting rainfall-runoff modelling. 3 refs., 1 tab., 11 figs.

  12. Remote Sensing Digital Image Analysis An Introduction

    CERN Document Server

    Richards, John A

    2013-01-01

    Remote Sensing Digital Image Analysis provides the non-specialist with a treatment of the quantitative analysis of satellite and aircraft derived remotely sensed data. Since the first edition of the book there have been significant developments in the algorithms used for the processing and analysis of remote sensing imagery; nevertheless many of the fundamentals have substantially remained the same.  This new edition presents material that has retained value since those early days, along with new techniques that can be incorporated into an operational framework for the analysis of remote sensing data. The book is designed as a teaching text for the senior undergraduate and postgraduate student, and as a fundamental treatment for those engaged in research using digital image processing in remote sensing.  The presentation level is for the mathematical non-specialist.  Since the very great number of operational users of remote sensing come from the earth sciences communities, the text is pitched at a leve...

  13. Geological remote sensing signatures of terrestrial impact craters

    International Nuclear Information System (INIS)

    Garvin, J.B.; Schnetzler, C.; Grieve, R.A.F.

    1988-01-01

    Geological remote sensing techniques can be used to investigate structural, depositional, and shock metamorphic effects associated with hypervelocity impact structures, some of which may be linked to global Earth system catastrophies. Although detailed laboratory and field investigations are necessary to establish conclusive evidence of an impact origin for suspected crater landforms, the synoptic perspective provided by various remote sensing systems can often serve as a pathfinder to key deposits which can then be targetted for intensive field study. In addition, remote sensing imagery can be used as a tool in the search for impact and other catastrophic explosion landforms on the basis of localized disruption and anomaly patterns. In order to reconstruct original dimensions of large, complex impact features in isolated, inaccessible regions, remote sensing imagery can be used to make preliminary estimates in the absence of field geophysical surveys. The experienced gained from two decades of planetary remote sensing of impact craters on the terrestrial planets, as well as the techniques developed for recognizing stages of degradation and initial crater morphology, can now be applied to the problem of discovering and studying eroded impact landforms on Earth. Preliminary results of remote sensing analyses of a set of terrestrial impact features in various states of degradation, geologic settings, and for a broad range of diameters and hence energies of formation are summarized. The intention is to develop a database of remote sensing signatures for catastrophic impact landforms which can then be used in EOS-era global surveys as the basis for locating the possibly hundreds of missing impact structures

  14. National Satellite Land Remote Sensing Data Archive

    Science.gov (United States)

    Faundeen, John L.; Longhenry, Ryan

    2018-06-13

    The National Satellite Land Remote Sensing Data Archive is managed on behalf of the Secretary of the Interior by the U.S. Geological Survey’s Earth Resources Observation and Science Center. The Land Remote Sensing Policy Act of 1992 (51 U.S.C. §601) directed the U.S. Department of the Interior to establish a permanent global archive consisting of imagery over land areas obtained from satellites orbiting the Earth. The law also directed the U.S. Department of the Interior, delegated to the U.S. Geological Survey, to ensure proper storage and preservation of imagery, and timely access for all parties. Since 2008, these images have been available at no cost to the user.

  15. Remote sensing and vegetation mapping in South Africa

    Directory of Open Access Journals (Sweden)

    M. L. Jarman

    1983-12-01

    Full Text Available The kinds of imagery, types of data and general relationships between scale of study, scale of mapping and scale of remote sensing products that are appropriate to the South African situation for visual and digital analysis are presented. The type of remote sensing product and processing, the type of field exercise appropriate to each, and the purpose of producing maps at each scale are discussed. Lack of repetitive imagery to date has not allowed for the full investigation of monitoring potential and careful planning at national level is needed to ensure availability of imagery for monitoring purposes. Map production processes which are rapid and accurate should be utilized. An integrated approach to vegetation mapping and surveying, which incorporates the best features of both visual and digital processing, is recommended for use.

  16. Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

    Science.gov (United States)

    Maxwell, S.K.; Meliker, J.R.; Goovaerts, P.

    2010-01-01

    In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

  17. Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Qingling Zhang

    2015-09-01

    Full Text Available Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI composite and DMSP/OLS Night Time Light (NTL to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced cloud computing facilities and the open Landsat data archives available, NDUI has the potential for global studies at the 30 m scale.

  18. An overview of passive remote sensing for post-fire monitoring

    Directory of Open Access Journals (Sweden)

    2005-01-01

    Full Text Available Monitoring of forest burnt areas has several aims: to locate and estimate the extent of such areas; to assess the damages suffered by the forest stands; to check the ability of the ecosystem to naturally recover after the fire; to support the planning of reclamation interventions; to assess the dynamics (pattern and speed of the natural recovery; to check the outcome of any eventual restoration intervention. Remote sensing is an important source of information to support all such tasks. In the last decades, the effectiveness of remotely sensed imagery is increasing due to the advancement of tools and techniques, and to the lowering of the costs, in relative terms. For an effective support to post-fire management (burnt scar perimeter mapping, damage severity assessment, post-fire vegetation monitoring, a mapping scale of at least 1:10000-1:20000 is required: hence, the selection of remotely sensed data is restricted to aerial imagery and to satellite imagery characterized by high (HR and, above all, very high (VHR spatial resolution. In the last decade, HR and VHR passive remote sensing has widespread, providing affordable multitemporal and multispectral pictures of the considered phenomena, at different scales (spatial, temporal and spectral resolutions with reference to the monitoring needs. In the light of such a potential, the integration of GPS field survey and HR (Landsat 7, Spot HVR and VHR satellite imagery (Ikonos, Quickbird, Spot 5 is currently sought as a highly viable option for the post-fire monitoring.

  19. A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

    Science.gov (United States)

    Mui, Amy B.

    Freshwater turtles are a globally declining taxa, and estimates of population status are not available for many species. Primary causes of decline stem from widespread habitat loss and degradation, and obtaining spatially-explicit information on remaining habitat across a relevant spatial scale has proven challenging. The discipline of remote sensing science has been employed widely in studies of biodiversity conservation, but it has not been utilized as frequently for cryptic, and less vagile species such as turtles, despite their vulnerable status. The work presented in this thesis investigates how multi-temporal remote sensing imagery can contribute key information for building spatially-explicit and temporally dynamic models of habitat and connectivity for the threatened, Blanding's turtle (Emydoidea blandingii) in southern Ontario, Canada. I began with outlining a methodological approach for delineating freshwater wetlands from high spatial resolution remote sensing imagery, using a geographic object-based image analysis (GEOBIA) approach. This method was applied to three different landscapes in southern Ontario, and across two biologically relevant seasons during the active (non-hibernating) period of Blanding's turtles. Next, relevant environmental variables associated with turtle presence were extracted from remote sensing imagery, and a boosted regression tree model was developed to predict the probability of occurrence of this species. Finally, I analysed the movement potential for Blanding's turtles in a disturbed landscape using a combination of approaches. Results indicate that (1) a parsimonious GEOBIA approach to land cover mapping, incorporating texture, spectral indices, and topographic information can map heterogeneous land cover with high accuracy, (2) remote-sensing derived environmental variables can be used to build habitat models with strong predictive power, and (3) connectivity potential is best estimated using a variety of approaches

  20. NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

    Science.gov (United States)

    Chirayath, Ved

    2018-01-01

    We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and

  1. Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields

    Science.gov (United States)

    Chen, Huili; Liang, Zhongyao; Liu, Yong; Liang, Qiuhua; Xie, Shuguang

    2017-10-01

    The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process

  2. Calculating Viewing Angles Pixel by Pixel in Optical Remote Sensing Satellite Imagery Using the Rational Function Model

    OpenAIRE

    Kai Xu; Guo Zhang; Qingjun Zhang; Deren Li

    2018-01-01

    In studies involving the extraction of surface physical parameters using optical remote sensing satellite imagery, sun-sensor geometry must be known, especially for sensor viewing angles. However, while pixel-by-pixel acquisitions of sensor viewing angles are of critical importance to many studies, currently available algorithms for calculating sensor-viewing angles focus only on the center-point pixel or are complicated and are not well known. Thus, this study aims to provide a simple and ge...

  3. Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

    Science.gov (United States)

    Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

    2016-12-01

    Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

  4. An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Haiyan Gu

    2018-04-01

    Full Text Available Remote sensing (RS image segmentation is an essential step in geographic object-based image analysis (GEOBIA to ultimately derive “meaningful objects”. While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA. Specifically, a minimum spanning tree (MST algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the “reverse searching-forward processing” chain based on message passing interface (MPI parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2 and different selected landscapes (residential/industrial, residential/agriculture covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral, while the accuracy is comparable with that of the FNEA method.

  5. A DATA FIELD METHOD FOR URBAN REMOTELY SENSED IMAGERY CLASSIFICATION CONSIDERING SPATIAL CORRELATION

    Directory of Open Access Journals (Sweden)

    Y. Zhang

    2016-06-01

    Full Text Available Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary’s C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM for the classification of multi-features (e.g. the spectral feature and spatial correlation feature. In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

  6. The Potential of AI Techniques for Remote Sensing

    Science.gov (United States)

    Estes, J. E.; Sailer, C. T. (Principal Investigator); Tinney, L. R.

    1984-01-01

    The current status of artificial intelligence AI technology is discussed along with imagery data management, database interrogation, and decision making. Techniques adapted from the field of artificial intelligence (AI) have significant, wide ranging impacts upon computer-assisted remote sensing analysis. AI based techniques offer a powerful and fundamentally different approach to many remote sensing tasks. In addition to computer assisted analysis, AI techniques can also aid onboard spacecraft data processing and analysis and database access and query.

  7. Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

    Science.gov (United States)

    Westover, Matthew; Baxter, Jared; Baxter, Rick; Day, Casey; Jensen, Ryan; Petersen, Steve; Larsen, Randy

    2016-01-01

    Greater sage-grouse populations have decreased steadily since European settlement in western North America. Reduced availability of brood-rearing habitat has been identified as a limiting factor for many populations. We used radio-telemetry to acquire locations of sage-grouse broods from 1998 to 2012 in Strawberry Valley, Utah. Using these locations and remotely-sensed NAIP (National Agricultural Imagery Program) imagery, we 1) determined which characteristics of brood-rearing habitat could be used in widely available, high resolution imagery 2) assessed the spatial extent at which sage-grouse selected brood-rearing habitat, and 3) created a predictive habitat model to identify areas of preferred brood-rearing habitat. We used AIC model selection to evaluate support for a list of variables derived from remotely-sensed imagery. We examined the relationship of these explanatory variables at three spatial extents (45, 200, and 795 meter radii). Our top model included 10 variables (percent shrub, percent grass, percent tree, percent paved road, percent riparian, meters of sage/tree edge, meters of riparian/tree edge, distance to tree, distance to transmission lines, and distance to permanent structures). Variables from each spatial extent were represented in our top model with the majority being associated with the larger (795 meter) spatial extent. When applied to our study area, our top model predicted 75% of naïve brood locations suggesting reasonable success using this method and widely available NAIP imagery. We encourage application of our methodology to other sage-grouse populations and species of conservation concern.

  8. [Retrieval of crown closure of moso bamboo forest using unmanned aerial vehicle (UAV) remotely sensed imagery based on geometric-optical model].

    Science.gov (United States)

    Wang, Cong; Du, Hua-qiang; Zhou, Guo-mo; Xu, Xiao-jun; Sun, Shao-bo; Gao, Guo-long

    2015-05-01

    This research focused on the application of remotely sensed imagery from unmanned aerial vehicle (UAV) with high spatial resolution for the estimation of crown closure of moso bamboo forest based on the geometric-optical model, and analyzed the influence of unconstrained and fully constrained linear spectral mixture analysis (SMA) on the accuracy of the estimated results. The results demonstrated that the combination of UAV remotely sensed imagery and geometric-optical model could, to some degrees, achieve the estimation of crown closure. However, the different SMA methods led to significant differentiation in the estimation accuracy. Compared with unconstrained SMA, the fully constrained linear SMA method resulted in higher accuracy of the estimated values, with the coefficient of determination (R2) of 0.63 at 0.01 level, against the measured values acquired during the field survey. Root mean square error (RMSE) of approximate 0.04 was low, indicating that the usage of fully constrained linear SMA could bring about better results in crown closure estimation, which was closer to the actual condition in moso bamboo forest.

  9. A review and analysis of neural networks for classification of remotely sensed multispectral imagery

    Science.gov (United States)

    Paola, Justin D.; Schowengerdt, Robert A.

    1993-01-01

    A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

  10. The Federal Oil Spill Team for Emergency Response Remote Sensing (FOSTERRS)

    Science.gov (United States)

    Stough, T.; Jones, C. E.; Leifer, I.; Lindsay, F. E.; Murray, J. J.; Ramirez, E. M.; Salemi, A.; Streett, D.

    2014-12-01

    Oil spills can cause enormous ecological and economic devastation, necessitating application of the best science and technology available, for which remote sensing plays a critical role in detection and monitoring of oil spills. The FOSTERRS interagency working group seeks to ensure that during an oil spill, remote sensing assets (satellite/aircraft) and analysis techniques are quickly, effectively and seamlessly available to oil spills responders. FOSTERRS enables cooperation between agencies with core environmental remote sensing assets and capabilities and academic and industry experts to act as an oil spill remote sensing information clearinghouse. The US government and its collaborators have a broad variety of aircraft and satellite sensors, imagery interrogation techniques and other technology that can provide indispensable remote sensing information to agencies, emergency responders and the public during an oil spill. Specifically, FOSTERRS will work to ensure that (1) suitable aircraft and satellite imagery and radar observations are quickly made available in a manner that can be integrated into oil spill detection and mitigation efforts, (2) existing imagery interrogation techniques are in the hands of those who will provide the 24 x 7 operational support and (3) efforts are made to develop new technology where the existing techniques do not provide oil spills responders with important information they need. The FOSTERRS mission goal places it in an ideal place for identification of critical technological needs, and identifying bottlenecks in technology acceptance. The core FOSTERRS team incorporates representation for operations and science for agencies with relevant instrumental and platform assets (NASA, NOAA, USGS, NRL). FOSTERRS membership will open to a wide range of end-user agencies and planned observer status from industry and academic experts, and eventually international partners. Through these collaborations, FOSTERRS facilitates interagency

  11. Remote sensing inputs to landscape models which predict future spatial land use patterns for hydrologic models

    Science.gov (United States)

    Miller, L. D.; Tom, C.; Nualchawee, K.

    1977-01-01

    A tropical forest area of Northern Thailand provided a test case of the application of the approach in more natural surroundings. Remote sensing imagery subjected to proper computer analysis has been shown to be a very useful means of collecting spatial data for the science of hydrology. Remote sensing products provide direct input to hydrologic models and practical data bases for planning large and small-scale hydrologic developments. Combining the available remote sensing imagery together with available map information in the landscape model provides a basis for substantial improvements in these applications.

  12. Unmanned aerial systems for photogrammetry and remote sensing: A review

    OpenAIRE

    Colomina, Ismael; Molina, Pere

    2014-01-01

    We discuss the evolution and state-of-the-art of the use of Unmanned Aerial Systems (UAS) in the field of Photogrammetry and Remote Sensing (PaRS). UAS, Remotely-Piloted Aerial Systems, Unmanned Aerial Vehicles or simply, drones are a hot topic comprising a diverse array of aspects including technology, privacy rights, safety and regulations, and even war and peace. Modern photogrammetry and remote sensing identified the potential of UAS-sourced imagery more than thirty years ago. In the last...

  13. Remote Sensing Applications to Water Quality Management in Florida

    Science.gov (United States)

    Lehrter, J. C.; Schaeffer, B. A.; Hagy, J.; Spiering, B.; Barnes, B.; Hu, C.; Le, C.; McEachron, L.; Underwood, L. W.; Ellis, C.; Fisher, B.

    2013-12-01

    Optical datasets from estuarine and coastal systems are increasingly available for remote sensing algorithm development, validation, and application. With validated algorithms, the data streams from satellite sensors can provide unprecedented spatial and temporal data for local and regional coastal water quality management. Our presentation will highlight two recent applications of optical data and remote sensing to water quality decision-making in coastal regions of the state of Florida; (1) informing the development of estuarine and coastal nutrient criteria for the state of Florida and (2) informing the rezoning of the Florida Keys National Marine Sanctuary. These efforts involved building up the underlying science to demonstrate the applicability of satellite data as well as an outreach component to educate decision-makers about the use, utility, and uncertainties of remote sensing data products. Scientific developments included testing existing algorithms and generating new algorithms for water clarity and chlorophylla in case II (CDOM or turbidity dominated) estuarine and coastal waters and demonstrating the accuracy of remote sensing data products in comparison to traditional field based measurements. Including members from decision-making organizations on the research team and interacting with decision-makers early and often in the process were key factors for the success of the outreach efforts and the eventual adoption of satellite data into the data records and analyses used in decision-making. Florida coastal water bodies (black boxes) for which remote sensing imagery were applied to derive numeric nutrient criteria and in situ observations (black dots) used to validate imagery. Florida ocean color applied to development of numeric nutrient criteria

  14. Remote sensing approach to structural modelling

    International Nuclear Information System (INIS)

    El Ghawaby, M.A.

    1989-01-01

    Remote sensing techniques are quite dependable tools in investigating geologic problems, specially those related to structural aspects. The Landsat imagery provides discrimination between rock units, detection of large scale structures as folds and faults, as well as small scale fabric elements such as foliation and banding. In order to fulfill the aim of geologic application of remote sensing, some essential surveying maps might be done from images prior to the structural interpretation: land-use, land-form drainage pattern, lithological unit and structural lineament maps. Afterwards, the field verification should lead to interpretation of a comprehensive structural model of the study area to apply for the target problem. To deduce such a model, there are two ways of analysis the interpreter may go through: the direct and the indirect methods. The direct one is needed in cases where the resources or the targets are controlled by an obvious or exposed structural element or pattern. The indirect way is necessary for areas where the target is governed by a complicated structural pattern. Some case histories of structural modelling methods applied successfully for exploration of radioactive minerals, iron deposits and groundwater aquifers in Egypt are presented. The progress in imagery, enhancement and integration of remote sensing data with the other geophysical and geochemical data allow a geologic interpretation to be carried out which become better than that achieved with either of the individual data sets. 9 refs

  15. SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY

    Directory of Open Access Journals (Sweden)

    X. Han

    2016-06-01

    Full Text Available Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE. Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI datasets – Pavia University dataset and the Kennedy Space Centre (KSC dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

  16. A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    WANG Shaoyu

    2016-12-01

    Full Text Available Remote sensing imagery has abundant spatial correlation information, but traditional pixel-based clustering algorithms don't take the spatial information into account, therefore the results are often not good. To this issue, a modified FCM classifier constrained by conditional random field model is proposed. Adjacent pixels' priori classified information will have a constraint on the classification of the center pixel, thus extracting spatial correlation information. Spectral information and spatial correlation information are considered at the same time when clustering based on second order conditional random field. What's more, the global optimal inference of pixel's classified posterior probability can be get using loopy belief propagation. The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object, and the classification accuracy is higher than traditional algorithms.

  17. Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

    Science.gov (United States)

    McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

    2008-01-01

    The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify

  18. Multispectral remote sensing of inland wetlands in South Carolina: selecting the appropriate sensor

    International Nuclear Information System (INIS)

    Jensen, J.R.; Hodgson, M.; Christensen, E.J.; Mackey, H.E.; Sharitz, R.R.

    1984-01-01

    This research summarizes the utility of remote sensing for mapping both local (SRP) and regional wetlands including: stream delta areas, using aircraft multispectral scanner (MSS) imagery and large scale aerial photography; the SRP river swamp, using aircraft MSS and LANDSAT thematic mapper imagery; the Savannah River watershed, using LANDSAT MSS Imagery

  19. Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models

    Science.gov (United States)

    H. Viana; J. Aranha; D. Lopes; Warren B. Cohen

    2012-01-01

    Spatially crown biomass of Pinus pinaster stands and shrubland above-ground biomass (AGB) estimation was carried-out in a region located in Centre-North Portugal, by means of different approaches including forest inventory data, remotely sensed imagery and spatial prediction models. Two cover types (pine stands and shrubland) were inventoried and...

  20. Data management and dissemination challenges for commercial remote sensing

    Science.gov (United States)

    Straeter, Terry A.

    1996-12-01

    Looking toward 2000, the ways by which commercial satellite imagery and imagery products are managed by the various remote sensing companies will be dictated by financial considerations, not technical feasibility. In the convergence of technologies that will shape the commercial companies in 2000, the most influential will likely be electronic commerce via the Internet. This paper discusses the character of these combined forces and speculates on how the industry might respond.

  1. Bringing an ecological view of change to Landsat-based remote sensing

    Science.gov (United States)

    Kennedy, Robert E.; Andrefouet, Serge; Cohen, Warren; Gomez, Cristina; Griffiths, Patrick; Hais, Martin; Healey, Sean; Helmer, Eileen H.; Hostert, Patrick; Lyons, Mitchell; Meigs, Garrett; Pflugmacher, Dirk; Phinn, Stuart; Powell, Scott; Scarth, Peter; Susmita, Sen; Schroeder, Todd A.; Schneider, Annemarie; Sonnenschein, Ruth; Vogelmann, James; Wulder, Michael A.; Zhu, Zhe

    2014-01-01

    When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long-term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

  2. Noninvasive Remote Sensing Techniques for Infrastructures Diagnostics

    Directory of Open Access Journals (Sweden)

    Angelo Palombo

    2011-01-01

    Full Text Available The present paper aims at analyzing the potentialities of noninvasive remote sensing techniques used for detecting the conservation status of infrastructures. The applied remote sensing techniques are ground-based microwave radar interferometer and InfraRed Thermography (IRT to study a particular structure planned and made in the framework of the ISTIMES project (funded by the European Commission in the frame of a joint Call “ICT and Security” of the Seventh Framework Programme. To exploit the effectiveness of the high-resolution remote sensing techniques applied we will use the high-frequency thermal camera to measure the structures oscillations by high-frequency analysis and ground-based microwave radar interferometer to measure the dynamic displacement of several points belonging to a large structure. The paper describes the preliminary research results and discusses on the future applicability and techniques developments for integrating high-frequency time series data of the thermal imagery and ground-based microwave radar interferometer data.

  3. Modelling population distribution using remote sensing imagery and location-based data

    Science.gov (United States)

    Song, J.; Prishchepov, A. V.

    2017-12-01

    Detailed spatial distribution of population density is essential for city studies such as urban planning, environmental pollution and city emergency, even estimate pressure on the environment and human exposure and risks to health. However, most of the researches used census data as the detailed dynamic population distribution are difficult to acquire, especially in microscale research. This research describes a method using remote sensing imagery and location-based data to model population distribution at the function zone level. Firstly, urban functional zones within a city were mapped by high-resolution remote sensing images and POIs. The workflow of functional zones extraction includes five parts: (1) Urban land use classification. (2) Segmenting images in built-up area. (3) Identification of functional segments by POIs. (4) Identification of functional blocks by functional segmentation and weight coefficients. (5) Assessing accuracy by validation points. The result showed as Fig.1. Secondly, we applied ordinary least square and geographically weighted regression to assess spatial nonstationary relationship between light digital number (DN) and population density of sampling points. The two methods were employed to predict the population distribution over the research area. The R²of GWR model were in the order of 0.7 and typically showed significant variations over the region than traditional OLS model. The result showed as Fig.2.Validation with sampling points of population density demonstrated that the result predicted by the GWR model correlated well with light value. The result showed as Fig.3. Results showed: (1) Population density is not linear correlated with light brightness using global model. (2) VIIRS night-time light data could estimate population density integrating functional zones at city level. (3) GWR is a robust model to map population distribution, the adjusted R2 of corresponding GWR models were higher than the optimal OLS models

  4. Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Fan Hu

    2016-06-01

    Full Text Available Scene classification of high-resolution remote sensing (HRRS imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

  5. Remote sensing science - new concepts and applications

    Energy Technology Data Exchange (ETDEWEB)

    Gerstl, S.A.; Cooke, B.J.; Henderson, B.G.; Love, S.P.; Zardecki, A.

    1996-10-01

    This is the final report of a one-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The science and technology of satellite remote sensing is an emerging interdisciplinary field that is growing rapidly with many global and regional applications requiring quantitative sensing of earth`s surface features as well as its atmosphere from space. It is possible today to resolve structures on the earth`s surface as small as one meter from space. If this high spatial resolution is coupled with high spectral resolution, instant object identification can also be achieved. To interpret these spectral signatures correctly, it is necessary to perform a computational correction on the satellite imagery that removes the distorting effects of the atmosphere. This project studied such new concepts and applied innovative new approaches in remote sensing science.

  6. Integrated remotely sensed datasets for disaster management

    Science.gov (United States)

    McCarthy, Timothy; Farrell, Ronan; Curtis, Andrew; Fotheringham, A. Stewart

    2008-10-01

    Video imagery can be acquired from aerial, terrestrial and marine based platforms and has been exploited for a range of remote sensing applications over the past two decades. Examples include coastal surveys using aerial video, routecorridor infrastructures surveys using vehicle mounted video cameras, aerial surveys over forestry and agriculture, underwater habitat mapping and disaster management. Many of these video systems are based on interlaced, television standards such as North America's NTSC and European SECAM and PAL television systems that are then recorded using various video formats. This technology has recently being employed as a front-line, remote sensing technology for damage assessment post-disaster. This paper traces the development of spatial video as a remote sensing tool from the early 1980s to the present day. The background to a new spatial-video research initiative based at National University of Ireland, Maynooth, (NUIM) is described. New improvements are proposed and include; low-cost encoders, easy to use software decoders, timing issues and interoperability. These developments will enable specialists and non-specialists collect, process and integrate these datasets within minimal support. This integrated approach will enable decision makers to access relevant remotely sensed datasets quickly and so, carry out rapid damage assessment during and post-disaster.

  7. Remote Sensing

    CERN Document Server

    Khorram, Siamak; Koch, Frank H; van der Wiele, Cynthia F

    2012-01-01

    Remote Sensing provides information on how remote sensing relates to the natural resources inventory, management, and monitoring, as well as environmental concerns. It explains the role of this new technology in current global challenges. "Remote Sensing" will discuss remotely sensed data application payloads and platforms, along with the methodologies involving image processing techniques as applied to remotely sensed data. This title provides information on image classification techniques and image registration, data integration, and data fusion techniques. How this technology applies to natural resources and environmental concerns will also be discussed.

  8. Semantic Segmentation of Convolutional Neural Network for Supervised Classification of Multispectral Remote Sensing

    Science.gov (United States)

    Xue, L.; Liu, C.; Wu, Y.; Li, H.

    2018-04-01

    Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution output. In the network , We added BN layers, which is more conducive to the reverse pass. Moreover, after upsampling convolution , we add dropout layers to prevent overfitting. They are promoted to get more precise segmentation results. To verify this network architecture, we used a Kaggle dataset. Experimental results show that U-Net achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

  9. Spatial and temporal remote sensing data fusion for vegetation monitoring

    Science.gov (United States)

    The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery...

  10. Environmental assessment of coal waste mounds in Japan using remote sensing techniques

    Energy Technology Data Exchange (ETDEWEB)

    Lewis, A J; Gotoh, K; Aoyama, K; Aoki, S [Louisiana State University, Baton Rouge, LA (United States). Department of Geography and Anthropology

    1993-01-01

    Focuses on the application of remote sensing techniques to the study of coal waste mounds. The situation at the coal waste mounds in Fukuoka, Japan is cited. Guidelines on film parameters, photographic keys and tasks required to inventory, monitor and manage coal waste mounds in Japan are addressed. Application of photogrammetry, remote sensing, aerial photography and satellite imagery techniques in monitoring spoil banks is reviewed. Applicability of the techniques is discussed. 24 refs.

  11. Coral reef remote sensing a guide for mapping, monitoring and management

    CERN Document Server

    Goodman, James A; Phinn, Stuart R

    2013-01-01

    This book offers a multi-level examination of remote-sensing technologies for mapping and monitoring coral reef ecosystems, ranging from satellite and airborne imagery to ship-based observation. Includes examples of practical applications of the technologies.

  12. Remote sensing for global change, climate change and atmosphere and ocean forecasting. Volume 1

    International Nuclear Information System (INIS)

    1992-01-01

    This volume is separated in three sessions. First part is on remote sensing for global change (with global modelling, land cover change on global scale, ocean colour studies of marine biosphere, biological and hydrological interactions and large scale experiments). Second part is on remote sensing for climate change (with earth radiation and clouds, sea ice, global climate research programme). Third part is on remote sensing for atmosphere and ocean forecasting (with temperatures and humidity, winds, data assimilation, cloud imagery, sea surface temperature, ocean waves and topography). (A.B.). refs., figs., tabs

  13. Remote sensing education and Internet/World Wide Web technology

    Science.gov (United States)

    Griffith, J.A.; Egbert, S.L.

    2001-01-01

    Remote sensing education is increasingly in demand across academic and professional disciplines. Meanwhile, Internet technology and the World Wide Web (WWW) are being more frequently employed as teaching tools in remote sensing and other disciplines. The current wealth of information on the Internet and World Wide Web must be distilled, nonetheless, to be useful in remote sensing education. An extensive literature base is developing on the WWW as a tool in education and in teaching remote sensing. This literature reveals benefits and limitations of the WWW, and can guide its implementation. Among the most beneficial aspects of the Web are increased access to remote sensing expertise regardless of geographic location, increased access to current material, and access to extensive archives of satellite imagery and aerial photography. As with other teaching innovations, using the WWW/Internet may well mean more work, not less, for teachers, at least at the stage of early adoption. Also, information posted on Web sites is not always accurate. Development stages of this technology range from on-line posting of syllabi and lecture notes to on-line laboratory exercises and animated landscape flyovers and on-line image processing. The advantages of WWW/Internet technology may likely outweigh the costs of implementing it as a teaching tool.

  14. Remote sensing estimates of impervious surfaces for pluvial flood modelling

    DEFF Research Database (Denmark)

    Kaspersen, Per Skougaard; Drews, Martin

    This paper investigates the accuracy of medium resolution (MR) satellite imagery in estimating impervious surfaces for European cities at the detail required for pluvial flood modelling. Using remote sensing techniques enables precise and systematic quantification of the influence of the past 30...

  15. Water column correction for coral reef studies by remote sensing.

    Science.gov (United States)

    Zoffoli, Maria Laura; Frouin, Robert; Kampel, Milton

    2014-09-11

    Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

  16. Applications of ecological concepts and remote sensing technologies in archaeological site reconnaissance

    Science.gov (United States)

    Miller, W. Frank; Sever, Thomas L.; Lee, C. Daniel

    1991-01-01

    The concept of integrating ecological perspectives on early man's settlement patterns with advanced remote sensing technologies shows promise for predictive site modeling. Early work with aerial imagery and ecosystem analysis is discussed with respect to the development of a major project in Maya archaeology supported by NASA and the National Geographic Society with technical support from the Mississippi State Remote Sensing Center. A preliminary site reconnaissance model will be developed for testing during the 1991 field season.

  17. Man-Made Object Extraction from Remote Sensing Imagery by Graph-Based Manifold Ranking

    Science.gov (United States)

    He, Y.; Wang, X.; Hu, X. Y.; Liu, S. H.

    2018-04-01

    The automatic extraction of man-made objects from remote sensing imagery is useful in many applications. This paper proposes an algorithm for extracting man-made objects automatically by integrating a graph model with the manifold ranking algorithm. Initially, we estimate a priori value of the man-made objects with the use of symmetric and contrast features. The graph model is established to represent the spatial relationships among pre-segmented superpixels, which are used as the graph nodes. Multiple characteristics, namely colour, texture and main direction, are used to compute the weights of the adjacent nodes. Manifold ranking effectively explores the relationships among all the nodes in the feature space as well as initial query assignment; thus, it is applied to generate a ranking map, which indicates the scores of the man-made objects. The man-made objects are then segmented on the basis of the ranking map. Two typical segmentation algorithms are compared with the proposed algorithm. Experimental results show that the proposed algorithm can extract man-made objects with high recognition rate and low omission rate.

  18. An Overview on Data Mining of Nighttime Light Remote Sensing

    Directory of Open Access Journals (Sweden)

    LI Deren

    2015-06-01

    Full Text Available When observing the Earth from above at night, it is clear that the human settlement and major economic regions emit glorious light. At cloud-free nights, some remote sensing satellites can record visible radiance source, including city light, fishing boat light and fire, and these nighttime cloud-free images are remotely sensed nighttime light images. Different from daytime remote sensing, nighttime light remote sensing provides a unique perspective on human social activities, thus it has been widely used for spatial data mining of socioeconomic domains. Historically, researches on nighttime light remote sensing mostly focus on urban land cover and urban expansion mapping using DMSP/OLS imagery, but the nighttime light images are not the unique remote sensing source to do these works. Through decades of development of nighttime light product, the nighttime light remote sensing application has been extended to numerous interesting and scientific study domains such as econometrics, poverty estimation, light pollution, fishery and armed conflict. Among the application cases, it is surprising to see the Gross Domestic Production (GDP data can be corrected using the nighttime light data, and it is interesting to see mechanism of several diseases can be revealed by nighttime light images, while nighttime light are the unique remote sensing source to do the above works. As the nighttime light remote sensing has numerous applications, it is important to summarize the application of nighttime light remote sensing and its data mining fields. This paper introduced major satellite platform and sensors for observing nighttime light at first. Consequently, the paper summarized the progress of nighttime light remote sensing data mining in socioeconomic parameter estimation, urbanization monitoring, important event evaluation, environmental and healthy effects, fishery dynamic mapping, epidemiological research and natural gas flaring monitoring. Finally, future

  19. Remote sensing and GIS integration: Towards intelligent imagery within a spatial data infrastructure

    Science.gov (United States)

    Abdelrahim, Mohamed Mahmoud Hosny

    2001-11-01

    times as much as the IKONOS GEOCARTERRA(TM) products. The developed IISP is a step closer towards the direct and active involvement of high-resolution remote sensing imagery in querying the real world and performing exploratory types of spatial analysis. (Abstract shortened by UMI.)

  20. Forest biodiversity and its assessment by remote sensing

    International Nuclear Information System (INIS)

    Innes, J.L.; Koch, B.

    1998-01-01

    Several international conventions and agreements have stressed the importance of the assessment of forest biodiversity. However, the methods by which such assessments can be made remain unclear. Remote sensing represents an important tool for looking at ecosystem diversity and various structural aspects of individual ecosystems. It provides a means to make assessments across several different spatial scales, and is also critical for assessments of changes in ecosystem pattern over time. Many different forms of remote sensing are available. While lately the emphasis on laser scanner and synthetic aperture radar data has increased, most work to date has used photographs and digital optical imagery, primarily from airborne and spaceborne platforms. These provide the opportunity to assess different phenomena from the landscape to the stand scale. Remote sensing provides the most efficient tool available for determining landscape-scale elements of forest biodiversity, such as the relative proportion of matrix and patches and their physical arrangement. At intermediate scales, remote sensing provides an ideal tool for evaluating the presence of corridors and the nature of edges. At the stand scale, remote sensing technologies are likely to deliver an increasing amount of information about the structural attributes of forest stands, such as the nature of the canopy surface, the presence of layering within the canopy and presence of (very) coarse woody debris on the forest floor. Given the rate of development in the technology, even greater usage is likely in the future. (author)

  1. Integrated remotely sensed datasets for disaster management

    OpenAIRE

    McCarthy, Tim; Farrell, Ronan; Curtis, Andrew; Fotheringham, A. Stewart

    2008-01-01

    Video imagery can be acquired from aerial, terrestrial and marine based platforms and has been exploited for a range of remote sensing applications over the past two decades. Examples include coastal surveys using aerial video, routecorridor infrastructures surveys using vehicle mounted video cameras, aerial surveys over forestry and agriculture, underwater habitat mapping and disaster management. Many of these video systems are based on interlaced, television standards such as North...

  2. Advanced and applied remote sensing of environmental conditions

    Science.gov (United States)

    Slonecker, E. Terrence; Fisher, Gary B.; Marr, David A.; Milheim, Lesley E.; Roig-Silva, Coral M.

    2013-01-01

    "Remote sensing” is a general term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth’s surface. Remotely sensed data are fundamental to geographic science. The U.S. Geological Survey’s (USGS) Eastern Geographic Science Center (EGSC) is currently conducting and promoting the research and development of several different aspects of remote sensing science in both the laboratory and from overhead instruments. Spectroscopy is the science of recording interactions of energy and matter and is the bench science for all remote sensing. Visible and infrared analysis in the laboratory with special instruments called spectrometers enables the transfer of this research from the laboratory to multispectral (5–15 broad bands) and hyperspectral (50–300 narrow contiguous bands) analyses from aircraft and satellite sensors. In addition, mid-wave (3–5 micrometers, µm) and long-wave (8–14 µm) infrared data analysis, such as attenuated total reflectance (ATR) spectral analysis, are also conducted. ATR is a special form of vibrational infrared spectroscopy that has many applications in chemistry and biology but has recently been shown to be especially diagnostic for vegetation analysis.

  3. Remote Sensing Plant Stress Using Combined Fluorescence and Reflectance Measurements for Early Detection of Defoliants within the Battlefield Environment

    Science.gov (United States)

    2012-10-02

    Sensing Imagery, Instituto de Agricultura Sostenible, Córdoba, Spain Young, D.R. 2007. Leaf to landscape in a barrier island environment.” Workshop...on Vegetation Stress Detection with Remote Sensing Imagery, Instituto de Agricultura Sostenible, Córdoba, Spain Young, D.R. and J.C. Naumann. 2007

  4. Watermarking techniques for electronic delivery of remote sensing images

    Science.gov (United States)

    Barni, Mauro; Bartolini, Franco; Magli, Enrico; Olmo, Gabriella

    2002-09-01

    Earth observation missions have recently attracted a growing interest, mainly due to the large number of possible applications capable of exploiting remotely sensed data and images. Along with the increase of market potential, the need arises for the protection of the image products. Such a need is a very crucial one, because the Internet and other public/private networks have become preferred means of data exchange. A critical issue arising when dealing with digital image distribution is copyright protection. Such a problem has been largely addressed by resorting to watermarking technology. A question that obviously arises is whether the requirements imposed by remote sensing imagery are compatible with existing watermarking techniques. On the basis of these motivations, the contribution of this work is twofold: assessment of the requirements imposed by remote sensing applications on watermark-based copyright protection, and modification of two well-established digital watermarking techniques to meet such constraints. More specifically, the concept of near-lossless watermarking is introduced and two possible algorithms matching such a requirement are presented. Experimental results are shown to measure the impact of watermark introduction on a typical remote sensing application, i.e., unsupervised image classification.

  5. First European Workshop on 'Remote sensing in mineral exploration'

    International Nuclear Information System (INIS)

    Van Wambeke, L.; Sanderson, D.J.; Dolan, J.M.

    1986-01-01

    The First European Workshop on 'Remote sensing in mineral exploration' organized by the Commission of the European Communities in February 1985 took stock of the results obtained within the European Community on the application of remote sensing techniques in exploration. The papers presented in this publication are essentially based on data obtained with the first generation of satellites and some airborne experiments. Important progress in data processing and interpretation has been made in the EEC since 1979 and is continuing to be made. The main aim is to provide the EC mining industry with a new tool for exploration. Significant results have already been obtained with the EEC playing an important role in the promotion of this relatively new technique. The main R and D trend is towards an integration of multidata sets (remote sensing, geochemical, geophysical and other data) to improve the methodology for delineating new targets in exploration. Another general trend is the participation of mining companies in remote sensing experiments. Further improvement for exploration is expected in the near future with the thematic mapper and the spot imageries as well as new airborne sensors

  6. Remote sensing in Michigan for land resource management: Highway impact assessment

    Science.gov (United States)

    1972-01-01

    An existing section of M-14 freeway constructed in 1964 and a potential extension from Ann Arbor to Plymouth, Michigan provided an opportunity for investigating the potential uses of remote sensing techniques in providing projective information needed for assessing the impact of highway construction. Remote sensing data included multispectral scanner imagery and aerial photography. Only minor effects on vegetation, soils, and land use were found to have occurred in the existing corridor. Adverse changes expected to take place in the corridor proposed for extension of the freeway can be minimized by proper design of drainage ditches and attention to good construction practices. Remote sensing can be used to collect and present many types of data useful for highway impact assessment on land use, vegetation categories and species, soil properties and hydrologic characteristics.

  7. Water Column Correction for Coral Reef Studies by Remote Sensing

    Directory of Open Access Journals (Sweden)

    Maria Laura Zoffoli

    2014-09-01

    Full Text Available Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

  8. Water Column Correction for Coral Reef Studies by Remote Sensing

    Science.gov (United States)

    Zoffoli, Maria Laura; Frouin, Robert; Kampel, Milton

    2014-01-01

    Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application. PMID:25215941

  9. Archimedean Witness: The Application of Remote Sensing as an Aid to Human Rights Prosecutions

    Science.gov (United States)

    Walker, James Robin

    The 21st century has seen a significant increase in the use of remote sensing technology in the international human rights arena for the purposes of documenting crimes against humanity. The nexus between remote sensing, human rights activism, and international criminal prosecutions sits at a significant crossroads within geographic thought, calling attention to the epistemological and geopolitical implications that stem from the "view from nowhere" afforded by satellite imagery. Therefore, this thesis is divided into three sections. The first looks at the geographical questions raised by the expansion of remote sensing use in the context of international activism. The second explores the complications inherent in the presentation of remote sensing data as evidence of war crimes. Building upon the first two, the third section is a case study in alternate forms of analysis, aimed at expanding the utility of remote sensing data in international criminal prosecutions.

  10. Multisource Remote Sensing Imagery Fusion Scheme Based on Bidimensional Empirical Mode Decomposition (BEMD and Its Application to the Extraction of Bamboo Forest

    Directory of Open Access Journals (Sweden)

    Guang Liu

    2016-12-01

    Full Text Available Most bamboo forests grow in humid climates in low-latitude tropical or subtropical monsoon areas, and they are generally located in hilly areas. Bamboo trunks are very straight and smooth, which means that bamboo forests have low structural diversity. These features are beneficial to synthetic aperture radar (SAR microwave penetration and they provide special information in SAR imagery. However, some factors (e.g., foreshortening can compromise the interpretation of SAR imagery. The fusion of SAR and optical imagery is considered an effective method with which to obtain information on ground objects. However, most relevant research has been based on two types of remote sensing image. This paper proposes a new fusion scheme, which combines three types of image simultaneously, based on two fusion methods: bidimensional empirical mode decomposition (BEMD and the Gram-Schmidt transform. The fusion of panchromatic and multispectral images based on the Gram-Schmidt transform can enhance spatial resolution while retaining multispectral information. BEMD is an adaptive decomposition method that has been applied widely in the analysis of nonlinear signals and to the nonstable signal of SAR. The fusion of SAR imagery with fused panchromatic and multispectral imagery using BEMD is based on the frequency information of the images. It was established that the proposed fusion scheme is an effective remote sensing image interpretation method, and that the value of entropy and the spatial frequency of the fused images were improved in comparison with other techniques such as the discrete wavelet, à-trous, and non-subsampled contourlet transform methods. Compared with the original image, information entropy of the fusion image based on BEMD improves about 0.13–0.38. Compared with the other three methods it improves about 0.06–0.12. The average gradient of BEMD is 4%–6% greater than for other methods. BEMD maintains spatial frequency 3.2–4.0 higher than

  11. A feasibility study of using remotely sensed data for water resource models

    Science.gov (United States)

    Ruff, J. F.

    1973-01-01

    Remotely sensed data were collected to demonstrate the feasibility of applying the results to water resource problems. Photographs of the Wolf Creek watershed in southwestern Colorado were collected over a one year period. Cloud top temperatures were measured using a radiometer. Thermal imagery of the Wolf Creek Pass area was obtained during one pre-dawn flight. Remote sensing studies of water resource problems for user agencies were also conducted. The results indicated that: (1) remote sensing techniques could be used to assist in the solution of water resource problems; (2) photogrammetric determination of snow depths is feasible; (3) changes in turbidity or suspended material concentration can be observed; and (4) surface turbulence can be related to bed scour; and (5) thermal effluents into rivers can be monitored.

  12. A review of hyperspectral remote sensing and its application in ...

    African Journals Online (AJOL)

    Multispectral imagery has been used as the data source for water and land observational remote sensing from airborne and satellite systems since the early 1960s. Over the past two decades, advances in sensor technology have made it possible for the collection of several hundred spectral bands. This is commonly ...

  13. Use of remote sensing techniques for inventorying and planning utilization of land resources in South Dakota

    Science.gov (United States)

    Myers, V. I.; Frazee, C. J.; Rusche, A. E.; Moore, D. G.; Nelson, G. D.; Westin, F. C.

    1974-01-01

    The basic procedures for interpreting remote sensing imagery to rapidly develop general soils and land use inventories were developed and utilized in Pennington County, South Dakota. These procedures and remote sensing data products were illustrated and explained to many user groups, some of whom are interested in obtaining similar data. The general soils data were integrated with land soils data supplied by the county director of equalization to prepare a land value map. A computer print-out of this map indicating a land value for each quarter section is being used in tax reappraisal of Pennington County. The land use data provided the land use planners with the present use of land in Pennington County. Additional uses of remote sensing applications are also discussed including tornado damage assessment, hail damage evaluation, and presentation of soil and land value information on base maps assembled from ERTS-1 imagery.

  14. Study on the construction of multi-dimensional Remote Sensing feature space for hydrological drought

    International Nuclear Information System (INIS)

    Xiang, Daxiang; Tan, Debao; Wen, Xiongfei; Shen, Shaohong; Li, Zhe; Cui, Yuanlai

    2014-01-01

    Hydrological drought refers to an abnormal water shortage caused by precipitation and surface water shortages or a groundwater imbalance. Hydrological drought is reflected in a drop of surface water, decrease of vegetation productivity, increase of temperature difference between day and night and so on. Remote sensing permits the observation of surface water, vegetation, temperature and other information from a macro perspective. This paper analyzes the correlation relationship and differentiation of both remote sensing and surface measured indicators, after the selection and extraction a series of representative remote sensing characteristic parameters according to the spectral characterization of surface features in remote sensing imagery, such as vegetation index, surface temperature and surface water from HJ-1A/B CCD/IRS data. Finally, multi-dimensional remote sensing features such as hydrological drought are built on a intelligent collaborative model. Further, for the Dong-ting lake area, two drought events are analyzed for verification of multi-dimensional features using remote sensing data with different phases and field observation data. The experiments results proved that multi-dimensional features are a good method for hydrological drought

  15. The use of remote sensing for landslide studies in Europe

    Science.gov (United States)

    Tofani, Veronica; Agostini, Andrea; Segoni, Samuele; Catani, Filippo; Casagli, Nicola

    2013-04-01

    The existing remote sensing techniques and their actual application in Europe for landslide detection, mapping and monitoring have been investigated. Data and information necessary to evaluate the subjects have been collected through a questionnaire, designed using a Google form, which was disseminated among end-users and researchers involved in landslide. In total, 49 answers were collected, coming from 17 European countries and from different kinds of institutions (universities, research institutes, public institutes and private companies). The spatial distribution of the answers is consistent with the distribution of landslides in Europe, the significance of landslides impact on society and the estimated landslide susceptibility in the various countries. The outcomes showed that landslide detection and mapping is mainly performed with aerial photos, often associated with optical and radar imagery. Concerning landslide monitoring, satellite radars prevail over the other types of data followed by aerial photos and meteorological sensors. Since subsampling the answers according to the different typology of institutions it is not noticeable a clear gap between research institutes and end users, it is possible to infer that in landslide remote sensing the research is advancing at the same pace as its day-to-day application. Apart from optical and radar imagery, other techniques are less widespread and some of them are not so well established, notwithstanding their performances are increasing at a fast rate as scientific and technological improvements are accomplished. Remote sensing is mainly used for detection/mapping and monitoring of slides, flows and lateral spreads with a preferably large scale of analysis (1:5000 - 1:25000). All the compilers integrate remote sensing data with other thematic data, mainly geological maps, landslide inventory maps and DTMs and derived maps. Concerning landslide monitoring, the results of the questionnaire stressed that the best

  16. Object-oriented Method of Hierarchical Urban Building Extraction from High-resolution Remote-Sensing Imagery

    Directory of Open Access Journals (Sweden)

    TAO Chao

    2016-02-01

    Full Text Available An automatic urban building extraction method for high-resolution remote-sensing imagery,which combines building segmentation based on neighbor total variations with object-oriented analysis,is presented in this paper. Aimed at different extraction complexity from various buildings in the segmented image,a hierarchical building extraction strategy with multi-feature fusion is adopted. Firstly,we extract some rectangle buildings which remain intact after segmentation through shape analysis. Secondly,in order to ensure each candidate building target to be independent,multidirectional morphological road-filtering algorithm is designed which can separate buildings from the neighboring roads with similar spectrum. Finally,we take the extracted buildings and the excluded non-buildings as samples to establish probability model respectively,and Bayesian discriminating classifier is used for making judgment of the other candidate building objects to get the ultimate extraction result. The experimental results have shown that the approach is able to detect buildings with different structure and spectral features in the same image. The results of performance evaluation also support the robustness and precision of the approach developed.

