WorldWideScience

Sample records for modeling remote sensing

  1. 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.

  2. Remote Sensing

    Indian Academy of Sciences (India)

    netic radiation as a medium of interaction. Space borne remote sensing is fast emerging as a front running provider of information on natural resources in a spatial format. This article briefly discusses the physical basis of remote sensing, how information is extracted from images and various applications of remote sensing.

  3. Model-based acoustic remote sensing of seafloor characteristics

    Digital Repository Service at National Institute of Oceanography (India)

    De, Ch.; Chakraborty, B.

    =UTF-8 3868 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 10, OCTOBER 2011 Model-Based Acoustic Remote Sensing of Seafloor Characteristics Chanchal De and Bishwajit Chakraborty, Member, IEEE Abstract—The characterization... of the estimated values of seafloor roughness spectrum parameters with the values of sediment mean grain size are compared with published information available in the literature. Index Terms—Acoustic remote sensing, backscatter model, echo envelope, inversion, mean...

  4. 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 ...

  5. Remote Sensing

    Indian Academy of Sciences (India)

    Rangnath R Navalgund, after working for more than two decades at the. Space Applications. Centre (ISRO),. Ahmedabad has moved over to the National. Remote Sensing Agency,. Department of Space,. Hyderabad, as its. Director since May 2001. Definition of Indian spacebome remote sensing missions and formulation of ...

  6. Remote sensing models and methods for image processing

    CERN Document Server

    Schowengerdt, Robert A

    1997-01-01

    This book is a completely updated, greatly expanded version of the previously successful volume by the author. The Second Edition includes new results and data, and discusses a unified framework and rationale for designing and evaluating image processing algorithms.Written from the viewpoint that image processing supports remote sensing science, this book describes physical models for remote sensing phenomenology and sensors and how they contribute to models for remote-sensing data. The text then presents image processing techniques and interprets them in terms of these models. Spectral, s

  7. Remote Sensing

    Indian Academy of Sciences (India)

    application area. RS data in conjunction with collateral data has greatly facilitated integrated development of land and water resources on watershed basis leading to sustainable develop- ment. Disaster monitoring, damage assessment and mitigation has been a main beneficiary of spaceborne remote sensing. Sequen-.

  8. Modeling, simulation, and analysis of optical remote sensing systems

    Science.gov (United States)

    Kerekes, John Paul; Landgrebe, David A.

    1989-01-01

    Remote Sensing of the Earth's resources from space-based sensors has evolved in the past 20 years from a scientific experiment to a commonly used technological tool. The scientific applications and engineering aspects of remote sensing systems have been studied extensively. However, most of these studies have been aimed at understanding individual aspects of the remote sensing process while relatively few have studied their interrelations. A motivation for studying these interrelationships has arisen with the advent of highly sophisticated configurable sensors as part of the Earth Observing System (EOS) proposed by NASA for the 1990's. Two approaches to investigating remote sensing systems are developed. In one approach, detailed models of the scene, the sensor, and the processing aspects of the system are implemented in a discrete simulation. This approach is useful in creating simulated images with desired characteristics for use in sensor or processing algorithm development. A less complete, but computationally simpler method based on a parametric model of the system is also developed. In this analytical model the various informational classes are parameterized by their spectral mean vector and covariance matrix. These class statistics are modified by models for the atmosphere, the sensor, and processing algorithms and an estimate made of the resulting classification accuracy among the informational classes. Application of these models is made to the study of the proposed High Resolution Imaging Spectrometer (HRIS). The interrelationships among observational conditions, sensor effects, and processing choices are investigated with several interesting results.

  9. 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...

  10. Distributed calibrating snow models using remotely sensed snow cover information

    Science.gov (United States)

    Li, H.

    2015-12-01

    Distributed calibrating snow models using remotely sensed snow cover information Hongyi Li1, Tao Che1, Xin Li1, Jian Wang11. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China For improving the simulation accuracy of snow model, remotely sensed snow cover data are used to calibrate spatial parameters of snow model. A physically based snow model is developed and snow parameters including snow surface roughness, new snow density and critical threshold temperature distinguishing snowfall from precipitation, are spatially calibrated in this study. The study region, Babaohe basin, located in northwestern China, have seasonal snow cover and with complex terrain. The results indicates that the spatially calibration of snow model parameters make the simulation results more reasonable, and the simulated snow accumulation days, plot-scale snow depth are more better than lumped calibration.

  11. Validating firn compaction model with remote sensing data

    DEFF Research Database (Denmark)

    Simonsen, S. B.; Stenseng, Lars; Sørensen, Louise Sandberg

    A comprehensive understanding of firn processes is of outmost importance, when estimating present and future changes of the Greenland Ice Sheet. Especially, when remote sensing altimetry is used to assess the state of ice sheets and their contribution to global sea level rise, firn compaction...... of firn compaction to correct ICESat measurements and assessing the present mass loss of the Greenland ice sheet. Validation of the model against the radar data gives good results and confidence in using the model to answer important questions. Questions such as; how large is the firn compaction...... correction relative to the changes in the elevation of the surface observed with remote sensing altimetry? What model time resolution is necessary to resolved the observed layering? What model refinements are necessary to give better estimates of the surface mass balance of the Greenland ice sheet from...

  12. Hydrologic model calibration using remotely-sensed data in Alaska

    Science.gov (United States)

    Driscoll, J. M.; Hay, L.; Van Beusekom, A. E.

    2016-12-01

    Watershed models in Alaska are critical for understanding snow- and glacier-dominated hydrologic processes in a changing climate. The highly diverse, frozen, and remote landscapes in Alaska present a host of new challenges for broad-scale hydrologic model development, including a notable absence of field-measured streamflow data. Without this commonly-used data for model calibration, alternative methods need to be developed in order to model hydrologic processes Alaska. Calibration methods that use remotely-sensed data in multi-objective, step-wise procedures were developed in this study. A calibration method using snow covered area (SCA) measured by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) was developed for a daily, deterministic, physically-based watershed model. The model selected for this study was the U.S. Geological Survey's Precipitation Runoff Modeling System (PRMS), and focused on a 62,500-sqkm test basin in southeastern Alaska from 2000 to 2014. Watershed model calibration to SCA ensures model processes adequately estimate transitional model states during snowmelt, a dominant hydrologic process in the basin. Gridded SCA data measuring fractional coverage were spatially aggregated to hydrologic response units within the basin. Model results were aggregated to eleven subbasins for calibration, comparison, and evaluation. Calibration of the subbasin watersheds to intermediate snowmelt process states, such as daily SCA, will likely also improve model estimates of solar radiation, potential evapotranspiration, annual water balance, and components of daily runoff. In Alaska where snow and glacier-fed systems are abundant, observed data are often scarce; therefore the development of calibration methods with remotely-sensed data are critical for improvement of watershed models which can then be used to estimate response to climate change. This study provides a method using remotely-sensed snow cover data to overcome field-measured data

  13. Use of remotely sensed precipitation and leaf area index in a distributed hydrological model

    DEFF Research Database (Denmark)

    Andersen, Jens; Dybkjær, Gorm Ibsen; Jensen, Karsten Høgh

    2002-01-01

    distributed hydrological modelling, remote sensing, precipitation, leaf area index, NOAA AVHRR, cold cloud duration......distributed hydrological modelling, remote sensing, precipitation, leaf area index, NOAA AVHRR, cold cloud duration...

  14. Perspectives in using a remotely sensed dryness index in distributed hydrological models at river basin scale

    DEFF Research Database (Denmark)

    Andersen, J.; Sandholt, Inge; Jensen, Karsten Høgh

    2002-01-01

    Remote Sensing, hydrological modelling, dryness index, surface temperature, vegetation index, Africa, Senegal, soil moisture......Remote Sensing, hydrological modelling, dryness index, surface temperature, vegetation index, Africa, Senegal, soil moisture...

  15. Modeling Global Urbanization Supported by Nighttime Light Remote Sensing

    Science.gov (United States)

    Zhou, Y.

    2015-12-01

    Urbanization, a major driver of global change, profoundly impacts our physical and social world, for example, altering carbon cycling and climate. Understanding these consequences for better scientific insights and effective decision-making unarguably requires accurate information on urban extent and its spatial distributions. In this study, we developed a cluster-based method to estimate the optimal thresholds and map urban extents from the nighttime light remote sensing data, extended this method to the global domain by developing a computational method (parameterization) to estimate the key parameters in the cluster-based method, and built a consistent 20-year global urban map series to evaluate the time-reactive nature of global urbanization (e.g. 2000 in Fig. 1). Supported by urban maps derived from nightlights remote sensing data and socio-economic drivers, we developed an integrated modeling framework to project future urban expansion by integrating a top-down macro-scale statistical model with a bottom-up urban growth model. With the models calibrated and validated using historical data, we explored urban growth at the grid level (1-km) over the next two decades under a number of socio-economic scenarios. The derived spatiotemporal information of historical and potential future urbanization will be of great value with practical implications for developing adaptation and risk management measures for urban infrastructure, transportation, energy, and water systems when considered together with other factors such as climate variability and change, and high impact weather events.

  16. Remote sensing applied to numerical modelling. [water resources pollution

    Science.gov (United States)

    Sengupta, S.; Lee, S. S.; Veziroglu, T. N.; Bland, R.

    1975-01-01

    Progress and remaining difficulties in the construction of predictive mathematical models of large bodies of water as ecosystems are reviewed. Surface temperature is at present the only variable than can be measured accurately and reliably by remote sensing techniques, but satellite infrared data are of sufficient resolution for macro-scale modeling of oceans and large lakes, and airborne radiometers are useful in meso-scale analysis (of lakes, bays, and thermal plumes). Finite-element and finite-difference techniques applied to the solution of relevant coupled time-dependent nonlinear partial differential equations are compared, and the specific problem of the Biscayne Bay and environs ecosystem is tackled in a finite-differences treatment using the rigid-lid model and a rigid-line grid system.

  17. Hyperspectral Remote Sensing and Ecological Modeling Research and Education at Mid America Remote Sensing Center (MARC): Field and Laboratory Enhancement

    Science.gov (United States)

    Cetin, Haluk

    1999-01-01

    The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and

  18. 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.

  19. Use of remote sensing to model ungauged Chilean basins

    Science.gov (United States)

    Vasquez, Nicolas; Vargas, Ximena

    2016-04-01

    Calibration of hydrological models is usually performed in gauged basins with streamflow data, which is the result of the hydrological cycle processes, due to a poor monitoring system of other processes like melting, infiltration, evapotranspiration or sublimation. This approach can generate several parameters combinations with similar streamflow results and choosing a reliable set of parameters can be challenging, especially in ungauged basins. Remote sensing can be useful because is an additional source of ungauged variables, and is distributed in space and time. This is valuable information related to the processes of hydrological cycle, and it helps to represent the basin with physically based models where the focus is on the processes, such as the Cold Regional Hydrological Model (CRHM). There are several satellites products related to the hydrological cycle such as snow covered area, albedo, evapotranspiration or surface temperature, in the case of MODIS, rain rate from TRMM, Soil moisture from SMOS or snow water equivalent (SWE) from AMSR, and these can be used to improve the representation of the processes in a basin or, in the case of this work, to estimate stream flow using remote sensing only. The study area is Elqui River, in northern Chile, with a semi-arid mediterranean climate and a snow driven regime due to the Andes, where snow accumulation and snowmelt control water availability and the maximum snow covered area reach 50% of the total basin. Several satellite products related principally to snow are considered to represent the variation of the snowpack in space and time as inputs to the model or as state variables.

  20. Development of mathematical techniques for the assimilation of remote sensing data into atmospheric models

    International Nuclear Information System (INIS)

    Seinfeld, J.H.

    1982-01-01

    The problem of the assimilation of remote sensing data into mathematical models of atmospheric pollutant species was investigated. The data assimilation problem is posed in terms of the matching of spatially integrated species burden measurements to the predicted three-dimensional concentration fields from atmospheric diffusion models. General conditions were derived for the reconstructability of atmospheric concentration distributions from data typical of remote sensing applications, and a computational algorithm (filter) for the processing of remote sensing data was developed

  1. AN INFORMATION SERVICE MODEL FOR REMOTE SENSING EMERGENCY SERVICES

    Directory of Open Access Journals (Sweden)

    Z. Zhang

    2017-09-01

    Full Text Available This paper presents a method on the semantic access environment, which can solve the problem about how to identify the correct natural disaster emergency knowledge and return to the demanders. The study data is natural disaster knowledge text set. Firstly, based on the remote sensing emergency knowledge database, we utilize the sematic network to extract the key words in the input documents dataset. Then, using the semantic analysis based on words segmentation and PLSA, to establish the sematic access environment to identify the requirement of users and match the emergency knowledge in the database. Finally, the user preference model was established, which could help the system to return the corresponding information to the different users. The results indicate that semantic analysis can dispose the natural disaster knowledge effectively, which will realize diversified information service, enhance the precision of information retrieval and satisfy the requirement of users.

  2. Evaluation of Evapotranspiration Partitioning in Remote Sensing Models

    Science.gov (United States)

    Talsma, C.; Good, S. P.; Jimenez, C.; Martens, B.; Fisher, J.

    2017-12-01

    Satellite driven evapotranspiration(ET) products have become a preferred method in estimating global and regional evaporative fluxes. However, the estimated ET partitions of soil evaporation, transpiration, and canopy interception show strong divergence between models and have largely not been validated. This paper considers three such algorithms: The Penman-Montieth model from the Moderate Resolution Imaging Spectroradiometer (PM-MODIS), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL), and the Global Land Evaporation Amsterdam Model (GLEAM). Modeled partition outputs from 2005-2007 are validated against a compiled list of field studies spanning several decades. Field studies using stable isotope tracers, sap-flow measurements, radial flowmeters, and other techniques to measure ET components were included. The correlation between annual estimates of field and remote sensing modeled soil evaporation (r2= 0.12-0.22, N=38), interception (r2 = 0.32-0.85, N=24), and transpiration (r2 = 0.37-0.56, N=38) are relatively poor compared to the quality of total ET estimates (r2= 0.61-0.74, N=38). The modeled estimates of soil evaporation are generally poor across all models and are likely culpable for much of the partitioning error. Possible reasons for divergences between and amongst modeled estimates of ET partitions are also presented.

  3. Remote sensing models and methods for image processing

    CERN Document Server

    Schowengerdt, Robert A

    2007-01-01

    Remote sensing is a technology that engages electromagnetic sensors to measure and monitor changes in the earth's surface and atmosphere. Normally this is accomplished through the use of a satellite or aircraft. This book, in its 3rd edition, seamlessly connects the art and science of earth remote sensing with the latest interpretative tools and techniques of computer-aided image processing. Newly expanded and updated, this edition delivers more of the applied scientific theory and practical results that helped the previous editions earn wide acclaim and become classroom and industry standa

  4. Modeling of Aerosol Vertical Profiles Using GIS and Remote Sensing

    Directory of Open Access Journals (Sweden)

    Kwon Ho Lee

    2009-06-01

    Full Text Available The use of Geographic Information Systems (GIS and Remote Sensing (RS by climatologists, environmentalists and urban planners for three dimensional modeling and visualization of the landscape is well established. However no previous study has implemented these techniques for 3D modeling of atmospheric aerosols because air quality data is traditionally measured at ground points, or from satellite images, with no vertical dimension. This study presents a prototype for modeling and visualizing aerosol vertical profiles over a 3D urban landscape in Hong Kong. The method uses a newly developed technique for the derivation of aerosol vertical profiles from AERONET sunphotometer measurements and surface visibility data, and links these to a 3D urban model. This permits automated modeling and visualization of aerosol concentrations at different atmospheric levels over the urban landscape in near-real time. Since the GIS platform permits presentation of the aerosol vertical distribution in 3D, it can be related to the built environment of the city. Examples are given of the applications of the model, including diagnosis of the relative contribution of vehicle emissions to pollution levels in the city, based on increased near-surface concentrations around weekday rush-hour times. The ability to model changes in air quality and visibility from ground level to the top of tall buildings is also demonstrated, and this has implications for energy use and environmental policies for the tall mega-cities of the future.

  5. 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...

  6. Use of remote sensing data in distributed hydrological models: applications in the Senegal River basin

    DEFF Research Database (Denmark)

    Sandholt, Inge; Andersen, Jens Asger; Gybkjær, Gorm

    1999-01-01

    Earth observation, remote sensing, hydrology, distributed hydrological modelling, West Africa, Senegal river basin, land cover, soil moisture, NOAA AVHRR, SPOT, Mike-she......Earth observation, remote sensing, hydrology, distributed hydrological modelling, West Africa, Senegal river basin, land cover, soil moisture, NOAA AVHRR, SPOT, Mike-she...

  7. 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

  8. Remote Sensing and Modeling of Cyclone Monica near Peak Intensity

    Directory of Open Access Journals (Sweden)

    Stephen L. Durden

    2010-07-01

    Full Text Available Cyclone Monica was an intense Southern Hemisphere tropical cyclone of 2006. Although no in situ measurements of Monica’s inner core were made, microwave, infrared, and visible satellite instruments observed Monica before and during peak intensity through landfall on Australia’s northern coast. The author analyzes remote sensing measurements in detail to investigate Monica’s intensity. While Dvorak analysis of its imagery argues that it was of extreme intensity, infrared and microwave soundings indicate a somewhat lower intensity, especially as it neared landfall. The author also describes several numerical model runs that were made to investigate the maximum possible intensity for the observed environmental conditions; these simulations also suggest a lower intensity than estimates from Dvorak analysis alone. Based on the evidence from the various measurements and modeling, the estimated range for the minimum sea level pressure at peak intensity is 900 to 920 hPa. The estimated range for the one-minute averaged maximum wind speed at peak intensity is 72 to 82 m/s. These maxima were likely reached about 24 hours prior to landfall, with some weakening occurring afterward.

  9. Enhanced Modeling of Remotely Sensed Annual Land Surface Temperature Cycle

    Science.gov (United States)

    Zou, Z.; Zhan, W.; Jiang, L.

    2017-09-01

    Satellite thermal remote sensing provides access to acquire large-scale Land surface temperature (LST) data, but also generates missing and abnormal values resulting from non-clear-sky conditions. Given this limitation, Annual Temperature Cycle (ATC) model was employed to reconstruct the continuous daily LST data over a year. The original model ATCO used harmonic functions, but the dramatic changes of the real LST caused by the weather changes remained unclear due to the smooth sine curve. Using Aqua/MODIS LST products, NDVI and meteorological data, we proposed enhanced model ATCE based on ATCO to describe the fluctuation and compared their performances for the Yangtze River Delta region of China. The results demonstrated that, the overall root mean square errors (RMSEs) of the ATCE was lower than ATCO, and the improved accuracy of daytime was better than that of night, with the errors decreased by 0.64 K and 0.36 K, respectively. The improvements of accuracies varied with different land cover types: the forest, grassland and built-up areas improved larger than water. And the spatial heterogeneity was observed for performance of ATC model: the RMSEs of built-up area, forest and grassland were around 3.0 K in the daytime, while the water attained 2.27 K; at night, the accuracies of all types significantly increased to similar RMSEs level about 2 K. By comparing the differences between LSTs simulated by two models in different seasons, it was found that the differences were smaller in the spring and autumn, while larger in the summer and winter.

  10. Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

    Science.gov (United States)

    Yueh, Simon H.

    2004-01-01

    Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

  11. Intercomparisons between passive and active microwave remote sensing, and hydrological modeling for soil moisture

    Science.gov (United States)

    Wood, E. F.; Lin, D.-S.; Mancini, M.; Thongs, D.; Troch, P. A.; Jackson, T. J.; Famiglietti, J. S.; Engman, E. T.

    1993-01-01

    Soil moisture estimations from a distributed hydrological model and two microwave sensors were compared with ground measurements collected during the MAC-HYDRO'90 experiment. The comparison was done with the purpose of evaluating the performance of the hydrological model and examining the limitations of remote sensing techniques used in soil moisture estimation. An image integration technique was used to integrate and analyze rainfall, soil properties, land cover, topography, and remote sensing imagery. Results indicate that the hydrological model and microwave sensors successfully picked up temporal variations of soil moisture and that the spatial soil moisture pattern may be remotely sensed with reasonable accuracy using existing algorithms.

  12. A Space-Time Periodic Task Model for Recommendation of Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Xiuhong Zhang

    2018-01-01

    Full Text Available With the rapid development of remote sensing technology, the quantity and variety of remote sensing images are growing so quickly that proactive and personalized access to data has become an inevitable trend. One of the active approaches is remote sensing image recommendation, which can offer related image products to users according to their preference. Although multiple studies on remote sensing retrieval and recommendation have been performed, most of these studies model the user profiles only from the perspective of spatial area or image features. In this paper, we propose a spatiotemporal recommendation method for remote sensing data based on the probabilistic latent topic model, which is named the Space-Time Periodic Task model (STPT. User retrieval behaviors of remote sensing images are represented as mixtures of latent tasks, which act as links between users and images. Each task is associated with the joint probability distribution of space, time and image characteristics. Meanwhile, the von Mises distribution is introduced to fit the distribution of tasks over time. Then, we adopt Gibbs sampling to learn the random variables and parameters and present the inference algorithm for our model. Experiments show that the proposed STPT model can improve the capability and efficiency of remote sensing image data services.

  13. Remote Sensing and Modeling for Improving Operational Aquatic Plant Management

    Science.gov (United States)

    Bubenheim, Dave

    2016-01-01

    The California Sacramento-San Joaquin River Delta is the hub for California’s water supply, conveying water from Northern to Southern California agriculture and communities while supporting important ecosystem services, agriculture, and communities in the Delta. Changes in climate, long-term drought, water quality changes, and expansion of invasive aquatic plants threatens ecosystems, impedes ecosystem restoration, and is economically, environmentally, and sociologically detrimental to the San Francisco Bay/California Delta complex. NASA Ames Research Center and the USDA-ARS partnered with the State of California and local governments to develop science-based, adaptive-management strategies for the Sacramento-San Joaquin Delta. The project combines science, operations, and economics related to integrated management scenarios for aquatic weeds to help land and waterway managers make science-informed decisions regarding management and outcomes. The team provides a comprehensive understanding of agricultural and urban land use in the Delta and the major water sheds (San Joaquin/Sacramento) supplying the Delta and interaction with drought and climate impacts on the environment, water quality, and weed growth. The team recommends conservation and modified land-use practices and aids local Delta stakeholders in developing management strategies. New remote sensing tools have been developed to enhance ability to assess conditions, inform decision support tools, and monitor management practices. Science gaps in understanding how native and invasive plants respond to altered environmental conditions are being filled and provide critical biological response parameters for Delta-SWAT simulation modeling. Operational agencies such as the California Department of Boating and Waterways provide testing and act as initial adopter of decision support tools. Methods developed by the project can become routine land and water management tools in complex river delta systems.

  14. 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

  15. 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...

  16. Kent mixture model for classification of remote sensing data on spherical manifolds

    CSIR Research Space (South Africa)

    Lunga, D

    2011-10-01

    Full Text Available Modern remote sensing imaging sensor technology provides detailed spectral and spatial information that enables precise analysis of land cover usage. From a research point of view, traditional widely used statistical models are often limited...

  17. An object-based storage model for distributed remote sensing images

    Science.gov (United States)

    Yu, Zhanwu; Li, Zhongmin; Zheng, Sheng

    2006-10-01

    It is very difficult to design an integrated storage solution for distributed remote sensing images to offer high performance network storage services and secure data sharing across platforms using current network storage models such as direct attached storage, network attached storage and storage area network. Object-based storage, as new generation network storage technology emerged recently, separates the data path, the control path and the management path, which solves the bottleneck problem of metadata existed in traditional storage models, and has the characteristics of parallel data access, data sharing across platforms, intelligence of storage devices and security of data access. We use the object-based storage in the storage management of remote sensing images to construct an object-based storage model for distributed remote sensing images. In the storage model, remote sensing images are organized as remote sensing objects stored in the object-based storage devices. According to the storage model, we present the architecture of a distributed remote sensing images application system based on object-based storage, and give some test results about the write performance comparison of traditional network storage model and object-based storage model.

  18. 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.

  19. Towards Remotely Sensed Composite Global Drought Risk Modelling

    Science.gov (United States)

    Dercas, Nicholas; Dalezios, Nicolas

    2015-04-01

    Drought is a multi-faceted issue and requires a multi-faceted assessment. Droughts may have the origin on precipitation deficits, which sequentially and by considering different time and space scales may impact soil moisture, plant wilting, stream flow, wildfire, ground water levels, famine and social impacts. There is a need to monitor drought even at a global scale. Key variables for monitoring drought include climate data, soil moisture, stream flow, ground water, reservoir and lake levels, snow pack, short-medium-long range forecasts, vegetation health and fire danger. However, there is no single definition of drought and there are different drought indicators and indices even for each drought type. There are already four operational global drought risk monitoring systems, namely the U.S. Drought Monitor, the European Drought Observatory (EDO), the African and the Australian systems, respectively. These systems require further research to improve the level of accuracy, the time and space scales, to consider all types of drought and to achieve operational efficiency, eventually. This paper attempts to contribute to the above mentioned objectives. Based on a similar general methodology, the multi-indicator approach is considered. This has resulted from previous research in the Mediterranean region, an agriculturally vulnerable region, using several drought indices separately, namely RDI and VHI. The proposed scheme attempts to consider different space scaling based on agroclimatic zoning through remotely sensed techniques and several indices. Needless to say, the agroclimatic potential of agricultural areas has to be assessed in order to achieve sustainable and efficient use of natural resources in combination with production maximization. Similarly, the time scale is also considered by addressing drought-related impacts affected by precipitation deficits on time scales ranging from a few days to a few months, such as non-irrigated agriculture, topsoil moisture

  20. Regions-of-interest extraction from remote sensing imageries using visual attention modelling

    Science.gov (United States)

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

    2016-10-01

    Processing and analysing large volume of remote sensing data is both labour intensive and time consuming. Therefore, there is a need to effectively and efficiently identify meaningful regions in these remote sensing data for timely resource management. In this paper, we propose a visual attention model for identifying regions-of-interest in remote sensing data. The proposed model incorporates both bottom-up spatial saliency and top-down objectness, by fusing a co-occurrence histogram saliency model with the BING objectness model. The co-occurrence histogram saliency model is constructed by first building a 2D co-occurrence histogram that captures co-occurrence and occurrence of image intensities, and then using the 2D co-occurrence histogram to model local and global saliency. On the other hand, the BING objectness model is constructed by resizing image intensities in variable-sized windows to 8x8 windows, and then using the norms of the gradients in the 8x8 windows as features to train a generic objectness measure. Our experimental results show that the proposed model can effectively and efficiently identify regions-of-interest in remote sensing data. The proposed model may be applied in various remote sensing applications such as anomaly detection, urban area detection, target detection, or land use classification.

  1. Cooling tower and plume modeling for satellite remote sensing applications

    Energy Technology Data Exchange (ETDEWEB)

    Powers, B.J.

    1995-05-01

    It is often useful in nonproliferation studies to be able to remotely estimate the power generated by a power plant. Such information is indirectly available through an examination of the power dissipated by the plant. Power dissipation is generally accomplished either by transferring the excess heat generated into the atmosphere or into bodies of water. It is the former method with which we are exclusively concerned in this report. We discuss in this report the difficulties associated with such a task. In particular, we primarily address the remote detection of the temperature associated with the condensed water plume emitted from the cooling tower. We find that the effective emissivity of the plume is of fundamental importance for this task. Having examined the dependence of the plume emissivity in several IR bands and with varying liquid water content and droplet size distributions, we conclude that the plume emissivity, and consequently the plume brightness temperature, is dependent upon not only the liquid water content and band, but also upon the droplet size distribution. Finally, we discuss models dependent upon a detailed point-by-point description of the hydrodynamics and thermodynamics of the plume dynamics and those based upon spatially integrated models. We describe in detail a new integral model, the LANL Plume Model, which accounts for the evolution of the droplet size distribution. Some typical results obtained from this model are discussed.

  2. Advancements in Modelling of Land Surface Energy Fluxes with Remote Sensing at Different Spatial Scales

    DEFF Research Database (Denmark)

    Guzinski, Radoslaw

    uxes, such as sensible heat ux, ground heat ux and net radiation, are also necessary. While it is possible to measure those uxes with ground-based instruments at local scales, at region scales they usually need to be modelled or estimated with the help of satellite remote sensing data. Even though...... to increase the spatial resolution of the reliable DTD-modelled fluxes from 1 km to 30 m. Furthermore, synergies between remote sensing based models and distributed hydrological models were studied with the aim of improving spatial performance of the hydrological models through incorporation of remote sensing......Evaporation of water from soil and its transpiration by vegetation together form a ux between the land and the atmosphere called evapotranspiration (ET). ET is a key factor in many natural and anthropogenic processes. It forms the basis of the hydrological cycle and has a strong inuence on local...

  3. 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...

  4. 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.

  5. Comparison between remote sensing and a dynamic vegetation model for estimating terrestrial primary production of Africa.

    Science.gov (United States)

    Ardö, Jonas

    2015-12-01

    Africa is an important part of the global carbon cycle. It is also a continent facing potential problems due to increasing resource demand in combination with climate change-induced changes in resource supply. Quantifying the pools and fluxes constituting the terrestrial African carbon cycle is a challenge, because of uncertainties in meteorological driver data, lack of validation data, and potentially uncertain representation of important processes in major ecosystems. In this paper, terrestrial primary production estimates derived from remote sensing and a dynamic vegetation model are compared and quantified for major African land cover types. Continental gross primary production estimates derived from remote sensing were higher than corresponding estimates derived from a dynamic vegetation model. However, estimates of continental net primary production from remote sensing were lower than corresponding estimates from the dynamic vegetation model. Variation was found among land cover classes, and the largest differences in gross primary production were found in the evergreen broadleaf forest. Average carbon use efficiency (NPP/GPP) was 0.58 for the vegetation model and 0.46 for the remote sensing method. Validation versus in situ data of aboveground net primary production revealed significant positive relationships for both methods. A combination of the remote sensing method with the dynamic vegetation model did not strongly affect this relationship. Observed significant differences in estimated vegetation productivity may have several causes, including model design and temperature sensitivity. Differences in carbon use efficiency reflect underlying model assumptions. Integrating the realistic process representation of dynamic vegetation models with the high resolution observational strength of remote sensing may support realistic estimation of components of the carbon cycle and enhance resource monitoring, providing suitable validation data is available.

  6. A remote sensing driven distributed hydrological model of the Senegal River basin

    DEFF Research Database (Denmark)

    Stisen, Simon; Jensen, Karsten Høgh; Sandholt, Inge

    2008-01-01

    Distributed hydrological models require extensive data amounts for driving the models and for parameterization of the land surface and subsurface. This study investigates the potential of applying remote sensing (RS) based input data in a hydrological model for the 350,000 km2 Senegal River basin...

  7. Use of remotely sensed precipitation and leaf area index in a distributed hydrological model

    DEFF Research Database (Denmark)

    Andersen, J.; Dybkjær, G.; Jensen, Karsten Høgh

    2002-01-01

    Remotely sensed precipitation from METEOSAT data and leaf area index (LAI) from NOAA AVHRR data is used as input data to the distributed hydrological modelling of three sub catchments (82.000 km(2)) in the Senegal River Basin. Further, root depths of annual vegetation are related to the temporal...... and spatial variation of LAI. The modelling results are compared with results based on conventional input of precipitation and vegetation characteristics. The introduction of remotely sensed LAI shows improvements in the simulated hydrographs, a marked change in the relative proportions of actual...... evapotranspiration comprising canopy evaporation, soil evaporation and transpiration. while no clear trend in the spatial pattern could be found, The remotely sensed precipitation resulted in similar model performances with respect to the simulated hydrographs as with the conventional raingauge input. A simple...

  8. Mathematic modeling of the Earth's surface and the process of remote sensing

    Science.gov (United States)

    Balter, B. M.

    1979-01-01

    It is shown that real data from remote sensing of the Earth from outer space are not best suited to the search for optimal procedures with which to process such data. To work out the procedures, it was proposed that data synthesized with the help of mathematical modeling be used. A criterion for simularity to reality was formulated. The basic principles for constructing methods for modeling the data from remote sensing are recommended. A concrete method is formulated for modeling a complete cycle of radiation transformations in remote sensing. A computer program is described which realizes the proposed method. Some results from calculations are presented which show that the method satisfies the requirements imposed on it.

  9. Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing.

    Directory of Open Access Journals (Sweden)

    Yvonne Walz

    2015-11-01

    Full Text Available Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health.We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI. The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d'Ivoire and validated against readily available survey data from school-aged children.Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d'Ivoire.A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and

  10. Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing.

    Science.gov (United States)

    Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Vounatsou, Penelope; Poda, Jean-Noël; N'Goran, Eliézer K; Utzinger, Jürg; Raso, Giovanna

    2015-11-01

    Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d'Ivoire and validated against readily available survey data from school-aged children. Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d'Ivoire. A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail

  11. Remote Sensing Technologies and Geospatial Modelling Hierarchy for Smart City Support

    Science.gov (United States)

    Popov, M.; Fedorovsky, O.; Stankevich, S.; Filipovich, V.; Khyzhniak, A.; Piestova, I.; Lubskyi, M.; Svideniuk, M.

    2017-12-01

    The approach to implementing the remote sensing technologies and geospatial modelling for smart city support is presented. The hierarchical structure and basic components of the smart city information support subsystem are considered. Some of the already available useful practical developments are described. These include city land use planning, urban vegetation analysis, thermal condition forecasting, geohazard detection, flooding risk assessment. Remote sensing data fusion approach for comprehensive geospatial analysis is discussed. Long-term city development forecasting by Forrester - Graham system dynamics model is provided over Kiev urban area.

  12. REMOTE SENSING TECHNOLOGIES AND GEOSPATIAL MODELLING HIERARCHY FOR SMART CITY SUPPORT

    Directory of Open Access Journals (Sweden)

    M. Popov

    2017-12-01

    Full Text Available The approach to implementing the remote sensing technologies and geospatial modelling for smart city support is presented. The hierarchical structure and basic components of the smart city information support subsystem are considered. Some of the already available useful practical developments are described. These include city land use planning, urban vegetation analysis, thermal condition forecasting, geohazard detection, flooding risk assessment. Remote sensing data fusion approach for comprehensive geospatial analysis is discussed. Long-term city development forecasting by Forrester – Graham system dynamics model is provided over Kiev urban area.

  13. Spatial pattern evaluation of a calibrated national hydrological model - a remote-sensing-based diagnostic approach

    Science.gov (United States)

    Mendiguren, Gorka; Koch, Julian; Stisen, Simon

    2017-11-01

    Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds

  14. Estimating Net Primary Production of Swedish Forest Landscapes by Combining Mechanistic Modeling and Remote Sensing

    DEFF Research Database (Denmark)

    Tagesson, Håkan Torbern; Smith, Benjamin; Løfgren, Anders

    2009-01-01

    and the Beer-Lambert law. LAI estimates were compared with satellite-extrapolated field estimates of LAI, and the results were generally acceptable. NPP estimates directly from the dynamic vegetation model and estimates obtained by combining the model estimates with remote sensing information were, on average...

  15. Equivalent sensor radiance generation and remote sensing from model parameters - Part 1: Equivalent sensor radiance formulation

    Science.gov (United States)

    Wind, G.; da Silva, A. M.; Norris, P. M.; Platnick, S.

    2013-07-01

    In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate). The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement.

  16. 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

  17. 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

  18. Challenges of assessing fire and burn severity using field measures, remote sensing and modelling

    Science.gov (United States)

    Penelope Morgan; Robert E. Keane; Gregory K. Dillon; Theresa B. Jain; Andrew T. Hudak; Eva C. Karau; Pamela G. Sikkink; Zachery A. Holden; Eva K. Strand

    2014-01-01

    Comprehensive assessment of ecological change after fires have burned forests and rangelands is important if we are to understand, predict and measure fire effects. We highlight the challenges in effective assessment of fire and burn severity in the field and using both remote sensing and simulation models. We draw on diverse recent research for guidance on assessing...

  19. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment

    NARCIS (Netherlands)

    Curnel, Y.; Wit, de A.J.W.; Duveiller, G.; Defourny, P.

    2011-01-01

    An Observing System Simulation Experiment (OSSE) has been defined to assess the potentialities of assimilating winter wheat leaf area index (LAI) estimations derived from remote sensing into the crop growth model WOFOST. Two assimilation strategies are considered: one based on Ensemble Kalman Filter

  20. Combining inventories of land cover and forest resources with prediction models and remotely sensed data

    Science.gov (United States)

    Raymond L. Czaplewski

    1989-01-01

    It is difficult to design systems for national and global resource inventory and analysis that efficiently satisfy changing, and increasingly complex objectives. It is proposed that individual inventory, monitoring, modeling, and remote sensing systems be specialized to achieve portions of the objectives. These separate systems can be statistically linked to accomplish...

  1. Spatial aggregation of land surface characteristics : impact of resolution of remote sensing data on land surface modelling

    NARCIS (Netherlands)

    Pelgrum, H.

    2000-01-01

    Land surface models describe the exchange of heat, moisture and momentum between the land surface and the atmosphere. These models can be solved regionally using remote sensing measurements as input. Input variables which can be derived from remote sensing measurements are surface albedo,

  2. Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models

    Directory of Open Access Journals (Sweden)

    Carly Hyatt Hansen

    2017-04-01

    Full Text Available This study demonstrates a number of methods for using field sampling and observed lake characteristics and patterns to improve techniques for development of algae remote sensing models and applications. As satellite and airborne sensors improve and their data are more readily available, applications of models to estimate water quality via remote sensing are becoming more practical for local water quality monitoring, particularly of surface algal conditions. Despite the increasing number of applications, there are significant concerns associated with remote sensing model development and application, several of which are addressed in this study. These concerns include: (1 selecting sensors which are suitable for the spatial and temporal variability in the water body; (2 determining appropriate uses of near-coincident data in empirical model calibration; and (3 recognizing potential limitations of remote sensing measurements which are biased toward surface and near-surface conditions. We address these issues in three lakes in the Great Salt Lake surface water system (namely the Great Salt Lake, Farmington Bay, and Utah Lake through sampling at scales that are representative of commonly used sensors, repeated sampling, and sampling at both near-surface depths and throughout the water column. The variability across distances representative of the spatial resolutions of Landsat, SENTINEL-2 and MODIS sensors suggests that these sensors are appropriate for this lake system. We also use observed temporal variability in the system to evaluate sensors. These relationships proved to be complex, and observed temporal variability indicates the revisit time of Landsat may be problematic for detecting short events in some lakes, while it may be sufficient for other areas of the system with lower short-term variability. Temporal variability patterns in these lakes are also used to assess near-coincident data in empirical model development. Finally, relationships

  3. Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

    OpenAIRE

    West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Young, Nicholas E.; Stohlgren, Thomas J.; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

    2016-01-01

    Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tama...

  4. Section summary: Remote sensing

    Science.gov (United States)

    Belinda Arunarwati Margono

    2013-01-01

    Remote sensing is an important data source for monitoring the change of forest cover, in terms of both total removal of forest cover (deforestation), and change of canopy cover, structure and forest ecosystem services that result in forest degradation. In the context of Intergovernmental Panel on Climate Change (IPCC), forest degradation monitoring requires information...

  5. Synergistic linkage between remote sensing and biophysical models for estimating plant ecophysiological and ecosystem processes

    International Nuclear Information System (INIS)

    Inoue, Y.; Olioso, A.

    2004-01-01

    Abstract Information on the ecological and physiological status of crops is essential for growth diagnostics and yield prediction. Within-field or between-field spatial information is required, especially with the recent trend toward precision agriculture, which seeks the efficient use of agrochemicals, water, and energy. The study of carbon and nitrogen cycles as well as environmental management on local and regional scales requires assessment of the spatial variability of biophysical and ecophysiological variables, scaling up of which is also needed for scientific and decision-making purposes. Remote sensing has great potential for these applications because it enables wide-area non-destructive, and real-time acquisition of information about ecophysiological conditions of vegetation. With recent advances in sensor technology, a variety of electromagnetic signatures, such as hyperspectral reflectance, thermal-infrared temperature, and microwave backscattering coefficients, can be acquired for both plants and ecosystems using ground-based, airborne, and satellite platforms. Their spatial and temporal resolutions have both recently been improved. This article reviews the state of the art in the remote sensing of plant ecophysiological data, with special emphasis on the synergy between remote sensing signatures and biophysical and ecophysiological process models. Several case studies for the optical, thermal, and microwave domains have demonstrated the potential of this synergistic linkage. Remote sensing and process modeling methods complement each other when combined synergistically. Further research on this approach is needed f or a wide range of ecophysiological and ecosystem studies, as well as for practical crop management

  6. Spatial Irrigation Management Using Remote Sensing Water Balance Modeling and Soil Water Content Monitoring

    Science.gov (United States)

    Barker, J. Burdette

    Spatially informed irrigation management may improve the optimal use of water resources. Sub-field scale water balance modeling and measurement were studied in the context of irrigation management. A spatial remote-sensing-based evapotranspiration and soil water balance model was modified and validated for use in real-time irrigation management. The modeled ET compared well with eddy covariance data from eastern Nebraska. Placement and quantity of sub-field scale soil water content measurement locations was also studied. Variance reduction factor and temporal stability were used to analyze soil water content data from an eastern Nebraska field. No consistent predictor of soil water temporal stability patterns was identified. At least three monitoring locations were needed per irrigation management zone to adequately quantify the mean soil water content. The remote-sensing-based water balance model was used to manage irrigation in a field experiment. The research included an eastern Nebraska field in 2015 and 2016 and a western Nebraska field in 2016 for a total of 210 plot-years. The response of maize and soybean to irrigation using variations of the model were compared with responses from treatments using soil water content measurement and a rainfed treatment. The remote-sensing-based treatment prescribed more irrigation than the other treatments in all cases. Excessive modeled soil evaporation and insufficient drainage times were suspected causes of the model drift. Modifying evaporation and drainage reduced modeled soil water depletion error. None of the included response variables were significantly different between treatments in western Nebraska. In eastern Nebraska, treatment differences for maize and soybean included evapotranspiration and a combined variable including evapotranspiration and deep percolation. Both variables were greatest for the remote-sensing model when differences were found to be statistically significant. Differences in maize yield in

  7. An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation

    Directory of Open Access Journals (Sweden)

    Chuli Hu

    2014-01-01

    Full Text Available In the Earth Observation sensor web environment, the rapid, accurate, and unified discovery of diverse remote sensing satellite sensors, and their association to yield an integrated solution for a comprehensive response to specific emergency tasks pose considerable challenges. In this study, we propose a remote sensing satellite sensor object model, based on the object-oriented paradigm and the Open Geospatial Consortium Sensor Model Language. The proposed model comprises a set of sensor resource objects. Each object consists of identification, state of resource attribute, and resource method. We implement the proposed attribute state description by applying it to different remote sensors. A real application, involving the observation of floods at the Yangtze River in China, is undertaken. Results indicate that the sensor inquirer can accurately discover qualified satellite sensors in an accurate and unified manner. By implementing the proposed union operation among the retrieved sensors, the inquirer can further determine how the selected sensors can collaboratively complete a specific observation requirement. Therefore, the proposed model provides a reliable foundation for sharing and integrating multiple remote sensing satellite sensors and their observations.

  8. Improvements in irrigation system modelling when using remotely sensed ET for calibration

    Science.gov (United States)

    van Opstal, J. D.; Neale, C. M. U.; Lecina, S.

    2014-10-01

    Irrigation system modelling is often used to aid decision-makers in the agricultural sector. It gives insight on the consequences of potential management and infrastructure changes. However, simulating an irrigation district requires a considerable amount of input data to properly represent the system, which is not easily acquired or available. During the simulation process, several assumptions have to be made and the calibration is usually performed only with flow measurements. The advancement of estimating evapotranspiration (ET) using remote sensing is a welcome asset for irrigation system modelling. Remotely-sensed ET can be used to improve the model accuracy in simulating the water balance and the crop production. This study makes use of the Ador-Simulation irrigation system model, which simulates water flows in irrigation districts in both the canal infrastructure and on-field. ET is estimated using an energy balance model, namely SEBAL, which has been proven to function well for agricultural areas. The seasonal ET by the Ador model and the ET from SEBAL are compared. These results determine sub-command areas, which perform well under current assumptions or, conversely, areas that need re-evaluation of assumptions and a re-run of the model. Using a combined approach of the Ador irrigation system model and remote sensing outputs from SEBAL, gives great insights during the modelling process and can accelerate the process. Additionally cost-savings and time-savings are apparent due to the decrease in input data required for simulating large-scale irrigation areas.

  9. 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...

  10. Hydrological now- and forecasting : Integration of operationally available remotely sensed and forecasted hydrometeorological variables into distributed hydrological models

    NARCIS (Netherlands)

    Schuurmans, J.M.

    2008-01-01

    Keywords: hydrology, models, soil moisture, rainfall, radar, rain gauge, remote sensing, evapotranspiration, forecasting, numerical weather prediction, Netherlands, Langbroekerwetering, Lopikerwaard. Computer simulation models are an important tool for hydrologists. With these models they can

  11. Geospatial Image Stream Processing: Models, techniques, and applications in remote sensing change detection

    Science.gov (United States)

    Rueda-Velasquez, Carlos Alberto

    Detection of changes in environmental phenomena using remotely sensed data is a major requirement in the Earth sciences, especially in natural disaster related scenarios where real-time detection plays a crucial role in the saving of human lives and the preservation of natural resources. Although various approaches formulated to model multidimensional data can in principle be applied to the inherent complexity of remotely sensed geospatial data, there are still challenging peculiarities that demand a precise characterization in the context of change detection, particularly in scenarios of fast changes. In the same vein, geospatial image streams do not fit appropriately in the standard Data Stream Management System (DSMS) approach because these systems mainly deal with tuple-based streams. Recognizing the necessity for a systematic effort to address the above issues, the work presented in this thesis is a concrete step toward the foundation and construction of an integrated Geospatial Image Stream Processing framework, GISP. First, we present a data and metadata model for remotely sensed image streams. We introduce a precise characterization of images and image streams in the context of remotely sensed geospatial data. On this foundation, we define spatially-aware temporal operators with a consistent semantics for change analysis tasks. We address the change detection problem in settings where multiple image stream sources are available, and thus we introduce an architectural design for the processing of geospatial image streams from multiple sources. With the aim of targeting collaborative scientific environments, we construct a realization of our architecture based on Kepler, a robust and widely used scientific workflow management system, as the underlying computational support; and open data and Web interface standards, as a means to facilitate the interoperability of GISP instances with other processing infrastructures and client applications. We demonstrate our

  12. Remote sensing data assimilation for a prognostic phenology model

    Science.gov (United States)

    R. Stockli; T. Rutishauser; D. Dragoni; J. O' Keefe; P. E. Thornton; M. Jolly; L. Lu; A. S. Denning

    2008-01-01

    Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active...

  13. A Hybrid of Optical Remote Sensing and Hydrological Modeling Improves Water Balance Estimation

    Science.gov (United States)

    Gleason, Colin J.; Wada, Yoshihide; Wang, Jida

    2018-01-01

    Declining gauging infrastructure and fractious water politics have decreased available information about river flows globally. Remote sensing and water balance modeling are frequently cited as potential solutions, but these techniques largely rely on these same in-decline gauge data to make accurate discharge estimates. A different approach is therefore needed, and we here combine remotely sensed discharge estimates made via at-many-stations hydraulic geometry (AMHG) and the PCR-GLOBWB hydrological model to estimate discharge over the Lower Nile. Specifically, we first estimate initial discharges from 87 Landsat images and AMHG (1984-2015), and then use these flow estimates to tune the model, all without using gauge data. The resulting tuned modeled hydrograph shows a large improvement in flow magnitude: validation of the tuned monthly hydrograph against a historical gauge (1978-1984) yields an RMSE of 439 m3/s (40.8%). By contrast, the original simulation had an order-of-magnitude flow error. This improvement is substantial but not perfect: tuned flows have a 1-2 month wet season lag and a negative base flow bias. Accounting for this 2 month lag yields a hydrograph RMSE of 270 m3/s (25.7%). Thus, our results coupling physical models and remote sensing is a promising first step and proof of concept toward future modeling of ungauged flows, especially as developments in cloud computing for remote sensing make our method easily applicable to any basin. Finally, we purposefully do not offer prescriptive solutions for Nile management, and rather hope that the methods demonstrated herein can prove useful to river stakeholders in managing their own water.

  14. Value of using remotely sensed evapotranspiration for SWAT model calibration

    Science.gov (United States)

    Hydrologic models are useful management tools for assessing water resources solutions and estimating the potential impact of climate variation scenarios. A comprehensive understanding of the water budget components and especially the evapotranspiration (ET) is critical and often overlooked for adeq...

  15. Remotely sensed soil moisture input to a hydrologic model

    Science.gov (United States)

    Engman, E. T.; Kustas, W. P.; Wang, J. R.

    1989-01-01

    The possibility of using detailed spatial soil moisture maps as input to a runoff model was investigated. The water balance of a small drainage basin was simulated using a simple storage model. Aircraft microwave measurements of soil moisture were used to construct two-dimensional maps of the spatial distribution of the soil moisture. Data from overflights on different dates provided the temporal changes resulting from soil drainage and evapotranspiration. The study site and data collection are described, and the soil measurement data are given. The model selection is discussed, and the simulation results are summarized. It is concluded that a time series of soil moisture is a valuable new type of data for verifying model performance and for updating and correcting simulated streamflow.

  16. Remote sensing estimation of evapotranspiration for SWAT Model Calibration

    Science.gov (United States)

    Hydrological models are used to assess many water resource problems from water quantity to water quality issues. The accurate assessment of the water budget, primarily the influence of precipitation and evapotranspiration (ET), is a critical first-step evaluation, which is often overlooked in hydro...

  17. ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Na Li

    2018-03-01

    Full Text Available The diverse density (DD algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels. However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS sensor and the Push-broom Hyperspectral Imager (PHI are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively.

  18. The role of remote sensing in hydrological modelling of the Okavango Delta, Botswana.

    Science.gov (United States)

    Milzow, Christian; Kgotlhang, Lesego; Kinzelbach, Wolfgang; Meier, Philipp; Bauer-Gottwein, Peter

    2009-05-01

    A coupled surface water-groundwater model of the Okavango Delta has been built based on the United States Geological Survey software MODFLOW 2000 including the SFR2 package for stream-flow routing. It will provide a new tool for evaluating water management and climate change scenarios. The delta's size and limited accessibility make direct, on the ground data acquisition difficult. Remote sensing methods are the most promising source of acquiring spatially distributed data for both model input parameters and calibration. Topography, aquifer thickness, channel positions, evapotranspiration and precipitation data are all based on remote sensing. Simulated flooding patterns are compared to patterns derived from visible to thermal NOAA-AVHRR data and microwave radar ENVISAT-ASAR data.

  19. [Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status].

