WorldWideScience

Sample records for modeling remote sensing

  1. Remote sensing applications to hydrologic modeling

    Science.gov (United States)

    Dozier, J.; Estes, J. E.; Simonett, D. S.; Davis, R.; Frew, J.; Marks, D.; Schiffman, K.; Souza, M.; Witebsky, E.

    1977-01-01

    An energy balance snowmelt model for rugged terrain was devised and coupled to a flow model. A literature review of remote sensing applications to hydrologic modeling was included along with a software development outline.

  2. Mesoscale Modeling, Forecasting and Remote Sensing Research.

    Science.gov (United States)

    remote sensing , cyclonic scale diagnostic studies and mesoscale numerical modeling and forecasting are summarized. Mechanisms involved in the release of potential instability are discussed and simulated quantitatively, giving particular attention to the convective formulation. The basic mesoscale model is documented including the equations, boundary condition, finite differences and initialization through an idealized frontal zone. Results of tests including a three dimensional test with real data, tests of convective/mesoscale interaction and tests with a detailed

  3. Branching model for vegetation. [polarimetric remote sensing

    Science.gov (United States)

    Yueh, Simon H.; Kong, J. A.; Jao, Jen K.; Shin, Robert T.; Le Toan, Thuy

    1992-01-01

    In the present branching model for remote sensing of vegetation, the frequency and angular responses of a two-scale cylinder cluster are calculated to illustrate the importance of vegetation architecture. Attention is given to the implementation of a two-scale branching model for soybeans, where the relative location of soybean plants is described by a pair of distribution functions. Theoretical backscattering coefficients evaluated by means of hole-correction pair distribution are in agreement with extensive data collected from soybean fields. The hole-correction approximation is found to be the more realistic.

  4. Branching model for vegetation. [polarimetric remote sensing

    Science.gov (United States)

    Yueh, Simon H.; Kong, J. A.; Jao, Jen K.; Shin, Robert T.; Le Toan, Thuy

    1992-01-01

    In the present branching model for remote sensing of vegetation, the frequency and angular responses of a two-scale cylinder cluster are calculated to illustrate the importance of vegetation architecture. Attention is given to the implementation of a two-scale branching model for soybeans, where the relative location of soybean plants is described by a pair of distribution functions. Theoretical backscattering coefficients evaluated by means of hole-correction pair distribution are in agreement with extensive data collected from soybean fields. The hole-correction approximation is found to be the more realistic.

  5. Remote Sensing

    CERN Document Server

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

    2012-01-01

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

  6. Strategies for using remotely sensed data in hydrologic models

    Science.gov (United States)

    Peck, E. L.; Keefer, T. N.; Johnson, E. R. (Principal Investigator)

    1981-01-01

    Present and planned remote sensing capabilities were evaluated. The usefulness of six remote sensing capabilities (soil moisture, land cover, impervious area, areal extent of snow cover, areal extent of frozen ground, and water equivalent of the snow cover) with seven hydrologic models (API, CREAMS, NWSRFS, STORM, STANFORD, SSARR, and NWSRFS Snowmelt) were reviewed. The results indicate remote sensing information has only limited value for use with the hydrologic models in their present form. With minor modifications to the models the usefulness would be enhanced. Specific recommendations are made for incorporating snow covered area measurements in the NWSRFS Snowmelt model. Recommendations are also made for incorporating soil moisture measurements in NWSRFS. Suggestions are made for incorporating snow covered area, soil moisture, and others in STORM and SSARR. General characteristics of a hydrologic model needed to make maximum use of remotely sensed data are discussed. Suggested goals for improvements in remote sensing for use in models are also established.

  7. Streamflow modelling by remote sensing: a contribution to digital earth

    NARCIS (Netherlands)

    Tan, M.L.; Latif, A.B.; Pohl, C.; Duan, Z.

    2014-01-01

    Remote sensing contributes valuable information to streamflow estimates. This paper discusses its relevance to the digital earth concept. The authors categorize the role of remote sensing in streamflow modelling and estimation. This paper emphasizes the applications and challenges of satellite-based

  8. Remote Sensing.

    Science.gov (United States)

    Williams, Richard S., Jr.; Southworth, C. Scott

    1983-01-01

    The Landsat Program became the major event of 1982 in geological remote sensing with the successful launch of Landsat 4. Other 1982 remote sensing accomplishments, research, publications, (including a set of Landsat worldwide reference system index maps), and conferences are highlighted. (JN)

  9. 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

  10. Models for estimation of land remote sensing satellites operational efficiency

    Science.gov (United States)

    Kurenkov, Vladimir I.; Kucherov, Alexander S.

    2017-01-01

    The paper deals with the problem of estimation of land remote sensing satellites operational efficiency. Appropriate mathematical models have been developed. Some results obtained with the help of the software worked out in Delphi programming support environment are presented.

  11. Validating firn compaction model with remote sensing data

    OpenAIRE

    2011-01-01

    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 models have been shown to be a key component. Now, remote sensing data can also be used to validate the firn models. Radar penetrating the upper part of the firn column in the interior part of Greenland ...

  12. Utilization of remote sensing observations in hydrologic models

    Science.gov (United States)

    Ragan, R. M.

    1977-01-01

    Most of the remote sensing related work in hydrologic modeling has centered on modifying existing models to take advantage of the capabilities of new sensor techniques. There has been enough success with this approach to insure that remote sensing is a powerful tool in modeling the watershed processes. Unfortunately, many of the models in use were designed without recognizing the growth of remote sensing technology. Thus, their parameters were selected to be map or field crew definable. It is believed that the real benefits will come through the evolution of new models having new parameters that are developed specifically to take advantage of our capabilities in remote sensing. The ability to define hydrologically active areas could have a significant impact. The ability to define soil moisture and the evolution of new techniques to estimate evoportransportation could significantly modify our approach to hydrologic modeling. Still, without a major educational effort to develop an understanding of the techniques used to extract parameter estimates from remote sensing data, the potential offered by this new technology will not be achieved.

  13. User requirements for hydrological models with remote sensing input

    Energy Technology Data Exchange (ETDEWEB)

    Kolberg, Sjur

    1997-10-01

    Monitoring the seasonal snow cover is important for several purposes. This report describes user requirements for hydrological models utilizing remotely sensed snow data. The information is mainly provided by operational users through a questionnaire. The report is primarily intended as a basis for other work packages within the Snow Tools project which aim at developing new remote sensing products for use in hydrological models. The HBV model is the only model mentioned by users in the questionnaire. It is widely used in Northern Scandinavia and Finland, in the fields of hydroelectric power production, flood forecasting and general monitoring of water resources. The current implementation of HBV is not based on remotely sensed data. Even the presently used HBV implementation may benefit from remotely sensed data. However, several improvements can be made to hydrological models to include remotely sensed snow data. Among these the most important are a distributed version, a more physical approach to the snow depletion curve, and a way to combine data from several sources. 1 ref.

  14. 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...... models have been shown to be a key component. Now, remote sensing data can also be used to validate the firn models. Radar penetrating the upper part of the firn column in the interior part of Greenland shows a clear layering. The observed layers from the radar data can be used as an in-situ validation...... 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...

  15. Stochastic models of cover class dynamics. [remote sensing of vegetation

    Science.gov (United States)

    Barringer, T. H.; Robinson, V. B.

    1981-01-01

    Investigations related to satellite remote sensing of vegetation have been concerned with questions of signature identification and extension, cover inventory accuracy, and change detection and monitoring. Attention is given to models of ecological succession, present directions in successional modeling and analysis, nondynamic spatial models, issues in the analysis of spatial data, and aspects of spatial modeling. Issues in time-series analysis are considered along with dynamic spatial models, and problems of model specification and identification.

  16. 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...

  17. 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...

  18. 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...

  19. International Models and Methods of Remote Sensing Education and Training.

    Science.gov (United States)

    Anderson, Paul S.

    A classification of remote sensing courses throughout the world, the world-wide need for sensing instruction, and alternative instructional methods for meeting those needs are discussed. Remote sensing involves aerial photointerpretation or the use of satellite and other non-photographic imagery; its focus is to interpret what is in the photograph…

  20. Malaria Modeling using Remote Sensing and GIS Technologies

    Science.gov (United States)

    Kiang, Richard

    2004-01-01

    Malaria has been with the human race since the ancient time. In spite of the advances of biomedical research and the completion of genomic mapping of Plasmodium falciparum, the exact mechanisms of how the various strains of parasites evade the human immune system and how they have adapted and become resistant to multiple drugs remain elusive. Perhaps because of these reasons, effective vaccines against malaria are still not available. Worldwide, approximately one to three millions deaths are attributed to malaria annually. With the increased availability of remotely sensed data, researchers in medical entomology, epidemiology and ecology have started to associate environmental and ecological variables with malaria transmission. In several studies, it has been shown that transmission correlates well with certain environmental and ecological parameters, and that remote sensing can be used to measure these determinants. In a NASA project, we have taken a holistic approach to examine how remote sensing and GIs can contribute to vector and malaria controls. To gain a better understanding of the interactions among the possible promoting factors, we have been developing a habitat model, a transmission model, and a risk prediction model, all using remote sensing data as input. Our objectives are: 1) To identify the potential breeding sites of major vector species and the locations for larvicide and insecticide applications in order to reduce costs, lessen the chance of developing pesticide resistance, and minimize the damage to the environment; 2) To develop a malaria transmission model characterizing the interactions among hosts, vectors, parasites, landcover and environment in order to identify the key factors that sustain or intensify malaria transmission, and 3) To develop a risk model to predict the occurrence of malaria and its transmission intensity using epidemiological data and satellite-derived or ground-measured environmental and meteorological data.

  1. Malaria Modeling using Remote Sensing and GIS Technologies

    Science.gov (United States)

    Kiang, Richard

    2004-01-01

    Malaria has been with the human race since the ancient time. In spite of the advances of biomedical research and the completion of genomic mapping of Plasmodium falciparum, the exact mechanisms of how the various strains of parasites evade the human immune system and how they have adapted and become resistant to multiple drugs remain elusive. Perhaps because of these reasons, effective vaccines against malaria are still not available. Worldwide, approximately one to three millions deaths are attributed to malaria annually. With the increased availability of remotely sensed data, researchers in medical entomology, epidemiology and ecology have started to associate environmental and ecological variables with malaria transmission. In several studies, it has been shown that transmission correlates well with certain environmental and ecological parameters, and that remote sensing can be used to measure these determinants. In a NASA project, we have taken a holistic approach to examine how remote sensing and GIs can contribute to vector and malaria controls. To gain a better understanding of the interactions among the possible promoting factors, we have been developing a habitat model, a transmission model, and a risk prediction model, all using remote sensing data as input. Our objectives are: 1) To identify the potential breeding sites of major vector species and the locations for larvicide and insecticide applications in order to reduce costs, lessen the chance of developing pesticide resistance, and minimize the damage to the environment; 2) To develop a malaria transmission model characterizing the interactions among hosts, vectors, parasites, landcover and environment in order to identify the key factors that sustain or intensify malaria transmission, and 3) To develop a risk model to predict the occurrence of malaria and its transmission intensity using epidemiological data and satellite-derived or ground-measured environmental and meteorological data.