  17. Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Fan Hu

    2015-11-01

    Full Text Available Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs, which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.

  18. Unmanned aerial systems for photogrammetry and remote sensing: A review

    Science.gov (United States)

    Colomina, I.; Molina, P.

    2014-06-01

    We discuss the evolution and state-of-the-art of the use of Unmanned Aerial Systems (UAS) in the field of Photogrammetry and Remote Sensing (PaRS). UAS, Remotely-Piloted Aerial Systems, Unmanned Aerial Vehicles or simply, drones are a hot topic comprising a diverse array of aspects including technology, privacy rights, safety and regulations, and even war and peace. Modern photogrammetry and remote sensing identified the potential of UAS-sourced imagery more than thirty years ago. In the last five years, these two sister disciplines have developed technology and methods that challenge the current aeronautical regulatory framework and their own traditional acquisition and processing methods. Navety and ingenuity have combined off-the-shelf, low-cost equipment with sophisticated computer vision, robotics and geomatic engineering. The results are cm-level resolution and accuracy products that can be generated even with cameras costing a few-hundred euros. In this review article, following a brief historic background and regulatory status analysis, we review the recent unmanned aircraft, sensing, navigation, orientation and general data processing developments for UAS photogrammetry and remote sensing with emphasis on the nano-micro-mini UAS segment.

  19. Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty

    Science.gov (United States)

    Katharine White; Jennifer Pontius; Paul Schaberg

    2014-01-01

    Current remote sensing studies of phenology have been limited to coarse spatial or temporal resolution and often lack a direct link to field measurements. To address this gap, we compared remote sensing methodologies using Landsat Thematic Mapper (TM) imagery to extensive field measurements in a mixed northern hardwood forest. Five vegetation indices, five mathematical...

  20. DARLA: Data Assimilation and Remote Sensing for Littoral Applications

    Science.gov (United States)

    Jessup, A.; Holman, R. A.; Chickadel, C.; Elgar, S.; Farquharson, G.; Haller, M. C.; Kurapov, A. L.; Özkan-Haller, H. T.; Raubenheimer, B.; Thomson, J. M.

    2012-12-01

    the Field Research Facility at Duck, NC in September 2010 focused on assimilation of tower-based electo-optical, infrared, and radar measurements in predictions of longshore currents. Here we provide an overview of our contribution to the RIVET I experiment at New River Inlet, NC in May 2012. During the course of the 3-week measurement period, continuous tower-based remote sensing measurements were made using electro-optical, infrared, and radar techniques covering the nearshore zone and the inlet mouth. A total of 50 hours of airborne measurements were made using high-resolution infrared imagers and a customized along track interferometric synthetic aperture radar (ATI SAR). The airborne IR imagery provides kilometer-scale mapping of frontal features that evolve as the inlet flow interacts with the oceanic wave and current fields. The ATI SAR provides maps of the two-dimensional surface currents. Near-surface measurements of turbulent velocities and surface waves using SWIFT drifters, designed to measures near-surface properties relevant to remote sensing, complimented the extensive in situ measurements by RIVET investigators.

  1. Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields

    Directory of Open Access Journals (Sweden)

    Teerapong Panboonyuen

    2017-07-01

    Full Text Available Object segmentation of remotely-sensed aerial (or very-high resolution, VHS images and satellite (or high-resolution, HR images, has been applied to many application domains, especially in road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts at applying the deep convolutional neural network (DCNN to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction of remote sensing images by applying landscape metrics (LMs and conditional random fields (CRFs. To improve the DCNN, a modern activation function called the exponential linear unit (ELU, is employed in our network, resulting in a higher number of, and yet more accurate, extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as the Thailand Earth Observation System (THEOS satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, a state-of-the-art object segmentation technique, on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and F 1 .

  2. Remote sensing investigations at a hazardous-waste landfill

    Science.gov (United States)

    Stohr, C.; Su, W.-J.; DuMontelle, P.B.; Griffin, R.A.

    1987-01-01

    In 1976 state licensed landfilling of industrial chemicals was begun above an abandoned, underground coal mine in Illinois. Five years later organic chemical pollutants were discovered in a monitoring well, suggesting migration 100 to 1000 times faster than predicted by laboratory tests. Remote sensing contributed to the determination of the causes of faster-than-predicted pollutant migration at the hazardous-waste landfill. Aerial and satellite imagery were employed to supplement field studies of local surface and groundwater hydrology, and to chronicle site history. Drainage impediments and depressions in the trench covers collected runoff, allowing rapid recharge of surface waters to some burial trenches. These features can be more effectively identified by photointerpretation than by conventional field reconnaissance. A ground-based, post-sunset survey of the trench covers that showed that a distinction between depressions which hold moisture at the surface from freely-draining depressions which permit rapid recharge to the burial trenches could be made using thermal infrared imagery.In 1976 state licensed landfilling of industrial chemicals was begun above an abandoned, underground coal mine in Illinois. Five years later organic chemical pollutants were discovered in a monitoring well, suggesting migration 100 to 1000 times faster than predicted by laboratory tests. Remote sensing contributed to the determination of the causes of faster-than-predicted pollutant migration at the hazardous-waste landfill. Aerial and satellite imagery were employed to supplement field studies of local surface and groundwater hydrology, and to chronicle site history. Drainage impediments and depressions in the trench covers collected runoff, allowing rapid recharge of surface waters to some burial trenches.

  3. Looking back to inform the future: The role of cognition in forest disturbance characterization from remote sensing imagery

    Science.gov (United States)

    Bianchetti, Raechel Anne

    Remotely sensed images have become a ubiquitous part of our daily lives. From novice users, aiding in search and rescue missions using tools such as TomNod, to trained analysts, synthesizing disparate data to address complex problems like climate change, imagery has become central to geospatial problem solving. Expert image analysts are continually faced with rapidly developing sensor technologies and software systems. In response to these cognitively demanding environments, expert analysts develop specialized knowledge and analytic skills to address increasingly complex problems. This study identifies the knowledge, skills, and analytic goals of expert image analysts tasked with identification of land cover and land use change. Analysts participating in this research are currently working as part of a national level analysis of land use change, and are well versed with the use of TimeSync, forest science, and image analysis. The results of this study benefit current analysts as it improves their awareness of their mental processes used during the image interpretation process. The study also can be generalized to understand the types of knowledge and visual cues that analysts use when reasoning with imagery for purposes beyond land use change studies. Here a Cognitive Task Analysis framework is used to organize evidence from qualitative knowledge elicitation methods for characterizing the cognitive aspects of the TimeSync image analysis process. Using a combination of content analysis, diagramming, semi-structured interviews, and observation, the study highlights the perceptual and cognitive elements of expert remote sensing interpretation. Results show that image analysts perform several standard cognitive processes, but flexibly employ these processes in response to various contextual cues. Expert image analysts' ability to think flexibly during their analysis process was directly related to their amount of image analysis experience. Additionally, results show

  4. Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2002-07

    Science.gov (United States)

    Pearson, D.K.; Gary, R.H.; Wilson, Z.D.

    2007-01-01

    Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is particularly useful when analyzing a wide variety of spatial data such as with remote sensing and spatial analysis. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This document presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup from 2002 through 2007.

  5. Extraction of coastal ocean wave characteristics using remote sensing and computer vision technologies

    CSIR Research Space (South Africa)

    Johnson, M

    2017-05-01

    Full Text Available optical imagery from the RapidEye satellite can be used to extract ocean wave characteristics such as wave direction, wavelength, wave period and wave velocity. If successful, the advantage of the proposed remote sensing-based approach would...

  6. Environmental and Landscape Remote Sensing Using Free and Open Source Image Processing Tools

    Science.gov (United States)

    As global climate change and human activities impact the environment, there is a growing need for scientific tools to monitor and measure environmental conditions that support human and ecological health. Remotely sensed imagery from satellite and airborne platforms provides a g...

  7. Time-sensitive remote sensing

    CERN Document Server

    Lippitt, Christopher; Coulter, Lloyd

    2015-01-01

    This book documents the state of the art in the use of remote sensing to address time-sensitive information requirements. Specifically, it brings together a group of authors who are both researchers and practitioners, who work toward or are currently using remote sensing to address time-sensitive information requirements with the goal of advancing the effective use of remote sensing to supply time-sensitive information. The book addresses the theoretical implications of time-sensitivity on the remote sensing process, assessments or descriptions of methods for expediting the delivery and improving the quality of information derived from remote sensing, and describes and analyzes time-sensitive remote sensing applications, with an emphasis on lessons learned. This book is intended for remote sensing scientists, practitioners (e.g., emergency responders or administrators of emergency response agencies), and students, but will also be of use to those seeking to understand the potential of remote sensing to addres...

  8. Automated training site selection for large-area remote-sensing image analysis

    Science.gov (United States)

    McCaffrey, Thomas M.; Franklin, Steven E.

    1993-11-01

    A computer program is presented to select training sites automatically from remotely sensed digital imagery. The basic ideas are to guide the image analyst through the process of selecting typical and representative areas for large-area image classifications by minimizing bias, and to provide an initial list of potential classes for which training sites are required to develop a classification scheme or to verify classification accuracy. Reducing subjectivity in training site selection is achieved by using a purely statistical selection of homogeneous sites which then can be compared to field knowledge, aerial photography, or other remote-sensing imagery and ancillary data to arrive at a final selection of sites to be used to train the classification decision rules. The selection of the homogeneous sites uses simple tests based on the coefficient of variance, the F-statistic, and the Student's i-statistic. Comparisons of site means are conducted with a linear growing list of previously located homogeneous pixels. The program supports a common pixel-interleaved digital image format and has been tested on aerial and satellite optical imagery. The program is coded efficiently in the C programming language and was developed under AIX-Unix on an IBM RISC 6000 24-bit color workstation.

  9. The application of remote sensing to the development and formulation of hydrologic planning models

    Science.gov (United States)

    Fowler, T. R.; Castruccio, P. A.; Loats, H. L., Jr.

    1977-01-01

    The development of a remote sensing model and its efficiency in determining parameters of hydrologic models are reviewed. Procedures for extracting hydrologic data from LANDSAT imagery, and the visual analysis of composite imagery are presented. A hydrologic planning model is developed and applied to determine seasonal variations in watershed conditions. The transfer of this technology to a user community and contract arrangements are discussed.

  10. An Improved Algorithm Based on Minimum Spanning Tree for Multi-scale Segmentation of Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    LI Hui

    2015-07-01

    Full Text Available As the basis of object-oriented information extraction from remote sensing imagery,image segmentation using multiple image features,exploiting spatial context information, and by a multi-scale approach are currently the research focuses. Using an optimization approach of the graph theory, an improved multi-scale image segmentation method is proposed. In this method, the image is applied with a coherent enhancement anisotropic diffusion filter followed by a minimum spanning tree segmentation approach, and the resulting segments are merged with reference to a minimum heterogeneity criterion.The heterogeneity criterion is defined as a function of the spectral characteristics and shape parameters of segments. The purpose of the merging step is to realize the multi-scale image segmentation. Tested on two images, the proposed method was visually and quantitatively compared with the segmentation method employed in the eCognition software. The results show that the proposed method is effective and outperforms the latter on areas with subtle spectral differences.

  11. An Open Source Software and Web-GIS Based Platform for Airborne SAR Remote Sensing Data Management, Distribution and Sharing

    Science.gov (United States)

    Changyong, Dou; Huadong, Guo; Chunming, Han; Ming, Liu

    2014-03-01

    With more and more Earth observation data available to the community, how to manage and sharing these valuable remote sensing datasets is becoming an urgent issue to be solved. The web based Geographical Information Systems (GIS) technology provides a convenient way for the users in different locations to share and make use of the same dataset. In order to efficiently use the airborne Synthetic Aperture Radar (SAR) remote sensing data acquired in the Airborne Remote Sensing Center of the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), a Web-GIS based platform for airborne SAR data management, distribution and sharing was designed and developed. The major features of the system include map based navigation search interface, full resolution imagery shown overlaid the map, and all the software adopted in the platform are Open Source Software (OSS). The functions of the platform include browsing the imagery on the map navigation based interface, ordering and downloading data online, image dataset and user management, etc. At present, the system is under testing in RADI and will come to regular operation soon.

  12. Multi- and hyperspectral remote sensing of tropical marine benthic habitats

    Science.gov (United States)

    Mishra, Deepak R.

    Tropical marine benthic habitats such as coral reef and associated environments are severely endangered because of the environmental degradation coupled with hurricanes, El Nino events, coastal pollution and runoff, tourism, and economic development. To monitor and protect this diverse environment it is important to not only develop baseline maps depicting their spatial distribution but also to document their changing conditions over time. Remote sensing offers an important means of delineating and monitoring coral reef ecosystems. Over the last twenty years the scientific community has been investigating the use and potential of remote sensing techniques to determine the conditions of the coral reefs by analyzing their spectral characteristics from space. One of the problems in monitoring coral reefs from space is the effect of the water column on the remotely sensed signal. When light penetrates water its intensity decreases exponentially with increasing depth. This process, known as water column attenuation, exerts a profound effect on remotely sensed data collected over water bodies. The approach presented in this research focuses on the development of semi-analytical models that resolves the confounding influence water column attenuation on substrate reflectance to characterize benthic habitats from high resolution remotely sensed imagery on a per-pixel basis. High spatial resolution satellite and airborne imagery were used as inputs in the models to derive water depth and water column optical properties (e.g., absorption and backscattering coefficients). These parameters were subsequently used in various bio-optical algorithms to deduce bottom albedo and then to classify the benthos, generating a detailed map of benthic habitats. IKONOS and QuickBird multispectral satellite data and AISA Eagle hyperspectral airborne data were used in this research for benthic habitat mapping along the north shore of Roatan Island, Honduras. The AISA Eagle classification was

  13. Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification

    Directory of Open Access Journals (Sweden)

    Jian Zhang

    2016-03-01

    Full Text Available Remote sensing systems based on consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. However, the performance of consumer-grade cameras for practical applications has not been well documented in related studies. The objective of this research was to apply three commonly-used classification methods (unsupervised, supervised, and object-based to three-band imagery with RGB (red, green, and blue bands and four-band imagery with RGB and near-infrared (NIR bands to evaluate the performance of a dual-camera imaging system for crop identification. Airborne images were acquired from a cropping area in Texas and mosaicked and georeferenced. The mosaicked imagery was classified using the three classification methods to assess the usefulness of NIR imagery for crop identification and to evaluate performance differences between the object-based and pixel-based methods. Image classification and accuracy assessment showed that the additional NIR band imagery improved crop classification accuracy over the RGB imagery and that the object-based method achieved better results with additional non-spectral image features. The results from this study indicate that the airborne imaging system based on two consumer-grade cameras used in this study can be useful for crop identification and other agricultural applications.

  14. Prediction of reef fish spawning aggregations using remote sensing: A review

    International Nuclear Information System (INIS)

    Rosli, M R; Ibrahim, A L; Masron, T

    2014-01-01

    Spawning aggregation is a very important occurrence to particular reef fish species as they use this opportunity to reproduce. However, due to their predictable nature, these aggregations have always been vulnerable to overexploitation. This problem leads to the importance of identifying the exact time and location for reef fish spawning aggregation. Thus, this paper review a little bit about spawning aggregation of reef fish as well as their characteristics, and problems regarding this phenomena. The use of remote sensing in marine applications is also described here in order to discuss how remote sensing can be utilize to predict reef fish spawning aggregation. Based on the unique geomorphological characteristics of the spawning aggregation, remote sensing seems to be a powerful tool to determine their exact times and locations. It has been proved that satellite imagery was able to delineate specific reef geomorphologies such as shelf edges and reef promontories. Despite of the widely use of remote sensing in marine applications, in fact there are still lack of studies had been carried out regarding spawning aggregations of reef fish due to the skeptical point-of-view by certain researchers over the capability of this technique. However, there is actually no doubt that the use of remote sensing will provide a better hand to the authorities in order to establish a more effective monitoring and conservation plan for these spawning aggregations

  15. Theme issue ;State-of-the-art in photogrammetry, remote sensing and spatial information science;

    Science.gov (United States)

    Heipke, Christian; Madden, Marguerite; Li, Zhilin; Dowman, Ian

    2016-05-01

    Over the past few years, photogrammetry, remote sensing and spatial information science have witnessed great changes in virtually every stage of information from imagery. Indeed, we have seen, for example, a sharply increased interest in unmanned aerial vehicles,

  16. Remotely Sensed Land Imagery and Access Systems: USGS Updates

    Science.gov (United States)

    Lamb, R.; Pieschke, R.; Lemig, K.

    2017-12-01

    The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center has implemented a number of updates to its suite of remotely sensed products and distribution systems. These changes will greatly expand the availability, accessibility, and usability of the image products from USGS. As of late 2017, several new datasets are available for public download at no charge from USGS/EROS Center. These products include Multispectral Instrument (MSI) Level-1C data from the Sentinel-2B satellite, which was launched in March 2017. Along with Sentinel-2A, the Sentinel-2B images are now being distributed through USGS systems as part of a collaborative effort with the European Space Agency (ESA). The Sentinel-2 imagery is highly complementary to multispectral data collected by the USGS Landsat 7 and 8 satellites. With these two missions operating together, the potential local revisit rate can be reduced to 2-4 days. Another product addition is Resourcesat-2 data acquired over the United States by the Indian Space Research Organisation (ISRO). The Resourcesat-2 products from USGS consist of Advanced Wide Field Sensor (AWiFS) and Linear Imaging Self-Scanning Sensor Three (LISS-3) images acquired August 2016 to present. In an effort to maximize future Landsat data interoperability, including time series analysis of the 45+ year archive, the reprocessing of Collection 1 for all historical Landsat Level 1 products is nearly complete. The USGS is now working on operational release of higher-level science products to support analysis of the Landsat archive at the pixel level. Major upgrades were also completed in 2017 for several USGS data discovery and access systems, including the LandsatLook Viewer (https://landsatlook.usgs.gov/) and GloVis Tool (https://glovis.usgs.gov/). Other options are now being developed to further enhance data access and overall user experience. These future options will be discussed and community feedback will be encouraged.

  17. An Adaptive Web-Based Learning Environment for the Application of Remote Sensing in Schools

    Science.gov (United States)

    Wolf, N.; Fuchsgruber, V.; Riembauer, G.; Siegmund, A.

    2016-06-01

    Satellite images have great educational potential for teaching on environmental issues and can promote the motivation of young people to enter careers in natural science and technology. Due to the importance and ubiquity of remote sensing in science, industry and the public, the use of satellite imagery has been included into many school curricular in Germany. However, its implementation into school practice is still hesitant, mainly due to lack of teachers' know-how and education materials that align with the curricula. In the project "Space4Geography" a web-based learning platform is developed with the aim to facilitate the application of satellite imagery in secondary school teaching and to foster effective student learning experiences in geography and other related subjects in an interdisciplinary way. The platform features ten learning modules demonstrating the exemplary application of original high spatial resolution remote sensing data (RapidEye and TerraSAR-X) to examine current environmental issues such as droughts, deforestation and urban sprawl. In this way, students will be introduced into the versatile applications of spaceborne earth observation and geospatial technologies. The integrated web-based remote sensing software "BLIF" equips the students with a toolset to explore, process and analyze the satellite images, thereby fostering the competence of students to work on geographical and environmental questions without requiring prior knowledge of remote sensing. This contribution presents the educational concept of the learning environment and its realization by the example of the learning module "Deforestation of the rainforest in Brasil".

  18. Remote Sensing of Mangrove Ecosystems: A Review

    Directory of Open Access Journals (Sweden)

    Stefan Dech

    2011-04-01

    Full Text Available Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high- and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove

  19. Remote Sensing for Mineral Exploration in Central Portugal

    Directory of Open Access Journals (Sweden)

    Ricardo Manuel

    2017-09-01

    Full Text Available Central Portugal is well known for the existence of Sn-W and Au-Ag mineral occurrences primarily associated with hydrothermal processes. Despite the economic and strategic importance of such occurrences, the detailed geology of this particular region is poorly known and there is an obvious absence of geological mapping at an adequate scale. Remote sensing techniques were used in order to increase current geological knowledge of the Góis–Castanheira de Pêra area (600 km2 and to guide future exploration stages by targeting and prioritising potential locations. Digital image processing algorithms, such as Red, Green, Blue (RGB colour composites, digital spatial filters, band ratios and Principal Components Analysis, were applied to Landsat 8 imagery and elevation data. Lineaments were extracted relying on geological photointerpretation criteria, allowing the identification of new geological–structural elements. Fieldwork was carried out in order to validate the remote sensing interpretations. Integration of remote sensing data with other information sources led to the definition of locations possibly suitable for hosting Sn-W and Au-Ag mineral occurrences. These areas were ranked according to their mineral potential. Targeting the most promising locations resulted in a reduction to less than 10% of the original study area (50.5 km2.

  20. An evaluation of remote sensing technologies for the detection of fugitive contamination at selected Superfund hazardous waste sites in Pennsylvania

    Science.gov (United States)

    Slonecker, E. Terrence; Fisher, Gary B.

    2014-01-01

    This evaluation was conducted to assess the potential for using both traditional remote sensing, such as aerial imagery, and emerging remote sensing technology, such as hyperspectral imaging, as tools for postclosure monitoring of selected hazardous waste sites. Sixteen deleted Superfund (SF) National Priorities List (NPL) sites in Pennsylvania were imaged with a Civil Air Patrol (CAP) Airborne Real-Time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) sensor between 2009 and 2012. Deleted sites are those sites that have been remediated and removed from the NPL. The imagery was processed to radiance and atmospherically corrected to relative reflectance with standard software routines using the Environment for Visualizing Imagery (ENVI, ITT–VIS, Boulder, Colorado) software. Standard routines for anomaly detection, endmember collection, vegetation stress, and spectral analysis were applied.

  1. Dynamics Change of Honghu Lake's Water Surface Area and Its Driving Force Analysis Based on Remote Sensing Technique and TOPMODEL model

    International Nuclear Information System (INIS)

    Wen, X; Cao, B; Shen, S; Hu, D; Tang, X

    2014-01-01

    Honghu Lake is the largest freshwater lake in the Hubei Province of China. This paper introduces a remote sensing approach to monitor the lake's water surface area dynamics over the last 40 years by using multi-temporal remote sensing imagery including Landsat and HJ-1. Meanwhile, the daily precipitation and evaporation data provided by Honghu meteorological station since 1970s were also collected and used to analyze the influence of climate change factors. The typical situation for precipitation was selected as an input into the TOPMODEL model to simulate the hydrological process in Honghu Lake. The simulation result with the water surface area extracted from remote sensing imagery was analyzed. This experiment shows the precipitation and timing of precipitation effects changes in the lake with remote sensing data and it showed the potential of using TOPMODEL model to analyze the combined hydrological process in Honghu Lake

  2. Geologic remote sensing study of the Hayden pass-Orient Mine Area, Northern Sangre de Cristo Mountains, Colorado

    Science.gov (United States)

    Wychgram, D. C.

    1972-01-01

    Remote sensor data from a NASA Convair 990 radar flight and Mission 101 and 105 have been interpreted and evaluated. Based on interpretation of the remote sensor data, a geologic map has been prepared and compared with a second geologic map, prepared from interpretation of both remote sensor data and field data. Comparison of the two maps gives one indication of the usefulness and reliability of the remote sensor data. Color and color infrared photography provided the largest amount of valuable information. Multiband photography was of lesser value and side-looking radar imagery provided no new information that was not available on small scale photography. Thermal scanner imagery proved to be a very specialized remote sensing tool that should be applied to areas of low relief and sparse vegetation where geologic features produce known or suspected thermal contrast. Low sun angle photography may be a good alternative to side-looking radar imagery but must be flown with critical timing.

  3. Remote Sensing of Vegetation Nitrogen Content for Spatially Explicit Carbon and Water Cycle Estimation

    Science.gov (United States)

    Zhang, Y. L.; Miller, J. R.; Chen, J. M.

    2009-05-01

    Foliage nitrogen concentration is a determinant of photosynthetic capacity of leaves, thereby an important input to ecological models for estimating terrestrial carbon and water budgets. Recently, spectrally continuous airborne hyperspectral remote sensing imagery has proven to be useful for retrieving an important related parameter, total chlorophyll content at both leaf and canopy scales. Thus remote sensing of vegetation biochemical parameters has promising potential for improving the prediction of global carbon and water balance patterns. In this research, we explored the feasibility of estimating leaf nitrogen content using hyperspectral remote sensing data for spatially explicit estimation of carbon and water budgets. Multi-year measurements of leaf biochemical contents of seven major boreal forest species were carried out in northeastern Ontario, Canada. The variation of leaf chlorophyll and nitrogen content in response to various growth conditions, and the relationship between them,were investigated. Despite differences in plant type (deciduous and evergreen), leaf age, stand growth conditions and developmental stages, leaf nitrogen content was strongly correlated with leaf chlorophyll content on a mass basis during the active growing season (r2=0.78). With this general correlation, leaf nitrogen content was estimated from leaf chlorophyll content at an accuracy of RMSE=2.2 mg/g, equivalent to 20.5% of the average measured leaf nitrogen content. Based on this correlation and a hyperspectral remote sensing algorithm for leaf chlorophyll content retrieval, the spatial variation of leaf nitrogen content was inferred from the airborne hyperspectral remote sensing imagery acquired by Compact Airborne Spectrographic Imager (CASI). A process-based ecological model Boreal Ecosystem Productivity Simulator (BEPS) was used for estimating terrestrial carbon and water budgets. In contrast to the scenario with leaf nitrogen content assigned as a constant value without

  4. Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

    Science.gov (United States)

    Tucker, Compton; Puma, Michael

    2015-01-01

    Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.

  5. Proceedings of the sixth circumpolar symposium on remote sensing of polar environments. CD-ROM ed.

    International Nuclear Information System (INIS)

    Taylor, D.

    2000-09-01

    This international conference focused on the application of remote sensing to monitor morphological and environmental changes in polar environments to better understand the impacts of climatic change. Remote sensing included the use of satellite image mapping, LANDSAT imagery, and digitized aerial photography. The conference was divided into several sessions entitled: (1) techniques, (2) wildlife habitat, (3) regional mapping, (4) environment and climate, (5) geographical information systems (GIS) modeling, (6) geology and geomorphology, (7) snow and ice, and (8) monitoring. The work presented at this conference indicates that remote sensing, photogrammetry, GIS and cartography are cost-effective means to monitor hard to reach polar regions. A total of 27 papers were presented at this conference. Four have been processed separately for inclusion on the database. refs., tabs,. figs

  6. Remote sensing inputs to water demand modeling

    Science.gov (United States)

    Estes, J. E.; Jensen, J. R.; Tinney, L. R.; Rector, M.

    1975-01-01

    In an attempt to determine the ability of remote sensing techniques to economically generate data required by water demand models, the Geography Remote Sensing Unit, in conjunction with the Kern County Water Agency of California, developed an analysis model. As a result it was determined that agricultural cropland inventories utilizing both high altitude photography and LANDSAT imagery can be conducted cost effectively. In addition, by using average irrigation application rates in conjunction with cropland data, estimates of agricultural water demand can be generated. However, more accurate estimates are possible if crop type, acreage, and crop specific application rates are employed. An analysis of the effect of saline-alkali soils on water demand in the study area is also examined. Finally, reference is made to the detection and delineation of water tables that are perched near the surface by semi-permeable clay layers. Soil salinity prediction, automated crop identification on a by-field basis, and a potential input to the determination of zones of equal benefit taxation are briefly touched upon.

  7. Potential for boom-mounted remote sensing applications in seedling quality monitoring

    Science.gov (United States)

    Robert F. Keefe; Jan U. H. Eitel; Daniel S. Long; Anthony S. Davis; Paul Gessler; Alistair M. S. Smith

    2009-01-01

    Remotely sensed aerial and satellite sensor imagery is widely used for classification of vegetation structure and health on industrial and public lands. More intensively than at any other time in the life of a planted tree, its health and status will be maintained and monitored while under culture in a bareroot or container nursery. As a case in point, inventories to...

  8. Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image

    OpenAIRE

    Wang, Jianhua; Qin, Qiming; Zhao, Jianghua; Ye, Xin; Feng, Xiao; Qin, Xuebin; Yang, Xiucheng

    2015-01-01

    Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e., remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for ...

  9. Introduction to remote sensing

    CERN Document Server

    Cracknell, Arthur P

    2007-01-01

    Addressing the need for updated information in remote sensing, Introduction to Remote Sensing, Second Edition provides a full and authoritative introduction for scientists who need to know the scope, potential, and limitations in the field. The authors discuss the physical principles of common remote sensing systems and examine the processing, interpretation, and applications of data. This new edition features updated and expanded material, including greater coverage of applications from across earth, environmental, atmospheric, and oceanographic sciences. Illustrated with remotely sensed colo

  10. A Novel Technique for Time-Centric Analysis of Massive Remotely-Sensed Datasets

    Directory of Open Access Journals (Sweden)

    Glenn E. Grant

    2015-04-01

    Full Text Available Analyzing massive remotely-sensed datasets presents formidable challenges. The volume of satellite imagery collected often outpaces analytical capabilities, however thorough analyses of complete datasets may provide new insights into processes that would otherwise be unseen. In this study we present a novel, object-oriented approach to storing, retrieving, and analyzing large remotely-sensed datasets. The objective is to provide a new structure for scalable storage and rapid, Internet-based analysis of climatology data. The concept of a “data rod” is introduced, a conceptual data object that organizes time-series information into a temporally-oriented vertical column at any given location. To demonstrate one possible use, we ingest 25 years of Greenland imagery into a series of pure-object databases, then retrieve and analyze the data. The results provide a basis for evaluating the database performance and scientific analysis capabilities. The project succeeds in demonstrating the effectiveness of the prototype database architecture and analysis approach, not because new scientific information is discovered, but because quality control issues are revealed in the source data that had gone undetected for years.

  11. Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

    Directory of Open Access Journals (Sweden)

    Chelsea Cervantes

    2010-09-01

    Full Text Available Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remote sensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial investment of

  12. Autonomous Coral Reef Survey in Support of Remote Sensing

    Directory of Open Access Journals (Sweden)

    Steven G. Ackleson

    2017-10-01

    Full Text Available An autonomous surface vehicle instrumented with optical and acoustical sensors was deployed in Kane'ohe Bay, HI, U.S.A., to provide high-resolution, in situ observations of coral reef reflectance with minimal human presence. The data represented a wide range in bottom type, water depth, and illumination and supported more thorough investigations of remote sensing methods for identifying and mapping shallow reef features. The in situ data were used to compute spectral bottom reflectance and remote sensing reflectance, Rrs,λ, as a function of water depth and benthic features. The signals were used to distinguish between live coral and uncolonized sediment within the depth range of the measurements (2.5–5 m. In situRrs, λ were found to compare well with remotely sensed measurements from an imaging spectrometer, the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS, deployed on an aircraft at high altitude. Cloud cover and in situ sensor orientation were found to have minimal impact on in situRrs, λ, suggesting that valid reflectance data may be collected using autonomous surveys even when atmospheric conditions are not favorable for remote sensing operations. The use of reflectance in the red and near infrared portions of the spectrum, expressed as the red edge height, REHλ, was investigated for detecting live aquatic vegetative biomass, including coral symbionts and turf algae. The REHλ signal from live coral was detected in Kane'ohe Bay to a depth of approximately 4 m with in situ measurements. A remote sensing algorithm based on the REHλ signal was defined and applied to AVIRIS imagery of the entire bay and was found to reveal areas of shallow, dense coral and algal cover. The peak wavelength of REHλ decreased with increasing water depth, indicating that a more complete examination of the red edge signal may potentially yield a remote sensing approach to simultaneously estimate vegetative biomass and bathymetry in shallow water.

  13. Daytime multispectral scanner aerial surveys of the Oak Ridge Reservation, 1992--1994: Overview of data processing and analysis by the Environmental Restoration Remote Sensing Program, Fiscal year 1995

    International Nuclear Information System (INIS)

    Smyre, J.L.; Hodgson, M.E.; Moll, B.W.; King, A.L.; Cheng, Yang.

    1995-11-01

    Environmental Restoration (ER) Remote Sensing and Special Surveys Program was in 1992 to apply the benefits of remote sensing technologies to Environmental Restoration Management (ERWM) programs at all of the five United States Department of Energy facilities operated and managed by Martin Marietta Energy Systems, Inc. (now Lockheed Martin Energy Systems)-the three Oak Ridge Reservation (ORR) facilities, the Paducah Gaseous Diffusion Plant (PGDP), the Portsmouth Gaseous Diffusion Plant (PORTS)-and adjacent off-site areas. The Remote Sensing Program includes the management of routine and special surveys at these sites, application of state-of-the-art remote sensing and geophysical technologies, and data transformation, integration, and analyses required to make the information valuable to ER. Remotely-sensed data collected of the ORR include natural color and color infrared (IR) aerial photography, 12-band multispectral scanner imagery, predawn thermal IR sensor imagery, magnetic and electromagnetic geophysical surveys, and gamma radiological data

  14. Remote sensing and conservation of isolated indigenous villages in Amazonia.

    Science.gov (United States)

    Walker, Robert S; Hamilton, Marcus J; Groth, Aaron A

    2014-11-01

    The vast forests on the border between Brazil and Peru harbour a number of indigenous groups that have limited contact with the outside world. Accurate estimates of population sizes and village areas are essential to begin assessing the immediate conservation needs of such isolated groups. In contrast to overflights and encounters on the ground, remote sensing with satellite imagery offers a safe, inexpensive, non-invasive and systematic approach to provide demographic and land-use information for isolated peoples. Satellite imagery can also be used to understand the growth of isolated villages over time. There are five isolated villages in the headwaters of the Envira River confirmed by overflights that are visible with recent satellite imagery further confirming their locations and allowing measurement of their cleared gardens, village areas and thatch roofed houses. These isolated villages appear to have population densities that are an order of magnitude higher than averages for other Brazilian indigenous villages. Here, we report on initial results of a remote surveillance programme designed to monitor movements and assess the demographic health of isolated peoples as a means to better mitigate against external threats to their long-term survival.

  15. Offshore wind potential evaluation and remote sensing imagery; Evaluation du potentiel eolien offshore et imagerie satellitale

    Energy Technology Data Exchange (ETDEWEB)

    Fichaux, N.

    2003-12-15

    Offshore wind energy may help to contribute to the respect of the Kyoto objectives by Europe. It is a key issue to struggle against global change. To sit the future offshore wind parks, it is necessary to accurately evaluate the spatial repartition of the wind potential. We demonstrate that the offshore wind potential shall be represented by maps of wind statistics. As remote sensing is a tool for measuring space physical phenomena, we evaluate its potentialities for mapping wind statistics. Space-borne scatterometers enables the obtention of wind statistics, but far from our areas of interest and at low spatial resolution. Synthetic Aperture Radar (SAR) enables the computation of high resolution wind maps over our areas of interest, but are unsuitable to compute wind statistics. We define the mathematical framework of a statistical method. That method enables to take advantage of both scatterometer and SAR to compute maps of wind statistics at high spatial resolution over the areas of interest. It enables remote sensing to be used operationally to map the offshore wind potential. (author)

  16. Watermarking-based protection of remote sensing images: requirements and possible solutions

    Science.gov (United States)

    Barni, Mauro; Bartolini, Franco; Cappellini, Vito; Magli, Enrico; Olmo, Gabriella

    2001-12-01

    Earth observation missions have recently attracted ag rowing interest form the scientific and industrial communities, mainly due to the large number of possible applications capable to exploit remotely sensed data and images. Along with the increase of market potential, the need arises for the protection of the image products from non-authorized use. Such a need is a very crucial one even because the Internet and other public/private networks have become preferred means of data exchange. A crucial issue arising when dealing with digital image distribution is copyright protection. Such a problem has been largely addressed by resorting to watermarking technology. A question that obviously arises is whether the requirements imposed by remote sensing imagery are compatible with existing watermarking techniques. On the basis of these motivations, the contribution of this work is twofold: i) assessment of the requirements imposed by the characteristics of remotely sensed images on watermark-based copyright protection ii) analysis of the state-of-the-art, and performance evaluation of existing algorithms in terms of the requirements at the previous point.

  17. Comparision of Bathymetry and Bottom Characteristics From Hyperspectral Remote Sensing Data and Shipborne Acoustic Measurements

    Science.gov (United States)

    McIntyre, M. L.; Naar, D. F.; Carder, K. L.; Howd, P. A.; Lewis, J. M.; Donahue, B. T.; Chen, F. R.

    2002-12-01

    There is growing interest in applying optical remote sensing techniques to shallow-water geological applications such as bathymetry and bottom characterization. Model inversions of hyperspectral remote-sensing reflectance imagery can provide estimates of bottom albedo and depth. This research was conducted in support of the HyCODE (Hyperspectral Coupled Ocean Dynamics Experiment) project in order to test optical sensor performance and the use of a hyperspectral remote-sensing reflectance algorithm for shallow waters in estimating bottom depths and reflectance. The objective of this project was to compare optically derived products of bottom depths and reflectance to shipborne acoustic measurements of bathymetry and backscatter. A set of three high-resolution, multibeam surveys within an 18 km by 1.5 km shore-perpendicular transect 5 km offshore of Sarasota, Florida were collected at water depths ranging from 8 m to 16 m. These products are compared to bottom depths derived from aircraft remote-sensing data collected with the AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) instrument data by means of a semi-analytical remote sensing reflectance model. The pixel size of the multibeam bathymetry and AVIRIS data are 0.25 m and 10 m, respectively. When viewed at full resolution, the multibeam bathymetry data show small-scale sedimentary bedforms (wavelength ~10m, amplitude ~1m) that are not observed in the lower resolution hyperspectral bathymetry. However, model-derived bottom depths agree well with a smoothed version of the multibeam bathymetry. Depths derived from shipborne hyperspectral measurements were accurate within 13%. In areas where diver observations confirmed biological growth and bioturbation, derived bottom depths were less accurate. Acoustic backscatter corresponds well with the aircraft hyperspectral imagery and in situ measurements of bottom reflectance. Acoustic backscatter was used to define the distribution of different bottom types

  18. Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing.

    Science.gov (United States)

    Hakkenberg, C R; Peet, R K; Urban, D L; Song, C

    2018-01-01

    In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling. © 2017 by the Ecological Society of America.

  19. Applications of remote sensing techniques to the assessment of dam safety: A progress report

    International Nuclear Information System (INIS)

    Bowlby, J.R.; Grass, J.D.; Singhroy, V.H.

    1990-01-01

    Remote sensing detection and data collection techniques, combined with data from image analyses, have become effective tools that can be used for rapid identification, interpretation and evaluation of the geological and environmental information required in some areas of performance analysis of hydraulic dams. Potential geological hazards to dams such as faults, landslides and liquefaction, regional crustal warping or tilting, stability of foundation materials, flooding and volcanic hazards are applications in which remote sensing may aid analysis. Details are presented of remote sensing techiques, optimal time of data acquisition, interpreting techniques, and application. Techniques include LANDSAT thematic mapper (TM), SPOT images, thermal infrared scanning, colour infrared photography, normal colour photography, panchromatic black and white, normal colour video, infrared video, airborne multi-spectral electronic imagery, airborne synthetic aperture radar, side scan sonar, and LIDAR (optical radar). 3 tabs

  20. Towards automated statewide land cover mapping in Wisconsin using satellite remote sensing and GIS techniques

    International Nuclear Information System (INIS)

    Cosentino, B.L.; Lillesand, T.M.

    1991-01-01

    Attention is given to an initial research project being performed by the University of Wisconsin-Madison, Environmental Remote Sensing Center in conjunction with seven local, state, and federal agencies to implement automated statewide land cover mapping using satellite remote sensing and geographical information system (GIS) techniques. The basis, progress, and future research needs for this mapping program are presented. The research efforts are directed toward strategies that integrate satellite remote sensing and GIS techniques in the generation of land cover data for multiple users of land cover information. The project objectives are to investigate methodologies that integrate satellite data with other imagery and spatial data resident in emerging GISs in the state for particular program needs, and to develop techniques that can improve automated land cover mapping efficiency and accuracy. 10 refs

  1. Using remote sensing imagery and GIS to identify land cover and land use within Ceahlau Massif (Romania

    Directory of Open Access Journals (Sweden)

    GEORGE CRACU

    2014-11-01

    Full Text Available Using remote sensing imagery and GIS to identify land cover and land use within Ceahlău Massif (Romania. In this study we considerer land cover and land use asessment within Ceahlău Massif (Romania using satellite imagery and GIS . To achieve this goal, we used a Landsat 7 ETM + satellite image, which was processed using specialized software in analyzing satellite images and GIS software in several stages:  Downloading, importing and layer stack of all spectral bands composing satellite image;  Establishment of areas of interest for each category of land cover and land use, which were digitized on - screen and for which spectral signatures characteristics were established;  Supervised image classification using Maximum Likelihood Method;  Importing the resulting m ap (raster in GIS environment and creating the final land cover/land use map for Ceahlău Massif. In the study area we identified nine land cover/land use classes: deciduous forests, mixed forests, coniferous forests, secondary grasslands, subalpine vegeta tion, alpine meadows, agricultural land, lakes and built area. By analizing the spatial distribution of these classes, it was found that forests are the best represented class, occupying an area of 188.4 km² (56.4% of total, followed by secondary grassl and, which occupies an area of 68.2 km² (20.4% of total, lakes (26.6 km² or 7.98% of total and agricultural land (16.1 km² or 4.86%

  2. 2006, REMOTE SENSING AND GIS IN THE REMEDIATION OF CHEMICAL WEAPONS CONTAMINATION IN AN URBAN LANDSCAPE

    Science.gov (United States)

    This presentation will document the use of historical imagery, GIS, photogrammetry and hyperspectral remote sensing in locating and removing chemical weapons such as Mustard Gas, Phosgene, Ricin, and Lewisite from the environment and establishing a risk assessment methodology for...

  3. Remote sensing with simulated unmanned aircraft imagery for precision agriculture applications

    Science.gov (United States)

    Hunt, E. Raymond; Daughtry, Craig S.T.; Mirsky, Steven B.; Hively, W. Dean

    2014-01-01

    An important application of unmanned aircraft systems (UAS) may be remote-sensing for precision agriculture, because of its ability to acquire images with very small pixel sizes from low altitude flights. The objective of this study was to compare information obtained from two different pixel sizes, one about a meter (the size of a small vegetation plot) and one about a millimeter. Cereal rye (Secale cereale) was planted at the Beltsville Agricultural Research Center for a winter cover crop with fall and spring fertilizer applications, which produced differences in biomass and leaf chlorophyll content. UAS imagery was simulated by placing a Fuji IS-Pro UVIR digital camera at 3-m height looking nadir. An external UV-IR cut filter was used to acquire true-color images; an external red cut filter was used to obtain color-infrared-like images with bands at near-infrared, green, and blue wavelengths. Plot-scale Green Normalized Difference Vegetation Index was correlated with dry aboveground biomass ( ${mbi {r}} = 0.58$ ), whereas the Triangular Greenness Index (TGI) was not correlated with chlorophyll content. We used the SamplePoint program to select 100 pixels systematically; we visually identified the cover type and acquired the digital numbers. The number of rye pixels in each image was better correlated with biomass ( ${mbi {r}} = 0.73$ ), and the average TGI from only leaf pixels was negatively correlated with chlorophyll content ( ${mbi {r}} = -0.72$ ). Thus, better information for crop requirements may be obtained using very small pixel sizes, but new algorithms based on computer vision are needed for analysis. It may not be necessary to geospatially register large numbers of photographs with very small pixel sizes. Instead, images could be analyzed as single plots along field transects.

  4. PAN-SHARPENING APPROACHES BASED ON UNMIXING OF MULTISPECTRAL REMOTE SENSING IMAGERY

    Directory of Open Access Journals (Sweden)

    G. Palubinskas

    2016-06-01

    Full Text Available Model based analysis or explicit definition/listing of all models/assumptions used in the derivation of a pan-sharpening method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods ‘better’ satisfying the needs of a particular application. Most existing pan-sharpening methods are based mainly on the two models/assumptions: spectral consistency for high resolution multispectral data (physical relationship between multispectral and panchromatic data in a high resolution scale and spatial consistency for multispectral data (so-called Wald’s protocol first property or relationship between multispectral data in different resolution scales. Two methods, one based on a linear unmixing model and another one based on spatial unmixing, are described/proposed/modified which respect models assumed and thus can produce correct or physically justified fusion results. Earlier mentioned property ‘better’ should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion or pan-sharpening is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions used are not valid or not fulfilled. From a model based view it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in all fusion methods. Thus in this paper a comparison of the two earlier proposed/modified pan-sharpening methods is performed. Preliminary experiments based on visual analysis are carried out in the urban area of Munich city for optical remote sensing multispectral data and panchromatic imagery of the WorldView-2 satellite sensor.

  5. Assessing the effects of subtropical forest fragmentation on leaf nitrogen distribution using remote sensing data

    CSIR Research Space (South Africa)

    Cho, Moses A

    2013-10-01

    Full Text Available the utility of new remote sensing tools to model the spatial distribution of leaf N concentration in a forested landscape undergoing deforestation in KwaZulu-Natal, South Africa. Leaf N was mapped using models developed from RapidEye imagery; a relatively new...

  6. Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

    Science.gov (United States)

    Axel, Colin

    Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial

  7. 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.

  8. Optical Remote Sensing Potentials for Looting Detection

    Directory of Open Access Journals (Sweden)

    Athos Agapiou

    2017-10-01

    Full Text Available Looting of archaeological sites is illegal and considered a major anthropogenic threat for cultural heritage, entailing undesirable and irreversible damage at several levels, such as landscape disturbance, heritage destruction, and adverse social impact. In recent years, the employment of remote sensing technologies using ground-based and/or space-based sensors has assisted in dealing with this issue. Novel remote sensing techniques have tackled heritage destruction occurring in war-conflicted areas, as well as illicit archeological activity in vast areas of archaeological interest with limited surveillance. The damage performed by illegal activities, as well as the scarcity of reliable information are some of the major concerns that local stakeholders are facing today. This study discusses the potential use of remote sensing technologies based on the results obtained for the archaeological landscape of Ayios Mnason in Politiko village, located in Nicosia district, Cyprus. In this area, more than ten looted tombs have been recorded in the last decade, indicating small-scale, but still systematic, looting. The image analysis, including vegetation indices, fusion, automatic extraction after object-oriented classification, etc., was based on high-resolution WorldView-2 multispectral satellite imagery and RGB high-resolution aerial orthorectified images. Google Earth© images were also used to map and diachronically observe the site. The current research also discusses the potential for wider application of the presented methodology, acting as an early warning system, in an effort to establish a systematic monitoring tool for archaeological areas in Cyprus facing similar threats.