    Science.gov (United States)

    Tan, Chang-Wei; Zhou, Qing-Bo; Qi, La; Zhuang, Heng-Yang

    2008-06-01

    The correlations of rice plant nitrogen content with raw hyperspectral reflectance, first derivative hyperspectral reflectance, and hyperspectral characteristic parameters were analyzed, and the hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status with these remote sensing parameters as independent variables were constructed and validated. The results indicated that the nitrogen content in rice plant organs had a variation trend of stem plant nitrogen nutritional status, with the decisive coefficients (R2) being 0.7996 and 0.8606, respectively; while the model with vegetation index (SDr - SDb) / (SDr + SDb) as independent variable, i. e., y = 365.871 + 639.323 ((SDr - SDb) / (SDr + SDb)), was most fit rice plant nitrogen content, with R2 = 0.8755, RMSE = 0.2372 and relative error = 11.36%, being able to quantitatively diagnose the nitrogen nutritional status of rice.

  20. Using remote sensing and machine learning for the spatial modelling of a bluetongue virus vector

    Science.gov (United States)

    Van doninck, J.; Peters, J.; De Baets, B.; Ducheyne, E.; Verhoest, N. E. C.

    2012-04-01

    Bluetongue is a viral vector-borne disease transmitted between hosts, mostly cattle and small ruminants, by some species of Culicoides midges. Within the Mediterranean basin, C. imicola is the main vector of the bluetongue virus. The spatial distribution of this species is limited by a number of environmental factors, including temperature, soil properties and land cover. The identification of zones at risk of bluetongue outbreaks thus requires detailed information on these environmental factors, as well as appropriate epidemiological modelling techniques. We here give an overview of the environmental factors assumed to be constraining the spatial distribution of C. imicola, as identified in different studies. Subsequently, remote sensing products that can be used as proxies for these environmental constraints are presented. Remote sensing data are then used together with species occurrence data from the Spanish Bluetongue National Surveillance Programme to calibrate a supervised learning model, based on Random Forests, to model the probability of occurrence of the C. imicola midge. The model will then be applied for a pixel-based prediction over the Iberian peninsula using remote sensing products for habitat characterization.

  1. Lidar Remote Sensing of Forests: New Instruments and Modeling Capabilities

    Science.gov (United States)

    Cook, Bruce D.

    2012-01-01

    Lidar instruments provide scientists with the unique opportunity to characterize the 3D structure of forest ecosystems. This information allows us to estimate properties such as wood volume, biomass density, stocking density, canopy cover, and leaf area. Structural information also can be used as drivers for photosynthesis and ecosystem demography models to predict forest growth and carbon sequestration. All lidars use time-in-flight measurements to compute accurate ranging measurements; however, there is a wide range of instruments and data types that are currently available, and instrument technology continues to advance at a rapid pace. This seminar will present new technologies that are in use and under development at NASA for airborne and space-based missions. Opportunities for instrument and data fusion will also be discussed, as Dr. Cook is the PI for G-LiHT, Goddard's LiDAR, Hyperspectral, and Thermal airborne imager. Lastly, this talk will introduce radiative transfer models that can simulate interactions between laser light and forest canopies. Developing modeling capabilities is important for providing continuity between observations made with different lidars, and to assist the design of new instruments. Dr. Bruce Cook is a research scientist in NASA's Biospheric Sciences Laboratory at Goddard Space Flight Center, and has more than 25 years of experience conducting research on ecosystem processes, soil biogeochemistry, and exchange of carbon, water vapor and energy between the terrestrial biosphere and atmosphere. His research interests include the combined use of lidar, hyperspectral, and thermal data for characterizing ecosystem form and function. He is Deputy Project Scientist for the Landsat Data Continuity Mission (LDCM); Project Manager for NASA s Carbon Monitoring System (CMS) pilot project for local-scale forest biomass; and PI of Goddard's LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager.

  2. On the use of remotely sensed data to constrain process modeling and ecological forecasting

    Science.gov (United States)

    Serbin, S.; Greenberg, J. A.

    2014-12-01

    The ability to seamlessly integrate information on vegetation structure, function, and dynamics across a continuum of scales, from the field to satellite observations, greatly enhances our ability to understand how terrestrial vegetation-atmosphere interactions change over time and in response
to disturbances and global change. For example, ecosystem process models require detailed information on the state (e.g. structure, leaf area index), surface properties (e.g. albedo), and dynamics (e.g. phenology, succession) of ecosystems in order to properly simulate the fluxes of carbon (C), water, and energy from the land to the atmosphere as well as address the vulnerability of ecosystems to environmental, pest and pathogen, and other anthropogenic perturbations. Other activities such as species distribution and environmental niche modeling (SDENM) require not only presence/absence information but also detailed spatial and temporal datasets, including climate and remotely sensed observations, to accurately project species ranges under current and often future climatic scenarios. Despite the many challenges of adequately initializing and parameterizing models, the last several decades have shown a substantial increase in the amount of available data useful for improving ecological predictions. Specifically remote sensing data provides an important data constraint for projecting species and successional changes as well as vegetation dynamics and the fluxes of C, water and energy and the storage of C in ecosystems, principally as a synoptic observational dataset for capturing short- to long-term plant-climate interactions. In this talk we will highlight the current and potential uses of various remotely sensed data sources in constraining process modeling and SDENM activities. We will pay particular attention to the uses of remote sensing as a direct constraint on ecological forecasts or a key observational dataset used to capture plant-climate interactions

  3. 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)

  4. Wind erosion in semiarid landscapes: Predictive models and remote sensing methods for the influence of vegetation

    Science.gov (United States)

    Musick, H. Brad

    1993-01-01

    The objectives of this research are: to develop and test predictive relations for the quantitative influence of vegetation canopy structure on wind erosion of semiarid rangeland soils, and to develop remote sensing methods for measuring the canopy structural parameters that determine sheltering against wind erosion. The influence of canopy structure on wind erosion will be investigated by means of wind-tunnel and field experiments using structural variables identified by the wind-tunnel and field experiments using model roughness elements to simulate plant canopies. The canopy structural variables identified by the wind-tunnel and field experiments as important in determining vegetative sheltering against wind erosion will then be measured at a number of naturally vegetated field sites and compared with estimates of these variables derived from analysis of remotely sensed data.

  5. Modeling the Hydrological Regime of Turkana Lake (Kenya, Ethiopia) by Combining Spatially Distributed Hydrological Modeling and Remote Sensing Datasets

    Science.gov (United States)

    Anghileri, D.; Kaelin, A.; Peleg, N.; Fatichi, S.; Molnar, P.; Roques, C.; Longuevergne, L.; Burlando, P.

    2017-12-01

    Hydrological modeling in poorly gauged basins can benefit from the use of remote sensing datasets although there are challenges associated with the mismatch in spatial and temporal scales between catchment scale hydrological models and remote sensing products. We model the hydrological processes and long-term water budget of the Lake Turkana catchment, a transboundary basin between Kenya and Ethiopia, by integrating several remote sensing products into a spatially distributed and physically explicit model, Topkapi-ETH. Lake Turkana is the world largest desert lake draining a catchment of 145'500 km2. It has three main contributing rivers: the Omo river, which contributes most of the annual lake inflow, the Turkwel river, and the Kerio rivers, which contribute the remaining part. The lake levels have shown great variations in the last decades due to long-term climate fluctuations and the regulation of three reservoirs, Gibe I, II, and III, which significantly alter the hydrological seasonality. Another large reservoir is planned and may be built in the next decade, generating concerns about the fate of Lake Turkana in the long run because of this additional anthropogenic pressure and increasing evaporation driven by climate change. We consider different remote sensing datasets, i.e., TRMM-V7 for precipitation, MERRA-2 for temperature, as inputs to the spatially distributed hydrological model. We validate the simulation results with other remote sensing datasets, i.e., GRACE for total water storage anomalies, GLDAS-NOAH for soil moisture, ERA-Interim/Land for surface runoff, and TOPEX/Poseidon for satellite altimetry data. Results highlight how different remote sensing products can be integrated into a hydrological modeling framework accounting for their relative uncertainties. We also carried out simulations with the artificial reservoirs planned in the north part of the catchment and without any reservoirs, to assess their impacts on the catchment hydrological

  6. Handbook on advances in remote sensing and geographic information systems paradigms and applications in forest landscape modeling

    CERN Document Server

    Favorskaya, Margarita N

    2017-01-01

    This book presents the latest advances in remote-sensing and geographic information systems and applications. It is divided into four parts, focusing on Airborne Light Detection and Ranging (LiDAR) and Optical Measurements of Forests; Individual Tree Modelling; Landscape Scene Modelling; and Forest Eco-system Modelling. Given the scope of its coverage, the book offers a valuable resource for students, researchers, practitioners, and educators interested in remote sensing and geographic information systems and applications.

  7. Applications of remote sensing to watershed management

    Science.gov (United States)

    Rango, A.

    1975-01-01

    Aircraft and satellite remote sensing systems which are capable of contributing to watershed management are described and include: the multispectral scanner subsystem on LANDSAT and the basic multispectral camera array flown on high altitude aircraft such as the U-2. Various aspects of watershed management investigated by remote sensing systems are discussed. Major areas included are: snow mapping, surface water inventories, flood management, hydrologic land use monitoring, and watershed modeling. It is indicated that technological advances in remote sensing of hydrological data must be coupled with an expansion of awareness and training in remote sensing techniques of the watershed management community.

  8. Real-time remote sensing driven river basin modeling using radar altimetry

    DEFF Research Database (Denmark)

    Pereira Cardenal, Silvio Javier; Riegels, Niels; Bauer-Gottwein, Peter

    2011-01-01

    reservoir level variation. Because of its easy accessibility and immediate availability, radar altimetry lends itself to being used in real-time hydrological applications. As an impartial source of information about the hydrological system that can be updated in real time, the modeling approach described......Many river basins have a weak in-situ hydrometeorological monitoring infrastructure. However, water resources practitioners depend on reliable hydrological models for management purposes. Remote sensing (RS) data have been recognized as an alternative to in-situ hydrometeorological data in remote...... and poorly monitored areas and are increasingly used to force, calibrate, and update hydrological models. In this study, we evaluate the potential of informing a river basin model with real-time radar altimetry measurements over reservoirs. We present a lumped, conceptual, river basin water balance modeling...

  9. Informing a hydrological model of the Ogooué with multi-mission remote sensing data

    DEFF Research Database (Denmark)

    Kittel, Cecile Marie Margaretha; Nielsen, Karina; Tøttrup, C.

    2018-01-01

    with publicly available and free remote sensing observations. We used a rainfall–runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) and forced it using precipitation from...... two satellite-based rainfall estimates, FEWS-RFE (Famine Early Warning System rainfall estimate) and the Tropical Rainfall Measuring Mission (TRMM) 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function...

  10. Remote sensing and modeling to fill the “gap” in missing natural capital

    Science.gov (United States)

    Bagstad, Kenneth J.; Willcock, Simon; Lange, Glenn-Marie

    2018-01-01

    This chapter reviews recent advances in remote sensing and environmental modeling that address the first step in ecosystem accounting: biophysical quantification of ecosystem services. The chapter focuses on those ecosystem services in which the most rapid advances are likely, including crop pollination, sediment regulation, carbon sequestration and storage, and coastal flood regulation. The discussion highlights data sources and modeling approaches that can support wealth accounting, next steps for mapping and biophysical modeling of ecosystem services, and considerations for integrating biophysical modeling and monetary valuation. These approaches could make the inclusion of some ecosystem services increasingly feasible in future versions of wealth accounts.

  11. Perspectives in using a remotely sensed dryness index in distributed hydrological models at river basin scale

    DEFF Research Database (Denmark)

    Andersen, Jens Asger; Sandholt, Inge; Jensen, Karsten Høgh

    2002-01-01

    evaluation of the modelling performance. The study further examined the spatial patterns in the model input and output, and it was found that particularly the spatial resolution of the precipitation input had a major impact on the model response. In an attempt to improve the model performance, this study...... examines a remotely sensed dryness index for its relationship to simulated soil moisture and evaporation for six days in the wet season 1990. The index is derived from observations of surface temperature and vegetation index as measured by the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor...

  12. Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application

    Directory of Open Access Journals (Sweden)

    Pengyun Chen

    2017-06-01

    Full Text Available Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information’s relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM, the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM, the pointwise approach and graph theory (PA-GT, and the Principal Component Analysis-Nonlocal Means (PCA-NLM denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection.

  13. Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application.

    Science.gov (United States)

    Chen, Pengyun; Zhang, Yichen; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

    2017-06-06

    Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information's relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection.

  14. 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.…

  15. Remote sensing of natural phenomena

    Directory of Open Access Journals (Sweden)

    Miodrag D. Regodić

    2014-06-01

    after the withdrawal of water, for the estimation of damage and flood recovery. Usage of satellite images in detectingearthquakes Remote sensing is widely used in the procedure of detecting and locating earthquakes. Earthquakes can be detected by the combination of geophysical methods with multispectral and radar images. By combining these nethods, we can monitor the conditions of seizmic areas. The obtained information can be computed and sent to information centres in stationary stations where the modelling of earthquake-affected terrains is carried out. Usage of satellite images in monitoring volcanos Remote sensing has been used ifor examining a large number of active vulcanos. Monitoring is performed several times, during and after eruptions. The modelling of volcanic areas enables the definition of lava-effusion zones,and  potentially dangerous zones, which is further used for  planning the protection of affected areas. Usage of satellite images in monitoring fire (blaze One of important methods of investigating, forecasting and monitoring forest fires is remote sensing. Satellite images are valuable in discovering fires and in mapping affected areas within the geographical-information system (GIS, as well as in the estimation of demage caused by fire. Satellite images can also be usedto estimate the temperature on the Earth surface. Conclusion Remote sensing becomes an increasingly important and unavoidable method of the acquisition of data on  geospacein general. The importance of thus obtained data  is invaluable in all phases of monitoring  catastrophic events, from detecting their onsets through monitoring their spreading and effects  to the phase of recovery. New generations of sensors enable systematic monitoring, recording and measuring different data important for detecting changes and processes in the sea, on the ground and in the atmosphere. The procedures of remote sensing enable surveying (recording and registration of different natural

  16. Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

    Directory of Open Access Journals (Sweden)

    T. Vischel

    2008-05-01

    Full Text Available The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa. The first estimate is derived from a physically-based hydrological model (TOPKAPI. The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS. Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i remote sensing in general (ii the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii for hydrological models to assimilate the remotely sensed soil moisture.

  17. Visible-infrared remote-sensing model and applications for ocean waters. Ph.D. Thesis

    Science.gov (United States)

    Lee, Zhongping

    1994-01-01

    Remote sensing has become important in the ocean sciences, especially for research involving large spatial scales. To estimate the in-water constituents through remote sensing, whether carried out by satellite or airplane, the signal emitted from beneath the sea surface, the so called water-leaving radiance (L(w)), is of prime importance. The magnitude of L(w) depends on two terms: one is the intensity of the solar input, and the other is the reflectance of the in-water constituents. The ratio of the water-leaving radiance to the downwelling irradiance (E(d)) above the sear surface (remote-sensing reflectance, R(sub rs)) is independent of the intensity of the irradiance input, and is largely a function of the optical properties of the in-water constituents. In this work, a model is developed to interpret r(sub rs) for ocean water in the visible-infrared range. In addition to terms for the radiance scattered from molecules and particles, the model includes terms that describe contributions from bottom reflectance, fluorescence of gelbstoff or colored dissolved organic matter (CDOM), and water Raman scattering. By using this model, the measured R(sub rs) of waters from the West Florida Shelf to the Mississippi River plume, which covered a (concentration of chlorophyll a) range of 0.07 - 50 mg/cu m, were well interpreted. The average percentage difference (a.p.d.) between the measured and modeled R(sub rs) is 3.4%, and, for the shallow waters, the model-required water depth is within 10% of the chart depth. Simple mathematical simulations for the phytoplankton pigment absorption coefficient (a(sub theta)) are suggested for using the R(sub rs) model. The inverse problem of R(sub rs), which is to analytically derive the in-water constituents from R(sub rs) data alone, can be solved using the a(sub theta) functions without prior knowledge of the in-water optical properties. More importantly, this method avoids problems associated with a need for knowledge of the shape

  18. Remote sensing prospection

    Directory of Open Access Journals (Sweden)

    Jeremy Bennett

    2012-12-01

    Full Text Available During the Capo Mannu Project 2011 fieldwork season, three separate sites were selected for remote sensing prospection: Su Pallosu (Beachfront and Upper Platform, Sa Rocca Tunda (Beachfront and Serra Is Araus. These areas have in common the presence of buried structures and/or ceramic deposits, and represent the favourite candidates for future excavations in the area. The level of success attained across the sites was not very high, which awkward topography and/or unusual geological circumstances hindering the usually reliant magnetometer survey method.

  19. Applications of Remote Sensing

    Science.gov (United States)

    Jacha, Charlene

    2015-04-01

    Remote sensing is one of the best ways to be able to monitor and see changes in the Earth. The use of satellite images in the classroom can be a practical way to help students understand the importance and use of remote sensing and Geographic Information Systems (GIS). It is essential in helping students to understand that underlying individual data points are converted to a broad spatial form. The use of actual remote sensing data makes this more understandable to the students e.g. an online map of recent earthquake events, geologic maps, satellite imagery. For change detection, images of years ten or twenty years apart of the same area can be compared and observations recorded. Satellite images of different places can be available on the Internet or from the local space agency. In groups of mixed abilities, students can observe changes in land use over time and also give possible reasons and explanations to those changes. Students should answer essential questions like, how does satellite imagery offer valuable information to different faculties e.g. military, weather, environmental departments and others. Before and after images on disasters for example, volcanoes, floods and earthquakes should be obtained and observed. Key questions would be; how can scientists use these images to predict, or to change the future outcomes over time. How to manage disasters and how the archived images can assist developers in planning land use around that area in the future. Other material that would be useful includes maps and aerial photographs of the area. A flight should be organized over the area for students to acquire aerial photographs of their own; this further enhances their understanding of the concept "remote sensing". Environmental issues such as air, water and land pollution can also be identified on satellite images. Key questions for students would include causes, effects and possible solutions to the problem. Conducting a fieldwork exercise around the area would

  20. A discussion of remote sensing geologic image model of a uranium deposit

    International Nuclear Information System (INIS)

    Qiao Rui

    1998-12-01

    Based on the geologic study on the uranium deposit, the items about remote sensing images of the mineral deposit were discussed, such as the best choice of temporal, the best compose of band, the image characteristics and image model. It is considered that the mineral deposit was controlled by the line, ring and arc through the interpretation of color-composite images. The geologic image model of the mineral deposit is annular fault block controlled by northeast and northwest structures. It is recommended that much attentions should be paid to mineralized regions being similar with this image model in long-range forecast

  1. Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

    OpenAIRE

    T. Vischel; G. Pegram; S. Sinclair; W. Wagner; A. Bartsch

    2007-01-01

    The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil ...

  2. Near-earth orbital guidance and remote sensing

    Science.gov (United States)

    Powers, W. F.

    1972-01-01

    The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

  3. Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

    Science.gov (United States)

    Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, P.; Williams, C.; Ardö, J.; Boucher, M.; Cappelaere, B.; de Grandcourt, A.; Nickless, A.; Nouvellon, Y.; Scholes, R.; Kutsch, W.

    2013-03-01

    Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

  4. Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

    Directory of Open Access Journals (Sweden)

    M. Marshall

    2013-03-01

    Full Text Available Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET, a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET, driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

  5. 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....

  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 water resources

    CERN Document Server

    Champollion, N; Benveniste, J; Chen, J

    2016-01-01

    This book is a collection of overview articles showing how space-based observations, combined with hydrological modeling, have considerably improved our knowledge of the continental water cycle and its sensitivity to climate change. Two main issues are highlighted: (1) the use in combination of space observations for monitoring water storage changes in river basins worldwide, and (2) the use of space data in hydrological modeling either through data assimilation or as external constraints. The water resources aspect is also addressed, as well as the impacts of direct anthropogenic forcing on land hydrology (e.g. ground water depletion, dam building on rivers, crop irrigation, changes in land use and agricultural practices, etc.). Remote sensing observations offer important new information on this important topic as well, which is highly useful for achieving water management objectives. Over the past 15 years, remote sensing techniques have increasingly demonstrated their capability to monitor components of th...

  8. Sensitivity analysis in remote sensing

    CERN Document Server

    Ustinov, Eugene A

    2015-01-01

    This book contains a detailed presentation of general principles of sensitivity analysis as well as their applications to sample cases of remote sensing experiments. An emphasis is made on applications of adjoint problems, because they are more efficient in many practical cases, although their formulation may seem counterintuitive to a beginner. Special attention is paid to forward problems based on higher-order partial differential equations, where a novel matrix operator approach to formulation of corresponding adjoint problems is presented. Sensitivity analysis (SA) serves for quantitative models of physical objects the same purpose, as differential calculus does for functions. SA provides derivatives of model output parameters (observables) with respect to input parameters. In remote sensing SA provides computer-efficient means to compute the jacobians, matrices of partial derivatives of observables with respect to the geophysical parameters of interest. The jacobians are used to solve corresponding inver...

  9. 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.

  10. 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)

  11. The Use of Remote Sensing Data for Modeling Air Quality in the Cities

    Science.gov (United States)

    Putrenko, V. V.; Pashynska, N. M.

    2017-12-01

    Monitoring of environmental pollution in the cities by the methods of remote sensing of the Earth is actual area of research for sustainable development. Ukraine has a poorly developed network of monitoring stations for air quality, the technical condition of which is deteriorating in recent years. Therefore, the possibility of obtaining data about the condition of air by remote sensing methods is of great importance. The paper considers the possibility of using the data about condition of atmosphere of the project AERONET to assess the air quality in Ukraine. The main pollution indicators were used data on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) content in the atmosphere. The main indicator of air quality in Ukraine is the air pollution index (API). We have built regression models the relationship between indicators of NO2, which are measured by remote sensing methods and ground-based measurements of indicators. There have also been built regression models, the relationship between the data given to the land of NO2 and API. To simulate the relationship between the API and PM2.5 were used geographically weighted regression model, which allows to take into account the territorial differentiation between these indicators. As a result, the maps that show the distribution of the main types of pollution in the territory of Ukraine, were constructed. PM2.5 data modeling is complicated with using existing indicators, which requires a separate organization observation network for PM2.5 content in the atmosphere for sustainable development in cities of Ukraine.

  12. Modelling size-fractionated primary production in the Atlantic Ocean from remote sensing

    Science.gov (United States)

    Brewin, Robert J. W.; Tilstone, Gavin H.; Jackson, Thomas; Cain, Terry; Miller, Peter I.; Lange, Priscila K.; Misra, Ankita; Airs, Ruth L.

    2017-11-01

    Marine primary production influences the transfer of carbon dioxide between the ocean and atmosphere, and the availability of energy for the pelagic food web. Both the rate and the fate of organic carbon from primary production are dependent on phytoplankton size. A key aim of the Atlantic Meridional Transect (AMT) programme has been to quantify biological carbon cycling in the Atlantic Ocean and measurements of total primary production have been routinely made on AMT cruises, as well as additional measurements of size-fractionated primary production on some cruises. Measurements of total primary production collected on the AMT have been used to evaluate remote-sensing techniques capable of producing basin-scale estimates of primary production. Though models exist to estimate size-fractionated primary production from satellite data, these have not been well validated in the Atlantic Ocean, and have been parameterised using measurements of phytoplankton pigments rather than direct measurements of phytoplankton size structure. Here, we re-tune a remote-sensing primary production model to estimate production in three size fractions of phytoplankton (10 μm) in the Atlantic Ocean, using measurements of size-fractionated chlorophyll and size-fractionated photosynthesis-irradiance experiments conducted on AMT 22 and 23 using sequential filtration-based methods. The performance of the remote-sensing technique was evaluated using: (i) independent estimates of size-fractionated primary production collected on a number of AMT cruises using 14C on-deck incubation experiments and (ii) Monte Carlo simulations. Considering uncertainty in the satellite inputs and model parameters, we estimate an average model error of between 0.27 and 0.63 for log10-transformed size-fractionated production, with lower errors for the small size class (10 μm), and errors generally higher in oligotrophic waters. Application to satellite data in 2007 suggests the contribution of cells 2 μm to total

  13. 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.

  14. Use of remote sensing derived parameters in a crop model for biomass prediction of hay crop

    Science.gov (United States)

    El Hajj, Mohammad; Baghdadi, Nicolas; Cheviron, Bruno; Belaud, Gilles; Zribi, Mehrez

    2016-04-01

    Pre-harvest yield forecasting is a critical challenge for producers, especially for large agricultural areas. During previous decades, numerous crop models were developed to predict crop growth and yield at daily time, most often for wheat or maize, and also for grasslands. Crop models require several input parameters that describe soil properties (e.g. field capacity), plant characteristics (e.g. maximal rooting depth) and management options (e.g. sowing dates, irrigation and harvest dates), which are referred to as the soil, plant and management families of parameters. Remote sensing technology has been extensively applied to identify spatially distributed values of some of the accessible parameters in the soil, plant and management families. The aim of this study was to address the feasibility, merits and limitations of forcing remote-sensing-derived parameters (LAI values, harvest and irrigation dates) in the PILOTE crop model, targeting the Total Dry Matter (TDM) of hay crops. Results show that optical images are suitable to feed PILOTE with LAI values without inducing significant errors on the predicted Total Dry Matter (TDM) values (Root Mean Square Error "RMSE" = 0.41 t/ha and Mean Absolute Percentage Error "MAPE" = 22%). Moreover, optical images with revisit times lower than 16 days are adequate to feed PILOTE with remotely sensed harvest dates (RMSE irrigation dates that were estimated from SAR images also enabled reliable model predictions, at least when attaching a random uncertainty of "only" 3 days to the real known irrigation dates. The case of one or several undetected irrigations has also been explored, with the expected conclusion that undetected irrigations significantly affect model predictions only in dry periods. For the tested soil properties and climatic conditions, a maximum underestimation of TDM of approximately 1.55 t/ha (reference TDM of 3.43 t/ha) was observed in the second crop growth cycle when ignoring two irrigations out of four in

  15. Evaluation of remotely sensed actual evapotranspiration data for modeling small scale irrigation in Ethiopia.

    Science.gov (United States)

    Taddele, Y. D.; Ayana, E.; Worqlul, A. W.; Srinivasan, R.; Gerik, T.; Clarke, N.

    2017-12-01

    The research presented in this paper is conducted in Ethiopia, which is located in the horn of Africa. Ethiopian economy largely depends on rainfed agriculture, which employs 80% of the labor force. The rainfed agriculture is frequently affected by droughts and dry spells. Small scale irrigation is considered as the lifeline for the livelihoods of smallholder farmers in Ethiopia. Biophysical models are highly used to determine the agricultural production, environmental sustainability, and socio-economic outcomes of small scale irrigation in Ethiopia. However, detailed spatially explicit data is not adequately available to calibrate and validate simulations from biophysical models. The Soil and Water Assessment Tool (SWAT) model was setup using finer resolution spatial and temporal data. The actual evapotranspiration (AET) estimation from the SWAT model was compared with two remotely sensed data, namely the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectrometer (MODIS). The performance of the monthly satellite data was evaluated with correlation coefficient (R2) over the different land use groups. The result indicated that over the long term and monthly the AVHRR AET captures the pattern of SWAT simulated AET reasonably well, especially on agricultural dominated landscapes. A comparison between SWAT simulated AET and AVHRR AET provided mixed results on grassland dominated landscapes and poor agreement on forest dominated landscapes. Results showed that the AVHRR AET products showed superior agreement with the SWAT simulated AET than MODIS AET. This suggests that remotely sensed products can be used as valuable tool in properly modeling small scale irrigation.

  16. Calibration of a distributed hydrologic model for six European catchments using remote sensing data

    Science.gov (United States)

    Stisen, S.; Demirel, M. C.; Mendiguren González, G.; Kumar, R.; Rakovec, O.; Samaniego, L. E.

    2017-12-01

    While observed streamflow has been the single reference for most conventional hydrologic model calibration exercises, the availability of spatially distributed remote sensing observations provide new possibilities for multi-variable calibration assessing both spatial and temporal variability of different hydrologic processes. In this study, we first identify the key transfer parameters of the mesoscale Hydrologic Model (mHM) controlling both the discharge and the spatial distribution of actual evapotranspiration (AET) across six central European catchments (Elbe, Main, Meuse, Moselle, Neckar and Vienne). These catchments are selected based on their limited topographical and climatic variability which enables to evaluate the effect of spatial parameterization on the simulated evapotranspiration patterns. We develop a European scale remote sensing based actual evapotranspiration dataset at a 1 km grid scale driven primarily by land surface temperature observations from MODIS using the TSEB approach. Using the observed AET maps we analyze the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mHM model. This model allows calibrating one-basin-at-a-time or all-basins-together using its unique structure and multi-parameter regionalization approach. Results will indicate any tradeoffs between spatial pattern and discharge simulation during model calibration and through validation against independent internal discharge locations. Moreover, added value on internal water balances will be analyzed.

  17. A DNA-based semantic fusion model for remote sensing data.

    Science.gov (United States)

    Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H

    2013-01-01

    Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.

  18. Informing a hydrological model of the Ogooué with multi-mission remote sensing data

    Science.gov (United States)

    Kittel, Cecile; Bauer-Gottwein, Peter; Nielsen, Karina; Tøttrup, Christian

    2017-04-01

    Knowledge on hydrological regimes of river basins is crucial for water management. However, data requirements often limit the applicability of hydrological models in basins with scarce in-situ data. Remote sensing provides a unique possibility to acquire information on hydrological variables in these basins. This study explores how multi-mission remote sensing data can inform a hydrological model. The Ogooué basin in Gabon is used as study area. No previous modelling efforts have been conducted for the basin and only historical flow and precipitation observations are available. Publicly available remote sensing observations are used to parametrize, force, calibrate and validate a hydrological model of the Ogooué. The modelling framework used in the study, is a lumped conceptual rainfall-runoff model based on the Budyko framework coupled to a Muskingum routing scheme. Precipitation is a crucial driver of the land-surface water balance, therefore two satellite-based rainfall estimates, Tropical Rainfall Measuring Mission (TRMM) product 3B42 version 7 and Famine Early Warning System - Rainfall Estimate (FEWS-RFE), are compared. The comparison shows good seasonal and spatial agreement between the products; however, TRMM consistently predicts significantly more precipitation: 1726 mm on average per year against 1556 mm for FEWS-RFE. Best modeling results are obtained with the TRMM precipitation forcing. Model calibration combines historical in-situ flow observations and GRACE total water storage observations using the Jet Propulsion Laboratory (JPL) mascon solution in a multi-objective approach. The two models are calibrated using flow duration curves and climatology benchmarks to overcome the lack of simultaneity between simulated and observed discharge. The objectives are aggregated into a global objective function, and the models are calibrated using the Shuffled Complex Evolution Algorithm. Water height observations from drifting orbit altimetry missions are

  19. Assimilation of remote sensing observations into a sediment transport model of China's largest freshwater lake: spatial and temporal effects.

    Science.gov (United States)

    Zhang, Peng; Chen, Xiaoling; Lu, Jianzhong; Zhang, Wei

    2015-12-01

    Numerical models are important tools that are used in studies of sediment dynamics in inland and coastal waters, and these models can now benefit from the use of integrated remote sensing observations. This study explores a scheme for assimilating remotely sensed suspended sediment (from charge-coupled device (CCD) images obtained from the Huanjing (HJ) satellite) into a two-dimensional sediment transport model of Poyang Lake, the largest freshwater lake in China. Optimal interpolation is used as the assimilation method, and model predictions are obtained by combining four remote sensing images. The parameters for optimal interpolation are determined through a series of assimilation experiments evaluating the sediment predictions based on field measurements. The model with assimilation of remotely sensed sediment reduces the root-mean-square error of the predicted sediment concentrations by 39.4% relative to the model without assimilation, demonstrating the effectiveness of the assimilation scheme. The spatial effect of assimilation is explored by comparing model predictions with remotely sensed sediment, revealing that the model with assimilation generates reasonable spatial distribution patterns of suspended sediment. The temporal effect of assimilation on the model's predictive capabilities varies spatially, with an average temporal effect of approximately 10.8 days. The current velocities which dominate the rate and direction of sediment transport most likely result in spatial differences in the temporal effect of assimilation on model predictions.

  20. Modelling desertification risk in the north-west of Jordan using geospatial and remote sensing techniques

    Directory of Open Access Journals (Sweden)

    Jawad T. Al-Bakri

    2016-03-01

    Full Text Available Remote sensing, climate, and ground data were used within a geographic information system (GIS to map desertification risk in the north-west of Jordan. The approach was based on modelling wind and water erosion and incorporating the results with a map representing the severity of drought. Water erosion was modelled by the universal soil loss equation, while wind erosion was modelled by a dust emission model. The extent of drought was mapped using the evapotranspiration water stress index (EWSI which incorporated actual and potential evapotranspiration. Output maps were assessed within GIS in terms of spatial patterns and the degree of correlation with soil surficial properties. Results showed that both topography and soil explained 75% of the variation in water erosion, while soil explained 25% of the variation in wind erosion, which was mainly controlled by natural factors of topography and wind. Analysis of the EWSI map showed that drought risk was dominating most of the rainfed areas. The combined effects of soil erosion and drought were reflected on the desertification risk map. The adoption of these geospatial and remote sensing techniques is, therefore, recommended to map desertification risk in Jordan and in similar arid environments.

  1. Hydrodynamic and Inundation Modeling of China’s Largest Freshwater Lake Aided by Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Peng Zhang

    2015-04-01

    Full Text Available China’s largest freshwater lake, Poyang Lake, is characterized by rapid changes in its inundation area and hydrodynamics, so in this study, a hydrodynamic model of Poyang Lake was established to simulate these long-term changes. Inundation information was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS remote sensing data and used to calibrate the wetting and drying parameter by assessing the accuracy of the simulated inundation area and its boundary. The bottom friction parameter was calibrated using current velocity measurements from Acoustic Doppler Current Profilers (ADCP. The results show the model is capable of predicting the inundation area dynamic through cross-validation with remotely sensed inundation data, and can reproduce the seasonal dynamics of the water level, and water discharge through a comparison with hydrological data. Based on the model results, the characteristics of the current velocities of the lake in the wet season and the dry season of the lake were explored, and the potential effect of the current dynamic on water quality patterns was discussed. The model is a promising basic tool for prediction and management of the water resource and water quality of Poyang Lake.

  2. Ground-based grasslands data to support remote sensing and ecosystem modeling of terrestrial primary production

    Energy Technology Data Exchange (ETDEWEB)

    Olson, R.J.; Turner, R.S. [Oak Ridge National Lab., TN (United States); Scurlock, J.M.O. [King`s College London, (England); Jennings, S.V. [Tennessee Univ., Knoxville, TN (United States)

    1995-12-31

    Estimating terrestrial net primary production (NPP) using remote- sensing tools and ecosystem models requires adequate ground-based measurements for calibration, parameterization, and validation. These data needs were strongly endorsed at a recent meeting of ecosystem modelers organized by the International Geosphere-Biosphere Programme`s (IGBP`s) Data and Information System (DIS) and its Global Analysis, Interpretation, and Modelling (GAIM) Task Force. To meet these needs, a multinational, multiagency project is being coordinated by the IGBP DIS to compile existing NPP data from field sites and to regionalize NPP point estimates to various-sized grid cells. Progress at Oak Ridge National Laboratory (ORNL) on compiling NPP data for grasslands as part of the IGBP DIS data initiative is described. Site data and associated documentation from diverse field studies are being acquired for selected grasslands and are being reviewed for completeness, consistency, and adequacy of documentation, including a description of sampling methods. Data are being compiled in a database with spatial, temporal, and thematic characteristics relevant to remote sensing and global modeling. NPP data are available from the ORNL Distributed Active Archive Center (DAAC) for biogeochemical dynamics. The ORNL DAAC is part of the Earth Observing System Data and Information System, of the US National Aeronautics and Space Administration.

  3. Estimating Net Primary Production of Swedish Forest Landscapes by Combining Mechanistic Modeling and Remote Sensing

    DEFF Research Database (Denmark)

    Tagesson, Håkan Torbern; Smith, Benjamin; Løfgren, Anders

    2009-01-01

    and the Beer-Lambert law. LAI estimates were compared with satellite-extrapolated field estimates of LAI, and the results were generally acceptable. NPP estimates directly from the dynamic vegetation model and estimates obtained by combining the model estimates with remote sensing information were, on average......The aim of this study was to investigate a combination of satellite images of leaf area index (LAI) with processbased vegetation modeling for the accurate assessment of the carbon balances of Swedish forest ecosystems at the scale of a landscape. Monthly climatologic data were used as inputs...... in a dynamic vegetation model, the Lund Potsdam Jena-General Ecosystem Simulator. Model estimates of net primary production (NPP) and the fraction of absorbed photosynthetic active radiation were constrained by combining them with satellite-based LAI images using a general light use efficiency (LUE) model...

  4. Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

    Science.gov (United States)

    Herman, Matthew R.; Nejadhashemi, A. Pouyan; Abouali, Mohammad; Hernandez-Suarez, Juan Sebastian; Daneshvar, Fariborz; Zhang, Zhen; Anderson, Martha C.; Sadeghi, Ali M.; Hain, Christopher R.; Sharifi, Amirreza

    2018-01-01

    As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73-0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error - observations standard deviation ratio (RSR) values <0.7 (0.39-0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly

  5. Informing a hydrological model of the Ogooué with multi-mission remote sensing data

    Science.gov (United States)

    Kittel, Cecile M. M.; Nielsen, Karina; Tøttrup, Christian; Bauer-Gottwein, Peter

    2018-02-01

    Remote sensing provides a unique opportunity to inform and constrain a hydrological model and to increase its value as a decision-support tool. In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations. We used a rainfall-runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) and forced it using precipitation from two satellite-based rainfall estimates, FEWS-RFE (Famine Early Warning System rainfall estimate) and the Tropical Rainfall Measuring Mission (TRMM) 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function with contributions from (1) historical in situ discharge observations from the period 1953-1984 at six locations in the basin, (2) radar altimetry measurements of river stages by Envisat and Jason-2 at 12 locations in the basin and (3) GRACE (Gravity Recovery and Climate Experiment) total water storage change (TWSC). Additionally, we extracted CryoSat-2 observations throughout the basin using a Sentinel-1 SAR (synthetic aperture radar) imagery water mask and used the observations for validation of the model. The use of new satellite missions, including Sentinel-1 and CryoSat-2, increased the spatial characterization of river stage. Throughout the basin, we achieved good agreement between observed and simulated discharge and the river stage, with an RMSD between simulated and observed water amplitudes at virtual stations of 0.74 m for the TRMM-forced model and 0.87 m for the FEWS-RFE-forced model. The hydrological model also captures overall total water storage change patterns, although the amplitude of storage change is generally underestimated. By combining hydrological modeling

  6. A Nonlinear Multiparameters Temperature Error Modeling and Compensation of POS Applied in Airborne Remote Sensing System

    Directory of Open Access Journals (Sweden)

    Jianli Li

    2014-01-01

    Full Text Available The position and orientation system (POS is a key equipment for airborne remote sensing systems, which provides high-precision position, velocity, and attitude information for various imaging payloads. Temperature error is the main source that affects the precision of POS. Traditional temperature error model is single temperature parameter linear function, which is not sufficient for the higher accuracy requirement of POS. The traditional compensation method based on neural network faces great problem in the repeatability error under different temperature conditions. In order to improve the precision and generalization ability of the temperature error compensation for POS, a nonlinear multiparameters temperature error modeling and compensation method based on Bayesian regularization neural network was proposed. The temperature error of POS was analyzed and a nonlinear multiparameters model was established. Bayesian regularization method was used as the evaluation criterion, which further optimized the coefficients of the temperature error. The experimental results show that the proposed method can improve temperature environmental adaptability and precision. The developed POS had been successfully applied in airborne TSMFTIS remote sensing system for the first time, which improved the accuracy of the reconstructed spectrum by 47.99%.

  7. A remote sensing computer-assisted learning tool developed using the unified modeling language

    Science.gov (United States)

    Friedrich, J.; Karslioglu, M. O.

    The goal of this work has been to create an easy-to-use and simple-to-make learning tool for remote sensing at an introductory level. Many students struggle to comprehend what seems to be a very basic knowledge of digital images, image processing and image arithmetic, for example. Because professional programs are generally too complex and overwhelming for beginners and often not tailored to the specific needs of a course regarding functionality, a computer-assisted learning (CAL) program was developed based on the unified modeling language (UML), the present standard for object-oriented (OO) system development. A major advantage of this approach is an easier transition from modeling to coding of such an application, if modern UML tools are being used. After introducing the constructed UML model, its implementation is briefly described followed by a series of learning exercises. They illustrate how the resulting CAL tool supports students taking an introductory course in remote sensing at the author's institution.

  8. Comparing Global Atmospheric CO2 Flux and Transport Models with Remote Sensing (and Other) Observations

    Science.gov (United States)

    Kawa, S. R.; Collatz, G. J.; Pawson, S.; Wennberg, P. O.; Wofsy, S. C.; Andrews, A. E.

    2010-01-01

    We report recent progress derived from comparison of global CO2 flux and transport models with new remote sensing and other sources of CO2 data including those from satellite. The overall objective of this activity is to improve the process models that represent our understanding of the workings of the atmospheric carbon cycle. Model estimates of CO2 surface flux and atmospheric transport processes are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, to provide the basic framework for carbon data assimilation, and ultimately for future projections of carbon-climate interactions. Models can also be used to test consistency within and between CO2 data sets under varying geophysical states. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 2000 through 2009. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at 1x1 degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-3), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to remote sensing observations from TCCON, GOSAT, and AIRS as well as relevant in situ observations. Examples of the influence of key process representations are shown from both forward and inverse model comparisons. We find that the model can resolve much of the synoptic, seasonal, and interannual

  9. Remote Sensing Digital Image Analysis

    Science.gov (United States)

    Richards, John A.; Jia, Xiuping

    Remote Sensing Digital Image Analysis provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations. Problems conclude each chapter. This fourth edition has been developed to reflect the changes that have occurred in this area over the past several years.

  10. Flood Inundation Modelling Under Uncertainty Using Globally and Freely Available Remote Sensing Data

    Science.gov (United States)

    Yan, K.; Di Baldassarre, G.; Giustarini, L.; Solomatine, D. P.

    2012-04-01

    The extreme consequences of recent catastrophic events have highlighted that flood risk prevention still needs to be improved to reduce human losses and economic damages, which have considerably increased worldwide in recent years. Flood risk management and long term floodplain planning are vital for living with floods, which is the currently proposed approach to cope with floods. To support the decision making processes, a significant issue is the availability of data to build appropriate and reliable models, from which the needed information could be obtained. The desirable data for model building, calibration and validation are often not sufficient or available. A unique opportunity is offered nowadays by globally available data which can be freely downloaded from internet. This might open new opportunities for filling the gap between available and needed data, in order to build reliable models and potentially lead to the development of global inundation models to produce floodplain maps for the entire globe. However, there remains the question of what is the real potential of those global remote sensing data, characterized by different accuracy, for global inundation monitoring and how to integrate them with inundation models. This research aims at contributing to understand whether the current globally and freely available remote sensing data (e.g. SRTM, SAR) can be actually used to appropriately support inundation modelling. In this study, the SRTM DEM is used for hydraulic model building, while ENVISAT-ASAR satellite imagery is used for model validation. To test the usefulness of these globally and freely available data, a model based on the high resolution LiDAR DEM and ground data (high water marks) is used as benchmark. The work is carried out on a data-rich test site: the River Alzette in the north of Luxembourg City. Uncertainties are estimated for both SRTM and LiDAR based models. Probabilistic flood inundation maps are produced under the framework of

  11. Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

    Science.gov (United States)

    Steyaert, Louis T.; Loveland, Thomas R.; Brown, Jesslyn F.; Reed, Bradley C.

    1993-01-01

    Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues

  12. Generalized texture models for detecting high-level structures in remotely sensed images

    OpenAIRE

    Doğrusöz, Emel

    2007-01-01

    Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2007. Thesis (Master's) -- Bilkent University, 2007. Includes bibliographical references leaves 90-97 With the rapid increase in the amount and resolution of remotely sensed image data, automatic extraction and classification of information obtained from such images have been an important problem in the field of pattern recognition since remotely sensed imagery...

  13. Predicting wheat production at regional scale by integration of remote sensing data with a simulation model

    NARCIS (Netherlands)

    Jongschaap, R.E.E.; Schouten, L.S.M.

    2005-01-01

    Optical remote sensing satellite data (SPOT HRV XS, Landsat 5 TM) were used to estimate winter wheat area in a pilot area of 5 × 5 km in the Southeast of France. The approach was scaled up to a larger area of 45 × 50 km and finally to the regional level covering several departments. Microwave remote

  14. 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.

  15. Modeling Relationships among 217 Fires Using Remote Sensing of Burn Severity in Southern Pine Forests

    Directory of Open Access Journals (Sweden)

    Amr Abd-Elrahman

    2011-09-01

    Full Text Available Pine flatwoods forests in the southeastern US have experienced severe wildfires over the past few decades, often attributed to fuel load build-up. These forest communities are fire dependent and require regular burning for ecosystem maintenance and health. Although prescribed fire has been used to reduce wildfire risk and maintain ecosystem integrity, managers are still working to reintroduce fire to long unburned areas. Common perception holds that reintroduction of fire in long unburned forests will produce severe fire effects, resulting in a reluctance to prescribe fire without first using expensive mechanical fuels reduction techniques. To inform prioritization and timing of future fire use, we apply remote sensing analysis to examine the set of conditions most likely to result in high burn severity effects, in relation to vegetation, years since the previous fire, and historical fire frequency. We analyze Landsat imagery-based differenced Normalized Burn Ratios (dNBR to model the relationships between previous and future burn severity to better predict areas of potential high severity. Our results show that remote sensing techniques are useful for modeling the relationship between elevated risk of high burn severity and the amount of time between fires, the type of fire (wildfire or prescribed burn, and the historical frequency of fires in pine flatwoods forests.

  16. Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae habitat and population densities

    Directory of Open Access Journals (Sweden)

    Khalifa M. Al-Kindi

    2017-08-01

    Full Text Available In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

  17. 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

  18. Potential for monitoring soil erosion features and soil erosion modeling components from remotely sensed data

    Science.gov (United States)

    Langran, K. J.

    1983-01-01

    Accurate estimates of soil erosion and its effects on soil productivity are essential in agricultural decision making and planning from the field scale to the national level. Erosion models have been primarily developed for designing erosion control systems, predicting sediment yield for reservoir design, predicting sediment transport, and simulating water quality. New models proposed are more comprehensive in that the necessary components (hydrology, erosion-sedimentation, nutrient cycling, tillage, etc.) are linked in a model appropriate for studying the erosion-productivity problem. Recent developments in remote sensing systems, such as Landsat Thematic Mapper, Shuttle Imaging Radar (SIR-B), etc., can contribute significantly to the future development and operational use of these models.

  19. A Multivariate Model for Coastal Water Quality Mapping Using Satellite Remote Sensing Images.

    Science.gov (United States)

    Su, Yuan-Fong; Liou, Jun-Jih; Hou, Ju-Chen; Hung, Wei-Chun; Hsu, Shu-Mei; Lien, Yi-Ting; Su, Ming-Daw; Cheng, Ke-Sheng; Wang, Yeng-Fung

    2008-10-10

    his study demonstrates the feasibility of coastal water quality mapping using satellite remote sensing images. Water quality sampling campaigns were conducted over a coastal area in northern Taiwan for measurements of three water quality variables including Secchi disk depth, turbidity, and total suspended solids. SPOT satellite images nearly concurrent with the water quality sampling campaigns were also acquired. A spectral reflectance estimation scheme proposed in this study was applied to SPOT multispectral images for estimation of the sea surface reflectance. Two models, univariate and multivariate, for water quality estimation using the sea surface reflectance derived from SPOT images were established. The multivariate model takes into consideration the wavelength-dependent combined effect of individual seawater constituents on the sea surface reflectance and is superior over the univariate model. Finally, quantitative coastal water quality mapping was accomplished by substituting the pixel-specific spectral reflectance into the multivariate water quality estimation model.

  20. Detection of mesoscale zones of atmospheric instabilities using remote sensing and weather forecasting model data

    Science.gov (United States)

    Winnicki, I.; Jasinski, J.; Kroszczynski, K.; Pietrek, S.

    2009-04-01

    The paper presents elements of research conducted in the Faculty of Civil Engineering and Geodesy of the Military University of Technology, Warsaw, Poland, concerning application of mesoscale models and remote sensing data to determining meteorological conditions of aircraft flight directly related with atmospheric instabilities. The quality of meteorological support of aviation depends on prompt and effective forecasting of weather conditions changes. The paper presents a computer module for detecting and monitoring zones of cloud cover, precipitation and turbulence along the aircraft flight route. It consists of programs and scripts for managing, processing and visualizing meteorological and remote sensing databases. The application was developed in Matlab® for Windows®. The module uses products of COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) mesoscale non-hydrostatic model of the atmosphere developed by the US Naval Research Laboratory, satellite images acquisition system from the MSG-2 (Meteosat Second Generation) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and meteorological radars data acquired from the Institute of Meteorology and Water Management (IMGW), Warsaw, Poland. The satellite images acquisition system and the COAMPS model are run operationally in the Faculty of Civil Engineering and Geodesy. The mesoscale model is run on an IA64 Feniks multiprocessor 64-bit computer cluster. The basic task of the module is to enable a complex analysis of data sets of miscellaneous information structure and to verify COAMPS results using satellite and radar data. The research is conducted using uniform cartographic projection of all elements of the database. Satellite and radar images are transformed into the Lambert Conformal projection of COAMPS. This facilitates simultaneous interpretation and supports decision making process for safe execution of flights. Forecasts are based on horizontal

  1. Characterizing the Surface Connectivity of Depressional Wetlands: Linking Remote Sensing and Hydrologic Modeling Approaches

    Science.gov (United States)

    Christensen, J.; Evenson, G. R.; Vanderhoof, M.; Wu, Q.; Golden, H. E.; Lane, C.