  2. Biogeochemical cycling in terrestrial ecosystems - Modeling, measurement, and remote sensing

    Science.gov (United States)

    Peterson, D. L.; Matson, P. A.; Lawless, J. G.; Aber, J. D.; Vitousek, P. M.

    1985-01-01

    The use of modeling, remote sensing, and measurements to characterize the pathways and to measure the rate of biogeochemical cycling in forest ecosystems is described. The application of the process-level model to predict processes in intact forests and ecosystems response to disturbance is examined. The selection of research areas from contrasting climate regimes and sites having a fertility gradient in that regime is discussed, and the sites studied are listed. The use of remote sensing in determining leaf area index and canopy biochemistry is analyzed. Nitrous oxide emission is investigated by using a gas measurement instrument. Future research projects, which include studying the influence of changes on nutrient cycling in ecosystems and the effect of pollutants on the ecosystems, are discussed.

  3. Model for the Interpretation of Hyperspectral Remote-Sensing Reflectance

    Science.gov (United States)

    Lee, Zhongping; Carder, Kendall L.; Hawes, Steve K.; Steward, Robert G.; Peacock, Thomas G.; Davis, Curtiss O.

    1994-01-01

    Remote-sensing reflectance is easier to interpret for the open ocean than for coastal regions because the optical signals are highly coupled to the phytoplankton (e.g., chlorophyll) concentrations. For estuarine or coastal waters, variable terrigenous colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance, all factors that do not covary with the pigment concentration, confound data interpretation. In this research, remote-sensing reflectance models are suggested for coastal waters, to which contributions that are due to bottom reflectance, CDOM fluorescence, and water Raman scattering are included. Through the use of two parameters to model the combination of the backscattering coefficient and the Q factor, excellent agreement was achieved between the measured and modeled remote-sensing reflectance for waters from the West Florida Shelf to the Mississippi River plume. These waters cover a range of chlorophyll of 0.2-40 mg/cu m and gelbstoff absorption at 440 nm from 0.02-0.4/m. Data with a spectral resolution of 10 nm or better, which is consistent with that provided by the airborne visible and infrared imaging spectrometer (AVIRIS) and spacecraft spectrometers, were used in the model evaluation.

  4. Cloud vertical distribution from radiosonde, remote sensing, and model simulations

    Science.gov (United States)

    Zhang, Jinqiang; Li, Zhanqing; Chen, Hongbin; Yoo, Hyelim; Cribb, Maureen

    2014-08-01

    Knowledge of cloud vertical structure is important for meteorological and climate studies due to the impact of clouds on both the Earth's radiation budget and atmospheric adiabatic heating. Yet it is among the most difficult quantities to observe. In this study, we develop a long-term (10 years) radiosonde-based cloud profile product over the Southern Great Plains and along with ground-based and space-borne remote sensing products, use it to evaluate cloud layer distributions simulated by the National Centers for Environmental Prediction global forecast system (GFS) model. The primary objective of this study is to identify advantages and limitations associated with different cloud layer detection methods and model simulations. Cloud occurrence frequencies are evaluated on monthly, annual, and seasonal scales. Cloud vertical distributions from all datasets are bimodal with a lower peak located in the boundary layer and an upper peak located in the high troposphere. In general, radiosonde low-level cloud retrievals bear close resemblance to the ground-based remote sensing product in terms of their variability and gross spatial patterns. The ground-based remote sensing approach tends to underestimate high clouds relative to the radiosonde-based estimation and satellite products which tend to underestimate low clouds. As such, caution must be exercised to use any single product. Overall, the GFS model simulates less low-level and more high-level clouds than observations. In terms of total cloud cover, GFS model simulations agree fairly well with the ground-based remote sensing product. A large wet bias is revealed in GFS-simulated relative humidity fields at high levels in the atmosphere.

  5. Global Urbanization Modeling Supported by Remote Sensing

    Science.gov (United States)

    Zhou, Y.; Smith, S.; Zhao, K.; Imhoff, M. L.; Thomson, A. M.; Bond-Lamberty, B. P.; Elvidge, C.

    2014-12-01

    Urbanization, one of the major human induced land cover and land use change, has profound impacts on the Earth system, and plays important roles in a variety of processes such as biodiversity loss, water and carbon cycle, and climate change. Accurate information on urban areas and their spatial distribution at the regional and global scales is important in both scientific and policy-making communities. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime stable light data (NTL) provide a potential way to map urban area and its dynamics economically and timely. In this study, we developed a cluster-based method to estimate the optimal thresholds and map urban extents from the DMSP/OLS NTL data. The sensitivity analysis demonstrates the robustness of the derived optimal thresholds and the reliability of the cluster-based method. Compared to existing threshold techniques, our method reduces the over- and under-estimation issue, when mapping urban extent over a large area. Using this cluster-based method, we built new global maps of 1-km urban extent from the NTL data (Figure 1) and evaluated its temporal dynamics from 1992 to 2013. Supported by the derived global urban maps and socio-economic drivers, we developed an integrated modeling framework by integrating a top-down macro-scale statistical model with a bottom-up urban growth model and projected future urban expansion.

  6. Hyperspectral Remote Sensing for Shallow Waters. I. A Semianalytical Model

    Science.gov (United States)

    Lee, Zhongping; Carder, Kendall L.; Mobley, Curtis D.; Steward, Robert G.; Patch, Jennifer S.

    1998-09-01

    For analytical or semianalytical retrieval of shallow-water bathymetry and or optical properties of the water column from remote sensing, the contribution to the remotely sensed signal from the water column has to be separated from that of the bottom. The mathematical separation involves three diffuse attenuation coefficients: one for the downwelling irradiance ( K d ), one for the upwelling radiance of the water column ( K u C ), and one for the upwelling radiance from bottom reflection ( K u B ). Because of the differences in photon origination and path lengths, these three coefficients in general are not equal, although their equality has been assumed in many previous studies. By use of the Hydrolight radiative-transfer numerical model with a particle phase function typical of coastal waters, the remote-sensing reflectance above ( R rs ) and below ( r rs ) the surface is calculated for various combinations of optical properties, bottom albedos, bottom depths, and solar zenith angles. A semianalytical (SA) model for r rs of shallow waters is then developed, in which the diffuse attenuation coefficients are explicitly expressed as functions of in-water absorption ( a ) and backscattering ( b b ). For remote-sensing inversion, parameters connecting R rs and r rs are also derived. It is found that r rs values determined by the SA model agree well with the exact values computed by Hydrolight ( 3% error), even for Hydrolight r rs values calculated with different particle phase functions. The Hydrolight calculations included b b a values as high as 1.5 to simulate high-turbidity situations that are occasionally found in coastal regions.

  7. Potential for Remotely Sensed Soil Moisture Data in Hydrologic Modeling

    Science.gov (United States)

    Engman, Edwin T.

    1997-01-01

    Many hydrologic processes display a unique signature that is detectable with microwave remote sensing. These signatures are in the form of the spatial and temporal distributions of surface soil moisture and portray the spatial heterogeneity of hydrologic processes and properties that one encounters in drainage basins. The hydrologic processes that may be detected include ground water recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential ET, and information about the hydrologic properties of soils and heterogeneity of hydrologic parameters. Microwave remote sensing has the potential to detect these signatures within a basin in the form of volumetric soil moisture measurements in the top few cm. These signatures should provide information on how and where to apply soil physical parameters in distributed and lumped parameter models and how to subdivide drainage basins into hydrologically similar sub-basins.

  8. A stochastic atmospheric model for remote sensing applications

    Science.gov (United States)

    Turner, R. E.

    1983-01-01

    There are many factors which reduce the accuracy of classification of objects in the satellite remote sensing of Earth's surface. One important factor is the variability in the scattering and absorptive properties of the atmospheric components such as particulates and the variable gases. For multispectral remote sensing of the Earth's surface in the visible and infrared parts of the spectrum the atmospheric particulates are a major source of variability in the received signal. It is difficult to design a sensor which will determine the unknown atmospheric components by remote sensing methods, at least to the accuracy needed for multispectral classification. The problem of spatial and temporal variations in the atmospheric quantities which can affect the measured radiances are examined. A method based upon the stochastic nature of the atmospheric components was developed, and, using actual data the statistical parameters needed for inclusion into a radiometric model was generated. Methods are then described for an improved correction of radiances. These algorithms will then result in a more accurate and consistent classification procedure.

  9. On the nature of models in remote sensing

    Science.gov (United States)

    Strahler, A. H.; Woodcock, C. E.; Smith, J. A.

    1986-01-01

    An explicit framework can provide a better understanding of remote sensing models and their interrelationships. This framework distinguishes between the scene, which is real and exists on the ground, and the image, which is a collection of spatially arranged masurements drawn from the scene. The scene model generalizes and parameterizes the essential qualities of the scene. Scene models may be discrete, in which the scene model consists of discrete elements with boundaries, or continuous, in which matter and energy flows are taken to be continuous and there are no clear or sharp boundaries in the scene. In the discrete case, there are two possibilities for models: H- and L-resolution. In the H-resolution case, the resolution cells of the image are smaller than the elements, and thus the elements may be individually resolved. In the L-resolution case, the resolution cells are larger than the elements and cannot be resolved. Most canopy models are L-resolution, deterministic, and noninvertible in nature; image processing models, however, tend to be H-resolution, empirical, and invertible. This taxonomy helps add insight to the development of remote sensing theory and point the way to new, productive areas of research.

  10. An Ecophysiological Model for Remote Sensing of GPP

    Science.gov (United States)

    Tu, K. P.

    2010-12-01

    Remote sensing light use efficiency (LUE) models of terrestrial gross primary productivity (GPP) are currently limited by three main problems: 1) the ability to distinguish light absorption by the photosynthetically-active (FAPAR) and the non-photosynthetically active (FIPAR) portions of the canopy, 2) the spatial and temporal variation of the maximum LUE within and across biomes, and 3) parameterization of temperature and moisture scalars for different vegetation types. We address these three issues by 1) using the Enhanced Vegetation Index (EVI) or Soil-Adjusted Vegetation Index (SAVI) to estimate light absorption by the photosynthetically active fraction of the canopy (FAPAR), as opposed to using NDVI which appears to be sensitive to the non-photosynthetically active fraction and is therefore more indicative of the total canopy light interception (FIPAR), 2) estimating the maximum unstressed LUE based on the maximum quantum yield of photosynthesis, a physiological and well-constrained parameter, and 3) inferring seasonal variation in temperature and moisture stress using the phenological information in FAPAR time series, with a unique temperature optimum (Topt) determined for each pixel and moisture stress estimated from relative changes in FAPAR. In this approach, the model can be applied entirely with remote sensing observations of EVI (or EVI2 or SAVI), air temperature (Tair), and incident photosynthetically-active radiation (PAR). Aside from improved parameterization of stress functions based entirely on remote sensing observations, this approach is similar to previous LUE models based on the quantum yield of photosynthesis. However, it differs in that we incorporate recent evidence indicating that the time-averaged quantum yield is roughly one-half that of the instantaneous maximum quantum yield. This translates to time-averaged rates of GPP being roughly one-half the maximum instantaneous rates or GPPmean=GPPmax/2, consistent with studies showing strong

  11. 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.