  9. Hyperspectral remote sensing

    CERN Document Server

    Eismann, Michael

    2012-01-01

    Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. This book provides a holistic treatment that captures its multidisciplinary nature, emphasizing the physical principles of hyperspectral remote sensing.

  10. Recent Advances in Registration, Integration and Fusion of Remotely Sensed Data: Redundant Representations and Frames

    Science.gov (United States)

    Czaja, Wojciech; Le Moigne-Stewart, Jacqueline

    2014-01-01

    In recent years, sophisticated mathematical techniques have been successfully applied to the field of remote sensing to produce significant advances in applications such as registration, integration and fusion of remotely sensed data. Registration, integration and fusion of multiple source imagery are the most important issues when dealing with Earth Science remote sensing data where information from multiple sensors, exhibiting various resolutions, must be integrated. Issues ranging from different sensor geometries, different spectral responses, differing illumination conditions, different seasons, and various amounts of noise need to be dealt with when designing an image registration, integration or fusion method. This tutorial will first define the problems and challenges associated with these applications and then will review some mathematical techniques that have been successfully utilized to solve them. In particular, we will cover topics on geometric multiscale representations, redundant representations and fusion frames, graph operators, diffusion wavelets, as well as spatial-spectral and operator-based data fusion. All the algorithms will be illustrated using remotely sensed data, with an emphasis on current and operational instruments.

  11. Object-based Morphological Building Index for Building Extraction from High Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    LIN Xiangguo

    2017-06-01

    Full Text Available Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. In this article, an object-based morphological building index (OBMBI is constructed based on both image segmentation and graph-based top-hat reconstruction, and OBMBI is used for building extraction from high resolution remote sensing images. First, bidirectional mapping relationship between pixels, objects and graph-nodes are constructed. Second, the OBMBI image is built based on both graph-based top-hat reconstruction and the above mapping relationship. Third, a binary thresholding is performed on the OBMBI image, and the binary image is converted into vector format to derive the building polygons. Finally, the post-processing is made to optimize the extracted building polygons. Two images, including an aerial image and a panchromatic satellite image, are used to test both the proposed method and classic PanTex method. The experimental results suggest that our proposed method has a higher accuracy in building extraction than the classic PanTex method. On average, the correctness, the completeness and the quality of our method are respectively 9.49%, 11.26% and 14.11% better than those of the PanTex.

  12. Source Apportionment of Annual Water Pollution Loads in River Basins by Remote-Sensed Land Cover Classification

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2016-08-01

    Full Text Available In this study, in order to determine the efficiency of estimating annual water pollution loads from remote-sensed land cover classification and ground-observed hydrological data, an empirical model was investigated. Remote sensing data imagery from National Oceanic and Atmospheric Administration (NOAA Advanced Very High Resolution Radiometer were applied to an 11 year (1994–2004 water quality dataset for 30 different rivers in Japan. Six water quality indicators—total nitrogen (TN, total phosphorus (TP, biochemical oxygen demand (BOD, chemical oxygen demand (COD, and dissolved oxygen (DO—were examined by using the observed river water quality data and generated land cover map. The TN, TP, BOD, COD, and DO loads were estimated for the 30 river basins using the empirical model. Calibration (1994–1999 and validation (2000–2004 results showed that the proposed simulation technique was useful for predicting water pollution loads in the river basins. We found that vegetation land cover had a larger impact on TP export into all rivers. Urban areas had a very small impact on DO export into rivers, but a relatively large impact on BOD and TN export. The results indicate that the application of land cover data generated from the remote-sensed imagery could give a useful interpretation about the river water quality.

  13. REMOTE SENSING IN OCEANOGRAPHY.

    Science.gov (United States)

    remote sensing from satellites. Sensing of oceanographic variables from aircraft began with the photographing of waves and ice. Since then remote measurement of sea surface temperatures and wave heights have become routine. Sensors tested for oceanographic applications include multi-band color cameras, radar scatterometers, infrared spectrometers and scanners, passive microwave radiometers, and radar imagers. Remote sensing has found its greatest application in providing rapid coverage of large oceanographic areas for synoptic and analysis and

  14. A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery.

    Science.gov (United States)

    Meng, Fan; Yang, Xiaomei; Zhou, Chenghu; Li, Zhi

    2017-09-15

    Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

  15. BisQue: cloud-based system for management, annotation, visualization, analysis and data mining of underwater and remote sensing imagery

    Science.gov (United States)

    Fedorov, D.; Miller, R. J.; Kvilekval, K. G.; Doheny, B.; Sampson, S.; Manjunath, B. S.

    2016-02-01

    Logistical and financial limitations of underwater operations are inherent in marine science, including biodiversity observation. Imagery is a promising way to address these challenges, but the diversity of organisms thwarts simple automated analysis. Recent developments in computer vision methods, such as convolutional neural networks (CNN), are promising for automated classification and detection tasks but are typically very computationally expensive and require extensive training on large datasets. Therefore, managing and connecting distributed computation, large storage and human annotations of diverse marine datasets is crucial for effective application of these methods. BisQue is a cloud-based system for management, annotation, visualization, analysis and data mining of underwater and remote sensing imagery and associated data. Designed to hide the complexity of distributed storage, large computational clusters, diversity of data formats and inhomogeneous computational environments behind a user friendly web-based interface, BisQue is built around an idea of flexible and hierarchical annotations defined by the user. Such textual and graphical annotations can describe captured attributes and the relationships between data elements. Annotations are powerful enough to describe cells in fluorescent 4D images, fish species in underwater videos and kelp beds in aerial imagery. Presently we are developing BisQue-based analysis modules for automated identification of benthic marine organisms. Recent experiments with drop-out and CNN based classification of several thousand annotated underwater images demonstrated an overall accuracy above 70% for the 15 best performing species and above 85% for the top 5 species. Based on these promising results, we have extended bisque with a CNN-based classification system allowing continuous training on user-provided data.

  16. Design and Verification of Remote Sensing Image Data Center Storage Architecture Based on Hadoop

    Science.gov (United States)

    Tang, D.; Zhou, X.; Jing, Y.; Cong, W.; Li, C.

    2018-04-01

    The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

  17. Strategic plan for the utilization of remote sensing technologies in the environmental restoration program

    International Nuclear Information System (INIS)

    King, A.D.; Doll, W.E.; Durfee, R.C.; Luxmoore, R.J.; Conder, S.R.; Nyquist, J.E.

    1993-12-01

    The objectives of the Environmental Restoration (ER) Remote Sensing and Special Surveys Program are to apply state-of-the-art remote sensing and geophysical technologies and to manage routine and remotely-sensed examinations of the Oak Ridge Reservation (ORR), the Paducah Gaseous Diffusion Plant (PGDP), the Portsmouth Gaseous Diffusion Plant (PORTS), and their adjacent off-site areas. Repeated multispectral scanner (MSS) imagery, gamma, and photographic surveys will allow monitoring of the degradation that might occur in waste containment vessels and monitoring (at a later stage in the remediation life cycle) of improvements from restoration efforts and cleanup. These technologies, in combination with geophysical surveys, will provide an effective means for identifying unknown waste sites and contaminant transport pathways. All of the data will be maintained in a data base that will be accessible to site managers in the ER Program. The complete analysis of collected data will provide site-specific data to the ER Program for characterizing and monitoring ER Program hazardous waste sites

  18. Strategic plan for the utilization of remote sensing technologies in the Environmental Restoration Program

    International Nuclear Information System (INIS)

    King, A.D.; Doll, W.E.; Durfee, R.C.; Luxmoore, R.J.; Conder, S.R.; Nyquist, J.E.

    1994-03-01

    The objectives of the Environmental Restoration (ER) Remote Sensing and Special Surveys Program are to apply state-of-the-art remote sensing and geophysical technologies and to manage routine and remotely-sensed examinations of the Oak Ridge Reservation (ORR), the Paducah Gaseous Diffusion Plant (PGDP), the Portsmouth Gaseous Diffusion Plant (PORTS), and their adjacent off-site areas. Repeated multispectral scanner (MSS) imagery, gamma, and photographic surveys will allow monitoring of the degradation that might occur in waste containment vessels and monitoring (at a later stage in the remediation life cycle) of improvements from restoration efforts and cleanup. These technologies, in combination with geophysical surveys, will provide an effective means for identifying unknown waste sites and contaminant transport pathways. All of the data will be maintained in a data base that will be accessible to site managers in the ER Program. The complete analysis of collected data will provide site-specific data to the ER Program for characterizing and monitoring ER Program hazardous waste sites

  19. Learning multiscale and deep representations for classifying remotely sensed imagery

    Science.gov (United States)

    Zhao, Wenzhi; Du, Shihong

    2016-03-01

    It is widely agreed that spatial features can be combined with spectral properties for improving interpretation performances on very-high-resolution (VHR) images in urban areas. However, many existing methods for extracting spatial features can only generate low-level features and consider limited scales, leading to unpleasant classification results. In this study, multiscale convolutional neural network (MCNN) algorithm was presented to learn spatial-related deep features for hyperspectral remote imagery classification. Unlike traditional methods for extracting spatial features, the MCNN first transforms the original data sets into a pyramid structure containing spatial information at multiple scales, and then automatically extracts high-level spatial features using multiscale training data sets. Specifically, the MCNN has two merits: (1) high-level spatial features can be effectively learned by using the hierarchical learning structure and (2) multiscale learning scheme can capture contextual information at different scales. To evaluate the effectiveness of the proposed approach, the MCNN was applied to classify the well-known hyperspectral data sets and compared with traditional methods. The experimental results shown a significant increase in classification accuracies especially for urban areas.

  20. Examining fire-induced forest changes using novel remote sensing technique: a case study in a mixed pine-oak forest

    Science.gov (United States)

    Meng, R.; Wu, J.; Zhao, F. R.; Cook, B.; Hanavan, R. P.; Serbin, S.

    2017-12-01

    Fire-induced forest changes has long been a central focus for forest ecology and global carbon cycling studies, and is becoming a pressing issue for global change biologists particularly with the projected increases in the frequency and intensity of fire with a warmer and drier climate. Compared with time-consuming and labor intensive field-based approaches, remote sensing offers a promising way to efficiently assess fire effects and monitor post-fire forest responses across a range of spatial and temporal scales. However, traditional remote sensing studies relying on simple optical spectral indices or coarse resolution imagery still face a number of technical challenges, including confusion or contamination of the signal by understory dynamics and mixed pixels with moderate to coarse resolution data (>= 30 m). As such, traditional remote sensing may not meet the increasing demand for more ecologically-meaningful monitoring and quantitation of fire-induced forest changes. Here we examined the use of novel remote sensing technique (i.e. airborne imaging spectroscopy and LiDAR measurement, very high spatial resolution (VHR) space-borne multi-spectral measurement, and high temporal-spatial resolution UAS-based (Unmanned Aerial System) imagery), in combination with field and phenocam measurements to map forest burn severity across spatial scales, quantify crown-scale post-fire forest recovery rate, and track fire-induced phenology changes in the burned areas. We focused on a mixed pine-oak forest undergoing multiple fire disturbances for the past several years in Long Island, NY as a case study. We demonstrate that (1) forest burn severity mapping from VHR remote sensing measurement can capture crown-scale heterogeneous fire patterns over large-scale; (2) the combination of VHR optical and structural measurements provides an efficient means to remotely sense species-level post-fire forest responses; (3) the UAS-based remote sensing enables monitoring of fire

  1. Food, water, and fault lines: Remote sensing opportunities for earthquake-response management of agricultural water

    International Nuclear Information System (INIS)

    Rodriguez, Jenna; Ustin, Susan; Sandoval-Solis, Samuel; O'Geen, Anthony Toby

    2016-01-01

    Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate post-earthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remote sensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remote sensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remote sensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remote sensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management. - Highlights: • Remote sensing to improve agricultural disaster management • Introduce post-earthquake agrohydrologic remote sensing (PEARS) framework • Apply PEARS framework to 2010 Maule Earthquake in Central Chile

  2. Food, water, and fault lines: Remote sensing opportunities for earthquake-response management of agricultural water

    Energy Technology Data Exchange (ETDEWEB)

    Rodriguez, Jenna, E-mail: jmmartin@ucdavis.edu; Ustin, Susan; Sandoval-Solis, Samuel; O' Geen, Anthony Toby

    2016-09-15

    Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate post-earthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remote sensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remote sensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remote sensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remote sensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management. - Highlights: • Remote sensing to improve agricultural disaster management • Introduce post-earthquake agrohydrologic remote sensing (PEARS) framework • Apply PEARS framework to 2010 Maule Earthquake in Central Chile.

  3. Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

    Science.gov (United States)

    Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

    2017-01-01

    In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

  4. 1991 ACSM-ASPRS Annual Convention, Baltimore, MD, Mar. 25-29, 1991, Technical Papers. Vol. 3 - Remote Sensing

    International Nuclear Information System (INIS)

    Mcdonald, A.J.

    1991-01-01

    The present volume on remote sensing discusses a system of integrated acquisition procedures with satellite data, photointerpretation and ground measurements of forest structure, Landsat TM image classification with an artificial neural network, and the use of transputers to reduce maximum likelihood classification time. Attention is given to the automated extraction of metadata from remotely sensed satellite imagery, oceanographic analysis with NASA's Seapak software, strategies for coordinating natural resource information management programs, and an integrative approach to research of deforestation under concession management. Topics addressed include semiautomated point and search signature selection, information management challenges of the EOS Data and Information System, an integrating remote sensing/GIS approach for resource assessment in the National Park Service, and the development of land data sets for studies of global climate change

  5. Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

    Science.gov (United States)

    Eken, S.; Aydın, E.; Sayar, A.

    2017-11-01

    In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

  6. Theory and approach of information retrievals from electromagnetic scattering and remote sensing

    CERN Document Server

    Jin, Ya-Qiu

    2006-01-01

    Covers several hot topics in current research of electromagnetic scattering, and radiative transfer in complex and random media, polarimetric scattering and SAR imagery technology, data validation and information retrieval from space-borne remote sensing, computational electromagnetics, etc.Including both forward modelling and inverse problems, analytic theory and numerical approachesAn overall summary of the author's works during most recent yearsAlso presents some insight for future research topics.

  7. Tropical forest degradation monitoring using ETM+ and MODIS remote sensing data in the Peninsular Malaysia

    International Nuclear Information System (INIS)

    Hashim, M; Pour, A B; Chong, K W

    2014-01-01

    This study was undertaken in order to test the use of remote sensing technology to assess forest degradation in the Peninsular Malaysia. In order to analyse the effect of spatial resolution on forest degradation assessment, course and moderate spatial resolution remote sensing data were examined in this study. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used as coarse spatial resolution data, while Landsat Enhanced Thematic Mapper + (ETM + ) imagery was used as moderate spatial resolution to compare the accuracy. Geometric and radiometric correction and re-sampling were performed in preprocessing section to enhance the analysis and results. Canopy fractional cover was used as an approach to assess the forest degradation in this study. Then, an optimum vegetation index was selected to apply on canopy fractional cover to enhance the detection of forest canopy damage. At the same time, accuracy assessment for the approach was referred to the location of Neobalanocarpus Heimii and correlate with global evapotranspiration rate. The forest degradation analysis was also applied and compared for all of the states in the Peninsular Malaysia. In conclusion, Landsat ETM + imagery obtained higher accuracy compare to MODIS using canopy fractional cover approach for forest degradation assessment, and can be more broadly applicable to use for forest degradation investigation

  8. High resolution color imagery for orthomaps and remote sensing

    Energy Technology Data Exchange (ETDEWEB)

    Fricker, Peter [Leica Geosystems GIS and Mapping, LLC (Switzerland); Gallo, M. Guillermo [Leica Geosystems GIS and Mapping, LLC (United States)

    2005-07-01

    The ADS40 Airborne Digital Pushbroom Sensor is currently the only commercial sensor capable of acquiring color and false color strip images in the low decimeter range at the same high resolution as the black and white stereo images. This high resolution of 12,000 pixels across the entire swath and 100% forward overlap in the image strips result in high quality DSM's, True Ortho's and at the same time allow unbiased remote sensing applications due to color strip images unchanged by pan-sharpening. The paper gives details on how the pushbroom sensor achieves these seemingly difficult technical challenges. It describes how a variety of mapping applications benefit from this sensor, a sensor which acts as a satellite pushbroom sensor within the airborne environment. (author)

  9. Remote sensing for nuclear power plant siting

    International Nuclear Information System (INIS)

    Siegal, B.S.; Welby, C.W.

    1981-01-01

    It is shown that satellite remote sensing provides timely and cost-effective information for siting and site evaluation of nuclear power plants. Side-looking airborne radar (SLAR) imagery is especially valuable in regions of prolonged cloud cover and haze, and provides additional assurance in siting and licensing. In addition, a wide range of enhancement techniques should be employed and different types of image should be color-combined to provide structural and lithologic information. Coastal water circulation can also be studied through repetitive coverage and the inherently synoptic nature of imaging satellites. Among the issues discussed are snow cover, sun angle, and cloud cover, and actual site evaluation studies in the Bataan peninsula of the Philippines and Laguna Verde, California

  10. Open Access Data in Polar and Cryospheric Remote Sensing

    Directory of Open Access Journals (Sweden)

    Allen Pope

    2014-07-01

    Full Text Available This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use.

  11. Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments

    Science.gov (United States)

    Jungho Im; John R. Jensen; Mark Coleman; Eric. Nelson

    2009-01-01

    Hyperspectral remote sensing research was conducted to document the biophysical and biochemical characteristics of controlled forest plots subjected to various nutrient and irrigation treatments. The experimental plots were located on the Savannah River Site near Aiken, SC. AISA hyperspectral imagery were analysed using three approaches, including: (1) normalized...

  12. Remote Sensing Analysis Techniques and Sensor Requirements to Support the Mapping of Illegal Domestic Waste Disposal Sites in Queensland, Australia

    Directory of Open Access Journals (Sweden)

    Katharine Glanville

    2015-10-01

    Full Text Available Illegal disposal of waste is a significant management issue for contemporary governments with waste posing an economic, social, and environmental risk. An improved understanding of the distribution of illegal waste disposal sites is critical to enhance the cost-effectiveness and efficiency of waste management efforts. Remotely sensed data has the potential to address this knowledge gap. However, the literature regarding the use of remote sensing to map illegal waste disposal sites is incomplete. This paper aims to analyze existing remote sensing methods and sensors used to monitor and map illegal waste disposal sites. The purpose of this paper is to support the evaluation of existing remote sensing methods for mapping illegal domestic waste sites in Queensland, Australia. Recent advances in technology and the acquisition of very high-resolution remote sensing imagery provide an important opportunity to (1 revisit established analysis techniques for identifying illegal waste disposal sites, (2 examine the applicability of different remote sensors for illegal waste disposal detection, and (3 identify opportunities for future research to increase the accuracy of any illegal waste disposal mapping products.

  13. NOAA Emergency Response Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is in response to natural disasters. The aerial photography missions were conducted by the NOAA Remote Sensing Division. The majority...

  14. Geological remote sensing-evaluation of image data

    Energy Technology Data Exchange (ETDEWEB)

    Nagatani, H

    1977-01-01

    During the nationwide geothermal investigation program (Japan) begun in 1973, five districts were chosen for evaluation of the effectiveness and limitations of aerial thermography. Using thermal images of Ibusuki City and Yamakawa-cho, geothermal resource areas were detected and related fracture zones were established by combining the thermal imagery and geological maps. At Ibusuki City, it was determined that the heat source was the Ata caldera, and that a fracture system connecting it to Lake Ikeda provides a conduit for geothermal fluids. Image plotting of thermal anomalies in the Hachimantai geothermal field was found to be an effective method for monitoring variation in thermal activity. LANDSAT imagery was anlayzed and lineament systems were detected in the mountains of the Kanto district. Followup of LANDSAT data by mapping teams confirmed a fault which intersects the major Kamitsuna fault in that district. This successful use of remote sensing data is encouraging but it is possible to draw only limited conclusions from it at present. Further refinement of analytical techniques is required.

  15. Remote sensing of impervious surface growth: A framework for quantifying urban expansion and re-densification mechanisms

    Science.gov (United States)

    Shahtahmassebi, Amir Reza; Song, Jie; Zheng, Qing; Blackburn, George Alan; Wang, Ke; Huang, Ling Yan; Pan, Yi; Moore, Nathan; Shahtahmassebi, Golnaz; Sadrabadi Haghighi, Reza; Deng, Jing Song

    2016-04-01

    A substantial body of literature has accumulated on the topic of using remotely sensed data to map impervious surfaces which are widely recognized as an important indicator of urbanization. However, the remote sensing of impervious surface growth has not been successfully addressed. This study proposes a new framework for deriving and summarizing urban expansion and re-densification using time series of impervious surface fractions (ISFs) derived from remotely sensed imagery. This approach integrates multiple endmember spectral mixture analysis (MESMA), analysis of regression residuals, spatial statistics (Getis_Ord) and urban growth theories; hence, the framework is abbreviated as MRGU. The performance of MRGU was compared with commonly used change detection techniques in order to evaluate the effectiveness of the approach. The results suggested that the ISF regression residuals were optimal for detecting impervious surface changes while Getis_Ord was effective for mapping hotspot regions in the regression residuals image. Moreover, the MRGU outputs agreed with the mechanisms proposed in several existing urban growth theories, but importantly the outputs enable the refinement of such models by explicitly accounting for the spatial distribution of both expansion and re-densification mechanisms. Based on Landsat data, the MRGU is somewhat restricted in its ability to measure re-densification in the urban core but this may be improved through the use of higher spatial resolution satellite imagery. The paper ends with an assessment of the present gaps in remote sensing of impervious surface growth and suggests some solutions. The application of impervious surface fractions in urban change detection is a stimulating new research idea which is driving future research with new models and algorithms.

  16. A method for geological hazard extraction using high-resolution remote sensing

    International Nuclear Information System (INIS)

    Wang, Q J; Chen, Y; Bi, J T; Lin, Q Z; Li, M X

    2014-01-01

    Taking Yingxiu, the epicentre of the Wenchuan earthquake, as the study area, a method for geological disaster extraction using high-resolution remote sensing imagery was proposed in this study. A high-resolution Digital Elevation Model (DEM) was used to create mask imagery to remove interfering factors such as buildings and water at low altitudes. Then, the mask imagery was diced into several small parts to reduce the large images' inconsistency, and they were used as the sources to be classified. After that, vector conversion was done on the classified imagery in ArcGIS. Finally, to ensure accuracy, other interfering factors such as buildings at high altitudes, bare land, and land covered by little vegetation were removed manually. Because the method can extract geological hazards in a short time, it is of great importance for decision-makers and rescuers who need to know the degree of damage in the disaster area, especially within 72 hours after an earthquake. Therefore, the method will play an important role in decision making, rescue, and disaster response planning

  17. Remote Sensing for Wind Energy

    DEFF Research Database (Denmark)

    The Remote Sensing in Wind Energy Compendium provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind this compendium began in year 2008 at Risø DTU during the first PhD Summer School: Remote Sensing in Wind Energy. Thus......-of-the-art compendium available for people involved in Remote Sensing in Wind Energy....

  18. Geospatial Analysis and Remote Sensing from Airplanes and Satellites for Cultural Resources Management

    Science.gov (United States)

    Giardino, Marco J.; Haley, Bryan S.

    2005-01-01

    Cultural resource management consists of research to identify, evaluate, document and assess cultural resources, planning to assist in decision-making, and stewardship to implement the preservation, protection and interpretation of these decisions and plans. One technique that may be useful in cultural resource management archaeology is remote sensing. It is the acquisition of data and derivative information about objects or materials (targets) located on the Earth's surface or in its atmosphere by using sensor mounted on platforms located at a distance from the targets to make measurements on interactions between the targets and electromagnetic radiation. Included in this definition are systems that acquire imagery by photographic methods and digital multispectral sensors. Data collected by digital multispectral sensors on aircraft and satellite platforms play a prominent role in many earth science applications, including land cover mapping, geology, soil science, agriculture, forestry, water resource management, urban and regional planning, and environmental assessments. Inherent in the analysis of remotely sensed data is the use of computer-based image processing techniques. Geographical information systems (GIS), designed for collecting, managing, and analyzing spatial information, are also useful in the analysis of remotely sensed data. A GIS can be used to integrate diverse types of spatially referenced digital data, including remotely sensed and map data. In archaeology, these tools have been used in various ways to aid in cultural resource projects. For example, they have been used to predict the presence of archaeological resources using modern environmental indicators. Remote sensing techniques have also been used to directly detect the presence of unknown sites based on the impact of past occupation on the Earth's surface. Additionally, remote sensing has been used as a mapping tool aimed at delineating the boundaries of a site or mapping previously

  19. The Inylchek Glacier in Kyrgyzstan, Central Asia: Insight on Surface Kinematics from Optical Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Mohamad Nobakht

    2014-01-01

    Full Text Available Mountain chains of Central Asia host a large number of glaciated areas that provide critical water supplies to the semi-arid populated foothills and lowlands of this region. Spatio-temporal variations of glacier flows are a key indicator of the impact of climate change on water resources as the glaciers react sensitively to climate. Satellite remote sensing using optical imagery is an efficient method for studying ice-velocity fields on mountain glaciers. In this study, temporal and spatial changes in surface velocity associated with the Inylchek glacier in Kyrgyzstan are investigated. We present a detailed map for the kinematics of the Inylchek glacier obtained by cross-correlation analysis of Landsat images, acquired between 2000 and 2011, and a set of ASTER images covering the time period between 2001 and 2007. Our results indicate a high-velocity region in the elevated part of the glacier, moving up to a rate of about 0.5 m/day. Time series analysis of optical data reveals some annual variations in the mean surface velocity of the Inylchek during 2000–2011. In particular, our findings suggest an opposite trend between periods of the northward glacial flow in Proletarskyi and Zvezdochka glacier, and the rate of westward motion observed for the main stream of the Inylchek.

  20. DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    S. Eken

    2017-11-01

    Full Text Available In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

  1. Remotely Sensed, catchment scale, estimations of flow resistance

    Science.gov (United States)

    Carbonneau, P.; Dugdale, S. J.

    2009-12-01

    Despite a decade of progress in the field of fluvial remote sensing, there are few published works using this new technology to advance and explore fundamental ideas and theories in fluvial geomorphology. This paper will apply remote sensing methods in order to re-visit a classic concept in fluvial geomorphology: flow resistance. Classic flow resistance equations such as those of Strickler and Keulegan typically use channel slope, channel depth or hydraulic radius and some measure channel roughness usually equated to the 50th or 84th percentile of the bed material size distribution. In this classic literature, empirical equations such as power laws are usually calibrated and validated with a maximum of a few hundred data points. In contrast, fluvial remote sensing methods are now capable of delivering millions of high resolution data points in continuous, catchment scale, surveys. On the river Tromie in Scotland, a full dataset or river characteristics is now available. Based on low altitude imagery and NextMap topographic data, this dataset has a continuous sampling of channel width at a resolution of 3cm, of depth and median grain size at a resolution of 1m, and of slope at a resolution of 5m. This entire data set is systematic and continuous for the entire 20km length of the river. When combined with discharge at the time of data acquisition, this new dataset offers the opportunity to re-examine flow resistance equations with a 2-4 orders of magnitude increase in calibration data. This paper will therefore re-examine the classic approaches of Strickler and Keulagan along with other more recent flow resistance equations. Ultimately, accurate predictions of flow resistance from remotely sensed parameters could lead to acceptable predictions of velocity. Such a usage of classic equations to predict velocity could allow lotic habitat models to account for microhabitat velocity at catchment scales without the recourse to advanced and computationally intensive

  2. Tunnel-Site Selection by Remote Sensing Techniques

    Science.gov (United States)

    A study of the role of remote sensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remote sensing was defined...conventional remote sensing . Future research directions are suggested, and the extension of remote sensing to include airborne passive microwave

  3. Remote Sensing Best Paper Award 2013

    OpenAIRE

    Prasad Thenkabail

    2013-01-01

    Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper Award” for 2013. Nominations were selected by the Editor-in-Chief and selected editorial board members from among all the papers published in 2009. Reviews and research papers were evaluated separately.

  4. The contribution of remote sensing to an understanding of the geology of Gabon

    International Nuclear Information System (INIS)

    Bassot, J.P.

    1988-01-01

    A major remote-sensing operation involving radar imagery and airbone magnetism and spectrometry has been successfully conducted in Gabon. The three methods used give complementary results. Lateral radar imagery and radiometry (U, K, Th) have supplied much new information on the Upper and Lower Proterozoic, but in areas affected by intense peneplanation and lateritisation they are less effective. Conversely, Airbone magnetism gives a deeper vision into the ground: particularly it revealed that the extent of greenstone belts had been significantly underestimated on existing geological maps. In addition, the trends in these belts have given a new insight into late Archaean tectonics in northern Gabon [fr

  5. A data fusion framework for floodplain analysis using GIS and remotely sensed data

    Science.gov (United States)

    Necsoiu, Dorel Marius

    Throughout history floods have been part of the human experience. They are recurring phenomena that form a necessary and enduring feature of all river basin and lowland coastal systems. In an average year, they benefit millions of people who depend on them. In the more developed countries, major floods can be the largest cause of economic losses from natural disasters, and are also a major cause of disaster-related deaths in the less developed countries. Flood disaster mitigation research was conducted to determine how remotely sensed data can effectively be used to produce accurate flood plain maps (FPMs), and to identify/quantify the sources of error associated with such data. Differences were analyzed between flood maps produced by an automated remote sensing analysis tailored to the available satellite remote sensing datasets (rFPM), the 100-year flooded areas "predicted" by the Flood Insurance Rate Maps, and FPMs based on DEM and hydrological data (aFPM). Landuse/landcover was also examined to determine its influence on rFPM errors. These errors were identified and the results were integrated in a GIS to minimize landuse/landcover effects. Two substantial flood events were analyzed. These events were selected because of their similar characteristics (i.e., the existence of FIRM or Q3 data; flood data which included flood peaks, rating curves, and flood profiles; and DEM and remote sensing imagery). Automatic feature extraction was determined to be an important component for successful flood analysis. A process network, in conjunction with domain specific information, was used to map raw remotely sensed data onto a representation that is more compatible with a GIS data model. From a practical point of view, rFPM provides a way to automatically match existing data models to the type of remote sensing data available for each event under investigation. Overall, results showed how remote sensing could contribute to the complex problem of flood management by

  6. Remote Sensing for Wind Energy

    DEFF Research Database (Denmark)

    Peña, Alfredo; Hasager, Charlotte Bay; Lange, Julia

    The Remote Sensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risø) during the first PhD Summer School: Remote Sensing in Wind Energy...... state-of-the-art ‘guideline’ available for people involved in Remote Sensing in Wind Energy....

  7. Remote Sensing and Imaging Physics

    Science.gov (United States)

    2012-03-07

    Program Manager AFOSR/RSE Air Force Research Laboratory Remote Sensing and Imaging Physics 7 March 2012 Report Documentation Page Form...00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Remote Sensing And Imaging Physics 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT...Imaging of Space Objects •Information without Imaging •Predicting the Location of Space Objects • Remote Sensing in Extreme Conditions •Propagation

  8. From Pixels to Population Stress: Global Multispectral Remote Sensing for Vulnerable Communities

    Science.gov (United States)

    Prashad, L.; Kaplan, E.; Letouze, E.; Kirkpatrick, R.; Luengo-Oroz, M.; Christensen, P. R.

    2011-12-01

    The Arizona State University (ASU) School of Earth and Space Exploration's Mars Space Flight Facility (MSFF) and 100 Cities Project, in collaboration with the United Nations Global Pulse initiative are utilizing NASA multispectral satellite data to visualize and analyze socioeconomic characteristics and human activity in Uganda. The Global Pulse initiative is exploring how new kinds of real-time data and innovative technologies can be leveraged to detect early social impacts of slow-onset crisis and global shocks. Global Pulse is developing a framework for real-time monitoring, assembling an open-source toolkit for analyzing new kinds of data and establishing a global network of country-level "Pulse Labs" where governments, UN agencies, academia and the private sector learn together how to harness the new world of "big data" to protect the vulnerable with targeted and agile policy responses. The ASU MSFF and 100 Cities Project are coordinating with the Global Pulse team to utilize NASA remote sensing data in this effort. Human behavior and socioeconomic parameters have been successfully studied via proxy through remote sensing of the physical environment by measuring the growth of city boundaries and transportation networks, crop health, soil moisture, and slum development from visible and infrared imagery. The NASA/ NOAA image of Earth's "Lights at Night" is routinely used to estimate economic development and population density. There are many examples of the conventional uses of remote sensing in humanitarian-related projects including the Famine Early Warning System Network (FEWS NET) and the UN's operational satellite applications programme (UNOSAT), which provides remote sensing for humanitarian and disaster relief. Since the Global Pulse project is focusing on new, innovative uses of technology for early crisis detection, we are focusing on three non-conventional uses of satellite remote sensing to understand what role NASA multispectral satellites can play

  9. Remote sensing: best practice

    Energy Technology Data Exchange (ETDEWEB)

    Brown, Gareth [Sgurr Energy (Canada)

    2011-07-01

    This paper presents remote sensing best practice in the wind industry. Remote sensing is a technique whereby measurements are obtained from the interaction of laser or acoustic pulses with the atmosphere. There is a vast diversity of tools and techniques available and they offer wide scope for reducing project uncertainty and risk but best practice must take into account versatility and flexibility. It should focus on the outcome in terms of results and data. However, traceability of accuracy requires comparison with conventional instruments. The framework for the Boulder protocol is given. Overviews of the guidelines for IEA SODAR and IEA LIDAR are also mentioned. The important elements of IEC 61400-12-1, an international standard for wind turbines, are given. Bankability is defined based on the Boulder protocol and a pie chart is presented that illustrates the uncertainty area covered by remote sensing. In conclusion it can be said that remote sensing is changing perceptions about how wind energy assessments can be made.

  10. Needs Assessment for the Use of NASA Remote Sensing Data in the Development and Implementation of Estuarine and Coastal Water Quality Standards

    Science.gov (United States)

    Spiering, Bruce; Underwood, Lauren; Ellis, Chris; Lehrter, John; Hagy, Jim; Schaeffer, Blake

    2010-01-01

    The goals of the project are to provide information from satellite remote sensing to support numeric nutrient criteria development and to determine data processing methods and data quality requirements to support nutrient criteria development and implementation. The approach is to identify water quality indicators that are used by decision makers to assess water quality and that are related to optical properties of the water; to develop remotely sensed data products based on algorithms relating remote sensing imagery to field-based observations of indicator values; to develop methods to assess estuarine water quality, including trends, spatial and temporal variability, and seasonality; and to develop tools to assist in the development and implementation of estuarine and coastal nutrient criteria. Additional slides present process, criteria development, typical data sources and analyses for criteria process, the power of remote sensing data for the process, examples from Pensacola Bay, spatial and temporal variability, pixel matchups, remote sensing validation, remote sensing in coastal waters, requirements for remotely sensed data products, and needs assessment. An additional presentation examines group engagement and information collection. Topics include needs assessment purpose and objectives, understanding water quality decision making, determining information requirements, and next steps.

  11. Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

    Directory of Open Access Journals (Sweden)

    Marc Cattet

    2010-11-01

    Full Text Available Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC. Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI, inversion algorithm, data fusion, and the integration of remote sensing (RS and geographic information system (GIS.

  12. Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

    Science.gov (United States)

    Wang, Kai; Franklin, Steven E; Guo, Xulin; Cattet, Marc

    2010-01-01

    Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

  13. Remote sensing in meteorology, oceanography and hydrology

    Energy Technology Data Exchange (ETDEWEB)

    Cracknell, A P [ed.

    1981-01-01

    Various aspects of remote sensing are discussed. Topics include: the EARTHNET data acquisition, processing, and distribution facility the design and implementation of a digital interactive image processing system geometrical aspects of remote sensing and space cartography remote sensing of a complex surface legal aspects of remote sensing remote sensing of pollution, dust storms, ice masses, and ocean waves and currents use of satellite images for weather forecasting. Notes on field trips and work-sheets for laboratory exercises are included.

  14. Remote sensing as a preliminary analysis for the detection of active tectonic structures: an application to the Albanian orogenic system

    Directory of Open Access Journals (Sweden)

    Andrea Favretto

    2013-12-01

    Full Text Available As is well known, both the traditional direct geological and geophysical survey methods used to identify geologic features are very expensive and time-consuming procedures. In this regard, remote sensing methods applied to multispectral and medium spatial resolution satellite images allow a more focused approach with respect to the more specific geologic methods. This is achieved by a preliminary land inspection carried out by the semi-automated analysis of satellite imagery. This avoids wasting resources as the geological/geophysical survey methods can be later applied only to those zones suspected of having certain tectonic activity (derived by the remotely sensed imagery. This paper will evaluate an ASTER sensor satellite image (and its derived Digital Elevation Model or DEM, in order to point out the suspected presence of active geologic structures (faults. The area in question is west – central Albania. The results of the remote sensing procedures are later compared with the established data for the same area taken by satellite images, in order to verify the reliability of the adopted method. The source of the established data has been from the bibliography.

  15. Documentation of archaeological sites in northern iraq using remote sensing methods

    Science.gov (United States)

    Matoušková, E.; Pavelka, K.; Nováček, K.; Starková, L.

    2015-08-01

    The MULINEM (The Medieval Urban Landscape in Northeastern Mesopotamia) project is aiming to investigate a Late Sasanian and Islamic urban network in the land of Erbil, historic province of Hidyab (Adiabene) that is located in the northern Iraq. The research of the hierarchical urban network in a defined area belongs to approaches rarely used in the study of the Islamic urbanism. The project focuses on the cluster of urban sites of the 6th-17th centuries A.D. This paper focuses on remote sensing analysis of historical sites with special interest of FORMOSAT-2 data that have been gained through a research announcement: Free FORMOSAT-2 satellite Imagery. Documentation of two archaeological sites (Makhmúr al-Qadima and Kushaf) are introduced. FORMOSAT-2 data results have been compared to historic CORONA satellite data of mentioned historical sites purchased earlier by the University of West Bohemia. Remote sensing methods were completed using in-situ measurements.

  16. The dog and cat population on Maio Island, Cape Verde: characterisation and prediction based on household survey and remotely sensed imagery.

    Science.gov (United States)

    Lopes Antunes, Ana Carolina; Ducheyne, Els; Bryssinckx, Ward; Vieira, Sara; Malta, Manuel; Vaz, Yolanda; Nunes, Telmo; Mintiens, Koen

    2015-11-04

    The objective was to estimate and characterise the dog and cat population on Maio Island, Cape Verde. Remotely sensed imagery was used to document the number of houses across the island and a household survey was carried out in six administrative areas recording the location of each animal using a global positioning system instrument. Linear statistical models were applied to predict the dog and cat populations based on the number of houses found and according to various levels of data aggregation. In the surveyed localities, a total of 457 dogs and 306 cats were found. The majority of animals had owners and only a few had free access to outdoor activities. The estimated population size was 531 dogs [95% confidence interval (CI): 453-609] and 354 cats (95% CI: 275-431). Stray animals were not a concern on the island in contrast to the rest of the country.

  17. Illumination invariant feature point matching for high-resolution planetary remote sensing images

    Science.gov (United States)

    Wu, Bo; Zeng, Hai; Hu, Han

    2018-03-01

    Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

  18. Analysis of remote sensing data for evaluation of vegetation resources

    Science.gov (United States)

    1970-01-01

    Research has centered around: (1) completion of a study on the use of remote sensing techniques as an aid to multiple use management; (2) determination of the information transfer at various image resolution levels for wildland areas; and (3) determination of the value of small scale multiband, multidate photography for the analysis of vegetation resources. In addition, a substantial effort was made to upgrade the automatic image classification and spectral signature acquisition capabilities of the laboratory. It was found that: (1) Remote sensing techniques should be useful in multiple use management to provide a first-cut analysis of an area. (2) Imagery with 400-500 feet ground resolvable distance (GRD), such as that expected from ERTS-1, should allow discriminations to be made between woody vegetation, grassland, and water bodies with approximately 80% accuracy. (3) Barley and wheat acreages in Maricopa County, Arizona could be estimated with acceptable accuracies using small scale multiband, multidate photography. Sampling errors for acreages of wheat, barley, small grains (wheat and barley combined), and all cropland were 13%, 11%, 8% and 3% respectively.

  19. Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

    Directory of Open Access Journals (Sweden)

    Mao-Gui Hu

    2009-10-01

    Full Text Available Satellite remote sensing (RS is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intraurban. In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolutionenhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well indetail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics.

  20. Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model

    Science.gov (United States)

    Wu, Z.; Chen, X.; Gao, Y.; Li, Y.

    2018-04-01

    Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

  1. a Framework for Capacity Building in Mapping Coastal Resources Using Remote Sensing in the Philippines

    Science.gov (United States)

    Tamondong, A.; Cruz, C.; Ticman, T.; Peralta, R.; Go, G. A.; Vergara, M.; Estabillo, M. S.; Cadalzo, I. E.; Jalbuena, R.; Blanco, A.

    2016-06-01

    Remote sensing has been an effective technology in mapping natural resources by reducing the costs and field data gathering time and bringing in timely information. With the launch of several earth observation satellites, an increase in the availability of satellite imageries provides an immense selection of data for the users. The Philippines has recently embarked in a program which will enable the gathering of LiDAR data in the whole country. The capacity of the Philippines to take advantage of these advancements and opportunities is lacking. There is a need to transfer the knowledge of remote sensing technology to other institutions to better utilize the available data. Being an archipelagic country with approximately 36,000 kilometers of coastline, and most of its people depending on its coastal resources, remote sensing is an optimal choice in mapping such resources. A project involving fifteen (15) state universities and colleges and higher education institutions all over the country headed by the University of the Philippines Training Center for Applied Geodesy and Photogrammetry and funded by the Department of Science and Technology was formed to carry out the task of capacity building in mapping the country's coastal resources using LiDAR and other remotely sensed datasets. This paper discusses the accomplishments and the future activities of the project.

  2. Performing and updating an inventory of Oregon's expanding irrigated agricultural lands utilizing remote sensing technology

    Science.gov (United States)

    Hall, M. J.

    1981-01-01

    An inventory technique based upon using remote sensing technology, interpreting both high altitude aerial photography and LANDSAT multispectral scanner imagery, is discussed. It is noted that once the final land use inventory maps of irrigated agricultural lands are available and approximately scaled they may be overlaid directly onto either multispectral scanner or return beam vidicon prints, thereby providing an inexpensive updating procedure.

  3. Remote Sensing and the Earth.

    Science.gov (United States)

    Brosius, Craig A.; And Others

    This document is designed to help senior high school students study remote sensing technology and techniques in relation to the environmental sciences. It discusses the acquisition, analysis, and use of ecological remote data. Material is divided into three sections and an appendix. Section One is an overview of the basics of remote sensing.…

  4. Landscape Pattern Detection in Archaeological Remote Sensing

    Directory of Open Access Journals (Sweden)

    Arianna Traviglia

    2017-12-01

    Full Text Available Automated detection of landscape patterns on Remote Sensing imagery has seen virtually little or no development in the archaeological domain, notwithstanding the fact that large portion of cultural landscapes worldwide are characterized by land engineering applications. The current extraordinary availability of remotely sensed images makes it now urgent to envision and develop automatic methods that can simplify their inspection and the extraction of relevant information from them, as the quantity of information is no longer manageable by traditional “human” visual interpretation. This paper expands on the development of automatic methods for the detection of target landscape features—represented by field system patterns—in very high spatial resolution images, within the framework of an archaeological project focused on the landscape engineering embedded in Roman cadasters. The targets of interest consist of a variety of similarly oriented objects of diverse nature (such as roads, drainage channels, etc. concurring to demark the current landscape organization, which reflects the one imposed by Romans over two millennia ago. The proposed workflow exploits the textural and shape properties of real-world elements forming the field patterns using multiscale analysis of dominant oriented response filters. Trials showed that this approach provides accurate localization of target linear objects and alignments signaled by a wide range of physical entities with very different characteristics.

  5. Radar Remote Sensing

    Science.gov (United States)

    Rosen, Paul A.

    2012-01-01

    This lecture was just a taste of radar remote sensing techniques and applications. Other important areas include Stereo radar grammetry. PolInSAR for volumetric structure mapping. Agricultural monitoring, soil moisture, ice-mapping, etc. The broad range of sensor types, frequencies of observation and availability of sensors have enabled radar sensors to make significant contributions in a wide area of earth and planetary remote sensing sciences. The range of applications, both qualitative and quantitative, continue to expand with each new generation of sensors.

  6. Dissemination of Earth Remote Sensing Data for Use in the NOAA/NWS Damage Assessment Toolkit

    Science.gov (United States)

    Molthan, Andrew; Burks, Jason; Camp, Parks; McGrath, Kevin; Bell, Jordan

    2015-01-01

    The National Weather Service has developed the Damage Assessment Toolkit (DAT), an application for smartphones and tablets that allows for the collection, geolocation, and aggregation of various damage indicators that are collected during storm surveys. The DAT supports the often labor-intensive process where meteorologists venture into the storm-affected area, allowing them to acquire geotagged photos of the observed damage while also assigning estimated EF-scale categories based upon their observations. Once the data are collected, the DAT infrastructure aggregates the observations into a server that allows other meteorologists to perform quality control and other analysis steps before completing their survey and making the resulting data available to the public. In addition to in-person observations, Earth remote sensing from operational, polar-orbiting satellites can support the damage assessment process by identifying portions of damage tracks that may be missed due to road limitations, access to private property, or time constraints. Products resulting from change detection techniques can identify damage to vegetation and the land surface, aiding in the survey process. In addition, higher resolution commercial imagery can corroborate ground-based surveys by examining higher-resolution commercial imagery. As part of an ongoing collaboration, NASA and NOAA are working to integrate near real-time Earth remote sensing observations into the NOAA/NWS Damage Assessment Toolkit. This presentation will highlight recent developments in a streamlined approach for disseminating Earth remote sensing data via web mapping services and a new menu interface that has been integrated within the DAT. A review of current and future products will be provided, including products derived from MODIS and VIIRS for preliminary track identification, along with conduits for higher-resolution Landsat, ASTER, and commercial imagery as they become available. In addition to tornado damage

  7. Remote sensing and implications for variable-rate application using agricultural aircraft

    Science.gov (United States)

    Thomson, Steven J.; Smith, Lowrey A.; Ray, Jeffrey D.; Zimba, Paul V.