    2017-12-01

    Surface connectivity of wetlands in the 700,000 km2 Prairie Pothole Region of North America (PPR) can occur through fill-spill and fill-merge mechanisms, with some wetlands eventually spilling into stream/river systems. These wetland-to-wetland and wetland-to-stream connections vary both spatially and temporally in PPR watersheds and are important to understanding hydrologic and biogeochemical processes in the landscape. To explore how to best characterize spatial and temporal variability in aquatic connectivity, we compared three approaches, 1) hydrological modeling alone, 2) remotely-sensed data alone, and 3) integrating remotely-sensed data into a hydrological model. These approaches were tested in the Pipestem Creek Watershed, North Dakota across a drought to deluge cycle (1990-2011). A Soil and Water Assessment Tool (SWAT) model was modified to include the water storage capacity of individual non-floodplain wetlands identified in the National Wetland Inventory (NWI) dataset. The SWAT-NWI model simulated the water balance and storage of each wetland and the temporal variability of their hydrologic connections between wetlands during the 21-year study period. However, SWAT-NWI only accounted for fill-spill, and did not allow for the expansion and merging of wetlands situated within larger depressions. Alternatively, we assessed the occurrence of fill-merge mechanisms using inundation maps derived from Landsat images on 19 cloud-free days during the 21 years. We found fill-merge mechanisms to be prevalent across the Pipestem watershed during times of deluge. The SWAT-NWI model was then modified to use LiDAR-derived depressions that account for the potential maximum depression extent, including the merging of smaller wetlands. The inundation maps were used to evaluate the ability of the SWAT-depression model to simulate fill-merge dynamics in addition to fill-spill dynamics throughout the study watershed. Ultimately, using remote sensing to inform and validate

  2. Carbon fluxes in ecosystems of Yellowstone National Park predicted from remote sensing data and simulation modeling

    Directory of Open Access Journals (Sweden)

    Huang Shengli

    2011-08-01

    Full Text Available Abstract Background A simulation model based on remote sensing data for spatial vegetation properties has been used to estimate ecosystem carbon fluxes across Yellowstone National Park (YNP. The CASA (Carnegie Ames Stanford Approach model was applied at a regional scale to estimate seasonal and annual carbon fluxes as net primary production (NPP and soil respiration components. Predicted net ecosystem production (NEP flux of CO2 is estimated from the model for carbon sinks and sources over multi-year periods that varied in climate and (wildfire disturbance histories. Monthly Enhanced Vegetation Index (EVI image coverages from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS instrument (from 2000 to 2006 were direct inputs to the model. New map products have been added to CASA from airborne remote sensing of coarse woody debris (CWD in areas burned by wildfires over the past two decades. Results Model results indicated that relatively cooler and wetter summer growing seasons were the most favorable for annual plant production and net ecosystem carbon gains in representative landscapes of YNP. When summed across vegetation class areas, the predominance of evergreen forest and shrubland (sagebrush cover was evident, with these two classes together accounting for 88% of the total annual NPP flux of 2.5 Tg C yr-1 (1 Tg = 1012 g for the entire Yellowstone study area from 2000-2006. Most vegetation classes were estimated as net ecosystem sinks of atmospheric CO2 on annual basis, making the entire study area a moderate net sink of about +0.13 Tg C yr-1. This average sink value for forested lands nonetheless masks the contribution of areas burned during the 1988 wildfires, which were estimated as net sources of CO2 to the atmosphere, totaling to a NEP flux of -0.04 Tg C yr-1 for the entire burned area. Several areas burned in the 1988 wildfires were estimated to be among the lowest in overall yearly NPP, namely the Hellroaring Fire, Mink

  3. New lessons on the Sudd hydrology learned from remote sensing and climate modeling

    Directory of Open Access Journals (Sweden)

    Y. A. Mohamed

    2006-01-01

    Full Text Available Despite its local and regional importance, hydro-meteorological data on the Sudd (one of Africa's largest wetlands is very scanty. This is due to the physical and political situation of this area of Sudan. The areal size of the wetland, the evaporation rate, and the influence on the micro and meso climate are still unresolved questions of the Sudd hydrology. The evaporation flux from the Sudd wetland has been estimated using thermal infrared remote sensing data and a parameterization of the surface energy balance (SEBAL model. It is concluded that the actual spatially averaged evaporation from the Sudd wetland over 3 years of different hydrometeorological characteristics varies between 1460 and 1935 mm/yr. This is substantially less than open water evaporation. The wetland area appears to be 70% larger than previously assumed when the Sudd was considered as an open water body. The temporal analysis of the Sudd evaporation demonstrated that the variation of the atmospheric demand in combination with the inter-annual fluctuation of the groundwater table results into a quasi-constant evaporation rate in the Sudd, while open water evaporation depicts a clear seasonal variability. The groundwater table characterizes a distinct seasonality, confirming that substantial parts of the Sudd are seasonal swamps. The new set of spatially distributed evaporation parameters from remote sensing form an important dataset for calibrating a regional climate model enclosing the Nile Basin. The Regional Atmospheric Climate Model (RACMO provides an insight not only into the temporal evolution of the hydro-climatological parameters, but also into the land surface climate interactions and embedded feedbacks. The impact of the flooding of the Sudd on the Nile hydroclimatology has been analysed by simulating two land surface scenarios (with and without the Sudd wetland. The paper presents some of the model results addressing the Sudd's influence on rainfall, evaporation

  4. Mapping Tamarix: New techniques for field measurements, spatial modeling and remote sensing

    Science.gov (United States)

    Evangelista, Paul H.

    Native riparian ecosystems throughout the southwestern United States are being altered by the rapid invasion of Tamarix species, commonly known as tamarisk. The effects that tamarisk has on ecosystem processes have been poorly quantified largely due to inadequate survey methods. I tested new approaches for field measurements, spatial models and remote sensing to improve our ability measure and to map tamarisk occurrence, and provide new methods that will assist in management and control efforts. Examining allometric relationships between basal cover and height measurements collected in the field, I was able to produce several models to accurately estimate aboveground biomass. The best two models were explained 97% of the variance (R 2 = 0.97). Next, I tested five commonly used predictive spatial models to identify which methods performed best for tamarisk using different types of data collected in the field. Most spatial models performed well for tamarisk, with logistic regression performing best with an Area Under the receiver-operating characteristic Curve (AUC) of 0.89 and overall accuracy of 85%. The results of this study also suggested that models may not perform equally with different invasive species, and that results may be influenced by species traits and their interaction with environmental factors. Lastly, I tested several approaches to improve the ability to remotely sense tamarisk occurrence. Using Landsat7 ETM+ satellite scenes and derived vegetation indices for six different months of the growing season, I examined their ability to detect tamarisk individually (single-scene analyses) and collectively (time-series). My results showed that time-series analyses were best suited to distinguish tamarisk from other vegetation and landscape features (AUC = 0.96, overall accuracy = 90%). June, August and September were the best months to detect unique phenological attributes that are likely related to the species' extended growing season and green-up during

  5. Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling

    Directory of Open Access Journals (Sweden)

    Sean Sweeney

    2015-11-01

    Full Text Available Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region’s food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of food security in these countries, and to understanding the processes behind it, are reliable, high-quality datasets of cultivated land. Remote sensing has been frequently used for this purpose but distinguishing crops under certain stages of growth from savanna woodlands has remained a major challenge. Yet, crop production in dryland ecosystems is most vulnerable to seasonal climate variability, amplifying the need for high quality products showing the distribution and extent of cropland. The key objective in this analysis is the development of a classification protocol for African savanna landscapes, emphasizing the delineation of cropland. We integrate remote sensing techniques with probabilistic modeling into an innovative workflow. We present summary results for this methodology applied to a land cover classification of Zambia’s Southern Province. Five primary land cover categories are classified for the study area, producing an overall map accuracy of 88.18%. Omission error within the cropland class is 12.11% and commission error 9.76%.

  6. A framework for sharing and integrating remote sensing and GIS models based on Web service.

    Science.gov (United States)

    Chen, Zeqiang; Lin, Hui; Chen, Min; Liu, Deer; Bao, Ying; Ding, Yulin

    2014-01-01

    Sharing and integrating Remote Sensing (RS) and Geographic Information System/Science (GIS) models are critical for developing practical application systems. Facilitating model sharing and model integration is a problem for model publishers and model users, respectively. To address this problem, a framework based on a Web service for sharing and integrating RS and GIS models is proposed in this paper. The fundamental idea of the framework is to publish heterogeneous RS and GIS models into standard Web services for sharing and interoperation and then to integrate the RS and GIS models using Web services. For the former, a "black box" and a visual method are employed to facilitate the publishing of the models as Web services. For the latter, model integration based on the geospatial workflow and semantic supported marching method is introduced. Under this framework, model sharing and integration is applied for developing the Pearl River Delta water environment monitoring system. The results show that the framework can facilitate model sharing and model integration for model publishers and model users.

  7. Signal processing for remote sensing

    CERN Document Server

    Chen, CH

    2007-01-01

    Written by leaders in the field, Signal Processing for Remote Sensing explores the data acquisitions segment of remote sensing. Each chapter presents a major research result or the most up to date development of a topic. The book includes a chapter by Dr. Norden Huang, inventor of the Huang-Hilbert transform who, along with and Dr. Steven Long discusses the application of the transform to remote sensing problems. It also contains a chapter by Dr. Enders A. Robinson, who has made major contributions to seismic signal processing for over half a century, on the basic problem of constructing seism

  8. 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...

  9. Combining land surface models and remote sensing data to estimate evapotranspiration for drought monitoring in Europe

    Science.gov (United States)

    Cammalleri, C.; Sepulcre-Cantó, G.; Vogt, J.

    2014-10-01

    The main hydrologic feedback from the land-surface to the atmosphere is the evapotranspiration, ET, which embraces the response of both the soil and vegetated surface to the atmospheric forcing (e.g., precipitation and temperature), as well as influences locally atmospheric humidity, cloud formation and precipitation, the main driver for drought. Actual ET is regulated by several factors, including biological quantities (e.g., rooting depth, leaf area, fraction of absorbed photosynthetically active radiation) and soil water status. The ET temporal dynamic is strongly affected by rainfall deficits, and in turn it represents a robust proxy of the effects of water shortage on plants. These characteristics make ET a promising quantity for monitoring environmental drought, defined as a shortage of water availability that reduces the ecosystem productivity. In the last few decades, the capability to accurately model ET over large areas in a spatial-distributed fashion has increased notably. Most of the improvements in this field are related to the increasing availability of remote sensing data, and the achievements in modelling of ET-related quantities. Several land-surface models exploit the richness of newly available datasets, including the Community Land Model (CLM) and the Meteosat Second Generation (MSG) ET outputs. Here, the potentiality of ET maps obtained by combining land-surface models and remote sensing data through these two schemes is explored, with a special focus on the reliability of ET (and derived standardized variables) as drought indicator. Tests were performed over Europe at moderate spatial resolution (3-5 km), with the final goal to improve the estimation of soil water status as a contribution to the European Drought Observatory (EDO, http://edo.jrc.ec.europa.eu).

  10. MULTI-SOURCE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR FEATURE FUSION OF REMOTE SENSING IMAGES AND LIDAR DATA

    Directory of Open Access Journals (Sweden)

    Z. Zhang

    2013-05-01

    Full Text Available Feature fusion of remote sensing images and LiDAR points cloud data, which have strong complementarity, can effectively play the advantages of multi-class features to provide more reliable information support for the remote sensing applications, such as object classification and recognition. In this paper, we introduce a novel multi-source hierarchical conditional random field (MSHCRF model to fuse features extracted from remote sensing images and LiDAR data for image classification. Firstly, typical features are selected to obtain the interest regions from multi-source data, then MSHCRF model is constructed to exploit up the features, category compatibility of images and the category consistency of multi-source data based on the regions, and the outputs of the model represents the optimal results of the image classification. Competitive results demonstrate the precision and robustness of the proposed method.

  11. The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

    Science.gov (United States)

    Wanders, N.; Bierkens, M. F. P.; de Jong, S. M.; de Roo, A.; Karssenberg, D.

    2014-08-01

    Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas. This article was corrected on 15 SEP 2014. See the end of the full text for details.

  12. Linear planimetric feature domains modeling for multisensor fusion in remote sensing

    Science.gov (United States)

    Pigeon, Luc; Solaiman, Basel; Toutin, Thierry; Thomson, Keith P. B.

    2000-04-01

    The availability of multi-sensed data, especially in remote sensing, leads to new possibilities in the area of target recognition. In fact, the information contained in an individual sensor represents only one facet of the reality. The use of several sensors aims at covering different facets of real world objects. In this study, the targets to recognize are the planimetric features (i.e. roads, energy transmission lines, railroads and rivers). The sensors used are visible type satellite sensors (SPOT Panchromatic and Landsat TM) as well as radar satellites (Radarsat fine mode and ERS-1). Sensor resolutions range from 8 to 30 meters/pixel. In this study, the modeling is not limited, as it is generally the case, to the problem feature's reality, but to each sensor that will be used. Moreover, the decision space (here a 3D symbolic map) has to be modeled in the same way as the reality and sensors to lead to a coherent and uniform system. Each model is developed using an object- oriented approach. Each reality-object is defined through its radiometric, geometric and topologic feature. The sensor model objects are defined in accordance to image acquisition and definition, including the stereo image cases (for SPOT and Radarsat). Finally, the decision space objects define the resulting 3D symbolic map where, for instance, a pixel attributes contain classification information as well as position, accuracy, reality object's membership values, etc.

  13. Remote Sensing Image Enhancement Based on Non-subsampled Shearlet Transform and Parameterized Logarithmic Image Processing Model

    Directory of Open Access Journals (Sweden)

    TAO Feixiang

    2015-08-01

    Full Text Available Aiming at parts of remote sensing images with dark brightness and low contrast, a remote sensing image enhancement method based on non-subsampled Shearlet transform and parameterized logarithmic image processing model is proposed in this paper to improve the visual effects and interpretability of remote sensing images. Firstly, a remote sensing image is decomposed into a low-frequency component and high frequency components by non-subsampled Shearlet transform.Then the low frequency component is enhanced according to PLIP (parameterized logarithmic image processing model, which can improve the contrast of image, while the improved fuzzy enhancement method is used to enhance the high frequency components in order to highlight the information of edges and details. A large number of experimental results show that, compared with five kinds of image enhancement methods such as bidirectional histogram equalization method, the method based on stationary wavelet transform and the method based on non-subsampled contourlet transform, the proposed method has advantages in both subjective visual effects and objective quantitative evaluation indexes such as contrast and definition, which can more effectively improve the contrast of remote sensing image and enhance edges and texture details with better visual effects.

  14. 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

  15. 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...

  16. Remote Sensing Data in Wind Velocity Field Modelling: a Case Study from the Sudetes (SW Poland)

    Science.gov (United States)

    Jancewicz, Kacper

    2014-06-01

    The phenomena of wind-field deformation above complex (mountainous) terrain is a popular subject of research related to numerical modelling using GIS techniques. This type of modelling requires, as input data, information on terrain roughness and a digital terrain/elevation model. This information may be provided by remote sensing data. Consequently, its accuracy and spatial resolution may affect the results of modelling. This paper represents an attempt to conduct wind-field modelling in the area of the Śnieżnik Massif (Eastern Sudetes). The modelling process was conducted in WindStation 2.0.10 software (using the computable fluid dynamics solver Canyon). Two different elevation models were used: the Global Land Survey Digital Elevation Model (GLS DEM) and Digital Terrain Elevation Data (DTED) Level 2. The terrain roughness raster was generated on the basis of Corine Land Cover 2006 (CLC 2006) data. The output data were post-processed in ArcInfo 9.3.1 software to achieve a high-quality cartographic presentation. Experimental modelling was conducted for situations from 26 November 2011, 25 May 2012, and 26 May 2012, based on a limited number of field measurements and using parameters of the atmosphere boundary layer derived from the aerological surveys provided by the closest meteorological stations. The model was run in a 100-m and 250-m spatial resolution. In order to verify the model's performance, leave-one-out cross-validation was used. The calculated indices allowed for a comparison with results of former studies pertaining to WindStation's performance. The experiment demonstrated very subtle differences between results in using DTED or GLS DEM elevation data. Additionally, CLC 2006 roughness data provided more noticeable improvements in the model's performance, but only in the resolution corresponding to the original roughness data. The best input data configuration resulted in the following mean values of error measure: root mean squared error of velocity

  17. Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

    Science.gov (United States)

    Montero, Rodolfo Alvarado; Schwanenberg, Dirk; Krahe, Peter; Lisniak, Dmytro; Sensoy, Aynur; Sorman, A. Arda; Akkol, Bulut

    2016-06-01

    Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables. The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand

  18. Remote-Sensing Biophysical Models for Estimating Lai of Irrigated Crops in Murry Darling Basin

    Science.gov (United States)

    Wittamperuma, I.; Hafeez, M.; Pakparvar, M.; Louis, J.

    2012-07-01

    Remote sensing is a rapid and reliable method for estimating crop growth data from individual plant to crops in irrigated agriculture ecosystem. The LAI is one of the important biophysical parameter for determining vegetation health, biomass, photosynthesis and evapotranspiration (ET) for the modelling of crop yield and water productivity. Ground measurement of this parameter is tedious and time-consuming due to heterogeneity across the landscape over time and space. This study deals with the development of remote-sensing based empirical relationships for the estimation of ground-based LAI (LAIG) using NDVI, modelled with and without atmospheric correction models for three irrigated crops (corn, wheat and rice) grown in irrigated farms within Coleambally Irrigation Area (CIA) which is located in southern Murray Darling basin, NSW in Australia. Extensive ground truthing campaigns were carried out to measure crop growth and to collect field samples of LAI using LAI- 2000 Plant Canopy Analyser and reflectance using CROPSCAN Multi Spectral Radiometer at several farms within the CIA. A Set of 12 cloud free Landsat 5 TM satellite images for the period of 2010-11 were downloaded and regression analysis was carried out to analyse the co-relationships between satellite and ground measured reflectance and to check the reliability of data sets for the crops. Among all the developed regression relationships between LAI and NDVI, the atmospheric correction process has significantly improved the relationship between LAI and NDVI for Landsat 5 TM images. The regression analysis also shows strong correlations for corn and wheat but weak correlations for rice which is currently being investigated.

  19. Using remotely sensed vegetation indices to model ecological pasture conditions in Kara-Unkur watershed, Kyrgyzstan

    Science.gov (United States)

    Masselink, Loes; Baartman, Jantiene; Verbesselt, Jan; Borchardt, Peter

    2017-04-01

    Kyrgyzstan has a long history of nomadic lifestyle in which pastures play an important role. However, currently the pastures are subject to severe grazing-induced degradation. Deteriorating levels of biomass, palatability and biodiversity reduce the pastures' productivity. To counter this and introduce sustainable pasture management, up-to-date information regarding the ecological conditions of the pastures is essential. This research aimed to investigate the potential of a remote sensing-based methodology to detect changing ecological pasture conditions in the Kara-Unkur watershed, Kyrgyzstan. The relations between Vegetation Indices (VIs) from Landsat ETM+ images and biomass, palatability and species richness field data were investigated. Both simple and multiple linear regression (MLR) analyses, including terrain attributes, were applied. Subsequently, trends of these three pasture conditions were mapped using time series analysis. The results show that biomass is most accurately estimated by a model including the Modified Soil Adjusted Vegetation Index (MSAVI) and a slope factor (R2 = 0.65, F = 0.0006). Regarding palatability, a model including the Enhanced Vegetation Index (EVI), Northness Index, Near Infrared (NIR) and Red band was most accurate (R2 = 0.61, F = 0.0160). Species richness was most accurately estimated by a model including Topographic Wetness Index (TWI), Eastness Index and estimated biomass (R2 = 0.81, F = 0.0028). Subsequent trend analyses of all three estimated ecological pasture conditions presented very similar trend patterns. Despite the need for a more robust validation, this study confirms the high potential of a remote sensing based methodology to detect changing ecological pasture conditions.

  20. Understanding and Modeling Tropical Grasslands Using Remotely Sensed Fluorescence and Soil Moisture

    Science.gov (United States)

    Smith, D.; Denning, S.; Baker, I. T.; Haynes, K. D.

    2016-12-01

    Seasonal grasslands account for a large area of Earth's land cover. Annual and seasonal changes in these grasslands have profound impacts on Earth's carbon, energy, and water cycles. In tropical grasslands, growth is commonly water-limited and the landscape oscillates between highly productive and unproductive. As the monsoon begins, soils moisten providing dry grasses the water necessary to photosynthesize. However, along with seasonal rains come clouds that obscure satellite products (MODIS fPAR/LAI) that are commonly used to quantify phenology and productivity in these areas. To mitigate this issue, we used solar induced fluorescence (SIF) products from GOSAT, GOME-2, and OCO-2 along with soil moisture products from SMAP which see through the clouds to monitor grassland productivity. To get a broader understanding of the vegetation dynamics, we used the Simple Biosphere Model (SiB) to simulate the seasonal cycles of vegetation. In conjunction with SiB, the remotely sensed SIF and soil moisture observations were utilized to paint a clearer picture of seasonal productivity in tropical grasslands. We focused on the growing season onset and senescence of vegetation in both SiB and remotely sensed observations. We investigated the threshold relationships between observed soil moisture and SIF during these "green-up" and "brown-down" periods. SIF and SMAP provide an unprecedented number of observations of these transitions and revealed substantial model biases in the treatment of grassland phenology. Comparing the observed thresholds to model phenology allowed us to improve SiB to more accurately represent the carbon cycle in tropical grasslands across the world.

  1. Developing A Model for Lake Ice Phenology Using Satellite Remote Sensing Observations

    Science.gov (United States)

    Skoglund, S. K.; Weathers, K. C.; Norouzi, H.; Prakash, S.; Ewing, H. A.

    2017-12-01

    Many northern temperate freshwater lakes are freezing over later and thawing earlier. This shift in timing, and the resulting shorter duration of seasonal ice cover, is expected to impact ecological processes, negatively affecting aquatic species and the quality of water we drink. Long-term, direct observations have been used to analyze changes in ice phenology, but those data are sparse relative to the number of lakes affected. Here we develop a model to utilize remote sensing data in approximating the dates of ice-on and ice-off for many years over a variety of lakes. Day and night surface temperatures from MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and Terra (MYD11A1 and MOD11A1 data products) for 2002-2017 were utilized in combination with observed ice-on and ice-off dates of Lake Auburn, Maine, to determine the ability of MODIS data to match ground-based observations. A moving average served to interpolate MODIS temperature data to fill data gaps from cloudy days. The nighttime data were used for ice-off, and the daytime measurements were used for ice-on predictions to avoid fluctuations between day and night ice/water status. The 0˚C intercepts of those data were used to mark approximate days of ice-on or ice-off. This revealed that approximations for ice-off dates were satisfactory (average ±8.2 days) for Lake Auburn as well as for Lake Sunapee, New Hampshire (average ±8.1 days), while approximations for Lake Auburn ice-on were less accurate and showed consistently earlier-than-observed ice-on dates (average -33.8 days). The comparison of observed and remotely sensed Lake Auburn ice cover duration showed relative agreement with a correlation coefficient of 0.46. Other remote sensing observations, such as the new GOES-R satellite, and further exploration of the ice formation process can improve ice-on approximation methods. The model shows promise for estimating ice-on, ice-off, and ice cover duration for northern temperate lakes.

  2. 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.

  3. 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.

  4. Wind farm related mortality among avian migrants - a remote sensing study and model analysis

    DEFF Research Database (Denmark)

    Desholm, M.

    -2006) of migrating birds at the Nysted offshore wind farm in the Baltic Sea, Denmark. This thesis poses and answers the following questions: a) what hazard factors do offshore wind farming pose to wild birds, b) how should one choose the key focal species to study, c) how can remote sensing techniques be applied......This thesis is the result of a PhD study on bird-wind farm collisions and consists of a synopsis, five published papers, one submitted manuscript and another ready for submission. The papers describe the fi ndings from pre- and post-construction visual, radar and thermal imaging studies (1999...... to the study of bird wind farm interactions, and d) specifi cally, how do waterbirds react when approaching an offshore wind farm? The main aim of the study was the development of a predictive bird-wind farm collision model that incorporates the avoidance rate of birds at multiple scales. Out of 235...

  5. Remote sensing inputs to National Model Implementation Program for water resources quality improvement

    Science.gov (United States)

    Eidenshink, J. C.; Schmer, F. A.

    1979-01-01

    The Lake Herman watershed in southeastern South Dakota has been selected as one of seven water resources systems in the United States for involvement in the National Model Implementation Program (MIP). MIP is a pilot program initiated to illustrate the effectiveness of existing water resources quality improvement programs. The Remote Sensing Institute (RSI) at South Dakota State University has produced a computerized geographic information system for the Lake Herman watershed. All components necessary for the monitoring and evaluation process were included in the data base. The computerized data were used to produce thematic maps and tabular data for the land cover and soil classes within the watershed. These data are being utilized operationally by SCS resource personnel for planning and management purposes.

  6. Remote Sensing-based Methodologies for Snow Model Adjustments in Operational Streamflow Prediction

    Science.gov (United States)

    Bender, S.; Miller, W. P.; Bernard, B.; Stokes, M.; Oaida, C. M.; Painter, T. H.

    2015-12-01

    Water management agencies rely on hydrologic forecasts issued by operational agencies such as NOAA's Colorado Basin River Forecast Center (CBRFC). The CBRFC has partnered with the Jet Propulsion Laboratory (JPL) under funding from NASA to incorporate research-oriented, remotely-sensed snow data into CBRFC operations and to improve the accuracy of CBRFC forecasts. The partnership has yielded valuable analysis of snow surface albedo as represented in JPL's MODIS Dust Radiative Forcing in Snow (MODDRFS) data, across the CBRFC's area of responsibility. When dust layers within a snowpack emerge, reducing the snow surface albedo, the snowmelt rate may accelerate. The CBRFC operational snow model (SNOW17) is a temperature-index model that lacks explicit representation of snowpack surface albedo. CBRFC forecasters monitor MODDRFS data for emerging dust layers and may manually adjust SNOW17 melt rates. A technique was needed for efficient and objective incorporation of the MODDRFS data into SNOW17. Initial development focused in Colorado, where dust-on-snow events frequently occur. CBRFC forecasters used retrospective JPL-CBRFC analysis and developed a quantitative relationship between MODDRFS data and mean areal temperature (MAT) data. The relationship was used to generate adjusted, MODDRFS-informed input for SNOW17. Impacts of the MODDRFS-SNOW17 MAT adjustment method on snowmelt-driven streamflow prediction varied spatially and with characteristics of the dust deposition events. The largest improvements occurred in southwestern Colorado, in years with intense dust deposition events. Application of the method in other regions of Colorado and in "low dust" years resulted in minimal impact. The MODDRFS-SNOW17 MAT technique will be implemented in CBRFC operations in late 2015, prior to spring 2016 runoff. Collaborative investigation of remote sensing-based adjustment methods for the CBRFC operational hydrologic forecasting environment will continue over the next several years.

  7. Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).

    Science.gov (United States)

    West, Amanda M; Evangelista, Paul H; Jarnevich, Catherine S; Young, Nicholas E; Stohlgren, Thomas J; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

    2016-10-11

    Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

  8. Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

    Science.gov (United States)

    West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Young, Nicholas E.; Stohlgren, Thomas J.; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

    2016-01-01

    Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

  9. Urban Landscape Characterization Using Remote Sensing Data For Input into Air Quality Modeling

    Science.gov (United States)

    Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Khan, Maudood

    2005-01-01

    The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.

  10. Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

    Science.gov (United States)

    Wang, Aihui; Barlage, Michael; Zeng, Xubin; Draper, Clara Sophie

    2014-01-01

    Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models.

  11. Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

    Science.gov (United States)

    Goetz, Scott J.; Bockstael, Nancy E.; Jantz, Claire A.

    2005-01-01

    This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has

  12. Multi-sensor cloud retrieval simulator and remote sensing from model parameters - Part 1: Synthetic sensor radiance formulation

    Science.gov (United States)

    Wind, G.; da Silva, A. M.; Norris, P. M.; Platnick, S.

    2013-11-01

    In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

  13. Remote Sensing Dynamic Monitoring of Biological Invasive Species Based on Adaptive PCNN and Improved C-V Model

    Directory of Open Access Journals (Sweden)

    PENG Gang

    2014-12-01

    Full Text Available Biological species invasion problem bring serious damage to the ecosystem, and have become one of the six major enviromental problems that affect the future economic development, also have become one of the hot topic in domestic and foreign scholars. Remote sensing technology has been successfully used in the investigation of coastal zone resources, dynamic monitoring of the resources and environment, and other fields. It will cite a new remote sensing image change detection algorithm based on adaptive pulse coupled neural network (PCNN and improved C-V model, for remote sensing dynamic monitoring of biological species invasion. The experimental results show that the algorithm is effective in the test results of biological species invasions.

  14. 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.

  15. Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling.

    Science.gov (United States)

    Gumma, Murali Krishna; Pavelic, Paul

    2013-04-01

    Groundwater development across much of sub-Saharan Africa is constrained by a lack of knowledge on the suitability of aquifers for borehole construction. The main objective of this study was to map groundwater potential at the country-scale for Ghana to identify locations for developing new supplies that could be used for a range of purposes. Groundwater potential zones were delineated using remote sensing and geographical information system (GIS) techniques drawing from a database that includes climate, geology, and satellite data. Subjective scores and weights were assigned to each of seven key spatial data layers and integrated to identify groundwater potential according to five categories ranging from very good to very poor derived from the total percentage score. From this analysis, areas of very good groundwater potential are estimated to cover 689,680 ha (2.9 % of the country), good potential 5,158,955 ha (21.6 %), moderate potential 10,898,140 ha (45.6 %), and poor/very poor potential 7,167,713 ha (30 %). The results were independently tested against borehole yield data (2,650 measurements) which conformed to the anticipated trend between groundwater potential and borehole yield. The satisfactory delineation of groundwater potential zones through spatial modeling suggests that groundwater development should first focus on areas of the highest potential. This study demonstrates the importance of remote sensing and GIS techniques in mapping groundwater potential at the country-scale and suggests that similar methods could be applied across other African countries and regions.

  16. Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment.

    Science.gov (United States)

    Shahabi, Himan; Hashim, Mazlan

    2015-04-22

    This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning.

  17. Cholera in the Lake Kivu region (DRC): Integrating remote sensing and spatially explicit epidemiological modeling

    Science.gov (United States)

    Finger, Flavio; Knox, Allyn; Bertuzzo, Enrico; Mari, Lorenzo; Bompangue, Didier; Gatto, Marino; Rodriguez-Iturbe, Ignacio; Rinaldo, Andrea

    2014-07-01

    Mathematical models of cholera dynamics can not only help in identifying environmental drivers and processes that influence disease transmission, but may also represent valuable tools for the prediction of the epidemiological patterns in time and space as well as for the allocation of health care resources. Cholera outbreaks have been reported in the Democratic Republic of the Congo since the 1970s. They have been ravaging the shore of Lake Kivu in the east of the country repeatedly during the last decades. Here we employ a spatially explicit, inhomogeneous Markov chain model to describe cholera incidence in eight health zones on the shore of the lake. Remotely sensed data sets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers in addition to baseline seasonality. The effect of human mobility is also modelled mechanistically. We test several models on a multiyear data set of reported cholera cases. The best fourteen models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via proper cross validation. Among these, the one accounting for seasonality, El Niño Southern Oscillation, precipitation and human mobility outperforms the others in cross validation. Some drivers (such as human mobility and rainfall) are retained only by a few models, possibly indicating that the mechanisms through which they influence cholera dynamics in the area will have to be investigated further.

  18. Remote sensing of water quality

    Science.gov (United States)

    Hovis, W. A.

    1978-01-01

    Remote sensing from aircraft has been used to determine water content in areas such as the New York Bight. Extension of the techniques developed to satellite sensing of the Chesapeake Bay will begin in 1978 with the launch of Nimbus-G. Remote sensing offers a number of interesting possibilities for investigating a reasonably large body of water, such as the Chesapeake Bay, coupled with some disadvantages. The chief advantage of remote sensing is that it offers the opportunity to cover large areas in relatively short periods of time. Low altitude satellites traveling at about 7 km/s can cover the Chesapeake Bay in about 1 minute so that the entire Bay can be studied under almost identical conditions of solar illumination.

  19. Modeling residential lawn fertilization practices: integrating high resolution remote sensing with socioeconomic data

    Science.gov (United States)

    Weiqi Zhou; Austin Troy; Morgan. Grove

    2008-01-01

    This article investigates how remotely sensed lawn characteristics, such as parcel lawn area and parcel lawn greenness, combined with household characteristics, can be used to predict household lawn fertilization practices on private residential lands. This study involves two watersheds, Glyndon and Baisman's Run, in Baltimore County, Maryland, USA. Parcel lawn...

  20. Modeling of Culicidae population capacity using remote sensing, GIS, and fractal geometry

    Science.gov (United States)

    Johnson, Daniel P.

    A remote sensing and fractal geometric approach is proposed for the surveillance of mosquito population. The spread of vector-borne pathogens is becoming increasingly important with recent discussions of extensive human urbanization and global climate change. To date most studies dealing with mosquito transmitted disease surveyed by remote sensing and Geographic Information Systems have been conducted in tropical homogenous locales. This study approached the issue in a rapidly suburbanizing county in the Midwestern portion of the United States. Adult mosquitoes were trapped over a several year period and the landscape of the surrounding patches analyzed via remote sensing, fractal geometry, and areal summation. Additionally, frequently used vegetation indices were employed in the analysis of the landscape. The fractal method outperformed the areal metric slightly. Future methods of mosquito surveillance using remote sensing are also discussed. Mosquito abatement policy may also be enhanced by the use of such methods and systems. The implication of ecological fallacy and other limitations of the current study are revealed.

  1. Modeling Accumulated Volume of Landslides Using Remote Sensing and DTM Data

    Directory of Open Access Journals (Sweden)

    Zhengchao Chen

    2014-02-01

    Full Text Available Landslides, like other natural hazards, such as avalanches, floods, and debris flows, may result in a lot of property damage and human casualties. The volume of landslide deposits is a key parameter for landslide studies and disaster relief. Using remote sensing and digital terrain model (DTM data, this paper analyzes errors that can occur in calculating landslide volumes using conventional models. To improve existing models, the mechanisms and laws governing the material deposited by landslides are studied and then the mass balance principle and mass balance line are defined. Based on these ideas, a novel and improved model (Mass Balance Model, MBM is proposed. By using a parameter called the “height adaptor”, MBM translates the volume calculation into an automatic search for the mass balance line within the scope of the landslide. Due to the use of mass balance constraints and the height adaptor, MBM is much more effective and reliable. A test of MBM was carried out for the case of a typical landslide, triggered by the Wenchuan Earthquake of 12 May 2008.

  2. Real-time remote sensing driven river basin modeling using radar altimetry

    Directory of Open Access Journals (Sweden)

    S. J. Pereira-Cardenal

    2011-01-01

    Full Text Available Many river basins have a weak in-situ hydrometeorological monitoring infrastructure. However, water resources practitioners depend on reliable hydrological models for management purposes. Remote sensing (RS data have been recognized as an alternative to in-situ hydrometeorological data in remote and poorly monitored areas and are increasingly used to force, calibrate, and update hydrological models.

    In this study, we evaluate the potential of informing a river basin model with real-time radar altimetry measurements over reservoirs. We present a lumped, conceptual, river basin water balance modeling approach based entirely on RS and reanalysis data: precipitation was obtained from the Tropical Rainfall Measuring Mission (TRMM Multisatellite Precipitation Analysis (TMPA, temperature from the European Centre for Medium-Range Weather Forecast's (ECMWF Operational Surface Analysis dataset and reference evapotranspiration was derived from temperature data. The Ensemble Kalman Filter was used to assimilate radar altimetry (ERS2 and Envisat measurements of reservoir water levels. The modeling approach was applied to the Syr Darya River Basin, a snowmelt-dominated basin with large topographical variability, several large reservoirs and scarce hydrometeorological data that is located in Central Asia and shared between 4 countries with conflicting water management interests.

    The modeling approach was tested over a historical period for which in-situ reservoir water levels were available. Assimilation of radar altimetry data significantly improved the performance of the hydrological model. Without assimilation of radar altimetry data, model performance was limited, probably because of the size and complexity of the model domain, simplifications inherent in model design, and the uncertainty of RS and reanalysis data. Altimetry data assimilation reduced the mean absolute error of the simulated reservoir water levels from 4.7 to 1.9 m, and

  3. Assessing Crop Conditions through the Use of Remote Sensing and Agricultural and Radar Modelling

    Science.gov (United States)

    Davitt, A. W. D.; McDonald, K. C.; Winter, J.

    2016-12-01

    California and The Central Valley are presently experiencing one of that region's worst, persistent droughts on record. Due to the drought, the majority of the agricultural regions located in the Central Valley have faced reduced surface water allocations, negatively impacting crop production and the socioeconomics of the region. As a result of these effects, there has been an increased incentive to consider new ways to maximize agricultural water use efficiency. Recent advances in remote sensing and agricultural modelling provide the ability to improve agricultural management for end users to better understand field conditions, allowing the users to make informed decisions on irrigation timing and amounts. Our research explores the effectiveness of Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) in simulating wheat, corn, and rice for water year 2015 in Yolo County, California. This will be accomplished through the evaluation of multi-temporal C-band radar backscatter from the Sentinel-1 satellite and the Michigan's microwave canopy scattering model (MIMICS) to simulate Sentinel-1 radar for each crop type based on vegetation structure outputs from DSSAT-CSM. By linking Sentinel-1 and MIMICS to DSSAT-CSM, we create a verified model for agricultural monitoring that can potentially support the assessment of conditions within agricultural fields. This would allow growers to optimize use of limited water supplies through informed water management practices, improving crop conditions and yield in a water stressed region.

  4. STUDY ON MODELING AND VISUALIZING THE POSITIONAL UNCERTAINTY OF REMOTE SENSING IMAGE

    Directory of Open Access Journals (Sweden)

    W. Jiao

    2016-06-01

    Full Text Available It is inevitable to bring about uncertainty during the process of data acquisition. The traditional method to evaluate the geometric positioning accuracy is usually by the statistical method and represented by the root mean square errors (RMSEs of control points. It is individual and discontinuous, so it is difficult to describe the error spatial distribution. In this paper the error uncertainty of each control point is deduced, and the uncertainty spatial distribution model of each arbitrary point is established. The error model is proposed to evaluate the geometric accuracy of remote sensing image. Then several visualization methods are studied to represent the discrete and continuous data of geometric uncertainties. The experiments show that the proposed evaluation method of error distribution model compared with the traditional method of RMSEs can get the similar results but without requiring the user to collect control points as checkpoints, and error distribution information calculated by the model can be provided to users along with the geometric image data. Additionally, the visualization methods described in this paper can effectively and objectively represents the image geometric quality, and also can help users probe the reasons of bringing the image uncertainties in some extent.

  5. National fire danger assessment and ecosystem restoration using remote sensing and ecological modeling

    Science.gov (United States)

    Zhu, Z.; Rollins, M.

    Hazardous fuel reduction, ecosystem rehabilitation and restoration, and firefighting safety, are land management priorities emphasized by recent national fire policies such as the National Fire Plan. Implementation of these policies requires geospatial data of vegetation conditions, fire fuels, risks, and ecosystem status developed consistently nationwide that can be used at multiple scales (i.e., local, regional, and national). A new research and development project called LANDFIRE is being conducted to develop an integrated methodology to produce geospatial fire data and predictive models for the land management community and a broad range of other applications. Main deliverables include mapped potential and existing vegetation types and vegetation structure parameters, various biophysical data layers, fire fuels models, fire risk layers, as well as state-of-the-art computer models for assessing fire risk, behavior and effects. In this presentation, we will review research results and findings of the LANDFIRE project using results from a prototype study covering central Utah Uinta and Wasatch ecosystems. Particularly we will describe how a consistent and operational vegetation mapping component may be achieved by integrating machine-learning algorithms, field reference data, satellite imagery, and ecologically significant biophysical variables. We will discuss how remotely sensed vegetation cover types and structure can be successfully converted to fire fuel classes and risk layers which are necessary input into fire behavior and fire effect models. Finally we will discuss challenges and opportunities for national implementation of the methodology.

  6. National Fire Fuels and Risks Assessment Using Remote Sensing and Ecological Modeling: Prototype Results

    Science.gov (United States)

    Zhu, Z.; Rollins, M.

    2003-12-01

    Hazardous fuel reduction, ecosystem rehabilitation and restoration, and firefighting safety, are land management priorities emphasized by recent national fire policies such as the National Fire Plan. Implementation of these policies requires geospatial data of vegetation conditions, fire fuels, risks, and ecosystem status developed consistently nationwide that can be used at multiple scales (i.e., local, regional, and national). A new research and development project called LANDFIRE has been conducted to develop an integrated methodology to produce geospatial fire data and predictive models for the land management community and a broad range of other applications. Main deliverables include mapped potential and existing vegetation types and structure variables, various biophysical data layers, fire fuels models, fire risk layers, as well as state-of-the-art computer models for assessing fire risk, behavior and effects. In this presentation, we will review research results and findings of the LANDFIRE project using results from a prototype study covering central Utah Uinta and Wasatch ecosystems. Particularly we will describe how a consistent and operational vegetation mapping component may be achieved by integrating machine-learning algorithms, field reference data, satellite imagery, and ecologically significant biophysical variables. We will discuss how remotely sensed vegetation cover types and structure can be successfully converted to fire fuel classes and risk layers which are necessary input into fire behavior and fire effect models. Finally we will discuss challenges and opportunities for national implementation of the methodology.

  7. Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations

    OpenAIRE

    Zhiqiang Cheng; Jihua Meng; Yanyou Qiao; Yiming Wang; Wenquan Dong; Yanxin Han

    2018-01-01

    The approach of using multispectral remote sensing (RS) to estimate soil available nutrients (SANs) has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, we replaced the statistical model by integrating the World Food Studies (WOFOST) model and time seri...

  8. The Application of Spaceborne Remote Sensing Datasets for Species Distribution Modeling in South America

    Science.gov (United States)

    McDonald, K. C.; Carnaval, A.; Waltari, E.; Schroeder, R.

    2012-12-01

    For the last 40 years, the fields of evolutionary biogeography and conservation biology witnessed substantial improvement and usage of correlative species distribution in studies of biodiversity patterns and their underlying processes Thanks to a suite of new algorithms and the integration of maximum entropy concepts into the biological sciences, field scientists are now able to better predict species ranges from environmental surrogates. To date, biologists have been relying on a single major set of global environmental grids for the purpose of both delimiting species environmental envelopes and projecting envelopes through space and time. The data set, also known as the WorldClim database, relies on measurements of elevation, precipitation, mean, maximum and minimum temperature collected from weather-stations across the world. These data were used to derive worldwide grids, at 1 km resolution, through interpolation of average monthly climate data from stations. Nineteen bioclimatic grids have been derived from the air temperature and precipitation values and have been used extensively in predictive studies. Although these WorldClim grids have been successfully applied to a suite of ecological and evolutionary research questions, their performance can be suboptimal especially in topographically complex areas where interpolation methods fail to capture true variation in local climate, in biological systems impacted by environmental phenomena occurring at finer temporal or spatial scales, and in regions with few weather stations. The objective of this work is to assess the utility of remote sensing data sets for providing environmental fields to derive novel bioclimatic grids relative to the WorldClim dataset, and test the associated improvement to species distribution models in a topographically complex tropical area. We generate a novel set of bioclimatic grids for biodiversity analysis from such sources as MODIS, AMSR-E, TRMM and MERRA. Using these grids, we

  9. Flood Hazard Mapping Combining Hydrodynamic Modeling and Multi Annual Remote Sensing data

    Directory of Open Access Journals (Sweden)

    Laura Giustarini

    2015-10-01

    Full Text Available This paper explores a method to combine the time and space continuity of a large-scale inundation model with discontinuous satellite microwave observations, for high-resolution flood hazard mapping. The assumption behind this approach is that hydraulic variables computed from continuous spatially-distributed hydrodynamic modeling and observed as discrete satellite-derived flood extents are correlated in time, so that probabilities can be transferred from the model series to the observations. A prerequisite is, therefore, the existence of a significant correlation between a modeled variable (i.e., flood extent or volume and the synchronously-observed flood extent. If this is the case, the availability of model simulations over a long time period allows for a robust estimate of non-exceedance probabilities that can be attributed to corresponding synchronously-available satellite observations. The generated flood hazard map has a spatial resolution equal to that of the satellite images, which is higher than that of currently available large scale inundation models. The method was applied on the Severn River (UK, using the outputs of a global inundation model provided by the European Centre for Medium-range Weather Forecasts and a large collection of ENVISAT ASAR imagery. A comparison between the hazard map obtained with the proposed method and with a more traditional numerical modeling approach supports the hypothesis that combining model results and satellite observations could provide advantages for high-resolution flood hazard mapping, provided that a sufficient number of remote sensing images is available and that a time correlation is present between variables derived from a global model and obtained from satellite observations.

  10. Remote Sensing Characterization of the Urban Landscape for Improvement of Air Quality Modeling

    Science.gov (United States)

    Quattrochi, Dale A.; Estes, Maurice G., Jr.; Khan, Maudood

    2005-01-01

    The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, in moderating ground-level ozone and air temperature, compared to "business as usual" simulations in which heat island mitigation strategies are not applied. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data has been found to better characterize low densityhburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for fiture scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the state Environmental Protection agency to evaluate how these

  11. GIS, remote sensing and spatial modeling for conservation of stone forest landscape in Lunan, China

    Science.gov (United States)

    Zhang, Chuanrong

    The Lunan Stone Forest is the World's premier pinnacle karst landscape, with considerable scientific and cultural importance. Because of its inherent ecological fragility and ongoing human disruption, especially recently burgeoning tourism development, the landscape is stressed and is in danger of being destroyed. Conservation policies have been implemented by the local and national governments, but many problems remain in the national park. For example, there is no accurate detailed map and no computer system to help authorities manage the natural resources. By integrating GIS, remote sensing and spatial modeling this dissertation investigates the issue of landscape conservation and develops some methodologies to assist in management of the natural resources in the national park. Four elements are involved: (1) To help decision-makers and residents understand the scope of resource exploitation and develop appropriate protective strategies, the dissertation documents how the landscape has been changed by human activities over the past 3 decades; (2) To help authorities scientifically designate different levels of protection in the park and to let the public actively participate in conservation decision making, a web-based Spatial Decision Support System for the conservation of the landscape was developed; (3) To make data sharing and integration easy in the future, a GML-based interoperable database for the park was implemented; and (4) To acquire more information and provide the uncertainty information to landscape conservation decision-makers, spatial land use patterns were modeled and the distributional uncertainty of land cover categories was assessed using a triplex Markov chain (TMC) model approach.

  12. Large-scale functional models of visual cortex for remote sensing

    Energy Technology Data Exchange (ETDEWEB)

    Brumby, Steven P [Los Alamos National Laboratory; Kenyon, Garrett [Los Alamos National Laboratory; Rasmussen, Craig E [Los Alamos National Laboratory; Swaminarayan, Sriram [Los Alamos National Laboratory; Bettencourt, Luis [Los Alamos National Laboratory; Landecker, Will [PORTLAND STATE UNIV.

    2009-01-01

    Neuroscience has revealed many properties of neurons and of the functional organization of visual cortex that are believed to be essential to human vision, but are missing in standard artificial neural networks. Equally important may be the sheer scale of visual cortex requiring {approx}1 petaflop of computation. In a year, the retina delivers {approx}1 petapixel to the brain, leading to massively large opportunities for learning at many levels of the cortical system. We describe work at Los Alamos National Laboratory (LANL) to develop large-scale functional models of visual cortex on LANL's Roadrunner petaflop supercomputer. An initial run of a simple region VI code achieved 1.144 petaflops during trials at the IBM facility in Poughkeepsie, NY (June 2008). Here, we present criteria for assessing when a set of learned local representations is 'complete' along with general criteria for assessing computer vision models based on their projected scaling behavior. Finally, we extend one class of biologically-inspired learning models to problems of remote sensing imagery.

  13. DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence

    Science.gov (United States)

    Gastellu-Etchegorry, Jean-Phil; Lauret, Nicolas; Yin, Tiangang; Landier, Lucas; Kallel, Abdelaziz; Malenovsky, Zbynek; Bitar, Ahmad Al; Aval, Josselin; Benhmida, Sahar; Qi, Jianbo; hide

    2017-01-01

    To better understand the life-essential cycles and processes of our planet and to further develop remote sensing (RS) technology, there is an increasing need for models that simulate the radiative budget (RB) and RS acquisitions of urban and natural landscapes using physical approaches and considering the three-dimensional (3-D) architecture of Earth surfaces. Discrete anisotropic radiative transfer (DART) is one of the most comprehensive physically based 3-D models of Earth-atmosphere radiative transfer, covering the spectral domain from ultraviolet to thermal infrared wavelengths. It simulates the optical 3-DRB and optical signals of proximal, aerial, and satellite imaging spectrometers and laser scanners, for any urban and/or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. In this paper, we briefly introduce DART theory and present recent advances in simulated sensors (LiDAR and cameras with finite field of view) and modeling mechanisms (atmosphere, specular reflectance with polarization and chlorophyll fluorescence). A case study demonstrating a novel application of DART to investigate urban landscapes is also presented.

  14. MODELING AND SIMULATION OF HIGH RESOLUTION OPTICAL REMOTE SENSING SATELLITE GEOMETRIC CHAIN

    Directory of Open Access Journals (Sweden)

    Z. Xia

    2018-04-01

    Full Text Available The high resolution satellite with the longer focal length and the larger aperture has been widely used in georeferencing of the observed scene in recent years. The consistent end to end model of high resolution remote sensing satellite geometric chain is presented, which consists of the scene, the three line array camera, the platform including attitude and position information, the time system and the processing algorithm. The integrated design of the camera and the star tracker is considered and the simulation method of the geolocation accuracy is put forward by introduce the new index of the angle between the camera and the star tracker. The model is validated by the geolocation accuracy simulation according to the test method of the ZY-3 satellite imagery rigorously. The simulation results show that the geolocation accuracy is within 25m, which is highly consistent with the test results. The geolocation accuracy can be improved about 7 m by the integrated design. The model combined with the simulation method is applicable to the geolocation accuracy estimate before the satellite launching.

  15. Remote sensing for urban planning

    Science.gov (United States)

    Davis, Bruce A.; Schmidt, Nicholas; Jensen, John R.; Cowen, Dave J.; Halls, Joanne; Narumalani, Sunil; Burgess, Bryan

    1994-01-01

    Utility companies are challenged to provide services to a highly dynamic customer base. With factory closures and shifts in employment becoming a routine occurrence, the utility industry must develop new techniques to maintain records and plan for expected growth. BellSouth Telecommunications, the largest of the Bell telephone companies, currently serves over 13 million residences and 2 million commercial customers. Tracking the movement of customers and scheduling the delivery of service are major tasks for BellSouth that require intensive manpower and sophisticated information management techniques. Through NASA's Commercial Remote Sensing Program Office, BellSouth is investigating the utility of remote sensing and geographic information system techniques to forecast residential development. This paper highlights the initial results of this project, which indicate a high correlation between the U.S. Bureau of Census block group statistics and statistics derived from remote sensing data.

  16. 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....

  17. 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.

  18. Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings.