  12. Northern Forest Ecosystem Dynamics Using Coupled Models and Remote Sensing

    Science.gov (United States)

    Ranson, K. J.; Sun, G.; Knox, R. G.; Levine, E. R.; Weishampel, J. F.; Fifer, S. T.

    1999-01-01

    Forest ecosystem dynamics modeling, remote sensing data analysis, and a geographical information system (GIS) were used together to determine the possible growth and development of a northern forest in Maine, USA. Field measurements and airborne synthetic aperture radar (SAR) data were used to produce maps of forest cover type and above ground biomass. These forest attribute maps, along with a conventional soils map, were used to identify the initial conditions for forest ecosystem model simulations. Using this information along with ecosystem model results enabled the development of predictive maps of forest development. The results obtained were consistent with observed forest conditions and expected successional trajectories. The study demonstrated that ecosystem models might be used in a spatial context when parameterized and used with georeferenced data sets.

  13. Integrating remote sensing and spatially explicit epidemiological modeling

    Science.gov (United States)

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

    2015-04-01

    Spatially explicit epidemiological models are a crucial tool for the prediction of epidemiological patterns in time and space as well as for the allocation of health care resources. In addition they can provide valuable information about epidemiological processes and allow for the identification of environmental drivers of the disease spread. Most epidemiological models rely on environmental data as inputs. They can either be measured in the field by the means of conventional instruments or using remote sensing techniques to measure suitable proxies of the variables of interest. The later benefit from several advantages over conventional methods, including data availability, which can be an issue especially in developing, and spatial as well as temporal resolution of the data, which is particularly crucial for spatially explicit models. Here we present the case study of a spatially explicit, semi-mechanistic model applied to recurring cholera outbreaks in the Lake Kivu area (Democratic Republic of the Congo). The model describes the cholera incidence in eight health zones on the shore of the lake. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers. Human mobility and its effect on the disease spread is also taken into account. Several model configurations are tested on a data set of reported cases. The best models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via cross validation. The best performing model accounts for seasonality, El Niño Southern Oscillation, precipitation and human mobility.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. Use of remote sensing data in distributed hydrological models: Applications in the Senegal river basin

    DEFF Research Database (Denmark)

    Sandholt, Inge; Andersen, Jens; Dybkjær, Gorm Ibsen;

    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...

  2. Salinity modeling by remote sensing in central and southern Iraq

    Science.gov (United States)

    Wu, W.; Mhaimeed, A. S.; Platonov, A.; Al-Shafie, W. M.; Abbas, A. M.; Al-Musawi, H. H.; Khalaf, A.; Salim, K. A.; Chrsiten, E.; De Pauw, E.; Ziadat, F.

    2012-12-01

    Salinization, leading to a significant loss of cultivated land and crop production, is one of the most active land degradation phenomena in the Mesopotamian region in Iraq. The objectives of this study (under the auspices of ACIAR and Italian Government) are to investigate the possibility to use remote sensing technology to establish salinity-sensitive models which can be further applied to local and regional salinity mapping and assessment. Case studies were conducted in three pilot sites namely Musaib, Dujaila and West Garraf in the central and southern Iraq. Fourteen spring (February - April), seven June and four summer Landsat ETM+ images in the period 2009-2012, RapidEye data (April 2012), and 95 field EM38 measurements undertaken in this spring and summer, 16 relevant soil laboratory analysis result (Dujaila) were employed in this study. The procedure we followed includes: (1) Atmospheric correction using FLAASH model; (2) Multispectral transformation of a set of vegetation and non-vegetation indices such as GDVI (Generalized Difference Vegetation Index), NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), SARVI (Soil Adjusted and Atmospherically Resistant Vegetation Index), NDII (Normalized Difference Infrared Index), Principal Components and surface temperature (T); (3) Derivation of the spring maximum (Musaib) and annual maximum (Dujaila and West Garraf) value in each pixel of each index of the observed period to avoid problems related to crop rotation (e.g. fallow) and the SLC-Off gaps in ETM+ images; (4) Extraction of the values of each vegetation and non-vegetation index corresponding to the field sampling locations (about 3 to 5 controversial samples very close to the roads or located in fallow were excluded); and (5) Coupling remote sensing indices with the available EM38 and soil electrical conductivity (EC) data using multiple linear least-square regression model at the confidence

  3. Model-based acoustic remote sensing of seafloor characteristics

    Digital Repository Service at National Institute of Oceanography (India)

    De, Ch.; Chakraborty, B.

    characterization using time- dependent acoustic backscatter: Study of Arabian Sea,” in Proc. IEEE Oceans, Kobe, Japan, 2008, pp. 1–4. 3876 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 10, OCTOBER 2011 [6] C. De and B. Chakraborty, “Acoustic... characterization of seafloor sediment employing a hybrid method of neural network architecture and fuzzy algorithm,” IEEE Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 743–747, Oct. 2009. [7] C. De and B. Chakraborty, “Preference of echo features...

  4. Modelling the Spatial Distribution of Culicoides imicola: Climatic versus Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Jasper Van Doninck

    2014-07-01

    Full Text Available Culicoides imicola is the main vector of the bluetongue virus in the Mediterranean Basin. Spatial distribution models for this species traditionally employ either climatic data or remotely sensed data, or a combination of both. Until now, however, no studies compared the accuracies of C. imicola distribution models based on climatic versus remote sensing data, even though remotely sensed datasets may offer advantages over climatic datasets with respect to spatial and temporal resolution. This study performs such an analysis for datasets over the peninsula of Calabria, Italy. Spatial distribution modelling based on climatic data using the random forests machine learning technique resulted in a percentage of correctly classified C. imicola trapping sites of nearly 88%, thereby outperforming the linear discriminant analysis and logistic regression modelling techniques. When replacing climatic data by remote sensing data, random forests modelling accuracies decreased only slightly. Assessment of the different variables’ importance showed that precipitation during late spring was the most important amongst 48 climatic variables. The dominant remotely sensed variables could be linked to climatic variables. Notwithstanding the slight decrease in predictive performance in this study, remotely sensed datasets could be preferred over climatic datasets for the modelling of C. imicola. Unlike climatic observations, remote sensing provides an equally high spatial resolution globally. Additionally, its high temporal resolution allows for investigating changes in species’ presence and changing environment.

  5. Application of remote sensed precipitation for landslide hazard assessment models

    Science.gov (United States)

    Kirschbaum, D. B.; Peters-Lidard, C. D.; Adler, R. F.; Kumar, S.; Harrison, K.

    2010-12-01

    The increasing availability of remotely sensed land surface and precipitation information provides new opportunities to improve upon existing landslide hazard assessment methods. This research considers how satellite precipitation information can be applied in two types of landslide hazard assessment frameworks: a global, landslide forecasting framework and a deterministic slope-stability model. Examination of both landslide hazard frameworks points to the need for higher resolution spatial and temporal precipitation inputs to better identify small-scale precipitation forcings that contribute to significant landslide triggering. This research considers how satellite precipitation information may be downscaled to account for local orographic impacts and better resolve peak intensities. Precipitation downscaling is employed in both models to better approximate local rainfall distribution, antecedent conditions, and intensities. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale and have the potential to significantly advance landslide hazard assessment tools. The first landslide forecasting tool, running in near real-time at http://trmm.gsfc.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. Results of the algorithm evaluation indicate that considering higher resolution susceptibility information is a key factor in better resolving potentially hazardous areas. However, success in resolving when landslide activity is probable is closely linked to appropriate characterization of the empirical rainfall intensity-duration thresholds. We test a variety of rainfall thresholds to evaluate algorithmic performance accuracy and determine the optimal set of conditions that

  6. Parameterization of a hydrological model using remote sensing data

    Science.gov (United States)

    Oppelt, N.; Rathjens, H.; Müller, T.-L.

    2012-04-01

    model interface to manage input and output data based on grid cells. This enabled us to model the changes in evapotranspiration patterns in the catchment with changing land use more realistically and to calculate the water balance for each grid cell without losing its geographic reference. Therefore, the grid cells can interact with each other and exchange matter and energy, which was not possible using the sub-watershed approach. Therefore, the grid-cell interface enables the implementation of remote sensing data to provide a spatially distributed modelling.

  7. 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

  8. REMOTE SENSING IN OCEANOGRAPHY.

    Science.gov (United States)

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

  9. 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.

  10. 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...

  11. Passive Remote Sensing of Oceanic Whitecaps: Updated Geophysical Model Function

    Science.gov (United States)

    Anguelova, M. D.; Bettenhausen, M. H.; Johnston, W.; Gaiser, P. W.

    2016-12-01

    Many air-sea interaction processes are quantified in terms of whitecap fraction W because oceanic whitecaps are the most visible and direct way of observing breaking of wind waves in the open ocean. Enhanced by breaking waves, surface fluxes of momentum, heat, and mass are critical for ocean-atmosphere coupling and thus affect the accuracy of models used to forecast weather, predict storm intensification, and study climate change. Whitecap fraction has been traditionally measured from photographs or video images collected from towers, ships, and aircrafts. Satellite-based passive remote sensing of whitecap fraction is a recent development that allows long term, consistent observations of whitecapping on a global scale. The method relies on changes of ocean surface emissivity at microwave frequencies (e.g., 6 to 37 GHz) due to presence of sea foam on a rough sea surface. These changes at the ocean surface are observed from the satellite as brightness temperature TB. A year-long W database built with this algorithm has proven useful in analyzing and quantifying the variability of W, as well as estimating fluxes of CO2 and sea spray production. The algorithm to obtain W from satellite observations of TB was developed at the Naval Research Laboratory within the framework of WindSat mission. The W(TB) algorithm estimates W by minimizing the differences between measured and modeled TB data. A geophysical model function (GMF) calculates TB at the top of the atmosphere as contributions from the atmosphere and the ocean surface. The ocean surface emissivity combines the emissivity of rough sea surface and the emissivity of areas covered with foam. Wind speed and direction, sea surface temperature, water vapor, and cloud liquid water are inputs to the atmospheric, roughness and foam models comprising the GMF. The W(TB) algorithm has been recently updated to use new sources and products for the input variables. We present new version of the W(TB) algorithm that uses updated

  12. 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...