    2004-01-01

    Aircraft routinely used for agricultural spray application are finding utility for remote sensing. Data obtained from remote sensing can be used for prescription application of pesticides, fertilizers, cotton growth regulators, and water (the latter with the assistance of hyperspectral indices and thermal imaging). Digital video was used to detect weeds in early cotton, and preliminary data were obtained to see if nitrogen status could be detected in early soybeans. Weeds were differentiable from early cotton at very low altitudes (65-m), with the aid of supervised classification algorithms in the ENVI image analysis software. The camera was flown at very low altitude for acceptable pixel resolution. Nitrogen status was not detectable by statistical analysis of digital numbers (DNs) obtained from images, but soybean cultivar differences were statistically discernable (F=26, p=0.01). Spectroradiometer data are being analyzed to identify narrow spectral bands that might aid in selecting camera filters for determination of plant nitrogen status. Multiple camera configurations are proposed to allow vegetative indices to be developed more readily. Both remotely sensed field images and ground data are to be used for decision-making in a proposed variable-rate application system for agricultural aircraft. For this system, prescriptions generated from digital imagery and data will be coupled with GPS-based swath guidance and programmable flow control.

  8. Remote sensing for water quality

    International Nuclear Information System (INIS)

    Giardino, Claudia

    2006-01-01

    The application of remote sensing to the study of lakes is begun in years 80 with the lunch of the satellites of second generation. Many experiences have indicated the contribution of remote sensing for the limnology [it

  9. Sensing our Environment: Remote sensing in a physics classroom

    Science.gov (United States)

    Isaacson, Sivan; Schüttler, Tobias; Cohen-Zada, Aviv L.; Blumberg, Dan G.; Girwidz, Raimund; Maman, Shimrit

    2017-04-01

    Remote sensing is defined as data acquisition of an object, deprived physical contact. Fundamentally, most remote sensing applications are referred to as the use of satellite- or aircraft-based sensor technologies to detect and classify objects mainly on Earth or other planets. In the last years there have been efforts to bring the important subject of remote sensing into schools, however, most of these attempts focused on geography disciplines - restricting to the applications of remote sensing and to a less extent the technique itself and the physics behind it. Optical remote sensing is based on physical principles and technical devices, which are very meaningful from a theoretical point of view as well as for "hands-on" teaching. Some main subjects are radiation, atom and molecular physics, spectroscopy, as well as optics and the semiconductor technology used in modern digital cameras. Thus two objectives were outlined for this project: 1) to investigate the possibilities of using remote sensing techniques in physics teaching, and 2) to identify its impact on pupil's interest in the field of natural sciences. This joint project of the DLR_School_Lab, Oberpfaffenhofen of the German Aerospace Center (DLR) and the Earth and Planetary Image Facility (EPIF) at BGU, was conducted in 2016. Thirty teenagers (ages 16-18) participated in the project and were exposed to the cutting edge methods of earth observation. The pupils on both sides participated in the project voluntarily, knowing that at least some of the project's work had to be done in their leisure time. The pupil's project started with a day at EPIF and DLR respectively, where the project task was explained to the participants and an introduction to remote sensing of vegetation was given. This was realized in lectures and in experimental workshops. During the following two months both groups took several measurements with modern optical remote sensing systems in their home region with a special focus on flora

  10. Spatial-Spectral Approaches to Edge Detection in Hyperspectral Remote Sensing

    Science.gov (United States)

    Cox, Cary M.

    This dissertation advances geoinformation science at the intersection of hyperspectral remote sensing and edge detection methods. A relatively new phenomenology among its remote sensing peers, hyperspectral imagery (HSI) comprises only about 7% of all remote sensing research - there are five times as many radar-focused peer reviewed journal articles than hyperspectral-focused peer reviewed journal articles. Similarly, edge detection studies comprise only about 8% of image processing research, most of which is dedicated to image processing techniques most closely associated with end results, such as image classification and feature extraction. Given the centrality of edge detection to mapping, that most important of geographic functions, improving the collective understanding of hyperspectral imagery edge detection methods constitutes a research objective aligned to the heart of geoinformation sciences. Consequently, this dissertation endeavors to narrow the HSI edge detection research gap by advancing three HSI edge detection methods designed to leverage HSI's unique chemical identification capabilities in pursuit of generating accurate, high-quality edge planes. The Di Zenzo-based gradient edge detection algorithm, an innovative version of the Resmini HySPADE edge detection algorithm and a level set-based edge detection algorithm are tested against 15 traditional and non-traditional HSI datasets spanning a range of HSI data configurations, spectral resolutions, spatial resolutions, bandpasses and applications. This study empirically measures algorithm performance against Dr. John Canny's six criteria for a good edge operator: false positives, false negatives, localization, single-point response, robustness to noise and unbroken edges. The end state is a suite of spatial-spectral edge detection algorithms that produce satisfactory edge results against a range of hyperspectral data types applicable to a diverse set of earth remote sensing applications. This work

  11. Emergency Response Imagery Related to Hurricanes Harvey, Irma, and Maria

    Science.gov (United States)

    Worthem, A. V.; Madore, B.; Imahori, G.; Woolard, J.; Sellars, J.; Halbach, A.; Helmricks, D.; Quarrick, J.

    2017-12-01

    NOAA's National Geodetic Survey (NGS) and Remote Sensing Division acquired and rapidly disseminated emergency response imagery related to the three recent hurricanes Harvey, Irma, and Maria. Aerial imagery was collected using a Trimble Digital Sensor System, a high-resolution digital camera, by means of NOAA's King Air 350ER and DeHavilland Twin Otter (DHC-6) Aircraft. The emergency response images are used to assess the before and after effects of the hurricanes' damage. The imagery aids emergency responders, such as FEMA, Coast Guard, and other state and local governments, in developing recovery strategies and efforts by prioritizing areas most affected and distributing appropriate resources. Collected imagery is also used to provide damage assessment for use in long-term recovery and rebuilding efforts. Additionally, the imagery allows for those evacuated persons to see images of their homes and neighborhoods remotely. Each of the individual images are processed through ortho-rectification and merged into a uniform mosaic image. These remotely sensed datasets are publically available, and often used by web-based map servers as well as, federal, state, and local government agencies. This poster will show the imagery collected for these three hurricanes and the processes involved in getting data quickly into the hands of those that need it most.

  12. Ground-Based Correction of Remote-Sensing Spectral Imagery

    Science.gov (United States)

    Alder-Golden, Steven M.; Rochford, Peter; Matthew, Michael; Berk, Alexander

    2007-01-01

    Software has been developed for an improved method of correcting for the atmospheric optical effects (primarily, effects of aerosols and water vapor) in spectral images of the surface of the Earth acquired by airborne and spaceborne remote-sensing instruments. In this method, the variables needed for the corrections are extracted from the readings of a radiometer located on the ground in the vicinity of the scene of interest. The software includes algorithms that analyze measurement data acquired from a shadow-band radiometer. These algorithms are based on a prior radiation transport software model, called MODTRAN, that has been developed through several versions up to what are now known as MODTRAN4 and MODTRAN5 . These components have been integrated with a user-friendly Interactive Data Language (IDL) front end and an advanced version of MODTRAN4. Software tools for handling general data formats, performing a Langley-type calibration, and generating an output file of retrieved atmospheric parameters for use in another atmospheric-correction computer program known as FLAASH have also been incorporated into the present soft-ware. Concomitantly with the soft-ware described thus far, there has been developed a version of FLAASH that utilizes the retrieved atmospheric parameters to process spectral image data.

  13. Introductory remote sensing principles and concepts principles and concepts

    CERN Document Server

    Gibson, Paul

    2013-01-01

    Introduction to Remote Sensing Principles and Concepts provides a comprehensive student introduction to both the theory and application of remote sensing. This textbook* introduces the field of remote sensing and traces its historical development and evolution* presents detailed explanations of core remote sensing principles and concepts providing the theory required for a clear understanding of remotely sensed images.* describes important remote sensing platforms - including Landsat, SPOT and NOAA * examines and illustrates many of the applications of remotely sensed images in various fields.

  14. Mississippi Sound Remote Sensing Study

    Science.gov (United States)

    Atwell, B. H.

    1973-01-01

    The Mississippi Sound Remote Sensing Study was initiated as part of the research program of the NASA Earth Resources Laboratory. The objective of this study is development of remote sensing techniques to study near-shore marine waters. Included within this general objective are the following: (1) evaluate existing techniques and instruments used for remote measurement of parameters of interest within these waters; (2) develop methods for interpretation of state-of-the-art remote sensing data which are most meaningful to an understanding of processes taking place within near-shore waters; (3) define hardware development requirements and/or system specifications; (4) develop a system combining data from remote and surface measurements which will most efficiently assess conditions in near-shore waters; (5) conduct projects in coordination with appropriate operating agencies to demonstrate applicability of this research to environmental and economic problems.

  15. Using Satellite Remote Sensing to assist the National Weather Service (NWS) in Storm Damage Surveys

    Science.gov (United States)

    Schultz, L. A.; Molthan, A.; McGrath, K.; Bell, J. R.; Cole, T.; Burks, J.

    2016-12-01

    In recent years, the NWS has developed a GIS-based application, called the Damage Assessment Toolkit (DAT), to conduct storm surveys after severe weather events. At present, the toolkit is primarily used for tornado damage surveys and facilitates the identification of damage indicators in accordance with the Enhanced Fujita (EF) intensity scale by allowing surveyors to compare time- and geo-tagged photos against the EF scale guidelines. Mobile and web-based applications provide easy access to the DAT for NWS personnel while performing their duties in the field or office. Multispectral satellite remote sensing imagery has demonstrated benefits for the detection and mapping of damage tracks caused by tornadoes, especially for long-track events and/or areas not easily accessed by NWS personnel. For example, imagery from MODIS, Landsat 7, Landsat 8, ASTER, Sentinel 2, and commercial satellites, collected and distributed in collaboration with the USGS Hazards Data Distribution System, have been useful for refining track location and extent through a "bird's eye" view of the damaged areas. The NASA Short-term Prediction Research and Transition (SPoRT) Center has been working with the NWS and USGS to provide imagery and derived products from polar-orbiting satellite platforms to assist in the detection and refinement of tornado tracks as part of a NASA Applied Science: Disasters project. Working closely with select Weather Forecast Offices (WFOs) and Regional Operations Centers (ROCs) in both the NWS Central and Southern regions, high- and medium-resolution (0.5 - 30 m and 250 m - 1 km resolutions, respectively) imagery and derived products have been provided to the DAT interface for evaluation of operational utility by the NWS for their use in both the field and in the office during post event analysis. Highlighted in this presentation will be case studies where the remotely sensed imagery assisted in the adjustment of a tornado track. Examples will be shown highlighting

  16. Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

    Science.gov (United States)

    Schmidt, Johannes; Fassnacht, Fabian Ewald; Neff, Christophe; Lausch, Angela; Kleinschmit, Birgit; Förster, Michael; Schmidtlein, Sebastian

    2017-08-01

    Remote sensing can be a valuable tool for supporting nature conservation monitoring systems. However, for many areas of conservation interest, there is still a considerable gap between field-based operational monitoring guidelines and the current remote sensing-based approaches. This hampers application in practice of the latter. Here, we propose a remote sensing approach for mapping the conservation status of Calluna-dominated Natura 2000 dwarf shrub habitats that is closely related to field mapping schemes. We transferred the evaluation criteria of the field guidelines to three related variables that can be captured by remote sensing: (1) coverage of the key species, (2) stand structural diversity, and (3) co-occurring species. Continuous information on these variables was obtained by regressing ground reference data from field surveys and UAV flights against airborne hyperspectral imagery. Merging the three resulting quality layers in an RGB representation allowed for illustrating the habitat quality in a continuous way. User-defined thresholds can be applied to this stack of quality layers to derive an overall assessment of habitat quality in terms of nature conservation, i.e. the conservation status. In our study, we found good accordance of the remotely sensed data with field-based information for the three variables key species, stand structural diversity and co-occurring vegetation (R2 of 0.79, 0.69, and 0.71, respectively) and it was possible to derive meaningful habitat quality maps. The conservation status could be derived with an accuracy of 65%. In interpreting these results it should be considered that the remote sensing based layers are independent estimates of habitat quality in their own right and not a mere replacement of the criteria used in the field guidelines. The approach is thought to be transferable to similar regions with minor adaptions. Our results refer to Calluna heathland which we consider a comparably easy target for remote sensing

  17. Remote Sensing and Reflectance Profiling in Entomology.

    Science.gov (United States)

    Nansen, Christian; Elliott, Norman

    2016-01-01

    Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

  18. Spatiotemporal remote sensing of ecosystem change and causation across Alaska.

    Science.gov (United States)

    Pastick, Neal J; Jorgenson, M Torre; Goetz, Scott J; Jones, Benjamin M; Wylie, Bruce K; Minsley, Burke J; Genet, Hélène; Knight, Joseph F; Swanson, David K; Jorgenson, Janet C

    2018-05-28

    Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio-environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high-latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time-series analysis of moderate-and high-resolution imagery was used to characterize land- and water-surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land-surface greening, browning, and wetness/moisture trend parameters derived from peak-growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km 2 ) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface-water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery

  19. Imagery for Disaster Response and Recovery

    Science.gov (United States)

    Bethel, G. R.

    2011-12-01

    Exposing the remotely sensed imagery for disaster response and recovery can provide the basis for an unbiased understanding of current conditions. Having created consolidated remotely sensed and geospatial data sources documents for US and Foreign disasters over the past six years, availability and usability are continuing to evolve. By documenting all existing sources of imagery and value added products, the disaster response and recovery community can develop actionable information. The past two years have provided unique situations to use imagery including a major humanitarian disaster and response effort in Haiti, a major environmental disaster in the Gulf of Mexico, a killer tornado in Joplin Missouri and long-term flooding in the Midwest. Each disaster presents different challenges and requires different spatial resolutions, spectral properties and/or multi-temporal collections. The community of data providers continues to expand with organized actives such as the International Charter for Space and Major Disasters and acquisitions by the private sector for the public good rather than for profit. However, data licensing, the lack of cross-calibration and inconsistent georeferencing hinder optimal use. Recent pre-event imagery is a critial component to any disaster response.

  20. Photogrammetry - Remote Sensing and Geoinformation

    Science.gov (United States)

    Lazaridou, M. A.; Patmio, E. N.

    2012-07-01

    Earth and its environment are studied by different scientific disciplines as geosciences, science of engineering, social sciences, geography, etc. The study of the above, beyond pure scientific interest, is useful for the practical needs of man. Photogrammetry and Remote Sensing (defined by Statute II of ISPRS) is the art, science, and technology of obtaining reliable information from non-contact imaging and other sensor systems about the Earth and its environment, and other physical objects and of processes through recording, measuring, analyzing and representation. Therefore, according to this definition, photogrammetry and remote sensing can support studies of the above disciplines for acquisition of geoinformation. This paper concerns basic concepts of geosciences (geomorphology, geology, hydrology etc), and the fundamentals of photogrammetry-remote sensing, in order to aid the understanding of the relationship between photogrammetry-remote sensing and geoinformation and also structure curriculum in a brief, concise and coherent way. This curriculum can represent an appropriate research and educational outline and help to disseminate knowledge in various directions and levels. It resulted from our research and educational experience in graduate and post-graduate level (post-graduate studies relative to the protection of environment and protection of monuments and historical centers) in the Lab. of Photogrammetry - Remote Sensing in Civil Engineering Faculty of Aristotle University of Thessaloniki.

  1. Irrigated Area Maps and Statistics of India Using Remote Sensing and National Statistics

    Directory of Open Access Journals (Sweden)

    Prasad S. Thenkabail

    2009-04-01

    with inadequate statistical analysis. Overall, the factors that influenced uncertainty in irrigated areas in remote sensing and national statistics were: (a inadequate accounting of irrigated areas, especially minor irrigation from groundwater, in the national statistics, (b definition issues involved in mapping using remote sensing as well as national statistics, (c difficulties in arriving at precise estimates of irrigated area fractions (IAFs using remote sensing, and (d imagery resolution in remote sensing. The study clearly established the existing uncertainties in irrigated area estimates and indicates that both remote sensing and national statistical approaches require further refinement. The need for accurate estimates of irrigated areas are crucial for water use assessments and food security studies and requires high emphasis.

  2. Estimating actual evapotranspiration from remote sensing imagery using R: the package 'TriangleMethod'.

    Science.gov (United States)

    Gampe, David; Huber García, Verena; Marzahn, Philip; Ludwig, Ralf

    2017-04-01

    Actual evaporation (Eta) is an essential variable to assess water availability, drought risk and food security, among others. Measurements of Eta are however limited to a small footprint, hampering a spatially explicit analysis and application and are very often not available at all. To overcome the problem of data scarcity, Eta can be assessed by various remote sensing approaches such as the Triangle Method (Jiang & Islam, 1999). Here, Eta is estimated by using the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST). In this study, the R-package 'TriangleMethod' was compiled to efficiently perform the calculations of NDVI and processing LST to finally derive Eta from the applied data set. The package contains all necessary calculation steps and allows easy processing of a large data base of remote sensing images. By default, the parameterization for the Landsat TM and ETM+ sensors are implemented, however, the algorithms can be easily extended to additional sensors. The auxiliary variables required to estimate Eta with this method, such as elevation, solar radiation and air temperature at the overpassing time, can be processed as gridded information to allow for a better representation of the study area. The package was successfully applied in various studies in Spain, Palestine, Costa Rica and Canada.

  3. Monitoring of "all-weather" evapotranspiration using optical and passive microwave remote sensing imagery over the River Source Region in Southwest China

    Science.gov (United States)

    Ma, Y.; Liu, S.

    2017-12-01

    Accurate estimation of surface evapotranspiration (ET) with high quality is one of the biggest obstacles for routine applications of remote sensing in eco-hydrological studies and water resource management at basin scale. However, many aspects urgently need to deeply research, such as the applicability of the ET models, the parameterization schemes optimization at the regional scale, the temporal upscaling, the selecting and developing of the spatiotemporal data fusion method and ground-based validation over heterogeneous land surfaces. This project is based on the theoretically robust surface energy balance system (SEBS) model, which the model mechanism need further investigation, including the applicability and the influencing factors, such as local environment, and heterogeneity of the landscape, for improving estimation accuracy. Due to technical and budget limitations, so far, optical remote sensing data is missing due to frequent cloud contamination and other poor atmospheric conditions in Southwest China. Here, a multi-source remote sensing data fusion method (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) method will be proposed through blending multi-source remote sensing data acquired by optical, and passive microwave remote sensors on board polar satellite platforms. The accurate "all-weather" ET estimation will be carried out for daily ET of the River Source Region in Southwest China, and then the remotely sensed ET results are overlapped with the footprint-weighted images of EC (eddy correlation) for ground-based validation.

  4. Image Fusion Technologies In Commercial Remote Sensing Packages

    OpenAIRE

    Al-Wassai, Firouz Abdullah; Kalyankar, N. V.

    2013-01-01

    Several remote sensing software packages are used to the explicit purpose of analyzing and visualizing remotely sensed data, with the developing of remote sensing sensor technologies from last ten years. Accord-ing to literature, the remote sensing is still the lack of software tools for effective information extraction from remote sensing data. So, this paper provides a state-of-art of multi-sensor image fusion technologies as well as review on the quality evaluation of the single image or f...

  5. Association of American Geographers, Remote Sensing Specialty Group Special Issue of Geocarto International

    Science.gov (United States)

    Allen, Thomas R. (Editor); Emerson, Charles W. (Editor); Quattrochi, Dale A. (Editor); Arnold, James E. (Technical Monitor)

    2001-01-01

    This special issue continues the precedence of the Association of American Geographers (AAG), Remote Sensing Specialty Group (RSSG) for publishing selected articles in Geocarto International as a by-product from the AAG annual meeting. As editors, we issued earlier this year, a solicitation for papers to be published in a special issue of Geocarto International that were presented in RSSG-sponsored sessions at the 2001 AAG annual meeting held in New York City on February 27-March 3. Although not an absolute requisite for publication, the vast majority of the papers in this special issue were presented at this year's AAG meeting in New York. Other articles in this issue that were not part of a paper or poster session at the 2001 AAG meeting are authored by RSSG members. Under the auspices of the RSSG, this special Geocarto International issue provides even more compelling evidence of the inextricable linkage between remote sensing and geography. The papers in this special issue fall into four general themes: 1) Urban Analysis and Techniques for Urban Analysis; 2) Land Use/Land Cover Analysis; 3) Fire Modeling Assessment; and 4) Techniques. The first four papers herein are concerned with the use of remote sensing for analysis of urban areas, and with use or development of techniques to better characterize urban areas using remote sensing data. As the lead paper in this grouping, Rashed et al., examine the usage of spectral mixture analysis (SMA) for analyzing satellite imagery of urban areas as opposed to more 'standard' methods of classification. Here SMA has been applied to IRS-1C satellite multispectral imagery to extract measures that better describe the 'anatomy' of the greater Cairo, Egypt region. Following this paper, Weng and Lo describe how Landsat TM data have been used to monitor land cover types and to estimate biomass parameters within an urban environment. The research reported in this paper applies an integrated GIS (Geographic Information System

  6. Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2008-09

    Science.gov (United States)

    ,

    2009-01-01

    Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is useful for analyzing a wide variety of spatial data. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This fact sheet presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup during 2008 and 2009. After a summary of GIS Workgroup capabilities, brief descriptions of activities by project at the local and national levels are presented. Projects are grouped by the fiscal year (October-September 2008 or 2009) the project ends and include overviews, project images, and Internet links to additional project information and related publications or articles.

  7. A fully automatic tool to perform accurate flood mapping by merging remote sensing imagery and ancillary data

    Science.gov (United States)

    D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco; Pasquariello, Guido

    2016-04-01

    describing the dynamics of each analysed event, combining time series of images, acquired by different sensors, with ancillary information. Some experiments have been performed by combining multi-temporal SAR intensity images, InSAR coherence and optical data, with geomorphic and other ground information. The tool has been tested on different flood events occurred in the Basilicata region (Italy) during the last years, showing good capabilities of identification of a large area interested by the flood phenomenon, partially overcoming the obstacle constituted by the presence of scattering/coherence classes corresponding to different land cover types, which respond differently to the presence of water and to inundation evolution [1] A. Refice et al, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2711-2722, 2014. [2] L. Pulvirenti et al., IEEE Trans. Geosci. Rem. Sens., Vol. PP, pp. 1- 13, 2015. [3] A. D'Addabbo et al., "A Bayesian Network for Flood Detection combining SAR Imagery and Ancillary Data," IEEE Trans. Geosci. Rem. Sens., in press.

  8. Multiscale and Multitemporal Urban Remote Sensing

    Science.gov (United States)

    Mesev, V.

    2012-07-01

    The remote sensing of urban areas has received much attention from scientists conducting studies on measuring sprawl, congestion, pollution, poverty, and environmental encroachment. Yet much of the research is case and data-specific where results are greatly influenced by prevailing local conditions. There seems to be a lack of epistemological links between remote sensing and conventional theoretical urban geography; in other words, an oversight for the appreciation of how urban theory fuels urban change and how urban change is measured by remotely sensed data. This paper explores basic urban theories such as centrality, mobility, materiality, nature, public space, consumption, segregation and exclusion, and how they can be measured by remote sensing sources. In particular, the link between structure (tangible objects) and function (intangible or immaterial behavior) is addressed as the theory that supports the wellknow contrast between land cover and land use classification from remotely sensed data. The paper then couches these urban theories and contributions from urban remote sensing within two analytical fields. The first is the search for an "appropriate" spatial scale of analysis, which is conveniently divided between micro and macro urban remote sensing for measuring urban structure, understanding urban processes, and perhaps contributions to urban theory at a variety of scales of analysis. The second is on the existence of a temporal lag between materiality of urban objects and the planning process that approved their construction, specifically how time-dependence in urban structural-functional models produce temporal lags that alter the causal links between societal and political functional demands and structural ramifications.

  9. Deep Salient Feature Based Anti-Noise Transfer Network for Scene Classification of Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Xi Gong

    2018-03-01

    Full Text Available Remote sensing (RS scene classification is important for RS imagery semantic interpretation. Although tremendous strides have been made in RS scene classification, one of the remaining open challenges is recognizing RS scenes in low quality variance (e.g., various scales and noises. This paper proposes a deep salient feature based anti-noise transfer network (DSFATN method that effectively enhances and explores the high-level features for RS scene classification in different scales and noise conditions. In DSFATN, a novel discriminative deep salient feature (DSF is introduced by saliency-guided DSF extraction, which conducts a patch-based visual saliency (PBVS algorithm using “visual attention” mechanisms to guide pre-trained CNNs for producing the discriminative high-level features. Then, an anti-noise network is proposed to learn and enhance the robust and anti-noise structure information of RS scene by directly propagating the label information to fully-connected layers. A joint loss is used to minimize the anti-noise network by integrating anti-noise constraint and a softmax classification loss. The proposed network architecture can be easily trained with a limited amount of training data. The experiments conducted on three different scale RS scene datasets show that the DSFATN method has achieved excellent performance and great robustness in different scales and noise conditions. It obtains classification accuracy of 98.25%, 98.46%, and 98.80%, respectively, on the UC Merced Land Use Dataset (UCM, the Google image dataset of SIRI-WHU, and the SAT-6 dataset, advancing the state-of-the-art substantially.

  10. Remote Sensing for Wind Energy

    DEFF Research Database (Denmark)

    The Remote Sensing in Wind Energy Compendium provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind this compendium began in year 2008 at Risø DTU during the first PhD Summer School: Remote Sensing in Wind Energy. Thus...... in the Meteorology and Test and Measurements Programs from the Wind Energy Division at Risø DTU in the PhD Summer Schools. We hope to add more topics in future editions and to update as necessary, to provide a truly state-of-the-art compendium available for people involved in Remote Sensing in Wind Energy....

  11. Remote Sensing for Wind Energy

    DEFF Research Database (Denmark)

    Peña, Alfredo; Hasager, Charlotte Bay; Badger, Merete

    The Remote Sensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risø) during the first PhD Summer School: Remote Sensing in Wind Energy...... colleagues in the Meteorology and Test and Measurements Sections from DTU Wind Energy in the PhD Summer Schools. We hope to continue adding more topics in future editions and to update and improve as necessary, to provide a truly state-of-the-art ‘guideline’ available for people involved in Remote Sensing...

  12. Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

    Science.gov (United States)

    Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

    2015-12-01

    Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in

  13. Resolution Enhancement of Multilook Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Galbraith, Amy E. [Univ. of Arizona, Tucson, AZ (United States)

    2004-07-01

    This dissertation studies the feasibility of enhancing the spatial resolution of multi-look remotely-sensed imagery using an iterative resolution enhancement algorithm known as Projection Onto Convex Sets (POCS). A multi-angle satellite image modeling tool is implemented, and simulated multi-look imagery is formed to test the resolution enhancement algorithm. Experiments are done to determine the optimal con guration and number of multi-angle low-resolution images needed for a quantitative improvement in the spatial resolution of the high-resolution estimate. The important topic of aliasing is examined in the context of the POCS resolution enhancement algorithm performance. In addition, the extension of the method to multispectral sensor images is discussed and an example is shown using multispectral confocal fluorescence imaging microscope data. Finally, the remote sensing issues of atmospheric path radiance and directional reflectance variations are explored to determine their effect on the resolution enhancement performance.

  14. Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

    Science.gov (United States)

    Christopher D. Lippitt; Douglas A. Stow; Philip J. Riggan

    2016-01-01

    Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication...

  15. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding

    Directory of Open Access Journals (Sweden)

    Maria Tattaris

    2016-08-01

    Full Text Available Remote sensing (RS of plant canopies permits non-intrusive, high-throughput monitoring of plant physiological characteristics. This study compared three RS approaches using a low flying UAV (unmanned aerial vehicle, with that of proximal sensing, and satellite-based imagery. Two physiological traits were considered, canopy temperature (CT and a vegetation index (NDVI, to determine the most viable approaches for large scale crop genetic improvement. The UAV-based platform achieves plot-level resolution while measuring several hundred plots in one mission via high-resolution thermal and multispectral imagery measured at altitudes of 30-100 m. The satellite measures multispectral imagery from an altitude of 770 km. Information was compared with proximal measurements using IR thermometers and an NDVI sensor at a distance of 0.5-1m above plots. For robust comparisons, CT and NDVI were assessed on panels of elite cultivars under irrigated and drought conditions, in different thermal regimes, and on un-adapted genetic resources under water deficit. Correlations between airborne data and yield/biomass at maturity were generally higher than equivalent proximal correlations. NDVI was derived from high-resolution satellite imagery for only larger sized plots (8.5 x 2.4 m due to restricted pixel density. Results support use of UAV-based RS techniques for high-throughput phenotyping for both precision and efficiency.

  16. Slovenian experience in applicability of remote sensing data in hydrology

    Energy Technology Data Exchange (ETDEWEB)

    Horvat, A; Vidmar, A; Petan, S; Brilly, M [University of Ljubljana, Faculty of Civil and Geodetic Engineering, Chair of Hydrology and Hydraulic Engineering, Hajdrihova 28, Ljubljana (Slovenia)], E-mail: mbrillygg@uni-lj.si

    2008-11-01

    Nowadays remote sensing data are great value for many ways of use as for snow hydrology. Snow cover mapping on basis of satellite images is widely spread because it is easier than in-situ measuring and interpolating. To verify the use of satellite imagery (MODIS product) we used all available data of snow cover and satellite images and compared detected snow cover on images and in-situ measured snow cover. The information about clouds and snow cover measured on the Ljubljana climatological station were acquired and analysed. The use of satellite images showed out to be useless because of climate characteristics of Slovenia and low elevation of much area.

  17. Slovenian experience in applicability of remote sensing data in hydrology

    International Nuclear Information System (INIS)

    Horvat, A; Vidmar, A; Petan, S; Brilly, M

    2008-01-01

    Nowadays remote sensing data are great value for many ways of use as for snow hydrology. Snow cover mapping on basis of satellite images is widely spread because it is easier than in-situ measuring and interpolating. To verify the use of satellite imagery (MODIS product) we used all available data of snow cover and satellite images and compared detected snow cover on images and in-situ measured snow cover. The information about clouds and snow cover measured on the Ljubljana climatological station were acquired and analysed. The use of satellite images showed out to be useless because of climate characteristics of Slovenia and low elevation of much area.

  18. The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery

    Science.gov (United States)

    Han, Xiaopeng; Huang, Xin; Li, Jiayi; Li, Yansheng; Yang, Michael Ying; Gong, Jianya

    2018-04-01

    In recent years, the availability of high-resolution imagery has enabled more detailed observation of the Earth. However, it is imperative to simultaneously achieve accurate interpretation and preserve the spatial details for the classification of such high-resolution data. To this aim, we propose the edge-preservation multi-classifier relearning framework (EMRF). This multi-classifier framework is made up of support vector machine (SVM), random forest (RF), and sparse multinomial logistic regression via variable splitting and augmented Lagrangian (LORSAL) classifiers, considering their complementary characteristics. To better characterize complex scenes of remote sensing images, relearning based on landscape metrics is proposed, which iteratively quantizes both the landscape composition and spatial configuration by the use of the initial classification results. In addition, a novel tri-training strategy is proposed to solve the over-smoothing effect of relearning by means of automatic selection of training samples with low classification certainties, which always distribute in or near the edge areas. Finally, EMRF flexibly combines the strengths of relearning and tri-training via the classification certainties calculated by the probabilistic output of the respective classifiers. It should be noted that, in order to achieve an unbiased evaluation, we assessed the classification accuracy of the proposed framework using both edge and non-edge test samples. The experimental results obtained with four multispectral high-resolution images confirm the efficacy of the proposed framework, in terms of both edge and non-edge accuracy.

  19. Geomorphometry through remote sensing and GIS for watershed management

    International Nuclear Information System (INIS)

    Venkateswarlu, P.; Reddy, M.A.; Gokhale, K.V.G.K.

    2005-01-01

    Application of remote sensing and GIS for effective determination of the quantitative description of drainage basin geometry for watershed management prioritization forms the theme of this paper. In the present study, each of the eight sub watersheds of Racherla watershed of Prakasam (District) Andhra Pradesh, have been studied in terms of the morphometric parameters -stream length, bifurcation ratio, length ratio, drainage density, stream frequency, texture ratio, form factor area, perimeter, circularity ratio, elongation ratio and sediment yield index. The prioritization of the eight sub watersheds is carried out considering morphometry and sediment yield index. Using IRS IC satellite imagery, a computerized database is created availing ARC / INFO software. The initial drainage map prepared from the survey of India toposheets was later unified with satellite imagery. The prioritization of sub sheds based on morphometry compared with sediment yield prioritization and found nearly same for the study area. The information obtained from all the thematic map is integrated and action plan is suggested for land and water resources development on a sustainable basis. (author)

  20. Using Remote Sensing to Evaluate Wetland Recovery in the Northern Tampa Bay Area Following Reduction in Groundwater Withdrawals

    Science.gov (United States)

    Elder, Amor

    In the past, the Northern Tampa Bay Area (NTBA) wetlands saw severe declines in hydrologic conditions due to excessive groundwater withdrawal rates. Eventually these rates were reduced to allow the wetlands to recover. To monitor this recovery, the Southwest Florida Water Management district (SWFWMD) set up a fieldwork based scoring methodology, called the Wetlands Assessment Procedure (WAP). WAP has been used in many studies of the area since groundwater withdrawal reductions; with many of those studies finding the recovery to be mixed at best. However, these studies were very limited in the number of wetlands they could assess due to the limitations of fieldwork. Therefore, it was proposed that remotely sensed variables associated with water consumption and stress be used to assess the recovery of the NTBA wetlands, as remote sensing allows for efficient assessments of targets over large area. Utilizing ASTER imagery scenes from 2005 and 2014, 211 wetlands' remotely sensed responses of NDVI, Land Surface Temperature (LST), and Evapotranspiration (ET) were mapped and statistically examined for trends indicating improvement or decline. Furthermore, a subset of WAP scores for the two years were examined and compared to the remotely sensed values. The results were contradictory, with remotely sensed responses showing an improvement over the time period, WAP scores indicating a decline in hydrologic conditions, and the two methods showing little to no fit when modeled against each other. As such, it is believed at this time that the remotely sensed method is not suitable for measuring the indicators of wetland recovery used in the WAP methodology.

  1. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria

    Science.gov (United States)

    Stumpf, Rick P; Davis, Timothy W.; Wynne, Timothy T.; Graham, Jennifer L.; Loftin, Keith A.; Johengen, T.H.; Gossiaux, D.; Palladino, D.; Burtner, A.

    2016-01-01

    Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments – since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins – especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates

  2. Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing

    Directory of Open Access Journals (Sweden)

    Margaret Kosmala

    2016-09-01

    Full Text Available The impact of a rapidly changing climate on the biosphere is an urgent area of research for mitigation policy and management. Plant phenology is a sensitive indicator of climate change and regulates the seasonality of carbon, water, and energy fluxes between the land surface and the climate system, making it an important tool for studying biosphere–atmosphere interactions. To monitor plant phenology at regional and continental scales, automated near-surface cameras are being increasingly used to supplement phenology data derived from satellite imagery and data from ground-based human observers. We used imagery from a network of phenology cameras in a citizen science project called Season Spotter to investigate whether information could be derived from these images beyond standard, color-based vegetation indices. We found that engaging citizen science volunteers resulted in useful science knowledge in three ways: first, volunteers were able to detect some, but not all, reproductive phenology events, connecting landscape-level measures with field-based measures. Second, volunteers successfully demarcated individual trees in landscape imagery, facilitating scaling of vegetation indices from organism to ecosystem. And third, volunteers’ data were used to validate phenology transition dates calculated from vegetation indices and to identify potential improvements to existing algorithms to enable better biological interpretation. As a result, the use of citizen science in combination with near-surface remote sensing of phenology can be used to link ground-based phenology observations to satellite sensor data for scaling and validation. Well-designed citizen science projects targeting improved data processing and validation of remote sensing imagery hold promise for providing the data needed to address grand challenges in environmental science and Earth observation.

  3. Ten ways remote sensing can contribute to conservation

    Science.gov (United States)

    Rose, Robert A.; Byler, Dirck; Eastman, J. Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A.; Laporte, Nadine; Leidner, Allison K.; Leimgruber, Peter; Morisette, Jeffrey T.; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C.; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

    2014-01-01

    In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to

  4. Ten ways remote sensing can contribute to conservation.

    Science.gov (United States)

    Rose, Robert A; Byler, Dirck; Eastman, J Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A; Laporte, Nadine; Leidner, Allison; Leimgruber, Peter; Morisette, Jeffrey; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

    2015-04-01

    In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to

  5. Remote sensing for oil spill detection and response

    International Nuclear Information System (INIS)

    Engelhardt, F.R.

    1999-01-01

    This paper focuses on the use of remote sensing for marine oil spill detection and response. The surveillance and monitoring of discharges, and the main elements of effective surveillance are discussed. Tactical emergency response and the requirements for selecting a suitable remote sensing approach, airborne remote sensing systems, and the integration of satellite and airborne imaging are examined. Specifications of satellite surveillance systems potentially usable for oil spill detection, and specifications of airborne remote sensing systems suitable for oil spill detection, monitoring and supplemental actions are tabulated, and a schema of integrated satellite-airborne remote sensing (ISARS) is presented. (UK)

  6. Field calibration and validation of remote-sensing surveys

    Science.gov (United States)

    Pe'eri, Shachak; McLeod, Andy; Lavoie, Paul; Ackerman, Seth D.; Gardner, James; Parrish, Christopher

    2013-01-01

    The Optical Collection Suite (OCS) is a ground-truth sampling system designed to perform in situ measurements that help calibrate and validate optical remote-sensing and swath-sonar surveys for mapping and monitoring coastal ecosystems and ocean planning. The OCS system enables researchers to collect underwater imagery with real-time feedback, measure the spectral response, and quantify the water clarity with simple and relatively inexpensive instruments that can be hand-deployed from a small vessel. This article reviews the design and performance of the system, based on operational and logistical considerations, as well as the data requirements to support a number of coastal science and management projects. The OCS system has been operational since 2009 and has been used in several ground-truth missions that overlapped with airborne lidar bathymetry (ALB), hyperspectral imagery (HSI), and swath-sonar bathymetric surveys in the Gulf of Maine, southwest Alaska, and the US Virgin Islands (USVI). Research projects that have used the system include a comparison of backscatter intensity derived from acoustic (multibeam/interferometric sonars) versus active optical (ALB) sensors, ALB bottom detection, and seafloor characterization using HSI and ALB.

  7. Satellite Remote Sensing For Aluminum And Nickel Laterites

    Science.gov (United States)

    Henderson, Frederick B.; Penfield, Glen T.; Grubbs, Donald K.

    1984-08-01

    The new LANDSAT-4,-5/Thematic Mapper (TM) land observational satellite remote sensing systems are providing dramatically new and important short wave infrared (SWIR) data, which combined with Landsat's Multi-Spectral Scanner (MSS) visible (VIS), very near infrared (VNIR), and thermal infrared (TI) data greatly improves regional geological mapping on a global scale. The TM will significantly improve clay, iron oxide, aluminum, and nickel laterite mapping capabilities over large areas of the world. It will also improve the ability to discriminate vegetation stress and species distribution associated with lateritic environments. Nickel laterites on Gag Island, Indonesia are defined by MSS imagery. Satellite imagery of the Cape Bougainville and the Darling Range, Australia bauxite deposits show the potential use of MSS data for exploration and mining applications. Examples of satellite syn-thetic aperture radar (SAR) for Jamaica document the use of this method for bauxite exploration. Thematic Mapper data will be combined with the French SPOT satellite's high spatial resolution and stereoscopic digital data, and U.S., Japanese, European, and Canadian Synthetic Aperture Radar (SAR) data to assist with logistics, mine development, and environ-mental concerns associated with aluminum and nickel lateritic deposits worldwide.

  8. Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

    Science.gov (United States)

    Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

    2015-03-01

    A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

  9. Satellite Remote Sensing: Aerosol Measurements

    Science.gov (United States)

    Kahn, Ralph A.

    2013-01-01

    Aerosols are solid or liquid particles suspended in the air, and those observed by satellite remote sensing are typically between about 0.05 and 10 microns in size. (Note that in traditional aerosol science, the term "aerosol" refers to both the particles and the medium in which they reside, whereas for remote sensing, the term commonly refers to the particles only. In this article, we adopt the remote-sensing definition.) They originate from a great diversity of sources, such as wildfires, volcanoes, soils and desert sands, breaking waves, natural biological activity, agricultural burning, cement production, and fossil fuel combustion. They typically remain in the atmosphere from several days to a week or more, and some travel great distances before returning to Earth's surface via gravitational settling or washout by precipitation. Many aerosol sources exhibit strong seasonal variability, and most experience inter-annual fluctuations. As such, the frequent, global coverage that space-based aerosol remote-sensing instruments can provide is making increasingly important contributions to regional and larger-scale aerosol studies.

  10. Airborne remote sensing of estuarine intertidal radionuclide concentrations

    International Nuclear Information System (INIS)

    Rainey, M.P.

    1999-08-01

    The ability to map industrial discharges through remote sensing provides a powerful tool in environmental monitoring. Radionuclide effluents have been discharged, under authorization, into the Irish Sea from BNFL (British Nuclear Fuels Pic.) sites at Sellafield and Springfields since 1952. The quantitative mapping of this anthropogenic radioactivity in estuarine intertidal zones is crucial for absolute interpretations of radionuclide transport. The spatial resolutions of traditional approaches e.g. point sampling and airborne gamma surveys are insufficient to support geomorphic interpretations of the fate of radionuclides in estuaries. The research presented in this thesis develops the use of airborne remote sensing to derive high-resolution synoptic data on the distribution of anthropogenic radionuclides in the intertidal areas of the Ribble Estuary, Lancashire, UK. From multidate surface sediment samples a significant relationship was identified between the Sellafield-derived 137 Cs and 241 Am and clay content (r 2 = 0.93 and 0.84 respectively). Detailed in situ, and laboratory, reflectance (0.4-2.5μm) experiments demonstrated that significant relationships exist between Airborne Thematic Mapper (ATM) simulated reflectance and intertidal sediment grain-size. The spectral influence of moisture on the reflectance characteristics of the intertidal area is also evident. This had substantial implications for the timing of airborne image acquisition. Low-tide Daedalus ATM imagery (Natural Environmental Research Council) was collected of the Ribble Estuary on May 30th 1997. Preprocessing and linear unmixing of the imagery allowed accurate sub-pixel determinations of sediment clay content distributions (r 2 = 0.81). Subsequently, the established relationships between 137 Cs and 241 Am and sediment grain-size enabled the radionuclide activity distributions across the entire intertidal area (92 km 2 ) to be mapped at a geomorphic scale (1.75 m). The accuracy of these maps

  11. Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Ke Wu

    2016-08-01

    Full Text Available In recent years, a novel matching classification strategy inspired by the artificial deoxyribonucleic acid (DNA technology has been proposed for hyperspectral remote sensing imagery. Such a method can describe brightness and shape information of a spectrum by encoding the spectral curve into a DNA strand, providing a more comprehensive way for spectral similarity comparison. However, it suffers from two problems: data volume is amplified when all of the bands participate in the encoding procedure and full-band comparison degrades the importance of bands carrying key information. In this paper, a new multi-probe based artificial DNA encoding and matching (MADEM method is proposed. In this method, spectral signatures are first transformed into DNA code words with a spectral feature encoding operation. After that, multiple probes for interesting classes are extracted to represent the specific fragments of DNA strands. During the course of spectral matching, the different probes are compared to obtain the similarity of different types of land covers. By computing the absolute vector distance (AVD between different probes of an unclassified spectrum and the typical DNA code words from the database, the class property of each pixel is set as the minimum distance class. The main benefit of this strategy is that the risk of redundant bands can be deeply reduced and critical spectral discrepancies can be enlarged. Two hyperspectral image datasets were tested. Comparing with the other classification methods, the overall accuracy can be improved from 1.22% to 10.09% and 1.19% to 15.87%, respectively. Furthermore, the kappa coefficient can be improved from 2.05% to 15.29% and 1.35% to 19.59%, respectively. This demonstrated that the proposed algorithm outperformed other traditional classification methods.

  12. Applications of Earth Remote Sensing for Identifying Tornado and Severe Weather Damage

    Science.gov (United States)

    Schultz, Lori; Molthan, Andrew; Burks, Jason E.; Bell, Jordan; McGrath, Kevin; Cole, Tony

    2016-01-01

    NASA SPoRT (Short-term Prediction Research and Transition Center) provided MODIS (Moderate Resolution Imaging Spectrometer) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery to WFOs (Weather Forecast Offices) in Alabama to support April 27th, 2011 damage assessments across the state. SPoRT was awarded a NASA Applied Science: Disasters Feasibility award to investigate the applicability of including remote sensing imagery and derived products into the NOAA/NWS (National Oceanic and Atmospheric Administration/National Weather System) Damage Assessment Toolkit (DAT). Proposal team was awarded the 3-year proposal to implement a web mapping service and associate data feeds from the USGS (U.S. Geological Survey) to provide satellite imagery and derived products directly to the NWS thru the DAT. In the United States, NOAA/NWS is charged with performing damage assessments when storm or tornado damage is suspected after a severe weather event. This has led to the development of the Damage Assessment Toolkit (DAT), an application for smartphones, tablets and web browsers that allows for the collection, geo-location, and aggregation of various damage indicators collected during storm surveys.

  13. Remote sensing for agriculture, ecosystems, and hydrology

    International Nuclear Information System (INIS)

    Engman, E.T.

    1998-01-01

    This volume contains the proceedings of SPIE's remote sensing symposium which was held September 22--24, 1998, in Barcelona, Spain. Topics of discussion include the following: calibration techniques for soil moisture measurements; remote sensing of grasslands and biomass estimation of meadows; evaluation of agricultural disasters; monitoring of industrial and natural radioactive elements; and remote sensing of vegetation and of forest fires

  14. Geometric registration of remotely sensed data with SAMIR

    Science.gov (United States)

    Gianinetto, Marco; Barazzetti, Luigi; Dini, Luigi; Fusiello, Andrea; Toldo, Roberto

    2015-06-01

    The commercial market offers several software packages for the registration of remotely sensed data through standard one-to-one image matching. Although very rapid and simple, this strategy does not take into consideration all the interconnections among the images of a multi-temporal data set. This paper presents a new scientific software, called Satellite Automatic Multi-Image Registration (SAMIR), able to extend the traditional registration approach towards multi-image global processing. Tests carried out with high-resolution optical (IKONOS) and high-resolution radar (COSMO-SkyMed) data showed that SAMIR can improve the registration phase with a more rigorous and robust workflow without initial approximations, user's interaction or limitation in spatial/spectral data size. The validation highlighted a sub-pixel accuracy in image co-registration for the considered imaging technologies, including optical and radar imagery.

  15. Remote Sensing of Environmental Pollution

    Science.gov (United States)

    North, G. W.

    1971-01-01

    Environmental pollution is a problem of international scope and concern. It can be subdivided into problems relating to water, air, or land pollution. Many of the problems in these three categories lend themselves to study and possible solution by remote sensing. Through the use of remote sensing systems and techniques, it is possible to detect and monitor, and in some cases, identify, measure, and study the effects of various environmental pollutants. As a guide for making decisions regarding the use of remote sensors for pollution studies, a special five-dimensional sensor/applications matrix has been designed. The matrix defines an environmental goal, ranks the various remote sensing objectives in terms of their ability to assist in solving environmental problems, lists the environmental problems, ranks the sensors that can be used for collecting data on each problem, and finally ranks the sensor platform options that are currently available.

  16. Commercial future: making remote sensing a media event

    Science.gov (United States)

    Lurie, Ian

    1999-12-01

    The rapid growth of commercial remote sensing has made high quality digital sensing data widely available -- now, remote sensing must become and remain a strong, commercially viable industry. However, this new industry cannot survive without an educated consumer base. To access markets, remote sensing providers must make their product more accessible, both literally and figuratively: Potential customers must be able to find the data they require, when they require it, and they must understand the utility of the information available to them. The Internet and the World Wide Web offer the perfect medium to educate potential customers and to sell remote sensing data to those customers. A well-designed web presence can provide both an information center and a market place for companies offering their data for sale. A very high potential web-based market for remote sensing lies in media. News agencies, web sites, and a host of other visual media services can use remote sensing data to provide current, relevant information regarding news around the world. This paper will provide a model for promotion and sale of remote sensing data via the Internet.