    Science.gov (United States)

    Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P

    2017-09-15

    Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workers are faced with choosing the appropriate remote sensing data. The objective of this research is to analyze the spatial resolution effects of different remote sensing images on soil prediction models in two smallholder farms in Southern India called Kothapally (Telangana State), and Masuti (Karnataka State), and provide empirical guidelines to choose the appropriate remote sensing images in DSM. Bayesian kriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (K ex ) in the topsoil (0-15 cm) at different spatial resolutions by incorporating spectral indices from Landsat 8 (30 m), RapidEye (5 m), and WorldView-2/GeoEye-1/Pleiades-1A images (2 m). Some spectral indices such as band reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multiple images showed relatively strong correlations with soil K ex in two study areas. The research also suggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-based soil prediction models would not necessarily have higher prediction performance than coarse spatial resolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settings need select the appropriate spectral indices and consider different factors such as the spatial resolution, band width, spectral resolution, temporal frequency, cost, and processing time of different remote sensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted to smallholder farm settings all over the world and help smallholder farmers implement sustainable and field-specific soil nutrient management scheme. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Verification of the two-dimensional hydrodynamic model based on remote sensing

    Science.gov (United States)

    Sazonov, Alexey; Mikhailukova, Polina; Krylenko, Inna; Frolova, Natalya; Kireeva, Mariya

    2016-04-01

    Mathematical modeling methods are used more and more actively to evaluate possible damage, identify potential flood zone and the influence of individual factors affecting the river during the passage of the flood. Calculations were performed by means of domestic software complex «STREAM-2D» which is based on the numerical solution of two-dimensional St. Venant equations. One of the major challenges in mathematical modeling is the verification of the model. This is usually made using data on water levels from hydrological stations: the smaller the difference of the actual level and the simulated one, the better the quality of the model used. Data from hydrological stations are not always available, so alternative sources of verification, such as remote sensing, are increasingly used. The aim of this work is to develop a method of verification of hydrodynamic model based on a comparison of actual flood zone area, which in turn is determined on the basis of the automated satellite image interpretation methods for different imaging systems and flooded area obtained in the course of the model. The study areas are Lena River, The North Dvina River, Amur River near Blagoveshchensk. We used satellite images made by optical and radar sensors: SPOT-5/HRG, Resurs-F, Radarsat-2. Flooded area were calculated using unsupervised classification (ISODATA and K-mean) for optical images and segmentation for Radarsat-2. Knowing the flow rate and the water level at a given date for the upper and lower limits of the model, respectively, it is possible to calculate flooded area by means of program STREAM-2D and GIS technology. All the existing vector layers with the boundaries of flooding are included in a GIS project for flood area calculation. This study was supported by the Russian Science Foundation, project no. 14-17-00155.

  20. The Isinglass Auroral Sounding Rocket Campaign: data synthesis incorporating remote sensing, in situ observations, and modelling

    Science.gov (United States)

    Lynch, K. A.; Clayton, R.; Roberts, T. M.; Hampton, D. L.; Conde, M.; Zettergren, M. D.; Burleigh, M.; Samara, M.; Michell, R.; Grubbs, G. A., II; Lessard, M.; Hysell, D. L.; Varney, R. H.; Reimer, A.

    2017-12-01

    The NASA auroral sounding rocket mission Isinglass was launched from Poker Flat Alaska in winter 2017. This mission consists of two separate multi-payload sounding rockets, over an array of groundbased observations, including radars and filtered cameras. The science goal is to collect two case studies, in two different auroral events, of the gradient scale sizes of auroral disturbances in the ionosphere. Data from the in situ payloads and the groundbased observations will be synthesized and fed into an ionospheric model, and the results will be studied to learn about which scale sizes of ionospheric structuring have significance for magnetosphere-ionosphere auroral coupling. The in situ instrumentation includes thermal ion sensors (at 5 points on the second flight), thermal electron sensors (at 2 points), DC magnetic fields (2 point), DC electric fields (one point, plus the 4 low-resource thermal ion RPA observations of drift on the second flight), and an auroral precipitation sensor (one point). The groundbased array includes filtered auroral imagers, the PFISR and SuperDarn radars, a coherent scatter radar, and a Fabry-Perot interferometer array. The ionospheric model to be used is a 3d electrostatic model including the effects of ionospheric chemistry. One observational and modelling goal for the mission is to move both observations and models of auroral arc systems into the third (along-arc) dimension. Modern assimilative tools combined with multipoint but low-resource observations allow a new view of the auroral ionosphere, that should allow us to learn more about the auroral zone as a coupled system. Conjugate case studies such as the Isinglass rocket flights allow for a test of the models' intepretation by comparing to in situ data. We aim to develop and improve ionospheric models to the point where they can be used to interpret remote sensing data with confidence without the checkpoint of in situ comparison.

  1. Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

    Science.gov (United States)

    Newcomer, Michelle; Kuss, Amber; Kentron, Tyler; Remar, Alex; Choksi, Vivek; Skiles, J. W.

    2011-01-01

    Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

  2. Satellite remote sensing data can be used to model marine microbial metabolite turnover

    Energy Technology Data Exchange (ETDEWEB)

    Larsen, Peter E.; Scott, Nicole; Post, Anton F.; Field, Dawn; Knight, Rob; Hamada, Yuki; Gilbert, Jack A.

    2014-07-29

    Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km2) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes’ predicted relative abundance was highly correlated (Pearson Correlation 0.72, P-value <10-6) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO2 were significantly correlated with observed surface CO2 fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ~3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology.

  3. Wind farm related mortality among avian migrants - a remote sensing study and model analysis

    Energy Technology Data Exchange (ETDEWEB)

    Desholm, M.

    2006-11-15

    This thesis is the result of a PhD study on bird-wind farm collisions and consists of a synopsis, five published papers, one submitted manuscript and another ready for submission. The papers describe the findings from pre- and post-construction visual, radar and thermal imaging studies (1999-2006) of migrating birds at the Nysted offshore wind farm in the Baltic Sea, Denmark. This thesis poses and answers the following questions: a) what hazard factors do offshore wind farming pose to wild birds, b) how should one choose the key focal species to study, c) how can remote sensing techniques be applied to the study of bird wind farm interactions, and d) specifically, how do water birds react when approaching an offshore wind farm? The main aim of the study was the development of a predictive bird-wind farm collision model that incorporates the avoidance rate of birds at multiple scales. Out of 235,136 migrating sea ducks only 47 individuals were predicted to collide with the wind turbine rotor-blades, equivalent to an overall mean collision risk of c. 0.02%. This thesis shows the added value of modelling in supplementing sound empirical studies in accessing the effects of major human development pressures on migratory bird populations. (au)

  4. Capturing Micro-topography of an Arctic Tundra Landscape through Digital Elevation Models (DEMs) Acquired from Various Remote Sensing Platforms

    Science.gov (United States)

    Vargas, S. A., Jr.; Tweedie, C. E.; Oberbauer, S. F.

    2013-12-01

    The need to improve the spatial and temporal scaling and extrapolation of plot level measurements of ecosystem structure and function to the landscape level has been identified as a persistent research challenge in the arctic terrestrial sciences. Although there has been a range of advances in remote sensing capabilities on satellite, fixed wing, helicopter and unmanned aerial vehicle platforms over the past decade, these present costly, logistically challenging (especially in the Arctic), technically demanding solutions for applications in an arctic environment. Here, we present a relatively low cost alternative to these platforms that uses kite aerial photography (KAP). Specifically, we demonstrate how digital elevation models (DEMs) were derived from this system for a coastal arctic landscape near Barrow, Alaska. DEMs of this area acquired from other remote sensing platforms such as Terrestrial Laser Scanning (TLS), Airborne Laser Scanning, and satellite imagery were also used in this study to determine accuracy and validity of results. DEMs interpolated using the KAP system were comparable to DEMs derived from the other platforms. For remotely sensing acre to kilometer square areas of interest, KAP has proven to be a low cost solution from which derived products that interface ground and satellite platforms can be developed by users with access to low-tech solutions and a limited knowledge of remote sensing.

  5. Landslide hazard assessment along a mountain highway in the Indian Himalayan Region (IHR) using remote sensing and computational models

    Science.gov (United States)

    Krishna, Akhouri P.; Kumar, Santosh

    2013-10-01

    Landslide hazard assessments using computational models, such as artificial neural network (ANN) and frequency ratio (FR), were carried out covering one of the important mountain highways in the Central Himalaya of Indian Himalayan Region (IHR). Landslide influencing factors were either calculated or extracted from spatial databases including recent remote sensing data of LANDSAT TM, CARTOSAT digital elevation model (DEM) and Tropical Rainfall Measuring Mission (TRMM) satellite for rainfall data. ANN was implemented using the multi-layered feed forward architecture with different input, output and hidden layers. This model based on back propagation algorithm derived weights for all possible parameters of landslides and causative factors considered. The training sites for landslide prone and non-prone areas were identified and verified through details gathered from remote sensing and other sources. Frequency Ratio (FR) models are based on observed relationships between the distribution of landslides and each landslide related factor. FR model implementation proved useful for assessing the spatial relationships between landslide locations and factors contributing to its occurrence. Above computational models generated respective susceptibility maps of landslide hazard for the study area. This further allowed the simulation of landslide hazard maps on a medium scale using GIS platform and remote sensing data. Upon validation and accuracy checks, it was observed that both models produced good results with FR having some edge over ANN based mapping. Such statistical and functional models led to better understanding of relationships between the landslides and preparatory factors as well as ensuring lesser levels of subjectivity compared to qualitative approaches.

  6. A Distributed Multi-dimensional SOLAP Model of Remote Sensing Data and Its Application in Drought Analysis

    Directory of Open Access Journals (Sweden)

    LI Jiyuan

    2014-06-01

    Full Text Available SOLAP (Spatial On-Line Analytical Processing has been applied to multi-dimensional analysis of remote sensing data recently. However, its computation performance faces a considerable challenge from the large-scale dataset. A geo-raster cube model extended by Map-Reduce is proposed, which refers to the application of Map-Reduce (a data-intensive computing paradigm in the OLAP field. In this model, the existing methods are modified to adapt to distributed environment based on the multi-level raster tiles. Then the multi-dimensional map algebra is introduced to decompose the SOLAP computation into multiple distributed parallel map algebra functions on tiles under the support of Map-Reduce. The drought monitoring by remote sensing data is employed as a case study to illustrate the model construction and application. The prototype is also implemented, and the performance testing shows the efficiency and scalability of this model.

  7. Assimilation of remotely sensed chlorophyll fluorescence data into the land surface model CLM4

    Science.gov (United States)

    Wieneke, S.; Ahrends, H. E.; Rascher, U.; Schween, J.; Schickling, A.; Crewell, S.

    2013-12-01

    Photosynthesis is the most important exchange process of CO2 between the atmosphere and the land-surface. Therefore, the prediction of vegetation response to environmental conditions like increasing CO2 concentrations or plant stress is crucial for a reliable prediction of climate change. Photosynthesis is a complex physiological process that consists of numerous bio-physical sub-processes and chemical reactions. Spatial and temporal patterns of photosynthesis depend on dynamic plant-specific adaptation strategies to highly variable environmental conditions. Photosynthesis can be estimated using land-surface models, but, while state-of-the-art models often rely on Plant Functional Type (PFT) specific constants, they poorly simulate the dynamic adaptation of the physiological status of plant canopies in space and time. Remotely sensed sun-induced chlorophyll fluorescence (SICF) gives us now the possibility to estimate the diurnal dynamic vitality of the photosynthetic apparatus at both, the leaf and canopy levels. We installed within the framework of the Transregio32 project (www.tr32.de) automated hyperspectral fluorescence sensors at an agricultural site (winter wheat) in the Rur catchment area in West Germany at the end of July 2012. End of August, additional measurements of SIFC on nearby temperate grassland site (riparian meadow) and on a sugar beet field were performed. Spatial covering SICF data of the region were obtained during a measurement campaign using the newly developed air-borne hyperspectral sensor HyPlant on the 23 and 27 August 2012. SIFC data and data provided by eddy covariance measurements will be used to update certain model parameters that are normally set as constants. First model results demonstrate that the assimilation of SIFC into the Community Land Model 4 (CLM4) will result in a more realistic simulation of plant-specific adaptation strategies and therefore in a more realistic simulation of photosynthesis in space and time.

  8. Offshore winds mapped from satellite remote sensing

    DEFF Research Database (Denmark)

    Hasager, Charlotte Bay

    2014-01-01

    the uncertainty on the model results on the offshore wind resource, it is necessary to compare model results with observations. Observations from ground-based wind lidar and satellite remote sensing are the two main technologies that can provide new types of offshore wind data at relatively low cost....... The advantages of microwave satellite remote sensing are 1) horizontal spatial coverage, 2) long data archives and 3) high spatial detail both in the coastal zone and of far-field wind farm wake. Passive microwave ocean wind speed data are available since 1987 with up to 6 observations per day with near...

  9. Developing Theoretical Marine Habitat Suitability Models from Remotely-Sensed Data and Traditional Ecological Knowledge

    Directory of Open Access Journals (Sweden)

    Patrick M. Olsen

    2015-09-01

    Full Text Available There is a lack of information regarding critical habitats for many marine species, including the bearded seal, an important subsistence species for the indigenous residents of Arctic regions. A systematic approach to modeling marine mammal habitat in arctic regions using the lifetime and multi-generational Traditional Ecological Knowledge (TEK of Alaska Native hunters is developed to address this gap. The approach uses lifetime and cross-generational knowledge of subsistence hunters and their harvest data in the place of observational knowledge gained from Western scientific field surveys of marine mammal sightings. TEK information for mid-June to October was transformed to seal presence/pseudo-absence and used to train Classification Tree Analyses of environmental predictor variables to predict suitable habitat for bearded seals in the Bering Strait region. Predictor variables were derived from a suite of terrestrial, oceanic, and atmospheric remote sensing products, transformed using trend analysis techniques, and aggregated. A Kappa of 0.883 was achieved for habitat classifications. The TEK information used is spatially restricted, but provides a viable, replicable data source that can replace or complement Western scientific observational data.

  10. Modelling of light pollution in suburban areas using remotely sensed imagery and GIS.

    Science.gov (United States)

    Chalkias, C; Petrakis, M; Psiloglou, B; Lianou, M

    2006-04-01

    This paper describes a methodology for modelling light pollution using geographical information systems (GIS) and remote sensing (RS) technology. The proposed approach attempts to address the issue of environmental assessment in sensitive suburban areas. The modern way of life in developing countries is conductive to environmental degradation in urban and suburban areas. One specific parameter for this degradation is light pollution due to intense artificial night lighting. This paper aims to assess this parameter for the Athens metropolitan area, using modern analytical and data capturing technologies. For this purpose, night-time satellite images and analogue maps have been used in order to create the spatial database of the GIS for the study area. Using GIS advanced analytical functionality, visibility analysis was implemented. The outputs for this analysis are a series of maps reflecting direct and indirect light pollution around the city of Athens. Direct light pollution corresponds to optical contact with artificial night light sources, while indirect light pollution corresponds to optical contact with the sky glow above the city. Additionally, the assessment of light pollution in different periods allows for dynamic evaluation of the phenomenon. The case study demonstrates high levels of light pollution in Athens suburban areas and its increase over the last decade.

  11. Parameterization and sensitivity analyses of a radiative transfer model for remote sensing plant canopies

    Science.gov (United States)

    Hall, Carlton Raden

    A major objective of remote sensing is determination of biochemical and biophysical characteristics of plant canopies utilizing high spectral resolution sensors. Canopy reflectance signatures are dependent on absorption and scattering processes of the leaf, canopy properties, and the ground beneath the canopy. This research investigates, through field and laboratory data collection, and computer model parameterization and simulations, the relationships between leaf optical properties, canopy biophysical features, and the nadir viewed above-canopy reflectance signature. Emphasis is placed on parameterization and application of an existing irradiance radiative transfer model developed for aquatic systems. Data and model analyses provide knowledge on the relative importance of leaves and canopy biophysical features in estimating the diffuse absorption a(lambda,m-1), diffuse backscatter b(lambda,m-1), beam attenuation alpha(lambda,m-1), and beam to diffuse conversion c(lambda,m-1 ) coefficients of the two-flow irradiance model. Data sets include field and laboratory measurements from three plant species, live oak (Quercus virginiana), Brazilian pepper (Schinus terebinthifolius) and grapefruit (Citrus paradisi) sampled on Cape Canaveral Air Force Station and Kennedy Space Center Florida in March and April of 1997. Features measured were depth h (m), projected foliage coverage PFC, leaf area index LAI, and zenith leaf angle. Optical measurements, collected with a Spectron SE 590 high sensitivity narrow bandwidth spectrograph, included above canopy reflectance, internal canopy transmittance and reflectance and bottom reflectance. Leaf samples were returned to laboratory where optical and physical and chemical measurements of leaf thickness, leaf area, leaf moisture and pigment content were made. A new term, the leaf volume correction index LVCI was developed and demonstrated in support of model coefficient parameterization. The LVCI is based on angle adjusted leaf

  12. 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.

  13. Water management and remote sensing

    NARCIS (Netherlands)

    Assem, S. van den; Bastiaanssen, W.G.M.; Claassen, T.H.L.; Feddes, R.A.; Menenti, M.; Minderhoud, P.; Nieuwenhuis, G.J.A.; Nieuwkoop, J. van; Stokkom, H.T.C. van; Stokman, N.G.M.; Thunnissen, H.A.M.; Visser, T.N.M.

    1990-01-01

    In modern water management detailed information is required on processes that occur and on the state of water systems, including the way they are influenced by human activities. Remote sensing can contribute significantly to these information. For example, areal patterns of water quality parameters

  14. Remote Sensing of Water Pollution

    Science.gov (United States)

    White, P. G.

    1971-01-01

    Remote sensing, as a tool to aid in the control of water pollution, offers a means of making rapid, economical surveys of areas that are relatively inaccessible on the ground. At the same time, it offers the only practical means of mapping pollution patterns that cover large areas. Detection of oil slicks, thermal pollution, sewage, and algae are discussed.

  15. A comparison between remotely-sensed and modelled surface soil moisture (and frozen status) at high latitudes

    Science.gov (United States)

    Gouttevin, I.; Bartsch, A.; Krinner, G.; Naeimi, V.

    2013-08-01

    In this study, the combined surface status and surface soil moisture products retrieved by the ASCAT sensor within the ESA-DUE Permafrost project are compared to the hydrological outputs of the land surface model ORCHIDEE over Northern Eurasia. The objective is to derive broad conclusions as to the strengths and weaknesses of hydrological modelling and, to a minor extent, remote sensing of soil moisture over an area where data is rare and hydrological modelling is though crucial for climate and ecological applications. The spatial and temporal resolutions of the ASCAT products make them suitable for comparison with model outputs. Modelled and remotely-sensed surface frozen and unfrozen statuses agree reasonably well, which allows for a seasonal comparison between modelled and observed (liquid) surface soil moisture. The atmospheric forcing and the snow scheme of the land surface model are identified as causes of moderate model-to-data divergence in terms of surface status. For unfrozen soils, the modelled and remotely-sensed surface soil moisture signals are positively correlated over most of the study area. The correlation deteriorates in the North-Eastern Siberian regions, which is consistent with the lack of accurate model parameters and the scarcity of meteorological data. The model shows a reduced ability to capture the main seasonal dynamics and spatial patterns of observed surface soil moisture in Northern Eurasia, namely a characteristic spring surface moistening resulting from snow melt and flooding. We hypothesize that these weak performances mainly originate from the non-representation of flooding and surface ponding in the model. Further identified limitations proceed from the coarse treatment of the hydrological specificities of mountainous areas and spatial inaccuracies in the meteorological forcing in remote, North-Eastern Siberian areas. Investigations are currently underway to determine to which extent plausible inaccuracies in the satellite data

  16. Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: The impact on predictions at gauged and ungauged locations

    Science.gov (United States)

    Li, Yuan; Grimaldi, Stefania; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

    2018-02-01

    The skill of hydrologic models, such as those used in operational flood prediction, is currently restricted by the availability of flow gauges and by the quality of the streamflow data used for calibration. The increased availability of remote sensing products provides the opportunity to further improve the model forecasting skill. A joint calibration scheme using streamflow measurements and remote sensing derived soil moisture values was examined and compared with a streamflow only calibration scheme. The efficacy of the two calibration schemes was tested in three modelling setups: 1) a lumped model; 2) a semi-distributed model with only the outlet gauge available for calibration; and 3) a semi-distributed model with multiple gauges available for calibration. The joint calibration scheme was found to slightly degrade the streamflow prediction at gauged sites during the calibration period compared with streamflow only calibration, but improvement was found at the same gauged sites during the independent validation period. A more consistent and statistically significant improvement was achieved at gauged sites not used in the calibration, due to the spatial information introduced by the remotely sensed soil moisture data. It was also found that the impact of using soil moisture for calibration tended to be stronger at the upstream and tributary sub-catchments than at the downstream sub-catchments.

  17. Hydrological and sedimentological modeling of the Okavango Delta, Botswana, using remotely sensed input and calibration data

    Science.gov (United States)

    Milzow, C.; Kgotlhang, L.; Kinzelbach, W.; Bauer-Gottwein, P.

    2006-12-01

    medium-term. The Delta's size and limited accessibility make direct data acquisition on the ground difficult. Remote sensing methods are the most promising source of acquiring spatially distributed data for both, model input and calibration. Besides ground data, METEOSAT and NOAA data are used for precipitation and evapotranspiration inputs respectively. The topography is taken from a study from Gumbricht et al. (2004) where the SRTM shuttle mission data is refined using remotely sensed vegetation indexes. The aquifer thickness was determined with an aeromagnetic survey. For calibration, the simulated flooding patterns are compared to patterns derived from satellite imagery: recent ENVISAT ASAR and older NOAA AVHRR scenes. The final objective is to better understand the hydrological and hydraulic aspects of this complex ecosystem and eventually predict the consequences of human interventions. It will provide a tool for decision makers involved to assess the impact of possible upstream dams and water abstraction scenarios.

  18. Vegetation water stress monitoring with remote sensing-based energy balance modelling

    Science.gov (United States)

    González-Dugo, Maria P.; Andreu, Ana; Carpintero, Elisabet; Gómez-Giráldez, Pedro; José Polo, María

    2014-05-01

    Drought is one of the major hazards faced by agroforestry systems in southern Europe, and an increase in frequency is predicted under the conditions of climate change for the region. Timely and accurate monitoring of vegetation water stress using remote sensing time series may assist early-warning services, helping to assess drought impacts and the design of management actions leading to reduce the economic and environmental vulnerability of these systems. A holm oak savanna, known as dehesa in Spain and montado in Portugal, is an agro-silvo-pastoral system occupying more than 3 million hectares the Iberian Peninsula and Greece. It consists of widely-spaced oak trees (mostly Quercus ilex L.), combined with crops, pasture and Mediterranean shrubs, and it is considered an example of sustainable land use, with great importance in the rural economy. Soil water dynamics is known to have a central role in current tree decline and the reduction of the forested area that is threatening its conservation. A two-source thermal-based evapotranspiration model (TSEB) has been applied to monitor the effect on vegetation water use of soil moisture stress in a dehesa located in southern Spain. The TSEB model separates the soil and canopy contributions to the radiative temperature and to the exchange of surface energy fluxes, so it is especially suited for partially vegetated landscapes. The integration of remotely sensed data in this model may support an evaluation of the whole ecosystem state at a large scale. During two consecutive summers, in 2012 and 2013, time series of optical and thermal MODIS images, with 250m and 1 km of spatial resolution respectively, have been combined with meteorological data provided by a ground station to monitor the evapotranspiration (ET) of the system. An eddy covariance tower (38°12' N; 4°17' W, 736 m a.s.l), equipped with instruments to measure all the components of the energy balance and 1 km of homogeneous fetch in the predominant wind

  19. Mapping three-dimensional geological features from remotely-sensed images and digital elevation models

    Science.gov (United States)

    Morris, Kevin Peter

    Accurate mapping of geological structures is important in numerous applications, ranging from mineral exploration through to hydrogeological modelling. Remotely sensed data can provide synoptic views of study areas enabling mapping of geological units within the area. Structural information may be derived from such data using standard manual photo-geologic interpretation techniques, although these are often inaccurate and incomplete. The aim of this thesis is, therefore, to compile a suite of automated and interactive computer-based analysis routines, designed to help a the user map geological structure. These are examined and integrated in the context of an expert system. The data used in this study include Digital Elevation Model (DEM) and Airborne Thematic Mapper images, both with a spatial resolution of 5m, for a 5 x 5 km area surrounding Llyn Cow lyd, Snowdonia, North Wales. The geology of this area comprises folded and faulted Ordo vician sediments intruded throughout by dolerite sills, providing a stringent test for the automated and semi-automated procedures. The DEM is used to highlight geomorphological features which may represent surface expressions of the sub-surface geology. The DEM is created from digitized contours, for which kriging is found to provide the best interpolation routine, based on a number of quantitative measures. Lambertian shading and the creation of slope and change of slope datasets are shown to provide the most successful enhancement of DEMs, in terms of highlighting a range of key geomorphological features. The digital image data are used to identify rock outcrops as well as lithologically controlled features in the land cover. To this end, a series of standard spectral enhancements of the images is examined. In this respect, the least correlated 3 band composite and a principal component composite are shown to give the best visual discrimination of geological and vegetation cover types. Automatic edge detection (followed by line

  20. Drought monitoring and assessment: Remote sensing and modeling approaches for the Famine Early Warning Systems Network

    Science.gov (United States)

    Senay, Gabriel; Velpuri, Naga Manohar; Bohms, Stefanie; Budde, Michael; Young, Claudia; Rowland, James; Verdin, James

    2015-01-01

    Drought monitoring is an essential component of drought risk management. It is usually carried out using drought indices/indicators that are continuous functions of rainfall and other hydrometeorological variables. This chapter presents a few examples of how remote sensing and hydrologic modeling techniques are being used to generate a suite of drought monitoring indicators at dekadal (10-day), monthly, seasonal, and annual time scales for several selected regions around the world. Satellite-based rainfall estimates are being used to produce drought indicators such as standardized precipitation index, dryness indicators, and start of season analysis. The Normalized Difference Vegetation Index is being used to monitor vegetation condition. Several satellite data products are combined using agrohydrologic models to produce multiple short- and long-term indicators of droughts. All the data sets are being produced and updated in near-real time to provide information about the onset, progression, extent, and intensity of drought conditions. The data and products produced are available for download from the Famine Early Warning Systems Network (FEWS NET) data portal at http://earlywarning.usgs.gov. The availability of timely information and products support the decision-making processes in drought-related hazard assessment, monitoring, and management with the FEWS NET. The drought-hazard monitoring approach perfected by the U.S. Geological Survey for FEWS NET through the integration of satellite data and hydrologic modeling can form the basis for similar decision support systems. Such systems can operationally produce reliable and useful regional information that is relevant for local, district-level decision making.

  1. EVALUATION OF SOIL LOSS IN GUARAÍRA BASIN BY GIS AND REMOTE SENSING BASED MODEL

    Directory of Open Access Journals (Sweden)

    Richarde Marques da Silva

    2007-01-01

    Full Text Available Environmental degradation, and specifically erosion, is a serious and extensive problem in many areas in Brazil. Prediction of runoff and erosion in ungauged basins is one of the most challenging tasks anywhere and it is especially a very difficult one in developing countries where monitoring and continuous measurements of these quantities are carried out in very few basins either due to the costs involved or due to the lack of trained personnel. The erosion processes and land use in the Guaraíra River Experimental Basin, located in Paraíba state, Brazil, are evaluated using remote sensing and a runoff-erosion model. WEPP is a process-based continuous simulation erosion model that can be applied to hillslope profiles and small watersheds. WEPP erosion model have been compared in numerous studies to observed values for soil loss and sediment delivery from cropland plots, forest roads, irrigated lands and small watersheds. A number of different techniques for evaluating WEPP have been used, including one recently developed in which the ability of WEPP to accurately predict soil erosion can be compared to the accuracy of replicated plots to predict soil erosion. WEPP was calibrated with daily rainfall data from five rain gauges for the period of 2003 to 2005. The obtained results showed the susceptible areas to the erosion process within Guaraíra river basin, and that the mean sediment yield could be in the order of 3.0 ton/ha/year (in an area of 5.84 ha.

  2. Utilization of Hydrologic Remote Sensing Data in Land Surface Modeling and Data Assimilation: Current Status and Challenges

    Science.gov (United States)

    Kumar, Sujay V.; Peters-Lidard, Christa; Reichl, Rolf; Harrison, Kenneth; Santanello, Joseph

    2010-01-01

    Recent advances in remote sensing technologies have enabled the monitoring and measurement of the Earth's land surface at an unprecedented scale and frequency. The myriad of these land surface observations must be integrated with the state-of-the-art land surface model forecasts using data assimilation to generate spatially and temporally coherent estimates of environmental conditions. These analyses are of critical importance to real-world applications such as agricultural production, water resources management and flood, drought, weather and climate prediction. This need motivated the development of NASA Land Information System (LIS), which is an expert system encapsulating a suite of modeling, computational and data assimilation tools required to address challenging hydrological problems. LIS integrates the use of several community land surface models, use of ground and satellite based observations, data assimilation and uncertainty estimation techniques and high performance computing and data management tools to enable the assessment and prediction of hydrologic conditions at various spatial and temporal scales of interest. This presentation will focus on describing the results, challenges and lessons learned from the use of remote sensing data for improving land surface modeling, within LIS. More specifically, studies related to the improved estimation of soil moisture, snow and land surface temperature conditions through data assimilation will be discussed. The presentation will also address the characterization of uncertainty in the modeling process through Bayesian remote sensing and computational methods.

  3. 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.

  4. Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

    Directory of Open Access Journals (Sweden)

    David M. Makori

    2017-02-01

    Full Text Available Bee keeping is indispensable to global food production. It is an alternate income source, especially in rural underdeveloped African settlements, and an important forest conservation incentive. However, dwindling honeybee colonies around the world are attributed to pests and diseases whose spatial distribution and influences are not well established. In this study, we used remotely sensed data to improve the reliability of pest ecological niche (EN models to attain reliable pest distribution maps. Occurrence data on four pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor were collected from apiaries within four main agro-ecological regions responsible for over 80% of Kenya’s bee keeping. Africlim bioclimatic and derived normalized difference vegetation index (NDVI variables were used to model their ecological niches using Maximum Entropy (MaxEnt. Combined precipitation variables had a high positive logit influence on all remotely sensed and biotic models’ performance. Remotely sensed vegetation variables had a substantial effect on the model, contributing up to 40.8% for G. mellonella and regions with high rainfall seasonality were predicted to be high-risk areas. Projections (to 2055 indicated that, with the current climate change trend, these regions will experience increased honeybee pest risk. We conclude that honeybee pests could be modelled using bioclimatic data and remotely sensed variables in MaxEnt. Although the bioclimatic data were most relevant in all model results, incorporating vegetation seasonality variables to improve mapping the ‘actual’ habitat of key honeybee pests and to identify risk and containment zones needs to be further investigated.

  5. Inferential monitoring of global change impact on biodiversity through remote sensing and species distribution modeling

    Science.gov (United States)

    Sangermano, Florencia

    2009-12-01

    The world is suffering from rapid changes in both climate and land cover which are the main factors affecting global biodiversity. These changes may affect ecosystems by altering species distributions, population sizes, and community compositions, which emphasizes the need for a rapid assessment of biodiversity status for conservation and management purposes. Current approaches on monitoring biodiversity rely mainly on long term observations of predetermined sites, which require large amounts of time, money and personnel to be executed. In order to overcome problems associated with current field monitoring methods, the main objective of this dissertation is the development of framework for inferential monitoring of the impact of global change on biodiversity based on remotely sensed data coupled with species distribution modeling techniques. Several research pieces were performed independently in order to fulfill this goal. First, species distribution modeling was used to identify the ranges of 6362 birds, mammals and amphibians in South America. Chapter 1 compares the power of different presence-only species distribution methods for modeling distributions of species with different response curves to environmental gradients and sample sizes. It was found that there is large variability in the power of the methods for modeling habitat suitability and species ranges, showing the importance of performing, when possible, a preliminary gradient analysis of the species distribution before selecting the method to be used. Chapter 2 presents a new methodology for the redefinition of species range polygons. Using a method capable of establishing the uncertainty in the definition of existing range polygons, the automated procedure identifies the relative importance of bioclimatic variables for the species, predicts their ranges and generates a quality assessment report to explore prediction errors. Analysis using independent validation data shows the power of this

  6. Constraining model parameters on remotely sensed evaporation: justification for distribution in ungauged basins?

    Directory of Open Access Journals (Sweden)

    H. C. Winsemius

    2008-12-01

    Full Text Available In this study, land surface related parameter distributions of a conceptual semi-distributed hydrological model are constrained by employing time series of satellite-based evaporation estimates during the dry season as explanatory information. The approach has been applied to the ungauged Luangwa river basin (150 000 (km2 in Zambia. The information contained in these evaporation estimates imposes compliance of the model with the largest outgoing water balance term, evaporation, and a spatially and temporally realistic depletion of soil moisture within the dry season. The model results in turn provide a better understanding of the information density of remotely sensed evaporation. Model parameters to which evaporation is sensitive, have been spatially distributed on the basis of dominant land cover characteristics. Consequently, their values were conditioned by means of Monte-Carlo sampling and evaluation on satellite evaporation estimates. The results show that behavioural parameter sets for model units with similar land cover are indeed clustered. The clustering reveals hydrologically meaningful signatures in the parameter response surface: wetland-dominated areas (also called dambos show optimal parameter ranges that reflect vegetation with a relatively small unsaturated zone (due to the shallow rooting depth of the vegetation which is easily moisture stressed. The forested areas and highlands show parameter ranges that indicate a much deeper root zone which is more drought resistent. Clustering was consequently used to formulate fuzzy membership functions that can be used to constrain parameter realizations in further calibration. Unrealistic parameter ranges, found for instance in the high unsaturated soil zone values in the highlands may indicate either overestimation of satellite-based evaporation or model structural deficiencies. We believe that in these areas, groundwater uptake into the root zone and lateral movement of

  7. Modeling urban growth and spatial structure in Nanjing, China with GIS and remote sensing

    Science.gov (United States)

    Luo, Jun

    This research focuses on the use of GIS, remote sensing and spatial modeling for studies on urban growth and spatial structure. Previous studies on urban growth modeling have not elaborated the spatial heterogeneity of urban growth pattern, which, however, is well recognized. The census population data is widely used for investigating urban spatial structure, but it has inherent various problems which can lead to biased analysis results. Studies on urban growth and spatial structure of Chinese cities remain limited due to the data availability and methodology development. In this dissertation, I initiate a new analysis framework and a new method to address these critical issues through a case study of Nanjing, China. The study first set up urban land expansion models for Nanjing in the period of 1988-2000. Landsat imageries are processed and classified to provide land use data in 1988 and 2000. GIS data are used to provide spatial variables inputs for the land use conversion models. A combined land use data sampling is conducted to obtain land use sample points for the proposed models. Classic logistic regression is used to reveal the urban land expansion from a global view. Furthermore, a logistic geographically weighted regression (GWR) model is set up to reveal the local variations of influence of spatial factors on urban land expansion. The study finds that the logistic GWR significantly improved the global logistic regression model and verifies that the influences of explanatory variables of urban growth are spatially varying. An urban growth probability surface is then generated based on the variable and parameter surfaces. This new framework for analyzing urban growth pattern may open a new direction for urban growth modeling. Second, the dissertation develops a new method, which utilizes detailed urban land parcel and building data to generate population surface of Nanjing in 2000. With this method, populations of small areas at intraurban level can be

  8. Assessing irrigated agriculture's surface water and groundwater consumption by combining satellite remote sensing and hydrologic modelling.

    Science.gov (United States)

    Peña-Arancibia, Jorge L; Mainuddin, Mohammed; Kirby, John M; Chiew, Francis H S; McVicar, Tim R; Vaze, Jai

    2016-01-15

    Globally, irrigation accounts for more than two thirds of freshwater demand. Recent regional and global assessments indicate that groundwater extraction (GWE) for irrigation has increased more rapidly than surface water extraction (SWE), potentially resulting in groundwater depletion. Irrigated agriculture in semi-arid and arid regions is usually from a combination of stored surface water and groundwater. This paper assesses the usefulness of remotely-sensed (RS) derived information on both irrigation dynamics and rates of actual evapotranspiration which are both input to a river-reach water balance model in order to quantify irrigation water use and water provenance (either surface water or groundwater). The assessment is implemented for the water-years 2004/05-2010/11 in five reaches of the Murray-Darling Basin (Australia); a heavily regulated basin with large irrigated areas and periodic droughts and floods. Irrigated area and water use are identified each water-year (from July to June) through a Random Forest model which uses RS vegetation phenology and actual evapotranspiration as predicting variables. Both irrigated areas and actual evapotranspiration from irrigated areas were compared against published estimates of irrigated areas and total water extraction (SWE+GWE).The river-reach model determines the irrigated area that can be serviced with stored surface water (SWE), and the remainder area (as determined by the Random Forest Model) is assumed to be supplemented by groundwater (GWE). Model results were evaluated against observed SWE and GWE. The modelled SWE generally captures the observed interannual patterns and to some extent the magnitudes, with Pearson's correlation coefficients >0.8 and normalised root-mean-square-error<30%. In terms of magnitude, the results were as accurate as or better than those of more traditional (i.e., using areas that fluctuate based on water resource availability and prescribed crop factors) irrigation modelling. The RS

  9. Development of a remote sensing-based rice yield forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H.

    2016-11-01

    This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh. (Author)

  10. Atmospheric turbulence in complex terrain: Verifying numerical model results with observations by remote-sensing instruments

    Science.gov (United States)

    Chan, P. W.

    2009-03-01

    The Hong Kong International Airport (HKIA) is situated in an area of complex terrain. Turbulent flow due to terrain disruption could occur in the vicinity of HKIA when winds from east to southwest climb over Lantau Island, a mountainous island to the south of the airport. Low-level turbulence is an aviation hazard to the aircraft flying into and out of HKIA. It is closely monitored using remote-sensing instruments including Doppler LIght Detection And Ranging (LIDAR) systems and wind profilers in the airport area. Forecasting of low-level turbulence by numerical weather prediction models would be useful in the provision of timely turbulence warnings to the pilots. The feasibility of forecasting eddy dissipation rate (EDR), a measure of turbulence intensity adopted in the international civil aviation community, is studied in this paper using the Regional Atmospheric Modelling System (RAMS). Super-high resolution simulation (within the regime of large eddy simulation) is performed with a horizontal grid size down to 50 m for some typical cases of turbulent airflow at HKIA, such as spring-time easterly winds in a stable boundary layer and gale-force southeasterly winds associated with a typhoon. Sensitivity of the simulation results with respect to the choice of turbulent kinetic energy (TKE) parameterization scheme in RAMS is also examined. RAMS simulation with Deardorff (1980) TKE scheme is found to give the best result in comparison with actual EDR observations. It has the potential for real-time forecasting of low-level turbulence in short-term aviation applications (viz. for the next several hours).

  11. Evaluating the Potential Use of Remotely-Sensed and Model-Simulated Soil Moisture for Agricultural Drought Risk Monitoring

    Science.gov (United States)

    Yan, Hongxiang; Moradkhani, Hamid

    2016-04-01

    Current two datasets provide spatial and temporal resolution of soil moisture at large-scale: the remotely-sensed soil moisture retrievals and the model-simulated soil moisture products. Drought monitoring using remotely-sensed soil moisture is emerging, and the soil moisture simulated using land surface models (LSMs) have been used operationally to monitor agriculture drought in United States. Although these two datasets yield important drought information, their drought monitoring skill still needs further quantification. This study provides a comprehensive assessment of the potential of remotely-sensed and model-simulated soil moisture data in monitoring agricultural drought over the Columbia River Basin (CRB), Pacific Northwest. Two satellite soil moisture datasets were evaluated, the LPRM-AMSR-E (unscaled, 2002-2011) and ESA-CCI (scaled, 1979-2013). The USGS Precipitation Runoff Modeling System (PRMS) is used to simulate the soil moisture from 1979-2011. The drought monitoring skill is quantified with two indices: drought area coverage (the ability of drought detection) and drought severity (according to USDM categories). The effects of satellite sensors (active, passive), multi-satellite combined, length of climatology, climate change effect, and statistical methods are also examined in this study.

  12. Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin-up.

    Science.gov (United States)

    Hanan, Erin J; Tague, Christina; Choate, Janet; Liu, Mingliang; Kolden, Crystal; Adam, Jennifer

    2018-03-24

    Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state, or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate; this approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one, or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in

  13. 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

  14. Assessment of long-term channel changes in the Mekong River using remote sensing and a channel-evolution model

    Science.gov (United States)

    Miyazawa, N.

    2011-12-01

    River-channel changes are a key factor affecting physical, ecological and management issues in the fluvial environment. In this study, long-term channel changes in the Mekong River were assessed using remote sensing and a channel-evolution model. A channel-evolution model for calculating long-term channel changes of a measndering river was developed using a previous fluid-dynamic model [Zolezzi and Seminara, 2001], and was applied in order to quantify channel changes of two meandering reaches in the Mekong River. Quite few attempts have been made so far to combine remote sensing observation of meandering planform change with the application of channel evolution models within relatively small-scale gravel-bed systems in humid temperate regions. The novel point of the present work is to link state-of-art meandering planform evolution model with observed morphological changes within large-scale sand-bed rivers with higher bank height in tropical monsoonal climate regions, which are the highly dynamic system, and assess the performance. Unstable extents of the reaches could be historically identified using remote-sensing technique. The instability caused i) bank erosion and accretion of meander bends and ii) movement or development of bars and changes in the flow around the bars. The remote sensing measurements indicate that maximum erosion occurred downstream of the maximum curvature of the river-center line in both reaches. The model simulations indicates that under the mean annual peak discharge the maximum of excess longitudinal velocity near the banks occurs downstream of the maximum curvature in both reaches. The channel migration coefficients of the reaches were calibrated by comparing remote-sensing measurements and model simulations. The diffrence in the migration coefficients between both reaches depends on the diffrence in bank height rather than the geotechnical properties of floodplain sediments. Possible eroded floodplain areas and accreted floodplain

  15. Assimilation of Remotely Sensed Soil Moisture Profiles into a Crop Modeling Framework for Reliable Yield Estimations

    Science.gov (United States)

    Mishra, V.; Cruise, J.; Mecikalski, J. R.

    2017-12-01

    Much effort has been expended recently on the assimilation of remotely sensed soil moisture into operational land surface models (LSM). These efforts have normally been focused on the use of data derived from the microwave bands and results have often shown that improvements to model simulations have been limited due to the fact that microwave signals only penetrate the top 2-5 cm of the soil surface. It is possible that model simulations could be further improved through the introduction of geostationary satellite thermal infrared (TIR) based root zone soil moisture in addition to the microwave deduced surface estimates. In this study, root zone soil moisture estimates from the TIR based Atmospheric Land Exchange Inverse (ALEXI) model were merged with NASA Soil Moisture Active Passive (SMAP) based surface estimates through the application of informational entropy. Entropy can be used to characterize the movement of moisture within the vadose zone and accounts for both advection and diffusion processes. The Principle of Maximum Entropy (POME) can be used to derive complete soil moisture profiles and, fortuitously, only requires a surface boundary condition as well as the overall mean moisture content of the soil column. A lower boundary can be considered a soil parameter or obtained from the LSM itself. In this study, SMAP provided the surface boundary while ALEXI supplied the mean and the entropy integral was used to tie the two together and produce the vertical profile. However, prior to the merging, the coarse resolution (9 km) SMAP data were downscaled to the finer resolution (4.7 km) ALEXI grid. The disaggregation scheme followed the Soil Evaporative Efficiency approach and again, all necessary inputs were available from the TIR model. The profiles were then assimilated into a standard agricultural crop model (Decision Support System for Agrotechnology, DSSAT) via the ensemble Kalman Filter. The study was conducted over the Southeastern United States for the

  16. Review: Estimating evapotranspiration using remote sensing and ...

    African Journals Online (AJOL)

    Review: Estimating evapotranspiration using remote sensing and the Surface Energy Balance System – A South African perspective. ... It is therefore recommended that any further research using the SEBS model in South Africa should be limited to agricultural areas where accurate vegetation parameters can be obtained, ...

  17. Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing.

    Science.gov (United States)

    Feng, X; Liu, G; Chen, J M; Chen, M; Liu, J; Ju, W M; Sun, R; Zhou, W

    2007-11-01

    The terrestrial carbon cycle is one of the foci in global climate change research. Simulating net primary productivity (NPP) of terrestrial ecosystems is important for carbon cycle research. In this study, China's terrestrial NPP was simulated using the Boreal Ecosystem Productivity Simulator (BEPS), a carbon-water coupled process model based on remote sensing inputs. For these purposes, a national-wide database (including leaf area index, land cover, meteorology, vegetation and soil) at a 1 km resolution and a validation database were established. Using these databases and BEPS, daily maps of NPP for the entire China's landmass in 2001 were produced, and gross primary productivity (GPP) and autotrophic respiration (RA) were estimated. Using the simulated results, we explore temporal-spatial patterns of China's terrestrial NPP and the mechanisms of its responses to various environmental factors. The total NPP and mean NPP of China's landmass were 2.235 GtC and 235.2 gCm(-2)yr(-1), respectively; the total GPP and mean GPP were 4.418 GtC and 465 gCm(-2)yr(-1); and the total RA and mean RA were 2.227 GtC and 234 gCm(-2)yr(-1), respectively. On average, NPP was 50.6% of GPP. In addition, statistical analysis of NPP of different land cover types was conducted, and spatiotemporal patterns of NPP were investigated. The response of NPP to changes in some key factors such as LAI, precipitation, temperature, solar radiation, VPD and AWC are evaluated and discussed.

  18. Environmental modelling of Omerli catchment area in Istanbul, Turkey using remote sensing and GIS techniques.

    Science.gov (United States)

    Coskun, H Gonca; Alparslan, Erhan

    2009-06-01

    Omerli Reservoir is one of the major drinking water reservoirs of Greater Metropolis Istanbul, providing 40% of the overall water demand. Istanbul where is one of the greatest metropolitan areas of the world with a population over 10 million and a rate of population increase about twice that of Turkey. As a result of population growth and industrial development, Omerli watershed is highly affected by the wastewater discharges from the residential areas and industrial plants. The main objective of this study is to investigate the temporal assessment of the land-use/cover of the Omerli Watershed and the water quality changes in the Reservoir. It is not possible to adequately control urbanization and other pollution sources affecting the water quality. Responses of these detrimental effects are due to rapidly increasing population, unplanned and illegal housing, and irrelevant industries at the protection zones of the watershed, together with insufficient infrastructure. The study is focused on the assessment of urbanization in relation to land use and water quality using Remote Sensing (RS) and Geographic Information Systems (GIS) techniques for all the four protection zones of the Reservoir and a time variant analyzing model is obtained. IRS-1C LISS and IRS-1C PAN, LANDSAT-5 TM satellite data of 1997, 1998, 2000, 2001 and 2006 are analyzed by confirmation through the ground truth data. RS data have been transferred into UTM coordinate system and image enhancement and classification techniques were used. Raster data were converted to vector data that belongs to study area to analyze in GIS for the purpose of planning and decision-making on protected watersheds.

  19. Integrated Modeling of Drought-Impacted Areas using Remote Sensing and Microenvironmental Data in California

    Science.gov (United States)

    Rao, M.; Silber-coats, Z.; Lawrence, F.

    2015-12-01

    California's ongoing drought condition shriveled not just the agricultural sector, but also the natural resources sector including forestry, wildlife, and fisheries. As future predictions of drought and fire severity become more real in California, there is an increased awareness to pursue innovative and cost-effective solutions that are based on silvicultural treatments and controlled burns to improve forest health and reduce the risk of high-severity wildfires. The main goal of this study is to develop a GIS map of the drought-impacted region of northern and central California using remote sensing data for the summer period of 2014. Specifically, Landsat/NAIP imagery will be analyzed using a combination of object-oriented classification and spectral indices such as the Modified Perpendicular Drought Index (MPDI). This spectral index basically scales the line perpendicular to the soil line defined in the Red-NIR feature space in conjunction with added information about vegetative fraction derived using NDVI. The resulting output will be correlated with USGS-produced estimates of climatic water deficit (CWD) data to characterize the severity of the drought. The CWD is simulated based on hydrological tool, Basin Characterization Model (BCM) that ingests historical climate data in conjunction with soils, topography, and geological data to predict other monthly hydrological outputs including runoff, recharge, and snowpack. In addition to field data, data collected by state agencies including USFS, calforests.org will be used in the classification and accuracy assessment procedures. Visual assessment using high-resolution imagery such as NAIP will be used to further refine the spatial maps. The drought severity maps produced will greatly facilitate site-specific planning efforts aimed at implementing resource management decisions.

  20. Modeling Snow Aggregates and their Single Scattering Properties: Implications to Snowfall Remote Sensing

    Science.gov (United States)

    Nowell, H.; Liu, G.

    2012-12-01

    With the advent of satellites, we can now observe areas of the globe that have sparse to no ground data coverage. Both active and passive satellite sensors aboard satellites including CloudSat's Cloud Profiling Radar (CPR), Aqua's Advanced Microwave Scanning Radiometer (AMSR-E) and the upcoming Global Precipitation Measurement's (GPM) Dual-Frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) study ice and snow particles. A good retrieval algorithm for these satellite sensors can only be developed when the single scattering properties of the snowflakes are accurately calculated in radiative transfer models. This becomes crucial at frequencies at and above the W-band when aggregate ice crystals become detectable by satellite radiometers. Snowflakes are often modeled as spheres or oblate spheroids to ease the complexity of calculations, despite the fact that they are typically aggregates of crystals. For improved accuracy in satellite remote sensing, it is important to model snowflakes as close to nature as possible. Several recent studies model flakes as pristine crystal types [Liu, 2008], generate aggregate flakes as fractals [Ishimoto, 2008] or via the Monte Carlo method [Maruyama and Fujioshi, 2005]. Modeling snowflakes as pristine crystals, however, has the drawback of not accurately reflecting snowflakes as most tend to be aggregates of different crystal types. Other studies where aggregates are generated tend to overlook size-density relationships of aggregate flakes or other studied statistical parameters such as aspect ratio. In an effort to improve available single-scattering properties of aggregate flakes, we developed a new method of generating flakes. Starting out with a six-bullet rosette crystal of accurate size and density, aggregate flakes are generated with two different bullet rosette crystal sizes of 200 and/or 400 microns in maximum dimension. The flakes similarly follow size-density relationships of aggregate as determined from

  1. Flood susceptibility analysis through remote sensing, GIS and frequency ratio model

    Science.gov (United States)

    Samanta, Sailesh; Pal, Dilip Kumar; Palsamanta, Babita

    2018-05-01

    Papua New Guinea (PNG) is saddled with frequent natural disasters like earthquake, volcanic eruption, landslide, drought, flood etc. Flood, as a hydrological disaster to humankind's niche brings about a powerful and often sudden, pernicious change in the surface distribution of water on land, while the benevolence of flood manifests in restoring the health of the thalweg from excessive siltation by redistributing the fertile sediments on the riverine floodplains. In respect to social, economic and environmental perspective, flood is one of the most devastating disasters in PNG. This research was conducted to investigate the usefulness of remote sensing, geographic information system and the frequency ratio (FR) for flood susceptibility mapping. FR model was used to handle different independent variables via weighted-based bivariate probability values to generate a plausible flood susceptibility map. This study was conducted in the Markham riverine precinct under Morobe province in PNG. A historical flood inventory database of PNG resource information system (PNGRIS) was used to generate 143 flood locations based on "create fishnet" analysis. 100 (70%) flood sample locations were selected randomly for model building. Ten independent variables, namely land use/land cover, elevation, slope, topographic wetness index, surface runoff, landform, lithology, distance from the main river, soil texture and soil drainage were used into the FR model for flood vulnerability analysis. Finally, the database was developed for areas vulnerable to flood. The result demonstrated a span of FR values ranging from 2.66 (least flood prone) to 19.02 (most flood prone) for the study area. The developed database was reclassified into five (5) flood vulnerability zones segmenting on the FR values, namely very low (less that 5.0), low (5.0-7.5), moderate (7.5-10.0), high (10.0-12.5) and very high susceptibility (more than 12.5). The result indicated that about 19.4% land area as `very high

  2. Integrating remote sensing and conventional grazing/browsing models for modelling carrying capacity in southern African rangelands

    Science.gov (United States)

    Adjorlolo, C.; Botha, J. O.; Mhangara, P.; Mutanga, O.; Odindi, J.