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

    National Research Council Canada - National Science Library

    Carly Hyatt Hansen; Steven J Burian; Philip E Dennison; Gustavious P Williams

    2017-01-01

    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...

  14. 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.

  15. Modeling Chemical Detection Sensitivities of Active and Passive Remote Sensing Systems

    Energy Technology Data Exchange (ETDEWEB)

    Scharlemann, E T

    2003-07-28

    During nearly a decade of remote sensing programs under the auspices of the U. S. Department of Energy (DOE), LLNL has developed a set of performance modeling codes--called APRS--for both Active and Passive Remote Sensing systems. These codes emphasize chemical detection sensitivity in the form of minimum detectable quantities with and without background spectral clutter and in the possible presence of other interfering chemicals. The codes have been benchmarked against data acquired in both active and passive remote sensing programs at LLNL and Los Alamos National Laboratory (LANL). The codes include, as an integral part of the performance modeling, many of the data analysis techniques developed in the DOE's active and passive remote sensing programs (e.g., ''band normalization'' for an active system, principal component analysis for a passive system).

  16. 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

  17. Objected-oriented remote sensing image classification method based on geographic ontology model

    Science.gov (United States)

    Chu, Z.; Liu, Z. J.; Gu, H. Y.

    2016-11-01

    Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application

  18. 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.

  19. Remote Sensing and Modelling of Solar Induced Fluorescence

    Science.gov (United States)

    Maier, S. W.

    Since many years passive optical remote sensing is applied for deducing plant and canopy parameters from ground, aircraft or satellite measurements. E.g. the "Normalized Difference Vegetation Index"] (NDVI) is widely used to estimate changes in vegetation state. The NDVI was originally used as a measure of green biomass. Later a theoretical basis was presented to use the NDVI as a measure of the solar photosynthetic active radiation absorbed by the canopy. Besides the NDVI various other vegetation indices and algorithms have been developed to derive plant and canopy parameters from reflectance measurements. These parameters are indicators of the integrated long-term status of plants. To observe the actual physiological or photosynthetic status of leaves, fluorescence techniques have been applied in the past in close contact to plant organs as e.g. leaves or chloroplasts. These optical methods are based on the fact that plant pigments as e.g. chlorophyll show a typical fluorescence in the red spectral region when excited with a laser beam or with any artificial light source having a wavelength in the blue to orange spectral region. Most investigations on chlorophyll fluorescence were done in the laboratory under well controlled conditions in order to understand the internal bio-molecular processes of photosynthesis. Chlorophyll fluorescence and photosynthesis are closely linked. In general, the absorbed photosynthetic active radiation (PAR) of the solar irradiation (380-750nm) is used by plants primarily to convert and store the absorbed energy in chemically bound energy (photosynthesis). This process is directly linked with the uptake of carbon dioxide and the release of oxygen (called primary productivity). But not all absorbed energy is used for photosynthesis. Two other pathways are possible for the absorbed energy to keep plants energetically balanced. First, the emission of thermal energy and second the emission as fluorescence light may be used for regulation

  20. 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.

  1. Parameter selection and model research on remote sensing evaluation for nearshore water quality

    Institute of Scientific and Technical Information of China (English)

    LEI Guibin; ZHANG Ying; PAN Delu; WANG Difeng; FU Dongyang

    2016-01-01

    Using remote sensing technology for water quality evaluation is an inevitable trend in marine environmental monitoring. However, fewer categories of water quality parameters can be monitored by remote sensing technology than the 35 specified in GB3097-1997 Marine Water Quality Standard. Therefore, we considered which parameters must be selected by remote sensing and how to model for water quality evaluation using the finite parameters. In this paper, focused on Leizhou Peninsula nearshore waters, we found N, P, COD, PH and DO to be the dominant parameters of water quality by analyzing measured data. Then, mathematical statistics was used to determine that the relationship among the five parameters was COD>DO>P>N>pH. Finally, five-parameter, four-parameter and three-parameter water quality evaluation models were established and compared. The results showed that COD, DO, P and N were the necessary parameters for remote sensing evaluation of the Leizhou Peninsula nearshore water quality, and the optimal comprehensive water quality evaluation model was the four-parameter model. This work may serve as a reference for monitoring the quality of other marine waters by remote sensing.

  2. Monitoring soil moisture through assimilation of active microwave remote sensing observation into a hydrologic model

    Science.gov (United States)

    Liu, Qian; Zhao, Yingshi

    2015-08-01

    Soil moisture can be estimated from point measurements, hydrologic models, and remote sensing. Many researches indicated that the most promising approach for soil moisture is the integration of remote sensing surface soil moisture data and computational modeling. Although many researches were conducted using passive microwave remote sensing data in soil moisture assimilation with coarse spatial resolution, few researches were carried out using active microwave remote sensing observation. This research developed and tested an operational approach of assimilation for soil moisture prediction using active microwave remote sensing data ASAR (Advanced Synthetic Aperture Radar) in Heihe Watershed. The assimilation was based on ensemble Kalman filter (EnKF), a forward radiative transfer model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The forward radiative transfer model, as a semi-empirical backscattering model, was used to eliminate the effect of surface roughness and vegetation cover on the backscatter coefficient. The impact of topography on soil water movement and the vertical and lateral exchange of soil water were considered. We conducted experiments to assimilate active microwave remote sensing data (ASAR) observation into a hydrologic model at two field sites, which had different underlying conditions. The soil moisture ground-truth data were collected through the field Time Domain Reflectometry (TDR) tools, and were used to assess the assimilation method. The temporal evolution of soil moisture measured at point-based monitoring locations were compared with EnKF based model predictions. The results indicated that the estimate of soil moisture was improved through assimilation with ASAR observation and the soil moisture based on data assimilation can be monitored in moderate spatial resolution.

  3. 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.

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

    Science.gov (United States)

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

    1977-01-01

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

  5. Exploitation of homogeneous isotropic turbulence models for optimization of turbulence remote sensing

    NARCIS (Netherlands)

    Oude Nijhuis, A.C.P.; Krasnov, O.K.; Unal, C.M.H.; Russchenberg, H.W.J.; Yarovoy, A.

    2015-01-01

    Homogeneous isotropic turbulence (HIT) models are compared, with respect to optimization of turbulence remote sensing. HIT models have different applications such as load calculation for wind turbines (Mann, 1998) or droplet track modelling (Pinsky and Khain, 2006). Details of vortices seem of less

  6. 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...

  7. Estimation of Fractional Vegetation Cover Based on Digital Camera Survey Data and a Remote Sensing Model

    Institute of Scientific and Technical Information of China (English)

    HU Zhen-qi; HE Fen-qin; YIN Jian-zhong; LU Xia; TANG Shi-lu; WANG Lin-lin; LI Xiao-jing

    2007-01-01

    The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover (fc). It mainly focuses onfc estimation when fcmax andfcmin are not approximately equal to 100% and 0%, respectively due to using remote sensing image with medium or low spatial resolution. Meanwhile, we present a new method offc estimation based on a random set offc maximum and minimum values from digital camera (DC) survey data and a dimidiate pixel model. The results show that this is a convenient, efficient and accurate method forfc monitoring, with the maximum error -0.172 and correlation coefficient of 0.974 between DC survey data and the estimated value of the remote sensing model. The remaining DC survey data can be used as verification data for the precision of thefc estimation. In general, the estimation offc based on DC survey data and a remote sensing model is a brand-new development trend and deserves further extensive utilization.

  8. Establishment of Winter Wheat Regional Simulation Model Based on Remote Sensing Data and Its Application

    Institute of Scientific and Technical Information of China (English)

    MA Yuping; WANG Shili; ZHANG Li; HOU Yingyu; ZHUANG Liwei; WANG Futang

    2006-01-01

    Accurate crop growth monitoring and yield forecasting are significant to the food security and the sus tainable development of agriculture. Crop yield estimation by remote sensing and crop growth simulation models have highly potential application in crop growth monitoring and yield forecasting. However, both of them have limitations in mechanism and regional application, respectively. Therefore, approach and methodology study on the combination of remote sensing data and crop growth simulation models are con cerned by many researchers. In this paper, adjusted and regionalized WOFOST (World Food Study) in North China and Scattering by Arbitrarily Inclined Leaves-a model of leaf optical PROperties SPECTra (SAIL-PROSFPECT) were coupled through LAI to simulate Soil Adjusted Vegetation Index (SAVI) of crop canopy, by which crop model was re-initialized by minimizing differences between simulated and synthesized SAVI from remote sensing data using an optimization software (FSEOPT). Thus, a regional remote-sensing crop-simulation-framework-model (WSPFRS) was established under potential production level (optimal soil water condition). The results were as follows: after re-initializing regional emergence date by using remote sensing data, anthesis, and maturity dates simulated by WSPFRS model were more close to measured values than simulated results of WOFOST; by re-initializing regional biomass weight at turn-green stage, the spa tial distribution of simulated storage organ weight was more consistent with measured yields and the area with high values was nearly consistent with actual high yield area. This research is a basis for developing regional crop model in water stress production level based on remote sensing data.

  9. 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

  10. 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

  11. Accessing and Utilizing Remote Sensing Data for Vectorborne Infectious Diseases Surveillance and Modeling

    Science.gov (United States)

    Kiang, Richard; Adimi, Farida; Kempler, Steven

    2008-01-01

    Background: The transmission of vectorborne infectious diseases is often influenced by environmental, meteorological and climatic parameters, because the vector life cycle depends on these factors. For example, the geophysical parameters relevant to malaria transmission include precipitation, surface temperature, humidity, elevation, and vegetation type. Because these parameters are routinely measured by satellites, remote sensing is an important technological tool for predicting, preventing, and containing a number of vectorborne infectious diseases, such as malaria, dengue, West Nile virus, etc. Methods: A variety of NASA remote sensing data can be used for modeling vectorborne infectious disease transmission. We will discuss both the well known and less known remote sensing data, including Landsat, AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), TRMM (Tropical Rainfall Measuring Mission), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), EO-1 (Earth Observing One) ALI (Advanced Land Imager), and SIESIP (Seasonal to Interannual Earth Science Information Partner) dataset. Giovanni is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center. It provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. After remote sensing data is obtained, a variety of techniques, including generalized linear models and artificial intelligence oriented methods, t 3 can be used to model the dependency of disease transmission on these parameters. Results: The processes of accessing, visualizing and utilizing precipitation data using Giovanni, and acquiring other data at additional websites are illustrated. Malaria incidence time series for some parts of Thailand and Indonesia are used to demonstrate that malaria incidences are reasonably well modeled with generalized linear models and artificial

  12. Modeling the Effect of Vegetation on Passive Microwave Remote Sensing of Soil Moisture

    Science.gov (United States)

    Liu, Y. P.; Inguva, R.; Crosson, W. L.; Coleman, T. L.; Laymon, C.; Fahsi, A.