  17. Essential climatic variables estimation with satellite imagery

    Science.gov (United States)

    Kolotii, A.; Kussul, N.; Shelestov, A.; Lavreniuk, M. S.

    2016-12-01

    According to Sendai Framework for Disaster Risk Reduction 2015 - 2030 Leaf Area Index (LAI) is considered as one of essential climatic variables. This variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes and enables monitoring and quantitative assessment of vegetation state. LAI has added value for such important global resources monitoring tasks as drought mapping and crop yield forecasting with use of data from different sources [1-2]. Remote sensing data from space can be used to estimate such biophysical parameter at regional and national scale. High temporal satellite imagery is usually required to capture main parameters of crop growth [3]. Sentinel-2 mission launched in 2015 be ESA is a source of high spatial and temporal resolution satellite imagery for mapping biophysical parameters. Products created with use of automated Sen2-Agri system deployed during Sen2-Agri country level demonstration project for Ukraine will be compared with our independent results of biophysical parameters mapping. References Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736 Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., ... & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 39-44. Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508.

  18. Remote Sensing Best Paper Award for the Year 2014

    OpenAIRE

    Prasad Thenkabail

    2014-01-01

    Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper Award” for the year 2014.

  19. An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery

    Science.gov (United States)

    Tong, Xiaohua; Luo, Xin; Liu, Shuguang; Xie, Huan; Chao, Wei; Liu, Shuang; Liu, Shijie; Makhinov, A. N.; Makhinova, A. F.; Jiang, Yuying

    2018-02-01

    Remote sensing techniques offer potential for effective flood detection with the advantages of low-cost, large-scale, and real-time surface observations. The easily accessible data sources of optical remote sensing imagery provide abundant spectral information for accurate surface water body extraction, and synthetic aperture radar (SAR) systems represent a powerful tool for flood monitoring because of their all-weather capability. This paper introduces a new approach for flood monitoring by the combined use of both Landsat 8 optical imagery and COSMO-SkyMed radar imagery. Specifically, the proposed method applies support vector machine and the active contour without edges model for water extent determination in the periods before and during the flood, respectively. A map difference method is used for the flood inundation analysis. The proposed approach is particularly suitable for large-scale flood monitoring, and it was tested on a serious flood that occurred in northeastern China in August 2013, which caused immense loss of human lives and properties. High overall accuracies of 97.46% for the optical imagery and 93.70% for the radar imagery are achieved by the use of the techniques presented in this study. The results show that about 12% of the whole study area was inundated, corresponding to 5466 km2 of land surface.

  20. Basic Remote Sensing Investigations for Beach Reconnaissance.

    Science.gov (United States)

    Progress is reported on three tasks designed to develop remote sensing beach reconnaissance techniques applicable to the benthic, beach intertidal...and beach upland zones. Task 1 is designed to develop remote sensing indicators of important beach composition and physical parameters which will...ultimately prove useful in models to predict beach conditions. Task 2 is designed to develop remote sensing techniques for survey of bottom features in

  1. Developing status of satellite remote sensing and its application

    International Nuclear Information System (INIS)

    Zhang Wanliang; Liu Dechang

    2005-01-01

    This paper has discussed the latest development of satellite remote sensing in sensor resolutions, satellite motion models, load forms, data processing and its application. The authors consider that sensor resolutions of satellite remote sensing have increased largely. Valid integration of multisensors is a new idea and technology of satellite remote sensing in the 21st century, and post-remote sensing application technology is the important part of deeply applying remote sensing information and has great practical significance. (authors)

  2. Current perspective on remote sensing

    International Nuclear Information System (INIS)

    Goodman, R.H.

    1992-01-01

    Surveillance and tracking of oil spills has been a feature of most spill response situations for many years. The simplest and most direct method uses visual observations from an aircraft and hand-plotting of the data on a map. This technique has proven adequate for most small spills and for responses in fair weather. As the size of the spill increases or the weather deteriorates, there is a need to augment visual aerial observations with remote sensing methods. Remote sensing and its associated systems are one of the most technically complex and sophisticated elements of an oil spill response. During the past few years, a number of initiatives have been undertaken to use contemporary electronic and computing systems to develop new and improved remote sensing systems

  3. Criteria for the optimal selection of remote sensing optical images to map event landslides

    Science.gov (United States)

    Fiorucci, Federica; Giordan, Daniele; Santangelo, Michele; Dutto, Furio; Rossi, Mauro; Guzzetti, Fausto

    2018-01-01

    Landslides leave discernible signs on the land surface, most of which can be captured in remote sensing images. Trained geomorphologists analyse remote sensing images and map landslides through heuristic interpretation of photographic and morphological characteristics. Despite a wide use of remote sensing images for landslide mapping, no attempt to evaluate how the image characteristics influence landslide identification and mapping exists. This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type (monoscopic or stereoscopic), on landslide mapping. We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system (dGPS) survey, which served as a benchmark. The eight maps were compared pairwise and to a benchmark. The mismatch between each map pair was quantified by the error index, E. Results show that the map closest to the benchmark delineation of the landslide was obtained using the higher resolution image, where the landslide signature was primarily photographical (in the landslide source and transport area). Conversely, where the landslide signature was mainly morphological (in the landslide deposit) the best mapping result was obtained using the stereoscopic images. Albeit conducted on a single landslide, the experiment results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps.

  4. Towards operational environmental applications using terrestrial remote sensing

    NARCIS (Netherlands)

    Veldkamp JG; Velde RJ van de; LBG

    1996-01-01

    Dit rapport beschrijft de resultaten van het Beleidscommissie Remote Sensing (BCRS) project 'Verankering van toepassingen van terrestrische remote sensing bij RIVM'. Het had ten eerste tot doel te voldoen aan de voorwaarden, zoals gesteld in de inventarisatie van remote sensing als

  5. Academic and Non-Profit Accessibility to Commercial Remote Sensing Software

    Science.gov (United States)

    O'Connor, A. S.; Farr, B.

    2013-12-01

    Remote Sensing as a topic of teaching and research at the university and college level continues to increase. As more data is made freely available and software becomes easier to use, more and more academic and non-profits institutions are turning to remote sensing to solve their tough and large spatial scale problems. Exelis Visual Information Solutions (VIS) has been supporting teaching and research endeavors for over 30 years with a special emphasis over the last 5 years with scientifically proven software and accessible training materials. The Exelis VIS academic program extends to US and Canadian 2 year and 4 year colleges and universities with tools for analyzing aerial and satellite multispectral and hyperspectral imagery, airborne LiDAR and Synthetic Aperture Radar. The Exelis VIS academic programs, using the ENVI Platform, enables labs and classrooms to be outfitted with software and makes software accessible to students. The ENVI software provides students hands on experience with remote sensing software, an easy teaching platform for professors and allows researchers scientifically vetted software they can trust. Training materials are provided at no additional cost and can either serve as a basis for course curriculum development or self paced learning. Non-profit organizations like The Nature Conservancy (TNC) and CGIAR have deployed ENVI and IDL enterprise wide licensing allowing researchers all over the world to have cost effective access COTS software for their research. Exelis VIS has also contributed licenses to the NASA DEVELOP program. Exelis VIS is committed to supporting the academic and NGO community with affordable enterprise licensing, access to training materials, and technical expertise to help researchers tackle today's Earth and Planetary science big data challenges.

  6. Integrating remote sensing techniques at Cuprite, Nevada: AVIRIS, Thematic Mapper, and field spectroscopy

    Science.gov (United States)

    Hill, Bradley; Nash, Greg; Ridd, Merrill; Hauff, Phoebe L.; Ebel, Phil

    1992-01-01

    The Cuprite mining district in southwestern Nevada has become a test site for remote sensing studies with numerous airborne scanners and ground sensor data sets collected over the past fifteen years. Structurally, the Cuprite region can be divided into two areas with slightly different alteration and mineralogy. These zones lie on either side of a postulated low-angle structural discontinuity that strikes nearly parallel to US Route 95. Hydrothermal alternation at Cuprite was classified into three major zones: silicified, opalized, and argillized. These alteration types form a bulls-eye pattern east of the highway and are more linear on the west side of the highway making a striking contrast from the air and the imagery. Cuprite is therefore an ideal location for remote sensing research as it exhibits easily identified hydrothermal zoning, is relatively devoid of vegetation, and contains a distinctive spectrally diagnostic mineral suite including the ammonium feldspar buddingtonite, several types of alunite, different jarosites, illite, kaolinite, smectite, dickite, and opal. This present study brings a new dimension to these previous remote sensing and ground data sets compiled for Cuprite. The development of a higher resolution field spectrometer now provides the capability to combine extensive in-situ mineralogical data with a new geologic field survey and detailed Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) images. The various data collection methods and the refinement of the integrated techniques are discussed.

  7. Geographic techniques and recent applications of remote sensing to landscape-water quality studies

    Science.gov (United States)

    Griffith, J.A.

    2002-01-01

    This article overviews recent advances in studies of landscape-water quality relationships using remote sensing techniques. With the increasing feasibility of using remotely-sensed data, landscape-water quality studies can now be more easily performed on regional, multi-state scales. The traditional method of relating land use and land cover to water quality has been extended to include landscape pattern and other landscape information derived from satellite data. Three items are focused on in this article: 1) the increasing recognition of the importance of larger-scale studies of regional water quality that require a landscape perspective; 2) the increasing importance of remotely sensed data, such as the imagery-derived normalized difference vegetation index (NDVI) and vegetation phenological metrics derived from time-series NDVI data; and 3) landscape pattern. In some studies, using landscape pattern metrics explained some of the variation in water quality not explained by land use/cover. However, in some other studies, the NDVI metrics were even more highly correlated to certain water quality parameters than either landscape pattern metrics or land use/cover proportions. Although studies relating landscape pattern metrics to water quality have had mixed results, this recent body of work applying these landscape measures and satellite-derived metrics to water quality analysis has demonstrated their potential usefulness in monitoring watershed conditions across large regions.

  8. New Directions in Land Remote Sensing Policy and International Cooperation

    Science.gov (United States)

    Stryker, Timothy

    2010-12-01

    Recent changes to land remote sensing satellite data policies in Brazil and the United States have led to the phenomenal growth in the delivery of land imagery to users worldwide. These new policies, which provide free and unrestricted access to land remote sensing data over a standard electronic interface, are expected to provide significant benefits to scientific and operational users, and open up new areas of Earth system science research and environmental monitoring. Freely-available data sets from the China-Brazil Earth Resources Satellites (CBERS), the U.S. Landsat satellites, and other satellite missions provide essential information for land surface monitoring, ecosystems management, disaster mitigation, and climate change research. These missions are making important contributions to the goals and objectives of regional and global terrestrial research and monitoring programs. These programs are in turn providing significant support to the goals and objectives of the United Nations Framework Convention on Climate Change (UN FCCC), the Global Earth Observation System of Systems (GEOSS), and the UN Reduction in Emissions from Deforestation and Degradation (REDD) program. These data policies are well-aligned with the "Data Democracy" initiative undertaken by the international Committee on Earth Observation Satellites (CEOS), through its current Chair, Brazil's National Institute for Space Research (Instituto Nacional de Pesquisas Espaciais, or INPE), and its former chairs, South Africa's Council for Scientific and Industrial Research (CSIR) and Thailand's Geo Informatics and Space Technology Development Agency (GISTDA). Comparable policies for land imaging data are under consideration within Europe and Canada. Collectively, these initiatives have the potential to accelerate and improve international mission collaboration, and greatly enhance the access, use, and application of land surface imagery for environmental monitoring and societal adaption to changing

  9. Remote sensing of wetlands applications and advances

    CERN Document Server

    Tiner, Ralph W; Klemas, Victor V

    2015-01-01

    Effectively Manage Wetland Resources Using the Best Available Remote Sensing Techniques Utilizing top scientists in the wetland classification and mapping field, Remote Sensing of Wetlands: Applications and Advances covers the rapidly changing landscape of wetlands and describes the latest advances in remote sensing that have taken place over the past 30 years for use in mapping wetlands. Factoring in the impact of climate change, as well as a growing demand on wetlands for agriculture, aquaculture, forestry, and development, this text considers the challenges that wetlands pose for remote sensing and provides a thorough introduction on the use of remotely sensed data for wetland detection. Taking advantage of the experiences of more than 50 contributing authors, the book describes a variety of techniques for mapping and classifying wetlands in a multitude of environments ranging from tropical to arctic wetlands including coral reefs and submerged aquatic vegetation. The authors discuss the advantages and di...

  10. Assessing the Impacts of US Landfall Hurricanes in 2012 using Aerial Remote Sensing

    Science.gov (United States)

    Bevington, John S.

    2013-04-01

    Remote sensing has become a widely-used technology for assessing and evaluating the extent and severity of impacts of natural disasters worldwide. Optical and radar data collected by air- and space-borne sensors have supported humanitarian and economic decision-making for over a decade. Advances in image spatial resolution and pre-processing speeds have meant images with centimetre spatial resolution are now available for analysis within hours following severe disaster events. This paper offers a retrospective view on recent large-scale responses to two of the major storms from the 2012 Atlantic hurricane season: Hurricane Isaac and post-tropical cyclone ("superstorm") Sandy. Although weak on the Saffir-Simpson hurricane wind scale, these slow-moving storms produced intense rainfall and coastal storm surges in the order of several metres in the Louisiana and Mississippi Gulf Coast (Isaac), and the Atlantic Seaboard (Sandy) of the United States. Data were generated for both events through interpretation of a combination of two types of aerial imagery: high spatial resolution optical imagery captured by fixed aerial sensors deployed by the National Oceanic and Atmospheric Administration (NOAA), and digital single lens reflex (DSLR) images captured by volunteers from the US Civil Air Patrol (CAP). Imagery for these events were collected over a period of days following the storms' landfall in the US, with availability of aerial data far outweighing the sub-metre satellite imagery. The imagery described were collected as vertical views (NOAA) and oblique views (CAP) over the whole affected coastal and major riverine areas. A network of over 150 remote sensing experts systematically and manually processed images through visual interpretation, culminating in hundreds of thousands of individual properties identified as damaged or destroyed by wind or surge. A discussion is presented on the challenges of responding at such a fine level of spatial granularity for coastal

  11. Environmental monitoring by means of remote sensing

    International Nuclear Information System (INIS)

    Theilen-Willige, B.

    1993-01-01

    Aircraft and satellite aerial photographs represent indispensible tools for environmental observation today. They contribute to a systematic inventory of important environmental parameters such as climate, vegetation or surface water. Their great importance lies in the continuous monitoring of large regions so that changes in environmental conditions are quickly detected. This book provides an overview of the capabilities of remote sensing in environmental monitoring and in the recognition of environmental problems as well as of the usefulness of remote sensing data for environmental planning. Also addressed is the role of remote sensing in the monitoring of natural hazards such as earthquakes and volcano eruptions as well as problems of remote sensing technology transfer to developing countries. (orig.) [de

  12. An ecological assessment of pasturelands in the Balkhash area of Kazakhstan with remote sensing and models

    International Nuclear Information System (INIS)

    Lebed, L; Qi, J; Heilman, P

    2012-01-01

    The 187 million hectares of pasturelands in Kazakhstan play a key role in the nation’s economy, as livestock production accounted for 54% of total agricultural production in 2010. However, more than half of these lands have been degraded as a result of unregulated grazing practices. Therefore, effective long term ecological monitoring of pasturelands in Kazakhstan is imperative to ensure sustainable pastureland management. As a case study in this research, we demonstrated how the ecological conditions could be assessed with remote sensing technologies and pastureland models. The example focuses on the southern Balkhash area with study sites on a foothill plain with Artemisia-ephemeral plants and a sandy plain with psammophilic vegetation in the Turan Desert. The assessment was based on remotely sensed imagery and meteorological data, a geobotanical archive and periodic ground sampling. The Pasture agrometeorological model was used to calculate biological, ecological and economic indicators to assess pastureland condition. The results showed that field surveys, meteorological observations, remote sensing and ecological models, such as Pasture, could be combined to effectively assess the ecological conditions of pasturelands and provide information about forage production that is critically important for balancing grazing and ecological conservation. (letter)

  13. Building change detection via a combination of CNNs using only RGB aerial imageries

    Science.gov (United States)

    Nemoto, Keisuke; Hamaguchi, Ryuhei; Sato, Masakazu; Fujita, Aito; Imaizumi, Tomoyuki; Hikosaka, Shuhei

    2017-10-01

    Building change information extracted from remote sensing imageries is important for various applications such as urban management and marketing planning. The goal of this work is to develop a methodology for automatically capturing building changes from remote sensing imageries. Recent studies have addressed this goal by exploiting 3-D information as a proxy for building height. In contrast, because in practice it is expensive or impossible to prepare 3-D information, we do not rely on 3-D data but focus on using only RGB aerial imageries. Instead, we employ deep convolutional neural networks (CNNs) to extract effective features, and improve change detection accuracy in RGB remote sensing imageries. We consider two aspects of building change detection, building detection and subsequent change detection. Our proposed methodology was tested on several areas, which has some differences such as dominant building characteristics and varying brightness values. On all over the tested areas, the proposed method provides good results for changed objects, with recall values over 75 % with a strict overlap requirement of over 50% in intersection-over-union (IoU). When the IoU threshold was relaxed to over 10%, resulting recall values were over 81%. We conclude that use of CNNs enables accurate detection of building changes without employing 3-D information.

  14. Remote sensing of natural phenomena

    Directory of Open Access Journals (Sweden)

    Miodrag D. Regodić

    2014-06-01

    Full Text Available There has always been a need to directly perceive and study the events whose extent is beyond people's possibilities. In order to get new data and to make observations and studying much more objective in comparison with past syntheses - a new method of examination called remote sensing has been adopted. The paper deals with the principles and elements of remote sensing, as well as with the basic aspects of using remote research in examining meteorological (weather parameters and the conditions of the atmosphere. The usage of satellite images is possible in all phases of the global and systematic research of different natural phenomena when airplane and satellite images of different characteristics are used and their analysis and interpretation is carried out by viewing and computer added procedures. Introduction Remote sensing of the Earth enables observing and studying global and local events that occur on it. Satellite images are nowadays used in geology, agriculture, forestry, geodesy, meteorology, spatial and urbanism planning, designing of infrastructure and other objects, protection from natural and technological catastrophes, etc. It it possible to use satellite images in all phases of global and systematic research of different natural phenomena. Basics of remote sensing Remote sensing is a method of the acquisition and interpretation of information about remote objects without making a physical contact with them. The term Daljinska detekcija is a literal translation of the English term Remote Sensing. In French it isTeledetection, in German - Fernerkundung, in Russian - дистанционие иследования. We also use terms such as: remote survailance, remote research, teledetection, remote methods, and distance research. The basic elements included in Remote Sensing are: object, electromagnetic energy, sensor, platform, image, analysis, interpretation and the information (data, fact. Usage of satellite remote research in

  15. Optimal Remote Sensing with Small Unmanned Aircraft Systems and Risk Management

    Science.gov (United States)

    Stark, Brandon

    Over the past decade, the rapid rise of Unmanned Aircraft Systems (UASs) has blossomed into a new component of the aviation industry. Though regulations within the United States lagged, the promise of the ability of Small Unmanned Aircraft Systems (SUASs), or those UAS that weigh less than 55 lbs, has driven significant advances in small scale aviation technology. The dream of a small, low-cost aerial platform that can fly anywhere and keep humans safely away from the `dull, dangerous and dirty' jobs, has encouraged many to examine the possibilities of utilizing SUAS in new and transformative ways, especially as a new tool in remote sensing. However, as with any new tool, there remains significant challenges in realizing the full potential of SUAS-based remote sensing. Within this dissertation, two specific challenges are addressed: validating the use of SUAS as a remote sensing platform and improving the safety and management of SUAS. The use of SUAS in remote sensing is a relatively new challenge and while it has many similarities to other remote sensing platforms, the dynamic nature of its operation makes it unique. In this dissertation, a closer look at the methodology of using SUAS reveals that while many view SUAS as an alternative to satellite imagery, this is an incomplete view and that the current common implementation introduces a new source of error that has significant implications on the reliability of the data collected. It can also be seen that a new approach to remote sensing with an SUAS can be developed by addressing the spatial, spectral and temporal factors that can now be more finely adjusted with the use of SUAS. However, to take the full advantage of the potential of SUAS, they must uphold the promise of improved safety. This is not a trivial challenge, especially for the integration into the National Airspace System (NAS) and for the safety management and oversight of diverse UAS operations. In this dissertation, the challenge of integrating

  16. Educational activities of remote sensing archaeology (Conference Presentation)

    Science.gov (United States)

    Hadjimitsis, Diofantos G.; Agapiou, Athos; Lysandrou, Vasilki; Themistocleous, Kyriacos; Cuca, Branka; Nisantzi, Argyro; Lasaponara, Rosa; Masini, Nicola; Krauss, Thomas; Cerra, Daniele; Gessner, Ursula; Schreier, Gunter

    2016-10-01

    Remote sensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remote sensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features. Education activities and training of remote sensing archaeology to young people is characterized of highly importance. Specific remote sensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a "remote sensing" approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remote sensing and of course to learn about history. The paper aims to present a modern didactical concept and some examples of practical implementation of remote sensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remote sensing for archaeological research in Cyprus. Through H2020 ATHENA project, the Remote Sensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.

  17. A COMPARISON OF LIDAR REFLECTANCE AND RADIOMETRICALLY CALIBRATED HYPERSPECTRAL IMAGERY

    Directory of Open Access Journals (Sweden)

    A. Roncat

    2016-06-01

    Full Text Available In order to retrieve results comparable under different flight parameters and among different flight campaigns, passive remote sensing data such as hyperspectral imagery need to undergo a radiometric calibration. While this calibration, aiming at the derivation of physically meaningful surface attributes such as a reflectance value, is quite cumbersome for passively sensed data and relies on a number of external parameters, the situation is by far less complicated for active remote sensing techniques such as lidar. This fact motivates the investigation of the suitability of full-waveform lidar as a “single-wavelength reflectometer” to support radiometric calibration of hyperspectral imagery. In this paper, this suitability was investigated by means of an airborne hyperspectral imagery campaign and an airborne lidar campaign recorded over the same area. Criteria are given to assess diffuse reflectance behaviour; the distribution of reflectance derived by the two techniques were found comparable in four test areas where these criteria were met. This is a promising result especially in the context of current developments of multi-spectral lidar systems.

  18. Some technical notes on using UAV-based remote sensing for post disaster assessment

    Science.gov (United States)

    Rokhmana, Catur Aries; Andaru, Ruli

    2017-07-01

    Indonesia is located in an area prone to disasters, which are various kinds of natural disasters happen. In disaster management, the geoinformation data are needed to be able to evaluate the impact area. The UAV (Unmanned Aerial Vehicle)-Based remote sensing technology is a good choice to produce a high spatial resolution of less than 15 cm, while the current resolution of the satellite imagery is still greater than 50 cm. This paper shows some technical notes that should be considered when using UAV-Based remote sensing system in post disaster for rapid assessment. Some cases are Aceh Earthquake in years 2013 for seeing infrastructure damages, Banjarnegara landslide in year 2014 for seeing the impact; and Kelud volcano eruption in year 2014 for seeing the impact and volumetric material calculation. The UAV-Based remote sensing system should be able to produce the Orthophoto image that can provide capabilities for visual interpretation the individual damage objects, and the changes situation. Meanwhile the DEM (digital Elevation model) product can derive terrain topography, and volumetric calculation with accuracy 3-5 pixel or sub-meter also. The UAV platform should be able for working remotely and autonomously in dangerous area and limited infrastructures. In mountainous or volcano area, an unconventional flight plan should implemented. Unfortunately, not all impact can be seen from above such as wall crack, some parcel boundaries, and many objects that covered by others higher object. The previous existing geoinformation data are also needed to be able to evaluate the change detection automatically.

  19. Scale issues in remote sensing

    CERN Document Server

    Weng, Qihao

    2014-01-01

    This book provides up-to-date developments, methods, and techniques in the field of GIS and remote sensing and features articles from internationally renowned authorities on three interrelated perspectives of scaling issues: scale in land surface properties, land surface patterns, and land surface processes. The book is ideal as a professional reference for practicing geographic information scientists and remote sensing engineers as well as a supplemental reading for graduate level students.

  20. Carbon dioxide effects research and assessment program. Measurement of changes in terrestrial carbon using remote sensing

    Energy Technology Data Exchange (ETDEWEB)

    Woodwell, G M [ed.

    1980-09-01

    Changes in the area of forests as well as changes in the storage of carbon within forest stands have large potential effects on atmospheric CO/sub 2/. This conference addressed the challenge of measuring changes in the area of forests globally through use of satellite remote sensing. The conclusion of the approximately seventy participants from around the world was that a program based on LANDSAT imagery supplemented by aerial photography is both possible and appropriate.

  1. Using Airborne Remote Sensing to Increase Situational Awareness in Civil Protection and Humanitarian Relief - the Importance of User Involvement

    Science.gov (United States)

    Römer, H.; Kiefl, R.; Henkel, F.; Wenxi, C.; Nippold, R.; Kurz, F.; Kippnich, U.

    2016-06-01

    Enhancing situational awareness in real-time (RT) civil protection and emergency response scenarios requires the development of comprehensive monitoring concepts combining classical remote sensing disciplines with geospatial information science. In the VABENE++ project of the German Aerospace Center (DLR) monitoring tools are being developed by which innovative data acquisition approaches are combined with information extraction as well as the generation and dissemination of information products to a specific user. DLR's 3K and 4k camera system which allow for a RT acquisition and pre-processing of high resolution aerial imagery are applied in two application examples conducted with end users: a civil protection exercise with humanitarian relief organisations and a large open-air music festival in cooperation with a festival organising company. This study discusses how airborne remote sensing can significantly contribute to both, situational assessment and awareness, focussing on the downstream processes required for extracting information from imagery and for visualising and disseminating imagery in combination with other geospatial information. Valuable user feedback and impetus for further developments has been obtained from both applications, referring to innovations in thematic image analysis (supporting festival site management) and product dissemination (editable web services). Thus, this study emphasises the important role of user involvement in application-related research, i.e. by aligning it closer to user's requirements.

  2. Remote Sensing Training for Middle School through the Center of Excellence in Remote Sensing Education

    Science.gov (United States)

    Hayden, L. B.; Johnson, D.; Baltrop, J.

    2012-12-01

    Remote sensing has steadily become an integral part of multiple disciplines, research, and education. Remote sensing can be defined as the process of acquiring information about an object or area of interest without physical contact. As remote sensing becomes a necessity in solving real world problems and scientific questions an important question to consider is why remote sensing training is significant to education and is it relevant to training students in this discipline. What has been discovered is the interest in Science, Technology, Engineering and Mathematics (STEM) fields, specifically remote sensing, has declined in our youth. The Center of Excellence in Remote Sensing Education and Research (CERSER) continuously strives to provide education and research opportunities on ice sheet, coastal, ocean, and marine science. One of those continued outreach efforts are Center for Remote Sensing of Ice Sheets (CReSIS) Middle School Program. Sponsored by the National Science Foundation CReSIS Middle School Program offers hands on experience for middle school students. CERSER and NSF offer students the opportunity to study and learn about remote sensing and its vital role in today's society as it relate to climate change and real world problems. The CReSIS Middle School Program is an annual two-week effort that offers middle school students experience with remote sensing and its applications. Specifically, participants received training with Global Positioning Systems (GPS) where the students learned the tools, mechanisms, and applications of a Garmin 60 GPS. As a part of the program the students were required to complete a fieldwork assignment where several longitude and latitude points were given throughout campus. The students had to then enter the longitude and latitude points into the Garmin 60 GPS, navigate their way to each location while also accurately reading the GPS to make sure travel was in the right direction. Upon completion of GPS training the

  3. LWIR Microgrid Polarimeter for Remote Sensing Studies

    Science.gov (United States)

    2010-02-28

    Polarimeter for Remote Sensing Studies 5b. GRANT NUMBER FA9550-08-1-0295 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 1. Scott Tyo 5e. TASK...and tested at the University of Arizona, and preliminary images are shown in this final report. 15. SUBJECT TERMS Remote Sensing , polarimetry 16...7.0 LWIR Microgrid Polarimeter for Remote Sensing Studies J. Scott Tyo College of Optical Sciences University of Arizona Tucson, AZ, 85721 tyo

  4. Hyperspectral remote sensing

    National Research Council Canada - National Science Library

    Eismann, Michael Theodore

    2012-01-01

    ..., and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remote sensing field, this book provides a holistic treatment...

  5. Assessment of the Impact of Reservoirs in the Upper Mekong River Using Satellite Radar Altimetry and Remote Sensing Imageries

    Directory of Open Access Journals (Sweden)

    Kuan-Ting Liu

    2016-04-01

    Full Text Available Water level (WL and water volume (WV of surface-water bodies are among the most crucial variables used in water-resources assessment and management. They fluctuate as a result of climatic forcing, and they are considered as indicators of climatic impacts on water resources. Quantifying riverine WL and WV, however, usually requires the availability of timely and continuous in situ data, which could be a challenge for rivers in remote regions, including the Mekong River basin. As one of the most developed rivers in the world, with more than 20 dams built or under construction, Mekong River is in need of a monitoring system that could facilitate basin-scale management of water resources facing future climate change. This study used spaceborne sensors to investigate two dams in the upper Mekong River, Xiaowan and Jinghong Dams within China, to examine river flow dynamics after these dams became operational. We integrated multi-mission satellite radar altimetry (RA, Envisat and Jason-2 and Landsat-5/-7/-8 Thematic Mapper (TM/Enhanced Thematic Mapper plus (ETM+/Operational  Land Imager (OLI optical remote sensing (RS imageries to construct composite WL time series with enhanced spatial resolutions and substantially extended WL data records. An empirical relationship between WL variation and water extent was first established for each dam, and then the combined long-term WL time series from Landsat images are reconstructed for the dams. The R2 between altimetry WL and Landsat water area measurements is >0.95. Next, the Tropical Rainfall Measuring Mission (TRMM data were used to diagnose and determine water variation caused by the precipitation anomaly within the basin. Finally, the impact of hydrologic dynamics caused by the impoundment of the dams is assessed. The discrepancy between satellite-derived WL and available in situ gauge data, in term of root-mean-square error (RMSE is at 2–5 m level. The estimated WV variations derived from combined RA

  6. PHOTOGRAMMETRY – REMOTE SENSING AND GEOINFORMATION

    Directory of Open Access Journals (Sweden)

    M. A. Lazaridou

    2012-07-01

    Full Text Available Earth and its environment are studied by different scientific disciplines as geosciences, science of engineering, social sciences, geography, etc. The study of the above, beyond pure scientific interest, is useful for the practical needs of man. Photogrammetry and Remote Sensing (defined by Statute II of ISPRS is the art, science, and technology of obtaining reliable information from non-contact imaging and other sensor systems about the Earth and its environment, and other physical objects and of processes through recording, measuring, analyzing and representation. Therefore, according to this definition, photogrammetry and remote sensing can support studies of the above disciplines for acquisition of geoinformation. This paper concerns basic concepts of geosciences (geomorphology, geology, hydrology etc, and the fundamentals of photogrammetry-remote sensing, in order to aid the understanding of the relationship between photogrammetry-remote sensing and geoinformation and also structure curriculum in a brief, concise and coherent way. This curriculum can represent an appropriate research and educational outline and help to disseminate knowledge in various directions and levels. It resulted from our research and educational experience in graduate and post-graduate level (post-graduate studies relative to the protection of environment and protection of monuments and historical centers in the Lab. of Photogrammetry – Remote Sensing in Civil Engineering Faculty of Aristotle University of Thessaloniki.

  7. Investigating the relationship between tree heights derived from SIBBORK forest model and remote sensing measurements

    Science.gov (United States)

    Osmanoglu, B.; Feliciano, E. A.; Armstrong, A. H.; Sun, G.; Montesano, P.; Ranson, K.

    2017-12-01

    Tree heights are one of the most commonly used remote sensing parameters to measure biomass of a forest. In this project, we investigate the relationship between remotely sensed tree heights (e.g. G-LiHT lidar and commercially available high resolution satellite imagery, HRSI) and the SIBBORK modeled tree heights. G-LiHT is a portable, airborne imaging system that simultaneously maps the composition, structure, and function of terrestrial ecosystems using lidar, imaging spectroscopy and thermal mapping. Ground elevation and canopy height models were generated using the lidar data acquired in 2012. A digital surface model was also generated using the HRSI technique from the commercially available WorldView data in 2016. The HRSI derived height and biomass products are available at the plot (10x10m) level. For this study, we parameterized the SIBBORK individual-based gap model for Howland forest, Maine. The parameterization was calibrated using field data for the study site and results show that the simulated forest reproduces the structural complexity of Howland old growth forest, based on comparisons of key variables including, aboveground biomass, forest height and basal area. Furthermore carbon cycle and ecosystem observational capabilities will be enhanced over the next 6 years via the launch of two LiDAR (NASA's GEDI and ICESAT 2) and two SAR (NASA's ISRO NiSAR and ESA's Biomass) systems. Our aim is to present the comparison of canopy height models obtained with SIBBORK forest model and remote sensing techniques, highlighting the synergy between individual-based forest modeling and high-resolution remote sensing.

  8. Recent Advances in Maya Studies Using Remotely Sensed Data

    Science.gov (United States)

    Sever, Tom; Irwin, Daniel; Arnold, James E. (Technical Monitor)

    2001-01-01

    The Peten region of northern Guatemala is one of the last places on earth where major archeological sites remain to be discovered. It was in this region that the Maya civilization began, flourished, and abruptly disappeared. Remote sensing technology is helping to locate and map ancient Maya sites that are threatened today by accelerating deforestation and looting. Thematic Mapper and IKONOS satellite and airborne Star3i radar data, combined with Global Positioning System (GPS) technology, are successfully detecting ancient Maya features such as cities, roadways, canals, and water reservoirs. Satellite imagery is also being used to map the baJos, which are seasonally flooded swamps that cover over 40% of the land surface. The use of bajos for farming has been a source of debate within the professional community for many years. But the recent detection and verification of cultural features within the bajo system by our research team are providing conclusive evidence that the ancient Maya had adapted well to wetland environments from the earliest times and utilized them until the time of the Maya collapse. The combination of water management and bajo farming is an important resource for the future of the current inhabitants who are experiencing rapid population growth. Remote sensing imagery is also demonstrating that in the Preclassic period (600 BC- AD 250), the Maya had already achieved a high organizational level as evidenced by the construction of massive temples and an elaborate inter-connecting roadway system. Although they experienced several setbacks such as droughts and hurricanes, the Maya nevertheless managed the delicate forest ecosystem successfully for several centuries. However, around AD 800, something happened to the Maya to cause their rapid decline and eventual disappearance from the region. The evidence indicates that at this time there was increased climatic dryness, extensive deforestation, overpopulation, and widespread warfare. This raises a

  9. Autonomous target recognition using remotely sensed surface vibration measurements

    Science.gov (United States)

    Geurts, James; Ruck, Dennis W.; Rogers, Steven K.; Oxley, Mark E.; Barr, Dallas N.

    1993-09-01

    The remotely measured surface vibration signatures of tactical military ground vehicles are investigated for use in target classification and identification friend or foe (IFF) systems. The use of remote surface vibration sensing by a laser radar reduces the effects of partial occlusion, concealment, and camouflage experienced by automatic target recognition systems using traditional imagery in a tactical battlefield environment. Linear Predictive Coding (LPC) efficiently represents the vibration signatures and nearest neighbor classifiers exploit the LPC feature set using a variety of distortion metrics. Nearest neighbor classifiers achieve an 88 percent classification rate in an eight class problem, representing a classification performance increase of thirty percent from previous efforts. A novel confidence figure of merit is implemented to attain a 100 percent classification rate with less than 60 percent rejection. The high classification rates are achieved on a target set which would pose significant problems to traditional image-based recognition systems. The targets are presented to the sensor in a variety of aspects and engine speeds at a range of 1 kilometer. The classification rates achieved demonstrate the benefits of using remote vibration measurement in a ground IFF system. The signature modeling and classification system can also be used to identify rotary and fixed-wing targets.

  10. Digital methods and remote sensing in archaeology archaeology in the age of sensing

    CERN Document Server

    Campana, Stefano

    2016-01-01

    This volume debuts the new scope of Remote Sensing, which was first defined as the analysis of data collected by sensors that were not in physical contact with the objects under investigation (using cameras, scanners, and radar systems operating from spaceborne or airborne platforms). A wider characterization is now possible: Remote Sensing can be any non-destructive approach to viewing the buried and nominally invisible evidence of past activity. Spaceborne and airborne sensors, now supplemented by laser scanning, are united using ground-based geophysical instruments and undersea remote sensing, as well as other non-invasive techniques such as surface collection or field-walking survey. Now, any method that enables observation of evidence on or beneath the surface of the earth, without impact on the surviving stratigraphy, is legitimately within the realm of Remote Sensing. The new interfaces and senses engaged in Remote Sensing appear throughout the book. On a philosophical level, this is about the landscap...

  11. [Using a modified remote sensing imagery for interpreting changes in cultivated saline-alkali land].

    Science.gov (United States)

    Gao, Hui; Liu, Hui-tao; Liu, Hong-juan; Liu, Jin-tong

    2015-04-01

    This paper developed a new interpretation symbol system for grading and classifying saline-alkali land, using Huanghua, a cosatal city in Hebei Province as a case. The system was developed by inverting remote sensing images from 1992 to 2011 based on site investigation, plant cover characteristics and features of remote sensing images. Combining this interpretation symbol system with supervising classification method, the information on arable land was obtained for the coastal saline-alkali ecosystem of Huanghua City, and the saline-alkali land area, changes in intensity of salinity-alkalinity and spatial distribution from 1992 to 2011 were analyzed. The results showed that salinization of arable land in Huanghua City alleviated from 1992 to 2011. The severely and moderately saline-alkali land area decreased in 2011 compared with 1992, while the non/slightly saline land area increased. The moderately saline-alkali land in southeast transformed to non/slightly saline-alkaline, while the severely saline-alkali land in west of the city far from the coastal zone became moderately saline-alkaline. The center of gravity (CG) of severely and non/slightly saline-alkali land moved closer the coastline, while that of the moderately saline-alkali land moved from southwest coastal line to northwest. Factors influencing changes in arable land within the saline-alkali ecosystem of Huanghua City were climate, hydrology and human activities.

  12. Oil spill remote sensing sensors and aircraft

    International Nuclear Information System (INIS)

    Fingas, M.; Fruhwirth, M.; Gamble, L.

    1992-01-01

    The most common form of remote sensing as applied to oil spills is aerial remote sensing. The technology of aerial remote sensing, mainly from aircraft, is reviewed along with aircraft-mounted remote sensors and aircraft modifications. The characteristics, advantages, and limitations of optical techniques, infrared and ultraviolet sensors, fluorosensors, microwave and radar sensors, and slick thickness sensors are discussed. Special attention is paid to remote sensing of oil under difficult circumstances, such as oil in water or oil on ice. An infrared camera is the first sensor recommended for oil spill work, as it is the cheapest and most applicable device, and is the only type of equipment that can be bought off-the-shelf. The second sensor recommended is an ultraviolet and visible-spectrum device. The laser fluorosensor offers the only potential for discriminating between oiled and un-oiled weeds or shoreline, and for positively identifying oil pollution on ice and in a variety of other situations. However, such an instrument is large and expensive. Radar, although low in priority for purchase, offers the only potential for large-area searches and foul-weather remote sensing. Most other sensors are experimental or do not offer good potential for oil detection or mapping. 48 refs., 8 tabs

  13. [Comparison of precision in retrieving soybean leaf area index based on multi-source remote sensing data].

    Science.gov (United States)

    Gao, Lin; Li, Chang-chun; Wang, Bao-shan; Yang Gui-jun; Wang, Lei; Fu, Kui

    2016-01-01

    With the innovation of remote sensing technology, remote sensing data sources are more and more abundant. The main aim of this study was to analyze retrieval accuracy of soybean leaf area index (LAI) based on multi-source remote sensing data including ground hyperspectral, unmanned aerial vehicle (UAV) multispectral and the Gaofen-1 (GF-1) WFV data. Ratio vegetation index (RVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), difference vegetation index (DVI), and triangle vegetation index (TVI) were used to establish LAI retrieval models, respectively. The models with the highest calibration accuracy were used in the validation. The capability of these three kinds of remote sensing data for LAI retrieval was assessed according to the estimation accuracy of models. The experimental results showed that the models based on the ground hyperspectral and UAV multispectral data got better estimation accuracy (R² was more than 0.69 and RMSE was less than 0.4 at 0.01 significance level), compared with the model based on WFV data. The RVI logarithmic model based on ground hyperspectral data was little superior to the NDVI linear model based on UAV multispectral data (The difference in E(A), R² and RMSE were 0.3%, 0.04 and 0.006, respectively). The models based on WFV data got the lowest estimation accuracy with R2 less than 0.30 and RMSE more than 0.70. The effects of sensor spectral response characteristics, sensor geometric location and spatial resolution on the soybean LAI retrieval were discussed. The results demonstrated that ground hyperspectral data were advantageous but not prominent over traditional multispectral data in soybean LAI retrieval. WFV imagery with 16 m spatial resolution could not meet the requirements of crop growth monitoring at field scale. Under the condition of ensuring the high precision in retrieving soybean LAI and working efficiently, the approach to acquiring agricultural information by UAV remote

  14. Multi-source remote sensing data management system

    International Nuclear Information System (INIS)

    Qin Kai; Zhao Yingjun; Lu Donghua; Zhang Donghui; Wu Wenhuan

    2014-01-01

    In this thesis, the author explored multi-source management problems of remote sensing data. The main idea is to use the mosaic dataset model, and the ways of an integreted display of image and its interpretation. Based on ArcGIS and IMINT feature knowledge platform, the author used the C# and other programming tools for development work, so as to design and implement multi-source remote sensing data management system function module which is able to simply, conveniently and efficiently manage multi-source remote sensing data. (authors)

  15. Optical Modeling of Spectral Backscattering and Remote Sensing Reflectance From Emiliania huxleyi Blooms

    Directory of Open Access Journals (Sweden)

    Griet Neukermans

    2018-05-01

    Full Text Available In this study we develop an analytical model for spectral backscattering and ocean color remote sensing of blooms of the calcifying phytoplankton species Emiliania huxleyi. Blooms of this coccolithophore species are ubiquitous and particularly intense in temperate and subpolar ocean waters. We first present significant improvements to our previous analytical light backscattering model for E. huxleyi coccoliths and coccospheres by accounting for the elliptical shape of coccoliths and the multi-layered coccosphere architecture observed on detailed imagery of E. huxleyi liths and coccospheres. Our new model also includes a size distribution function that closely matches measured E. huxleyi size distributions. The model for spectral backscattering is then implemented in an analytical radiative transfer model to evaluate the variability of spectral remote sensing reflectance with respect to changes in the size distribution of the coccoliths and during a hypothetical E. huxleyi bloom decay event in which coccospheres shed their liths. Our modeled remote sensing reflectance spectra reproduced well the bright milky turquoise coloring of the open ocean typically associated with the final stages of E. huxleyi blooms, with peak reflectance at a wavelength of 0.49 μm. Our results also show that the magnitude of backscattering from coccoliths when attached to or freed from the coccosphere does not differ much, contrary to what is commonly assumed, and that the spectral shape of backscattering is mainly controlled by the size and morphology of the coccoliths, suggesting that they may be estimated from spectral backscattering.

  16. Remote sensing and resource exploration

    International Nuclear Information System (INIS)

    El-Baz, F.; Hassan, M.H.A.; Cappellini, V.

    1989-01-01

    The purpose of the Workshop was to study in depth the application of remote sensing technology to the fields of archaeology, astronomy, geography, geology, and physics. Some emphasis was placed on utilizing remote sensing methods and techniques in the search for water, mineral and land resources. The Workshop was attended by 90 people from 35 countries. The proceedings of this meeting includes 15 papers, 12 of them have a separate abstract in the INIS Database. Refs, figs and tabs

  17. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

    Science.gov (United States)

    Dorigo, W. A.; Zurita-Milla, R.; de Wit, A. J. W.; Brazile, J.; Singh, R.; Schaepman, M. E.

    2007-05-01

    During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical-empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by

  18. Quantifying the City’s Green Area Potential Gain Using Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Teresa Santos

    2016-11-01

    Full Text Available Information about green spaces available in a city is essential for urban planning. Urban green areas are generally assessed through environmental indicators that reflect the city’s quality of life and urban comfort. A methodology based on 3D measure and analysis of green urban areas at the city scale is presented. Two products are proposed: (1 measuring current vegetation cover at ground level through object-oriented classification of WorldView-2 imagery; and (2 estimating potential green cover at rooftop level using 3D data obtained by LiDAR sensor. The methodology, implemented in Lisbon, Portugal, demonstrates that: (1 remote sensing imagery provides powerful tools for master planning and policy analysis regarding green urban area expansion; and (2 measures of urban sustainability cannot be solely based on indicators obtained from 2D geographical information. In fact, 2D urban indicators should be complemented by 3D modelling of geographic data.

  19. High resolution remote sensing of water surface patterns

    Science.gov (United States)

    Woodget, A.; Visser, F.; Maddock, I.; Carbonneau, P.

    2012-12-01

    The assessment of in-stream habitat availability within fluvial environments in the UK traditionally includes the mapping of patterns which appear on the surface of the water, known as 'surface flow types' (SFTs). The UK's River Habitat Survey identifies ten key SFTs, including categories such as rippled flow, upwelling, broken standing waves and smooth flow. SFTs result from the interaction between the underlying channel morphology, water depth and velocity and reflect the local flow hydraulics. It has been shown that SFTs can be both biologically and hydraulically distinct. SFT mapping is usually conducted from the river banks where estimates of spatial coverage are made by eye. This approach is affected by user subjectivity and inaccuracies in the spatial extent of mapped units. Remote sensing and specifically the recent developments in unmanned aerial systems (UAS) may now offer an alternative approach for SFT mapping, with the capability for rapid and repeatable collection of very high resolution imagery from low altitudes, under bespoke flight conditions. This PhD research is aimed at investigating the mapping of SFTs using high resolution optical imagery (less than 10cm) collected from a helicopter-based UAS flown at low altitudes (less than 100m). This paper presents the initial findings from a series of structured experiments on the River Arrow, a small lowland river in Warwickshire, UK. These experiments investigate the potential for mapping SFTs from still and video imagery of different spatial resolutions collected at different flying altitudes and from different viewing angles (i.e. vertical and oblique). Imagery is processed using 3D mosaicking software to create orthophotos and digital elevation models (DEM). The types of image analysis which are tested include a simple, manual visual assessment undertaken in a GIS environment, based on the high resolution optical imagery. In addition, an object-based image analysis approach which makes use of the

  20. Prospecting for coal in China with remote sensing

    Energy Technology Data Exchange (ETDEWEB)

    Ke-long Tan; Yu-qing Wan; Sun-xin Sun; Gui-bao Bao; Jing-shui Kuang [Aerophotogrammetry and Remote Sensing Center of China Coal, Xi' an (China)

    2008-12-15

    In China it is important to explore coal prospecting by taking advantage of modern remote sensing and geographic information system technologies. Given a theoretical basis for coal prospecting by remote sensing, the methodologies and existing problems are demonstrated systematically by summarizing past practices of coal prospecting with remote sensing. A new theory of coal prospecting with remote sensing is proposed. In uncovered areas, coal resources can be prospected by direct interpretation. In coal bearing strata of developed areas covered by thin Quaternary strata or vegetation, prospecting for coal can be carried out by indirect interpretation of geomorphology and vegetation. For deeply buried underground deposits, coal prospecting can rely on tectonic structures, interpretation and analysis of new tectonic clues and regularity of coal formation and preservation controlled by tectonic structures. By applying newly hyper-spectral, multi-polarization, multi-angle, multi-temporal and multi-resolution remote sensing data and carrying out integrated analysis of geographic attributes, ground attributes, geophysical exploration results, geochemical exploration results, geological drilling results and remote sensing data by GIS tools, coal geology resources and mineralogical regularities can be explored and coal resource information can be acquired with some confidence. 12 refs., 4 figs., 3 tabs.