    2014-10-01

    Woody vegetation encroachment into grasslands or bush thickening, a global phenomenon, is transforming the Southern African grassland systems into savanna-like landscapes. Estimation of woody vegetation is important to rangeland scientists and land managers for assessing its impact on grass production and calculating its grazing and browsing capacity. Assessment of grazing and browsing components is often challenging because agro-ecological landscapes of this region are largely characterized by small scale and heterogeneous land-use-land-cover patterns. In this study, we investigated the utility of high spatial resolution remotely sensing data for modelling grazing and browsing capacity at landscape level. Woody tree density or Tree Equivalents (TE) and Total Leaf Mass (LMASS) data were derived using the Biomass Estimation for Canopy Volume (BECVOL) program. The Random Forest (RF) regression algorithm was assessed to establish relationships between these variables and vegetation indices (Simple Ratio and Normalized Difference Vegetation Index), calculated using the red and near infrared bands of SPOT5. The RF analysis predicted LMASS with R2 = 0.63 and a Root Mean Square Error (RMSE) of 1256 kg/ha compared to a mean of 2291kg/ha. TE was predicted with R2 = 0.55 and a RMSE = 1614 TE/ha compared to a mean of 3746 TE/ha. Next, spatial distribution maps of LMASS/ha and TE/ha were derived using separate RF regression models. The resultant maps were then used as input data into conventional grazing and browsing capacity models to calculate grazing and browsing capacity maps for the study area. This study provides a sound platform for integrating currently available and future remote sensing satellite data into rangeland carrying capacity modelling and monitoring.

  3. Remote sensing of natural resources

    CERN Document Server

    Wang, Guangxing

    2013-01-01

    "… a comprehensive view on and real world examples of remote sensing technologies in natural resources assessment and monitoring. … state-of-the-art knowledge in this multidisciplinary field. Readers can expect to finish the book armed with the required knowledge to understand the immense literature available and apply their knowledge to the understanding of sampling design, the analysis of multi-source imagery, and the application of the techniques to specific problems relevant to natural resources."-Yuhong He, University of Toronto Mississauga, Ontario, Canada"The list of topics covered is so complete that I would recommend the book to anyone teaching a graduate course on vegetation analysis through digital image analysis. … I recommend this book then for anyone doing advanced digital image analysis and environmental GIS courses who want to cover topics related to applied remote sensing work involving vegetation analysis."-Charles Roberts, Florida Atlantic University, Boca Raton, USA, in Economic Bota...

  4. Estimation of Actual Evapotranspiration Using an Agro-Hydrological Model and Remote Sensing Techniques

    Directory of Open Access Journals (Sweden)

    mostafa yaghoobzadeh

    2017-02-01

    Full Text Available Introduction: Accurate estimation of evapotranspiration plays an important role in quantification of water balance at awatershed, plain and regional scale. Moreover, it is important in terms ofmanaging water resources such as water allocation, irrigation management, and evaluating the effects of changing land use on water yields. Different methods are available for ET estimation including Bowen ratio energy balance systems, eddy correlation systems, weighing lysimeters.Water balance techniques offer powerful alternatives for measuring ET and other surface energy fluxes. In spite of the elegance, high accuracy and theoretical attractions of these techniques for measuring ET, their practical use over large areas might be limited. They can be very expensive for practical applications at regional scales under heterogeneous terrains composed of different agro-ecosystems. To overcome aforementioned limitations by use of satellite measurements are appropriate approach. The feasibility of using remotely sensed crop parameters in combination of agro-hydrological models has been investigated in recent studies. The aim of the present study was to determine evapotranspiration by two methods, remote sensing and soil, water, atmosphere, and plant (SWAP model for wheat fields located in Neishabour plain. The output of SWAP has been validated by means of soil water content measurements. Furthermore, the actual evapotranspiration estimated by SWAP has been considered as the “reference” in the comparison between SEBAL energy balance models. Materials and Methods: Surface Energy Balance Algorithm for Land (SEBAL was used to estimate actual ET fluxes from Modis satellite images. SEBAL is a one-layer energy balance model that estimates latent heat flux and other energy balance components without information on soil, crop, and management practices. The near surface energy balance equation can be approximated as: Rn = G + H + λET Where Rn: net radiation (Wm2; G

  5. Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

    Science.gov (United States)

    Zhang, Xiaodong; Kirilenko, Andrei; Lim, Howe; Teng, Williams

    2010-01-01

    This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

  6. Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

    Science.gov (United States)

    Fan, Lei

    ., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems.

  7. 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)

  8. Estimating Daily Evapotranspiration From Remotely Sensed Instantaneous Observations With Simplified Derivations of a Theoretical Model

    Science.gov (United States)

    Tang, Ronglin; Li, Zhao-Liang

    2017-10-01

    Surface evapotranspiration (ET) is one of the key components in global hydrological cycle and energy budget on Earth. This paper designs a theoretical relationship between daily and instantaneous ETs with a multiplication of multiple fractions through a mathematical derivation of the physics-based Penman-Monteith equation and further develops five methods for converting remotely sensed instantaneous ET to daily values, one of which is equivalent to the conventional constant evaporative fraction (EF) method. The five methods are then evaluated and intercompared using long-term ground-based eddy covariance system-measured half-hourly latent heat flux (LE) and three groups of Moderate Resolution Imaging Spectroradiometer-based instantaneous LE data sets collected from April 2009 to late October 2011 at the Yucheng station. Overall, the constant decoupling factor (Ω) method, the constant surface resistance (Rc) method, and the constant ratio of surface resistance to aerodynamic resistance (Rc/Ra) method could produce daily LE estimates that are in reasonably good agreement with the ground-based eddy covariance measurements, whereas the constant EF method and the constant Priestley-Taylor parameter (α) method underestimate the daily LE with larger biases and root-mean-square errors. The former three methods are of more solid physical foundation and can effectively capture the effect of temporally variable meteorological factors on the diurnal pattern of surface ET. They provide good alternatives to the nowadays commonly applied methods for the conversion of remotely sensed instantaneous ET to daily values.

  9. Paleovalleys mapping using remote sensing

    Science.gov (United States)

    Baibatsha, A. B.

    2014-06-01

    For work materials used multispectral satellite imagery Landsat (7 channels), medium spatial resolution (14,25-90 m) and a digital elevation model (data SRTM). For interpretation of satellite images and especially their infrared and thermal channels allocated buried paleovalleys pre-paleogene age. Their total length is 228 km. By manifestation of the content of remote sensing paleovalleys distinctly divided into two types, long ribbon-like read in materials and space survey highlights a network of small lakes. By the nature of the relationship established that the second type of river paleovalleys flogs first. On this basis, proposed to allocate two uneven river paleosystem. The most ancient paleovalleys first type can presumably be attributed to karst erosion, blurry chalk and carbon deposits foundation. Paleovalleys may include significant groundwater resources as drinking and industrial purposes. Also we can control the position paleovalleys zinc and bauxite mineralization area and alluvial deposits include uranium mineralization valleys infiltration type and placer gold. Direction paleovalleys choppy, but in general they have a north-east orientation, which is controlled by tectonic zones of the foundation. These zones are defined as the burial place themselves paleovalleys and position of karst cavities in areas interfacing with other structures orientation. The association of mineralization to the caverns in the beds paleovalleys could generally present conditions of formation of mineralization and carry it to the "Niagara" type. The term is obviously best reflects the mechanism of formation of these ores.

  10. Paleovalleys mapping using remote sensing

    Directory of Open Access Journals (Sweden)

    A. B. Baibatsha

    2014-06-01

    Full Text Available For work materials used multispectral satellite imagery Landsat (7 channels, medium spatial resolution (14,25–90 m and a digital elevation model (data SRTM. For interpretation of satellite images and especially their infrared and thermal channels allocated buried paleovalleys pre-paleogene age. Their total length is 228 km. By manifestation of the content of remote sensing paleovalleys distinctly divided into two types, long ribbon-like read in materials and space survey highlights a network of small lakes. By the nature of the relationship established that the second type of river paleovalleys flogs first. On this basis, proposed to allocate two uneven river paleosystem. The most ancient paleovalleys first type can presumably be attributed to karst erosion, blurry chalk and carbon deposits foundation. Paleovalleys may include significant groundwater resources as drinking and industrial purposes. Also we can control the position paleovalleys zinc and bauxite mineralization area and alluvial deposits include uranium mineralization valleys infiltration type and placer gold. Direction paleovalleys choppy, but in general they have a north-east orientation, which is controlled by tectonic zones of the foundation. These zones are defined as the burial place themselves paleovalleys and position of karst cavities in areas interfacing with other structures orientation. The association of mineralization to the caverns in the beds paleovalleys could generally present conditions of formation of mineralization and carry it to the "Niagara" type. The term is obviously best reflects the mechanism of formation of these ores.

  11. Calculating Remote Sensing Reflectance Uncertainties Using an Instrument Model Propagated Through Atmospheric Correction via Monte Carlo Simulations

    Science.gov (United States)

    Karakoylu, E.; Franz, B.

    2016-01-01

    First attempt at quantifying uncertainties in ocean remote sensing reflectance satellite measurements. Based on 1000 iterations of Monte Carlo. Data source is a SeaWiFS 4-day composite, 2003. The uncertainty is for remote sensing reflectance (Rrs) at 443 nm.

  12. A COARSE-TO-FINE MODEL FOR AIRPLANE DETECTION FROM LARGE REMOTE SENSING IMAGES USING SALIENCY MODLE AND DEEP LEARNING

    Directory of Open Access Journals (Sweden)

    Z. N. Song

    2018-04-01

    Full Text Available High resolution remote sensing images are bearing the important strategic information, especially finding some time-sensitive-targets quickly, like airplanes, ships, and cars. Most of time the problem firstly we face is how to rapidly judge whether a particular target is included in a large random remote sensing image, instead of detecting them on a given image. The problem of time-sensitive-targets target finding in a huge image is a great challenge: 1 Complex background leads to high loss and false alarms in tiny object detection in a large-scale images. 2 Unlike traditional image retrieval, what we need to do is not just compare the similarity of image blocks, but quickly find specific targets in a huge image. In this paper, taking the target of airplane as an example, presents an effective method for searching aircraft targets in large scale optical remote sensing images. Firstly, we used an improved visual attention model utilizes salience detection and line segment detector to quickly locate suspected regions in a large and complicated remote sensing image. Then for each region, without region proposal method, a single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation is adopted to search small airplane objects. Unlike sliding window and region proposal-based techniques, we can do entire image (region during training and test time so it implicitly encodes contextual information about classes as well as their appearance. Experimental results show the proposed method is quickly identify airplanes in large-scale images.

  13. Spatially Distributed Assimilation of Remotely Sensed Leaf Area Index and Potential Evapotranspiration for Hydrologic Modeling in Wetland Landscapes

    Science.gov (United States)

    Rajib, A.; Evenson, G. R.; Golden, H. E.; Lane, C.

    2017-12-01

    Evapotranspiration (ET), a highly dynamic flux in wetland landscapes, regulates the accuracy of surface/sub-surface runoff simulation in a hydrologic model. Accordingly, considerable uncertainty in simulating ET-related processes remains, including our limited ability to incorporate realistic ground conditions, particularly those involved with complex land-atmosphere feedbacks, vegetation growth, and energy balances. Uncertainty persists despite using high resolution topography and/or detailed land use data. Thus, a good hydrologic model can produce right answers for wrong reasons. In this study, we develop an efficient approach for multi-variable assimilation of remotely sensed earth observations (EOs) into a hydrologic model and apply it in the 1700 km2 Pipestem Creek watershed in the Prairie Pothole Region of North Dakota, USA. Our goal is to employ EOs, specifically Leaf Area Index (LAI) and Potential Evapotranspiration (PET), as surrogates for the aforementioned processes without overruling the model's built-in physical/semi-empirical process conceptualizations. To do this, we modified the source code of an already-improved version of the Soil and Water Assessment Tool (SWAT) for wetland hydrology (Evenson et al. 2016 HP 30(22):4168) to directly assimilate remotely-sensed LAI and PET (obtained from the 500 m and 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) gridded products, respectively) into each model Hydrologic Response Unit (HRU). Two configurations of the model, one with and one without EO assimilation, are calibrated against streamflow observations at the watershed outlet. Spatio-temporal changes in the HRU-level water balance, based on calibrated outputs, are evaluated using MODIS Actual Evapotranspiration (AET) as a reference. It is expected that the model configuration having remotely sensed LAI and PET, will simulate more realistic land-atmosphere feedbacks, vegetation growth and energy balance. As a result, this will decrease simulated

  14. Merging Alternate Remotely-Sensed Soil Moisture Retrievals Using a Non-Static Model Combination Approach

    Directory of Open Access Journals (Sweden)

    Seokhyeon Kim

    2016-06-01

    Full Text Available Soil moisture is an important variable in the coupled hydrologic and climate system. In recent years, microwave-based soil moisture products have been shown to be a viable alternative to in situ measurements. A popular way to measure the performance of soil moisture products is to calculate the temporal correlation coefficient (R against in situ measurements or other appropriate reference datasets. In this study, an existing linear combination method improving R was modified to allow for a non-static or nonstationary model combination as the basis for improving remotely-sensed surface soil moisture. Previous research had noted that two soil moisture products retrieved using the Japan Aerospace Exploration Agency (JAXA and Land Parameter Retrieval Model (LPRM algorithms from the same Advanced Microwave Scanning Radiometer 2 (AMSR2 sensor are spatially complementary in terms of R against a suitable reference over a fixed period. Accordingly, a linear combination was proposed to maximize R using a set of spatially-varying, but temporally-fixed weights. Even though this approach showed promising results, there was room for further improvements, in particular using non-static or dynamic weights that take account of the time-varying nature of the combination algorithm being approximated. The dynamic weighting was achieved by using a moving window. A number of different window sizes was investigated. The optimal weighting factors were determined for the data lying within the moving window and then used to dynamically combine the two parent products. We show improved performance for the dynamically-combined product over the static linear combination. Generally, shorter time windows outperform the static approach, and a 60-day time window is suggested to be the optimum. Results were validated against in situ measurements collected from 124 stations over different continents. The mean R of the dynamically-combined products was found to be 0.57 and 0

  15. Advances in Remote Sensing of Flooding

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2015-11-01

    Full Text Available With the publication of eight original research articles, four types of advances in the remote sensing of floods are achieved. The uncertainty of modeled outputs using precipitation datasets derived from in situ observations and remote sensors is further understood. With the terrestrial laser scanner and airborne light detection and ranging (LiDAR coupled with high resolution optical and radar imagery, researchers improve accuracy levels in estimating the surface water height, extent, and flow of floods. The unmanned aircraft system (UAS can be the game changer in the acquisition and application of remote sensing data. The UAS may fly everywhere and every time when a flood event occurs. With the development of urban structure maps, the flood risk and possible damage is well assessed. The flood mitigation plans and response activities become effective and efficient using geographic information system (GIS-based urban flood vulnerability and risk maps.

  16. A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images.

    Science.gov (United States)

    Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun

    2017-06-22

    With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency.

  17. Creating Aerosol Types from CHemistry (CATCH): A New Algorithm to Extend the Link Between Remote Sensing and Models

    Science.gov (United States)

    Dawson, K. W.; Meskhidze, N.; Burton, S. P.; Johnson, M. S.; Kacenelenbogen, M. S.; Hostetler, C. A.; Hu, Y.

    2017-11-01

    Current remote sensing methods can identify aerosol types within an atmospheric column, presenting an opportunity to incrementally bridge the gap between remote sensing and models. Here a new algorithm was designed for Creating Aerosol Types from CHemistry (CATCH). CATCH-derived aerosol types—dusty mix, maritime, urban, smoke, and fresh smoke—are based on first-generation airborne High Spectral Resolution Lidar (HSRL-1) retrievals during the Ship-Aircraft Bio-Optical Research (SABOR) campaign, July/August 2014. CATCH is designed to derive aerosol types from model output of chemical composition. CATCH-derived aerosol types are determined by multivariate clustering of model-calculated variables that have been trained using retrievals of aerosol types from HSRL-1. CATCH-derived aerosol types (with the exception of smoke) compare well with HSRL-1 retrievals during SABOR with an average difference in aerosol optical depth (AOD) transport of smoke is underpredicted by the Goddard Earth Observing System- with Chemistry (GEOS-Chem) model. Spatial distributions of CATCH-derived aerosol types for the North American model domain during July/August 2014 show that aerosol type-specific AOD values occurred over representative locations: urban over areas with large population, maritime over oceans, smoke, and fresh smoke over typical biomass burning regions. This study demonstrates that model-generated information on aerosol chemical composition can be translated into aerosol types analogous to those retrieved from remote sensing methods. In the future, spaceborne HSRL-1 and CATCH can be used to gain insight into chemical composition of aerosol types, reducing uncertainties in estimates of aerosol radiative forcing.

  18. The use of remotely sensed soil moisture data in large-scale models of the hydrological cycle

    Science.gov (United States)

    Salomonson, V. V.; Gurney, R. J.; Schmugge, T. J.

    1985-01-01

    Manabe (1982) has reviewed numerical simulations of the atmosphere which provided a framework within which an examination of the dynamics of the hydrological cycle could be conducted. It was found that the climate is sensitive to soil moisture variability in space and time. The challenge arises now to improve the observations of soil moisture so as to provide up-dated boundary condition inputs to large scale models including the hydrological cycle. Attention is given to details regarding the significance of understanding soil moisture variations, soil moisture estimation using remote sensing, and energy and moisture balance modeling.

  19. Remote Sensing and Modeling of Landslides: Detection, Monitoring and Risk Evaluation

    Science.gov (United States)

    Kirschbaum, Dalia; Fukuoka, Hiroshi

    2012-01-01

    Landslides are one of the most pervasive hazards in the world, resulting in more fatalities and economic damage than is generally recognized_ Occurring over an extensive range of lithologies, morphologies, hydrologies, and climates, mass movements can be triggered by intense or prolonged rainfall, seismicity, freeze/thaw processes, and antbropogertic activities, among other factors. The location, size, and timing of these processes are characteristically difficult to predict and assess because of their localized spatial scales, distribution, and complex interactions between rainfall infiltration, hydromechanical properties of the soil, and the underlying surface composition. However, the increased availability, accessibility, and resolution of remote sensing data offer a new opportunity to explore issues of landslide susceptibility, hazard, and risk over a variety of spatial scales. This special issue presents a series of papers that investigate the sources, behavior, and impacts of different mass movement types using a diverse set of data sources and evaluation methodologies.

  20. Remote Sensing of Plastic Debris

    Science.gov (United States)

    Garaba, S. P.; Dierssen, H. M.

    2016-02-01

    Plastic debris is becoming a nuisance in the environment and as a result there has been a dire need to synoptically detect and quantify them in the ocean and on land. We investigate the possible utility of spectral information determined from hand held, airborne and satellite remote sensing tools in the detection and identification polymer source of plastic debris. Sampled debris will be compared to our derived spectral library of typical raw polymer sources found at sea and in household waste. Additional work will be to determine ways to estimate the abundance of plastic debris in target areas. Implications of successful remote detection, tracking and quantification of plastic debris will be towards validating field observations over large areas and at repeated time intervals both on land and at sea.

  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. Modeling river discharge and sediment transport in the Wax Lake-Atchafalaya basin with remote sensing parametrization.

    Science.gov (United States)

    Simard, M.; Liu, K.; Denbina, M. W.; Jensen, D.; Rodriguez, E.; Liao, T. H.; Christensen, A.; Jones, C. E.; Twilley, R.; Lamb, M. P.; Thomas, N. A.

    2017-12-01

    Our goal is to estimate the fluxes of water and sediments throughout the Wax Lake-Atchafalaya basin. This was achieved by parametrization of a set of 1D (HEC-RAS) and 2D (DELFT3D) hydrology models with state of the art remote sensing measurements of water surface elevation, water surface slope and total suspended sediment (TSS) concentrations. The model implementations are spatially explicit, simulating river currents, lateral flows to distributaries and marshes, and spatial variations of sediment concentrations. Three remote sensing instruments were flown simultaneously to collect data over the Wax Lake-Atchafalaya basin, and along with in situ field data. A Riegl Lidar was used to measure water surface elevation and slope, while the UAVSAR L-band radar collected data in repeat-pass interferometric mode to measure water level change within adjacent marshes and islands. These data were collected several times as the tide rose and fell. AVRIS-NG instruments measured water surface reflectance spectra, used to estimate TSS. Bathymetry was obtained from sonar transects and water level changes were recorded by 19 water level pressure transducers. We used several Acoustic Doppler Current Profiler (ADCP) transects to estimate river discharge. The remotely sensed measurements of water surface slope were small ( 1cm/km) and varied slightly along the channel, especially at the confluence with bayous and the intra-coastal waterway. The slope also underwent significant changes during the tidal cycle. Lateral fluxes to island marshes were mainly observed by UAVSAR close to the distributaries. The extensive remote sensing measurements showed significant disparity with the hydrology model outputs. Observed variations in water surface slopes were unmatched by the model and tidal wave propagation was much faster than gauge measurements. The slope variations were compensated for in the models by tuning local lateral fluxes, bathymetry and riverbed friction. Overall, the simpler 1D

  3. Sources of remotely sensed data

    Science.gov (United States)

    Applications Branch, EROS Data Center

    1978-01-01

    NCIC was established within the USGS to provide a single-point contact source for cartographic-related information, including remotely sensed data. A computerized indexing system, the Aerial Photography Summary Record System (APSRS), shows all holding for Federal agencies, with the long range goal of including data acquired on the state and local levels and (eventually) by private industry. The system directs the used to a particular agency which holds coverage over a particular unit area, based on the 7 1/2 minute USGS quadrangle system. The data will remain in the hands of the source agency.

  4. Microwave remote sensing laboratory design

    Science.gov (United States)

    Friedman, E.

    1979-01-01

    Application of active and passive microwave remote sensing to the study of ocean pollution is discussed. Previous research efforts, both in the field and in the laboratory were surveyed to derive guidance for the design of a laboratory program of research. The essential issues include: choice of radar or radiometry as the observational technique; choice of laboratory or field as the research site; choice of operating frequency; tank sizes and material; techniques for wave generation and appropriate wavelength spectrum; methods for controlling and disposing of pollutants used in the research; and pollutants other than oil which could or should be studied.

  5. Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Tina Gerl

    2014-08-01

    Full Text Available The modeling of flood damage is an important component for risk analyses, which are the basis for risk-oriented flood management, risk mapping, and financial appraisals. An automatic urban structure type mapping approach was applied on a land use/land cover classification generated from multispectral Ikonos data and LiDAR (Light Detection And Ranging data in order to provide spatially detailed information about the building stock of the case study area of Dresden, Germany. The multi-parameter damage models FLEMOps (Flood Loss Estimation Model for the private sector and regression-tree models have been adapted to the information derived from remote sensing data and were applied on the basis of the urban structure map. To evaluate this approach, which is suitable for risk analyses, as well as for post-disaster event analyses, an estimation of the flood losses caused by the Elbe flood in 2002 was undertaken. The urban structure mapping approach delivered a map with a good accuracy of 74% and on this basis modeled flood losses for the Elbe flood in 2002 in Dresden were in the same order of magnitude as official damage data. It has been shown that single-family houses suffered significantly higher damages than other urban structure types. Consequently, information on their specific location might significantly improve damage modeling, which indicates a high potential of remote sensing methods to further improve risk assessments.

  6. Study of Influence of Effluent on Ground Water Using Remote Sensing, GIS and Modeling Techniques

    Science.gov (United States)

    Pathak, S.; Bhadra, B. K.; Sharma, J. R.

    2012-07-01

    The area lies in arid zone of western Rajasthan having very scanty rains and very low ground water reserves. Some of the other problems that are faced by the area are disposal of industrial effluent posing threat to its sustainability of water resource. Textiles, dyeing and printing industries, various mechanical process and chemical/synthetic dyes are used and considerable wastewater discharged from these textile units contains about high amount of the dyes into the adjoining drainages. This has caused degradation of water quality in this water scarce semi-arid region of the country. Pali city is located South-West, 70 Kms from Jodhpur in western Rajasthan (India). There are four Common Effluent Treatment Plant (CETP) treating wastewater to meet the pollutant level permissible to river discharge, a huge amount of effluent water of these factories directly meets the into the river Bandi - a tributary of river Luni. In order to monitor the impact of industrial effluents on the environment, identifying the extent of the degradation and evolving possible means of minimizing the impacts studies on quality of effluents, polluted river water and water of adjoining wells, the contamination migration of the pollutants from the river to ground water were studied. Remote sensing analysis has been carried out using Resourcesat -1 multispectral satellite data along with DEM derived from IRS P5 stereo pair. GIS database generated of various thematic layers viz. base layer - inventorying all waterbodies in the vicinity, transport network and village layer, drainage, geomorphology, structure, land use. Analysis of spatial distribution of the features and change detection in land use/cover carried out. GIS maps have been used to help factor in spatial location of source and hydro-geomorphological settings. DEM & elevation contour helped in delineation of watershed and identifying flow modelling boundaries. Litholog data analysis carried out for aquifer boundaries using specialized

  7. STUDY OF INFLUENCE OF EFFLUENT ON GROUND WATER USING REMOTE SENSING, GIS AND MODELING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    S. Pathak

    2012-07-01

    Full Text Available The area lies in arid zone of western Rajasthan having very scanty rains and very low ground water reserves. Some of the other problems that are faced by the area are disposal of industrial effluent posing threat to its sustainability of water resource. Textiles, dyeing and printing industries, various mechanical process and chemical/synthetic dyes are used and considerable wastewater discharged from these textile units contains about high amount of the dyes into the adjoining drainages. This has caused degradation of water quality in this water scarce semi-arid region of the country. Pali city is located South-West, 70 Kms from Jodhpur in western Rajasthan (India. There are four Common Effluent Treatment Plant (CETP treating wastewater to meet the pollutant level permissible to river discharge, a huge amount of effluent water of these factories directly meets the into the river Bandi – a tributary of river Luni. In order to monitor the impact of industrial effluents on the environment, identifying the extent of the degradation and evolving possible means of minimizing the impacts studies on quality of effluents, polluted river water and water of adjoining wells, the contamination migration of the pollutants from the river to ground water were studied. Remote sensing analysis has been carried out using Resourcesat −1 multispectral satellite data along with DEM derived from IRS P5 stereo pair. GIS database generated of various thematic layers viz. base layer – inventorying all waterbodies in the vicinity, transport network and village layer, drainage, geomorphology, structure, land use. Analysis of spatial distribution of the features and change detection in land use/cover carried out. GIS maps have been used to help factor in spatial location of source and hydro-geomorphological settings. DEM & elevation contour helped in delineation of watershed and identifying flow modelling boundaries. Litholog data analysis carried out for aquifer

  8. Inter-Comparison of Retrieved and Modelled Soil Moisture and Coherency of Remotely Sensed Hydrology Data

    Science.gov (United States)

    Kolassa, Jana; Aires, Filipe

    2013-04-01

    A neural network algorithm has been developed for the retrieval of Soil Moisture (SM) from global satellite observations. The algorithm estimates soil moisture from a synergy of passive and active microwave, infrared and visible satellite observations in order to capture the different SM variabilities that the individual sensors are sensitive to. The advantages and drawbacks of each satellite observation have been analysed and the information type and content carried by each observation have been determined. A global data set of monthly mean soil moisture for the 1993-2000 period has been computed with the neural network algorithm (Kolassa et al., in press, 2012). The resulting soil moisture retrieval product has then been used in an inter-comparison study including soil moisture from (1) the HTESSEL model (Balsamo et al., 2009), (2) the WACMOS satellite product (Liu et al., 2011), and (3) in situ measurements from the International Soil Moisture Network (Dorigo et al., 2011). The analysis showed that the satellite remote sensing products are well-suited to capture the spatial variability of the in situ data and even show the potential to improve the modelled soil moisture. Both satellite retrievals also display a good agreement with the temporal structures of the in situ data, however, HTESSEL appears to be more suitable for capturing the temporal variability (Kolassa et al., in press, 2012). The use of this type of neural network approach is currently being investigated as a retrieval option for the SMOS mission. Our soil moisture retrieval product has also been used in a coherence study with precipitation data from GPCP (Adler et al., 2003) and inundation estimates from GIEMS (Prigent et al., 2007). It was investigated on a global scale whether the three observation-based datasets are coherent with each other and show the expected behaviour. For most regions of the Earth, the datasets were consistent and the behaviour observed could be explained with the known

  9. Innovative use of soft data for the validation of a rainfall-runoff model forced by remote sensing data

    Science.gov (United States)

    van Emmerik, Tim; Eilander, Dirk; Piet, Marijn; Mulder, Gert

    2013-04-01

    The Chamcar Bei catchment in southern Cambodia is a typical ungauged basin. Neither meteorological data or discharge measurements are available. In this catchment, local farmers are highly dependent on the irrigation system. However, due to the unreliability of the water supply, it was required to make a hydrological model, with which further improvements of the irrigation system could be planned. First, we used knowledge generated in the IAHS decade on Predictions in Ungauged Basins (PUB) to estimate the annual water balance of the Chamcar Bei catchment. Next, using remotely sensed precipitation, vegetation, elevation and transpiration data, a monthly rainfall-runoff model has been developed. The rainfall-runoff model was linked to the irrigation system reservoir, which allowed to validate the model based on soft data such as historical knowledge of the reservoir water level and groundwater levels visible in wells. This study shows that combining existing remote sensing data and soft ground data can lead to useful modeling results. The approach presented in this study can be applied in other ungauged basins, which can be extremely helpful in managing water resources in developing countries.

  10. Recent Advances Combining Remote Sensing Data with Advanced Models to Assess Disturbance Related Plant-Climate Interactions.

    Science.gov (United States)

    Hurtt, G. C.

    2015-12-01

    Terrestrial ecosystem dynamics are strongly influenced by processes of disturbance and recovery across a range of spatial and temporal scales, from large catastrophic events including tropical cyclones, fires, and pest outbreaks, to fine-scale forest canopy gap dynamics. Natural disturbances episodically alter vegetation structure and create important fluxes of carbon from vegetation to coarse woody debris and litter, and can alter land surface properties important for climate. Similarly, anthropogenic disturbances have the capacity to alter important land surface properties. Recovery following disturbances tends to restore vegetation structure and carbon over longer time scales as vegetation regrows and debris decomposes and land surface properties are restored. The complex spatial pattern from a legacy of past events, together with ongoing and potentially changing future events, presents a challenge not only for understanding, but also for prediction. As many disturbance processes are climate related, being climate driven and/or producing affects on climate through biophysical or biogeochemical alterations of the land surface, disturbance is a critical link in understanding plant-climate interactions. Here we review past progress, current results, and future priorities for utilizing remote sensing data in advanced models to understand of the role of disturbance in plant-climate interactions. Recent advances have helped to quantify the long term impacts of hurricanes on forests, account for recent forest disturbance events, quantify the vulnerability of ecosystems to potential future disturbance rates, and project future vegetation change in response to climate change, and reduce uncertainty through improved initial conditions accounting for the history of past disturbance events. Now, a new generation of land use data are being developed constrained by remote sensing to drive the next generation of Earth system models to estimate the effects of anthropogenic

  11. 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.

  12. Monitoring landscape level processes using remote sensing of large plots

    Science.gov (United States)

    Raymond L. Czaplewski

    1991-01-01

    Global and regional assessaents require timely information on landscape level status (e.g., areal extent of different ecosystems) and processes (e.g., changes in land use and land cover). To measure and understand these processes at the regional level, and model their impacts, remote sensing is often necessary. However, processing massive volumes of remotely sensing...

  13. 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...

  14. A framework for developing remote sensing applications

    International Nuclear Information System (INIS)

    Ahmad, T.; Hayat, M.F.; Afzal, M.; Asif, H.M.S.; Asif, K.H.

    2014-01-01

    Remote Sensing Application (RSA) is important as one of the critical enabler of e-systems such as e- governments, e-commerce, and e-sciences. In this study, we argued that owning to the specialized needs of RSA such as volatility and interactive nature, a customized Software Engineering (SE) approach should be adapted for their development. Based on this argument we have also identified the shortcomings of the conventional SE approaches and the classical waterfall software development life cycle model. In this study, we have proposed a modification to the classical waterfall software development life cycle model for proposing a customized software development Framework for RSAs. We have identified four (4) different types of changes that can occur to an already developed RS application. The proposed framework was capable to incorporate all four types of changes. Remote Sensing, software engineering, functional requirements, types of changes. (author)

  15. Data Quality in Remote Sensing

    Science.gov (United States)

    Batini, C.; Blaschke, T.; Lang, S.; Albrecht, F.; Abdulmutalib, H. M.; Barsi, Á.; Szabó, G.; Kugler, Zs.

    2017-09-01

    The issue of data quality (DQ) is of growing importance in Remote Sensing (RS), due to the widespread use of digital services (incl. apps) that exploit remote sensing data. In this position paper a body of experts from the ISPRS Intercommission working group III/IVb "DQ" identifies, categorises and reasons about issues that are considered as crucial for a RS research and application agenda. This ISPRS initiative ensures to build on earlier work by other organisations such as IEEE, CEOS or GEO, in particular on the meritorious work of the Quality Assurance Framework for Earth Observation (QA4EO) which was established and endorsed by the Committee on Earth Observation Satellites (CEOS) but aims to broaden the view by including experts from computer science and particularly database science. The main activities and outcomes include: providing a taxonomy of DQ dimensions in the RS domain, achieving a global approach to DQ for heterogeneous-format RS data sets, investigate DQ dimensions in use, conceive a methodology for managing cost effective solutions on DQ in RS initiatives, and to address future challenges on RS DQ dimensions arising in the new era of the big Earth data.

  16. Taiwan's second remote sensing satellite

    Science.gov (United States)

    Chern, Jeng-Shing; Ling, Jer; Weng, Shui-Lin

    2008-12-01

    FORMOSAT-2 is Taiwan's first remote sensing satellite (RSS). It was launched on 20 May 2004 with five-year mission life and a very unique mission orbit at 891 km altitude. This orbit gives FORMOSAT-2 the daily revisit feature and the capability of imaging the Arctic and Antarctic regions due to the high enough altitude. For more than three years, FORMOSAT-2 has performed outstanding jobs and its global effectiveness is evidenced in many fields such as public education in Taiwan, Earth science and ecological niche research, preservation of the world heritages, contribution to the International Charter: space and major disasters, observation of suspected North Korea and Iranian nuclear facilities, and scientific observation of the atmospheric transient luminous events (TLEs). In order to continue the provision of earth observation images from space, the National Space Organization (NSPO) of Taiwan started to work on the second RSS from 2005. This second RSS will also be Taiwan's first indigenous satellite. Both the bus platform and remote sensing instrument (RSI) shall be designed and manufactured by NSPO and the Instrument Technology Research Center (ITRC) under the supervision of the National Applied Research Laboratories (NARL). Its onboard computer (OBC) shall use Taiwan's indigenous LEON-3 central processing unit (CPU). In order to achieve cost effective design, the commercial off the shelf (COTS) components shall be widely used. NSPO shall impose the up-screening/qualification and validation/verification processes to ensure their normal functions for proper operations in the severe space environments.

  17. 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.

  18. A novel airport extraction model based on saliency region detection for high spatial resolution remote sensing images

    Science.gov (United States)

    Lv, Wen; Zhang, Libao; Zhu, Yongchun

    2017-06-01

    The airport is one of the most crucial traffic facilities in military and civil fields. Automatic airport extraction in high spatial resolution remote sensing images has many applications such as regional planning and military reconnaissance. Traditional airport extraction strategies usually base on prior knowledge and locate the airport target by template matching and classification, which will cause high computation complexity and large costs of computing resources for high spatial resolution remote sensing images. In this paper, we propose a novel automatic airport extraction model based on saliency region detection, airport runway extraction and adaptive threshold segmentation. In saliency region detection, we choose frequency-tuned (FT) model for computing airport saliency using low level features of color and luminance that is easy and fast to implement and can provide full-resolution saliency maps. In airport runway extraction, Hough transform is adopted to count the number of parallel line segments. In adaptive threshold segmentation, the Otsu threshold segmentation algorithm is proposed to obtain more accurate airport regions. The experimental results demonstrate that the proposed model outperforms existing saliency analysis models and shows good performance in the extraction of the airport.

  19. Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area

    Directory of Open Access Journals (Sweden)

    J. Cristóbal

    2011-05-01

    Full Text Available Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method, that combines remote sensing imagery with GIS-based climate modelling to estimate daily evapotranspiration (ETd for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (Landsat-7 ETM+, Landsat-5 TM and TERRA/AQUA MODIS, with a spatial resolution of 60, 120 and 1000 m, respectively and combining three different approaches to calculate the B parameter, which represents an average bulk conductance for the daily-integrated sensible heat flux. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L. stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, R2 test of 0.89, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30 %. The poor agreement obtained using TERRA/AQUA MODIS, with a mean RMSE value of 1.8 and 2.4 mm day−1 and an estimation error of about ±57 and 50 %, respectively. This

  20. Function modeling improves the efficiency of spatial modeling using big data from remote sensing

    Science.gov (United States)

    John Hogland; Nathaniel Anderson

    2017-01-01

    Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have...

  1. Archeological methodology and remote sensing.

    Science.gov (United States)

    Gumerman, G J; Lyons, T R

    1971-04-09

    We have shown that the different spectral surveying techniques and the resultant imagery vary in their applicability to archeological prediction and exploration, but their applications are far broader than we have indicated. Their full potential, to a considerable extent, still remains unexplored. Table 1 is a chart of the more common sensor systems useful to archeological investigators. Several kinds of photography, thermal infrared imagery, and radar imagery are listed. Checks in various categories of direct and indirect utility in archeological research indicate that the different systems do provide varying degrees of input for studies in these areas. Photography and multispectral photography have the broadest applications in this field. Standard black-and-white aerial photography generally serves the purposes of archeological exploration and site analysis better than infrared scanner imagery, radar, or color photography. However, the real value of remotesensing experimentation lies in the utilization of different instruments and in the comparison and correlation of their data output. It can be stated without doubt that there is no one all-purpose remotesensing device on which the archeologist can rely that will reveal all evidence of human occupations. Remote-sensing data will not replace the traditional ground-based site survey, but, used judiciously, data gathered from aerial reconnaissance can reveal many cultural features unsuspected from the ground. The spectral properties of sites distinguishable by various types of remote sensors may perhaps be one of their most characteristic features, and yet the meaning of the differential discrimnination of features has not been determined for the most part, since such spectral properties are poorly understood at this date. The difficulty in isolating the causes of acceptable definition in certain portion of the spectrum and the lack of acceptable definition in others suggests that the evaluation of remote-sensing

  2. Examining the Reliability of Survey Data with Remote Sensing and Geographic Information Systems to Improve Deforestation Modeling

    OpenAIRE

    Caviglia-Harris, Jill L.; Harris, Daniel W.

    2005-01-01

    Tropical deforestation has environmental consequences at local, regional and global scales. The Brazilian Amazon's deforestation resulted largely from conversion to farmland by landholders. These conversions caused deforestation that researchers have examined locally by interviews with landowners and regionally by satellite remote sensing. This paper merges data from these methods to validate survey-based deforestation levels with remote-sensing information. We determine household characteris...

  3. A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models

    Directory of Open Access Journals (Sweden)

    J. C. Vogeler

    2016-02-01

    Full Text Available Spatially explicit maps of wildlife habitat relationships have proven to be valuable tools for conservation and management applications including evaluating how and which species may be impacted by large scale climate change, ongoing fragmentation of habitat, and local land-use practices. Studies have turned to remote sensing datasets as a way to characterize vegetation for the examination of habitat selection and for mapping realized relationships across the landscape. Potentially one of the more difficult habitat types to try to characterize with remote sensing are the vertically and horizontally complex forest systems. Characterizing this complexity is needed to explore which aspects may represent driving and/or limiting factors for wildlife species. Active remote sensing data from lidar and radar sensors has thus caught the attention of the forest wildlife research and management community in its potential to represent three dimensional habitat features. The purpose of this review was to examine the applications of active remote sensing for characterizing forest in wildlife habitat studies through a keyword search within Web of Science. We present commonly used active remote sensing metrics and methods, discuss recent advances in characterizing aspects of forest habitat, and provide suggestions for future research in the area of new remote sensing data/techniques that could benefit forest wildlife studies that are currently not represented or may be underutilized within the wildlife literature. We also highlight the potential value in data fusion of active and passive sensor data for representing multiple dimensions and scales of forest habitat. While the use of remote sensing has increased in recent years within wildlife habitat studies, continued communication between the remote sensing, forest management, and wildlife communities is vital to ensure appropriate data sources and methods are understood and utilized, and so that creators of

  4. Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

    Directory of Open Access Journals (Sweden)

    Alfonso Calera

    2017-05-01

    Full Text Available The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

  5. Modeling the Impact of Drizzle and 3D Cloud Structure on Remote Sensing of Effective Radius

    Science.gov (United States)

    Platnick, Steven; Zinner, Tobias; Ackerman, S.

    2008-01-01

    Remote sensing of cloud particle size with passive sensors like MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave infrared channels. MODIS observations sometimes show significantly larger effective radii in marine boundary layer cloud fields derived from the 1.6 and 2.1 pm channel observations than for 3.7 pm retrievals. Possible explanations range from 3D radiative transport effects and sub-pixel cloud inhomogeneity to the impact of drizzle formation on the droplet distribution. To investigate the potential influence of these factors, we use LES boundary layer cloud simulations in combination with 3D Monte Carlo simulations of MODIS observations. LES simulations of warm cloud spectral microphysics for cases of marine stratus and broken stratocumulus, each for two different values of cloud condensation nuclei density, produce cloud structures comprising droplet size distributions with and without drizzle size drops. In this study, synthetic MODIS observations generated from 3D radiative transport simulations that consider the full droplet size distribution will be generated for each scene. The operational MODIS effective radius retrievals will then be applied to the simulated reflectances and the results compared with the LES microphysics.

  6. Flexible simulation strategy for modeling 3D cultural objects based on multisource remotely sensed imagery

    Science.gov (United States)

    Guienko, Guennadi; Levin, Eugene

    2003-01-01

    New ideas and solutions never come alone. Although automated feature extraction is not sufficiently mature to move from the realm of scientific investigation into the category of production technology, a new goal has arisen: 3D simulation of real-world objects, extracted from images. This task, which evolved from feature extraction and is not an easy task itself, becomes even more complex, multi-leveled, and often uncertain and fuzzy when one exploits time-sequenced multi-source remotely sensed visual data. The basic components of the process are familiar image processing tasks: fusion of various types of imagery, automatic recognition of objects, removng those objects from the source images, and replacing them in the images with their realistic simulated "twin" object rendering. This paper discusses how to aggregate the most appropriate approach to each task into one technological process in order to develop a Manipulator for Visual Simulation of 3D objects (ManVIS) that is independent or imagery/format/media. The technology could be made general by combining a number of competent special purpose algorithms under appropriate contextual, geometric, spatial, and temporal constraints derived from a-priori knowledge. This could be achieved by planning the simulation in an Open Structure Simulation Strategy Manager (O3SM) a distinct component of ManVIS building the simulation strategy before beginning actual image manipulation.

  7. REMOTE SENSING AND SURFACE ENERGY FLUX MODELS TO DERIVE EVAPOTRANSPIRATION AND CROP COEFFICIENT

    Directory of Open Access Journals (Sweden)

    Salvatore Barbagallo

    2008-06-01

    Full Text Available Remote sensing techniques using high resolution satellite images provide opportunities to evaluate daily crop water use and its spatial and temporal distribution on a field by field basis. Mapping this indicator with pixels of few meters of size on extend areas allows to characterize different processes and parameters. Satellite data on vegetation reflectance, integrated with in field measurements of canopy coverage features and the monitoring of energy fluxes through the soil-plant-atmosphere system, allow to estimate conventional irrigation components (ET, Kc thus improving irrigation strategies. In the study, satellite potential evapotranspiration (ETp and crop coefficient (Kc maps of orange orchards are derived using semi-empirical approaches between reflectance data from IKONOS imagery and ground measurements of vegetation features. The monitoring of energy fluxes through the orchard allows to estimate actual crop evapotranspiration (ETa using energy balance and the Surface Renewal theory. The approach indicates substantial promise as an efficient, accurate and relatively inexpensive procedure to predict actual ET fluxes and Kc from irrigated lands.

  8. Remote Sensing of Landscapes with Spectral Images

    Science.gov (United States)

    Adams, John B.; Gillespie, Alan R.

    2006-05-01

    Remote Sensing of Landscapes with Spectral Images describes how to process and interpret spectral images using physical models to bridge the gap between the engineering and theoretical sides of remote-sensing and the world that we encounter when we venture outdoors. The emphasis is on the practical use of images rather than on theory and mathematical derivations. Examples are drawn from a variety of landscapes and interpretations are tested against the reality seen on the ground. The reader is led through analysis of real images (using figures and explanations); the examples are chosen to illustrate important aspects of the analytic framework. This textbook will form a valuable reference for graduate students and professionals in a variety of disciplines including ecology, forestry, geology, geography, urban planning, archeology and civil engineering. It is supplemented by a web-site hosting digital color versions of figures in the book as well as ancillary images (www.cambridge.org/9780521662214). Presents a coherent view of practical remote sensing, leading from imaging and field work to the generation of useful thematic maps Explains how to apply physical models to help interpret spectral images Supplemented by a website hosting digital colour versions of figures in the book, as well as additional colour figures

  9. Monitoring arid-land groundwater abstraction through optimization of a land surface model with remote sensing-based evaporation

    KAUST Repository

    Lopez Valencia, Oliver Miguel

    2018-02-01

    The increase in irrigated agriculture in Saudi Arabia is having a large impact on its limited groundwater resources. While large-scale water storage changes can be estimated using satellite data, monitoring groundwater abstraction rates is largely non-existent at either farm or regional level, so water management decisions remain ill-informed. Although determining water use from space at high spatiotemporal resolutions remains challenging, a number of approaches have shown promise, particularly in the retrieval of crop water use via evaporation. Apart from satellite-based estimates, land surface models offer a continuous spatial-temporal evolution of full land-atmosphere water and energy exchanges. In this study, we first examine recent trends in terrestrial water storage depletion within the Arabian Peninsula and explore its relation to increased agricultural activity in the region using satellite data. Next, we evaluate a number of large-scale remote sensing-based evaporation models, giving insight into the challenges of evaporation retrieval in arid environments. Finally, we present a novel method aimed to retrieve groundwater abstraction rates used in irrigated fields by constraining a land surface model with remote sensing-based evaporation observations. The approach is used to reproduce reported irrigation rates over 41 center-pivot irrigation fields presenting a range of crop dynamics over the course of one year. The results of this application are promising, with mean absolute errors below 3 mm:day-1, bias of -1.6 mm:day-1, and a first rough estimate of total annual abstractions of 65.8 Mm3 (close to the estimated value using reported farm data, 69.42 Mm3). However, further efforts to address the overestimation of bare soil evaporation in the model are required. The uneven coverage of satellite data within the study site allowed us to evaluate its impact on the optimization, with a better match between observed and obtained irrigation rates on fields with

  10. Detection of pollution-induced forest decline in the Kola Peninsula using remote sensing and mathematical modelling

    Energy Technology Data Exchange (ETDEWEB)

    Rigina, Olga

    1998-07-01

    Forests on the Kola Peninsula in Northern Russia grow close to the northern tree line. They are subjected to both natural and anthropogenic stress factors. The Cu-Ni smelter 'Severonikel' (Lat. 67 deg 55'N; Long. 32 deg 57'E) near Monchegorsk is one of the two major sources of sulphur dioxide and heavy metals emissions on the Kola Peninsula. These emissions have caused significant deterioration of the surrounding vegetation. The thesis demonstrates how methods of Remote sensing, ground survey and mathematical modelling can be integrated for monitoring of the smelter's environmental impact on the surrounding vegetation: ground truth data are used for calibration of remote-sensed data, which further serve to verify mathematical models. The study aims were: * to estimate the scale of airborne sulphur pollution from the smelting industry on the Kola Peninsula and its effect on vegetation; * to assess spatial extent of the forest decline in the 'Severonikel' smelter impact zone; * to display dynamics of the forest damage area in spatial and temporal perspective; * to validate the Gaussian plume sector model and the IIASA forest impact model as components of the analysis of forest damage. The Regional Acidification Information and Simulation model (RAINS) was applied to calculate sulphur deposition and loads in Fennoscandia in order to assess the contribution of the Kola sources to the deposition pattern in the region. The percentage of the ecosystems where the critical load had been exceeded was calculated. For more detailed assessments, calculations based on local and meso-scale models were made. Landsat-MSS summer images from 1978, 1986 and 1992 and a Landsat -TM image from 1996 were used for change-detection analyses. The methods applied were bandwise histogram matching and subsequent differencing. An unsupervised classification of land-cover was made using the 1996 Landsat-TM image. In situ observations of vegetation type and

  11. Detection of pollution-induced forest decline in the Kola Peninsula using remote sensing and mathematical modelling

    International Nuclear Information System (INIS)

    Rigina, Olga

    1998-01-01

    Forests on the Kola Peninsula in Northern Russia grow close to the northern tree line. They are subjected to both natural and anthropogenic stress factors. The Cu-Ni smelter 'Severonikel' (Lat. 67 deg 55'N; Long. 32 deg 57'E) near Monchegorsk is one of the two major sources of sulphur dioxide and heavy metals emissions on the Kola Peninsula. These emissions have caused significant deterioration of the surrounding vegetation. The thesis demonstrates how methods of Remote sensing, ground survey and mathematical modelling can be integrated for monitoring of the smelter's environmental impact on the surrounding vegetation: ground truth data are used for calibration of remote-sensed data, which further serve to verify mathematical models. The study aims were: * to estimate the scale of airborne sulphur pollution from the smelting industry on the Kola Peninsula and its effect on vegetation; * to assess spatial extent of the forest decline in the 'Severonikel' smelter impact zone; * to display dynamics of the forest damage area in spatial and temporal perspective; * to validate the Gaussian plume sector model and the IIASA forest impact model as components of the analysis of forest damage. The Regional Acidification Information and Simulation model (RAINS) was applied to calculate sulphur deposition and loads in Fennoscandia in order to assess the contribution of the Kola sources to the deposition pattern in the region. The percentage of the ecosystems where the critical load had been exceeded was calculated. For more detailed assessments, calculations based on local and meso-scale models were made. Landsat-MSS summer images from 1978, 1986 and 1992 and a Landsat -TM image from 1996 were used for change-detection analyses. The methods applied were bandwise histogram matching and subsequent differencing. An unsupervised classification of land-cover was made using the 1996 Landsat-TM image. In situ observations of vegetation type and degradation levels on permanent field

  12. Lunar remote sensing and measurements

    Science.gov (United States)

    Moore, H.J.; Boyce, J.M.; Schaber, G.G.; Scott, D.H.