    1998-01-01

    The effect of vegetation on passive microwave remote sensing of soil moisture is studied. The radiative transfer modeling work of Njoku and Kong is applied to a stratified medium of which the upper layer is treated as a layer of vegetation. An effective dielectric constant for this vegetation layer is computed using estimates of the dielectric constant of individual components of the vegetation layer. The horizontally-polarized brightness temperature is then computed as a function of the incidence angle. Model predictions are used to compare with the data obtained in the Huntsville '96, remote sensing of soil moisture experiment, and with predictions obtained using a correction procedure of Jackson and Schmugge.

  13. Evaluation of the international vehicle emission (IVE) model with on-road remote sensing measurements

    Institute of Scientific and Technical Information of China (English)

    GUO Hui; ZHANG Qing-yu; SHI Yao; WANG Da-hui

    2007-01-01

    International Vehicle Emissions (IVE) model funded by U.S. Environmental Protection (USEPA) is designed to estimate emissions from motor vehicles in developing countries. In this study, the IVE model was evaluated by utilizing a dataset available from the remote sensing measurements on a large number of vehicles at five different sites in Hangzhou, China, in 2004 and 2005. Average fuel-based emission factors derived from the remote sensing measurements were compared with corresponding emission factors derived from IVE calculations for urban, hot stabilized condition. The results show a good agreement between the two methods for gasoline passenger cars' HC emission for all IVE subsectors and technology classes. In the case of CO emissions, the modeled results were reasonably good, although systematically underestimate the emissions by almost 12%-50% for different technology classes. However, the model totally overestimated NOx emissions. The IVE NOx emission factors were 1.5-3.5 times of the remote sensing measured ones. The IVE model was also evaluated for light duty gasoline truck (LDGT), heavy duty gasoline vehicles (HDGV) and motor cycles (MC). A notable result was observed that the decrease in emissions from technology class State Ⅱ to State Ⅰ were overestimated by the IVE model compared to remote sensing measurements for all the three pollutants. Finally, in order to improve emission estimation, the adjusted base emission factors from local studies are strongly recommended to be used in the IVE model.

  14. Comparison of various remote sensing snow products in a distributed hydrological model

    Science.gov (United States)

    Berezowski, Tomasz; Chormański, Jarosław; Batelaan, Okke

    2014-05-01

    With the development of remote sensing, more and more data series with spatially distributed snow cover become available. These data can be obtained for free, from many sources varying in spatial and temporal resolution, the length of the time series and the method of acquisition (VIS-NIR or microwave sensors). A popular use of remotely sensed snow distribution data is in hydrological modelling. However, a suitability test of different remote sensing snow products for hydrological models was so far not conducted. In this work, some of the most common remote sensing snow products (MOD10A1, IMS , GLOBSNOW and AMSR-E_DySno) are used as input data in the WetSpa distributed hydrological model. Each of the snow products has different properties and is based on different algorithms, which makes the analysis interesting and multidimensional. The area of research is the Biebrza River catchment - located in north-eastern Poland, comprising approximately 7000 km2. Biebrza is a natural river with a snow melt regime, making it very suitable for this kind of analysis. In total 6 modelling scenarios were conducted (4 with remote sensing data, 1 standard approach - temperature threshold for snow accumulation and melting, 1 based on snow data from meteorological stations). Each model was calibrated against discharge with the Shuffled Complex Evolution (SCE) algorithm. The calibration was repeated three times for each model to make sure that the global optimum was found. The calibration and validation periods were both 3 years long. The next stage was a comparison with the GLUE uncertainty analysis for each of the models, on a shorter, one-year period. The best model in terms of Nash-Sutcliffe efficiency and r2 was using the MOD10A1 data; however, the models using GLOBSNOW SWE and the standard approach received similar scores. In terms of the model bias the best results were obtained for the IMS and MOD10A1 data. Nevertheless, the lowest root mean square error was found for the

  15. The added value of remote sensing products in constraining hydrological models

    Science.gov (United States)

    Hrachowitz, M.; Nijzink, R.; Savenije, H. H. G.

    2016-12-01

    A typical calibration of a hydrological model relies on the availability of discharge data, which is, however, not always present and not the largest outgoing flux in many parts of the world. At the same time, more remote sensing products are becoming available that can aid in deriving model parameters and model structures, but also more traditional analytical approaches (e.g. the Budyko framework) can still be of high value. In this research, models are constrained in a step-wise approach with different combinations of remote sensing data and/or analytical frameworks. For example, the temporal resolution can be a driving principle leading to the formulation of a set of constraints. More specific, in a first step the Budyko framework can be used as a means to filter out solutions that cannot reproduce the long-term dynamics of the system. In the following steps, remote sensing data of respectively GRACE (monthly resolution), NDII (16-day resolution) and LSA-SAF evaporation (daily) can lead to final parameterizations of a model. Nevertheless, the choice of these driving principles, the applied order of constraints and the strictness of the applied boundaries of the constraints, will lead to varying solutions. Therefore, variations in these factors, and thus different combinations with different remote sensing products, should lead to an enhanced understanding of the strengths and weaknesses the approaches have with regard to finding optimal parameter sets for hydrological models.

  16. Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters. Part 1; Equivalent Sensor Radiance Formulation

    Science.gov (United States)

    Wind, Galina; DaSilva, Arlindo M.; Norris, Peter M.; Platnick, Steven E.

    2013-01-01

    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 probably 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.

  17. Estimation of Soil Moisture Profile using a Simple Hydrology Model and Passive Microwave Remote Sensing

    Science.gov (United States)

    Soman, Vishwas V.; Crosson, William L.; Laymon, Charles; Tsegaye, Teferi

    1998-01-01

    Soil moisture is an important component of analysis in many Earth science disciplines. Soil moisture information can be obtained either by using microwave remote sensing or by using a hydrologic model. In this study, we combined these two approaches to increase the accuracy of profile soil moisture estimation. A hydrologic model was used to analyze the errors in the estimation of soil moisture using the data collected during Huntsville '96 microwave remote sensing experiment in Huntsville, Alabama. Root mean square errors (RMSE) in soil moisture estimation increase by 22% with increase in the model input interval from 6 hr to 12 hr for the grass-covered plot. RMSEs were reduced for given model time step by 20-50% when model soil moisture estimates were updated using remotely-sensed data. This methodology has a potential to be employed in soil moisture estimation using rainfall data collected by a space-borne sensor, such as the Tropical Rainfall Measuring Mission (TRMM) satellite, if remotely-sensed data are available to update the model estimates.

  18. LIDAR and atmosphere remote sensing

    CSIR Research Space (South Africa)

    Venkataraman, S

    2008-05-01

    Full Text Available and to consist of theory and practical exercises • Theory: Remote sensing process, Photogrammetry, introduction to multispectral, remote sensing systems, Thermal infra-red remote sensing, Active and passive remote sensing, LIDAR, Application of remotely... Aerosol measurements and cloud characteristics head2right Water vapour measurements in the lower troposphere region up to 8 km head2right Ozone measurements in the troposphere regions up to 18 km Slide 22 © CSIR 2008 www...

  19. A remote sensing model for monitoring soil evaporation based on differential thermal inertia and its validation

    Institute of Scientific and Technical Information of China (English)

    张仁华; 孙晓敏; 朱治林; 苏红波; 唐新斋

    2003-01-01

    The presently applied remote sensing algorithms and approaches to monitor soil surface fluxes are reviewed at the beginning of this paper, and the bottleneck of the estimation of soil surface fluxes lies in the dependence on non remotely sensed parameters (NRSP). A soil surface evaporation model based on differential thermal inertia, only using remotely sensed information, has thus been proposed after many experiments. The key of the model is to derive soil moisture availability by differential thermal inertia rather than local soil parameters such as soil properties and type. Bowen ratio is estimated by means of soil moisture availability instead of NRSP, such as temperature and wind velocity. Net radiation flux and apparent thermal inertia have been used for soil heat flux parameterization, therefore, the objective of evaporation (latent heat flux) inversion for bare soil only by remotely sensed information can be realized. Two NOAA-AVHRR five-band images, taken at Shapotou northwest of China when soil surface temperature approximated to the highest and lowest of the region, were applied in combination with the ground surface information measured synchronously. The distribution of soil evaporation in Shapotou could be determined. Model verification has been performed between the measured soil surface evaporation and the corresponding calculated value of the images, and the result has proved model to be feasible. Finally, the possible errors and further modifications when applying model to fulling vegetation canopy have been discussed.

  20. 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

  1. Remote sensing image fusion

    CERN Document Server

    Alparone, Luciano; Baronti, Stefano; Garzelli, Andrea

    2015-01-01

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

  2. Application of Remote Sensing Data in a Distributed Hydrological Model for the Gambia River Basin

    Science.gov (United States)

    Stisen, S.; Sandholt, I.; Jensen, K. H.

    2003-12-01

    Distributed hydrological models have an extensive demand of high resolution spatial and temporal data for driving and validating the models. Most of the variables are only available as point measurements which impose serious constraints to the applicability and the credibility of such models particularly for large regional scales and in areas where the availability and quality of hydrological data are limited. Remote sensing information appears to offer useful data that not only can fill some of the gaps in data availability but also can supply data at the appropriate scale for distributed hydrological models. In this study we tested three types of remote sensing derived variables in a distributed hydrological model of the 42,000 km2 Gambia River Basin in West Africa: (1) potential evapotranspiration estimated by the Makkink equation and based on daily global radiation fields derived from the geostationary meteorological satellite Meteosat, (2) leaf area index (LAI) based on data from the MODIS satellite, and (3) Temperature Vegetation Dryness Index (TVDI) derived form NOAA AVHRR images. The remote sensing derived time series of potential evapotranspiration and LAI were used as input to the model while TVDI was used for validating the spatial simulations of soil moisture. The effects of introducing remote sensing based input were evaluated for both discharge simulations and spatial outputs by comparing the model simulations to those based on traditional data. Application of remote sensing based input of potential evapotranspiration and LAI had in both cases little effects on the simulated discharges while some effects were seen on the spatial and temporal variation of variables like actual evapotranspiration and soil moisture. Improved spatial simulations of these variables may potentially allow for better design of e.g. irrigation schemes. The comparative analysis of TVDI estimates and spatial model simulations of soil moisture content in the root zone was however

  3. Review of Hydrologic Models for Evaluating Use of Remote Sensing Capabilities

    Science.gov (United States)

    Peck, E. L.; Mcquivey, R. S.; Keefer, T.; Johnson, E. R.; Erekson, J. L.

    1982-01-01

    Hydrologic models most commonly used by federal agencies for hydrologic forecasting are reviewed. Six catchment models and one snow accumulation and ablation model are reviewed. Information on the structure, parameters, states, and required inputs is presented in schematic diagrams and in tables. The primary and secondary roles of parameters and state variables with respect to their function in the models are identified. The information will be used to evaluate the usefulness of remote sensing capabilities in the operational use of hydrologic models.