  1. Hurricane Sandy: Rapid Response Imagery of the Surrounding Regions

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of Hurricane Sandy. The aerial photography missions were conducted by the NOAA Remote Sensing Division. The images were acquired...

  2. USING AIRBORNE REMOTE SENSING TO INCREASE SITUATIONAL AWARENESS IN CIVIL PROTECTION AND HUMANITARIAN RELIEF – THE IMPORTANCE OF USER INVOLVEMENT

    Directory of Open Access Journals (Sweden)

    H. Römer

    2016-06-01

    Full Text Available Enhancing situational awareness in real-time (RT civil protection and emergency response scenarios requires the development of comprehensive monitoring concepts combining classical remote sensing disciplines with geospatial information science. In the VABENE++ project of the German Aerospace Center (DLR monitoring tools are being developed by which innovative data acquisition approaches are combined with information extraction as well as the generation and dissemination of information products to a specific user. DLR’s 3K and 4k camera system which allow for a RT acquisition and pre-processing of high resolution aerial imagery are applied in two application examples conducted with end users: a civil protection exercise with humanitarian relief organisations and a large open-air music festival in cooperation with a festival organising company. This study discusses how airborne remote sensing can significantly contribute to both, situational assessment and awareness, focussing on the downstream processes required for extracting information from imagery and for visualising and disseminating imagery in combination with other geospatial information. Valuable user feedback and impetus for further developments has been obtained from both applications, referring to innovations in thematic image analysis (supporting festival site management and product dissemination (editable web services. Thus, this study emphasises the important role of user involvement in application-related research, i.e. by aligning it closer to user’s requirements.

  3. Kite Aerial Photography as a Tool for Remote Sensing

    Science.gov (United States)

    Sallee, Jeff; Meier, Lesley R.

    2010-01-01

    As humans, we perform remote sensing nearly all the time. This is because we acquire most of our information about our surroundings through the senses of sight and hearing. Whether viewed by the unenhanced eye or a military satellite, remote sensing is observing objects from a distance. With our current technology, remote sensing has become a part…

  4. Physics teaching by infrared remote sensing of vegetation

    Science.gov (United States)

    Schüttler, Tobias; Maman, Shimrit; Girwidz, Raimund

    2018-05-01

    Context- and project-based teaching has proven to foster different affective and cognitive aspects of learning. As a versatile and multidisciplinary scientific research area with diverse applications for everyday life, satellite remote sensing is an interesting context for physics education. In this paper we give a brief overview of satellite remote sensing of vegetation and how to obtain your own, individual infrared remote sensing data with affordable converted digital cameras. This novel technique provides the opportunity to conduct individual remote sensing measurement projects with students in their respective environment. The data can be compared to real satellite data and is of sufficient accuracy for educational purposes.

  5. National Satellite Land Remote Sensing Data Archive

    Science.gov (United States)

    Faundeen, John L.; Kelly, Francis P.; Holm, Thomas M.; Nolt, Jenna E.

    2013-01-01

    The National Satellite Land Remote Sensing Data Archive (NSLRSDA) resides at the U.S. Geological Survey's (USGS) Earth Resources Observation and Science (EROS) Center. Through the Land Remote Sensing Policy Act of 1992, the U.S. Congress directed the Department of the Interior (DOI) to establish a permanent Government archive containing satellite remote sensing data of the Earth's land surface and to make this data easily accessible and readily available. This unique DOI/USGS archive provides a comprehensive, permanent, and impartial observational record of the planet's land surface obtained throughout more than five decades of satellite remote sensing. Satellite-derived data and information products are primary sources used to detect and understand changes such as deforestation, desertification, agricultural crop vigor, water quality, invasive plant species, and certain natural hazards such as flood extent and wildfire scars.

  6. Remote sensing research in geographic education: An alternative view

    Science.gov (United States)

    Wilson, H.; Cary, T. K.; Goward, S. N.

    1981-01-01

    It is noted that within many geography departments remote sensing is viewed as a mere technique a student should learn in order to carry out true geographic research. This view inhibits both students and faculty from investigation of remotely sensed data as a new source of geographic knowledge that may alter our understanding of the Earth. The tendency is for geographers to accept these new data and analysis techniques from engineers and mathematicians without questioning the accompanying premises. This black-box approach hinders geographic applications of the new remotely sensed data and limits the geographer's contribution to further development of remote sensing observation systems. It is suggested that geographers contribute to the development of remote sensing through pursuit of basic research. This research can be encouraged, particularly among students, by demonstrating the links between geographic theory and remotely sensed observations, encouraging a healthy skepticism concerning the current understanding of these data.

  7. Classification of permafrost active layer depth from remotely sensed and topographic evidence

    International Nuclear Information System (INIS)

    Peddle, D.R.; Franklin, S.E.

    1993-01-01

    The remote detection of permafrost (perennially frozen ground) has important implications to environmental resource development, engineering studies, natural hazard prediction, and climate change research. In this study, the authors present results from two experiments into the classification of permafrost active layer depth within the zone of discontinuous permafrost in northern Canada. A new software system based on evidential reasoning was implemented to permit the integrated classification of multisource data consisting of landcover, terrain aspect, and equivalent latitude, each of which possessed different formats, data types, or statistical properties that could not be handled by conventional classification algorithms available to this study. In the first experiment, four active layer depth classes were classified using ground based measurements of the three variables with an accuracy of 83% compared to in situ soil probe determination of permafrost active layer depth at over 500 field sites. This confirmed the environmental significance of the variables selected, and provided a baseline result to which a remote sensing classification could be compared. In the second experiment, evidence for each input variable was obtained from image processing of digital SPOT imagery and a photogrammetric digital elevation model, and used to classify active layer depth with an accuracy of 79%. These results suggest the classification of evidence from remotely sensed measures of spectral response and topography may provide suitable indicators of permafrost active layer depth

  8. History and future of remote sensing technology and education

    Science.gov (United States)

    Colwell, R. N.

    1980-01-01

    A historical overview of the discovery and development of photography, related sciences, and remote sensing technology is presented. The role of education to date in the development of remote sensing is discussed. The probable future and potential of remote sensing and training is described.

  9. Remote sensing for vineyard management

    Science.gov (United States)

    Philipson, W. R.; Erb, T. L.; Fernandez, D.; Mcleester, J. N.

    1980-01-01

    Cornell's Remote Sensing Program has been involved in a continuing investigation to assess the value of remote sensing for vineyard management. Program staff members have conducted a series of site and crop analysis studies. These include: (1) panchromatic aerial photography for planning artificial drainage in a new vineyard; (2) color infrared aerial photography for assessing crop vigor/health; and (3) color infrared aerial photography and aircraft multispectral scanner data for evaluating yield related factors. These studies and their findings are reviewed.

  10. On the Use of Ocean Color Remote Sensing to Measure the Transport of Dissolved Organic Carbon by the Mississippi River Plume

    Science.gov (United States)

    DelCastillo, Carlos E.; Miller, Richard L.

    2007-01-01

    We investigated the use of ocean color remote sensing to measure transport of dissolved organic carbon (DOC) by the Mississippi River to the Gulf of Mexico. From 2000 to 2005 we recorded surface measurements of DOC, colored dissolved organic matter (CDOM), salinity, and water-leaving radiances during five cruises to the Mississippi River Plume. These measurements were used to develop empirical relationships to derive CDOM, DOC, and salinity from monthly composites of SeaWiFS imagery collected from 1998 through 2005. We used river flow data and a two-end-member mixing model to derive DOC concentrations in the river end-member, river flow, and DOC transport using remote sensing data. We compared our remote sensing estimates of river flow and DOC transport with data collected by the United States Geological Survey (USGS) from 1998 through 2005. Our remote sensing estimates of river flow and DOC transport correlated well (r2 0.70) with the USGS data. Our remote sensing estimates and USGS field data showed low variability in DOC concentrations in the river end-member (7-11%), and high seasonal variability in river flow (50%). Therefore, changes in river flow control the variability in DOC transport, indicating that the remote sensing estimate of river flow is the most critical element of our DOC transport measurement. We concluded that it is possible to use this method to estimate DOC transport by other large rivers if there are data on the relationship between CDOM, DOC, and salinity in the river plume.

  11. Remote sensing of coral reefs and their physical environment

    International Nuclear Information System (INIS)

    Mumby, Peter J.; Skirving, William; Strong, Alan E.; Hardy, John T.; LeDrew, Ellsworth F.; Hochberg, Eric J.; Stumpf, Rick P.; David, Laura T.

    2004-01-01

    There has been a vast improvement in access to remotely sensed data in just a few recent years. This revolution of information is the result of heavy investment in new technology by governments and industry, rapid developments in computing power and storage, and easy dissemination of data over the internet. Today, remotely sensed data are available to virtually anyone with a desktop computer. Here, we review the status of one of the most popular areas of marine remote sensing research: coral reefs. Previous reviews have focused on the ability of remote sensing to map the structure and habitat composition of coral reefs, but have neglected to consider the physical environment in which reefs occur. We provide a holistic review of what can, might, and cannot be mapped using remote sensing at this time. We cover aspects of reef structure and health but also discuss the diversity of physical environmental data such as temperature, winds, solar radiation and water quality. There have been numerous recent advances in the remote sensing of reefs and we hope that this paper enhances awareness of the diverse data sources available, and helps practitioners identify realistic objectives for remote sensing in coral reef areas

  12. Remote sensing of coral reefs and their physical environment

    Energy Technology Data Exchange (ETDEWEB)

    Mumby, Peter J.; Skirving, William; Strong, Alan E.; Hardy, John T.; LeDrew, Ellsworth F.; Hochberg, Eric J.; Stumpf, Rick P.; David, Laura T

    2004-02-01

    There has been a vast improvement in access to remotely sensed data in just a few recent years. This revolution of information is the result of heavy investment in new technology by governments and industry, rapid developments in computing power and storage, and easy dissemination of data over the internet. Today, remotely sensed data are available to virtually anyone with a desktop computer. Here, we review the status of one of the most popular areas of marine remote sensing research: coral reefs. Previous reviews have focused on the ability of remote sensing to map the structure and habitat composition of coral reefs, but have neglected to consider the physical environment in which reefs occur. We provide a holistic review of what can, might, and cannot be mapped using remote sensing at this time. We cover aspects of reef structure and health but also discuss the diversity of physical environmental data such as temperature, winds, solar radiation and water quality. There have been numerous recent advances in the remote sensing of reefs and we hope that this paper enhances awareness of the diverse data sources available, and helps practitioners identify realistic objectives for remote sensing in coral reef areas.

  13. Comparison of Random Forest and Support Vector Machine classifiers using UAV remote sensing imagery

    Science.gov (United States)

    Piragnolo, Marco; Masiero, Andrea; Pirotti, Francesco

    2017-04-01

    Since recent years surveying with unmanned aerial vehicles (UAV) is getting a great amount of attention due to decreasing costs, higher precision and flexibility of usage. UAVs have been applied for geomorphological investigations, forestry, precision agriculture, cultural heritage assessment and for archaeological purposes. It can be used for land use and land cover classification (LULC). In literature, there are two main types of approaches for classification of remote sensing imagery: pixel-based and object-based. On one hand, pixel-based approach mostly uses training areas to define classes and respective spectral signatures. On the other hand, object-based classification considers pixels, scale, spatial information and texture information for creating homogeneous objects. Machine learning methods have been applied successfully for classification, and their use is increasing due to the availability of faster computing capabilities. The methods learn and train the model from previous computation. Two machine learning methods which have given good results in previous investigations are Random Forest (RF) and Support Vector Machine (SVM). The goal of this work is to compare RF and SVM methods for classifying LULC using images collected with a fixed wing UAV. The processing chain regarding classification uses packages in R, an open source scripting language for data analysis, which provides all necessary algorithms. The imagery was acquired and processed in November 2015 with cameras providing information over the red, blue, green and near infrared wavelength reflectivity over a testing area in the campus of Agripolis, in Italy. Images were elaborated and ortho-rectified through Agisoft Photoscan. The ortho-rectified image is the full data set, and the test set is derived from partial sub-setting of the full data set. Different tests have been carried out, using a percentage from 2 % to 20 % of the total. Ten training sets and ten validation sets are obtained from

  14. Surface Biophysical Parameters Derived From Remote Sensing Data For Urban Changes Assessment

    International Nuclear Information System (INIS)

    Zoran, M.; Pavelescu, G.; Nicolae, D.N.; Talianu, C.

    2007-01-01

    Remote sensing is a key application in global-change science, being very useful for urban climatology and land use-Landcover dynamics analysis.Multi-spectral and multi-temporal satellite imagery (LANDSAT TM, ETM ;SAR ) over 1984 - 2004 period for Bucharest urban area provide the most reliable technique of monitoring of different urban structures regarding the net radiation and heat fluxes associated with urbanization at the regional scale. This study attempts to provide environmental awareness to urban planners in future urban development. The land cover information, properly classified, can provide a spatially and temporally explicit view of societal and environmental attributes and can be an important complement to in-situ measurements

  15. Remote Sensing of Landslides—A Review

    Directory of Open Access Journals (Sweden)

    Chaoying Zhao

    2018-02-01

    Full Text Available Triggered by earthquakes, rainfall, or anthropogenic activities, landslides represent widespread and problematic geohazards worldwide. In recent years, multiple remote sensing techniques, including synthetic aperture radar, optical, and light detection and ranging measurements from spaceborne, airborne, and ground-based platforms, have been widely applied for the analysis of landslide processes. Current techniques include landslide detection, inventory mapping, surface deformation monitoring, trigger factor analysis and mechanism inversion. In addition, landslide susceptibility modelling, hazard assessment, and risk evaluation can be further analyzed using a synergic fusion of multiple remote sensing data and other factors affecting landslides. We summarize the 19 articles collected in this special issue of Remote Sensing of Landslide, in the terms of data, methods and applications used in the papers.

  16. SYMPOSIUM ON REMOTE SENSING IN THE POLAR REGIONS

    Science.gov (United States)

    The Arctic Institute of North America long has been interested in encouraging full and specific attention to applications of remote sensing to polar...research problems. The major purpose of the symposium was to acquaint scientists and technicians concerned with remote sensing with some of the...special problems of the polar areas and, in turn, to acquaint polar scientists with the potential of the use of remote sensing . The Symposium therefore was

  17. Hyperspectral remote sensing for light pollution monitoring

    Directory of Open Access Journals (Sweden)

    P. Marcoionni

    2006-06-01

    Full Text Available industries. In this paper we introduce the results from a remote sensing campaign performed in September 2001 at night time. For the first time nocturnal light pollution was measured at high spatial and spectral resolution using two airborne hyperspectral sensors, namely the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS and the Visible InfraRed Scanner (VIRS-200. These imagers, generally employed for day-time Earth remote sensing, were flown over the Tuscany coast (Italy on board of a Casa 212/200 airplane from an altitude of 1.5-2.0 km. We describe the experimental activities which preceded the remote sensing campaign, the optimization of sensor configuration, and the images as far acquired. The obtained results point out the novelty of the performed measurements and highlight the need to employ advanced remote sensing techniques as a spectroscopic tool for light pollution monitoring.

  18. Use of microwave remote sensing in salinity estimation

    International Nuclear Information System (INIS)

    Singh, R.P.; Kumar, V.; Srivastav, S.K.

    1990-01-01

    Soil-moisture interaction and the consequent liberation of ions causes the salinity of waters. The salinity of river, lake, ocean and ground water changes due to seepage and surface runoff. We have studied the feasibility of using microwave remote sensing for the estimation of salinity by carrying out numerical calculations to study the microwave remote sensing responses of various models representative of river, lake and ocean water. The results show the dependence of microwave remote sensing responses on the salinity and surface temperature of water. The results presented in this paper will be useful in the selection of microwave sensor parameters and in the accurate estimation of salinity from microwave remote sensing data

  19. Remote sensing techniques in monitoring areas affected by forest fire

    Science.gov (United States)

    Karagianni, Aikaterini Ch.; Lazaridou, Maria A.

    2017-09-01

    Forest fire is a part of nature playing a key role in shaping ecosystems. However, fire's environmental impacts can be significant, affecting wildlife habitat and timber, human settlements, man-made technical constructions and various networks (road, power networks) and polluting the air with emissions harmful to human health. Furthermore, fire's effect on the landscape may be long-lasting. Monitoring the development of a fire occurs as an important aspect at the management of natural hazards in general. Among the used methods for monitoring, satellite data and remote sensing techniques can be proven of particular importance. Satellite remote sensing offers a useful tool for forest fire detection, monitoring, management and damage assessment. Especially for fire scars detection and monitoring, satellite data derived from Landsat 8 can be a useful research tool. This paper includes critical considerations of the above and concerns in particular an example of the Greek area (Thasos Island). This specific area was hit by fires several times in the past and recently as well (September 2016). Landsat 8 satellite data are being used (pre and post fire imagery) and digital image processing techniques are applied (enhancement techniques, calculation of various indices) for fire scars detection. Visual interpretation of the example area affected by the fires is also being done, contributing to the overall study.

  20. Online catalog access and distribution of remotely sensed information

    Science.gov (United States)

    Lutton, Stephen M.

    1997-09-01

    Remote sensing is providing voluminous data and value added information products. Electronic sensors, communication electronics, computer software, hardware, and network communications technology have matured to the point where a distributed infrastructure for remotely sensed information is a reality. The amount of remotely sensed data and information is making distributed infrastructure almost a necessity. This infrastructure provides data collection, archiving, cataloging, browsing, processing, and viewing for applications from scientific research to economic, legal, and national security decision making. The remote sensing field is entering a new exciting stage of commercial growth and expansion into the mainstream of government and business decision making. This paper overviews this new distributed infrastructure and then focuses on describing a software system for on-line catalog access and distribution of remotely sensed information.

  1. Polarbrdf: A General Purpose Python Package for Visualization Quantitative Analysis of Multi-Angular Remote Sensing Measurements

    Science.gov (United States)

    Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

    2016-01-01

    The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

  2. Developing Remote Sensing Products for Monitoring and Modeling Great Lakes Coastal Wetland Vulnerability to Climate Change and Land Use

    Science.gov (United States)

    Bourgeau-Chavez, L. L.; Miller, M. E.; Battaglia, M.; Banda, E.; Endres, S.; Currie, W. S.; Elgersma, K. J.; French, N. H. F.; Goldberg, D. E.; Hyndman, D. W.

    2014-12-01

    Spread of invasive plant species in the coastal wetlands of the Great Lakes is degrading wetland habitat, decreasing biodiversity, and decreasing ecosystem services. An understanding of the mechanisms of invasion is crucial to gaining control of this growing threat. To better understand the effects of land use and climatic drivers on the vulnerability of coastal zones to invasion, as well as to develop an understanding of the mechanisms of invasion, research is being conducted that integrates field studies, process-based ecosystem and hydrological models, and remote sensing. Spatial data from remote sensing is needed to parameterize the hydrological model and to test the outputs of the linked models. We will present several new remote sensing products that are providing important physiological, biochemical, and landscape information to parameterize and verify models. This includes a novel hybrid radar-optical technique to delineate stands of invasives, as well as natural wetland cover types; using radar to map seasonally inundated areas not hydrologically connected; and developing new algorithms to estimate leaf area index (LAI) using Landsat. A coastal map delineating wetland types including monocultures of the invaders (Typha spp. and Phragmites austrailis) was created using satellite radar (ALOS PALSAR, 20 m resolution) and optical data (Landsat 5, 30 m resolution) fusion from multiple dates in a Random Forests classifier. These maps provide verification of the integrated model showing areas at high risk of invasion. For parameterizing the hydrological model, maps of seasonal wetness are being developed using spring (wet) imagery and differencing that with summer (dry) imagery to detect the seasonally wet areas. Finally, development of LAI remote sensing high resolution algorithms for uplands and wetlands is underway. LAI algorithms for wetlands have not been previously developed due to the difficulty of a water background. These products are being used to

  3. Review of research on remote sensing with digital map. Remote sensing to suchi chizu no ketsugo ni yoru kenkyu no shokai

    Energy Technology Data Exchange (ETDEWEB)

    Tanaka, S; Sugimura, T [Remote Sensing Technology Center of Japan, Tokyo (Japan)

    1990-12-05

    This paper describes the relationship between remote sensing and digital map. The relation between remote sensing and digital map is roughly classified into two kinds. One of them is utilization of remote sensing and digital map in combination to analyze phenomena, and the other is normalization of remote sensing data by use of digital map. For examples of utilizing remote sensing and digital map, there are the creation of a perspective image of ground scene from Landsat MSS data by use of a mesh type digital map of the orthogonal co-ordinates, and the creation of an image of the enviromental research along roads from satilite data by use of a vector type digital map. Furthermore, this paper introduces a procedure of correcting geographical strains by use of a digital map and converting a radar image to corrected plane image, and the use of a digital map in the global scale for the analysis of floods and other purposes. 20 refs., 5 figs., 1 tab.

  4. Remote sensing in operational range management programs in Western Canada

    Science.gov (United States)

    Thompson, M. D.

    1977-01-01

    A pilot program carried out in Western Canada to test remote sensing under semi-operational conditions and display its applicability to operational range management programs was described. Four agencies were involved in the program, two in Alberta and two in Manitoba. Each had different objectives and needs for remote sensing within its range management programs, and each was generally unfamiliar with remote sensing techniques and their applications. Personnel with experience and expertise in the remote sensing and range management fields worked with the agency personnel through every phase of the pilot program. Results indicate that these agencies have found remote sensing to be a cost effective tool and will begin to utilize remote sensing in their operational work during ensuing seasons.

  5. Optical Remote Sensing Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — The Optical Remote Sensing Laboratory deploys rugged, cutting-edge electro-optical instrumentation for the collection of various event signatures, with expertise in...

  6. Current NASA Earth Remote Sensing Observations

    Science.gov (United States)

    Luvall, Jeffrey C.; Sprigg, William A.; Huete, Alfredo; Pejanovic, Goran; Nickovic, Slobodan; Ponce-Campos, Guillermo; Krapfl, Heide; Budge, Amy; Zelicoff, Alan; Myers, Orrin; hide

    2011-01-01

    This slide presentation reviews current NASA Earth Remote Sensing observations in specific reference to improving public health information in view of pollen sensing. While pollen sampling has instrumentation, there are limitations, such as lack of stations, and reporting lag time. Therefore it is desirable use remote sensing to act as early warning system for public health reasons. The use of Juniper Pollen was chosen to test the possibility of using MODIS data and a dust transport model, Dust REgional Atmospheric Model (DREAM) to act as an early warning system.

  7. Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

    Science.gov (United States)

    Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

    2018-04-01

    Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

  8. Accuracy Dimensions in Remote Sensing

    Science.gov (United States)

    Barsi, Á.; Kugler, Zs.; László, I.; Szabó, Gy.; Abdulmutalib, H. M.

    2018-04-01

    The technological developments in remote sensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users' needs. The present paper gives the theoretic overview of the issue, besides selected, practice

  9. ACCURACY DIMENSIONS IN REMOTE SENSING

    Directory of Open Access Journals (Sweden)

    Á. Barsi

    2018-04-01

    Full Text Available The technological developments in remote sensing (RS during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS, which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users’ needs. The present paper gives the theoretic overview of the issue, besides

  10. Initial results of the spatial distribution of rubber trees in Peninsular Malaysia using remotely sensed data for biomass estimate

    International Nuclear Information System (INIS)

    Shidiq, I P A; Ismail, M H; Kamarudin, N

    2014-01-01

    The preservation and sustainable management of forest and other land cover ecosystems such as rubber trees will help addressing two major recent issues: climate change and bio-resource energy. The rubber trees are dominantly distributed in the Negeri Sembilan and Kedah on the west coast side of Peninsular Malaysia. This study is aimed to analyse the spatial distribution and biomass of rubber trees in Peninsular Malaysia with special emphasis in Negeri Sembilan State. Geospatial data from remote sensors are used to tackle the time and labour consuming problem due to the large spatial coverage and the need of continuous temporal data. Remote sensing imagery used in this study is a Landsat 5 TM. The image from optical sensor was used to sense the rubber trees and further classified rubber tree by different age

  11. Initial results of the spatial distribution of rubber trees in Peninsular Malaysia using remotely sensed data for biomass estimate

    Science.gov (United States)

    Shidiq, I. P. A.; Ismail, M. H.; Kamarudin, N.

    2014-02-01

    The preservation and sustainable management of forest and other land cover ecosystems such as rubber trees will help addressing two major recent issues: climate change and bio-resource energy. The rubber trees are dominantly distributed in the Negeri Sembilan and Kedah on the west coast side of Peninsular Malaysia. This study is aimed to analyse the spatial distribution and biomass of rubber trees in Peninsular Malaysia with special emphasis in Negeri Sembilan State. Geospatial data from remote sensors are used to tackle the time and labour consuming problem due to the large spatial coverage and the need of continuous temporal data. Remote sensing imagery used in this study is a Landsat 5 TM. The image from optical sensor was used to sense the rubber trees and further classified rubber tree by different age.

  12. Optical/Infrared Signatures for Space-Based Remote Sensing

    National Research Council Canada - National Science Library

    Picard, R. H; Dewan, E. M; Winick, J. R; O'Neil, R. R

    2007-01-01

    This report describes work carried out under the Air Force Research Laboratory's basic research task in optical remote-sensing signatures, entitled Optical / Infrared Signatures for Space-Based Remote Sensing...

  13. New advance in the research of post-remote sensing application technology. Series of 'proposition and consideration of post-remote sensing application technology'

    International Nuclear Information System (INIS)

    Liu Dechang; Ye Fawang

    2005-01-01

    Based on deep consideration in post-remote sensing application technology, this article pays more attention to its technological meaning. The application idea of post-remote sensing application technology to uranium exploration is also discussed. The proposition and research on new concept of post-remote sensing application technology is an important search and of important theoretical and practical significance to uranium exploration. (authors)

  14. Satellite and airborne oil spill remote sensing: state of the art and application to the BP Deepwater Horizon oil spill

    Energy Technology Data Exchange (ETDEWEB)

    Leifer, Ira [University of California (United States); Clark, Roger; Swayze, Gregg [US Geology Survey (United States); Jones, Cathleen [California Institute of Technology (United States); Svejkovsky, Jan [Ocean Imaging Corporation (United States)

    2011-07-01

    This study stresses the value of using satellite technology in quantifying oil seepage impact, and how it can be applied to the case of Horizon oil spill. The purpose of the study is to clarify the remote sensing process as it applies to oil spills, and how testing resources should be properly allocated so as to come up with the optimal response strategy. Many parameters were involved in this research, of which the most important were the environmental factors, the active and passive remote sensing measures, satellite imagery and imaging spectroscopy, and oil thickness measurements using thermal infrared and laser-induced fluorescence. These parameters were later used to quantify the spills in the impacted regions. Results showed that remote sensing would always be accompanied by certain errors, however, in the case of the Horizon spill, the infrared approach proved to be a convenient and a reliable approach for impact analysis process. The study also put emphasis on the importance of oil spatial patterns in validating the reliability of a test procedure.

  15. Satellite and airborne oil spill remote sensing: state of the art and application to the BP Deepwater Horizon oil spill

    International Nuclear Information System (INIS)

    Leifer, Ira; Clark, Roger; Swayze, Gregg; Jones, Cathleen; Svejkovsky, Jan

    2011-01-01

    This study stresses the value of using satellite technology in quantifying oil seepage impact, and how it can be applied to the case of Horizon oil spill. The purpose of the study is to clarify the remote sensing process as it applies to oil spills, and how testing resources should be properly allocated so as to come up with the optimal response strategy. Many parameters were involved in this research, of which the most important were the environmental factors, the active and passive remote sensing measures, satellite imagery and imaging spectroscopy, and oil thickness measurements using thermal infrared and laser-induced fluorescence. These parameters were later used to quantify the spills in the impacted regions. Results showed that remote sensing would always be accompanied by certain errors, however, in the case of the Horizon spill, the infrared approach proved to be a convenient and a reliable approach for impact analysis process. The study also put emphasis on the importance of oil spatial patterns in validating the reliability of a test procedure.

  16. Comprehensive, integrated, remote sensing at DOE sites

    International Nuclear Information System (INIS)

    Lackey, J.G.; Burson, Z.G.

    1985-01-01

    The Department of Energy has established a program called Comprehensive, Integrated Remote Sensing (CIRS). The overall objective of the program is to provide a state-of-the-art data base of remotely sensed data for all users of such information at large DOE sites. The primary types of remote sensing provided, at present, consist of the following: large format aerial photography, video from aerial platforms, multispectral scanning, and airborne nuclear radiometric surveys. Implementation of the CIRS Program by EG and G Energy Measurements, Inc. began with field operations at the Savannah River Plant in 1982 and is continuing at that DOE site at a level of effort of about $1.5 m per year. Integrated remote sensing studies were subsequently extended to the West Valley Demonstration Project in this summer and fall of 1984. It is expected that the Program will eventually be extended to cover all large DOE sites on a continuing basis

  17. Polarimetric Remote Sensing of Atmospheric Particulate Pollutants

    Science.gov (United States)

    Li, Z.; Zhang, Y.; Hong, J.

    2018-04-01

    Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remote sensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remote sensing measurements including polarimetric, active and infrared remote sensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remote sensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF), whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.

  18. Research on Coal Exploration Technology Based on Satellite Remote Sensing

    Directory of Open Access Journals (Sweden)

    Dong Xiao

    2016-01-01

    Full Text Available Coal is the main source of energy. In China and Vietnam, coal resources are very rich, but the exploration level is relatively low. This is mainly caused by the complicated geological structure, the low efficiency, the related damage, and other bad situations. To this end, we need to make use of some advanced technologies to guarantee the resource exploration is implemented smoothly and orderly. Numerous studies show that remote sensing technology is an effective way in coal exploration and measurement. In this paper, we try to measure the distribution and reserves of open-air coal area through satellite imagery. The satellite picture of open-air coal mining region in Quang Ninh Province of Vietnam was collected as the experimental data. Firstly, the ENVI software is used to eliminate satellite imagery spectral interference. Then, the image classification model is established by the improved ELM algorithm. Finally, the effectiveness of the improved ELM algorithm is verified by using MATLAB simulations. The results show that the accuracies of the testing set reach 96.5%. And it reaches 83% of the image discernment precision compared with the same image from Google.

  19. Remote Sensing of Selected Water-Quality Indicators with the Hyperspectral Imager for the Coastal Ocean (HICO) Sensor

    Science.gov (United States)

    2014-01-01

    the bands needed for atmospheric correction. Spectral definition files for AVIRIS, HYDICE, HYMAP, HYPERION, CASI, and AISA sensors are included as...Satellite Visible Imagery – A Review.” In Lecture Notes on Coastal and Estuarine Studies, edited by R. T. Barber, N. K. Mooers, M. J. Bowman, and B...In Proceedings of SPIE Coastal Ocean Remote Sensing, edited by Robert J. Frouin, ZhongPing Lee, Vol. 6680, 668013-1-668013-9. doi:10.1117/12.736845

  20. Annotated bibliography of remote sensing methods for monitoring desertification

    Science.gov (United States)

    Walker, A.S.; Robinove, Charles J.

    1981-01-01

    Remote sensing techniques are valuable for locating, assessing, and monitoring desertification. Remotely sensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remote sensing methods that may be applied to desertification studies.

  1. Preface to: Pan Ocean Remote Sensing Conference (PORSEC)

    Digital Repository Service at National Institute of Oceanography (India)

    Desa, E.; Brown, R.; Shenoi, S.S.C.; Joseph, G.

    Conference (PORSEC), earlier known as the Paci c Ocean Remote Sensing Conference (PORSEC), was formed in 1992 to provide a venue for international cooperation in the increasingly important area of remote sensing of the ocean. Many countries that border... and ocean dynamics, and modeling with satellite sensor (mainly microwave) data. Some of the presentations are of regional interest, while others will nd an audience beyond the satellite remote sensing community. These rst results through their simple...

  2. Estimating Discharge in Low-Order Rivers With High-Resolution Aerial Imagery

    OpenAIRE

    King, Tyler V.; Neilson, Bethany T.; Rasmussen, Mitchell T.

    2018-01-01

    Remote sensing of river discharge promises to augment in situ gauging stations, but the majority of research in this field focuses on large rivers (>50 m wide). We present a method for estimating volumetric river discharge in low-order (wide) rivers from remotely sensed data by coupling high-resolution imagery with one-dimensional hydraulic modeling at so-called virtual gauging stations. These locations were identified as locations where the river contracted under low flows, exposing a substa...

  3. A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

    Directory of Open Access Journals (Sweden)

    Gillian V. Lui

    2015-03-01

    Full Text Available Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite software package and the Global Forest Change dataset (GFCD being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001–2007 and 2007–2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite’s comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today’s continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application.

  4. Assisting Groundwater Exploration for Refugee/IDP Camps by Remote Sensing and GIS

    Science.gov (United States)

    Wendt, Lorenz; Robl, Jörg; Hilberg, Sylke; Braun, Andreas; Rogenhofer, Edith; Dirnberger, Daniel; Strasser, Thomas; Füreder, Petra; Lang, Stefan

    2015-04-01

    Refugee camps and camps of internally displaced people (IDP) often form spontaneously or have to be established rapidly in remote, rural areas, where little is known about the hydrogeological situation. This requires a rapid assessment of the availability of groundwater to enable humanitarian organisations like Médecins Sans Frontières (MSF) to supply the camp population with sufficient potable water. Within the project EO4HumEn, hydrogeological reconnaissance maps are produced for MSF by integrating remote sensing data like SRTM, Landsat, ASTER, optical very-high resolution (VHR) imagery, and SAR data. Depending on the specific situation of the camps, these maps contain topography, permanent and temporary water bodies, hard rock outcrops and their geological variability, locations of existing boreholes and wells (if available), potential contamination sources, roads and obstacles (e.g. swampland). In areas characterized by unconsolidated sediments, specific landforms like alluvial fans, meanders, levees, deltas or beach ridges are identified. Here, the reconnaissance map can be sufficient to plan drill sites for groundwater abstraction. In hard rock areas, the lithology is determined, if the vegetation cover allows it. Fractures, faults and karst features are mapped to resolve the structural setting. Anomalous vegetation patterns are interpreted in terms of near-surface groundwater. The maps provide an overview of the camp surroundings, and allow the field hydrogeologists to focus their investigations on the most promising locations. The maps are complemented by a literature review on geological maps, articles and reports available for the area of interest. Assisting groundwater exploration by remote sensing data analysis is not a new development, but it has not been widely adopted by the humanitarian community as interfaces between humanitarian organisations and GI-scientists were missing. EO4HumEn fills this gap by a strong interdisciplinary cooperation

  5. Remote Sensing: Physics And Environmental Applications

    International Nuclear Information System (INIS)

    EI Raey, M.

    2007-01-01

    Full text: Basic principles of remote sensing of environment are outlined emphasizing inherent physical and target properties leading to proper identification and classification. Basic processing techniques are discussed. Applications of remote sensing techniques in various aspects of environmental monitoring and assessment is surveyed with emphasis on aspects of main concern to developing communities such as planning, sea level impacts, mine detection and earthquake prediction are all outlined and discussed

  6. Freeware for GIS and Remote Sensing

    OpenAIRE

    Lena Halounová

    2007-01-01

    Education in remote sensing and GIS is based on software utilization. The software needs to be installed in computer rooms with a certain number of licenses. The commercial software equipment is therefore financially demanding and not only for universities, but especially for students. Internet research brings a long list of free software of various capabilities. The paper shows a present state of GIS, image processing and remote sensing free software.

  7. Portable remote sensing image processing system; Kahangata remote sensing gazo shori system

    Energy Technology Data Exchange (ETDEWEB)

    Fujikawa, S; Uchida, K; Tanaka, S; Jingo, H [Dowa Engineering Co. Ltd., Tokyo (Japan); Hato, M [Earth Remote Sensing Data Analysis Center, Tokyo (Japan)

    1997-10-22

    Recently, geological analysis using remote sensing data has been put into practice due to data with high spectral resolution and high spatial resolution. There has been a remarkable increase in both software and hardware of personal computer. Software is independent of hardware due to Windows. It has become easy to develop softwares. Under such situation, a portable remote sensing image processing system coping with Window 95 has been developed. Using this system, basic image processing can be conducted, and present location can be displayed on the image in real time by linking with GPS. Accordingly, it is not required to bring printed images for the field works of image processing. This system can be used instead of topographic maps for overseas surveys. Microsoft Visual C++ ver. 2.0 is used for the software. 1 fig.

  8. Remote sensing terminology: past experience and recent needs

    Science.gov (United States)

    Kancheva, Rumiana

    2013-10-01

    Terminology is a key issue for a better understanding among people using various languages. Terminology accuracy is essential during all phases of international cooperation. It is crucial to keep up with the latest quantitative and qualitative developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remote sensing and geoinformatics which develop rapidly and have wide and ever extending applications in various domains of human activity. The importance of the correct use of remote sensing terms refers not only to people working in this field but also to experts in many disciplines who handle remote sensing data and information products. The paper is devoted to terminology issues that refer to all aspects of remote sensing research and application areas. The attention is drawn on the recent needs and peculiarities of compiling specialized dictionaries in the subject area of remote sensing. Details are presented about the work in progress on the preparation of an English-Bulgarian dictionary of remote sensing terms focusing on Earth observations and geoinformation science. Our belief is that the elaboration of bilingual and multilingual dictionaries and glossaries in this spreading, most technically advanced and promising field of human expertise is of great practical importance. Any interest in cooperation and initiating of suchlike collaborative multilingual projects is welcome and highly appreciated.

  9. Landscape Archeology: Remote Sensing Investigation of the Ancient Maya in the Peten Rainforest of Northern Guatemala

    Science.gov (United States)

    Sever, Thomas L.; Irwin, Daniel E.; Arnold, James E. (Technical Monitor)

    2002-01-01

    Through the use of airborne and satellite imagery we are improving our ability to investigate ancient Maya settlement, subsistence, and landscape modification in this dense forest region. Today the area is threatened by encroaching settlement and deforestation. However, it was in this region that the Maya civilization began, flourished, and abruptly disappeared for unknown reasons in the 9th century AD. At the time of their collapse they had attained one of the highest population densities in human history. How the Maya were able to successfully manage water and feed this dense population is not well understood at this time. A NASA-funded project used remote sensing technology to investigate large seasonal swamps (bajos) that make up 40 percent of the landscape. Through the use of remote sensing, ancient Maya features such as sites, roadways, canals and water reservoirs have been detected and verified through ground reconnaissance. The results of this preliminary research cast new light on the adaptation of the ancient Maya to their environment. Microenvironmental variation within the wetlands was elucidated and the different vegetation associations identified in the satellite imagery. More than 70 new archeological sites within and at the edges of the bajo were mapped and tested. Modification of the landscape by the Maya in the form of dams and reservoirs in the Holmul River and its tributaries and possible drainage canals in bajos was demonstrated. The use of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM), one-meter IKONOS satellite imagery, as well as high resolution airborne STAR-3i radar imagery--2.5 meter backscatter/10 meter Digital Elevation Model (DEM)--are opening new possibilities for understanding how a civilization was able to survive for centuries upon a karat topographic landscape. This understanding is critical for the current population that is currently experiencing rapid population growth and destroying the landscape through

  10. Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery.

    Science.gov (United States)

    Chen, Li; Tan, Chih-Hung; Kao, Shuh-Ji; Wang, Tai-Sheng

    2008-01-01

    Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors.

  11. Proceedings of the eighth thematic conference on geologic remote sensing

    International Nuclear Information System (INIS)

    Balmer, M.L.; Lange, F.F.; Levi, C.G.

    1991-01-01

    These proceedings contain papers presented at the Eighth Thematic Conference on Geologic Remote Sensing. This meeting was held April 29-May 2, 1991, in Denver, Colorado, USA. The conference was organized by the Environmental Research Institute of Michigan, in Cooperation with an international program committee composed primarily of geologic remote sensing specialists. The meeting was convened to discuss state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing as well as research and development activities aimed at increasing the future capabilities of this technology. The presentations in these volumes address the following topics: Spectral Geology; U.S. and International Hydrocarbon Exploration; Radar and Thermal Infrared Remote Sensing; Engineering Geology and Hydrogeology; Minerals Exploration; Remote Sensing for Marine and Environmental Applications; Image Processing and Analysis; Geobotanical Remote Sensing; Data Integration and Geographic Information Systems

  12. A Review of Ocean/Sea Subsurface Water Temperature Studies from Remote Sensing and Non-Remote Sensing Methods

    Directory of Open Access Journals (Sweden)

    Elahe Akbari

    2017-12-01

    Full Text Available Oceans/Seas are important components of Earth that are affected by global warming and climate change. Recent studies have indicated that the deeper oceans are responsible for climate variability by changing the Earth’s ecosystem; therefore, assessing them has become more important. Remote sensing can provide sea surface data at high spatial/temporal resolution and with large spatial coverage, which allows for remarkable discoveries in the ocean sciences. The deep layers of the ocean/sea, however, cannot be directly detected by satellite remote sensors. Therefore, researchers have examined the relationships between salinity, height, and temperature of the oceans/Seas to estimate their subsurface water temperature using dynamical models and model-based data assimilation (numerical based and statistical approaches, which simulate these parameters by employing remotely sensed data and in situ measurements. Due to the requirements of comprehensive perception and the importance of global warming in decision making and scientific studies, this review provides comprehensive information on the methods that are used to estimate ocean/sea subsurface water temperature from remotely and non-remotely sensed data. To clarify the subsurface processes, the challenges, limitations, and perspectives of the existing methods are also investigated.

  13. Contribution of non-negative matrix factorization to the classification of remote sensing images

    Science.gov (United States)

    Karoui, M. S.; Deville, Y.; Hosseini, S.; Ouamri, A.; Ducrot, D.

    2008-10-01

    Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. The classification process requires some pre-processing, especially for data size reduction. The most usual technique is Principal Component Analysis. Another approach consists in regarding each pixel of the multispectral image as a mixture of pure elements contained in the observed area. Using Blind Source Separation (BSS) methods, one can hope to unmix each pixel and to perform the recognition of the classes constituting the observed scene. Our contribution consists in using Non-negative Matrix Factorization (NMF) combined with sparse coding as a solution to BSS, in order to generate new images (which are at least partly separated images) using HRV SPOT images from Oran area, Algeria). These images are then used as inputs of a supervised classifier integrating textural information. The results of classifications of these "separated" images show a clear improvement (correct pixel classification rate improved by more than 20%) compared to classification of initial (i.e. non separated) images. These results show the contribution of NMF as an attractive pre-processing for classification of multispectral remote sensing imagery.

  14. Remote Sensing Image in the Application of Agricultural Tourism Planning

    Directory of Open Access Journals (Sweden)

    Guojing Fan

    2013-06-01

    Full Text Available This paper introduces the processing technology of high resolution remote sensing image, the specific making process of tourism map and different remote sensing data in the key application of tourism planning and so on. Remote sensing extracts agricultural tourism planning information, improving the scientificalness and operability of agricultural tourism planning. Therefore remote sensing image in the application of agricultural tourism planning will be the inevitable trend of tourism development.

  15. Coastal remote sensing – towards integrated coastal research and management

    CSIR Research Space (South Africa)

    Lück-Vogel, Melanie

    2012-10-01

    Full Text Available coastal resources and anthropogenic infrastructure for a safer future. What is the role of remote sensing? The coastal zone connects terrestrial biophysical systems with marine systems. Some marine ecosystems cannot function without intact inland... for the development of sound integrated management solutions. To date, however, remote sensing applications usually focus on areas landward from the highwater line (?terrestrial? remote sensing), while ?marine? remote sensing does not pay attention to the shallow...

  16. Forest structural assessment using remote sensing technologies: an ...

    African Journals Online (AJOL)

    -Natal and MONDI Business Paper have recently embarked on a remote sensing cooperative. The primary focus of this cooperative is to explore the potential benefits associated with using remote sensing for forestry-related activities.

  17. Using Very High Resolution Remotely Sensed Imagery to Estimate Agricultural Production: A comparison of food insecure and secure growing areas in Kenya

    Science.gov (United States)

    Grace, K.; Husak, G. J.; Bogle, S.

    2013-12-01

    Determining the amount of food produced in a food insecure, isolated, subsistence farming community can be used to help identify households or communities who may be in need of additional food resources. Measuring annual food production in developing countries, much less at a sub-national level, is complicated by lack of data. It can be difficult and costly to access all of the farming households engaged in subsistence farming. However, recent research has focused on the use of remotely sensed data to aid in the estimation of area under cultivation and because food production is the measure of yield (production per hectare) multiplied by area (number of hectares), we can use the area measure to reduce uncertainty in food production estimates. One strategy for estimating cultivated area relies on a fairly time intensive manual interpretation of very high resolution data. Due to the availability of very high resolution data it is possible to construct estimates of cultivated area, even in communities where fields are small. While this strategy has been used to effectively estimate cultivated area in a timely manner, questions remain about the spatial and temporal generalizability of this approach. The purpose of this paper is to produce and compare estimates of cultivated area in two very different agricultural areas of Kenya, a highly food insecure country in East Africa, during two different agricultural seasons. The areas selected represent two different livelihood zones: a marginal growing area where poor farmers rely on inconsistent rainfall and a lush growing area near the mountainous region of the middle-West area of the country where rainfall is consistent and therefore more suited to cultivation. The overarching goal is to determine the effectiveness of very high resolution remotely sensed imagery in calculating estimates of cultivated area in areas where food production strategies are different. Additionally the results of this research will explore the

  18. Potential for remote sensing of agriculture from the international space station

    International Nuclear Information System (INIS)

    Morgenthaler, George W.; Khatib, Nader

    1999-01-01

    Today's spatial resolution of orbital sensing systems is too coarse to economically serve the yield-improvement/contamination-reduction needs of the small to mid-size farm enterprise. Remote sensing from aircraft is being pressed into service. However, satellite remote sensing constellations with greater resolution and more spectral bands, i.e., with resolutions of 1 m in the panchromatic, 4 m in the multi-spectral, and 8 m in the hyper-spectral are expected to be in orbit by the year 2000. Such systems coupled with Global Positioning System (GPS) capability will make 'precision agriculture', i.e., the identification of specific and timely fertilizer, irrigation, herbicide, and insecticide needs on an acre-by-acre basis and the ability to meet these needs with precision delivery systems at affordable costs, is what is needed and can be achieved. Current plans for remote sensing systems on the International Space Station (ISS) include externally attached payloads and a window observation platform. The planned orbit of the Space Station will result in overflight of a specific latitude and longitude at the same clock time every 3 months. However, a pass over a specific latitude and longitude during 'daylight hours' could occur much more frequently. The ISS might thus be a space platform for experimental and developmental testing of future commercial space remote sensing precision agriculture systems. There is also a need for agricultural 'truth' sites so that predictive crop yield and pollution models can be devised and corrective suggestions delivered to farmers at affordable costs. In Summer 1998, the University of Colorado at Boulder and the Center for the Study of Terrestrial and Extraterrestrial Atmospheres (CSTEA) at Howard University, under NASA Goddard Space Flight Center funding, established an agricultural 'truth' site in eastern Colorado. The 'truth' site was highly instrumented for measuring trace gas concentrations (NO x , SO x , CO 2 , O 3 , organics

  19. Support vector machine as a binary classifier for automated object detection in remotely sensed data

    International Nuclear Information System (INIS)

    Wardaya, P D

    2014-01-01

    In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result

  20. Support vector machine as a binary classifier for automated object detection in remotely sensed data

    Science.gov (United States)

    Wardaya, P. D.