    1980-01-01

    Remote sensing and measurements of the Moon from Apollo orbiting spacecraft and Earth form a basis for extrapolation of Apollo surface data to regions of the Moon where manned and unmanned spacecraft have not been and may be used to discover target regions for future lunar exploration which will produce the highest scientific yields. Orbital remote sensing and measurements discussed include (1) relative ages and inferred absolute ages, (2) gravity, (3) magnetism, (4) chemical composition, and (5) reflection of radar waves (bistatic). Earth-based remote sensing and measurements discussed include (1) reflection of sunlight, (2) reflection and scattering of radar waves, and (3) infrared eclipse temperatures. Photographs from the Apollo missions, Lunar Orbiters, and other sources provide a fundamental source of data on the geology and topography of the Moon and a basis for comparing, correlating, and testing the remote sensing and measurements. Relative ages obtained from crater statistics and then empirically correlated with absolute ages indicate that significant lunar volcanism continued to 2.5 b.y. (billion years) ago-some 600 m.y. (million years) after the youngest volcanic rocks sampled by Apollo-and that intensive bombardment of the Moon occurred in the interval of 3.84 to 3.9 b.y. ago. Estimated fluxes of crater-producing objects during the last 50 m.y. agree fairly well with fluxes measured by the Apollo passive seismic stations. Gravity measurements obtained by observing orbiting spacecraft reveal that mare basins have mass concentrations and that the volume of material ejected from the Orientale basin is near 2 to 5 million km 3 depending on whether there has or has not been isostatic compensation, little or none of which has occurred since 3.84 b.y. ago. Isostatic compensation may have occurred in some of the old large lunar basins, but more data are needed to prove it. Steady fields of remanent magnetism were detected by the Apollo 15 and 16 subsatellites

  13. Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations

    Directory of Open Access Journals (Sweden)

    Zhiqiang Cheng

    2018-01-01

    Full Text Available The approach of using multispectral remote sensing (RS to estimate soil available nutrients (SANs has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, we replaced the statistical model by integrating the World Food Studies (WOFOST model and time series of remote sensing (T-RS observations to ensure stability and accuracy. Time series of HJ-1 A/B data was assimilated into the WOFOST model to extrapolate crop growth simulations from a single point to a large area using a specific assimilation method. Because nutrient-limited growth within the growing season is required and the SAN parameters can only be used at the end of the growing season in the original model, the WOFOST model was modified. Notably, the calculation order was changed, and new soil nutrient uptake algorithms were implemented in the model for nutrient-limited growth estimation. Finally, experiments were conducted in the spring maize plots of Hongxing Farm to analyze the effects of nutrient stress on crop growth and the SAN simulation accuracy. The results confirm the differences in crop growth status caused by a lack of soil nutrients. The new approach can take advantage of these differences to provide better SAN estimates. In general, the new approach can overcome the limitations of existing methods and simulate the SAN status with reliable accuracy.

  14. Spatial Modeling of Flood Duration in Amazonian Floodplains Through Radar Remote Sensing and Generalized Linear Models

    Science.gov (United States)

    Ferreira-Ferreira, J.; Francisco, M. S.; Silva, T. S. F.

    2017-12-01

    Amazon floodplains play an important role in biodiversity maintenance and provide important ecosystem services. Flood duration is the prime factor modulating biogeochemical cycling in Amazonian floodplain systems, as well as influencing ecosystem structure and function. However, due to the absence of accurate terrain information, fine-scale hydrological modeling is still not possible for most of the Amazon floodplains, and little is known regarding the spatio-temporal behavior of flooding in these environments. Our study presents an new approach for spatial modeling of flood duration, using Synthetic Aperture Radar (SAR) and Generalized Linear Modeling. Our focal study site was Mamirauá Sustainable Development Reserve, in the Central Amazon. We acquired a series of L-band ALOS-1/PALSAR Fine-Beam mosaics, chosen to capture the widest possible range of river stage heights at regular intervals. We then mapped flooded area on each image, and used the resulting binary maps as the response variable (flooded/non-flooded) for multiple logistic regression. Explanatory variables were accumulated precipitation 15 days prior and the water stage height recorded in the Mamirauá lake gauging station observed for each image acquisition date, Euclidean distance from the nearest drainage, and slope, terrain curvature, profile curvature, planform curvature and Height Above the Nearest Drainage (HAND) derived from the 30-m SRTM DEM. Model results were validated with water levels recorded by ten pressure transducers installed within the floodplains, from 2014 to 2016. The most accurate model included water stage height and HAND as explanatory variables, yielding a RMSE of ±38.73 days of flooding per year when compared to the ground validation sites. The largest disagreements were 57 days and 83 days for two validation sites, while remaining locations achieved absolute errors lower than 38 days. In five out of nine validation sites, the model predicted flood durations with

  15. Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

    Science.gov (United States)

    Jones, Emlyn M.; Baird, Mark E.; Mongin, Mathieu; Parslow, John; Skerratt, Jenny; Lovell, Jenny; Margvelashvili, Nugzar; Matear, Richard J.; Wild-Allen, Karen; Robson, Barbara; Rizwi, Farhan; Oke, Peter; King, Edward; Schroeder, Thomas; Steven, Andy; Taylor, John

    2016-12-01

    Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO3, NH4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations

  16. Natural Resource Information System. Remote Sensing Studies.

    Science.gov (United States)

    Leachtenauer, J.; And Others

    A major design objective of the Natural Resource Information System entailed the use of remote sensing data as an input to the system. Potential applications of remote sensing data were therefore reviewed and available imagery interpreted to provide input to a demonstration data base. A literature review was conducted to determine the types and…

  17. Modelling LULC for the period 2010-2030 using GIS and Remote sensing: a case study of Tikrit, Iraq

    Science.gov (United States)

    Jasim Hadi, Sinan; Shafri, Helmi Z. M.; Mahir, Mustafa D.

    2014-06-01

    This study extends upon the results of [1] to include the modeling of Land use/ Land cover (LULC). This study looks at the changes that occurred from 2010 to 2030 in Tikrit district, Iraq by predicting LULC for the year target 2030 by using the classified images for two points of time (2000 - 2010) as a foundation for the modeling process. The projected map, in comparison with 2010 LULC map, shows a significant decrease in vegetation area (45.11 km2) which must be regulated in order to maintain a green environment, and increase in the urban area (58.42 km2) which should be monitored to have sustainable development and control the eco-environment degradation. Also, in this study, it is shown clearly that the use of Geographic Information System (GIS) and remote sensing (specially IDRISI software) in modeling LULC is a suitable approach to understand the future pattern.

  18. A Self-Calibrating Runoff and Streamflow Remote Sensing Model for Ungauged Basins Using Open-Access Earth Observation Data

    Directory of Open Access Journals (Sweden)

    Ate Poortinga

    2017-01-01

    Full Text Available Due to increasing pressures on water resources, there is a need to monitor regional water resource availability in a spatially and temporally explicit manner. However, for many parts of the world, there is insufficient data to quantify stream flow or ground water infiltration rates. We present the results of a pixel-based water balance formulation to partition rainfall into evapotranspiration, surface water runoff and potential ground water infiltration. The method leverages remote sensing derived estimates of precipitation, evapotranspiration, soil moisture, Leaf Area Index, and a single F coefficient to distinguish between runoff and storage changes. The study produced significant correlations between the remote sensing method and field based measurements of river flow in two Vietnamese river basins. For the Ca basin, we found R2 values ranging from 0.88–0.97 and Nash–Sutcliffe efficiency (NSE values varying between 0.44–0.88. The R2 for the Red River varied between 0.87–0.93 and NSE values between 0.61 and 0.79. Based on these findings, we conclude that the method allows for a fast and cost-effective way to map water resource availability in basins with no gauges or monitoring infrastructure, without the need for application of sophisticated hydrological models or resource-intensive data.

  19. 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...

  20. 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...

  1. A remotely sensed index of deforestation/urbanization for use in climate models

    Science.gov (United States)

    Gillies, Robert R.; Carlson, Toby N.

    1995-01-01

    The object of this research is to use indirect measurements, notably thermal infrared, to describe urbanization and deforestation with parameters that can be used to assess, as well as predict, the effects of land use changes on local microclimate. More specifically, we use a new approach for the treatment of remotely sensed data; this is referred to as the 'triangle' method. The name triangle is given because the envelope of data points, when plotted as a function of surface radiant temperature versus vegetation index or fractional vegetation cover, exhibits the shape of a triangle. From the information contained on these 'scatter plots', land use changes can be related to two intrinsic surface variables, the surface moisture availability (M(sub 0))(sup 1) and fractional vegetation cover. Recent work by Carlson et al. indicate that the triangle shape on the scatter plots may be scale similar, suggesting that these two parameters are subject to the same interpretation on differing scales. A second objective in this research is to determine if historical data for Advanced Very High Resolution Radiometer (AVHRR) (NOAA satellite; 1.1 km resolution at nadir) can be used to assess changes in regional land use over time. To this end, two target areas were chosen for the investigation of urbanization and two for deforestation. The former comprise tow areas in Pennsylvania, one a small but rapidly growing population center (State College) and the other a medium-sized urban area which continues to undergo development (Chester County). The two deforestation sites consist of rain forest areas in western and central Costa Rica and a region in the Brazilian Amazon.

  2. A Volume Model for Urban Heat Island Based on Remote Sensing Imagery and Its Application: A Case Study in Beijing

    Science.gov (United States)

    Zhou, J.; Chen, Y. H.; Li, J.; Weng, Q. H.

    2007-12-01

    Along with urbanization, urban heat island (UHI) has become one of the most serious urban problems, because of its impacts on the urban microclimate, air quality, energy consuming, public health and so on. With the advent of thermal remote sensing technology, remote observations of UHIs become the focus of urban remote sensing. While some progresses have been made by scientists, remote sensing study of UHI has been slow to advance qualitative description of thermal patterns and simple correlations. As a common indicator of UHI, magnitude is often used to describe the degree of UHI occurrence. In order to develop a more quantitative and more effective indicator for UHI dynamic monitoring at regional scale, deriving its spatial feature and facilitating the comparisons between different UHIs occur at different time or different areas based on remote sensing imageries, this paper proposes a new parameter named UHI volume to integrate UHI magnitude and extent effectively. After the subtraction of the rural contribution in the land surface temperature (LST) image, the isolated UHI signature is fitted using a least-squares Gaussian surface and then the UHI volume is calculated as a double integral of the UHI signature function over its footprint. This study investigates the applicability of this volume model based on examination of thirty EOS-Terra MODIS level 1B imageries of Beijing City, China, acquired between 2004~2006. These imageries include fifteen nighttime scenes, with their corresponding daytime scenes. Firstly, the resampled digital elevation model (DEM) data, the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) are used to extract the urban areas. Secondly, a simplified method is performed to retrieve the land surface temperature from MODIS channel 31 and channel 32. Thirdly, four transects at different directions including N-S, W-E, NW-SE and NE-SW are selected to detect the UHIs to ensure the

  3. 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

  4. Winter wheat yield estimation of remote sensing research based on WOFOST crop model and leaf area index assimilation

    Science.gov (United States)

    Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei

    2017-04-01

    Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed

  5. Can we use remotely sensed land surface temperatures to evaluate and improve model simulations of the urban heat island?

    Science.gov (United States)

    Hu, L.; Monaghan, A. J.; Brunsell, N. A.; Barlage, M. J.; Feddema, J. J.; Wilhelmi, O.

    2013-12-01

    Extreme heat events are the leading cause of weather-related human mortality in the United States and in many countries world-wide, and the development of highly accurate urban climate models to predict heat waves and extreme heat events is critical. However, the heterogeneous urban surface with myriad energy and moisture fluxes increases model complexity and uncertainty. Remotely sensed land surface temperature (LST) offers advantages such as comparable spatial scale, global coverage, steady periodicity, and long-term observations, which can be applied to assess model simulations. This research proposes a sampling technique to select and compare MODIS LST and model-simulated radiative temperature for eight configurations of the High Resolution Land Data Assimilation System (HRLDAS) during 2003-2012 summers (JJA) for Houston, TX. The objective is to decrease comparison biases between MODIS and HRLDAS caused by clouds, view angles, and the LST retrieval algorithm, and to understand which urban surface properties are critical for accurate UHI simulations. The results show that the accurate description of urban fraction can effectively decrease more than 25% of RMSE for HRLDAS LST for both daytime and nighttime comparisons. Assuming irrigated vegetation in the urban area largely improved the RMSE by about 2K during the daytime, while there was no significant difference for the nighttime periods. In the most realistic scenario HRLDAS performed quite well at night, both temporally and spatially. HRLDAS daytime LST simulations are warmer than MODIS observations by approximately 5K but with relatively strong correlations. In summary, remotely sensed LST can be a good observational source for the assessment of UHI simulations, but requires careful pre-processing beforehand to avoid unrepresentative comparisons. The proposed sampling method is practical and effective for validation of long-term urban-scale model simulations.

  6. Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

    Directory of Open Access Journals (Sweden)

    Rui Ma

    2017-02-01

    Full Text Available Quantitative estimation of the magnitude and variability of gross primary productivity (GPP is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF and a proper orthogonal decomposition (POD-based ensemble four-dimensional (4D variational assimilation method (PODEn4DVar, incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of r = 0.923 and RMSD of 32.676 gC/m2/month compared with the LPJ-EnKF scheme (r = 0.887, RMSD = 38.531 gC/m2/month and with no data assimilation (r = 0.840, RMSD = 45.410 gC/m2/month. Applying the PODEn4DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003–2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems.

  7. [NDVI difference rate recognition model of deciduous broad-leaved forest based on HJ-CCD remote sensing data].

    Science.gov (United States)

    Wang, Yan; Tian, Qing-Jiu; Huang, Yan; Wei, Hong-Wei

    2013-04-01

    The present paper takes Chuzhou in Anhui Province as the research area, and deciduous broad-leaved forest as the research object. Then it constructs the recognition model about deciduous broad-leaved forest was constructed using NDVI difference rate between leaf expansion and flowering and fruit-bearing, and the model was applied to HJ-CCD remote sensing image on April 1, 2012 and May 4, 2012. At last, the spatial distribution map of deciduous broad-leaved forest was extracted effectively, and the results of extraction were verified and evaluated. The result shows the validity of NDVI difference rate extraction method proposed in this paper and also verifies the applicability of using HJ-CCD data for vegetation classification and recognition.

  8. A 30 meter soil properties map of the contiguous United States for use in remote sensing and land surface models

    Science.gov (United States)

    Chaney, N.; Morgan, C.; McBratney, A.; Wood, E. F.; Yimam, Y.

    2016-12-01

    Soil moisture plays a critical role in the terrestrial water, energy, and biogeochemical cycles. For this reason, numerical weather prediction, global circulation models, and hydrologic monitoring systems increasingly emphasize modeling soil moisture and assimilating soil moisture remote sensing products. In both cases, the prescribed soil hydraulic properties play a pivotal role in accurately describing the soil moisture state. However, an accurate characterization of soil hydraulic properties remains a persistent challenge—existing continental soil databases are too coarse and outdated for contemporary applications. To address this challenge, we have developed the Probabilistic Remapping of SSURGO database (POLARIS); a new soil database that covers the contiguous United States (CONUS) at a 30-meter spatial resolution. POLARIS was constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm to remap the rich yet incomplete Soil Survey Geographic (SSURGO) database to create spatially complete probabilistic soil series maps over CONUS (Chaney et al., 2016). These maps are then combined with the vertical profile information of each soil series to create the corresponding maps of soil hydraulic properties and their associated uncertainties. The mapped soil hydraulic properties include soil texture, saturated hydraulic conductivity, porosity, field capacity, and wilting point. POLARIS provides a breakthrough in soil information. To illustrate this database's potential, we will both explore the database at multiple spatial scales and discuss recent land surface modeling results that have used POLARIS to simulate soil moisture at a 30-meter spatial resolution over CONUS between 2004 and 2014. We will discuss the added benefit of using POLARIS and the opportunity it presents to improve the characterization of soil hydraulic properties in land surface models and soil moisture remote sensing. References

  9. Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain

    Science.gov (United States)

    Yao, Fengmei; Tang, Yanjing; Wang, Peijuan; Zhang, Jiahua

    Climate change significantly impact on agriculture in recent year, the accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C4 crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS-P-YEC (Remote-Sensing-Photosynthesis-Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS-P-YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002-2011. The 111 statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (p yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002 to 2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale.

  10. A novel linear physical model for remote sensing of snow wetness and snow density using the visible and infrared bands

    Science.gov (United States)

    Varade, D. M.; Dikshit, O.

    2017-12-01

    Modeling and forecasting of snowmelt runoff are significant for understanding the hydrological processes in the cryosphere which requires timely information regarding snow physical properties such as liquid water content and density of snow in the topmost layer of the snowpack. Both the seasonal runoffs and avalanche forecasting are vastly dependent on the inherent physical characteristics of the snowpack which are conventionally measured by field surveys in difficult terrains at larger impending costs and manpower. With advances in remote sensing technology and the increase in the availability of satellite data, the frequency and extent of these surveys could see a declining trend in future. In this study, we present a novel approach for estimating snow wetness and snow density using visible and infrared bands that are available with most multi-spectral sensors. We define a trapezoidal feature space based on the spectral reflectance in the near infrared band and the Normalized Differenced Snow Index (NDSI), referred to as NIR-NDSI space, where dry snow and wet snow are observed in the left diagonal upper and lower right corners, respectively. The corresponding pixels are extracted by approximating the dry and wet edges which are used to develop a linear physical model to estimate snow wetness. Snow density is then estimated using the modeled snow wetness. Although the proposed approach has used Sentinel-2 data, it can be extended to incorporate data from other multi-spectral sensors. The estimated values for snow wetness and snow density show a high correlation with respect to in-situ measurements. The proposed model opens a new avenue for remote sensing of snow physical properties using multi-spectral data, which were limited in the literature.

  11. Using Remote Sensing to Identify Changes in Land Use and Sources of Fecal Bacteria to Support a Watershed Transport Model

    Directory of Open Access Journals (Sweden)

    Sean Butler

    2014-07-01

    Full Text Available The contamination of shellfish harvesting areas by fecal bacteria in the Annapolis Basin of Nova Scotia, Canada, is a recurring problem which has consequences for industry, government, and local communities. This study contributes to the development of an integrated water quality forecasting system to improve the efficiency and effectiveness of industry management. The proposed integrated forecasting framework is composed of a database containing contamination sources, hydrodynamics of the Annapolis Basin, Escherichia coli (E. coli loadings and watershed hydrology scenarios, coupled with environmental conditions of the region (e.g., temperature, precipitation, evaporation, and ultraviolet light. For integration into this framework, this study presents a viable methodology for assessing the contribution of fecal bacteria originating from a watershed. The proposed methodology investigated the application of high resolution remote sensing, coupled with the commercially available product, MIKE 11, to monitor watershed land use and its impact on water quality. Remote sensing proved to be an extremely useful tool in the identification of sources of fecal bacteria contamination, as well as the detection of land use change over time. Validation of the MIKE 11 model produced very good agreement (R2 = 0.88, E = 0.85 between predicted and observed river flows, while model calibration of E. coli concentrations showed fair agreement (R2 = 0.51 and E = 0.38 between predicted and observed values. A proper evaluation of the MIKE 11 model was constrained due to limited water sampling. However, the model was very effective in predicting times of high contamination for use in the integrated forecasting framework, especially during substantial precipitation events.

  12. Remote sensing applications of the extended radiosity method

    Energy Technology Data Exchange (ETDEWEB)

    Gerstl, S.A.W.; Borel, C.C.

    1992-01-01

    In this paper we describe the progress made in the last three years on developing the radiosity method for remote sensing applications. The research covered canopy modeling, volumetric scattering and atmospheric corrections for future analysis of EOS imaging spectrometer data.

  13. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

    Science.gov (United States)

    Ebhuoma, Osadolor; Gebreslasie, Michael

    2016-06-14

    Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical

  14. Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories

    Science.gov (United States)

    Steen Magnussen; Ronald E. McRoberts; Erkki O. Tomppo

    2009-01-01

    New model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (knn) predictions of attribute Y from remotely-sensed ancillary data X are presented. Non-parametric functions predict Y from scalar 'Single Index Model' transformations of X. Variance functions generated...

  15. Modeling spatial patterns of wildfire susceptibility in southern California: Applications of MODIS remote sensing data and mesoscale numerical weather models

    Science.gov (United States)

    Schneider, Philipp

    This dissertation investigates the potential of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and mesoscale numerical weather models for mapping wildfire susceptibility in general and for improving the Fire Potential Index (FPI) in southern California in particular. The dissertation explores the use of the Visible Atmospherically Resistant Index (VARI) from MODIS data for mapping relative greenness (RG) of vegetation and subsequently for computing the FPI. VARI-based RG was validated against in situ observations of live fuel moisture. The results indicate that VARI is superior to the previously used Normalized Difference Vegetation Index (NDVI) for computing RG. FPI computed using VARI-based RG was found to outperform the traditional FPI when validated against historical fire detections using logistic regression. The study further investigates the potential of using Multiple Endmember Spectral Mixture Analysis (MESMA) on MODIS data for estimating live and dead fractions of vegetation. MESMA fractions were compared against in situ measurements and fractions derived from data of a high-resolution, hyperspectral sensor. The results show that live and dead fractions obtained from MODIS using MESMA are well correlated with the reference data. Further, FPI computed using MESMA-based green vegetation fraction in lieu of RG was validated against historical fire occurrence data. MESMA-based FPI performs at a comparable level to the traditional NDVI-based FPI, but can do so using a single MODIS image rather than an extensive remote sensing time series as required for the RG approach. Finally this dissertation explores the potential of integrating gridded wind speed data obtained from the MM5 mesoscale numerical weather model in the FPI. A new fire susceptibility index, the Wind-Adjusted Fire Potential Index (WAFPI), was introduced. It modifies the FPI algorithm by integrating normalized wind speed. Validating WAFPI against historical wildfire events using

  16. 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

  17. 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

  18. Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote-sensing based surface energy balance models

    Directory of Open Access Journals (Sweden)

    G. Rallo

    2009-07-01

    Full Text Available Actual evapotranspiration from typical Mediterranean crops has been assessed in a Sicilian study area by using surface energy balance (SEB and soil-water balance models. Both modelling approaches use remotely sensed data to estimate evapotranspiration fluxes in a spatially distributed way. The first approach exploits visible (VIS, near-infrared (NIR and thermal (TIR observations to solve the surface energy balance equation whereas the soil-water balance model uses only VIS-NIR data to detect the spatial variability of crop parameters. Considering that the study area is characterized by typical spatially sparse Mediterranean vegetation, i.e. olive, citrus and vineyards, alternating bare soil and canopy, we focused the attention on the main conceptual differences between one-source and two-sources energy balance models. Two different models have been tested: the widely used one-source SEBAL model, where soil and vegetation are considered as the sole source (mostly appropriate in the case of uniform vegetation coverage and the two-sources TSEB model, where soil and vegetation components of the surface energy balance are treated separately. Actual evapotranspiration estimates by means of the two surface energy balance models have been compared vs. the outputs of the agro-hydrological SWAP model, which was applied in a spatially distributed way to simulate one-dimensional water flow in the soil-plant-atmosphere continuum. Remote sensing data in the VIS and NIR spectral ranges have been used to infer spatially distributed vegetation parameters needed to set up the upper boundary condition of SWAP. Actual evapotranspiration values obtained from the application of the soil water balance model SWAP have been considered as the reference to be used for energy balance models accuracy assessment.

    Airborne hyperspectral data acquired during a NERC (Natural Environment Research Council, UK campaign in 2005 have been used. The results of this

  19. Comparison of remote sensing and plant trait-based modelling to predict ecosystem services in subalpine grasslands

    Czech Academy of Sciences Publication Activity Database

    Homolová, Lucie; Schaepman, M. E.; Lamarque, L.; Clevers, J.G.P.W.; de Bello, Francesco; Thuiller, W.; Lavorel, S.

    2014-01-01

    Roč. 5, č. 8 (2014), č. článku 100. ISSN 2150-8925 Institutional support: RVO:67179843 ; RVO:67985939 Keywords : land-use change * leaf chlorophyll content * imaging spectroscopy * water-content * aviris data * spectral reflectance * hyperspectral data * species richness * area index * vegetation * aisa * biomass * ecosystem properties * ecosystem services * linear regression * remote sensing * spatial heterogeneity * subalpine grasslands Subject RIV: EH - Ecology, Behaviour; EF - Botanics (BU-J) OBOR OECD: Remote sensing; Plant sciences, botany (BU-J) Impact factor: 2.255, year: 2014

  20. Clouds, Wind and the Biogeography of Central American Cloud Forests: Remote Sensing, Atmospheric Modeling, and Walking in the Jungle

    Science.gov (United States)

    Lawton, R.; Nair, U. S.

    2011-12-01

    Cloud forests stand at the core of the complex of montane ecosystems that provide the backbone to the multinational Mesoamerican Biological Corridor, which seeks to protect a biodiversity conservation "hotspot" of global significance in an area of rapidly changing land use. Although cloud forests are generally defined by frequent and prolonged immersion in cloud, workers differ in their feelings about "frequent" and "prolonged", and quantitative assessments are rare. Here we focus on the dry season, in which the cloud and mist from orographic cloud plays a critical role in forest water relations, and discuss remote sensing of orographic clouds, and regional and atmospheric modeling at several scales to quantitatively examine the distribution of the atmospheric conditions that characterize cloud forests. Remote sensing using data from GOES reveals diurnal and longer scale patterns in the distribution of dry season orographic clouds in Central America at both regional and local scales. Data from MODIS, used to calculate the base height of orographic cloud banks, reveals not only the geographic distributon of cloud forest sites, but also striking regional variation in the frequency of montane immersion in orographic cloud. At a more local scale, wind is known to have striking effects on forest structure and species distribution in tropical montane ecosystems, both as a general mechanical stress and as the major agent of ecological disturbance. High resolution regional atmospheric modeling using CSU RAMS in the Monteverde cloud forests of Costa Rica provides quantitative information on the spatial distribution of canopy level winds, insight into the spatial structure and local dynamics of cloud forest communities. This information will be useful in not only in local conservation planning and the design of the Mesoamerican Biological Corridor, but also in assessments of the sensitivity of cloud forests to global and regional climate changes.

  1. Monitoring Regional Changes in Alaskan Carbon Fluxes and Underlying Biophysical Processes Using In Situ Observations, Models and Satellite Remote Sensing

    Science.gov (United States)

    Watts, J. D.; Kimball, J. S.; Du, J.; Kim, Y.; Klene, A. E.; Moghaddam, M.; Commane, R.

    2016-12-01

    The effects of climate change within Alaskan boreal and Arctic ecosystems are evident in a lengthening non-frozen season, deepening of the permafrost active layer, and contrasting shifts in regional surface water inundation, soil wetness and patterns of vegetation greening and browning. These biophysical processes play a crucial role in greenhouse gas (CO2, CH4) exchange and the stability of carbon cycling in wetlands and other permafrost landscapes. Here we examine recent (2003-2015) changes and spatiotemporal variability in daily and seasonal carbon fluxes across Alaska, integrating observations from field measurements, eddy covariance flux towers and satellite data driven Terrestrial Carbon Flux (TCF) model simulations at 1-km resolution. The use of integrated multi-channel passive microwave remote sensing from AMSR (Advanced Microwave Scanning Radiometer) sensor records and new lower frequency (L-band) retrievals from the NASA SMAP (Soil Moisture Active Passive) mission provide a comprehensive assessment of dynamic (bi-weekly to daily) changes in vegetation biomass, surface water inundation, soil thermal and moisture conditions, with relative insensitivity to solar illumination and atmosphere constraints. The satellite microwave based environmental records are used in conjunction with MODIS optical-infrared remote sensing and ancillary meteorological data to assess daily net ecosystem carbon exchange, including CH4 emissions from anaerobic soil conditions. The flux tower observations and TCF model simulations indicate that boreal-Arctic CH4 emissions can substantially reduce the net ecosystem carbon sink, while the magnitude of reduction depends on wetland vegetation type, surface water inundation and soil moisture regimes, and the timing of seasonal warming. Considerable year-to-year variability observed in the flux tower observations and satellite records emphasizes the importance of long-term monitoring across the high northern latitudes through an

  2. A blended approach to analyze staple and high-value crops using remote sensing with radiative transfer and crop models.

    Science.gov (United States)

    Davitt, A. W. D.; Winter, J.; McDonald, K. C.; Escobar, V. M.; Steiner, N.

    2017-12-01

    The monitoring of staple and high-value crops is important for maintaining food security. The recent launch of numerous remote sensing satellites has created the ability to monitor vast amounts of crop lands, continuously and in a timely manner. This monitoring provides users with a wealth of information on various crop types over different regions of the world. However, a challenge still remains on how to best quantify and interpret the crop and surface characteristics that are measured by visible, near-infrared, and active and passive microwave radar. Currently, two NASA funded projects are examining the ability to monitor different types of crops in California with different remote sensing platforms. The goal of both projects is to develop a cost-effective monitoring tool for use by vineyard and crop managers. The first project is designed to examine the capability to monitor vineyard water management and soil moisture in Sonoma County using Soil Moisture Active Passive (SMAP), Sentinel-1A and -2, and Landsat-8. The combined mission products create thorough and robust measurements of surface and vineyard characteristics that can potentially improve the ability to monitor vineyard health. Incorporating the Michigan Microwave Canopy Scattering (MIMICS), a radiative transfer model, enables us to better understand surface and vineyard features that influence radar measurements from Sentinel-1A. The second project is a blended approach to analyze corn, rice, and wheat growth using Sentinel-1A products with Decision Support System for Agrotechnology Transfer (DSSAT) and MIMICS models. This project aims to characterize the crop structures that influence Sentinel-1A radar measurements. Preliminary results have revealed the corn, rice, and wheat structures that influence radar measurements during a growing season. The potential of this monitoring tool can be used for maintaining food security. This includes supporting sustainable irrigation practices, identifying crop

  3. Needs and emerging trends of remote sensing

    Science.gov (United States)

    McNair, Michael

    2014-06-01

    From the earliest need to be able to see an enemy over a hill to sending semi-autonomous platforms with advanced sensor packages out into space, humans have wanted to know more about what is around them. Issues of distance are being minimized through advances in technology to the point where remote control of a sensor is useful but sensing by way of a non-collocated sensor is better. We are not content to just sense what is physically nearby. However, it is not always practical or possible to move sensors to an area of interest; we must be able to sense at a distance. This requires not only new technologies but new approaches; our need to sense at a distance is ever changing with newer challenges. As a result, remote sensing is not limited to relocating a sensor but is expanded into possibly deducing or inferring from available information. Sensing at a distance is the heart of remote sensing. Much of the sensing technology today is focused on analysis of electromagnetic radiation and sound. While these are important and the most mature areas of sensing, this paper seeks to identify future sensing possibilities by looking beyond light and sound. By drawing a parallel to the five human senses, we can then identify the existing and some of the future possibilities. A further narrowing of the field of sensing causes us to look specifically at robotic sensing. It is here that this paper will be directed.

  4. Remote sensing for nuclear power plant siting

    International Nuclear Information System (INIS)

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

    1981-01-01

    Remote sensing techniques enhance the selection and evaluation process for nuclear power plant siting. The principal advantage is the synoptic view which improves recognition of linear features, possibly indicative of faults. The interpretation of such images, in conjunction with seismological studies, also permits delineation of seismo-tectonic provinces. In volcanic terrains, geomorphic-age boundaries can be delineated and volcanic centers identified, providing necessary guidance for field sampling and regional model derivation. The use of such techniques is considered for studies in the Philippines, Mexico, and Greece. 5 refs

  5. Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters . Pt. 1; Synthetic Sensor Radiance Formulation; [Synthetic Sensor Radiance Formulation

    Science.gov (United States)

    Wind, G.; DaSilva, A. M.; Norris, P. M.; Platnick, S.

    2013-01-01

    In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

  6. Evaluation of aerosol distributions in the GISS-TOMAS global aerosol microphysics model with remote sensing observations

    Directory of Open Access Journals (Sweden)

    Y. H. Lee

    2010-03-01

    Full Text Available The Aerosol Optical Depth (AOD and Angstrom Coefficient (AC predictions in the GISS-TOMAS model of global aerosol microphysics are evaluated against remote sensing data from MODIS, MISR, and AERONET. The model AOD agrees well (within a factor of two over polluted continental (or high sulfate, dusty, and moderate sea-salt regions but less well over the equatorial, high sea-salt, and biomass burning regions. Underprediction of sea-salt in the equatorial region is likely due to GCM meteorology (low wind speeds and high precipitation. For the Southern Ocean, overprediction of AOD is very likely due to high sea-salt emissions and perhaps aerosol water uptake in the model. However, uncertainties in cloud screening at high latitudes make it difficult to evaluate the model AOD there with the satellite-based AOD. AOD in biomass burning regions is underpredicted, a tendency found in other global models but more severely here. Using measurements from the LBA-SMOCC 2002 campaign, the surface-level OC concentration in the model are found to be underpredicted severely during the dry season while much less severely for EC concentration, suggesting the low AOD in the model is due to underpredictions in OM mass. The potential for errors in emissions and wet deposition to contribute to this bias is discussed.

  7. Biophysical applications of satellite remote sensing

    CERN Document Server

    Hanes, Jonathan

    2014-01-01

    Including an introduction and historical overview of the field, this comprehensive synthesis of the major biophysical applications of satellite remote sensing includes in-depth discussion of satellite-sourced biophysical metrics such as leaf area index.

  8. NOAA Coastal Mapping Remote Sensing Data

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Remote Sensing Division is responsible for providing data to support the Coastal Mapping Program, Emergency Response efforts, and the Aeronautical Survey Program...

  9. 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.

  10. 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

  11. Acoustic remote sensing of ocean flows

    Digital Repository Service at National Institute of Oceanography (India)

    Joseph, A.; Desa, E.

    surface layer of the ocean surface and hence these techniques are unusable for measurement of subsurface circulation. The three methods of ocean circulation measurement using acoustic remote sensing techniques are the Lagrangian, Eulerian and single...

  12. GNSS remote sensing theory, methods and applications

    CERN Document Server

    Jin, Shuanggen; Xie, Feiqin

    2014-01-01

    This book presents the theory and methods of GNSS remote sensing as well as its applications in the atmosphere, oceans, land and hydrology. It contains detailed theory and study cases to help the reader put the material into practice.

  13. Remote sensing of multimodal transportation systems.

    Science.gov (United States)

    2016-09-01

    Hyperspectral remote sensing is an emerging field with many potential applications in the observation, management, and maintenance of the global transportation infrastructure. This report describes the development of an affordable framework to captur...

  14. Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin

    Directory of Open Access Journals (Sweden)

    Xiaoduo Pan

    2017-09-01

    Full Text Available Individually, ground-based, in situ observations, remote sensing, and regional climate modeling cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrains. Data assimilation techniques can be used to bridge the gap between observations and models by assimilating ground observations and remote sensing products into models to improve precipitation simulation and forecasting. However, only a small portion of satellite-retrieved precipitation products assimilation research has been implemented over complex terrains in an arid region. Here, we used the weather research and forecasting (WRF model to assimilate two satellite precipitation products (The Tropical Rainfall Measuring Mission: TRMM 3B42 and Fengyun-2D: FY-2D using the 4D-Var data assimilation method for a typical inland river basin in northwest China’s arid region, the Heihe River Basin, where terrains are very complex. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly over regions with complex terrains.

  15. Application of a Hybrid Model Based on a Convolutional Auto-Encoder and Convolutional Neural Network in Object-Oriented Remote Sensing Classification

    Directory of Open Access Journals (Sweden)

    Wei Cui

    2018-01-01

    Full Text Available Variation in the format and classification requirements for remote sensing data makes establishing a standard remote sensing sample dataset difficult. As a result, few remote sensing deep neural network models have been widely accepted. We propose a hybrid deep neural network model based on a convolutional auto-encoder and a complementary convolutional neural network to solve this problem. The convolutional auto-encoder supports feature extraction and data dimension reduction of remote sensing data. The extracted features are input into the convolutional neural network and subsequently classified. Experimental results show that in the proposed model, the classification accuracy increases from 0.916 to 0.944, compared to a traditional convolutional neural network model; furthermore, the number of training runs is reduced from 40,000 to 22,000, and the number of labelled samples can be reduced by more than half, all while ensuring a classification accuracy of no less than 0.9, which suggests the effectiveness and feasibility of the proposed model.

  16. 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.

  17. Latent Heat Flux Estimate Through an Energy Water Balance Model and Land Surface Temperature from Remote Sensing

    Science.gov (United States)

    Corbari, Chiara; Sobrino, Jose A.; Mancini, Marco; Hidalgo, Victoria

    2011-01-01

    Soil moisture plays a key role in the terrestrial water cycle and is responsible for the partitioning of precipitation between runoff and infiltration. Moreover, surface soil moisture controls the redistribution of the incoming solar radiation on land surface into sensible and latent heat fluxes. Recent developments have been made to improve soil moisture dynamics predictions with hydrologic land surface models (LSMs) that compute water and energy balances between the land surface and the low atmosphere. However, most of the time soil moisture is confined to an internal numerical model variable mainly due to its intrinsic space and time variability and to the well known difficulties in assessing its value from remote sensing as from in situ measurements. In order to exploit the synergy between hydrological distributed models and thermal remote sensed data, FEST-EWB, a land surface model that solves the energy balance equation, was developed. In this hydrological model, the energy budget is solved looking for the representative thermodynamic equilibrium temperature (RET) defined as the land surface temperature that closes the energy balance equation. So using this approach, soil moisture is linked to the latent heat flux and then to LST. In this work the relationship between land surface temperature and soil moisture is analysed using LST from AHS (airborne hyperspectral scanner), with a spatial resolution of 2-4 m, LST from MODIS, with a spatial resolution of 1000 m, and thermal infrared radiometric ground measurements that are compared with the thermodynamic equilibrium temperature from the energy water balance model. Moreover soil moisture measurements were carried out during the airborne overpasses and then compared with SM from the hydrological model. An improvement of this well known inverse relationship between soil moisture and land surface temperature is obtained when the thermodynamic approach is used. The analysis of the scale effects of the different

  18. Carbon stock and carbon turnover in boreal and temperate forests - Integration of remote sensing data and global vegetation models

    Science.gov (United States)

    Thurner, Martin; Beer, Christian; Carvalhais, Nuno; Forkel, Matthias; Tito Rademacher, Tim; Santoro, Maurizio; Tum, Markus; Schmullius, Christiane

    2016-04-01

    Long-term vegetation dynamics are one of the key uncertainties of the carbon cycle. There are large differences in simulated vegetation carbon stocks and fluxes including productivity, respiration and carbon turnover between global vegetation models. Especially the implementation of climate-related mortality processes, for instance drought, fire, frost or insect effects, is often lacking or insufficient in current models and their importance at global scale is highly uncertain. These shortcomings have been due to the lack of spatially extensive information on vegetation carbon stocks, which cannot be provided by inventory data alone. Instead, we recently have been able to estimate northern boreal and temperate forest carbon stocks based on radar remote sensing data. Our spatially explicit product (0.01° resolution) shows strong agreement to inventory-based estimates at a regional scale and allows for a spatial evaluation of carbon stocks and dynamics simulated by global vegetation models. By combining this state-of-the-art biomass product and NPP datasets originating from remote sensing, we are able to study the relation between carbon turnover rate and a set of climate indices in northern boreal and temperate forests along spatial gradients. We observe an increasing turnover rate with colder winter temperatures and longer winters in boreal forests, suggesting frost damage and the trade-off between frost adaptation and growth being important mortality processes in this ecosystem. In contrast, turnover rate increases with climatic conditions favouring drought and insect outbreaks in temperate forests. Investigated global vegetation models from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT, are able to reproduce observation-based spatial climate - turnover rate relationships only to a limited extent. While most of the models compare relatively well in terms of NPP, simulated

  19. Using remotely sensed data and stochastic models to simulate realistic flood hazard footprints across the continental US

    Science.gov (United States)

    Bates, P. D.; Quinn, N.; Sampson, C. C.; Smith, A.; Wing, O.; Neal, J. C.

    2017-12-01

    Remotely sensed data has transformed the field of large scale hydraulic modelling. New digital elevation, hydrography and river width data has allowed such models to be created for the first time, and remotely sensed observations of water height, slope and water extent has allowed them to be calibrated and tested. As a result, we are now able to conduct flood risk analyses at national, continental or even global scales. However, continental scale analyses have significant additional complexity compared to typical flood risk modelling approaches. Traditional flood risk assessment uses frequency curves to define the magnitude of extreme flows at gauging stations. The flow values for given design events, such as the 1 in 100 year return period flow, are then used to drive hydraulic models in order to produce maps of flood hazard. Such an approach works well for single gauge locations and local models because over relatively short river reaches (say 10-60km) one can assume that the return period of an event does not vary. At regional to national scales and across multiple river catchments this assumption breaks down, and for a given flood event the return period will be different at different gauging stations, a pattern known as the event `footprint'. Despite this, many national scale risk analyses still use `constant in space' return period hazard layers (e.g. the FEMA Special Flood Hazard Areas) in their calculations. Such an approach can estimate potential exposure, but will over-estimate risk and cannot determine likely flood losses over a whole region or country. We address this problem by using a stochastic model to simulate many realistic extreme event footprints based on observed gauged flows and the statistics of gauge to gauge correlations. We take the entire USGS gauge data catalogue for sites with > 45 years of record and use a conditional approach for multivariate extreme values to generate sets of flood events with realistic return period variation in

  20. Infrared (IR) remote sensing of gases

    OpenAIRE

    López Martínez, Fernando

    2008-01-01

    The IR Imaging and Remote Sensing Laboratory – LIR-UC3M of Universidad Carlos III, has developed Multi and Hyper spectral Infrared (IR) analysis techniques for gas remote sensing. Design of specific sensors for the determination of gases and their concentration are proposed. Almost all gases (CO2, CO, NO2, O3, HC o NH, …) related to industrial, environmental or military safety can be detected. Companies or centres with interest in the use of specific application sensors are required.

  1. 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.

  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, imaging, and signal engineering

    Energy Technology Data Exchange (ETDEWEB)

    Brase, J.M.

    1993-03-01

    This report discusses the Remote Sensing, Imaging, and Signal Engineering (RISE) trust area which has been very active in working to define new directions. Signal and image processing have always been important support for existing programs at Lawrence Livermore National Laboratory (LLNL), but now these technologies are becoming central to the formation of new programs. Exciting new applications such as high-resolution telescopes, radar remote sensing, and advanced medical imaging are allowing us to participate in the development of new programs.

  4. Remote sensing education at the University of British Columbia

    Science.gov (United States)

    Murtha, P. A.

    1981-01-01

    The development, current status, and organization of the University of British Columbia's interdisciplinary graduate program in remote sensing are described. Specialized programs are tailored to meet student's needs and interest and can range from the theoretical development of technology (sensor development, modelling, and computer analysis) to specialized application of remote sensing in resource analysis such as the determination of vegetation damage, land classification, and land use. The courses and faculty members are listed.

  5. Passive microwave remote sensing of soil moisture

    International Nuclear Information System (INIS)

    Jackson, T.J.; Schmugge, T.J.

    1986-01-01

    Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive microwave soil moisture sensors currently considered for space operation are in the range 10–20 km. The most useful frequency range for soil moisture sensing is 1–5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations

  6. 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...

  7. Remote sensing from UAVs for hydrological monitoring

    DEFF Research Database (Denmark)

    Bandini, Filippo; Garcia, Monica; Bauer-Gottwein, Peter

    compared to other technologies: compared to field based techniques, remote sensing with UAVs is a non-destructive technique, less time consuming, ensures a reduced time between acquisition and interpretation of data and gives the possibility to access remote and unsafe areas. Compared to full...

  8. 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...

  9. Deforestation and Rice: Using Methods in Modeling and Remote Sensing to Project Patterns of Forest Change in Eastern Madagascar

    Science.gov (United States)

    Armstrong, A. H.; Fatoyinbo, T. E.; Fischer, R.; Huth, A.; Shugart, H. H.

    2013-12-01

    In the species rich tropics, forest conservation is often eclipsed by anthropogenic disturbance, resulting in a heightened need for an accurate assessment of biomass and the gaining of predictive capability before these ecosystems disappear. The combination of multi-temporal remote sensing data, field data and forest growth modeling to quantify carbon stocks and flux is therefore of great importance. In this study, we utilize these methods to (1) improve forest biomass and carbon flux estimates for the study region in Eastern Madagascar, and (2) initialize an individual-based growth model that incorporates the anthropogenic factors causing deforestation to project ecosystem response to future environmental change. Recent studies have shown that there is a direct correlation between the international rice market and rates of deforestation in tropical countries such as Madagascar (see Minten et al., 2006). Further, although law protects the remaining forest areas, dictatorships and recent political unrest have lead to poor or non-existent enforcement of precious wood and forest protection over the past 35 years. Our approach combined multi-temporal remote sensing analysis and ecological modeling using a theoretical and mathematical approach to assess biomass change and to understand how tree growth and life history (growth response patterns) relate to past and present economic variability in Madagascar forests of the eastern Toamasina region. We measured rates of change of deforestation with respect to politics and the price of rice by classifying and comparing biomass using 30m Landsat during 5 political regime time periods (1985-1992, 1993-1996, 1997-2001, 2002-2008, 2009 to present). Forest biomass estimations were calibrated using forest inventory data collected over 3 growing seasons over the study region (130 small circular plots in primary forest). This information was then built into the previously parameterized (Armstrong et al., in prep and Fischer et al in

  10. A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States

    Science.gov (United States)

    Byrd, Kristin B.; Ballanti, Laurel; Thomas, Nathan; Nguyen, Dung; Holmquist, James R.; Simard, Marc; Windham-Myers, Lisamarie

    2018-05-01

    Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m2, 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%-44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all

  11. Integrating remote sensing and a process-based hydrological model to evaluate water use and productivity in a south Indian catchment

    NARCIS (Netherlands)

    Immerzeel, W.; Gaur, A.; Zwart, S. J.

    2008-01-01

    The combined use of remote sensing and a distributed hydrological model have demonstrated the improved understanding of the entire water balance in an area where data are scarcely available. Water use and crop water productivity were assessed in the Upper Bhima catchment in southern India using an

  12. 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...

  13. Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions

    Science.gov (United States)

    The ability of remote sensing-based surface energy balance (SEB) models to track water stress in rain-fed switchgrass has not been explored yet. In this paper, the theoretical framework of crop water stress index (CWSI) was utilized to estimate CWSI in rain-fed switchgrass (Panicum virgatum L.) usin...

  14. Leveraging this Golden Age of Remote Sensing and Modeling of Terrestrial Hydrology to Understand Water Cycling in the Water Availability Grand Challenge for North America

    Science.gov (United States)

    Painter, T. H.; Famiglietti, J. S.; Stephens, G. L.

    2016-12-01

    We live in a time of increasing strains on our global fresh water availability due to increasing population, warming climate, changes in precipitation, and extensive depletion of groundwater supplies. At the same time, we have seen enormous growth in capabilities to remotely sense the regional to global water cycle and model complex systems with physically based frameworks. The GEWEX Water Availability Grand Challenge for North America is poised to leverage this convergence of remote sensing and modeling capabilities to answer fundamental questions on the water cycle. In particular, we envision an experiment that targets the complex and resource-critical Western US from California to just into the Great Plains, constraining physically-based hydrologic modeling with the US and international remote sensing capabilities. In particular, the last decade has seen the implementation or soon-to-be launch of water cycle missions such as GRACE and GRACE-FO for groundwater, SMAP for soil moisture, GPM for precipitation, SWOT for terrestrial surface water, and the Airborne Snow Observatory for snowpack. With the advent of convection-resolving mesoscale climate and water cycle modeling (e.g. WRF, WRF-Hydro) and mesoscale models capable of quantitative assimilation of remotely sensed data (e.g. the JPL Western States Water Mission), we can now begin to test hypotheses on the nature and changes in the water cycle of the Western US from a physical standpoint. In turn, by fusing water cycle science, water management, and ecosystem management while addressing these hypotheses, this golden age of remote sensing and modeling can bring all fields into a markedly less uncertain state of present knowledge and decadal scale forecasts.

  15. 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

  16. 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.

  17. Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating.

    Science.gov (United States)

    Lu, Xiaoman; Zheng, Guang; Miller, Colton; Alvarado, Ernesto

    2017-09-08

    Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% ( n = 35, p BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.

  18. The Spatio-Temporal Modeling of Urban Growth Using Remote Sensing and Intelligent Algorithms, Case of Mahabad, Iran

    Directory of Open Access Journals (Sweden)

    Alì Soltani

    2013-06-01

    Full Text Available The simulation of urban growth can be considered as a useful way for analyzing the complex process of urban physical evolution. The aim of this study is to model and simulate the complex patterns of land use change by utilizing remote sensing and artificial intelligence techniques in the fast growing city of Mahabad, north-west of Iran which encountered with several environmental subsequences. The key subject is how to allocate optimized weight into effective parameters upon urban growth and subsequently achieving an improved simulation. Artificial Neural Networks (ANN algorithm was used to allocate the weight via an iteration approach. In this way, weight allocation was carried out by the ANN training accomplishing through time-series satellite images representing urban growth process. Cellular Automata (CA was used as the principal motor of the model and then ANN applied to find suitable scale of parameters and relations between potential factors affecting urban growth. The general accuracy of the suggested model and obtained Fuzzy Kappa Coefficient confirms achieving better results than classic CA models in simulating nonlinear urban evolution process.

  19. A new Dynamic Dust-emission rate (DDR) scheme base on Satellite remote sensing data for air quality model

    Science.gov (United States)

    Tang, Yu Jia; Li, Ling Jun; Zhou, Yi Ming; Zhang, Da Wei; Yin, Wen Jun; Zhang, Meng; Xie, Bao Guo; Cheng, Nianliang

    2017-04-01

    Dust produced by wind erosion is a major source of atmospheric dust pollutions which have impacts on air quality, weather and climate. It is difficult to calculate dust concentration in the atmosphere with certainty unless the dust-emission rate can be estimated with accuracy. Hence, due to the unreliable estimation of dust-emission rate flux from ground surface, the dust forecast accuracy in air quality models is low. The main reason is that the parameter that describes the dust-emission rate in the regional air quality model is constant and cannot reflect the reality of surface dust-emission changes. A new scheme which uses the vegetation information from satellite remote sensing data and meteorological condition provided by meteorological forecast model is developed to estimate the actual dust-emission rete from the ground surface. The results shows that the new scheme can improve dust simulation and forecast performance significantly and reduce the root mean square error by 25% 68%. The DDR scheme can be coupled with any current air quality model (e.g. WRF-Chem, CMAQ, CAMx) and produce more accurate dust forecast.