  4. Remote sensing and simulation modelling for on-demand irrigation systems management

    NARCIS (Netherlands)

    D'Urso, G.; Menenti, M.; Santini, A.

    1996-01-01

    This paper describes a procedure for monitoring and improving the performance of on-demand irrigation networks, based on the integration of remote sensing techniques and simulation modelling of water flow in each component of the system. In order to adequately reproduce the actual operation of an on

  5. Assimilation of remotely sensed latent heat flux in a distributed hydrological model

    NARCIS (Netherlands)

    Schuurmans, J.M.; Troch, P.A.A.; Veldhuizen, A.A.; Bastiaanssen, W.G.M.; Bierkens, M.F.P.

    2003-01-01

    This paper addresses the question of whether remotely sensed latent heat flux estimates over a catchment can be used to improve distributed hydrological model water balance computations by the process of data assimilation. The data used is a series of satellite images for the Drentse Aa catchment in

  6. Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling.

    Science.gov (United States)

    Walz, Yvonne; Wegmann, Martin; Leutner, Benjamin; Dech, Stefan; Vounatsou, Penelope; N'Goran, Eliézer K; Raso, Giovanna; Utzinger, Jürg

    2015-01-01

    Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d'Ivoire using high- and moderate-resolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixel-based modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.

  7. Integration of remote sensing based surface information into a three-dimensional microclimate model

    Science.gov (United States)

    Heldens, Wieke; Heiden, Uta; Esch, Thomas; Mueller, Andreas; Dech, Stefan

    2017-03-01

    Climate change urges cities to consider the urban climate as part of sustainable planning. Urban microclimate models can provide knowledge on the climate at building block level. However, very detailed information on the area of interest is required. Most microclimate studies therefore make use of assumptions and generalizations to describe the model area. Remote sensing data with area wide coverage provides a means to derive many parameters at the detailed spatial and thematic scale required by urban climate models. This study shows how microclimate simulations for a series of real world urban areas can be supported by using remote sensing data. In an automated process, surface materials, albedo, LAI/LAD and object height have been derived and integrated into the urban microclimate model ENVI-met. Multiple microclimate simulations have been carried out both with the dynamic remote sensing based input data as well as with manual and static input data to analyze the impact of the RS-based surface information and the suitability of the applied data and techniques. A valuable support of the integration of the remote sensing based input data for ENVI-met is the use of an automated processing chain. This saves tedious manual editing and allows for fast and area wide generation of simulation areas. The analysis of the different modes shows the importance of high quality height data, detailed surface material information and albedo.

  8. Rice Yield Estimation by Integrating Remote Sensing with Rice Growth Simulation Model

    Institute of Scientific and Technical Information of China (English)

    O. ABOU-ISMAIL; HUANG Jing-Feng; WANG Ren-Chao

    2004-01-01

    Since remote sensing can provide information on the actual status of an agricultural crop, the integration between remote sensing data and crop growth simulation models has become an important trend for yield estimation and prediction.The main objective of this research was to combine a rice growth simulation model with remote sensing data to estimate rice grain yield for different growing seasons leading to an assessment of rice yield at regional levels. Integration between NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data and the rice growth simulation model ORYZA1 to develop a new software, which was named as Rice-SRS Model, resulted in accurate estimates for rice yield in Shaoxing, China, with an estimation error reduced to 1.03% and 0.79% over-estimation and 0.79% under-estimation for early, single and late season rice, respectively. Selecting suitable dates for remote sensing images was an important factor which could influence estimation accuracy. Thus, given the different growing periods for each rice season, four images were needed for early and late rice, while five images were preferable for single season rice.Estimating rice yield using two or three images was possible, however, if images were obtained during the panicle initiation and heading stages.

  9. 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, surface te

  10. Assimilating data from remote sensing into a high-resolution global hydrological model

    Science.gov (United States)

    Lu, Yang; Sutanudjaja, Edwin; Drost, Niels; Hut, Rolf; Steele-Dunne, Susan; van de Giesen, Nick; de Jong, Kor; van Beek, Ludovicus; Bierkens, Marc

    2014-05-01

    This study is focused on the challenges of assimilating current and planned remote sensing data into the modified PCR-GLOB-WB model to yield optimal results. The development of a high-resolution (1 km or finer) global hydrological model has been put forward as 'Grand Challenge' for the hydrological community. Extensive assimilation of remote sensing data is a promising route to constrain and ensure the accuracy of such a hydrological model, but it poses a great challenge in many aspects. Over the last 30 years, advances in remote sensing techniques have triggered the exponential growth of hydrologically useful data from remote sensing. Aside from the ICT challenge of streaming and handing the sheer volume of data, and selecting an appropriate assimilation algorithm, the fundamental questions of which datasets contain the most useful information and how to use them must be addressed. The first task is to divide the candidate datasets into those that will be assimilated and those that will be used to parameterize or force the model. As the time step is reduced from daily to ~hourly, remote sensing data may play a crucial role in providing a more dynamic description of the land surface, or in downscaling the forcing data. Here, we will present a outline of the key processes in the PCR-GLOB-WB and a summary of which states and fluxes will benefit most from assimilation, and which model parameters can be modified to incorporate real-time information from remote sensing. Finally, we need to consider the gap in spatial scales. The PCR-GLOB-WB model is now running at 10 km resolution and will be modified to run at 1 km scale, while the spatial resolution of many remote sensing products is considerably coarser. We will present an overview of the downscaling approaches under consideration for key state variables. The eWaterCycle project is a collaboration between Delft University of Technology, Utrecht University and the Netherlands eScience Center. The final aim is to

  11. 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

  12. 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...... the geostationary METEOSAT-7 and the polar orbiting advanced very high resolution radiometer (AVHRR) sensors using well documented techniques. The distributed hydrological model MIKE SHE was calibrated and validated against observed discharge for six individual subcatchments during the period 1998-2005. The model...... outputs of AET from both model setups was carried out. This revealed substantial differences in the spatial patterns of AET for the examined subcatchment, in spite of similar values of predicted discharge and average AET. The potential for driving large scale hydrological models using remote sensing data...

  13. Remote sensing reflectance model of optically active components of turbid waters

    Science.gov (United States)

    Kutser, Tiit; Arst, Helgi

    1994-12-01

    A mathematical model that simulates the spectral curves of remote sensing reflectance is developed. The model is compared to measurements obtained from research vessel or boat in the Baltic Sea and Estonian lakes. The model simulates the effects of light backscattering from water and suspended matter, and the effects of its absorption due to water, phytoplankton, suspended matter and yellow substance. Measured by remote sensing spectral curves are compared by multiple of spectra obtained from model calculations to find the theoretical spectrum which is closest to experimental. It is assumed that in case of coincidence of the spectral curves concentrations of optically active substances in the model correspond to real ones. Preliminary testing of the model demonstrates that this model is useful for estimation of concentration of optically active substances in the waters of the Baltic Sea and Estonian lakes.

  14. EPA REMOTE SENSING RESEARCH

    Science.gov (United States)

    The 2006 transgenic corn imaging research campaign has been greatly assisted through a cooperative effort with several Illinois growers who provided planting area and crop composition. This research effort was designed to evaluate the effectiveness of remote sensed imagery of var...

  15. 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...

  16. Remote sensing: best practice

    Energy Technology Data Exchange (ETDEWEB)

    Brown, Gareth [Sgurr Energy (Canada)

    2011-07-01

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

  17. Modeling the land surface reflectance for optical remote sensing data in rugged terrain

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented.Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively,the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model.A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper.The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.

  18. Modeling the land surface reflectance for optical remote sensing data in rugged terrain

    Institute of Scientific and Technical Information of China (English)

    WEN JianGuang; LIU QinHuo; XIAO Qing; LIU Qiang; LI XiaoWen

    2008-01-01

    A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented. Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively, the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model. A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper. The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.

  19. 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.

  20. Simulation model of SAR remote sensing of turbulent wake of semi-elliptical submerged body

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    In terms of the 2-dimensional hydrodynamic simplified model of a semi-elliptical submerged body moving horizontally at high speed,by using the full-spectrum model of SAR(synthetic aperture radar) remote sensing and taking the effect of oceanic interior turbulence on surface gravity capillary waves into account, applying the k-ε model of turbulence with internal wave mixing, and adopting the Nasmyth spectrum of oceanic turbulence, the 2-dimensional simulation model of SAR remote sensing of this semi-elliptical submerged body is built up. Simulation by using this model at X band and C band is made in the northeastern South China Sea (21°00'N,119°00'E). Satisfactory results of the delay time and delay distance of turbulent surface wake of this semi-elliptical submerged body, as well as the minimum submerged depth at which this submerged body which cannot be discovered by SAR, are obtained through simulation.

  1. Remote sensing and numerical modeling of suspended sediment in Laguna de Terminos, Campeche, Mexico

    Science.gov (United States)

    Jensen, John R.; Kjerfve, Bjorn; Ramsey, Elijah W., III; Magill, Karen E.; Medeiros, Carmen

    1989-01-01

    It is necessary to understand the complex physical processes at work in coastal lagoons in order to manage them effectively. Improved methods of data collection and analysis must be found to provide synoptic, timely hydrodynamic information because of the sheer size of some lagoons and the difficulty of acquiring in situ data (particularly in the tropics). This paper summarizes research to model salinity and suspended sediment distributions in Laguna de Terminos, Mexico, using (1) a coupled hydrodynamic and dispersion model and (2) analysis of two Landsat Thematic Mapper images collected on November 25, 1984 and April 24, 1987. Atmospherically corrected chromaticity data derived from Thermatic Mapper data were significantly correlated with modeled total suspended sediment concentrations for the two dates. Comparison between numerically modeled and remotely sensed suspended sediment maps at 1.5 x 1.5 km resolution yielded a covariation map useful for identifying areas of discrepancy between the remotely sensed data and model output.

  2. Preface: Remote Sensing in Coastal Environments

    OpenAIRE

    Deepak R. Mishra; Gould, Richard W.

    2016-01-01

    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 i...

  3. Modeling α- and β-diversity in a tropical forest from remotely sensed and spatial data

    Science.gov (United States)

    Hernández-Stefanoni, J. Luis; Gallardo-Cruz, J. Alberto; Meave, Jorge A.; Rocchini, Duccio; Bello-Pineda, Javier; López-Martínez, J. Omar

    2012-10-01

    Comprehensive information on species distribution and species composition patterns of plant communities is required for effective conservation and management of biodiversity. Remote sensing offers an inexpensive means of attaining complete spatial coverage for large areas, at regular time intervals, and can therefore be extremely useful for estimating both species richness and spatial variation of species composition (α- and β-diversity). An essential step to map such attributes is to identify and understand their main drivers. We used remotely sensed data as a surrogate of plant productivity and habitat structure variables for explaining α- and β-diversity, and evaluated the relative roles of productivity-habitat structure and spatial variables in explaining observed patterns of α- and β-diversity by using a Principal Coordinates of Neighbor Matrices analysis. We also examined the relationship between remotely sensed and field data, in order to map α- and β-diversity at the landscape-level in the Yucatan Peninsula, using a regression kriging procedure. These two procedures integrate the relationship of species richness and spatial species turnover both with remotely sensed data and spatial structure. The empirical models so obtained can be used to predict species richness and variation in species composition, and they can be regarded as valuable tools not only for identifying areas with high local species richness (α-diversity), but also areas with high species turnover (β-diversity). Ultimately, information obtained in this way can help maximize the number of species preserved in a landscape.