    2014-02-01

    In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

  1. Remote Sensing Information Science Research

    Science.gov (United States)

    Clarke, Keith C.; Scepan, Joseph; Hemphill, Jeffrey; Herold, Martin; Husak, Gregory; Kline, Karen; Knight, Kevin

    2002-01-01

    This document is the final report summarizing research conducted by the Remote Sensing Research Unit, Department of Geography, University of California, Santa Barbara under National Aeronautics and Space Administration Research Grant NAG5-10457. This document describes work performed during the period of 1 March 2001 thorough 30 September 2002. This report includes a survey of research proposed and performed within RSRU and the UCSB Geography Department during the past 25 years. A broad suite of RSRU research conducted under NAG5-10457 is also described under themes of Applied Research Activities and Information Science Research. This research includes: 1. NASA ESA Research Grant Performance Metrics Reporting. 2. Global Data Set Thematic Accuracy Analysis. 3. ISCGM/Global Map Project Support. 4. Cooperative International Activities. 5. User Model Study of Global Environmental Data Sets. 6. Global Spatial Data Infrastructure. 7. CIESIN Collaboration. 8. On the Value of Coordinating Landsat Operations. 10. The California Marine Protected Areas Database: Compilation and Accuracy Issues. 11. Assessing Landslide Hazard Over a 130-Year Period for La Conchita, California Remote Sensing and Spatial Metrics for Applied Urban Area Analysis, including: (1) IKONOS Data Processing for Urban Analysis. (2) Image Segmentation and Object Oriented Classification. (3) Spectral Properties of Urban Materials. (4) Spatial Scale in Urban Mapping. (5) Variable Scale Spatial and Temporal Urban Growth Signatures. (6) Interpretation and Verification of SLEUTH Modeling Results. (7) Spatial Land Cover Pattern Analysis for Representing Urban Land Use and Socioeconomic Structures. 12. Colorado River Flood Plain Remote Sensing Study Support. 13. African Rainfall Modeling and Assessment. 14. Remote Sensing and GIS Integration.

  2. Freeware for GIS and Remote Sensing

    Directory of Open Access Journals (Sweden)

    Lena Halounová

    2007-12-01

    Full Text Available Education in remote sensing and GIS is based on software utilization. The software needs to be installed in computer rooms with a certain number of licenses. The commercial software equipment is therefore financially demanding and not only for universities, but especially for students. Internet research brings a long list of free software of various capabilities. The paper shows a present state of GIS, image processing and remote sensing free software.

  3. Remote sensing programs and courses in engineering and water resources

    Science.gov (United States)

    Kiefer, R. W.

    1981-01-01

    The content of typical basic and advanced remote sensing and image interpretation courses are described and typical remote sensing graduate programs of study in civil engineering and in interdisciplinary environmental remote sensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remote sensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remote sensing upon which the more specialized advanced remote sensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.

  4. EarthTutor: An Interactive Intelligent Tutoring System for Remote Sensing

    Science.gov (United States)

    Bell, A. M.; Parton, K.; Smith, E.

    2005-12-01

    Earth science classes in colleges and high schools use a variety of satellite image processing software to teach earth science and remote sensing principles. However, current tutorials for image processing software are often paper-based or lecture-based and do not take advantage of the full potential of the computer context to teach, immerse, and stimulate students. We present EarthTutor, an adaptive, interactive Intelligent Tutoring System (ITS) being built for NASA (National Aeronautics and Space Administration) that is integrated directly with an image processing application. The system aims to foster the use of satellite imagery in classrooms and encourage inquiry-based, hands-on earth science scientific study by providing students with an engaging imagery analysis learning environment. EarthTutor's software is available as a plug-in to ImageJ, a free image processing system developed by the NIH (National Institute of Health). Since it is written in Java, it can be run on almost any platform and also as an applet from the Web. Labs developed for EarthTutor combine lesson content (such as HTML web pages) with interactive activities and questions. In each lab the student learns to measure, calibrate, color, slice, plot and otherwise process and analyze earth science imagery. During the activities, EarthTutor monitors students closely as they work, which allows it to provide immediate feedback that is customized to a particular student's needs. As the student moves through the labs, EarthTutor assesses the student, and tailors the presentation of the content to a student's demonstrated skill level. EarthTutor's adaptive approach is based on emerging Artificial Intelligence (AI) research. Bayesian networks are employed to model a student's proficiency with different earth science and image processing concepts. Agent behaviors are used to track the student's progress through activities and provide guidance when a student encounters difficulty. Through individual

  5. Project THEMIS: A Center for Remote Sensing.

    Science.gov (United States)

    This report summarizes the technical work accomplished under Project THEMIS, A Center for Remote Sensing at the University of Kansas during the...period 16 September 1967 through 15 September 1973. The highlights of the four major areas forming the remote sensing system are presented. A detailed description of the latest radar spectrometer results is presented.

  6. Remote sensing and eLearning 2.0 for school education

    Science.gov (United States)

    Voss, Kerstin; Goetzke, Roland; Hodam, Henryk

    2010-10-01

    The "Remote Sensing in Schools" project aims at improving the integration of "Satellite remote sensing" into school teaching. Therefore, it is the project's overall objective to teach students in primary and secondary schools the basics and fields of application of remote sensing. Existing results show that many teachers are interested in remote sensing and at same time motivated to integrate it into their teaching. Despite the good intention, in the end, the implementation often fails due to the complexity and poor set-up of the information provided. Therefore, a comprehensive and well-structured learning platform on the topic of remote sensing is developed. The platform shall allow a structured introduction to the topic.

  7. Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing.

    Science.gov (United States)

    Richardson, Andrew D; Hufkens, Koen; Milliman, Tom; Frolking, Steve

    2018-04-09

    Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both "greenness rising" and "greenness falling" transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.

  8. POLARIMETRIC REMOTE SENSING OF ATMOSPHERIC PARTICULATE POLLUTANTS

    Directory of Open Access Journals (Sweden)

    Z. Li

    2018-04-01

    Full Text Available Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remote sensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remote sensing measurements including polarimetric, active and infrared remote sensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remote sensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF, whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.

  9. Review of Remote Sensing Needs and Applications in Africa

    Science.gov (United States)

    Brown, Molly E.

    2007-01-01

    Remote sensing data has had an important role in identifying and responding to inter-annual variations in the African environment during the past three decades. As a largely agricultural region with diverse but generally limited government capacity to acquire and distribute ground observations of rainfall, temperature and other parameters, remote sensing is sometimes the only reliable measure of crop growing conditions in Africa. Thus, developing and maintaining the technical and scientific capacity to analyze and utilize satellite remote sensing data in Africa is critical to augmenting the continent's local weather/climate observation networks as well as its agricultural and natural resource development and management. The report Review of Remote Sensing Needs and Applications in Africa' has as its central goal to recommend to the US Agency for International Development an appropriate approach to support sustainable remote sensing applications at African regional remote sensing centers. The report focuses on "RS applications" to refer to the acquisition, maintenance and archiving, dissemination, distribution, analysis, and interpretation of remote sensing data, as well as the integration of interpreted data with other spatial data products. The report focuses on three primary remote sensing centers: (1) The AGRHYMET Regional Center in Niamey, Niger, created in 1974, is a specialized institute of the Permanent Interstate Committee for Drought Control in the Sahel (CILSS), with particular specialization in science and techniques applied to agricultural development, rural development, and natural resource management. (2) The Regional Centre for Maiming of Resources for Development (RCMRD) in Nairobi, Kenya, established in 1975 under the auspices of the United Nations Economic Commission for Africa and the Organization of African Unity (now the African Union), is an intergovernmental organization, with 15 member states from eastern and southern Africa. (3) The

  10. Estimating urban forest carbon sequestration potential in the southern United States using current remote sensing imagery sources

    Science.gov (United States)

    Krista Merry; Pete Bettinger; Jacek Siry; J. Michael Bowker

    2015-01-01

    With an increased interest in reducing carbon dioxide in the atmosphere, tree planting and maintenance in urban areas has become a viable option for increasing carbon sequestration. Methods for assessing the potential for planting trees within an urban area should allow for quick, inexpensive, and accurate estimations of available land using current remote sensing...

  11. Economic optimization and evolutionary programming when using remote sensing data

    OpenAIRE

    Shamin Roman; Alberto Gabriel Enrike; Uryngaliyeva Ayzhana; Semenov Aleksandr

    2018-01-01

    The article considers the issues of optimizing the use of remote sensing data. Built a mathematical model to describe the economic effect of the use of remote sensing data. It is shown that this model is incorrect optimisation task. Given a numerical method of solving this problem. Also discusses how to optimize organizational structure by using genetic algorithm based on remote sensing. The methods considered allow the use of remote sensing data in an optimal way. The proposed mathematical m...

  12. The Short Wave Aerostat-Mounted Imager (SWAMI): A novel platform for acquiring remotely sensed data from a tethered balloon

    Science.gov (United States)

    Vierling, L.A.; Fersdahl, M.; Chen, X.; Li, Z.; Zimmerman, P.

    2006-01-01

    We describe a new remote sensing system called the Short Wave Aerostat-Mounted Imager (SWAMI). The SWAMI is designed to acquire co-located video imagery and hyperspectral data to study basic remote sensing questions and to link landscape level trace gas fluxes with spatially and temporally appropriate spectral observations. The SWAMI can fly at altitudes up to 2 km above ground level to bridge the spatial gap between radiometric measurements collected near the surface and those acquired by other aircraft or satellites. The SWAMI platform consists of a dual channel hyperspectral spectroradiometer, video camera, GPS, thermal infrared sensor, and several meteorological and control sensors. All SWAMI functions (e.g. data acquisition and sensor pointing) can be controlled from the ground via wireless transmission. Sample data from the sampling platform are presented, along with several potential scientific applications of SWAMI data.

  13. Mapping Surface Water DOC in the Northern Gulf of Mexico Using CDOM Absorption Coefficients and Remote Sensing Imagery

    Science.gov (United States)

    Kelly, B.; Chelsky, A.; Bulygina, E.; Roberts, B. J.

    2017-12-01

    Remote sensing techniques have become valuable tools to researchers, providing the capability to measure and visualize important parameters without the need for time or resource intensive sampling trips. Relationships between dissolved organic carbon (DOC), colored dissolved organic matter (CDOM) and spectral data have been used to remotely sense DOC concentrations in riverine systems, however, this approach has not been applied to the northern Gulf of Mexico (GoM) and needs to be tested to determine how accurate these relationships are in riverine-dominated shelf systems. In April, July, and October 2017 we sampled surface water from 80+ sites over an area of 100,000 km2 along the Louisiana-Texas shelf in the northern GoM. DOC concentrations were measured on filtered water samples using a Shimadzu TOC-VCSH analyzer using standard techniques. Additionally, DOC concentrations were estimated from CDOM absorption coefficients of filtered water samples on a UV-Vis spectrophotometer using a modification of the methods of Fichot and Benner (2011). These values were regressed against Landsat visible band spectral data for those same locations to establish a relationship between the spectral data, CDOM absorption coefficients. This allowed us to spatially map CDOM absorption coefficients in the Gulf of Mexico using the Landsat spectral data in GIS. We then used a multiple linear regressions model to derive DOC concentrations from the CDOM absorption coefficients and applied those to our map. This study provides an evaluation of the viability of scaling up CDOM absorption coefficient and remote-sensing derived estimates of DOC concentrations to the scale of the LA-TX shelf ecosystem.

  14. Ontology-Guided Image Interpretation for GEOBIA of High Spatial Resolution Remote Sense Imagery: A Coastal Area Case Study

    Directory of Open Access Journals (Sweden)

    Helingjie Huang

    2017-03-01

    Full Text Available Image interpretation is a major topic in the remote sensing community. With the increasing acquisition of high spatial resolution (HSR remotely sensed images, incorporating geographic object-based image analysis (GEOBIA is becoming an important sub-discipline for improving remote sensing applications. The idea of integrating the human ability to understand images inspires research related to introducing expert knowledge into image object–based interpretation. The relevant work involved three parts: (1 identification and formalization of domain knowledge; (2 image segmentation and feature extraction; and (3 matching image objects with geographic concepts. This paper presents a novel way that combines multi-scaled segmented image objects with geographic concepts to express context in an ontology-guided image interpretation. Spectral features and geometric features of a single object are extracted after segmentation and topological relationships are also used in the interpretation. Web ontology language–query language (OWL-QL formalize domain knowledge. Then the interpretation matching procedure is implemented by the OWL-QL query-answering. Compared with a supervised classification, which does not consider context, the proposed method validates two HSR images of coastal areas in China. Both the number of interpreted classes increased (19 classes over 10 classes in Case 1 and 12 classes over seven in Case 2, and the overall accuracy improved (0.77 over 0.55 in Case 1 and 0.86 over 0.65 in Case 2. The additional context of the image objects improved accuracy during image classification. The proposed approach shows the pivotal role of ontology for knowledge-guided interpretation.

  15. The Potential and Uptake of Remote Sensing in Insurance: A Review

    Directory of Open Access Journals (Sweden)

    Jan de Leeuw

    2014-11-01

    Full Text Available Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1 indices can be constructed that correlate well with what is insured; (2 these indices can be delivered at low cost; and (3 it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community.

  16. Photogrammetry and remote sensing education subjects

    Science.gov (United States)

    Lazaridou, Maria A.; Karagianni, Aikaterini Ch.

    2017-09-01

    The rapid technologic advances in the scientific areas of photogrammetry and remote sensing require continuous readjustments at the educational programs and their implementation. The teaching teamwork should deal with the challenge to offer the volume of the knowledge without preventing the understanding of principles and methods and also to introduce "new" knowledge (advances, trends) followed by evaluation and presentation of relevant applications. This is of particular importance for a Civil Engineering Faculty as this in Aristotle University of Thessaloniki, as the framework of Photogrammetry and Remote Sensing is closely connected with applications in the four educational Divisions of the Faculty. This paper refers to the above and includes subjects of organizing the courses in photogrammetry and remote sensing in the Civil Engineering Faculty of Aristotle University of Thessaloniki. A scheme of the general curriculum as well the teaching aims and methods are also presented.

  17. Remote sensing applications in environmental research

    CERN Document Server

    Srivastava, Prashant K; Gupta, Manika; Islam, Tanvir

    2014-01-01

    Remote Sensing Applications in Environmental Research is the basis for advanced Earth Observation (EO) datasets used in environmental monitoring and research. Now that there are a number of satellites in orbit, EO has become imperative in today's sciences, weather and natural disaster prediction. This highly interdisciplinary reference work brings together diverse studies on remote sensing and GIS, from a theoretical background to its applications, represented through various case studies and the findings of new models. The book offers a comprehensive range of contributions by well-known scientists from around the world and opens a new window for students in presenting interdisciplinary and methodological resources on the latest research. It explores various key aspects and offers state-of-the-art research in a simplified form, describing remote sensing and GIS studies for those who are new to the field, as well as for established researchers.

  18. Graphic products used in the evaluation of traditional and emerging remote sensing technologies for the detection of fugitive contamination at selected superfund hazardous waste sites

    Science.gov (United States)

    Slonecker, E. Terrence; Fisher, Gary B.

    2011-01-01

    This report presents the overhead imagery and field sampling results used to prepare U.S. Geological Survey Open-File Report 2011-1050, 'Evaluation of Traditional and Emerging Remote Sensing Technologies for the Detection of Fugitive Contamination at Selected Superfund Hazardous Waste Sites'. These graphic products were used in the evaluation of remote sensing technology in postclosure monitoring of hazardous waste sites and represent an ongoing research effort. Soil sampling results presented here were accomplished with field portable x-ray fluoresence (XRF) technology and are used as screening tools only representing the current conditions of metals and other contaminants at selected Superfund hazardous waste sites.

  19. ESA remote-sensing programme - Present activities and future plans

    Energy Technology Data Exchange (ETDEWEB)

    Plevin, J [ESA, Directorate of Planning and Future Programmes, Paris, France; Pryke, I [ESA, Directorate of Applications Programmes, Toulouse, France

    1979-02-01

    The present activities and future missions of the ESA program of spaceborne remote sensing of earth resources and environment are discussed. Program objectives have been determined to be the satisfaction of European regional needs by agricultural, land use, water resources, coastal and polar surveys, and meeting the requirements of developing nations in the areas of agricultural production, mineral exploration and disaster warning and assessment. The Earthnet system of data processing centers presently is used for the distribution of remote sensing data acquired by NASA satellites. Remote sensing experiments to be flown aboard Spacelab are the Metric Camera, to test high resolution mapping capabilities of a large format camera, and the Microwave Remote-Sensing Experiment, which operates as a two-frequency scatterometer, a synthetic aperture radar and a passive microwave radiometer. Studies carried out on the definition of future remote sensing satellite systems are described, including studies of system concepts for land applications and coastal monitoring satellites.

  20. Remote sensing by satellite - Technical and operational implications for international cooperation

    Science.gov (United States)

    Doyle, S. E.

    1976-01-01

    International cooperation in the U.S. Space Program is discussed and related to the NASA program for remote sensing of the earth. Satellite remote sensing techniques are considered along with the selection of the best sensors and wavelength bands. The technology of remote sensing satellites is considered with emphasis on the Landsat system configuration. Future aspects of remote sensing satellites are considered.

  1. RSComPro: An Open Communication Protocol for Remote Sensing Systems

    DEFF Research Database (Denmark)

    Vasiljevic, Nikola; Trujillo, Juan-José

    The remote sensing protocol (RSComPro) is a communication protocol, which has been developed for controlling multiple remote sensing systems simultaneously through a UDP/IP and TPC/IP network. This protocol is meant to be open to the remote sensing community. The scope is the implementation of so...

  2. Remote Sensing and the Kyoto Protocol: A Workshop Summary

    Science.gov (United States)

    Rosenqvist, Ake; Imhoff, Marc; Milne, Anthony; Dobson, Craig

    2000-01-01

    The Kyoto Protocol to the United Nations Framework Convention on Climate Change contains quantified, legally binding commitments to limit or reduce greenhouse gas emissions to 1990 levels and allows carbon emissions to be balanced by carbon sinks represented by vegetation. The issue of using vegetation cover as an emission offset raises a debate about the adequacy of current remote sensing systems and data archives to both assess carbon stocks/sinks at 1990 levels, and monitor the current and future global status of those stocks. These concerns and the potential ratification of the Protocol among participating countries is stimulating policy debates and underscoring a need for the exchange of information between the international legal community and the remote sensing community. On October 20-22 1999, two working groups of the International Society for Photogrammetry and Remote Sensing (ISPRS) joined with the University of Michigan (Michigan, USA) to convene discussions on how remote sensing technology could contribute to the information requirements raised by implementation of, and compliance with, the Kyoto Protocol. The meeting originated as a joint effort between the Global Monitoring Working Group and the Radar Applications Working Group in Commission VII of the ISPRS, co-sponsored by the University of Michigan. Tile meeting was attended by representatives from national government agencies and international organizations and academic institutions. Some of the key themes addressed were: (1) legal aspects of transnational remote sensing in the context of the Kyoto Protocol; (2) a review of the current and future and remote sensing technologies that could be applied to the Kyoto Protocol; (3) identification of areas where additional research is needed in order to advance and align remote sensing technology with the requirements and expectations of the Protocol; and 94) the bureaucratic and research management approaches needed to align the remote sensing

  3. Remote sensing of coastal fronts and their effects on oil dispersion

    Energy Technology Data Exchange (ETDEWEB)

    Klemas, V

    1980-01-01

    The use of remote sensing techniques to determine the properties of coastal and estuarine fronts, which represent regions of discontinuities and high gradients in ocean physical parameters such as velocity and density, and to assess the influence of such fronts on oil pollutants is discussed. Results of an aircraft and boat verification study of an oil drift and spread model in Delaware Bay are indicated which illustrate the tendency of oil slicks to be attracted to frontal regions, where a denser fluid underlies a lighter fluid giving rise to an inclined interface with convergence zones. Landsat imagery of the bay acquired in order to incorporate frontal information into the interactive computer model is then presented which allows the locations of coastal fronts to be charted throughout a tidal cycle. It is noted that satellite observations of flood-associated fronts on the New Jersey side of the bay and ebb-associated fronts on the Delaware side agree with boat measurements and model predictions, and that the remote tracking of fronts by aircraft and satellites will aid in oil slick clean-up operations.

  4. Remote sensing observation used in offshore wind energy

    DEFF Research Database (Denmark)

    Hasager, Charlotte Bay; Pena Diaz, Alfredo; Christiansen, Merete Bruun

    2008-01-01

    Remote sensing observations used in offshore wind energy are described in three parts: ground-based techniques and applications, airborne techniques and applications, and satellite-based techniques and applications. Ground-based remote sensing of winds is relevant, in particular, for new large wind...

  5. Remote sensing education in NASA's technology transfer program

    Science.gov (United States)

    Weinstein, R. H.

    1981-01-01

    Remote sensing is a principal focus of NASA's technology transfer program activity with major attention to remote sensing education the Regional Program and the University Applications Program. Relevant activities over the past five years are reviewed and perspective on future directions is presented.

  6. Regularization destriping of remote sensing imagery

    Science.gov (United States)

    Basnayake, Ranil; Bollt, Erik; Tufillaro, Nicholas; Sun, Jie; Gierach, Michelle

    2017-07-01

    We illustrate the utility of variational destriping for ocean color images from both multispectral and hyperspectral sensors. In particular, we examine data from a filter spectrometer, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (NPP) orbiter, and an airborne grating spectrometer, the Jet Population Laboratory's (JPL) hyperspectral Portable Remote Imaging Spectrometer (PRISM) sensor. We solve the destriping problem using a variational regularization method by giving weights spatially to preserve the other features of the image during the destriping process. The target functional penalizes the neighborhood of stripes (strictly, directionally uniform features) while promoting data fidelity, and the functional is minimized by solving the Euler-Lagrange equations with an explicit finite-difference scheme. We show the accuracy of our method from a benchmark data set which represents the sea surface temperature off the coast of Oregon, USA. Technical details, such as how to impose continuity across data gaps using inpainting, are also described.

  7. Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.

    Science.gov (United States)

    Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi

    2015-03-15

    Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate

  8. Remote Sensing Terminology in a Global and Knowledge-Based World

    Science.gov (United States)

    Kancheva, Rumiana

    The paper is devoted to terminology issues related to all aspects of remote sensing research and applications. Terminology is the basis for a better understanding among people. It is crucial to keep up with the latest developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remote sensing and geoinformatics which develop rapidly and have ever extending applications in various domains of science and human activities. Remote sensing terminology issues are directly relevant to the contemporary worldwide policies on information accessibility, dissemination and utilization of research results in support of solutions to global environmental challenges and sustainable development goals. Remote sensing and spatial information technologies are an integral part of the international strategies for cooperation in scientific, research and application areas with a particular accent on environmental monitoring, ecological problems natural resources management, climate modeling, weather forecasts, disaster mitigation and many others to which remote sensing data can be put. Remote sensing researchers, professionals, students and decision makers of different counties and nationalities should fully understand, interpret and translate into their native language any term, definition or acronym found in papers, books, proceedings, specifications, documentation, and etc. The importance of the correct use, precise definition and unification of remote sensing terms refers not only to people working in this field but also to experts in a variety of disciplines who handle remote sensing data and information products. In this paper, we draw the attention on the specifics, peculiarities and recent needs of compiling specialized dictionaries in the area of remote sensing focusing on Earth observations and the integration of remote sensing with other geoinformation technologies such as photogrammetry, geodesy

  9. Space remote sensing systems an introduction

    CERN Document Server

    Chen, H S

    1985-01-01

    Space Remote Sensing Systems: An Introduction discusses the space remote sensing system, which is a modern high-technology field developed from earth sciences, engineering, and space systems technology for environmental protection, resource monitoring, climate prediction, weather forecasting, ocean measurement, and many other applications. This book consists of 10 chapters. Chapter 1 describes the science of the atmosphere and the earth's surface. Chapter 2 discusses spaceborne radiation collector systems, while Chapter 3 focuses on space detector and CCD systems. The passive space optical rad

  10. Retrieval operators of remote sensing applications

    International Nuclear Information System (INIS)

    Ahmad, T.; Shah, A.

    2014-01-01

    A set of operators of remote sensing applications have been proposed to fulfill most of the Functional Requirements (FR). These operators capture the functions of the applications, which can be considered as the services provided by the applications. In general, a good application meets maximum FR from user. In this paper, we have defined a remote sensing application by a set, having all images created at dissimilar time instances, and each image is categorized into set of different layers. (author)

  11. Hyperspectral remote sensing of plant pigments.

    Science.gov (United States)

    Blackburn, George Alan

    2007-01-01

    The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.

  12. Remote Sensing Information Gateway

    Science.gov (United States)

    Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

  13. Development of a Land Use Mapping and Monitoring Protocol for the High Plains Region: A Multitemporal Remote Sensing Application

    Science.gov (United States)

    Price, Kevin P.; Nellis, M. Duane

    1996-01-01

    The purpose of this project was to develop a practical protocol that employs multitemporal remotely sensed imagery, integrated with environmental parameters to model and monitor agricultural and natural resources in the High Plains Region of the United States. The value of this project would be extended throughout the region via workshops targeted at carefully selected audiences and designed to transfer remote sensing technology and the methods and applications developed. Implementation of such a protocol using remotely sensed satellite imagery is critical for addressing many issues of regional importance, including: (1) Prediction of rural land use/land cover (LULC) categories within a region; (2) Use of rural LULC maps for successive years to monitor change; (3) Crop types derived from LULC maps as important inputs to water consumption models; (4) Early prediction of crop yields; (5) Multi-date maps of crop types to monitor patterns related to crop change; (6) Knowledge of crop types to monitor condition and improve prediction of crop yield; (7) More precise models of crop types and conditions to improve agricultural economic forecasts; (8;) Prediction of biomass for estimating vegetation production, soil protection from erosion forces, nonpoint source pollution, wildlife habitat quality and other related factors; (9) Crop type and condition information to more accurately predict production of biogeochemicals such as CO2, CH4, and other greenhouse gases that are inputs to global climate models; (10) Provide information regarding limiting factors (i.e., economic constraints of pumping, fertilizing, etc.) used in conjunction with other factors, such as changes in climate for predicting changes in rural LULC; (11) Accurate prediction of rural LULC used to assess the effectiveness of government programs such as the U.S. Soil Conservation Service (SCS) Conservation Reserve Program; and (12) Prediction of water demand based on rural LULC that can be related to rates of

  14. A New Capability for Automated Target Selection and Sampling for use with Remote Sensing Instruments on the MER Rovers

    Science.gov (United States)

    Castano, R.; Estlin, T.; Anderson, R. C.; Gaines, D.; Bornstein, B.; de Granville, C.; Tang, B.; Thompson, D.; Judd, M.

    2008-12-01

    The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. The system is designed to operate onboard a rover identifying and reacting to serendipitous science opportunities, such as rocks with novel properties. OASIS operates by analyzing data the rover gathers, and then using machine learning techniques, prioritizing the data based on criteria set by the science team. This prioritization can be used to organize data for transmission back to Earth and it can be used to search for specific targets it has been told to find by the science team. If one of these targets is found, it is identified as a new science opportunity and a "science alert" is sent to a planning and scheduling system. After reviewing the rover's current operational status to ensure that it has enough resources to complete its traverse and act on the new science opportunity, OASIS can change the command sequence of the rover in order to obtain additional science measurements. Currently, OASIS is being applied on a new front. OASIS is providing a new rover mission technology that enables targeted remote-sensing science in an automated fashion during or after rover traverses. Currently, targets for remote sensing instruments, especially narrow field-of-view instruments (such as the MER Mini- TES spectrometer or the 2009 MSL ChemCam spectrometer) must be selected manually based on imagery already on the ground with the operations team. OASIS will enable the rover flight software to analyze imagery onboard in order to autonomously select and sequence targeted remote-sensing observations in an opportunistic fashion. We are in the process of scheduling an onboard MER experiment to demonstrate the OASIS capability in early 2009.

  15. Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling

    Science.gov (United States)

    Van Linn, Peter F.; Nussear, Kenneth E.; Esque, Todd C.; DeFalco, Lesley A.; Inman, Richard D.; Abella, Scott R.

    2013-01-01

    Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.

  16. CYBERNETIC BASIS AND SYSTEM PRACTICE OF REMOTE SENSING AND SPATIAL INFORMATION SCIENCE

    Directory of Open Access Journals (Sweden)

    X. Tan

    2017-09-01

    Full Text Available Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

  17. Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

    Science.gov (United States)

    Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

    2017-09-01

    Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

  18. PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

    Science.gov (United States)

    Poudyal, R.; Singh, M.; Gautam, R.; Gatebe, C. K.

    2016-12-01

    The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR)- http://car.gsfc.nasa.gov/. Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

  19. Oil spill remote sensing flights around Vancouver Island

    Energy Technology Data Exchange (ETDEWEB)

    Brown, C.E.; Fingas, M.F.; Marois, R. [Environment Canada, Ottawa, ON (Canada)

    2006-07-01

    A large number of oiled seabirds are found on beaches and shorelines in Canada each year. Although there are several programs in place to detect high-volume oily bilge dumping incidents, the sensors used in many surveillance procedures are not capable of detecting suspected chronic low-volume disposal of contaminated waste waters by ships. This paper described the development and testing procedures of the Scanning Laser Environmental Airborne Fluorosensor (SLEAF), which was designed to map and characterize oil contamination in marine coastal and shoreline environments. Laser-induced fluorescence is detected by SLEAF with a spectrometric receiver. Full-spectral resolution geo-referenced fluorescence data are collected for each laser pulse and recorded directly to a computer. Eight oil spill remote sensing flights using SLEAF were conducted during March and April 2006. Geo-referenced infrared, ultraviolet, colour video and digital still imagery was collected alongside the fluorosensor data. Several light patches of oil were observed with SLEAF, most of which were in shipping lanes in the Strait of Juan de Fuca. The oil patches were light, and some were not visible to the naked eye, and were only detected by the laser fluorosensor. Larger slicks were captured in video imagery. Approximately 50 marine vessels were overflown during the flight demonstration program, and only 2 vessels appeared to be associated with the oil slicks. It was concluded that chronic low-volume oil releases in shipping lanes around Vancouver Island are a cause for concern. 11 refs., 2 tabs., 5 figs.

  20. Use of Remote Sensing for Decision Support in Africa

    Science.gov (United States)

    Policelli, Frederick S.

    2007-01-01

    Over the past 30 years, the scientific community has learned a great deal about the Earth as an integrated system. Much of this research has been enabled by the development of remote sensing technologies and their operation from space. Decision makers in many nations have begun to make use of remote sensing data for resource management, policy making, and sustainable development planning. This paper makes an attempt to provide a survey of the current state of the requirements and use of remote sensing for sustainable development in Africa. This activity has shown that there are not many climate data ready decision support tools already functioning in Africa. There are, however, endusers with known requirements who could benefit from remote sensing data.

  1. Application of remote sensing to agricultural field trials

    NARCIS (Netherlands)

    Clevers, J.G.P.W.

    1986-01-01

    Remote sensing techniques enable quantitative information about a field trial to be obtained instantaneously and non-destructively. The aim of this study was to identify a method that can reduce inaccuracies in field trial analysis, and to identify how remote sensing can support and/or

  2. Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Till F. Sonnemann

    2017-12-01

    Full Text Available Satellite imagery has had limited application in the analysis of pre-colonial settlement archaeology in the Caribbean; visible evidence of wooden structures perishes quickly in tropical climates. Only slight topographic modifications remain, typically associated with middens. Nonetheless, surface scatters, as well as the soil characteristics they produce, can serve as quantifiable indicators of an archaeological site, detectable by analyzing remote sensing imagery. A variety of pre-processed, very diverse data sets went through a process of image registration, with the intention to combine multispectral bands to feed two different semi-automatic direct detection algorithms: a posterior probability, and a frequentist approach. Two 5 × 5 km2 areas in the northwestern Dominican Republic with diverse environments, having sufficient imagery coverage, and a representative number of known indigenous site locations, served each for one approach. Buffers around the locations of known sites, as well as areas with no likely archaeological evidence were used as samples. The resulting maps offer quantifiable statistical outcomes of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. These still very experimental and rather unvalidated trials, as they have not been subsequently groundtruthed, show variable potential of this method in diverse environments.

  3. Some problems on remote sensing geology for uranium prospecting

    International Nuclear Information System (INIS)

    Yang Tinghuai.

    1988-01-01

    Remote sensing is a kind of very effective method which can be used in all stages of geological prospecting. Geological prospecting with remote sensing method must be based on different genetic models of ore deposits, characteristics of geology-landscape and comprehensive analysis for geophysical and geochemical data, that is, by way of conceptual model prospecting. The prospecting results based on remote sensing geology should be assessed from three aspects such as direct, indirect and potential ones

  4. Monitoring terrestrial dissolved organic carbon export at land-water interfaces using remote sensing

    Science.gov (United States)

    Yu, Q.; Li, J.; Tian, Y. Q.

    2017-12-01

    Carbon flux from land to oceans and lakes is a crucial component of carbon cycling. However, this lateral carbon flow at land-water interface is often neglected in the terrestrial carbon cycle budget, mainly because observations of the carbon dynamics are very limited. Monitoring CDOM/DOC dynamics using remote sensing and assessing DOC export from land to water remains a challenge. Current CDOM retrieval algorithms in the field of ocean color are not simply applicable to inland aquatic ecosystems since they were developed for coarse resolution ocean-viewing imagery and less complex water types in open-sea. We developed a new semi-analytical algorithm, called SBOP (Shallow water Bio-Optical Properties algorithm) to adapt to shallow inland waters. SBOP was first developed and calibrated based on in situ hyperspectral radiometer data. Then we applied it to the Landsat-8 OLI images and evaluated the effectiveness of the multispectral images on inversion of CDOM absorption based on our field sampling at the Saginaw Bay in the Lake Huron. The algorithm performances (RMSE = 0.17 and R2 = 0.87 in the Saginaw Bay; R2 = 0.80 in the northeastern US lakes) is promising and we conclude the CDOM absorption can be derived from Landsat-8 OLI image in both optically deep and optically shallow waters with high accuracy. Our method addressed challenges on employing appropriate atmospheric correction, determining bottom reflectance influence for shallow waters, and improving for bio-optical properties retrieval, as well as adapting to both hyperspectral and the multispectral remote sensing imagery. Over 100 Landsat-8 images in Lake Huron, northeastern US lakes, and the Arctic major rivers were processed to understand the CDOM spatio-temporal dynamics and its associated driving factors.

  5. Report on geologic remote sensing of the Columbia Plateau

    International Nuclear Information System (INIS)

    Sandness, G.A.; Kimball, C.S.; Schmierer, K.E.; Lindberg, J.W.

    1982-05-01

    The purpose of this remote sensing study is to identify faults or other geologic features which may have a significant bearing on the structural and tectonic character of the Hanford Site and the surrounding region. Landsat imagery, Skylab photographs, and U-2 photographs were analyzed to identify and map geologic photolineaments in the Columbia Plateau. The Landsat and Skylab imagery provided a regional perspective and allowed the identification of large-scale linear features. The U-2 photography provided much greater spatial resolution as well as a stereoscopic viewing capability. This allowed identification of smaller structural or geologic features and the identification of many cultural and nongeologic lineaments detected in the Landsat and Skylab imagery. The area studied totals, approximately 85,000 square miles, and encompasses virtually all exposures of Columbia River Basalt in the states of Washington, Oregon, and Idaho. It also includes an area bordering the Columbia River Basalt outcrop. This border area was studied in order to identify significant structures that may extend into the plateau. Included are a description of the procedures used for image analysis, 20 lineament maps at a scale of 1:250,000, geological summaries for the areas covered by the lineament maps, and discussions of many of the lineaments shown on the maps. Comparisons of the lineament maps with available geologic maps showed that the number of detected lineaments was much greater than the number of known faults and other linear features. Approximately 70% of the faults shown on the geologic maps were detected and are characterized as lineaments. Lineament trends in the northwest-southeast and northeast-southwest directions were found to predominate throughout the study area

  6. Multi- and hyperspectral geologic remote sensing: A review

    Science.gov (United States)

    van der Meer, Freek D.; van der Werff, Harald M. A.; van Ruitenbeek, Frank J. A.; Hecker, Chris A.; Bakker, Wim H.; Noomen, Marleen F.; van der Meijde, Mark; Carranza, E. John M.; Smeth, J. Boudewijn de; Woldai, Tsehaie

    2012-02-01

    Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quantitative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive subpixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly

  7. Opportunities for Increasing Societal Value of Remote Sensing Data ...

    African Journals Online (AJOL)

    Opportunities for Increasing Societal Value of Remote Sensing Data in South Africa's Strategic Development Priorities: A Review. ... Despite the enormous capital required to fund remote sensing initiatives, governments ... HOW TO USE AJOL.

  8. Assessing the accuracy of remote sensing techniques in vegetation ...

    African Journals Online (AJOL)

    Assessing the accuracy of remote sensing techniques in vegetation fractions estimation. ... This study aimed at exploring different remote sensing (RS) techniques for quantitatively measuring vegetation and bare soil ... HOW TO USE AJOL.

  9. Mapping Water Use and Drought with Satellite Remote Sensing

    OpenAIRE

    Anderson, Martha

    2014-01-01

    Mapping water use and drought with satellite remote sensing. Martha C. Anderson, Bill Kustas, Feng Gao, Kate Semmens. USDA-Agricultural Research Service Hydrology and Remote Sensing Laboratory, Beltsville, MD. Chris Hain NOAA-NESDIS

  10. Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing.

    Science.gov (United States)

    Fricker, Geoffrey A; Wolf, Jeffrey A; Saatchi, Sassan S; Gillespie, Thomas W

    2015-10-01

    There is an increasing interest in identifying theories, empirical data sets, and remote-sensing metrics that can quantify tropical forest alpha diversity at a landscape scale. Quantifying patterns of tree species richness in the field is time consuming, especially in regions with over 100 tree species/ha. We examine species richness in a 50-ha plot in Barro Colorado Island in Panama and test if biophysical measurements of canopy reflectance from high-resolution satellite imagery and detailed vertical forest structure and topography from light detection and ranging (lidar) are associated with species richness across four tree size classes (>1, 1-10, >10, and >20 cm dbh) and three spatial scales (1, 0.25, and 0.04 ha). We use the 2010 tree inventory, including 204,757 individuals belonging to 301 species of freestanding woody plants or 166 ± 1.5 species/ha (mean ± SE), to compare with remote-sensing data. All remote-sensing metrics became less correlated with species richness as spatial resolution decreased from 1.0 ha to 0.04 ha and tree size increased from 1 cm to 20 cm dbh. When all stems with dbh > 1 cm in 1-ha plots were compared to remote-sensing metrics, standard deviation in canopy reflectance explained 13% of the variance in species richness. The standard deviations of canopy height and the topographic wetness index (TWI) derived from lidar were the best metrics to explain the spatial variance in species richness (15% and 24%, respectively). Using multiple regression models, we made predictions of species richness across Barro Colorado Island (BCI) at the 1-ha spatial scale for different tree size classes. We predicted variation in tree species richness among all plants (adjusted r² = 0.35) and trees with dbh > 10 cm (adjusted r² = 0.25). However, the best model results were for understory trees and shrubs (dbh 1-10 cm) (adjusted r² = 0.52) that comprise the majority of species richness in tropical forests. Our results indicate that high

  11. Remote sensing of land surface phenology

    Science.gov (United States)

    Meier, G.A.; Brown, Jesslyn F.

    2014-01-01

    Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

  12. Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system

    Science.gov (United States)

    Shi, Yeyin; Murray, Seth C.; Rooney, William L.; Valasek, John; Olsenholler, Jeff; Pugh, N. Ace; Henrickson, James; Bowden, Ezekiel; Zhang, Dongyan; Thomasson, J. Alex

    2016-05-01

    Recent development of unmanned aerial systems has created opportunities in automation of field-based high-throughput phenotyping by lowering flight operational cost and complexity and allowing flexible re-visit time and higher image resolution than satellite or manned airborne remote sensing. In this study, flights were conducted over corn and sorghum breeding trials in College Station, Texas, with a fixed-wing unmanned aerial vehicle (UAV) carrying two multispectral cameras and a high-resolution digital camera. The objectives were to establish the workflow and investigate the ability of UAV-based remote sensing for automating data collection of plant traits to develop genetic and physiological models. Most important among these traits were plant height and number of plants which are currently manually collected with high labor costs. Vegetation indices were calculated for each breeding cultivar from mosaicked and radiometrically calibrated multi-band imagery in order to be correlated with ground-measured plant heights, populations and yield across high genetic-diversity breeding cultivars. Growth curves were profiled with the aerial measured time-series height and vegetation index data. The next step of this study will be to investigate the correlations between aerial measurements and ground truth measured manually in field and from lab tests.

  13. Development of airborne remote sensing data assimilation system

    International Nuclear Information System (INIS)

    Gudu, B R; Bi, H Y; Wang, H Y; Qin, S X; Ma, J W

    2014-01-01

    In this paper, an airborne remote sensing data assimilation system for China Airborne Remote Sensing System is introduced. This data assimilation system is composed of a land surface model, data assimilation algorithms, observation data and fundamental parameters forcing the land surface model. In this data assimilation system, Variable Infiltration Capacity hydrologic model is selected as the land surface model, which also serves as the main framework of the system. Three-dimensional variation algorithm, four-dimensional variation algorithms, ensemble Kalman filter and Particle filter algorithms are integrated in this system. Observation data includes ground observations and remotely sensed data. The fundamental forcing parameters include soil parameters, vegetation parameters and the meteorological data

  14. Popularization of remote sensing education and general course construction in undergraduate education

    International Nuclear Information System (INIS)

    Wang, Jing'ai; Sheng, Zhongyao; Yu, Han

    2014-01-01

    The construction of a course focused on remote sensing is important because it cultivates college students' geographic abilities and popularizes remote sensing technology. Using internet datasets, this article compares data from general undergraduate courses at almost 100 universities located in the United States and China with 3 years of experimental teaching data from the general undergraduate ''Remote sensing Region'' course at Beijing Normal University. The comparison focuses on curricular concepts, course content, website construction and the popularity of the remote sensing topic. Our research shows that the ''remote sensing region'' course can promote the geographic abilities of college students by popularizing remote sensing observation technology. The course can improve the overall quality of college students by breaking major barriers, and it can promote global and national consciousness by presenting material with global and regional relevancy. Remote sensing imaging has become known as the third most intuitive geographic language after text and maps. The general remote sensing course have the three following developmental qualities: interdisciplinarity, popularization and internationalization

  15. Remote sensing and change detection in rangelands | Palmer ...

    African Journals Online (AJOL)

    To most land managers, remote sensing has remained illusive, seldom allowing the manager to use it to its full potential. In contrast, the policy maker, backed by GIS laboratories and remote sensing specialists, is confronted by plausible scenarios of degradation and transformation. After intervening, he is seldom active long ...

  16. Research on active imaging information transmission technology of satellite borne quantum remote sensing

    Science.gov (United States)

    Bi, Siwen; Zhen, Ming; Yang, Song; Lin, Xuling; Wu, Zhiqiang

    2017-08-01

    According to the development and application needs of Remote Sensing Science and technology, Prof. Siwen Bi proposed quantum remote sensing. Firstly, the paper gives a brief introduction of the background of quantum remote sensing, the research status and related researches at home and abroad on the theory, information mechanism and imaging experiments of quantum remote sensing and the production of principle prototype.Then, the quantization of pure remote sensing radiation field, the state function and squeezing effect of quantum remote sensing radiation field are emphasized. It also describes the squeezing optical operator of quantum light field in active imaging information transmission experiment and imaging experiments, achieving 2-3 times higher resolution than that of coherent light detection imaging and completing the production of quantum remote sensing imaging prototype. The application of quantum remote sensing technology can significantly improve both the signal-to-noise ratio of information transmission imaging and the spatial resolution of quantum remote sensing .On the above basis, Prof.Bi proposed the technical solution of active imaging information transmission technology of satellite borne quantum remote sensing, launched researches on its system composition and operation principle and on quantum noiseless amplifying devices, providing solutions and technical basis for implementing active imaging information technology of satellite borne Quantum Remote Sensing.

  17. Information from imagery: ISPRS scientific vision and research agenda

    Science.gov (United States)

    Chen, Jun; Dowman, Ian; Li, Songnian; Li, Zhilin; Madden, Marguerite; Mills, Jon; Paparoditis, Nicolas; Rottensteiner, Franz; Sester, Monika; Toth, Charles; Trinder, John; Heipke, Christian

    2016-05-01

    With the increased availability of very high-resolution satellite imagery, terrain based imaging and participatory sensing, inexpensive platforms, and advanced information and communication technologies, the application of imagery is now ubiquitous, playing an important role in many aspects of life and work today. As a leading organisation in this field, the International Society for Photogrammetry and Remote Sensing (ISPRS) has been devoted to effectively and efficiently obtaining and utilising information from imagery since its foundation in the year 1910. This paper examines the significant challenges currently facing ISPRS and its communities, such as providing high-quality information, enabling advanced geospatial computing, and supporting collaborative problem solving. The state-of-the-art in ISPRS related research and development is reviewed and the trends and topics for future work are identified. By providing an overarching scientific vision and research agenda, we hope to call on and mobilise all ISPRS scientists, practitioners and other stakeholders to continue improving our understanding and capacity on information from imagery and to deliver advanced geospatial knowledge that enables humankind to better deal with the challenges ahead, posed for example by global change, ubiquitous sensing, and a demand for real-time information generation.

  18. In Situ/Remote Sensing Integration to Assess Forest Health—A Review

    Directory of Open Access Journals (Sweden)

    Marion Pause

    2016-06-01

    Full Text Available For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted.

  19. Remote Sensing Image Registration Using Multiple Image Features

    Directory of Open Access Journals (Sweden)

    Kun Yang

    2017-06-01

    Full Text Available Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform distance which is endowed with the intensity information is used to measure the scale space extrema. (iii To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

  20. An economic value of remote-sensing information—Application to agricultural production and maintaining groundwater quality

    Science.gov (United States)

    Forney, William M.; Raunikar, Ronald P.; Bernknopf, Richard L.; Mishra, Shruti K.