  20. The utility of remotely-sensed vegetative and terrain covariates at different spatial resolutions in modelling soil and watertable depth (for digital soil mapping)

    OpenAIRE

    Taylor, J. A.; Jacob, Frédéric; Galleguillos, M.; Prevot, L.; Guix, N.; Lagacherie, P.

    2013-01-01

    Digital soil modelling and mapping is reliant on the availability and utility of easily derived and accessible covariates. In this paper, the value of covariates derived from a time-series of remotely-sensed ASTER satellite imagery and digital elevation models were evaluated for modelling two soil attributes - soil depth and watertable depth. Modelling was performed at two resolutions: a fine resolution (15 m pixels) that relates to the resolution of the ASTER Visible-NIR bands, and a larger ...

  1. Using NLDAS weather forcing data on remote sensing of evapotranspiration using a two source energy balance model

    Science.gov (United States)

    Geli, H. M.; Neale, C. M.; Verdin, J. P.; Senay, G. B.

    2012-12-01

    Using NLDAS weather forcing data on remote sensing of evapotranspiration using a two source model Hatim M. E. Geli, Christopher M. U. Neale, James P. Verdin, and Gabriel Senay Estimates of evapotranspiration (ET) on regional scale over agricultural areas provide a means for efficient water resource management and allocation. Different thermal infrared remote sensing-based energy balance models can be used to obtain such estimates with a reasonable accuracy. Ground-based point measurements of weather variable are generally used in the application of most of these models. However at large scales, using point weather data might not be representative of local variability. The North American Land Data Assimilation System (NLDAS) provides spatial weather forcing data on a gridded basis that can account for local and regional weather variability. The NLDAS forcing data is available at 1/8th degree grid (~12 km × 12 km) which is considered relatively coarse resolution to represent field to field variability in agricultural areas. In this study we will be investigating the associated uncertainty and bias of using NLDAS-2 forcing data with respect to ground-based measurements from automated weather stations. The two source energy balance (TSEB) model of Norman et al. (1995) is used to obtain estimates of surface energy balance fluxes (SEBF) and ET over the Palo Verde Irrigation District (PVID), CA. The PVID is about 500 km2 in area covered mostly with alfalfa and cotton crops as well as fallow fields. Estimates of SEBF/ET obtained using both type of weather data (NLDAS-2 and ground-based) are compared to ground-based eddy covariance and Bowen ratio flux measurements. The analysis is conducted during the 2008 growing season using thermal IR and multispectral images from the Landsat Thematic Mapper sensor. This study is conducted in an effort to, standardize and provide a framework for spatial ET maps that can be used by the USGS in the implementation of the Water

  2. A forward-looking, national-scale remote sensing-based model of tidal marsh aboveground carbon stocks

    Science.gov (United States)

    Holmquist, J. R.; Byrd, K. B.; Ballanti, L.; Nguyen, D.; Simard, M.; Windham-Myers, L.; Thomas, N.

    2017-12-01

    Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our goal was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). To meet this objective we developed the first national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest algorithm we tested Sentinel-1 radar backscatter metrics and Landsat vegetation indices as predictors of biomass. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n=409, RMSE=310 g/m2, 10.3% normalized RMSE), successfully predicted biomass and carbon for a range of marsh plant functional types defined by height, leaf angle and growth form. Model error was reduced by scaling field measured biomass by Landsat fraction green vegetation derived from object-based classification of National Agriculture Imagery Program imagery. We generated 30m resolution biomass maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map for each region. With a mean plant %C of 44.1% (n=1384, 95% C.I.=43.99% - 44.37%) we estimated mean aboveground carbon densities (Mg/ha) and total carbon stocks for each wetland type for each region. Louisiana palustrine emergent marshes had the highest C density (2.67 ±0.08 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ±0.06 Mg/ha). This modeling and data synthesis effort will allow for aboveground

  3. Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation

    Science.gov (United States)

    Qin, Changbo; Jia, Yangwen; Su, Z.(Bob); Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen

    2008-01-01

    This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems. PMID:27879946

  4. 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

  5. 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

  6. Plankton Biomass Models Based on GIS and Remote Sensing Technique for Predicting Marine Megafauna Hotspots in the Solor Waters

    Science.gov (United States)

    Putra, MIH; Lewis, SA; Kurniasih, EM; Prabuning, D.; Faiqoh, E.

    2016-11-01

    Geographic information system and remote sensing techniques can be used to assist with distribution modelling; a useful tool that helps with strategic design and management plans for MPAs. This study built a pilot model of plankton biomass and distribution in the waters off Solor and Lembata, and is the first study to identify marine megafauna foraging areas in the region. Forty-three samples of zooplankton were collected every 4 km according to the range time and station of aqua MODIS. Generalized additive model (GAM) we used to modelling zooplankton biomass response from environmental properties.Thirty one samples were used to build a model of inverse distance weighting (IDW) (cell size 0.01°) and 12 samples were used as a control to verify the models accuracy. Furthermore, Getis-Ord Gi was used to identify the significance of the hotspot and cold-spot for foraging area. The GAM models was explain 88.1% response of zooplankton biomass and percent to full moon, phytopankton biomassbeing strong predictors. The sampling design was essential in order to build highly accurate models. Our models 96% accurate for phytoplankton and 88% accurate for zooplankton. The foraging behaviour was significantly related to plankton biomass hotspots, which were two times higher compared to plankton cold-spots. In addition, extremely steep slopes of the Lamakera strait support strong upwelling with highly productive waters that affect the presence of marine megafauna. This study detects that the Lamakera strait provides the planktonic requirements for marine megafauna foraging, helping to explain why this region supports such high diversity and abundance of marine megafauna.

  7. Hydraulic Geometry, GIS and Remote Sensing, Techniques against Rainfall-Runoff Models for Estimating Flood Magnitude in Ephemeral Fluvial Systems

    Directory of Open Access Journals (Sweden)

    Rafael Garcia-Lorenzo

    2010-11-01

    Full Text Available This paper shows the combined use of remotely sensed data and hydraulic geometry methods as an alternative to rainfall-runoff models. Hydraulic geometric data and boolean images of water sheets obtained from satellite images after storm events were integrated in a Geographical Information System. Channel cross-sections were extracted from a high resolution Digital Terrain Model (DTM and superimposed on the image cover to estimate the peak flow using HEC-RAS. The proposed methodology has been tested in ephemeral channels (ramblas on the coastal zone in south-eastern Spain. These fluvial systems constitute an important natural hazard due to their high discharges and sediment loads. In particular, different areas affected by floods during the period 1997 to 2009 were delimited through HEC-GeoRAs from hydraulic geometry data and Landsat images of these floods (Landsat‑TM5 and Landsat-ETM+7. Such an approach has been validated against rainfall-surface runoff models (SCS Dimensionless Unit Hydrograph, SCSD, Témez gamma HU Tγ and the Modified Rational method, MRM comparing their results with flood hydrographs of the Automatic Hydrologic Information System (AHIS in several ephemeral channels in the Murcia Region. The results obtained from the method providing a better fit were used to calculate different hydraulic geometry parameters, especially in residual flood areas.

  8. Implication of remotely sensed data to incorporate land cover effect into a linear reservoir-based rainfall-runoff model

    Science.gov (United States)

    Nourani, Vahid; Fard, Ahmad Fakheri; Niazi, Faegheh; Gupta, Hoshin V.; Goodrich, David C.; Kamran, Khalil Valizadeh

    2015-10-01

    This study investigates the effect of land use on the Geomorphological Cascade of Unequal linear Reservoirs (GCUR) model using the Normalized Difference Vegetation Index (NDVI) derived from remotely sensed data as a measure of land use. The proposed modeling has two important aspects: it considers the effects of both watershed geomorphology and land use/cover, and it requires only one parameter to be estimated through the use of observed rainfall-runoff data. Geographic Information System (GIS) tools are employed to determine the parameters associated with watershed geomorphology, and the Vegetation Index parameter is extracted from historical Landsat images. The modeling is applied via three formulations to a watershed located in Southeastern Arizona, which consists of two gaged sub-watersheds with different land uses. The results show that while all of the formulations generate forecasts of the basin outlet hydrographs with acceptable accuracy, only the two formulations that consider the effects of land cover (using NDVI) provide acceptable results at the outlets of the sub-watersheds.

  9. Spatial and temporal patterns of land surface fluxes from remotely sensed surface temperatures within an uncertainty modelling framework

    Directory of Open Access Journals (Sweden)

    M. F. McCabe

    2005-01-01

    Full Text Available Characterising the development of evapotranspiration through time is a difficult task, particularly when utilising remote sensing data, because retrieved information is often spatially dense, but temporally sparse. Techniques to expand these essentially instantaneous measures are not only limited, they are restricted by the general paucity of information describing the spatial distribution and temporal evolution of evaporative patterns. In a novel approach, temporal changes in land surface temperatures, derived from NOAA-AVHRR imagery and a generalised split-window algorithm, are used as a calibration variable in a simple land surface scheme (TOPUP and combined within the Generalised Likelihood Uncertainty Estimation (GLUE methodology to provide estimates of areal evapotranspiration at the pixel scale. Such an approach offers an innovative means of transcending the patch or landscape scale of SVAT type models, to spatially distributed estimates of model output. The resulting spatial and temporal patterns of land surface fluxes and surface resistance are used to more fully understand the hydro-ecological trends observed across a study catchment in eastern Australia. The modelling approach is assessed by comparing predicted cumulative evapotranspiration values with surface fluxes determined from Bowen ratio systems and using auxiliary information such as in-situ soil moisture measurements and depth to groundwater to corroborate observed responses.

  10. Remote sensing applied in uranium exploration

    International Nuclear Information System (INIS)

    Conradsen, K.; Nilsson, G.; Thyrsted, T.

    1985-01-01

    A research project, aiming at investigation the use of remote sensing in uranium exploration, has been accomplished on data from South Greenland. During the project, analyses have been done on pure remote sensing data (Landsat MSS) and on integrated data of various types, including geochemical, aeromagnetic, radiometric and geological data in addition to the MSS data. Ratioing, factor analysis and discriminant analysis were used for enhancement of colour anomalies which correspond to oxidation zones. Some of the anomalies coincide with U and Nb mineralizations. Lineaments were mapped visually from photoprints, digitized and analysed statistically. A sinusoidal model could be applied to the general directional frequency distribution and was used to define ten classes of significant directions. Three of these directions were of major geological significance. Thus some of the major alkaline intrusions are situated at the intersections of some of the lineaments, a particular NE-SW trending lineament coincides with a geochemical boundary and pitchblende occurrences may be related to a WNW-ESE direction. The various types of data set were brought onto format of the Landsat images and collected in a data base. Representing three different types of data (Landsat MSS-band 7, aeromagnetic data and the geochemical Fe-content of stream sediments) on basis of intensity, hue and saturation revealed new features among which can be mentioned a possible indication of a subsurface continuation of one of the major alkaline intrusions. (author)

  11. Wetlands Evapotranspiration Using Remotely Sensed Solar Radiation

    Science.gov (United States)

    Jacobs, J. M.; Myers, D. A.; Anderson, M. C.

    2001-12-01

    The application of remote sensing methods to estimate evapotranspiration has the advantage of good spatial resolution and excellent spatial coverage, but may have the disadvantage of infrequent sampling and considerable expense. The GOES satellites provide enhanced temporal resolution with hourly estimates of solar radiation and have a spatial resolution that is significantly better than that available from most ground-based pyranometer networks. As solar radiation is the primary forcing variable in wetland evapotranspiration, the opportunity to apply GOES satellite data to wetland hydrologic analyses is great. An accuracy assessment of the remote sensing product is important and the subsequent validation of the evapotranspiration estimates are a critical step for the use of this product. A wetland field experiment was conducted in the Paynes Prairie Preserve, North Central Florida during a growing season characterized by significant convective activity. Evapotranspiration and other surface energy balance components of a wet prairie community dominated by Panicum hemitomon (maiden cane), Ptilimnium capillaceum (mock bishop's weed), and Eupatorium capillifolium (dog fennel) were investigated. Incoming solar radiation derived from GOES-8 satellite observations, in combination with local meteorological measurements, were used to model evapotranspiration from a wetland. The satellite solar radiation, derived net radiation and estimated evapotranspiration estimates were compared to measured data at 30-min intervals and daily times scales.

  12. Literature relevant to remote sensing of water quality

    Science.gov (United States)

    Middleton, E. M.; Marcell, R. F.

    1983-01-01

    References relevant to remote sensing of water quality were compiled, organized, and cross-referenced. The following general categories were included: (1) optical properties and measurement of water characteristics; (2) interpretation of water characteristics by remote sensing, including color, transparency, suspended or dissolved inorganic matter, biological materials, and temperature; (3) application of remote sensing for water quality monitoring; (4) application of remote sensing according to water body type; and (5) manipulation, processing and interpretation of remote sensing digital water data.

  13. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

    Science.gov (United States)

    Romaguera, Mireia; Vaughan, R. Greg; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F.D.

    2018-01-01

    This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of LST, the LSTgt. We hypothesize that in geothermal areas the LSM time series will underestimate the LST as compared to the remote sensing data, since the former does not account for the geothermal component in its model.In order to extract LSTgt, two approaches of different nature (physical based and data mining) were developed and tested in an area of about 560 × 560 km2 centered at the Kenyan Rift. Pre-dawn data in the study area during the first 45 days of 2012 were analyzed.The results show consistent spatial and temporal LSTgt patterns between the two approaches, and systematic differences of about 2 K. A geothermal area map from surface studies was used to assess LSTgt inside and outside the geothermal boundaries. Spatial means were found to be higher inside the geothermal limits, as well as the relative frequency of occurrence of high LSTgt. Results further show that areas with strong topography can result in anomalously high LSTgt values (false positives), which suggests the need for a slope and aspect correction in the inputs to achieve realistic results in those areas. The uncertainty analysis indicates that large uncertainties of the input parameters may limit detection of LSTgt anomalies. To validate the approaches, higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over the Olkaria geothermal field were used. An established method to estimate radiant geothermal flux was applied providing values between 9 and 24 W/m2 in the geothermal area, which coincides with the LSTgt flux rates obtained with the proposed approaches.The proposed approaches are a first step in estimating LSTgt

  14. Environmental Assessment and Monitoring with ICAMS (Image Characterization and Modeling System) Using Multiscale Remote-Sensing Data

    Science.gov (United States)

    Lam, N.; Qiu, H.-I.; Quattrochi, Dale A.; Zhao, Wei

    1997-01-01

    With the rapid increase in spatial data, especially in the NASA-EOS (Earth Observing System) era, it is necessary to develop efficient and innovative tools to handle and analyze these data so that environmental conditions can be assessed and monitored. A main difficulty facing geographers and environmental scientists in environmental assessment and measurement is that spatial analytical tools are not easily accessible. We have recently developed a remote sensing/GIS software module called Image Characterization and Modeling System (ICAMS) to provide specialized spatial analytical tools for the measurement and characterization of satellite and other forms of spatial data. ICAMS runs on both the Intergraph-MGE and Arc/info UNIX and Windows-NT platforms. The main techniques in ICAMS include fractal measurement methods, variogram analysis, spatial autocorrelation statistics, textural measures, aggregation techniques, normalized difference vegetation index (NDVI), and delineation of land/water and vegetated/non-vegetated boundaries. In this paper, we demonstrate the main applications of ICAMS on the Intergraph-MGE platform using Landsat Thematic Mapper images from the city of Lake Charles, Louisiana. While the utilities of ICAMS' spatial measurement methods (e.g., fractal indices) in assessing environmental conditions remain to be researched, making the software available to a wider scientific community can permit the techniques in ICAMS to be evaluated and used for a diversity of applications. The findings from these various studies should lead to improved algorithms and more reliable models for environmental assessment and monitoring.

  15. Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco

    Directory of Open Access Journals (Sweden)

    Aafaf El Jazouli

    2017-11-01

    Full Text Available Abstract The Ikkour watershed located in the Middle Atlas Mountain (Morocco has been a subject of serious soil erosion problems. This study aimed to assess the soil erosion susceptibility in this mountainous watershed using Universal Soil Loss Equation (USLE and spectral indices integrated with Geographic Information System (GIS environment. The USLE model required the integration of thematic factors’ maps which are rainfall aggressiveness, length and steepness of the slope, vegetation cover, soil erodibility, and erosion control practices. These factors were calculated using remote sensing data and GIS. The USLE-based assessment showed that the estimated total annual potential soil loss was about 70.66 ton ha−1 year−1. This soil loss is favored by the steep slopes and degraded vegetation cover. The spectral index method, offering a qualitative evaluation of water erosion, showed different degrees of soil degradation in the study watershed according to FI, BI, CI, and NDVI. The results of this study displayed an agreement between the USLE model and spectral index approach, and indicated that the predicted soil erosion rate can be due to the most rugged land topography and an increase in agricultural areas. Indeed, these results can further assist the decision makers in implementation of suitable conservation program to reduce soil erosion.

  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. Using Satellite Remote Sensing and Modelling for Insights into N02 Air Pollution and NO2 Emissions

    Science.gov (United States)

    Lamsal, L. N.; Martin, R. V.; Krotkov, N. A.; Bucsela, E. J.; Celarier, E. A.; vanDonkelaar, A.; Parrish, D.

    2012-01-01

    Nitrogen oxides (NO(x)) are key actors in air quality and climate change. Satellite remote sensing of tropospheric NO2 has developed rapidly with enhanced spatial and temporal resolution since initial observations in 1995. We have developed an improved algorithm and retrieved tropospheric NO2 columns from Ozone Monitoring Instrument. Column observations of tropospheric NO2 from the nadir-viewing satellite sensors contain large contributions from the boundary layer due to strong enhancement of NO2 in the boundary layer. We infer ground-level NO2 concentrations from the OMI satellite instrument which demonstrate significant agreement with in-situ surface measurements. We examine how NO2 columns measured by satellite, ground-level NO2 derived from satellite, and NO(x) emissions obtained from bottom-up inventories relate to world's urban population. We perform inverse modeling analysis of NO2 measurements from OMI to estimate "top-down" surface NO(x) emissions, which are used to evaluate and improve "bottom-up" emission inventories. We use NO2 column observations from OMI and the relationship between NO2 columns and NO(x) emissions from a GEOS-Chem model simulation to estimate the annual change in bottom-up NO(x) emissions. The emission updates offer an improved estimate of NO(x) that are critical to our understanding of air quality, acid deposition, and climate change.

  18. Anopheles fauna of coastal Cayenne, French Guiana: modelling and mapping of species presence using remotely sensed land cover data

    Directory of Open Access Journals (Sweden)

    Antoine Adde

    Full Text Available Little is known about the Anopheles species of the coastal areas of French Guiana, or their spatiotemporal distribution or environmental determinants. The present study aimed to (1 document the distribution of Anopheles fauna in the coastal area around Cayenne, and (2 investigate the use of remotely sensed land cover data as proxies of Anopheles presence. To characterise the Anopheles fauna, we combined the findings of two entomological surveys that were conducted during the period 2007-2009 and in 2014 at 37 sites. Satellite imagery data were processed to extract land cover variables potentially related to Anopheles ecology. Based on these data, a methodology was formed to estimate a statistical predictive model of the spatial-seasonal variations in the presence of Anopheles in the Cayenne region. Two Anopheles species, known as main malaria vectors in South America, were identified, including the more dominant An. aquasalis near town and rural sites, and An. darlingi only found in inland sites. Furthermore, a cross-validated model of An. aquasalis presence that integrated marsh and forest surface area was extrapolated to generate predictive maps. The present study supports the use of satellite imagery by health authorities for the surveillance of malaria vectors and planning of control strategies.

  19. Several thoughts for using new satellite remote sensing and global modeling for aerosol and cloud climate studies

    Science.gov (United States)

    Nakajima, Teruyuki; Hashimoto, Makiko; Takenaka, Hideaki; Goto, Daisuke; Oikawa, Eiji; Suzuki, Kentaroh; Uchida, Junya; Dai, Tie; Shi, Chong

    2017-04-01

    The rapid growth of satellite remote sensing technologies in the last two decades widened the utility of satellite data for understanding climate impacts of aerosols and clouds. The climate modeling community also has received the benefit of the earth observation and nowadays closed-collaboration of the two communities make us possible to challenge various applications for societal problems, such as for global warming and global-scale air pollution and others. I like to give several thoughts of new algorithm developments, model use of satellite data for climate impact studies and societal applications related with aerosols and clouds. Important issues are 1) Better aerosol detection and solar energy application using expanded observation ability of the third generation geostationary satellites, i.e. Himawari-8, GOES-R and future MTG, 2) Various observation functions by directional, polarimetric, and high resolution near-UV band by MISR, POLDER&PARASOL, GOSAT/CAI and future GOSAT2/CAI2, 3) Various applications of general purpose-imagers, MODIS, VIIRS and future GCOM-C/SGLI, and 4) Climate studies of aerosol and cloud stratification and convection with active and passive sensors, especially climate impact of BC aerosols using CLOUDSAT&CALIPSO and future Earth Explorer/EarthCARE.

  20. Remote sensing and modelling analysis of the extreme dust storm hitting the Middle East and eastern Mediterranean in September 2015

    Science.gov (United States)

    Solomos, Stavros; Ansmann, Albert; Mamouri, Rodanthi-Elisavet; Binietoglou, Ioannis; Patlakas, Platon; Marinou, Eleni; Amiridis, Vassilis

    2017-03-01

    The extreme dust storm that affected the Middle East and the eastern Mediterranean in September 2015 resulted in record-breaking dust loads over Cyprus with aerosol optical depth exceeding 5.0 at 550 nm. We analyse this event using profiles from the European Aerosol Research Lidar Network (EARLINET) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), geostationary observations from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and high-resolution simulations from the Regional Atmospheric Modeling System (RAMS). The analysis of modelling and remote sensing data reveals the main mechanisms that resulted in the generation and persistence of the dust cloud over the Middle East and Cyprus. A combination of meteorological and surface processes is found, including (a) the development of a thermal low in the area of Syria that results in unstable atmospheric conditions and dust mobilization in this area, (b) the convective activity over northern Iraq that triggers the formation of westward-moving haboobs that merge with the previously elevated dust layer, and (c) the changes in land use due to war in the areas of northern Iraq and Syria that enhance dust erodibility.

  1. Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

    Science.gov (United States)

    El Jazouli, Aafaf; Barakat, Ahmed; Ghafiri, Abdessamad; El Moutaki, Saida; Ettaqy, Abderrahim; Khellouk, Rida

    2017-12-01

    The Ikkour watershed located in the Middle Atlas Mountain (Morocco) has been a subject of serious soil erosion problems. This study aimed to assess the soil erosion susceptibility in this mountainous watershed using Universal Soil Loss Equation (USLE) and spectral indices integrated with Geographic Information System (GIS) environment. The USLE model required the integration of thematic factors' maps which are rainfall aggressiveness, length and steepness of the slope, vegetation cover, soil erodibility, and erosion control practices. These factors were calculated using remote sensing data and GIS. The USLE-based assessment showed that the estimated total annual potential soil loss was about 70.66 ton ha-1 year-1. This soil loss is favored by the steep slopes and degraded vegetation cover. The spectral index method, offering a qualitative evaluation of water erosion, showed different degrees of soil degradation in the study watershed according to FI, BI, CI, and NDVI. The results of this study displayed an agreement between the USLE model and spectral index approach, and indicated that the predicted soil erosion rate can be due to the most rugged land topography and an increase in agricultural areas. Indeed, these results can further assist the decision makers in implementation of suitable conservation program to reduce soil erosion.

  2. An Observation Task Chain Representation Model for Disaster Process-Oriented Remote Sensing Satellite Sensor Planning: A Flood Water Monitoring Application

    Directory of Open Access Journals (Sweden)

    Chao Yang

    2018-03-01

    Full Text Available An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning of remote-sensing satellite sensors. By considering the modeling requirements for a disaster observation task, we propose an observation task chain (OTChain representation model that includes four basic OTChain segments and eight-tuple observation task metadata description structures. A prototype system, namely OTChainManager, is implemented to provide functions for modeling, managing, querying, and visualizing observation tasks. In the case of flood water monitoring, we use a flood remote-sensing satellite sensor observation task for the experiment. The results show that the proposed OTChain representation model can be used in modeling process-owned flood disaster observation tasks. By querying and visualizing the flood observation task instances in the Jinsha River Basin, the proposed model can effectively express observation task processes, represent personalized observation constraints, and plan global remote-sensing satellite sensor observations. Compared with typical observation task information models or engines, the proposed OTChain representation model satisfies the information demands of the OTChain and its processes as well as impels the development of a long time-series sensor observation scheme.

  3. 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.

  4. Linking Remote Sensing Data and Energy Balance Models for a Scalable Agriculture Insurance System for sub-Saharan Africa

    Science.gov (United States)

    Brown, M. E.; Osgood, D. E.; McCarty, J. L.; Husak, G. J.; Hain, C.; Neigh, C. S. R.

    2014-12-01

    One of the most immediate and obvious impacts of climate change is on the weather-sensitive agriculture sector. Both local and global impacts on production of food will have a negative effect on the ability of humanity to meet its growing food demands. Agriculture has become more risky, particularly for farmers in the most vulnerable and food insecure regions of the world such as East Africa. Smallholders and low-income farmers need better financial tools to reduce the risk to food security while enabling productivity increases to meet the needs of a growing population. This paper will describe a recently funded project that brings together climate science, economics, and remote sensing expertise to focus on providing a scalable and sensor-independent remote sensing based product that can be used in developing regional rainfed agriculture insurance programs around the world. We will focus our efforts in Ethiopia and Kenya in East Africa and in Senegal and Burkina Faso in West Africa, where there are active index insurance pilots that can test the effectiveness of our remote sensing-based approach for use in the agriculture insurance industry. The paper will present the overall program, explain links to the insurance industry, and present comparisons of the four remote sensing datasets used to identify drought: the CHIRPS 30-year rainfall data product, the GIMMS 30-year vegetation data product from AVHRR, the ESA soil moisture ECV-30 year soil moisture data product, and a MODIS Evapotranspiration (ET) 15-year dataset. A summary of next year's plans for this project will be presented at the close of the presentation.

  5. Exploring the Vertical Distribution of Structural Parameters and Light Radiation in Rice Canopies by the Coupling Model and Remote Sensing

    Directory of Open Access Journals (Sweden)

    Yongjiu Guo

    2015-04-01

    Full Text Available Canopy structural parameters and light radiation are important for evaluating the light use efficiency and grain yield of crops. Their spatial variation within canopies and temporal variation over growth stages could be simulated using dynamic models with strong application and predictability. Based on an optimized canopy structure vertical distribution model and the Beer-Lambert law combined with hyperspectral remote sensing (RS technology, we established a new dynamic model for simulating leaf area index (LAI, leaf angle (LA distribution and light radiation at different vertical heights and growth stages. The model was validated by measuring LAI, LA and light radiation in different leaf layers at different growth stages of two different types of rice (Oryza sativa L., i.e., japonica (Wuxiangjing14 and indica (Shanyou63. The results show that the simulated values were in good agreement with the observed values, with an average RRMSE (relative root mean squared error between simulated and observed LAI and LA values of 14.75% and 21.78%, respectively. The RRMSE values for simulated photosynthetic active radiation (PAR transmittance and interception rates were 14.25% and 9.22% for Wuxiangjing14 and 15.71% and 4.40% for Shanyou63, respectively. In addition, the corresponding RRMSE values for red (R, green (G and blue (B radiation transmittance and interception rates were 16.34%, 15.96% and 15.36% for Wuxiangjing14 and 5.75%, 8.23% and 5.03% for Shanyou63, respectively. The results indicate that the model performed well for different rice cultivars and under different cultivation conditions.

  6. A Conceptual Model of Surface Reflectance Estimation for Satellite Remote Sensing Images Using in situ Reference Data

    Directory of Open Access Journals (Sweden)

    Ke-Sheng Cheng

    2012-03-01

    Full Text Available For satellite remote sensing, radiances received at the sensor are not only affected by the atmosphere but also by the topographic properties of the terrain surface. As a result, atmospheric correction alone does not yield output images that truly reflect terrain surface properties, namely surface reflectance (bidirectional reflectance factor, BRF of objects on the earth surface. Following the concept of the radiometric control area (RCA-based path radiance estimation method, we herein propose a statistical approach for surface reflectance estimation utilizing DEM data and surface reflectance of selected radiometric control areas. An algorithm for identification of shaded samples and a shape factor model were also developed in this study. The proposed RCA-based surface reflectance estimation method is capable of achieving good reflectance estimates in a region where elevation varies from 0 to approximately 600 m above the mean sea level. However, further study is recommended in order to extend the application of the proposed method to areas with substantial terrain variation.

  7. 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.

  8. Multi-variable SWAT model calibration with remotely sensed evapotranspiration and observed flow

    OpenAIRE

    Franco, Ana Clara Lazzari; Bonumá, Nadia Bernardi

    2017-01-01

    ABSTRACT Although intrinsic, uncertainty for hydrological model estimation is not always reported. The aim of this study is to evaluate the use of satellite-based evapotranspiration on SWAT model calibration, regarding uncertainty and model performance in streamflow simulation. The SWAT model was calibrated in a monthly step and validated in monthly (streamflow and evapotranspiration) and daily steps (streamflow only). The validation and calibration period covers the years from 2006 to 2009 a...

  9. Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France

    Science.gov (United States)

    Albergel, C.; Calvet, J.-C.; de Rosnay, P.; Balsamo, G.; Wagner, W.; Hasenauer, S.; Naeimi, V.; Martin, E.; Bazile, E.; Bouyssel, F.; Mahfouf, J.-F.

    2010-11-01

    The SMOSMANIA soil moisture network in Southwestern France is used to evaluate modelled and remotely sensed soil moisture products. The surface soil moisture (SSM) measured in situ at 5 cm permits to evaluate SSM from the SIM operational hydrometeorological model of Météo-France and to perform a cross-evaluation of the normalised SSM estimates derived from coarse-resolution (25 km) active microwave observations from the ASCAT scatterometer instrument (C-band, onboard METOP), issued by EUMETSAT and resampled to the Discrete Global Grid (DGG, 12.5 km gridspacing) by TU-Wien (Vienna University of Technology) over a two year period (2007-2008). A downscaled ASCAT product at one kilometre scale is evaluated as well, together with operational soil moisture products of two meteorological services, namely the ALADIN numerical weather prediction model (NWP) and the Integrated Forecasting System (IFS) analysis of Météo-France and ECMWF, respectively. In addition to the operational SSM analysis of ECMWF, a second analysis using a simplified extended Kalman filter and assimilating the ASCAT SSM estimates is tested. The ECMWF SSM estimates correlate better with the in situ observations than the Météo-France products. This may be due to the higher ability of the multi-layer land surface model used at ECMWF to represent the soil moisture profile. However, the SSM derived from SIM corresponds to a thin soil surface layer and presents good correlations with ASCAT SSM estimates for the very first centimetres of soil. At ECMWF, the use of a new data assimilation technique, which is able to use the ASCAT SSM, improves the SSM and the root-zone soil moisture analyses.

  10. Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France

    Directory of Open Access Journals (Sweden)

    C. Albergel

    2010-11-01

    Full Text Available The SMOSMANIA soil moisture network in Southwestern France is used to evaluate modelled and remotely sensed soil moisture products. The surface soil moisture (SSM measured in situ at 5 cm permits to evaluate SSM from the SIM operational hydrometeorological model of Météo-France and to perform a cross-evaluation of the normalised SSM estimates derived from coarse-resolution (25 km active microwave observations from the ASCAT scatterometer instrument (C-band, onboard METOP, issued by EUMETSAT and resampled to the Discrete Global Grid (DGG, 12.5 km gridspacing by TU-Wien (Vienna University of Technology over a two year period (2007–2008. A downscaled ASCAT product at one kilometre scale is evaluated as well, together with operational soil moisture products of two meteorological services, namely the ALADIN numerical weather prediction model (NWP and the Integrated Forecasting System (IFS analysis of Météo-France and ECMWF, respectively. In addition to the operational SSM analysis of ECMWF, a second analysis using a simplified extended Kalman filter and assimilating the ASCAT SSM estimates is tested. The ECMWF SSM estimates correlate better with the in situ observations than the Météo-France products. This may be due to the higher ability of the multi-layer land surface model used at ECMWF to represent the soil moisture profile. However, the SSM derived from SIM corresponds to a thin soil surface layer and presents good correlations with ASCAT SSM estimates for the very first centimetres of soil. At ECMWF, the use of a new data assimilation technique, which is able to use the ASCAT SSM, improves the SSM and the root-zone soil moisture analyses.

  11. Biomass Burning Emissions from Fire Remote Sensing

    Science.gov (United States)

    Ichoku, Charles

    2010-01-01

    Knowledge of the emission source strengths of different (particulate and gaseous) atmospheric constituents is one of the principal ingredients upon which the modeling and forecasting of their distribution and impacts depend. Biomass burning emissions are complex and difficult to quantify. However, satellite remote sensing is providing us tremendous opportunities to measure the fire radiative energy (FRE) release rate or power (FRP), which has a direct relationship with the rates of biomass consumption and emissions of major smoke constituents. In this presentation, we will show how the satellite measurement of FRP is facilitating the quantitative characterization of biomass burning and smoke emission rates, and the implications of this unique capability for improving our understanding of smoke impacts on air quality, weather, and climate. We will also discuss some of the challenges and uncertainties associated with satellite measurement of FRP and how they are being addressed.

  12. Constraining the dynamics of the water budget at high spatial resolution in the world's water towers using models and remote sensing data; Snake River Basin, USA

    Science.gov (United States)

    Watson, K. A.; Masarik, M. T.; Flores, A. N.

    2016-12-01

    Mountainous, snow-dominated basins are often referred to as the water towers of the world because they store precipitation in seasonal snowpacks, which gradually melt and provide water supplies to downstream communities. Yet significant uncertainties remain in terms of quantifying the stores and fluxes of water in these regions as well as the associated energy exchanges. Constraining these stores and fluxes is crucial for advancing process understanding and managing these water resources in a changing climate. Remote sensing data are particularly important to these efforts due to the remoteness of these landscapes and high spatial variability in water budget components. We have developed a high resolution regional climate dataset extending from 1986 to the present for the Snake River Basin in the northwestern USA. The Snake River Basin is the largest tributary of the Columbia River by volume and a critically important basin for regional economies and communities. The core of the dataset was developed using a regional climate model, forced by reanalysis data. Specifically the Weather Research and Forecasting (WRF) model was used to dynamically downscale the North American Regional Reanalysis (NARR) over the region at 3 km horizontal resolution for the period of interest. A suite of satellite remote sensing products provide independent, albeit uncertain, constraint on a number of components of the water and energy budgets for the region across a range of spatial and temporal scales. For example, GRACE data are used to constrain basinwide terrestrial water storage and MODIS products are used to constrain the spatial and temporal evolution of evapotranspiration and snow cover. The joint use of both models and remote sensing products allows for both better understanding of water cycle dynamics and associated hydrometeorologic processes, and identification of limitations in both the remote sensing products and regional climate simulations.

  13. Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5

    Science.gov (United States)

    Wang, Dagang; Wang, Guiling; Parr, Dana T.; Liao, Weilin; Xia, Youlong; Fu, Congsheng

    2017-07-01

    Land surface models bear substantial biases in simulating surface water and energy budgets despite the continuous development and improvement of model parameterizations. To reduce model biases, Parr et al. (2015) proposed a method incorporating satellite-based evapotranspiration (ET) products into land surface models. Here we apply this bias correction method to the Community Land Model version 4.5 (CLM4.5) and test its performance over the conterminous US (CONUS). We first calibrate a relationship between the observational ET from the Global Land Evaporation Amsterdam Model (GLEAM) product and the model ET from CLM4.5, and assume that this relationship holds beyond the calibration period. During the validation or application period, a simulation using the default CLM4.5 (CLM) is conducted first, and its output is combined with the calibrated observational-vs.-model ET relationship to derive a corrected ET; an experiment (CLMET) is then conducted in which the model-generated ET is overwritten with the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly improves the hydrological simulations over most of the CONUS, and the improvement is stronger in the eastern CONUS than the western CONUS and is strongest over the Southeast CONUS. For any specific region, the degree of the improvement depends on whether the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. (2015) method) and whether water is the limiting factor in places where ET is underestimated. While the bias correction method improves hydrological estimates without improving the physical parameterization of land surface models, results from this study do provide guidance for physically based model development effort.

  14. Remote Sensing Protocols for Parameterizing an Individual, Tree-Based, Forest Growth and Yield Model

    Science.gov (United States)

    2014-09-01

    Penelope Morgan. 2006. “Regression Modeling and Mapping of Coniferous Forest Basal Area and Tree Density from Discrete- Return LIDAR and... Basal Area Relationships of Open-Grown Southern Pines for Modeling Competition and Growth.” Canadian Journal of of Forest Research 22: 341–347... Forest Growth and Yield Model Co ns tr uc tio n En gi ne er in g R es ea rc h La bo ra to ry Scott A. Tweddale, Patrick J. Guertin, and

  15. Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5

    Directory of Open Access Journals (Sweden)

    D. Wang

    2017-07-01

    Full Text Available Land surface models bear substantial biases in simulating surface water and energy budgets despite the continuous development and improvement of model parameterizations. To reduce model biases, Parr et al. (2015 proposed a method incorporating satellite-based evapotranspiration (ET products into land surface models. Here we apply this bias correction method to the Community Land Model version 4.5 (CLM4.5 and test its performance over the conterminous US (CONUS. We first calibrate a relationship between the observational ET from the Global Land Evaporation Amsterdam Model (GLEAM product and the model ET from CLM4.5, and assume that this relationship holds beyond the calibration period. During the validation or application period, a simulation using the default CLM4.5 (CLM is conducted first, and its output is combined with the calibrated observational-vs.-model ET relationship to derive a corrected ET; an experiment (CLMET is then conducted in which the model-generated ET is overwritten with the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly improves the hydrological simulations over most of the CONUS, and the improvement is stronger in the eastern CONUS than the western CONUS and is strongest over the Southeast CONUS. For any specific region, the degree of the improvement depends on whether the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. (2015 method and whether water is the limiting factor in places where ET is underestimated. While the bias correction method improves hydrological estimates without improving the physical parameterization of land surface models, results from this study do provide guidance for physically based model development effort.

  16. Modeling surface water storage from space altimetry, remote sensing and gravity

    Science.gov (United States)

    Boy, Jean-Paul; Loomis, Bryant; Luthcke, Scott

    2017-04-01

    Since its launch in 2002, the GRACE (Gravity Recovery And Climate Experiment) is recording Earth gravity field variations with unprecedented temporal and spatial resolutions, mainly due to global circulation of surface geophysical fluids. Continental water storage variations estimated with GRACE are classically compared to global hydrology models such as GLDAS (Global Land Data Assimilation System) or MERRA (Modern Era-Retrospective Analysis) hydrology models. However most of these models do not take into account both the groundwater and the surface water (lakes and rivers) components of the hydrological cycle. We derive surface water storage in several large river basins, characterized by various climates, using a simple routing scheme, forced by runoff outputs of GLDAS and MERRA-land hydrology models. We adjust the flow velocity, i.e. the only free parameter in our modeling by fitting the modeled equivalent water height to the observed water elevation from radar altimetry measurements. The conversion of the observed geometric heights into the modeled equivalent water heights requires the knowledge of the variations of the river widths, which can be derived from MODIS observations. We validate river models by comparing the estimated discharge to independent in-situ measurements. We finally add to the soil-moisture and snow components of the GLDAS and MERRA-land models our estimates of surface water variations and show that they are in better agreement with GRACE. We also compare these estimates to WGHM, which includes both groundwater and surface components.

  17. Thermal infrared remote sensing sensors, methods, applications

    CERN Document Server

    Kuenzer, Claudia

    2013-01-01

    This book provides a comprehensive overview of the state of the art in the field of thermal infrared remote sensing. Temperature is one of the most important physical environmental variables monitored by earth observing remote sensing systems. Temperature ranges define the boundaries of habitats on our planet. Thermal hazards endanger our resources and well-being. In this book renowned international experts have contributed chapters on currently available thermal sensors as well as innovative plans for future missions. Further chapters discuss the underlying physics and image processing techni

  18. 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

  19. 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.

  20. 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.

  1. [Remote sensing resource monitoring on Atractylodes lancea].

    Science.gov (United States)

    Sun, Yu-Zhang; Guo, Lan-Ping; Zhu, Wen-Quan; Huang, Lu-Qi; Gu, Xiao-He; Han, Li-Jian; Pan, Yao-Zhong

    2008-02-01

    Remote sensing technology was used for investigation of the resources of Atractylodes lancea. Firstly, the general situation of Jiangshu Maoshan and A. lancea in Maoshan was introduced; Secondly, the methods of remote sensing on the resource of the wild drugs were explained. Thirdly, the TM images were interpret according to the differences of the objects reflex spectrum, and growth environments in Damao mountain, Ermao mountain and Xiaomao mountain were divided into different sub-areas according to the results of the field investigations. Finally, the resource of A. lancea in Jiangshu Maoshan was estimated.

  2. A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States

    Science.gov (United States)

    Byrd, Kristin B.; Ballanti, Laurel; Thomas, Nathan; Nguyen, Dung; Holmquist, James R.; Simard, Marc; Windham-Myers, Lisamarie

    2018-01-01

    Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m2, 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%–44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had

  3. Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

    CSIR Research Space (South Africa)

    Marshall, M

    2013-03-01

    Full Text Available insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a...

  4. Regional water resources management in the Andean region with numerical models and satellite remote sensing

    NARCIS (Netherlands)

    Menenti, M.; Mulders, C.W.B.

    1999-01-01

    This report describes the development and adaptation of distributed numerical simulation models of hydrological processes in complex watersheds typical of the Andean region. These distributed models take advantage of the synoptic capabilities of sensors on-board satellites and GIS procedures have

  5. Coupling Tritium Release Data with Remotely Sensed Precipitation Data to Assess Model Uncertainties

    Science.gov (United States)

    Avant, B. K.; Ignatius, A. R.; Rasmussen, T. C.; Grundstein, A.; Mote, T. L.; Shepherd, J. M.

    2010-12-01

    An accidental tritium release (570 L, 210 TBq) from the K-Reactor at the Savannah River Site (South Carolina, USA) occurred between December 22-25, 1991. Observed tritium concentrations in rivers and streams, as well as in the coastal estuary, are used to calibrate a hydrologic flow and transport model, BASINS 4.0 (Better Assessment Science Integrating Point and Non-Point Sources) environmental analysis system and the HSPF hydrologic model. The model is then used to investigate complex hydrometeorological and source attribution problems. Both source and meteorologic input uncertainties are evaluated with respect to model predictions. Meteorological inputs include ground-based rain gauges supplemented with radar along with several NASA products including TRMM 3B42, TRMM 3B42RT, and MERRA (Modern Era Retrospective-Analysis for Research and Applications) reanalysis data. Model parameter uncertainties are evaluated using PEST (Model-Independent Parameter Estimation and Uncertainty Analysis) and coupled to meteorologic uncertainties to provide bounding estimates of model accuracy.

  6. Evaluation of Stakeholder-Driven Groundwater Management through Integrated Modeling and Remote Sensing in the US High Plains Aquifer

    Science.gov (United States)

    Deines, J. M.; Kendall, A. D.; Butler, J. J., Jr.; Hyndman, D. W.

    2017-12-01

    Irrigation greatly enhances agricultural yields and stabilizes farmer incomes, but overexploitation of water resources has depleted groundwater aquifers around the globe. In much of the High Plains Aquifer (HPA) in the United States, water-level declines threaten the continued viability of agricultural operations reliant on irrigation. Policy and management institutions to address this sustainability challenge differ widely across the HPA and the world. In Kansas, grassroots-driven legislation in 2012 allowed local stakeholder groups to establish Local Enhanced Management Areas (LEMAs) and work with state officials to generate enforceable and monitored water use reduction programs. The pioneering LEMA was formed in 2013, following a popular vote by farmers within a 256 km2 region in northwestern Kansas. The group sought to reduce groundwater pumping by 20% through 2017 in order to stabilize water levels while minimally reducing crop productivity. Initial statistical estimates indicate the LEMA has been successful; planning is underway to extend it for five years (2018-2022) and to implement additional LEMAs in the wider groundwater management district. Here, we assess the efficacy of this first LEMA with coupled crop-hydrology models to quantify water budget impacts and any associated trade-offs in crop productivity. We drive these models with a novel data fusion of water use data and our recent remotely sensed Annual Irrigation Maps (AIM) dataset, allowing detailed tracking of irrigation water in space and time. Results from these process-based models provide detailed insights into changes in the physical system resulting from the LEMA program that can inform future stakeholder-driven management in Kansas and in stressed aquifers around the world.

  7. The possibility of using photogrammetric and remote sensing techniques to model lavaka (gully erosion) development in Madagascar

    Science.gov (United States)

    Raveloson, Andrea; Székely, Balázs; Molnár, Gábor; Rasztovits, Sascha

    2013-04-01

    Gully erosion is a worldwide problem for it has a number of undesirable effects and their development is hard to follow. Madagascar is one of the most affected countries for its highlands are densely covered with gullies named lavakas. Lavaka formation and development seems to be triggered by many regional and local causes but the actual reasons are still poorly understood. Furthermore lavakas differ from normal gullies due to their enormous size and special shape. Field surveys are time consuming and data from two-dimensional measurements and pictures (even aerial) might lack major information for morphologic studies. Therefore close range surveying technologies should be used to get three-dimensional information about these unusual and complex features. This contribution discusses which remote sensing and photogrammetric techniques are adequate to survey the development of lavakas, their volume change and sediment budget. Depending on the types and properties (such as volume, depth, shape, vegetation) of the lavaka different methods will be proposed showing pros and cons of each one of them. Our goal is to review techniques to model, survey and analyze lavakas development to better understand the cause of their formation, special size and shape. Different methods are evaluated and compared from field survey through data processing, analyzing cost-effectiveness, potential errors and accuracy for each one of them. For this purpose we will also consider time- and cost-effectiveness of the softwares able to render the images into 3D model as well as the resolution and accuracy of the outputs. Further studies will concentrate on using the three dimensional models of lavakas which will be later on used for geomorphological studies in order to understand their special shape and size. This is ILARG-contribution #07.

  8. A novel approach to model exposure of coastal-marine ecosystems to riverine flood plumes based on remote sensing techniques.

    Science.gov (United States)

    Álvarez-Romero, Jorge G; Devlin, Michelle; Teixeira da Silva, Eduardo; Petus, Caroline; Ban, Natalie C; Pressey, Robert L; Kool, Johnathan; Roberts, Jason J; Cerdeira-Estrada, Sergio; Wenger, Amelia S; Brodie, Jon

    2013-04-15

    Increased loads of land-based pollutants are a major threat to coastal-marine ecosystems. Identifying the affected marine areas and the scale of influence on ecosystems is critical to assess the impacts of degraded water quality and to inform planning for catchment management and marine conservation. Studies using remotely-sensed data have contributed to our understanding of the occurrence and influence of river plumes, and to our ability to assess exposure of marine ecosystems to land-based pollutants. However, refinement of plume modeling techniques is required to improve risk assessments. We developed a novel, complementary, approach to model exposure of coastal-marine ecosystems to land-based pollutants. We used supervised classification of MODIS-Aqua true-color satellite imagery to map the extent of plumes and to qualitatively assess the dispersal of pollutants in plumes. We used the Great Barrier Reef (GBR), the world's largest coral reef system, to test our approach. We combined frequency of plume occurrence with spatially distributed loads (based on a cost-distance function) to create maps of exposure to suspended sediment and dissolved inorganic nitrogen. We then compared annual exposure maps (2007-2011) to assess inter-annual variability in the exposure of coral reefs and seagrass beds to these pollutants. We found this method useful to map plumes and qualitatively assess exposure to land-based pollutants. We observed inter-annual variation in exposure of ecosystems to pollutants in the GBR, stressing the need to incorporate a temporal component into plume exposure/risk models. Our study contributes to our understanding of plume spatial-temporal dynamics of the GBR and offers a method that can also be applied to monitor exposure of coastal-marine ecosystems to plumes and explore their ecological influences. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. Where to Combat Shrub Encroachment in Alpine Timberline Ecosystems: Combining Remotely-Sensed Vegetation Information with Species Habitat Modelling.

    Science.gov (United States)

    Braunisch, Veronika; Patthey, Patrick; Arlettaz, Raphaël

    2016-01-01

    In many cultural landscapes, the abandonment of traditional grazing leads to encroachment of pastures by woody plants, which reduces habitat heterogeneity and impacts biodiversity typical of semi-open habitats. We developed a framework of mutually interacting spatial models to locate areas where shrub encroachment in Alpine treeline ecosystems deteriorates vulnerable species' habitat, using black grouse Tetrao tetrix (L.) in the Swiss Alps as a study model. Combining field observations and remote-sensing information we 1) identified and located the six predominant treeline vegetation types; 2) modelled current black grouse breeding habitat as a function thereof so as to derive optimal habitat profiles; 3) simulated from these profiles the theoretical spatial extension of breeding habitat when assuming optimal vegetation conditions throughout; and used the discrepancy between (2) and (3) to 4) locate major aggregations of homogeneous shrub vegetation in otherwise suitable breeding habitat as priority sites for habitat restoration. All six vegetation types (alpine pasture, coniferous forest, Alnus viridis (Chaix), Rhododendron-dominated, Juniperus-dominated and mixed heathland) were predicted with high accuracy (AUC >0.9). Breeding black grouse preferred a heterogeneous mosaic of vegetation types, with none exceeding 50% cover. While 15% of the timberline belt currently offered suitable breeding habitat, twice that fraction (29%) would potentially be suitable when assuming optimal shrub and ground vegetation conditions throughout the study area. Yet, only 10% of this difference was attributed to habitat deterioration by shrub-encroachment of dense heathland (all types 5.2%) and Alnus viridis (4.8%). The presented method provides both a general, large-scale assessment of areas covered by dense shrub vegetation as well as specific target values and priority areas for habitat restoration related to a selected target organism. This facilitates optimizing the spatial

  10. Where to Combat Shrub Encroachment in Alpine Timberline Ecosystems: Combining Remotely-Sensed Vegetation Information with Species Habitat Modelling.