  4. Remote sensing of aerosols in the Arctic for an evaluation of global climate model simulations.

    Science.gov (United States)

    Glantz, Paul; Bourassa, Adam; Herber, Andreas; Iversen, Trond; Karlsson, Johannes; Kirkevåg, Alf; Maturilli, Marion; Seland, Øyvind; Stebel, Kerstin; Struthers, Hamish; Tesche, Matthias; Thomason, Larry

    2014-07-16

    In this study Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua retrievals of aerosol optical thickness (AOT) at 555 nm are compared to Sun photometer measurements from Svalbard for a period of 9 years. For the 642 daily coincident measurements that were obtained, MODIS AOT generally varies within the predicted uncertainty of the retrieval over ocean (ΔAOT = ±0.03 ± 0.05 · AOT). The results from the remote sensing have been used to examine the accuracy in estimates of aerosol optical properties in the Arctic, generated by global climate models and from in situ measurements at the Zeppelin station, Svalbard. AOT simulated with the Norwegian Earth System Model/Community Atmosphere Model version 4 Oslo global climate model does not reproduce the observed seasonal variability of the Arctic aerosol. The model overestimates clear-sky AOT by nearly a factor of 2 for the background summer season, while tending to underestimate the values in the spring season. Furthermore, large differences in all-sky AOT of up to 1 order of magnitude are found for the Coupled Model Intercomparison Project phase 5 model ensemble for the spring and summer seasons. Large differences between satellite/ground-based remote sensing of AOT and AOT estimated from dry and humidified scattering coefficients are found for the subarctic marine boundary layer in summer. Remote sensing of AOT is very useful in validation of climate models.

  5. Remotely sensed latent heat fluxes for improving model predictions of soil moisture: a case study

    Directory of Open Access Journals (Sweden)

    J. M. Schuurmans

    2010-08-01

    Full Text Available This paper investigates whether the use of remotely sensed latent heat fluxes improves the accuracy of spatially-distributed soil moisture predictions by a hydrological model. By using real data we aim to show the potential and limitations in practice. We use (i satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL, provides us the spatial distribution of daily ETact and (ii an operational physically based distributed (25 m×25 m hydrological model of a small catchment (70 km2 in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Firstly, model outcomes of ETact are compared to the processed satellite data. Secondly, we perform data assimilation that updates the modelled soil moisture. We show that remotely sensed ETact is useful in hydrological modelling for two reasons. Firstly, in the procedure of model calibration: comparison of modeled and remotely sensed ETact together with the outcomes of our data assimilation procedure points out potential model errors (both conceptual and flux-related. Secondly, assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area with forest and deep groundwater levels. As both ASTER and MODIS images were available for the same days, this study provides also an excellent opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides much better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

  6. Recent Progresses in Atmospheric Remote Sensing Research in China-- Chinese National Report on Atmospheric Remote Sensing Research in China during 1999-2003

    Institute of Scientific and Technical Information of China (English)

    邱金桓; 陈洪滨

    2004-01-01

    Progresses of atmospheric remote sensing research in China during 1999-2003 are summarily introduced.This research includes: (1) microwave remote sensing of the atmosphere; (2) Lidar remote sensing; (3)remote sensing of aerosol optical properties; and (4) other research related to atmospheric remote sensing,including GPS remote sensing of precipitable water vapor and radiation model development.

  7. Estimation of Regional Evapotranspiration Using Remotely Sensed Land Surface Temperature. Part 2: Application of Equilibrium Evaporation Model to Estimate Evapotranspiration by Remote Sensing Technique. [Japan

    Science.gov (United States)

    Kotoda, K.; Nakagawa, S.; Kai, K.; Yoshino, M. M.; Takeda, K.; Seki, K.

    1985-01-01

    In a humid region like Japan, it seems that the radiation term in the energy balance equation plays a more important role for evapotranspiration then does the vapor pressure difference between the surface and lower atmospheric boundary layer. A Priestley-Taylor type equation (equilibrium evaporation model) is used to estimate evapotranspiration. Net radiation, soil heat flux, and surface temperature data are obtained. Only temperature data obtained by remotely sensed techniques are used.

  8. Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling

    Science.gov (United States)

    Thakur, Jay Krishna; Singh, Sudhir Kumar; Ekanthalu, Vicky Shettigondahalli

    2017-07-01

    Integration of remote sensing (RS), geographic information systems (GIS) and global positioning system (GPS) are emerging research areas in the field of groundwater hydrology, resource management, environmental monitoring and during emergency response. Recent advancements in the fields of RS, GIS, GPS and higher level of computation will help in providing and handling a range of data simultaneously in a time- and cost-efficient manner. This review paper deals with hydrological modeling, uses of remote sensing and GIS in hydrological modeling, models of integrations and their need and in last the conclusion. After dealing with these issues conceptually and technically, we can develop better methods and novel approaches to handle large data sets and in a better way to communicate information related with rapidly decreasing societal resources, i.e. groundwater.

  9. Basic Remote Sensing Investigations for Beach Reconnaissance.

    Science.gov (United States)

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

  10. Advanced laser remote sensing

    Energy Technology Data Exchange (ETDEWEB)

    Schultz, J.; Czuchlewski, S.; Karl, R. [and others

    1996-11-01

    This is the final report of a three-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory. Remote measurement of wind velocities is critical to a wide variety of applications such as environmental studies, weather prediction, aircraft safety, the accuracy of projectiles, bombs, parachute drops, prediction of the dispersal of chemical and biological warfare agents, and the debris from nuclear explosions. Major programs to develop remote sensors for these applications currently exist in the DoD and NASA. At present, however, there are no real-time, three-dimensional wind measurement techniques that are practical for many of these applications and we report on two new promising techniques. The first new technique uses an elastic backscatter lidar to track aerosol patterns in the atmosphere and to calculate three dimensional wind velocities from changes in the positions of the aerosol patterns. This was first done by Professor Ed Eloranta of the University of Wisconsin using post processing techniques and we are adapting Professor Eloranta`s algorithms to a real-time data processor and installing it in an existing elastic backscatter lidar system at Los Alamos (the XM94 helicopter lidar), which has a compatible data processing and control system. The second novel wind sensing technique is based on radio-frequency (RF) modulation and spatial filtering of elastic backscatter lidars. Because of their compactness and reliability, solid state lasers are the lasers of choice for many remote sensing applications, including wind sensing.

  11. Modeling and remote sensing of human induced water cycle change

    Science.gov (United States)

    Pokhrel, Yadu N.

    2016-04-01

    The global water cycle has been profoundly affected by human land-water management especially during the last century. Since the changes in water cycle can affect the functioning of a wide range of biophysical and biogeochemical processes of the Earth system, it is essential to account for human land-water management in land surface models (LSMs) which are used for water resources assessment and to simulate the land surface hydrologic processes within Earth system models (ESMs). During the last two decades, noteworthy progress has been made in modeling human impacts on the water cycle but sufficient advancements have not yet been made, especially in representing human factors in large-scale LSMs toward integrating them into ESMs. In this study, an integrated modeling framework of continental-scale water cycle, with explicit representation of climate and human induced forces (e.g., irrigation, groundwater pumping) is developed and used to reconstruct the observed water cycle changes in the past and to attribute the observed changes to climatic and human factors. The new model builds upon two different previously developed models: a global LSM called the Human Impacts and GroundWater in the MATSIRO (HiGW-MAT) and a high-resolution regional groundwater model called the LEAF-Hydro-Flood. The model is used to retro-simulate the hydrologic stores and fluxes in close dialogue with in-situ and GRACE satellite based observations at a wide range of river basin scales around the world, with a particular focus on the changes in groundwater dynamics in northwest India, Pakistan, and the High Plains and Central Valley aquifers in the US.

  12. FAO-56 Dual Model Combined with Multi-Sensor Remote Sensing for Regional Evapotranspiration Estimations

    Directory of Open Access Journals (Sweden)

    Rim Amri

    2014-06-01

    Full Text Available The main goal of this study is to evaluate the potential of the FAO-56 dual technique for the estimation of regional evapotranspiration (ET and its constituent components (crop transpiration and soil evaporation, for two classes of vegetation (olives trees and cereals in the semi-arid region of the Kairouan plain in central Tunisia. The proposed approach combines the FAO-56 technique with remote sensing (optical and microwave, not only for vegetation characterization, as proposed in other studies but also for the estimation of soil evaporation, through the use of satellite moisture products. Since it is difficult to use ground flux measurements to validate remotely sensed data at regional scales, comparisons were made with the land surface model ISBA-A-gs which is a physical SVAT (Soil–Vegetation–Atmosphere Transfer model, an operational tool developed by Météo-France. It is thus shown that good results can be obtained with this relatively simple approach, based on the FAO-56 technique combined with remote sensing, to retrieve temporal variations of ET. The approach proposed for the daily mapping of evapotranspiration at 1 km resolution is approved in two steps, for the period between 1991 and 2007. In an initial step, the ISBA-A-gs soil moisture outputs are compared with ERS/WSC products. Then, the output of the FAO-56 technique is compared with the output generated by the SVAT ISBA-A-gs model.

  13. 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.

  14. Remote Sensing of Alpha and Beta Sources - Modeling Summary

    Energy Technology Data Exchange (ETDEWEB)

    Dignon, J; Frank, M; Cherepy, N

    2005-10-20

    Evaluating the potential for optical detection of the products of interactions of energetic electrons or other particles with the background atmosphere depends on predictions of change in atmospheric concentrations of species which would generate detectable spectral signals within the range of observation. The solar blind region of the spectrum, in the ultra violet, would be the logical band for outdoor detection (see Figure 1). The chemistry relevant to these processes is composed of ion-molecule reactions involving the initially created N{sub 2}{sup +} and O{sub 2}{sup +} ions, and their subsequent interactions with ambient trace atmospheric constituents. Effective modeling of the atmospheric chemical system acted upon by energetic particles requires knowledge of the dominant mechanism that exchange charge and associate it with atmospheric constituents, kinetic parameters of the individual processes (see e.g. Brasseur and Solomon, 1995), and a solver for the coupled differential equations that is accurate for the very stiff set of time constants involved. The LLNL box model, VOLVO, simulates the diel cycle of trace constituent photochemistry for any point on the globe over the wide range of time scales present using a stiff Gear-type ODE solver, i.e. LSODE. It has been applied to problems such as tropospheric and stratospheric nitrogen oxides, stratospheric ozone production and loss, and tropospheric hydrocarbon oxidation. For this study we have included the appropriate ion flux.