    2012-01-01

    Does remote-sensing information provide economic benefits to society, and can a value be assigned to those benefits? Can resource management and policy decisions be better informed by coupling past and present Earth observations with groundwater nitrate measurements? Using an integrated assessment approach, the U.S. Geological Survey (USGS) applied an established conceptual framework to answer these questions, as well as to estimate the value of information (VOI) for remote-sensing imagery. The approach uses moderate-resolution land-imagery (MRLI) data from the Landsat and Advanced Wide Field Sensor satellites that has been classified by the National Agricultural Statistics Service into the Cropland Data Layer (CDL). Within the constraint of the U.S. Environmental Protection Agency's public health threshold for potable groundwater resources, the USGS modeled the relation between a population of the CDL's land uses and dynamic nitrate (NO3-) contamination of aquifers in a case study region in northeastern Iowa. Employing various multiscaled, multitemporal geospatial datasets with MRLI to maximize the value of agricultural production, the approach develops and uses multiple environmental science models to address dynamic nitrogen loading and transport at specified distances from specific sites (wells) and at landscape scales (for example, across 35 counties and two aquifers). In addition to the ecosystem service of potable groundwater, this effort focuses on the use of MRLI for the management of the major land uses in the study region-the production of corn and soybeans, which can impact groundwater quality. Derived methods and results include (1) economic and dynamic nitrate-pollution models, (2) probabilities of the survival of groundwater, and (3) a VOI for remote sensing. For the northeastern Iowa study region, the marginal benefit of the MRLI VOI (in 2010 dollars) is $858 million ±$197 million annualized, which corresponds to a net present value of $38

  1. A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery.

    Science.gov (United States)

    Huang, Huasheng; Deng, Jizhong; Lan, Yubin; Yang, Aqing; Deng, Xiaoling; Zhang, Lei

    2018-01-01

    Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

  2. Bilge dump detection from SAR imagery using local binary patterns

    CSIR Research Space (South Africa)

    Mdakane, LW

    2015-07-01

    Full Text Available 2015: Remote Sensing: Understanding the Earth for a Safer World, Milan, Italy, 26-31 July 2015 Bilge dump detection from SAR imagery using local binary patterns yz L.W. Mdakane,yz W. Kleynhans,yz C.P. Schwegmann yDepartment of Electrical...

  3. Searches over graphs representing geospatial-temporal remote sensing data

    Science.gov (United States)

    Brost, Randolph; Perkins, David Nikolaus

    2018-03-06

    Various technologies pertaining to identifying objects of interest in remote sensing images by searching over geospatial-temporal graph representations are described herein. Graphs are constructed by representing objects in remote sensing images as nodes, and connecting nodes with undirected edges representing either distance or adjacency relationships between objects and directed edges representing changes in time. Geospatial-temporal graph searches are made computationally efficient by taking advantage of characteristics of geospatial-temporal data in remote sensing images through the application of various graph search techniques.

  4. Landsat's role in ecological applications of remote sensing.

    Science.gov (United States)

    Warren B. Cohen; Samuel N. Goward

    2004-01-01

    Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotely sensed data, those acquired by landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on...

  5. Land use mapping and change detection using ERTS imagery in Montgomery County, Alabama

    Science.gov (United States)

    Wilms, R. P.

    1973-01-01

    The feasibility of using remotely sensed data from ERTS-1 for mapping land use and detecting land use change was investigated. Land use information was gathered from 1964 air photo mosaics and from 1972 ERTS data. The 1964 data provided the basis for comparison with ERTS-1 imagery. From this comparison, urban sprawl was quite evident for the city of Montgomery. A significant trend from forestland to agricultural was also discovered. The development of main traffic arteries between 1964 and 1972 was a vital factor in the development of some of the urban centers. Even though certain problems in interpreting and correlating land use data from ERTS imagery were encountered, it has been demonstrated that remotely sensed data from ERTS is useful for inventorying land use and detecting land use change.

  6. ESTIMATION OF SEAGRASS COVERAGE BY DEPTH INVARIANT INDICES ON QUICKBIRD IMAGERY

    Directory of Open Access Journals (Sweden)

    Muhammad Anshar Amran

    2010-01-01

    Full Text Available Management of seagrass ecosystem requires availability of information on the actual condition of seagrass coverage. Remote sensing technology for seagrass mapping has been used to detect the presence of seagrass coverage, but so far no information on the condition of seagrass could be obtained. Therefore, a research is required using remote sensing imagery to obtain information on the condition of seagrass coverage.The aim of this research is to formulate mathematical relationship between seagrass coverage and depth invariant indices on Quickbird imagery. Transformation was done on multispectral bands which could detect sea floor objects that are in the region of blue, green and red bands.The study areas covered are the seas around Barranglompo Island and Barrangcaddi Island, westward of Makassar city, Indonesia. Various seagrass coverages were detected within the region under study.Mathematical relationship between seagrass coverage and depth invariant indices was obtained by multiple linear regression method. Percentage of seagrass coverage (C was obtained by transformation of depth invariant indices (Xij on Quickbird imagery, with transformation equation as follows:C = 19.934 – 63.347 X12 + 23.239 X23.A good accuracy of 75% for the seagrass coverage was obtained by transformation of depth invariant indices (Xij on Quickbird imagery.

  7. Microrelief Associated with Gas Emission Craters: Remote-Sensing and Field-Based Study

    Directory of Open Access Journals (Sweden)

    Alexander Kizyakov

    2018-04-01

    Full Text Available Formation of gas emission craters (GEC is a new process in the permafrost zone, leading to considerable terrain changes. Yet their role in changing the relief is local, incomparable in the volume of the removed deposits to other destructive cryogenic processes. However, the relief-forming role of GECs is not limited to the appearance of the crater itself, but also results in positive and negative microforms as well. Negative microforms are rounded hollows, surrounded by piles of ejected or extruded deposits. Hypotheses related to the origin of these forms are put forward and supported by an analysis of multi-temporal satellite images, field observations and photographs of GECs. Remote sensing data specifically was used for interpretation of landform origin, measuring distances and density of material scattering, identifying scattered material through analysis of repeated imagery. Remote-sensing and field data reliably substantiate an impact nature of the hollows around GECs. It is found that scattering of frozen blocks at a distance of up to 293 m from a GEC is capable of creating an impact hollow. These data indicate the influence of GEC on the relief through the formation of a microrelief within a radius of 15–20 times the radius of the crater itself. Our study aims at the prediction of risk zones.

  8. Stream Tracker: Crowd sourcing and remote sensing to monitor stream flow intermittence

    Science.gov (United States)

    Puntenney, K.; Kampf, S. K.; Newman, G.; Lefsky, M. A.; Weber, R.; Gerlich, J.

    2017-12-01

    Streams that do not flow continuously in time and space support diverse aquatic life and can be critical contributors to downstream water supply. However, these intermittent streams are rarely monitored and poorly mapped. Stream Tracker is a community powered stream monitoring project that pairs citizen contributed observations of streamflow presence or absence with a network of streamflow sensors and remotely sensed data from satellites to track when and where water is flowing in intermittent stream channels. Citizens can visit sites on roads and trails to track flow and contribute their observations to the project site hosted by CitSci.org. Data can be entered using either a mobile application with offline capabilities or an online data entry portal. The sensor network provides a consistent record of streamflow and flow presence/absence across a range of elevations and drainage areas. Capacitance, resistance, and laser sensors have been deployed to determine the most reliable, low cost sensor that could be mass distributed to track streamflow intermittence over a larger number of sites. Streamflow presence or absence observations from the citizen and sensor networks are then compared to satellite imagery to improve flow detection algorithms using remotely sensed data from Landsat. In the first two months of this project, 1,287 observations have been made at 241 sites by 24 project members across northern and western Colorado.

  9. Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs

    Energy Technology Data Exchange (ETDEWEB)

    McLendon, William C.,; Brost, Randolph

    2016-05-01

    Remote sensing systems produce large volumes of high-resolution images that are difficult to search. The GeoGraphy (pronounced Geo-Graph-y) framework [2, 20] encodes remote sensing imagery into a geospatial-temporal semantic graph representation to enable high level semantic searches to be performed. Typically scene objects such as buildings and trees tend to be shaped like blocks with few holes, but other shapes generated from path networks tend to have a large number of holes and can span a large geographic region due to their connectedness. For example, we have a dataset covering the city of Philadelphia in which there is a single road network node spanning a 6 mile x 8 mile region. Even a simple question such as "find two houses near the same street" might give unexpected results. More generally, nodes arising from networks of paths (roads, sidewalks, trails, etc.) require additional processing to make them useful for searches in GeoGraphy. We have assigned the term Path Network Recovery to this process. Path Network Recovery is a three-step process involving (1) partitioning the network node into segments, (2) repairing broken path segments interrupted by occlusions or sensor noise, and (3) adding path-aware search semantics into GeoQuestions. This report covers the path network recovery process, how it is used, and some example use cases of the current capabilities.

  10. Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN and Landsat Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Karsten Schulz

    2009-11-01

    Full Text Available Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML or k-Nearest Neighbor (k-NN indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.

  11. Potential application of remote sensing in monitoring informal settlements in South Africa where complimentary data does not exist

    CSIR Research Space (South Africa)

    Busgeeth, K

    2008-06-01

    Full Text Available as only the study conducted by Hofmann was available [7]. The study reported how informal settlements can be detected from other land-use-forms by describing typical characteristics of colour, texture, shape and context using remote sensed data from.... This approach may be appropriate for larger buildings, but Hofmann found that individual shacks could not be identified on IKONOS imagery [7]. QuickBird has a higher spatial resolution than IKONOS, at 0.6m vs 1m in the panchromatic band; this represents a...

  12. REMOTE SENSING FOR ENVIRONMENTAL COMPLIANCE MONITORING

    Science.gov (United States)

    I. Remote Sensing Basics A. The electromagnetic spectrum demonstrates what we can see both in the visible and beyond the visible part of the spectrum through the use of various types of sensors. B. Resolution refers to what a remote sensor can see and how often. 1. Sp...

  13. An evaluation of traditional and emerging remote sensing technologies for the detection of fugitive contamination at selected Superfund hazardous waste sites

    Science.gov (United States)

    Slonecker, E. Terrence; Fisher, Gary B.

    2011-01-01

    This report represents a remote sensing research effort conducted by the U.S. Geological Survey in cooperation with the U.S. Environmental Protection Agency (EPA) for the EPA Office of Inspector General. The objective of this investigation was to explore the efficacy of remote sensing as a technology for postclosure monitoring of hazardous waste sites as defined under the Comprehensive Environmental Response Compensation and Liability Act of 1980 (Public Law 96-510, 42 U.S.C. §9601 et seq.), also known as \\"Superfund.\\" Five delisted Superfund sites in Maryland and Virginia were imaged with a hyperspectral sensor and visited for collection of soil, water, and spectral samples and inspection of general site conditions. This report evaluates traditional and hyperspectral imagery and field spectroscopic measurement techniques in the characterization and analysis of fugitive (anthropogenic, uncontrolled) contamination at previously remediated hazardous waste disposal sites.

  14. Application of remote sensing in aquatic ecosystems

    Science.gov (United States)

    Yousef, Foad

    I utilized state the art remote sensing and GIS (Geographical Information System) techniques to study large scale biological, physical and ecological processes of coastal, nearshore, and offshore waters of Lake Michigan and Lake Superior. These processes ranged from chlorophyll alpha and primary production time series analysies in Lake Michigan to coastal stamp sand threats on Buffalo Reef in Lake Superior. I used SeaWiFS (Sea-viewing Wide Field-of-view Sensor) satellite imagery to trace various biological, chemical and optical water properties of Lake Michigan during the past decade and to investigate the collapse of early spring primary production. Using spatial analysis techniques, I was able to connect these changes to some important biological processes of the lake (quagga mussels filtration). In a separate study on Lake Superior, using LiDAR (Light Detection and Ranging) and aerial photos, we examined natural coastal erosion in Grand Traverse Bay, Michigan, and discussed a variety of geological features that influence general sediment accumulation patterns and interactions with migrating tailings from legacy mining. These sediments are moving southwesterly towards Buffalo Reef, creating a threat to the lake trout and lake whitefish breeding ground.

  15. Geological remote sensing

    Science.gov (United States)

    Bishop, Charlotte; Rivard, Benoit; de Souza Filho, Carlos; van der Meer, Freek

    2018-02-01

    Geology is defined as the 'study of the planet Earth - the materials of which it is made, the processes that act on these materials, the products formed, and the history of the planet and its life forms since its origin' (Bates and Jackson, 1976). Remote sensing has seen a number of variable definitions such as those by Sabins and Lillesand and Kiefer in their respective textbooks (Sabins, 1996; Lillesand and Kiefer, 2000). Floyd Sabins (Sabins, 1996) defined it as 'the science of acquiring, processing and interpreting images that record the interaction between electromagnetic energy and matter' while Lillesand and Kiefer (Lillesand and Kiefer, 2000) defined it as 'the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation'. Thus Geological Remote Sensing can be considered the study of, not just Earth given the breadth of work undertaken in planetary science, geological features and surfaces and their interaction with the electromagnetic spectrum using technology that is not in direct contact with the features of interest.

  16. A NDVI assisted remote sensing image adaptive scale segmentation method

    Science.gov (United States)

    Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

    2018-03-01

    Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.

  17. Remote Sensing of shallow sea floor for digital earth environment

    International Nuclear Information System (INIS)

    Yahya, N N; Hashim, M; Ahmad, S

    2014-01-01

    Understanding the sea floor biodiversity requires spatial information that can be acquired from remote sensing satellite data. Species volume, spatial patterns and species coverage are some of the information that can be derived. Current approaches for mapping sea bottom type have evolved from field observation, visual interpretation from aerial photography, mapping from remote sensing satellite data along with field survey and hydrograhic chart. Remote sensing offers most versatile technique to map sea bottom type up to a certain scale. This paper reviews the technical characteristics of signal and light interference within marine features, space and remote sensing satellite. In addition, related image processing techniques that are applicable to remote sensing satellite data for sea bottom type digital mapping is also presented. The sea bottom type can be differentiated by classification method using appropriate spectral bands of satellite data. In order to verify the existence of particular sea bottom type, field observations need to be carried out with proper technique and equipment

  18. Remote sensing fire and fuels in southern California

    Science.gov (United States)

    Philip Riggan; Lynn Wolden; Bob Tissell; David Weise; J. Coen

    2011-01-01

    Airborne remote sensing at infrared wavelengths has the potential to quantify large-fire properties related to energy release or intensity, residence time, fuel-consumption rate, rate of spread, and soil heating. Remote sensing at a high temporal rate can track fire-line outbreaks and acceleration and spotting ahead of a fire front. Yet infrared imagers and imaging...

  19. Subsurface remote sensing

    International Nuclear Information System (INIS)

    Schweitzer, Jeffrey S.; Groves, Joel L.

    2002-01-01

    Subsurface remote sensing measurements are widely used for oil and gas exploration, for oil and gas production monitoring, and for basic studies in the earth sciences. Radiation sensors, often including small accelerator sources, are used to obtain bulk properties of the surrounding strata as well as to provide detailed elemental analyses of the rocks and fluids in rock pores. Typically, instrument packages are lowered into a borehole at the end of a long cable, that may be as long as 10 km, and two-way data and instruction telemetry allows a single radiation instrument to operate in different modes and to send the data to a surface computer. Because these boreholes are often in remote locations throughout the world, the data are frequently transmitted by satellite to various locations around the world for almost real-time analysis and incorporation with other data. The complete system approach that permits rapid and reliable data acquisition, remote analysis and transmission to those making decisions is described

  20. Remote Sensing for Mapping RAMSAR Heritage Site at Sungai Pulai Mangrove Forest Reserve, Johor, Malaysia

    International Nuclear Information System (INIS)

    Hasmadi, I.M.; Pakhriazad, H.Z.; Norlida, K.

    2011-01-01

    The Sungai Pulai Mangrove Forest Reserve (SPMFR) is the largest reverin mangrove system in Johore. In 2003 about 9,126 ha of the Sungai Pulai mangrove was designated as a RAMSAR site. RAMSAR sites are wetland areas that are deemed to have international importance and are included in the List of Wetlands of International Importance. The SPMFR plays a significant socio-economic role to the adjacent 38 villages. Satellite remote sensing is a useful source of information where it provides timely and complete coverage for vegetation mapping especially in mangroves where the accessibility is difficult. This study was carried out to identify and map land cover types using SPOT-4 imagery at the Sungai Pulai-RAMSAR site and its surrounding areas. Through unsupervised classification technique a total of seven classes of land cover type were mapped, where about 90 % mapping accuracy was gained from the accuracy assessment. Later, vegetation densities were classified into five levels namely very high, high, medium, low and very low based on crown density scale using vegetation indices model such as NDVI, AVI and OSAVI. Results from NDVI and OSAVI model were almost similar but AVI model detected more on medium vegetation which did not show the real ground condition. The study concludes that SPOT-4 imagery was able to discriminate mangrove area clearly from other land covers type. Vegetation indices model can be used as a tool for mapping vegetation density level in the SPMFR and its surrounding area. Therefore VIs models from remote sensing are useful to monitor and manage the mangrove forest for sustainable management and preserve the SPMFR as a RAMSAR site in Peninsular Malaysia. (author)

  1. Subsidence feature discrimination using deep convolutional neral networks in synthetic aperture radar imagery

    CSIR Research Space (South Africa)

    Schwegmann, Colin P

    2017-07-01

    Full Text Available International Geoscience and Remote Sensing Symposium (IGARSS), 23-28 July 2017, Fort Worth, TX, USA SUBSIDENCE FEATURE DISCRIMINATION USING DEEP CONVOLUTIONAL NEURAL NETWORKS IN SYNTHETIC APERTURE RADAR IMAGERY Schwegmann, Colin P Kleynhans, Waldo...

  2. Monitoring water quality by remote sensing

    Science.gov (United States)

    Brown, R. L. (Principal Investigator)

    1977-01-01

    The author has identified the following significant results. A limited study was conducted to determine the applicability of remote sensing for evaluating water quality conditions in the San Francisco Bay and delta. Considerable supporting data were available for the study area from other than overflight sources, but short-term temporal and spatial variability precluded their use. The study results were not sufficient to shed much light on the subject, but it did appear that, with the present state of the art in image analysis and the large amount of ground truth needed, remote sensing has only limited application in monitoring water quality.

  3. Gully Erosion Mapping and Monitoring at Multiple Scales Based on Multi-Source Remote Sensing Data of the Sancha River Catchment, Northeast China

    Directory of Open Access Journals (Sweden)

    Ranghu Wang

    2016-11-01

    Full Text Available This research is focused on gully erosion mapping and monitoring at multiple spatial scales using multi-source remote sensing data of the Sancha River catchment in Northeast China, where gullies extend over a vast area. A high resolution satellite image (Pleiades 1A, 0.7 m was used to obtain the spatial distribution of the gullies of the overall basin. Image visual interpretation with field verification was employed to map the geometric gully features and evaluate gully erosion as well as the topographic differentiation characteristics. Unmanned Aerial Vehicle (UAV remote sensing data and the 3D photo-reconstruction method were employed for detailed gully mapping at a site scale. The results showed that: (1 the sub-meter image showed a strong ability in the recognition of various gully types and obtained satisfactory results, and the topographic factors of elevation, slope and slope aspects exerted significant influence on the gully spatial distribution at the catchment scale; and (2 at a more detailed site scale, UAV imagery combined with 3D photo-reconstruction provided a Digital Surface Model (DSM and ortho-image at the centimeter level as well as a detailed 3D model. The resulting products revealed the area of agricultural utilization and its shaping by human agricultural activities and water erosion in detail, and also provided the gully volume. The present study indicates that using multi-source remote sensing data, including satellite and UAV imagery simultaneously, results in an effective assessment of gully erosion over multiple spatial scales. The combined approach should be continued to regularly monitor gully erosion to understand the erosion process and its relationship with the environment from a comprehensive perspective.

  4. Remote Sensing Analysis of the Sierra Blanca (Faskin Ranch) Low-Level Radioactive Waste Disposal Site, Hudspeth County, Texas

    International Nuclear Information System (INIS)

    LeMone, D. V.; Dodge, R.; Xie, H.; Langford, R. P.; Keller, G. R.

    2002-01-01

    Remote sensing images provide useful physical information, revealing such features as geological structure, vegetation, drainage patterns, and variations in consolidated and unconsolidated lithologies. That technology has been applied to the failed Sierra Blanca (Faskin Ranch) shallow burial low-level radioactive waste disposal site selected by the Texas Low-Level Radioactive Waste Disposal Authority. It has been re-examined using data from LANDSAT satellite series. The comparison of the earlier LANDSAT V (5/20/86) (30-m resolution) with the later new, higher resolution ETM imagery (10/23/99) LANDSAT VII data (15-m resolution) clearly shows the superiority of the LANDSAT VII data. The search for surficial indications of evidence of fatal flaws at the Sierra Blanca site utilizing was not successful, as it had been in the case of the earlier remote sensing analysis of the failed Fort Hancock site utilizing LANDSAT V data. The authors conclude that the tectonic activity at the Sierra Blanca site is much less recent and active than in the previously studied Fort Hancock site. The Sierra Blanca site failed primarily on the further needed documentation concerning a subsurface fault underneath the site and environmental justice issues. The presence of this fault was not revealed using the newer LANDSAT VII data. Despite this fact, it must be remembered that remote sensing provides baseline documentation for determining future physical and financial remediation responsibilities. On the basis of the two sites examined by LANDSAT remote sensing imaging, it is concluded that it is an essential, cost-effective tool that should be utilized not only in site examination but also in all nuclear-related facilities

  5. What is a picture worth? A history of remote sensing

    Science.gov (United States)

    Moore, Gerald K.

    1979-01-01

    Remote sensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remote sensing begins with photography. The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remote sensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m. Remote sensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remote sensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.

  6. Remote sensing for the geobotanical and biogeochemical assessment of environmental contamination

    International Nuclear Information System (INIS)

    Wickham, J.; Chesley, M.; Lancaster, J.; Mouat, D.

    1993-01-01

    Under Contract Number DE-AC08-90NV10845, the DOE has funded the Desert Research Institute (DRI) to examine several aspects of remote sensing, specifically with respect to how its use might help support Environmental Restoration and Waste Management (ERWM) activities at DOE sites located throughout the country. This report represents partial fulfillment of DRI's obligations under that contract and includes a review of relevant literature associated with remote sensing studies and our evaluation and recommendation as to the applicability of various remote sensing techniques for DOE needs. With respect to DOE ERWM activities, remote sensing may be broadly defined as collecting information about a target without actually being in physical contact with the object. As the common platforms for remote sensing observations are aircraft and satellites, there exists the possibility to rapidly and efficiently collect information over DOE sites that would allow for the identification and monitoring of contamination related to present and past activities. As DOE sites cover areas ranging from tens to hundreds of square miles, remote sensing may provide an effective, efficient, and economical method in support of ERWM activities. For this review, remote sensing has been limited to methods that employ electromagnetic (EM) energy as the means of detecting and measuring target characteristics

  7. Remote sensing for the geobotanical and biogeochemical assessment of environmental contamination

    Energy Technology Data Exchange (ETDEWEB)

    Wickham, J.; Chesley, M.; Lancaster, J.; Mouat, D.

    1993-01-01

    Under Contract Number DE-AC08-90NV10845, the DOE has funded the Desert Research Institute (DRI) to examine several aspects of remote sensing, specifically with respect to how its use might help support Environmental Restoration and Waste Management (ERWM) activities at DOE sites located throughout the country. This report represents partial fulfillment of DRI`s obligations under that contract and includes a review of relevant literature associated with remote sensing studies and our evaluation and recommendation as to the applicability of various remote sensing techniques for DOE needs. With respect to DOE ERWM activities, remote sensing may be broadly defined as collecting information about a target without actually being in physical contact with the object. As the common platforms for remote sensing observations are aircraft and satellites, there exists the possibility to rapidly and efficiently collect information over DOE sites that would allow for the identification and monitoring of contamination related to present and past activities. As DOE sites cover areas ranging from tens to hundreds of square miles, remote sensing may provide an effective, efficient, and economical method in support of ERWM activities. For this review, remote sensing has been limited to methods that employ electromagnetic (EM) energy as the means of detecting and measuring target characteristics.

  8. MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH

    Data.gov (United States)

    National Aeronautics and Space Administration — MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH VARUN CHANDOLA AND RANGA RAJU VATSAVAI Abstract. Multispectral remote sensing images have...

  9. Applications of Environmental Remote Sensing by HJ-1C SAR Imageries

    Directory of Open Access Journals (Sweden)

    Tian Wei

    2014-06-01

    Full Text Available The HJ-1C satellite was successfully launched in November 19, 2012. The HJ-1C and HJ-1A/1B satellites, which were launched in September 06, 2008, constitute the “2+1” small satellite constellation for environmental and disaster monitoring. This study focuses on the analysis and evaluation of the satellite performance with respect to environmental remote sensing, including land use interpretation, land cover classification, oil spill identification, retrieval of sea waves, and monitoring of coastal mariculture. The data used in this study cover the city of Beijing and the sea of the Fujian Province. Nine HJ-1C satellite images (level-2, S band, VV Pol, strip mode, 5 m resolution from December 2012 to January 2013 are used. The conclusions are as follows: (1 the HJ-1C SAR images can be used to manually identify farmland, woodland, roads, rivers, urban construction, and rural residential areas; (2 the accuracy of the automatic land cover classification increased significantly when the HJ-1C SAR and HJ-1B CCD fusion images are used; (3 the HJ-1C satellite can be used to identify oil spills, to invert wave parameters, and to extract information regarding inshore aquaculture.

  10. Landsat imagery: a unique resource

    Science.gov (United States)

    Miller, H.; Sexton, N.; Koontz, L.

    2011-01-01

    Landsat satellites provide high-quality, multi-spectral imagery of the surface of the Earth. These moderate-resolution, remotely sensed images are not just pictures, but contain many layers of data collected at different points along the visible and invisible light spectrum. These data can be manipulated to reveal what the Earth’s surface looks like, including what types of vegetation are present or how a natural disaster has impacted an area (Fig. 1).

  11. Remote sensing for wind energy

    Energy Technology Data Exchange (ETDEWEB)

    Pena, A.; Bay Hasager, C.; Lange, J. [Technical Univ. of Denmark. DTU Wind Energy, DTU Risoe Campus, Roskilde (Denmark) (and others

    2013-06-15

    The Remote Sensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risoe) during the first PhD Summer School: Remote Sensing in Wind Energy. Thus it is closely linked to the PhD Summer Schools where state-of-the-art is presented during the lecture sessions. The advantage of the report is to supplement with in-depth, article style information. Thus we strive to provide link from the lectures, field demonstrations, and hands-on exercises to theory. The report will allow alumni to trace back details after the course and benefit from the collection of information. This is the third edition of the report (first externally available), after very successful and demanded first two, and we warmly acknowledge all the contributing authors for their work in the writing of the chapters, and we also acknowledge all our colleagues in the Meteorology and Test and Measurements Sections from DTU Wind Energy in the PhD Summer Schools. We hope to continue adding more topics in future editions and to update and improve as necessary, to provide a truly state-of-the-art 'guideline' available for people involved in Remote Sensing in Wind Energy. (Author)

  12. Remote sensing of water and nitrogen stress in broccoli

    Science.gov (United States)

    Elsheikha, Diael-Deen Mohamed

    Remote sensing is being used in agriculture for crop management. Ground based remote sensing data acquisition system was used for collection of high spatial and temporal resolution data for irrigated broccoli crop. The system was composed of a small cart that ran back and forth on a rail system that was mounted on a linear move irrigation system. The cart was equipped with a sensor that had 4 discrete wavelengths; 550 nm, 660 nm, 720 nm, and 810 nm, and an infrared thermometer, all had 10 nm bandwidth. A global positioning system was used to indicate the cart position. The study consisted of two parts; the first was to evaluate remotely sensed reflectance and indices in broccoli during the growing season, and determine whether remotely sensed indices or standard deviation of indices can distinguish between nitrogen and water stress in broccoli, and the second part of the study was to evaluate remotely sensed indices and standard deviation of remotely sensed indices in broccoli during daily changes in solar zenith angle. Results indicated that nitrogen was detected using Ratio Vegetation index, RVI, Normalized Difference Vegetation Index, NDVI, Canopy Chlorophyll Concentration Index, CCCI, and also using the reflectance in the Near-Infrared, NIR, bands. The Red reflectance band capability of showing stress was not as clear as the previous indices and bands reflectance. The Canopy Chlorophyll Concentration Index, CCCI, was the most successful index. The Crop Water Stress Index was able to detect water stress but it was highly affected by the solar zenith angle change along the day.

  13. A Parallel Processing Algorithm for Remote Sensing Classification

    Science.gov (United States)

    Gualtieri, J. Anthony

    2005-01-01

    A current thread in parallel computation is the use of cluster computers created by networking a few to thousands of commodity general-purpose workstation-level commuters using the Linux operating system. For example on the Medusa cluster at NASA/GSFC, this provides for super computing performance, 130 G(sub flops) (Linpack Benchmark) at moderate cost, $370K. However, to be useful for scientific computing in the area of Earth science, issues of ease of programming, access to existing scientific libraries, and portability of existing code need to be considered. In this paper, I address these issues in the context of tools for rendering earth science remote sensing data into useful products. In particular, I focus on a problem that can be decomposed into a set of independent tasks, which on a serial computer would be performed sequentially, but with a cluster computer can be performed in parallel, giving an obvious speedup. To make the ideas concrete, I consider the problem of classifying hyperspectral imagery where some ground truth is available to train the classifier. In particular I will use the Support Vector Machine (SVM) approach as applied to hyperspectral imagery. The approach will be to introduce notions about parallel computation and then to restrict the development to the SVM problem. Pseudocode (an outline of the computation) will be described and then details specific to the implementation will be given. Then timing results will be reported to show what speedups are possible using parallel computation. The paper will close with a discussion of the results.

  14. 1999 IEEE international geoscience and remote sensing symposium

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1999-07-01

    The theme of IGARSS'99, ``Remote Sensing of the System Earth--A Challenge for the 21st Century,'' shows how earth observation based on satellite remote sensing can significantly contribute to the future study of the environment and the changes it is undergoing, whether from natural causes or human activities. The wide range of topics offers an interdisciplinary approach and suggests integrated techniques and theory in remote sensing are essential for modeling and understanding the environment. Topics covered include: new instrumentation and future systems; high resolution SAR/InSAR; earth system science educational initiative; data fusion; radar sensing of ice sheets; image processing techniques; clouds and ice particles; internal waves; natural hazards and disaster monitoring; advanced passive and active sensors and sensor calibration; radar assessment of rain, oil spills and natural slicks; data standards and distribution; and vegetation monitoring using BRDF approaches.

  15. A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites.

    Science.gov (United States)

    Maynard, Jonathan J; Karl, Jason W

    2017-01-01

    Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral 'fingerprint' of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites

  16. Scientific Programming Using Java: A Remote Sensing Example

    Science.gov (United States)

    Prados, Don; Mohamed, Mohamed A.; Johnson, Michael; Cao, Changyong; Gasser, Jerry

    1999-01-01

    This paper presents results of a project to port remote sensing code from the C programming language to Java. The advantages and disadvantages of using Java versus C as a scientific programming language in remote sensing applications are discussed. Remote sensing applications deal with voluminous data that require effective memory management, such as buffering operations, when processed. Some of these applications also implement complex computational algorithms, such as Fast Fourier Transformation analysis, that are very performance intensive. Factors considered include performance, precision, complexity, rapidity of development, ease of code reuse, ease of maintenance, memory management, and platform independence. Performance of radiometric calibration code written in Java for the graphical user interface and of using C for the domain model are also presented.

  17. Implementation of remote sensing data in research of coastal dynamics at the Baydaratskaya Bay, Kara Sea

    Science.gov (United States)

    Kuznetsov, D. E.; Belova, N.; Noskov, A.; Ogorodov, S.

    2011-12-01

    The development of Arctic coastal regions is now in progress due to significant amount of hydrocarbon deposits discovered. In high latitudes, natural hazards such as coastal erosion and thermoerosion, deflation, linear erosion and thermal denudation, ice gouging can make petroleum production and transport unprofitable. A prominent feature of Kara Sea, as well as other Arctic seas, is the development of coast in permafrost conditions. Despite the long ice period (up to 9 months), during the ice free period coastal dynamics are very intensive. If pipeline landfall site occurs at a shore section with high retreat rate (1 - 3m/year and higher), danger of pipeline damage due to exposure, line sagging and mechanical deformations becomes high. Protective measures may appear inefficient, since shore sections with active coastal erosion are subject not only to bluff retreat, but also to nearshore zone and coastal slope erosion. Exposed pipeline sections also get in danger of sea ice effect. For correct definition of coastal dynamics setting we use dual approach. The first part is perennial instrumental monitoring of shore morphology, relying on system of benchmarks used for repeated measures, together with in-field geomorphologic expertise. Measures include direct observations and geodetic leveling onshore and echosounding offshore. Being the most precise method, direct measurements are expensive. The other drawback is that they can't give an overview of long-span tendencies of coastal evolution for prolonged shore sections, which is essential for shore deformation forecast complying with lifetime of structures (usually 30 to 50 years). This is where the importance of the 2nd part, analysis of the different time remote sensing data, becomes decisive. Most important sources of remote sensing data include Corona imagery from 1960s - 70s, aerial photos of different times (but most of them are inaccessible for Russian Arctic coast), Landsat imagery (covering a long time span

  18. Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

    Directory of Open Access Journals (Sweden)

    Daniel A. Griffith

    2016-06-01

    Full Text Available Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pixels in realistically-sized remotely-sensed images, a situation that more recently has changed. Recent advances in spatial statistical estimation theory support the extraction of information and the distilling of knowledge from remotely-sensed images in a way that accounts for latent spatial autocorrelation. This paper summarizes an effective methodological approach to achieve this end, illustrating results with a 2002 remotely sensed-image of the Florida Everglades, and simulation experiments. Specifically, uncertainty of spatial autocorrelation parameter in a spatial autoregressive model is modeled with a beta-beta mixture approach and is further investigated with three different sampling strategies: coterminous sampling, random sub-region sampling, and increasing domain sub-regions. The results suggest that uncertainty associated with remotely-sensed data should be cast in consideration of spatial autocorrelation. It emphasizes that one remaining challenge is to better quantify the spatial variability of spatial autocorrelation estimates across geographic landscapes.

  19. Navigation and Remote Sensing Payloads and Methods of the Sarvant Unmanned Aerial System

    Science.gov (United States)

    Molina, P.; Fortuny, P.; Colomina, I.; Remy, M.; Macedo, K. A. C.; Zúnigo, Y. R. C.; Vaz, E.; Luebeck, D.; Moreira, J.; Blázquez, M.

    2013-08-01

    In a large number of scenarios and missions, the technical, operational and economical advantages of UAS-based photogrammetry and remote sensing over traditional airborne and satellite platforms are apparent. Airborne Synthetic Aperture Radar (SAR) or combined optical/SAR operation in remote areas might be a case of a typical "dull, dirty, dangerous" mission suitable for unmanned operation - in harsh environments such as for example rain forest areas in Brazil, topographic mapping of small to medium sparsely inhabited remote areas with UAS-based photogrammetry and remote sensing seems to be a reasonable paradigm. An example of such a system is the SARVANT platform, a fixed-wing aerial vehicle with a six-meter wingspan and a maximumtake- of-weight of 140 kilograms, able to carry a fifty-kilogram payload. SARVANT includes a multi-band (X and P) interferometric SAR payload, as the P-band enables the topographic mapping of densely tree-covered areas, providing terrain profile information. Moreover, the combination of X- and P-band measurements can be used to extract biomass estimations. Finally, long-term plan entails to incorporate surveying capabilities also at optical bands and deliver real-time imagery to a control station. This paper focuses on the remote-sensing concept in SARVANT, composed by the aforementioned SAR sensor and envisioning a double optical camera configuration to cover the visible and the near-infrared spectrum. The flexibility on the optical payload election, ranging from professional, medium-format cameras to mass-market, small-format cameras, is discussed as a driver in the SARVANT development. The paper also focuses on the navigation and orientation payloads, including the sensors (IMU and GNSS), the measurement acquisition system and the proposed navigation and orientation methods. The latter includes the Fast AT procedure, which performs close to traditional Integrated Sensor Orientation (ISO) and better than Direct Sensor Orientation (Di

  20. Methods of training the graduate level and professional geologist in remote sensing technology

    Science.gov (United States)

    Kolm, K. E.

    1981-01-01

    Requirements for a basic course in remote sensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remote sensing theory, the theory and data types relating to different remote sensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remote sensing in a given study, the integration of other data bases (geophysical and geochemical) into a remote sensing study, and geological remote sensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotely sensed data.

  1. Remote sensing technology: symposium proceedings

    International Nuclear Information System (INIS)

    1985-01-01

    Papers were presented in four subject areas: applications of remote sensing; data analysis, digital and analog; acquisition systems; and general. Abstracts of individual items from the conference were prepared separately for the data base

  2. Remote Sensing Wind and Wind Shear System.

    Science.gov (United States)

    Contents: Remote sensing of wind shear and the theory and development of acoustic doppler; Wind studies; A comparison of methods for the remote detection of winds in the airport environment; Acoustic doppler system development; System calibration; Airport operational tests.

  3. Ontology-based classification of remote sensing images using spectral rules

    Science.gov (United States)

    Andrés, Samuel; Arvor, Damien; Mougenot, Isabelle; Libourel, Thérèse; Durieux, Laurent

    2017-05-01

    Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for "domain" experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.

  4. Remote Sensing Image Classification Based on Stacked Denoising Autoencoder

    Directory of Open Access Journals (Sweden)

    Peng Liang

    2017-12-01

    Full Text Available Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1 remote sensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remote sensing image classification.

  5. Remote-sensing image encryption in hybrid domains

    Science.gov (United States)

    Zhang, Xiaoqiang; Zhu, Guiliang; Ma, Shilong

    2012-04-01

    Remote-sensing technology plays an important role in military and industrial fields. Remote-sensing image is the main means of acquiring information from satellites, which always contain some confidential information. To securely transmit and store remote-sensing images, we propose a new image encryption algorithm in hybrid domains. This algorithm makes full use of the advantages of image encryption in both spatial domain and transform domain. First, the low-pass subband coefficients of image DWT (discrete wavelet transform) decomposition are sorted by a PWLCM system in transform domain. Second, the image after IDWT (inverse discrete wavelet transform) reconstruction is diffused with 2D (two-dimensional) Logistic map and XOR operation in spatial domain. The experiment results and algorithm analyses show that the new algorithm possesses a large key space and can resist brute-force, statistical and differential attacks. Meanwhile, the proposed algorithm has the desirable encryption efficiency to satisfy requirements in practice.

  6. Remote sensing of oil slicks

    Digital Repository Service at National Institute of Oceanography (India)

    Fondekar, S.P.; Rao, L.V.G.

    the drawback of expensive conventional surveying methods. An airborne remote sensing system used for monitoring and surveillance of oil comprises different sensors such as side-looking airborne radar, synthetic aperture radar, infrared/ultraviolet line scanner...

  7. Remote sensing of ecosystem health: opportunities, challenges, and future perspectives.

    Science.gov (United States)

    Li, Zhaoqin; Xu, Dandan; Guo, Xulin

    2014-11-07

    Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

  8. Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives

    Directory of Open Access Journals (Sweden)

    Zhaoqin Li

    2014-11-01

    Full Text Available Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1 scale issue; (2 transportability issue; (3 data availability; and (4 uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

  9. Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives

    Science.gov (United States)

    Li, Zhaoqin; Xu, Dandan; Guo, Xulin

    2014-01-01

    Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges. PMID:25386759

  10. Suitability Evaluation for Products Generation from Multisource Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Jining Yan

    2016-12-01

    Full Text Available With the arrival of the big data era in Earth observation, the remote sensing communities have accumulated a large amount of invaluable and irreplaceable data for global monitoring. These massive remote sensing data have enabled large-area and long-term series Earth observation, and have, in particular, made standard, automated product generation more popular. However, there is more than one type of data selection for producing a certain remote sensing product; no single remote sensor can cover such a large area at one time. Therefore, we should automatically select the best data source from redundant multisource remote sensing data, or select substitute data if data is lacking, during the generation of remote sensing products. However, the current data selection strategy mainly adopts the empirical model, and has a lack of theoretical support and quantitative analysis. Hence, comprehensively considering the spectral characteristics of ground objects and spectra differences of each remote sensor, by means of spectrum simulation and correlation analysis, we propose a suitability evaluation model for product generation. The model will enable us to obtain the Production Suitability Index (PSI of each remote sensing data. In order to validate the proposed model, two typical value-added information products, NDVI and NDWI, and two similar or complementary remote sensors, Landsat-OLI and HJ1A-CCD1, were chosen, and the verification experiments were performed. Through qualitative and quantitative analysis, the experimental results were consistent with our model calculation results, and strongly proved the validity of the suitability evaluation model. The proposed production suitability evaluation model could assist with standard, automated, serialized product generation. It will play an important role in one-station, value-added information services during the big data era of Earth observation.

  11. Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

    Science.gov (United States)

    Brooks, Colin; Bourgeau-Chavez, Laura; Endres, Sarah; Battaglia, Michael; Shuchman, Robert

    2015-01-01

    Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA

  12. Classification of remotely sensed images

    CSIR Research Space (South Africa)

    Dudeni, N

    2008-10-01

    Full Text Available For this research, the researchers examine various existing image classification algorithms with the aim of demonstrating how these algorithms can be applied to remote sensing images. These algorithms are broadly divided into supervised...

  13. [Estimation of desert vegetation coverage based on multi-source remote sensing data].

    Science.gov (United States)

    Wan, Hong-Mei; Li, Xia; Dong, Dao-Rui

    2012-12-01

    Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remote sensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remote sensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remote sensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remote sensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remote sensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remote sensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remote sensing monitoring scheme for the ecological restoration in the study area.

  14. Relational Database Extension Oriented, Self-adaptive Imagery Pyramid Model

    Directory of Open Access Journals (Sweden)

    HU Zhenghua

    2015-06-01

    Full Text Available With the development of remote sensing technology, especially the improvement of sensor resolution, the amount of image data is increasing. This puts forward higher requirements to manage huge amount of data efficiently and intelligently. And how to access massive remote sensing data with efficiency and smartness becomes an increasingly popular topic. In this paper, against current development status of Spatial Data Management System, we proposed a self-adaptive strategy for image blocking and a method for LoD(level of detailmodel construction that adapts, with the combination of database storage, network transmission and the hardware of the client. Confirmed by experiments, this imagery management mechanism can achieve intelligent and efficient storage and access in a variety of different conditions of database, network and client. This study provides a feasible idea and method for efficient image data management, contributing to the efficient access and management for remote sensing image data which are based on database technology under network environment of C/S architecture.

  15. Geographic information systems and remote sensing techniques in environmental assessment

    International Nuclear Information System (INIS)

    Kenny, F.M.

    1996-01-01

    Digital map products and spatial inventories are becoming increasingly available from geological surveys, agricultural, natural resource, environmental, energy, transportation and forestry departments. As well there are now multitudes of specialized digital airborne and satellite image products available. This wide availability of geographically referenced data and the advances in spatial data analysis software are providing geoscientists with new tools and new ways of viewing traditionally used data. Through several examples, this paper will demonstrate how remote sensing and GIS technologies can contribute to environmental assessment of an urban fringe area. Nowhere is the need for spatial inventories and mapping greater than in such areas, where pre-existing information becomes rapidly outdated. A 260-km 2 site, north of Metropolitan Toronto was chosen as a study area. A spatial data base was constructed which included imagery from three different satellite sensors, a Digital Terrain Model (DTM), and digital drainage network, and a digital copy of the Ontario Geological Survey's Quaternary geological map. (author). 15 refs., 1 tab., 17 figs

  16. A geospatial database model for the management of remote sensing datasets at multiple spectral, spatial, and temporal scales

    Science.gov (United States)

    Ifimov, Gabriela; Pigeau, Grace; Arroyo-Mora, J. Pablo; Soffer, Raymond; Leblanc, George

    2017-10-01

    In this study the development and implementation of a geospatial database model for the management of multiscale datasets encompassing airborne imagery and associated metadata is presented. To develop the multi-source geospatial database we have used a Relational Database Management System (RDBMS) on a Structure Query Language (SQL) server which was then integrated into ArcGIS and implemented as a geodatabase. The acquired datasets were compiled, standardized, and integrated into the RDBMS, where logical associations between different types of information were linked (e.g. location, date, and instrument). Airborne data, at different processing levels (digital numbers through geocorrected reflectance), were implemented in the geospatial database where the datasets are linked spatially and temporally. An example dataset consisting of airborne hyperspectral imagery, collected for inter and intra-annual vegetation characterization and detection of potential hydrocarbon seepage events over pipeline areas, is presented. Our work provides a model for the management of airborne imagery, which is a challenging aspect of data management in remote sensing, especially when large volumes of data are collected.

  17. Remote sensing of soybean stress as an indicator of chemical concentration of biosolid amended surface soils

    Science.gov (United States)

    Sridhar, B. B. Maruthi; Vincent, Robert K.; Roberts, Sheila J.; Czajkowski, Kevin

    2011-08-01

    The accumulation of heavy metals in the biosolid amended soils and the risk of their uptake into different plant parts is a topic of great concern. This study examines the accumulation of several heavy metals and nutrients in soybeans grown on biosolid applied soils and the use of remote sensing to monitor the metal uptake and plant stress. Field and greenhouse studies were conducted with soybeans grown on soils applied with biosolids at varying rates. The plant growth was monitored using Landsat TM imagery and handheld spectroradiometer in field and greenhouse studies, respectively. Soil and plant samples were collected and then analyzed for several elemental concentrations. The chemical concentrations in soils and roots increased significantly with increase in applied biosolid concentrations. Copper (Cu) and Molybdenum (Mo) accumulated significantly in the shoots of the metal-treated plants. Our spectral and Landsat TM image analysis revealed that the Normalized Difference Vegetative Index (NDVI) can be used to distinguish the metal stressed plants. The NDVI showed significant negative correlation with increase in soil Cu concentrations followed by other elements. This study suggests the use of remote sensing to monitor soybean stress patterns and thus indirectly assess soil chemical characteristics.

  18. LACO-Wiki: A land cover validation tool and a new, innovative teaching resource for remote sensing and the geosciences

    Science.gov (United States)

    See, Linda; Perger, Christoph; Dresel, Christopher; Hofer, Martin; Weichselbaum, Juergen; Mondel, Thomas; Steffen, Fritz

    2016-04-01

    The validation of land cover products is an important step in the workflow of generating a land cover map from remotely-sensed imagery. Many students of remote sensing will be given exercises on classifying a land cover map followed by the validation process. Many algorithms exist for classification, embedded within proprietary image processing software or increasingly as open source tools. However, there is little standardization for land cover validation, nor a set of open tools available for implementing this process. The LACO-Wiki tool was developed as a way of filling this gap, bringing together standardized land cover validation methods and workflows into a single portal. This includes the storage and management of land cover maps and validation data; step-by-step instructions to guide users through the validation process; sound sampling designs; an easy-to-use environment for validation sample interpretation; and the generation of accuracy reports based on the validation process. The tool was developed for a range of users including producers of land cover maps, researchers, teachers and students. The use of such a tool could be embedded within the curriculum of remote sensing courses at a university level but is simple enough for use by students aged 13-18. A beta version of the tool is available for testing at: http://www.laco-wiki.net.

  19. Operational programs in forest management and priority in the utilization of remote sensing

    Science.gov (United States)

    Douglass, R. W.

    1978-01-01

    A speech is given on operational remote sensing programs in forest management and the importance of remote sensing in forestry is emphasized. Forest service priorities in using remote sensing are outlined.

  20. Innovative progress and sustainable development of remote sensing for uranium geology

    International Nuclear Information System (INIS)

    Liu Dechang; Zhao Yingjun; Ye Fawang

    2009-01-01

    The paper reviewes the innovative process of remote sensing for the uranium geology in Beijing Research Institute of Uranium Geology (BRIUG), discusses the science and technology progress of uranium geology due to remote sensing technique, and the way how to keep sustainable development of the remote sensing for uranium geology so as to play an important role in the uranium geology in the future. (authors)