    Directory of Open Access Journals (Sweden)

    Veronika Braunisch

    Full Text Available In many cultural landscapes, the abandonment of traditional grazing leads to encroachment of pastures by woody plants, which reduces habitat heterogeneity and impacts biodiversity typical of semi-open habitats. We developed a framework of mutually interacting spatial models to locate areas where shrub encroachment in Alpine treeline ecosystems deteriorates vulnerable species' habitat, using black grouse Tetrao tetrix (L. in the Swiss Alps as a study model. Combining field observations and remote-sensing information we 1 identified and located the six predominant treeline vegetation types; 2 modelled current black grouse breeding habitat as a function thereof so as to derive optimal habitat profiles; 3 simulated from these profiles the theoretical spatial extension of breeding habitat when assuming optimal vegetation conditions throughout; and used the discrepancy between (2 and (3 to 4 locate major aggregations of homogeneous shrub vegetation in otherwise suitable breeding habitat as priority sites for habitat restoration. All six vegetation types (alpine pasture, coniferous forest, Alnus viridis (Chaix, Rhododendron-dominated, Juniperus-dominated and mixed heathland were predicted with high accuracy (AUC >0.9. Breeding black grouse preferred a heterogeneous mosaic of vegetation types, with none exceeding 50% cover. While 15% of the timberline belt currently offered suitable breeding habitat, twice that fraction (29% would potentially be suitable when assuming optimal shrub and ground vegetation conditions throughout the study area. Yet, only 10% of this difference was attributed to habitat deterioration by shrub-encroachment of dense heathland (all types 5.2% and Alnus viridis (4.8%. The presented method provides both a general, large-scale assessment of areas covered by dense shrub vegetation as well as specific target values and priority areas for habitat restoration related to a selected target organism. This facilitates optimizing the

  11. Remote sensing of oceanic primary production: Computations using a spectral model

    Digital Repository Service at National Institute of Oceanography (India)

    Sathyendranath, S.; Platt, T.; Caverhill, C.M.; Warnock, R.E.; Lewis, M.R.

    A spectral model of underwater irradiance is coupled with a spectral version of the photosynthesis-light relationship to compute oceanic primary production. The results are shown to be significantly different from those obtained using...

  12. Optical modeling of aerosol extinction for remote sensing in the marine environment

    Science.gov (United States)

    Kaloshin, G. A.

    2013-05-01

    A microphysical model is presented for the surface layer marine and coastal atmospheric aerosols that is based on long-term observations of size distributions for 0.01-100 μm particles in different geographic sites. The fundamental feature of the model is a parameterization of amplitudes and widths for aerosol modes of the aerosol size distribution function (ASDF) as functions of fetch and wind speed. The shape of the ASDF and its dependence on meteorological parameters, altitudes above sea level (H), fetch (X), wind speed (U) and relative humidity (RH) are investigated. The spectral profiles of the aerosol extinction coefficients calculated by MaexPro (Marine Aerosol Extinction Profiles) are in good agreement with observational data and the numerical results obtained from the Navy Aerosol Model (NAM) and the Advanced Navy Aerosol Model (ANAM). Moreover, MaexPro was found to be an accurate and reliable tool for investigation of the optical properties of atmospheric aerosols.

  13. Global Flood Risk From Advanced Modeling and Remote Sensing in Collaboration With Google Earth Engine

    Data.gov (United States)

    National Aeronautics and Space Administration — As predictive accuracy of the climate response to greenhouse emissions improves, measurements of sea level rise are being coupled with modeling to better understand...

  14. Using Remote Sensing and Radar Meteorological Data to Support Watershed Assessments Comprising Integrated Environmental Modeling

    Science.gov (United States)

    Meteorological (MET) data required by watershed assessments comprising Integrated Environmental Modeling (IEM) traditionally have been provided by land-based weather (gauge) stations, although these data may not be the most appropriate for adequate spatial and temporal resolution...

  15. Laser Transmission Model, Balloon Experiment, and Satellite Remote Sensing for Thin Cirrus to Support ABL

    National Research Council Canada - National Science Library

    Liou, K. N; Ou, S. C; Takano, Y

    2006-01-01

    A three-dimensional (3-D) model has been developed for the transmission and backscattering of a high-energy laser beam through inhomogeneous high-level clouds in plane-parallel and spherical geometries...

  16. 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…

  17. Evaluating gridded crop model simulations of evapotranspiration and irrigation using survey and remotely sensed data

    Science.gov (United States)

    Lopez Bobeda, J. R.

    2017-12-01

    The increasing use of groundwater for irrigation of crops has exacerbated groundwater sustainability issues faced by water limited regions. Gridded, process-based crop models have the potential to help farmers and policymakers asses the effects water shortages on yield and devise new strategies for sustainable water use. Gridded crop models are typically calibrated and evaluated using county-level survey data of yield, planting dates, and maturity dates. However, little is known about the ability of these models to reproduce observed crop evapotranspiration and water use at regional scales. The aim of this work is to evaluate a gridded version of the Decision Support System for Agrotechnology Transfer (DSSAT) crop model over the continental United States. We evaluated crop seasonal evapotranspiration over 5 arc-minute grids, and irrigation water use at the county level. Evapotranspiration was assessed only for rainfed agriculture to test the model evapotranspiration equations separate from the irrigation algorithm. Model evapotranspiration was evaluated against the Atmospheric Land Exchange Inverse (ALEXI) modeling product. Using a combination of the USDA crop land data layer (CDL) and the USGS Moderate Resolution Imaging Spectroradiometer Irrigated Agriculture Dataset for the United States (MIrAD-US), we selected only grids with more than 60% of their area planted with the simulated crops (corn, cotton, and soybean), and less than 20% of their area irrigated. Irrigation water use was compared against the USGS county level irrigated agriculture water use survey data. Simulated gridded data were aggregated to county level using USDA CDL and USGS MIrAD-US. Only counties where 70% or more of the irrigated land was corn, cotton, or soybean were selected for the evaluation. Our results suggest that gridded crop models can reasonably reproduce crop evapotranspiration at the country scale (RRMSE = 10%).

  18. Comparison of multiple models for estimating gross primary production using remote sensing data and fluxnet observations

    Directory of Open Access Journals (Sweden)

    S. Wang

    2015-05-01

    Full Text Available In this study, gross primary production (GPP estimated from a temperature and greenness (TG model, a greenness and radiation (GR model, a vegetation photosynthesis model (VPM, and a MODIS product have been compared with eddy covariance measurements in cropland during 2003–2005. Results showed that the determination coefficients (R2 between fluxnet GPP and estimated GPP were all greater than 0.74, indicating that all these models offered reliable estimates of GPP. We also found that the VPM-based GPP estimates performed a bit better (R2 is 0.82, and RMSE is 16.75 gC m−2 (8 day−1 than other models, mainly due to its comprehensive consideration of the stresses from light, temperature and water. The actual GPP was overestimated in the non-growing season and underestimated in the growing season by MOD_GPP. The validation confirms that the above three models may be used to estimate crop production in the North China Plain, but there are still significant uncertainties.

  19. An efficient strategy for the inversion of bidirectional reflectance models with satellite remote sensing data

    Energy Technology Data Exchange (ETDEWEB)

    Privette, J.L.

    1994-12-31

    The angular distribution of radiation scattered by the earth surface contains information on the structural and optical properties of the surface. Potentially, this information may be retrieved through the inversion of surface bidirectional reflectance distribution function (BRDF) models. This report details the limitations and efficient application of BRDF model inversions using data from ground- and satellite-based sensors. A turbid medium BRDF model, based on the discrete ordinates solution to the transport equation, was used to quantify the sensitivity of top-of-canopy reflectance to vegetation and soil parameters. Results were used to define parameter sets for inversions. Using synthetic reflectance values, the invertibility of the model was investigated for different optimization algorithms, surface and sampling conditions. Inversions were also conducted with field data from a ground-based radiometer. First, a soil BRDF model was inverted for different soil and sampling conditions. A condition-invariant solution was determined and used as the lower boundary condition in canopy model inversions. Finally, a scheme was developed to improve the speed and accuracy of inversions.

  20. Remote Chemical Sensing Using Quantum Cascade Lasers

    Energy Technology Data Exchange (ETDEWEB)

    Harper, Warren W.; Schultz, John F.

    2003-01-30

    Spectroscopic chemical sensing research at Pacific Northwest National Laboratory (PNNL) is focused on developing advanced sensors for detecting the production of nuclear, chemical, or biological weapons; use of chemical weapons; or the presence of explosives, firearms, narcotics, or other contraband of significance to homeland security in airports, cargo terminals, public buildings, or other sensitive locations. For most of these missions, the signature chemicals are expected to occur in very low concentrations, and in mixture with ambient air or airborne waste streams that contain large numbers of other species that may interfere with spectroscopic detection, or be mistaken for signatures of illicit activity. PNNL’s emphasis is therefore on developing remote and sampling sensors with extreme sensitivity, and resistance to interferents, or selectivity. PNNL’s research activities include: 1. Identification of signature chemicals and quantification of their spectral characteristics, 2. Identification and development of laser and other technologies that enable breakthroughs in sensitivity and selectivity, 3. Development of promising sensing techniques through experimentation and modeling the physical phenomenology and practical engineering limitations affecting their performance, and 4. Development and testing of data collection methods and analysis algorithms. Close coordination of all aspects of the research is important to ensure that all parts are focused on productive avenues of investigation. Close coordination of experimental development and numerical modeling is particularly important because the theoretical component provides understanding and predictive capability, while the experiments validate calculations and ensure that all phenomena and engineering limitations are considered.

  1. Aerosol Sources, Absorption, and Intercontinental Transport: Synergies among Models, Remote Sensing, and Atmospheric Measurements

    Science.gov (United States)

    Chin, Mian; Ginoux, Paul; Dubovik, Oleg; Holben, Brent; Kaufman, Yoram; chu, Allen; Anderson, Tad; Quinn, Patricia

    2003-01-01

    Aerosol climate forcing is one of the largest uncertainties in assessing the anthropogenic impact on the global climate system. This uncertainty arises from the poorly quantified aerosol sources, especially black carbon emissions, our limited knowledge of aerosol mixing state and optical properties, and the consequences of intercontinental transport of aerosols and their precursors. Here we use a global model GOCART to simulate atmospheric aerosols, including sulfate, black carbon, organic carbon, dust, and sea salt, from anthropogenic, biomass burning, and natural sources. We compare the model calculated aerosol extinction and absorption with those quantities from the ground-based sun photometer measurements from AERONET at several different wavelengths and the field observations from ACE-Asia, and model calculated total aerosol optical depth and fine mode fractions with the MODIS satellite retrieval. We will also estimate the intercontinental transport of pollution and dust aerosols from their source regions to other areas in different seasons.

  2. Evaluation of remote-sensing-based rainfall products through predictive capability in hydrological runoff modelling

    DEFF Research Database (Denmark)

    Stisen, Simon; Sandholt, Inge

    2010-01-01

    were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC-FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global......The emergence of regional and global satellite-based rainfall products with high spatial and temporal resolution has opened up new large-scale hydrological applications in data-sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage...

  3. Remote sensing of the plasmasphere mass density using ground magnetometers and the FLIP model

    Science.gov (United States)

    Zesta, Eftyhia; Chi, Peter; Moldwin, Mark; Jorgensen, Anders; Richards, Phil; Boudouridis, Athanasios; Duffy, Jared

    2012-07-01

    The SAMBA (South American Meridional B-field Array) chain is a Southern Hemisphere meridional chain of 12 magnetometers, 11 of them at L=1.1 to L=2.5 along the coast of Chile and in the Antarctica peninsula, and one auroral station along the same meridian. SAMBA is ideal for low and mid-latitude studies of geophysical events and ULF waves. The MEASURE (Magnetometers along the Eastern Atlantic Seaboard for Undergraduate Research and Education) and McMAC (Mid-continent Magnetoseismic Chain) chains are Northern Hemisphere meridional chains in the same local time as SAMBA, but cover low to sub-auroral latitudes. SAMBA is partly conjugate to MEASURE and McMAC chains, offering unique opportunities for inter-hemispheric studies. We use 5 of the SAMBA stations and an even larger number of conjugate stations from the Northern hemisphere to determine the field line resonance (FLR) frequency of closely spaced flux tubes in the inner magnetosphere. Standard inversion techniques are used to derive the equatorial mass density of these flux tubes from the FLRs. We thus yield the mass density distribution of the plasmasphere for specific events and compare our results with results from the FLIP thermosphere-ionosphere model model. We find that for moderate activity the model determined FLR radial distribution is in excellent agreement with the observed distribution. During storm time observations indicate stronger depletion than predicted by the model initial runs.

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

    NARCIS (Netherlands)

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

    2007-01-01

    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

  5. Water harvest- and storage- location assessment model using GIS and remote sensing

    Science.gov (United States)

    Weerasinghe, H.; Schneider, U. A.; Löw, A.

    2011-04-01

    This study describes a globally applicable method to determine the local suitability to implement water supply management strategies within the context of a river catchment. We apply this method, and develop a spatial analysis model named Geographic Water Management Potential (GWAMP). We retrieve input data from global data repositories and rescale these data to 1km spatial resolution to obtain a set of manageable input data. Potential runoff is calculated as an intermediate input using the Soil Conservation Service Curve Number (SCS-CN) equation. Multi Criteria Evaluation techniques are used to determine the suitability levels and relative importance of input parameters for water supply management. Accordingly, the model identifies, potential water harvesting- and storage sites for on-farm water storage, regional dams, and soil moisture conservation. We apply the model to two case-study locations, the Sao-Francisco and Nile catchments, which differ in their geographic and climatic conditions. The model results are validated against existing data on hydrologic networks, reservoir capacities and runoff. On average, GWAMP predictions of sites with high rain water storage suitability correlate well (83%) with the locations of existing regional dams and farm tanks. According to the results from testing and validation of the GWAMP we point out that the GWAMP can be used identify potential sites for rain water harvesting and storage technologies in a given catchment.

  6. Theory of radiative transfer models applied in optical remote sensing of vegetation canopies

    NARCIS (Netherlands)

    Verhoef, W.

    1998-01-01


    In this thesis the work of the author on the modelling of radiative transfer in vegetation canopies and the terrestrial atmosphere is summarized. The activities span a period of more than fifteen years of research in this field carried out at the National Aerospace Laboratory

  7. Improving streamflow simulations and forecasting performance of SWAT model by assimilating remotely sensed soil moisture observations

    Science.gov (United States)

    Patil, Amol; Ramsankaran, RAAJ

    2017-12-01

    This article presents a study carried out using EnKF based assimilation of coarser-scale SMOS soil moisture retrievals to improve the streamflow simulations and forecasting performance of SWAT model in a large catchment. This study has been carried out in Munneru river catchment, India, which is about 10,156 km2. In this study, an EnkF based new approach is proposed for improving the inherent vertical coupling of soil layers of SWAT hydrological model during soil moisture data assimilation. Evaluation of the vertical error correlation obtained between surface and subsurface layers indicates that the vertical coupling can be improved significantly using ensemble of soil storages compared to the traditional static soil storages based EnKF approach. However, the improvements in the simulated streamflow are moderate, which is due to the limitations in SWAT model in reflecting the profile soil moisture updates in surface runoff computations. Further, it is observed that the durability of streamflow improvements is longer when the assimilation system effectively updates the subsurface flow component. Overall, the results of the present study indicate that the passive microwave-based coarser-scale soil moisture products like SMOS hold significant potential to improve the streamflow estimates when assimilating into large-scale distributed hydrological models operating at a daily time step.

  8. Evapotranspiration and Precipitation inputs for SWAT model using remotely sensed observations

    Science.gov (United States)

    The ability of numerical models, such as the Soil and Water Assessment Tool (or SWAT), to accurately represent the partition of the water budget and describe sediment loads and other pollutant conditions related to water quality strongly depends on how well spatiotemporal variability in precipitatio...

  9. Radiation absorption and use by humid savanna grassland: assessment using remote sensing and modelling

    International Nuclear Information System (INIS)

    Roux, X. le; Gauthier, H.; Begue, A.; Sinoquet, H.

    1997-01-01

    The components of the canopy radiation balance in photosynthetically active radiation (PAR), phytomass and leaf area index (LAI) were measured during a complete annual cycle in an annually burned African humid savanna. Directional reflectances measured by a hand-held radiometer were used to compute the canopy normalized difference vegetation index (NDVI). The fraction f APAR of PAR absorbed by the canopy (APAR) and canopy reflectances were simulated by the scattering from arbitrarily inclined leaves (SAIL) and the radiation interception in row intercropping (RIRI) models. The daily PAR to solar radiation ratio was linearly related to the daily fraction of diffuse solar radiation with an annual value around 0.47. The observed f APAR was non-linearly related to NDVI. The SAIL model simulated reasonably well directional reflectances but noticeably overestimated f APAR during most of the growing season. Comparison of simulations performed with the 1D and 3D versions of the RIRI model highlighted the weak influence of the heterogeneous structure of the canopy after fire and of the vertical distribution of dead and green leaves on total f APAR . Daily f APAR values simulated by the 3D-RIRI model were linearly related to and 9.8% higher than observed values. For sufficient soil water availability, the net production efficiency ϵ n of the savanna grass canopy was 1.92 and 1.28 g DM MJ −1 APAR (where DM stands for dry matter) during early regrowth and mature stage, respectively. In conclusion, the linear relationship between NDVI and f APAR used in most primary production models operating at large scales may slightly overestimate f APAR by green leaves for the humid savanna biome. Moreover, the net production efficiency of humid savannas is close to or higher than values reported for the other major natural biomes. (author)

  10. Merging remotely sensed data, models and indicators for a sustainable development of coastal aquaculture in Algeria

    Science.gov (United States)

    Brigolin, Daniele; Venier, Chiara; Amine Taji, Mohamed; Lourguioui, Hichem; Mangin, Antoine; Pastres, Roberto

    2014-05-01

    Finfish cage farming is an economically relevant activity, which exerts pressures on coastal systems and thus require a science-based management, based on the Ecosystem Approach, in order to be carry out in a sustainable way. Within MEDINA project (EU 282977), ocean color data and models were used for estimating indicators of pressures of aquaculture installations along the north African coast. These indicators can provide important support for decision makers in the allocation of new zones for aquaculture, by taking into account the suitability of an area for this activity and minimizing negative environmental effects, thus enhancing the social acceptability of aquaculture. The increase in the number of farms represents a strategic objective for the Algerian food production sector, which is currently being supported by different national initiatives. The case-study presented in this work was carried out in the Gulf of Bejaia. Water quality for aquaculture was first screened based on ocean color CDOM data (http://www.globcolour.info/). The SWAN model was subsequently used to propagate offshore wave data and to derive wave height statistics. On this basis, sub-areas of the Gulf were ranked, according their optimality in respect to cage resistance and fish welfare requirements. At the three best sites an integrated aquaculture impact assessment model was therefore applied: this tool allows one to obtain a detailed representation of fish growth and population dynamics inside the rearing cages, and to simulate the deposition of uneaten food and faeces on the sediment and the subsequent mineralization of organic matter. This integrated model was used to produce a set of indicators of the fish cages environmental interaction under different scenarios of forcings (water temperature, feeding, currents). These model-derived indicators could usefully contribute to the implementation of the ecosystem approach for the management of aquaculture activities, also required by the

  11. Satellite remote sensing for modeling and monitoring of water quality in the Great Lakes

    Science.gov (United States)

    Coffield, S. R.; Crosson, W. L.; Al-Hamdan, M. Z.; Barik, M. G.

    2017-12-01

    Consistent and accurate monitoring of the Great Lakes is critical for protecting the freshwater ecosystems, quantifying the impacts of climate change, understanding harmful algal blooms, and safeguarding public health for the millions who rely on the Lakes for drinking water. While ground-based monitoring is often hampered by limited sampling resolution, satellite data provide surface reflectance measurements at much more complete spatial and temporal scales. In this study, we implemented NASA data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to build robust water quality models. We developed and validated models for chlorophyll-a, nitrogen, phosphorus, and turbidity based on combinations of the six MODIS Ocean Color bands (412, 443, 488, 531, 547, and 667nm) for 2003-2016. Second, we applied these models to quantify trends in water quality through time and in relation to changing land cover, runoff, and climate for six selected coastal areas in Lakes Michigan and Erie. We found strongest models for chlorophyll-a in Lake Huron (R2 = 0.75), nitrogen in Lake Ontario (R2=0.66), phosphorus in Lake Erie (R2=0.60), and turbidity in Lake Erie (R2=0.86). These offer improvements over previous efforts to model chlorophyll-a while adding nitrogen, phosphorus, and turbidity. Mapped water quality parameters showed high spatial variability, with nitrogen concentrated largely in Superior and coastal Michigan and high turbidity, phosphorus, and chlorophyll near urban and agricultural areas of Erie. Temporal analysis also showed concurrence of high runoff or precipitation and nitrogen in Lake Michigan offshore of wetlands, suggesting that water quality in these areas is sensitive to changes in climate.

  12. Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA

    Science.gov (United States)

    Howard, Daniel M.; Wylie, Bruce K.; Tieszen, Larry L.

    2012-01-01

    With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the

  13. Remote sensing of sagebrush canopy nitrogen

    Science.gov (United States)

    Mitchell, Jessica J.; Glenn, Nancy F.; Sankey, Temuulen T.; Derryberry, DeWayne R.; Germino, Matthew J.

    2012-01-01

    This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublands – a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remote sensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R2 value of 0.72 and an R2 predicted value of 0.42 (n = 35). Subsetting the dataset to minimize the influence of bare ground (n = 19) increased R2 to 0.95 (R2 predicted = 0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform.

  14. Detection of aerosol pollution sources during sandstorms in Northwestern China using remote sensed and model simulated data

    Science.gov (United States)

    Filonchyk, Mikalai; Yan, Haowen; Yang, Shuwen; Lu, Xiaomin

    2018-02-01

    The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM10 and PM2.5 exceeded 400 μg/m3 and 150 μg/m3, respectively, the ratio PM2.5/PM10 being within the range of 0.123-0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75-1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km-1 × sr-1 with the distribution of the main types of aerosols in the troposphere of the region within 0-12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3-6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China.

  15. 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.

  16. 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.

  17. Regional crop yield forecasting using probalistic crop growth modelling and remote sensing data assimilation

    OpenAIRE

    Wit, de, A.J.W.

    2007-01-01

    Een belangrijk onderdeel van het MARS oogstvoorspellingssysteem is het zogenaamde CGMS (crop growth monitoring system). CGMS gebruikt een gewasgroeimodel om het effect van bodem, weer en teeltmaatregelen op de groei van het gewas te bepalen. Hiervoor worden relevante gegevens verzameld over Europa. Op basis van deze gegevens simuleert het model WOFOST de gewasgroei. In dit proefschrift wordt op praktische en theoretische gronden beargumenteerd dat de onzekerheid in het weer de bepalende facto...

  18. Estimation of intercepted radiation on row-structured orchards with remote sensing and radiative transfer models

    OpenAIRE

    Guillén Climent, M. Luz

    2012-01-01

    The light energy absorbed by plant leaves drives fundamental physiological processes such as photosynthesis. The absorption of light occurs within the 400-700 nm spectral region, so it is called Photosynthetic Active Radiation, PAR. Thus, the fraction of intercepted PAR is called fIPAR. This thesis studies the estimation of fIPAR with high spatial resolution sensors and radiative transfer models in heterogeneous orchards. The objective is to obtain maps showing the spatial v...

  19. Semi-automatic road extraction from very high resolution remote sensing imagery by RoadModeler

    Science.gov (United States)

    Lu, Yao

    Accurate and up-to-date road information is essential for both effective urban planning and disaster management. Today, very high resolution (VHR) imagery acquired by airborne and spaceborne imaging sensors is the primary source for the acquisition of spatial information of increasingly growing road networks. Given the increased availability of the aerial and satellite images, it is necessary to develop computer-aided techniques to improve the efficiency and reduce the cost of road extraction tasks. Therefore, automation of image-based road extraction is a very active research topic. This thesis deals with the development and implementation aspects of a semi-automatic road extraction strategy, which includes two key approaches: multidirectional and single-direction road extraction. It requires a human operator to initialize a seed circle on a road and specify a extraction approach before the road is extracted by automatic algorithms using multiple vision cues. The multidirectional approach is used to detect roads with different materials, widths, intersection shapes, and degrees of noise, but sometimes it also interprets parking lots as road areas. Different from the multidirectional approach, the single-direction approach can detect roads with few mistakes, but each seed circle can only be used to detect one road. In accordance with this strategy, a RoadModeler prototype was developed. Both aerial and GeoEye-1 satellite images of seven different types of scenes with various road shapes in rural, downtown, and residential areas were used to evaluate the performance of the RoadModeler. The experimental results demonstrated that the RoadModeler is reliable and easy-to-use by a non-expert operator. Therefore, the RoadModeler is much better than the object-oriented classification. Its average road completeness, correctness, and quality achieved 94%, 97%, and 94%, respectively. These results are higher than those of Hu et al. (2007), which are 91%, 90%, and 85

  20. Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture

    Science.gov (United States)

    Brown, Jesslyn; Pervez, Md Shahriar

    2014-01-01

    Over 22 million hectares (ha) of U.S. croplands are irrigated. Irrigation is an intensified agricultural land use that increases crop yields and the practice affects water and energy cycles at, above, and below the land surface. Until recently, there has been a scarcity of geospatially detailed information about irrigation that is comprehensive, consistent, and timely to support studies tying agricultural land use change to aquifer water use and other factors. This study shows evidence for a recent overall net expansion of 522 thousand ha across the U.S. (2.33%) and 519 thousand ha (8.7%) in irrigated cropped area across the High Plains Aquifer (HPA) from 2002 to 2007. In fact, over 97% of the net national expansion in irrigated agriculture overlays the HPA. We employed a modeling approach implemented at two time intervals (2002 and 2007) for mapping irrigated agriculture across the conterminous U.S. (CONUS). We utilized U.S. Department of Agriculture (USDA) county statistics, satellite imagery, and a national land cover map in the model. The model output, called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the U.S. (MIrAD-US), was then used to reveal relatively detailed spatial patterns of irrigation change across the nation and the HPA. Causes for the irrigation increase in the HPA are complex, but factors include crop commodity price increases, the corn ethanol industry, and government policies related to water use. Impacts of more irrigation may include shifts in local and regional climate, further groundwater depletion, and increasing crop yields and farm income.

  1. Estimating nitrogen oxides emissions at city scale in China with a nightlight remote sensing model.

    Science.gov (United States)

    Jiang, Jianhui; Zhang, Jianying; Zhang, Yangwei; Zhang, Chunlong; Tian, Guangming

    2016-02-15

    Increasing nitrogen oxides (NOx) emissions over the fast developing regions have been of great concern due to their critical associations with the aggravated haze and climate change. However, little geographically specific data exists for estimating spatio-temporal trends of NOx emissions. In order to quantify the spatial and temporal variations of NOx emissions, a spatially explicit approach based on the continuous satellite observations of artificial nighttime stable lights (NSLs) from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) was developed to estimate NOx emissions from the largest emission source of fossil fuel combustion. The NSL based model was established with three types of data including satellite data of nighttime stable lights, geographical data of administrative boundaries, and provincial energy consumptions in China, where a significant growth of NOx emission has experienced during three policy stages corresponding to the 9th-11th)Five-Year Plan (FYP, 1995-2010). The estimated national NOx emissions increased by 8.2% per year during the study period, and the total annual NOx emissions in China estimated by the NSL-based model were approximately 4.1%-13.8% higher than the previous estimates. The spatio-temporal variations of NOx emissions at city scale were then evaluated by the Moran's I indices. The global Moran's I indices for measuring spatial agglomerations of China's NOx emission increased by 50.7% during 1995-2010. Although the inland cities have shown larger contribution to the emission growth than the more developed coastal cities since 2005, the High-High clusters of NOx emission located in Beijing-Tianjin-Hebei regions, the Yangtze River Delta, and the Pearl River Delta should still be the major focus of NOx mitigation. Our results indicate that the readily available DMSP/OLS nighttime stable lights based model could be an easily accessible and effective tool for achieving strategic decision making

  2. LF/MF Propagation Modeling for D-Region Ionospheric Remote Sensing

    Science.gov (United States)

    Higginson-Rollins, M. A.; Cohen, M.

    2017-12-01

    The D-region of the ionosphere is highly inaccessible because it is too high for continuous in-situ measurement techniques and too low for satellite measurements. Very-Low Frequency (VLF) signals have been developed and used as a diagnostic tool for this region of the ionosphere and are favorable because of the low ionospheric attenuation rates, allowing global propagation - but this also creates an ill-posed multi-mode propagation problem. As an alternative, Low-Frequency (LF) and Medium-Frequency (MF) signals could be used as a diagnostic tool of the D-region. These higher frequencies have a higher attenuation rate, and thus only a few modes propagate in the Earth-ionosphere waveguide, creating a much simpler problem to analyze. The United States Coast Guard (USCG) operates a national network of radio transmitters that serve as an enhancement to the Global Positioning System (GPS). This network is termed Differential Global Positioning System (DGPS) and uses fixed reference stations as a method of determining the error in received GPS satellite signals and transmits the correction value using low frequency and medium frequency radio signals between 285 kHz and 385 kHz. Using sensitive receivers, we can detect this signal many hundreds of km away. We present modeling of the propagation of these transmitters' signals for use as a diagnostic tool for characterizing the D-region. The Finite-Difference Time-Domain (FDTD) method is implemented to model the groundwave radiated by the DGPS beacons and account for environmental effects, such as changing soil conductivities and terrain. A full wave numerical solver is used to model the skywave component of the propagating signal and specifically to ascertain the reflection coefficients for various ionospheric conditions. Preliminary results are shown and discussed, and comparisons with collected data are presented.

  3. Improving remote sensing flood assessment using volunteered geographical data

    Directory of Open Access Journals (Sweden)

    E. Schnebele

    2013-03-01

    Full Text Available A new methodology for the generation of flood hazard maps is presented fusing remote sensing and volunteered geographical data. Water pixels are identified utilizing a machine learning classification of two Landsat remote sensing scenes, acquired before and during the flooding event as well as a digital elevation model paired with river gage data. A statistical model computes the probability of flooded areas as a function of the number of adjacent pixels classified as water. Volunteered data obtained through Google news, videos and photos are added to modify the contour regions. It is shown that even a small amount of volunteered ground data can dramatically improve results.

  4. Soil surface roughness modeling: limit of global characterization in remote sensing

    Science.gov (United States)

    Chimi-Chiadjeu, O.; Vannier, E.; Dusséaux, R.; Taconet, O.

    2013-10-01

    Many scientists use a global characterization of bare soil surface random roughness. Surface roughness is often characterized by statistical parameters deduced from its autocorrelation function. Assuming an autocorrelation model and a Gaussian height distribution, some authors have developed algorithms for numerical generation of soil surfaces that have the same statistical properties. This approach is widespread and does not take into account morphological aspects of the soil surface micro-topography. Now a detail surface roughness analysis reveals that the micro-topography is structured by holes, aggregates and clods. In the present study, we clearly show that when describing surface roughness as a whole, some information related to morphological aspects is lost. Two Digital Elevation Model (DEM) of a same natural seedbed surface were recorded by stereo photogrammetry. After estimating global parameters of these natural surfaces, we generated numerical surfaces of the same average characteristics by linear filtering. Big aggregates and clods were then captured by a contour-based approach. We show that the two-dimensional autocorrelation functions of generated surfaces and of the two agricultural surfaces are close together. Nevertheless, the number and shape of segmented object contours change from generated surfaces to the natural surfaces. Generated surfaces show fewer and bigger segmented objects than in the natural case. Moreover, the shape of some segmented objects is unrealistic in comparison to real clods, which have to be convex and of low circularity.

  5. Assimilation of Remotely Sensed Fluorescence Data into the Land-Surface Model CLM4

    Science.gov (United States)

    Wieneke, S.; Ahrends, H. E.; Rascher, U.; Schickling, A.; Schween, J.; Crewell, S.

    2012-12-01

    The most important exchange process of CO2 between the atmosphere and the land-surface is photosynthesis. Therefore, the prediction of vegetation response to increasing atmospheric CO2 concentrations is crucial for a reliable prediction of climate change. Photosynthesis is a complex physiological process that consists of numerous bio-physical sub-processes and chemical reactions. Spatial and temporal patterns of photosynthesis depend on dynamic plant-specific adaptation strategies to highly variable environmental conditions. Photosynthesis can be estimated using land-surface models, but, while state-of-the-art models often rely on plant specific constants, they poorly simulate the dynamic adaptation of the physiological status of plant canopies. Another way to estimate photosynthesis is the measurement of sun-induced chlorophyll fluorescence (SICF). Several studies over the last decade have demonstrated that SICF is a promising proxy to estimate the diurnal dynamic vitality of the photosynthetic apparatus at both the leaf and canopy levels. Recent studies have shown, that the weak SICF signal is also detectable from air- and space-borne sensors. Time series of SICF can already be derived from ground based measurements and first spatial maps from air- and space-borne sensors are available. It is expected that in the preparation of the European satellite mission FLEX further spatial and temporal data sets will become available. Although, it is not possible to derive data sets that have a diurnal and spatial resolution at the same time, spatial data sets could be used to update certain model parameters that are normally set as constants. This will result in a better simulation of plant-specific adaptation strategies. In this study we focus on the retrieval of the maximum rate of carboxylation (Vcmax), by implementing the SICF signal into the photosynthesis module of the Community Land Model 4 (CLM4; http://www.cgd.ucar.edu/tss/clm), which is based on the Farquhar

  6. Remote Sensing Characteristics of Wave Breaking Rollers

    Science.gov (United States)

    Haller, M. C.; Catalan, P.

    2006-12-01

    The wave roller has a primary influence on the balances of mass and momentum in the surf zone (e.g. Svendsen, 1984; Dally and Brown, 1995; Ruessink et al., 2001). In addition, the roller area and its angle of inclination on the wave front are important quantities governing the dissipation rates in breaking waves (e.g Madsen et al., 1997). Yet, there have been very few measurements published of individual breaking wave roller geometries in shallow water. A number of investigators have focused on observations of the initial jet-like motion at the onset of breaking before the establishment of the wave roller (e.g. Basco, 1985; Jansen, 1986), while Govender et al. (2002) provide observations of wave roller vertical cross-sections and angles of inclination for a pair of laboratory wave conditions. Nonetheless, presently very little is known about the growth, evolution, and decay of this aerated region of white water as it propagates through the surf zone; mostly due to the inherent difficulties in making the relevant observations. The present work is focused on analyzing observations of the time and space scales of individual shallow water breaking wave rollers as derived from remote sensing systems. Using a high-resolution video system in a large-scale laboratory facility, we have obtained detailed measurements of the growth and evolution of the wave breaking roller. In addition, by synchronizing the remote video with in-situ wave gages, we are able to directly relate the video intensity signal to the underlying wave shape. Results indicate that the horizontal length scale of breaking wave rollers differs significantly from the previous observations of Duncan (1981), which has been a traditional basis for roller model parameterizations. The overall approach to the video analysis is new in the sense that we concentrate on individual breaking waves, as opposed to the more commonly used time-exposure technique. In addition, a new parameter of interest, denoted Imax, is

  7. Modeling and Risk Mapping of Forest Fires using Remote Sensing and GIS (Case Study: Baghe-Shadi Protected Area, Yazd Province)

    OpenAIRE

    A. Najafi; M. H. Irannezhad; A. Sotoudeh; M. H. Mokhtari; B. Kiani

    2016-01-01

    Baghe-shadi in Yazd province is one the forests which is reported to be an area with high rate fire occurrence. The aim of this study was to model and map the fire risk area using geographic information system and remote sensing. In this study Analytical Hierarchy Process (AHP) with human related factors (distance from road, distance from settlements, and distance from vegetation), climatic related factors (air temperature and rainfall), and physiographic related factors (elevation, slope, as...

  8. iPot: Improved potato monitoring in Belgium using remote sensing and crop growth modelling

    Science.gov (United States)

    Piccard, Isabelle; Gobin, Anne; Curnel, Yannick; Goffart, Jean-Pierre; Planchon, Viviane; Wellens, Joost; Tychon, Bernard; Cattoor, Nele; Cools, Romain

    2016-04-01

    Potato processors, traders and packers largely work with potato contracts. The close follow up of contracted parcels is important to improve the quantity and quality of the crop and reduce risks related to storage, packaging or processing. The use of geo-information by the sector is limited, notwithstanding the great benefits that this type of information may offer. At the same time, new sensor-based technologies continue to gain importance and farmers increasingly invest in these. The combination of geo-information and crop modelling might strengthen the competitiveness of the Belgian potato chain in a global market. The iPot project, financed by the Belgian Science Policy Office (Belspo), aims at providing the Belgian potato processing sector, represented by Belgapom, with near real time information on field condition (weather-soil), crop development and yield estimates, derived from a combination of satellite images and crop growth models. During the cropping season regular UAV flights (RGB, 3x3 cm) and high resolution satellite images (DMC/Deimos, 22m pixel size) were combined to elucidate crop phenology and performance at variety trials. UAV images were processed using a K-means clustering algorithm to classify the crop according to its greenness at 5m resolution. Vegetation indices such as %Cover and LAI were calculated with the Cyclopes algorithm (INRA-EMMAH) on the DMC images. Both DMC and UAV-based cover maps showed similar patterns, and helped detect different crop stages during the season. A wide spread field monitoring campaign with crop observations and measurements allowed for further calibration of the satellite image derived vegetation indices. Curve fitting techniques and phenological models were developed and compared with the vegetation indices during the season, both at trials and farmers' fields. Understanding and predicting crop phenology and canopy development is important for timely crop management and ultimately for yield estimates. An

  9. Advancements in Modelling of Land Surface Energy Fluxes with Remote Sensing at Different Spatial Scales

    DEFF Research Database (Denmark)

    Guzinski, Radoslaw

    Evaporation of water from soil and its transpiration by vegetation together form a ux between the land and the atmosphere called evapotranspiration (ET). ET is a key factor in many natural and anthropogenic processes. It forms the basis of the hydrological cycle and has a strong inuence on local...... climate, weather and numerous biophysical processes, such as plant productivity. As energy is required for ET to occur, it also forms a link between the land-surface energy uxes and water uxes. Therefore, to be able to obtain reliable estimates of ET, reliable estimates of the other land-surface energy...... of this study was to look at, and improve, various approaches for modelling the land-surface energy uxes at different spatial scales. The work was done using physically-based Two-Source Energy Balance (TSEB) approach as well as semi-empirical \\Triangle" approach. The TSEB-based approach was the main focus...

  10. Dynamic monitoring of cropland using remotely sensed data and ecosystem performance models

    Science.gov (United States)

    Wylie, B. K.; Gu, Y.; Phuyal, K. P.; Howard, D.

    2011-12-01

    Effective monitoring and assessing of cropland ecosystems (e.g., cropland growth condition and productivity) is essential for land managers and decision makers to make optimal land management decisions. Interpreting cropland growth conditions and productivity is very complex because of variable site characteristics (e.g., soil condition, surface geology, and climate conditions), weather conditions (e.g., surface temperature and precipitation), ecological disturbances (e.g., insect infestations), and management effects (e.g., irrigation, planting date, and fertilization). The goal of this study is to develop a scheme for dynamic monitoring of cropland ecosystems, enabling land managers to easily identify weather-related (e.g., drought) and non-weather-related (e.g., management) annual cropland performance variations. This scheme is based on satellite observations, biophysical and geophysical conditions, weather and climate data, and ecosystem performance models. Our study area is the Greater Platte River Basin, which includes a broad range of cropland productivities. We used the growing season averaged Normalized Difference Vegetation Index (NDVI) derived from satellite observations as a proxy for cropland productivity. We developed a simple cropland-saturation correction method to adjust saturated NDVI values. We classified cropland types (e.g., soybean, corn, and wheat), developed the cropland ecosystem site potential models, and mapped the multi-year weather-based expected ecosystem performance (EEP) for corn. We also identified productive grasslands and unproductive (or degraded) marginal croplands that are suitable for conversion to cellulosic feedstocks. Finally, we validated our cropland EEP results using crop yield data derived from the USDA National Agricultural Statistics Service. Results from this study provide land managers with pertinent information on management practices that result in more efficient cropland productivities.

  11. Remote sensing in uranium exploration. Basic guidance

    International Nuclear Information System (INIS)

    1981-01-01

    The purpose of this publication is to provide the reader with a basis for making an intelligent approach to the use of remote sensing in uranium exploration. It includes: A description of the various techniques; specific applications in view of exploration strategy and selection of appropriate techniques, and some examples of applications; availability and costs; a bibliography

  12. OPTICAL REMOTE SENSING FOR AIR QUALITY MONITORING

    Science.gov (United States)

    The paper outlines recent developments in using optical remote sensing (ORS) instruments for air quality monitoring both for gaseous pollutants and airborne particulate matter (PM). The U.S. Environmental Protection Agency (EPA) has been using open-path Fourier transform infrared...

  13. Annual Report Remote Sensing Activities Utrecht University

    NARCIS (Netherlands)

    Jong, S.M. de; Jetten, V.G.; Kwast, J. van der; Addink, E.A.

    2010-01-01

    The Faculty of Geosciences of Utrecht University in The Netherlands is a suc-cessful research and educational organi-sation (www.geo.uu.nl). The Faculty has four departments: Physical Geography, Earth Sciences, Human Geography & Planning and Innovation & Environmental Sciences. The remote sensing,

  14. Satellite Remote Sensing for Monitoring and Assessment

    Science.gov (United States)

    Remote sensing technology has the potential to enhance the engagement of communities and managers in the implementation and performance of best management practices. This presentation will use examples from U.S. numeric criteria development and state water quality monitoring prog...

  15. Remote sensing applications for monitoring rangeland vegetation ...

    African Journals Online (AJOL)

    Remote sensing techniques hold considerable promise for the inventory and monitoring of natural resources on rangelands. A significant lack of information concerning basic spectral characteristics of range vegetation and soils has resulted in a lack of rangeland applications. The parameters of interest for range condition ...

  16. Satellite Remote Sensing in Offshore Wind Energy

    DEFF Research Database (Denmark)

    Hasager, Charlotte Bay; Badger, Merete; Astrup, Poul

    2013-01-01

    Satellite remote sensing of ocean surface winds are presented with focus on wind energy applications. The history on operational and research-based satellite ocean wind mapping is briefly described for passive microwave, scatterometer and synthetic aperture radar (SAR). Currently 6 GW installed...

  17. Gully Features Extraction Using Remote Sensing Techniques ...

    African Journals Online (AJOL)

    Gullies are large and deep erosion depressions or channels normally occurring in drainage ways. They are spectrally heterogeneous, making them difficult to map using pixel based classification technique. The advancement of remote sensing in terms of Geographic Object Based Image Analysis (GEOBIA) provides new ...

  18. The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

    Science.gov (United States)

    Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William L.; Khan, Maudood N.

    2006-01-01

    The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

  19. The interpretation of remote sensing : a feasibility study

    NARCIS (Netherlands)

    Dulk, den J.A.

    1989-01-01

    This thesis describes research done to ascertain the possibilities and limitations of the use of remote sensing observations for agriculture. The topic is defined in Chapter 1. In Chapter 2 the possible applicability of certain existing models for this study is examined. Three models are

  20. Quantifying early-seral forest composition with remote sensing

    Science.gov (United States)

    Rayma A. Cooley; Peter T. Wolter; Brian R. Sturtevant

    2016-01-01

    Spatially explicit modeling of recovering forest structure within two years following wildfire disturbance has not been attempted, yet such knowledge is critical for determining successional pathways. We used remote sensing and field data, along with digital climate and terrain data, to model and map early-seral aspen structure and vegetation species richness following...

  1. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling

    Science.gov (United States)

    C. Vega, Greta; Pertierra, Luis R.; Olalla-Tárraga, Miguel Ángel

    2017-06-01

    Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe.

  2. Use of remotely-sensed observations and a data assimilating marine biogeochemical model to determine water quality on the Great Barrier Reef.

    Science.gov (United States)

    Baird, Mark; Jones, Emlyn; Wozniak, Monika; Mongin, Mathieu; Skerratt, Jennifer; Margvelashvilli, Nugzar; Wild-Allen, Karen; Robson, Barbara; Rizwi, Farhan; Schroeder, Thomas; Steven, Andy

    2017-04-01

    The health of the Great Barrier Reef is presently assessed using the water column concentration of chlorophyll and suspended solids, and measured light penetration. Quantifying these water column properties over 2,000 km of often cloud-covered, sparsely sampled, and highly variable coastal waters is problematic. To provide the best estimate of water quality, we assimilating satellite remote-sensing reflectance (the ratio of water-leaving radiance versus water-entering irradiance) using an in-water optical model to produce an equivalent simulated remote-sensing reflectance, and calculate the mis-match between the observed and simulated quantities to constrain a complex biogeochemical model (eReefs) with a Deterministic Ensemble Kalman Filter (DEnKF). We compare the water quality properties of the data assimilating model with in-situ observations, as well as with withheld remote-sensed observations. As a final step, we consider whether withheld observations can be combined with the data-assimilation generated chlorophyll fields to provide the best estimate of the chlorophyll concentration given all the available information.

  3. Integrating geographic information systems and remote sensing with spatial econometric and mixed logit models for environmental valuation

    Science.gov (United States)

    Wells, Aaron Raymond

    This research focuses on the Emory and Obed Watersheds in the Cumberland Plateau in Central Tennessee and the Lower Hatchie River Watershed in West Tennessee. A framework based on market and nonmarket valuation techniques was used to empirically estimate economic values for environmental amenities and negative externalities in these areas. The specific techniques employed include a variation of hedonic pricing and discrete choice conjoint analysis (i.e., choice modeling), in addition to geographic information systems (GIS) and remote sensing. Microeconomic models of agent behavior, including random utility theory and profit maximization, provide the principal theoretical foundation linking valuation techniques and econometric models. The generalized method of moments estimator for a first-order spatial autoregressive function and mixed logit models are the principal econometric methods applied within the framework. The dissertation is subdivided into three separate chapters written in a manuscript format. The first chapter provides the necessary theoretical and mathematical conditions that must be satisfied in order for a forest amenity enhancement program to be implemented. These conditions include utility, value, and profit maximization. The second chapter evaluates the effect of forest land cover and information about future land use change on respondent preferences and willingness to pay for alternative hypothetical forest amenity enhancement options. Land use change information and the amount of forest land cover significantly influenced respondent preferences, choices, and stated willingness to pay. Hicksian welfare estimates for proposed enhancement options ranged from 57.42 to 25.53, depending on the policy specification, information level, and econometric model. The third chapter presents economic values for negative externalities associated with channelization that affect the productivity and overall market value of forested wetlands. Results of robust

  4. Ground-based remote sensing profiling and numerical weather prediction model to manage nuclear power plants meteorological surveillance in Switzerland

    Directory of Open Access Journals (Sweden)

    B. Calpini

    2011-08-01

    Full Text Available The meteorological surveillance of the four nuclear power plants in Switzerland is of first importance in a densely populated area such as the Swiss Plateau. The project "Centrales Nucléaires et Météorologie" CN-MET aimed at providing a new security tool based on one hand on the development of a high resolution numerical weather prediction (NWP model. The latter is providing essential nowcasting information in case of a radioactive release from a nuclear power plant in Switzerland. On the other hand, the model input over the Swiss Plateau is generated by a dedicated network of surface and upper air observations including remote sensing instruments (wind profilers and temperature/humidity passive microwave radiometers. This network is built upon three main sites ideally located for measuring the inflow/outflow and central conditions of the main wind field in the planetary boundary layer over the Swiss Plateau, as well as a number of surface automatic weather stations (AWS. The network data are assimilated in real-time into the fine grid NWP model using a rapid update cycle of eight runs per day (one forecast every three hours. This high resolution NWP model has replaced the former security tool based on in situ observations (in particular one meteorological mast at each of the power plants and a local dispersion model. It is used to forecast the dynamics of the atmosphere in the planetary boundary layer (typically the first 4 km above ground layer and over a time scale of 24 h. This tool provides at any time (e.g. starting at the initial time of a nuclear power plant release the best picture of the 24-h evolution of the air mass over the Swiss Plateau and furthermore generates the input data (in the form of simulated values substituting in situ observations required for the local dispersion model used at each of the nuclear power plants locations. This paper is presenting the concept and two validation studies as well as the results of an

  5. Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

    Science.gov (United States)

    Albergel, C.; de Rosnay, P.; Gruhier, C.; Munoz-Sabater, J.; Hasenauer, S.; Isaksen, L.; Kerr, Y.; Wagner, W.

    2012-04-01

    In situ soil moisture data collected from more than 200 stations located in various biomes and climate (Africa, Australia, Europe and the United States) are used to determine the reliability of three soil moisture products, (i) one analysis from the ECMWF (European Centre for Medium-Range Weather Forecasts) numerical weather prediction system (SM-DAS-2) and two remotely sensed soil moisture products, namely (ii) ASCAT (Advanced Scatterometer) and (iii) SMOS (Soil Moisture Ocean Salinity). SM-DAS-2 is produced offline at ECMWF and relies on an advanced surface data assimilation system Extended Kalman Filter) used to optimally combine conventional observations with satellite measurements. ASCAT remotely sensed surface soil moisture is provided in near real time by EUMETSAT. At ECMWF, ASCAT is used for soil moisture analyses in SM-DAS-2, also. Finally the SMOS remotely sensed soil moisture data level two product developed at CESBIO is used. Evaluation of the times series as well as of the anomaly values, shows good performances of the three products to capture surface soil moisture annual cycle as well as its short term variability. Correlation values with in situ data are very satisfactory over most of the investigated sites located in contrasted biomes and climate conditions with averaged values of 0.70 for SM-DAS-2, 0.53 for ASCAT and 0.54 for SMOS. Although radio frequency interference disturbs the natural microwave emission of the Earth observed by SMOS in several parts of the world, hence the soil moisture retrieval, performances of SMOS over Australia are very encouraging.

  6. Mapping Latent Heat Flux in the Western Forest Covered Regions of Algeria Using Remote Sensing Data and a Spatialized Model

    Directory of Open Access Journals (Sweden)

    Khalladi Mederbal

    2009-10-01

    Full Text Available The present paper reports on an investigation to monitor the drought status in Algerian forest covered areas with satellite Earth observations because ground data are scarce and hard to collect. The main goal of this study is to map surface energy fluxes with remote sensing data, based on a simplified algorithm to solve the energy balance equation on each data pixel. Cultivated areas, forest cover and a large water surface were included in the investigated surfaces. The input parameters involve remotely sensed data in the visible, near infrared and thermal infrared. The surface energy fluxes are estimated by expressing the partitioning of energy available at the surface between the sensible heat flux (H and the latent heat flux (LE through the evaporative fraction (Λ according to the S-SEBI (Simplified Surface Energy Balance Index concept. The method is applicable under the assumptions of constant atmospheric conditions and sufficient wet and dry pixels over a Landsat 7 image. The results are analyzed and discussed considering instantaneous latent heat flux at the data acquisition time. The results confirm the relationships between albedo (r0, the surface temperature (T0 and the evaporative fraction. The method provides estimates of air temperature and LE close to reference measurements. The estimate of latent heat flux and other variables are comparable to those of previous studies. Their comparison with other methods shows reasonable agreement. This approach has demonstrated its simplicity and the fact that remote sensing data alone is sufficient; it could be very promising in areas where data are scarce and difficult to collect.

  7. Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

    Directory of Open Access Journals (Sweden)

    Rajchandar Padmanaban

    2017-04-01

    Full Text Available Urba