  15. Comparison of Cloud Resolving Model Simulations to Remote Sensing Data

    Science.gov (United States)

    Randall, David A.; Eitzen, Zachary

    2005-01-01

    The purpose of this research was to evaluate the ability of a cloud-resolving model (CRM) to simulate the dynamical, radiative, and microphysical properties of deep convective cloud objects identified using CERES (Clouds and the Earth s Radiant Energy System) on board the Tropical Rainfall Measuring Mission (TRMM) satellite platform, for many cases. A deep convective cloud object is a contiguous region that is composed of satellite footprints that fulfill the following selection criteria: 100% cloud fraction, cloud optical depth > 10, and a cloud top height of at least 10 km. Selection criteria have also been formed for different types of boundary-layer clouds, as described in Xu et al. (2005). The purpose of the cloud object approach is to identify specific areas of where the cloud properties simulated by the CRM systematically differ from the observed cloud properties. Where these systematic differences exist, concrete steps can be made to improve the CRM s simulation of an entire class of clouds, rather than by tuning the model to correctly simulate a single case study, as is often done. Additional information regarding detailed approaches and findings are presented.

  16. Theoretical models for polarimetric microwave remote sensing of earth terrain

    Science.gov (United States)

    Borgeaud, M.; Nghiem, S. V.; Shin, R. T.; Kong, J. A.

    1989-01-01

    Using the two-layer anisotropic random medium, a mathematically rigorous, fully polarimetric model is developed to compute the Mueller and covariance matrices in the backscattering direction for various kinds of earth terrain. The electric field is first written in the form of an integral equation involving the unperturbed dyadic Green's function in the absence of the permittivity fluctuations. The integral equation is then solved by an iterative series known as the Born series. With only the first term of the series, which physically describes a single scattering process, the fully polarimetric backscattering coefficients are derived. Four different kinds of upgoing and downgoing waves exist due to the excitation of both ordinary and extraordinary waves in the anisotropic random medium. An averaging scheme over the azimuthal direction is used to simulate the effects on the radar backscattering due to the azimuthal randomness in the growth direction of leaves in tree and grass fields.

  17. 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.…

  18. 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.…

  19. 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.

  20. Use of fractal models in the Earth's remote sensing of the arctic zone

    Science.gov (United States)

    Berg, D. B.; Medvedev, A. N.; Manzhurov, I. L.; Taubaev, A. A.

    2016-12-01

    The development and practical application of new mathematical methods of processing, image analysis and pattern recognition has significant prospects for mapping the Earth from space. In the paper, it is proposed to use the fractal model of the surface contamination distribution, previously developed by the authors, for the analysis of color multispectral satellite images on the example of the territory of the Polar Urals. The research has shown the following: 1) The brightness distribution on remote sensing snapshot has a fractal character. 2) The values of fractal dimension of the territory images in different spectral ranges significantly differ. 3) The hierarchy of geomorphological structures in the range of 13-1700 m may be considered as self-similar. Thus, the proposed method of calculating the fractal dimension value of the snapshot may become one of the informative attributes for remote sensing images interpretation.

  1. 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.

  2. 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

  3. Remote sensing clustering analysis based on object-based interval modeling

    Science.gov (United States)

    He, Hui; Liang, Tianheng; Hu, Dan; Yu, Xianchuan

    2016-09-01

    In object-based clustering, image data are segmented into objects (groups of pixels) and then clustered based on the objects' features. This method can be used to automatically classify high-resolution, remote sensing images, but requires accurate descriptions of object features. In this paper, we ascertain that interval-valued data model is appropriate for describing clustering prototype features. With this in mind, we developed an object-based interval modeling method for high-resolution, multiband, remote sensing data. We also designed an adaptive interval-valued fuzzy clustering method. We ran experiments utilizing images from the SPOT-5 satellite sensor, for the Pearl River Delta region and Beijing. The results indicate that the proposed algorithm considers both the anisotropy of the remote sensing data and the ambiguity of objects. Additionally, we present a new dissimilarity measure for interval vectors, which better separates the interval vectors generated by features of the segmentation units (objects). This approach effectively limits classification errors caused by spectral mixing between classes. Compared with the object-based unsupervised classification method proposed earlier, the proposed algorithm improves the classification accuracy without increasing computational complexity.

  4. Modeling Residential Lawn Fertilization Practices: Integrating High Resolution Remote Sensing with Socioeconomic Data

    Science.gov (United States)

    Zhou, Weiqi; Troy, Austin; Grove, Morgan

    2008-05-01

    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 area and lawn greenness were derived from high-resolution aerial imagery using an object-oriented classification approach. Four indicators of household characteristics, including lot size, square footage of the house, housing value, and housing age were obtained from a property database. Residential lawn care survey data combined with remotely sensed parcel lawn area and greenness data were used to estimate two measures of household lawn fertilization practices, household annual fertilizer nitrogen application amount ( N_yr) and household annual fertilizer nitrogen application rate ( N_ha_yr). Using multiple regression with multi-model inferential procedures, we found that a combination of parcel lawn area and parcel lawn greenness best predicts N_yr, whereas a combination of parcel lawn greenness and lot size best predicts variation in N_ha_yr. Our analyses show that household fertilization practices can be effectively predicted by remotely sensed lawn indices and household characteristics. This has significant implications for urban watershed managers and modelers.

  5. Integration and management of massive remote-sensing data based on GeoSOT subdivision model

    Science.gov (United States)

    Li, Shuang; Cheng, Chengqi; Chen, Bo; Meng, Li

    2016-07-01

    Owing to the rapid development of earth observation technology, the volume of spatial information is growing rapidly; therefore, improving query retrieval speed from large, rich data sources for remote-sensing data management systems is quite urgent. A global subdivision model, geographic coordinate subdivision grid with one-dimension integer coding on 2n-tree, which we propose as a solution, has been used in data management organizations. However, because a spatial object may cover several grids, ample data redundancy will occur when data are stored in relational databases. To solve this redundancy problem, we first combined the subdivision model with the spatial array database containing the inverted index. We proposed an improved approach for integrating and managing massive remote-sensing data. By adding a spatial code column in an array format in a database, spatial information in remote-sensing metadata can be stored and logically subdivided. We implemented our method in a Kingbase Enterprise Server database system and compared the results with the Oracle platform by simulating worldwide image data. Experimental results showed that our approach performed better than Oracle in terms of data integration and time and space efficiency. Our approach also offers an efficient storage management system for existing storage centers and management systems.

  6. 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.

  7. Remote sensing Extraction model of redtide biomass by airborne hyperspectral technique

    Institute of Scientific and Technical Information of China (English)

    MA Yi; ZHANG Jie; CUI Ting-wei

    2006-01-01

    Our work is based on the known research results of inherent optical quality of ocean color constituents.According to optimized parameters and induced fluorescence term of chlorophyll, this paper puts forward a remote sensing reflectance model of sea water, which is fitted in Liaodong Bay of Bohai. An inverse model that can evaluate redtide biomass according to chlorophyll retrieval is provided by inducing a functional extreme problem. The calculation example of the model indicates that the inversion model has explicit mathematic and physical meaning, but its practicability needs to be verified.

  8. 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.

  9. Advances in remote sensing and modeling of terrestrial hydro-meteorological processes and extremes

    Science.gov (United States)

    Remote sensing is an indispensable tool for monitoring and detecting the evolution of the Earth’s hydro-meteorological processes. Fast-growing remote sensing observations and technologies have been a primary impetus to advancing our knowledge of hydro-meteorological processes and their extremes ove...

  10. 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

  11. Preface: Remote Sensing of Water Resources

    OpenAIRE

    Deepak R. Mishra; Eurico J. D’Sa; Sachidananda Mishra

    2016-01-01

    The Special Issue (SI) on “Remote Sensing of Water Resources” presents a diverse range of papers studying remote sensing tools, methods, and models to better monitor water resources which include inland, coastal, and open ocean waters. The SI is comprised of fifteen articles on widely ranging research topics related to water bodies. This preface summarizes each article published in the SI.

  12. Better interpretation of snow remote sensing data with physics-based models

    Science.gov (United States)

    Sandells, M.; Davenport, I. J.; Quaife, T. L.; Flerchinger, G. N.; Marks, D. G.; Gurney, R. J.

    2012-12-01

    Interpretation of remote sensing data requires a model and some assumptions, and the quality of the end product depends on the accuracy and appropriateness of these. Snow is a vital component of the water cycle, both socially and economically, so accurate monitoring of this resource is important. However, the snow mass products from passive microwave data may have large errors in them, and were deemed too unreliable for consideration in the latest Intergovernmental Panel on Climate Change Assessment Report. The SSM/I passive microwave snow mass retrieval algorithm uses a linear brightness temperature difference model, and assumptions that snow has a fixed grain diameter of 0.8mm and density of 300 kg m-3. In reality, the properties of the snow vary in time and space depending on its thermal history, and scattering of microwave radiation is very sensitive to snow properties. If snow mass retrievals are to be made from remote sensing data, then these properties must be known rather well. Layered physics-based models are capable of simulating the evolution of profiles of temperature, water content in the snow or soil, and snow grain size. These simulations could be used to provide information to help understand remote sensing data. Additional information from other remote sensing sources could enhance the accuracy of the product. For example, surface snow grain size can be obtained from near-infrared reflectance observations, and these data can be used to constrain the physically-based model, as could thermal observations. Here, we will present a new method that could be used to derive better estimates of snow mass and soil moisture. The system is comprised of a physically-based model of the snow and soil to derive snow and soil properties, a snow microwave emission model to estimate the satellite observations and ancillary data to constrain the physically-based model. These components will be used to estimate snow mass from passive microwave data with data

  13. 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....... The performance of the DTD model was improved in forested ecosystems and during senescence, by taking into account the fraction of the vegetation that is green, as well as during dry conditions and in temperate climates, by modifying certain model formulations. A disaggregation algorithm was also developed...

  14. 3D subsurface geological modeling using GIS, remote sensing, and boreholes data

    Science.gov (United States)

    Kavoura, Katerina; Konstantopoulou, Maria; Kyriou, Aggeliki; Nikolakopoulos, Konstantinos G.; Sabatakakis, Nikolaos; Depountis, Nikolaos

    2016-08-01

    The current paper presents the combined use of geological-geotechnical insitu data, remote sensing data and GIS techniques for the evaluation of a subsurface geological model. High accuracy Digital Surface Model (DSM), airphotos mosaic and satellite data, with a spatial resolution of 0.5m were used for an othophoto base map compilation of the study area. Geological - geotechnical data obtained from exploratory boreholes and the 1:5000 engineering geological maps were digitized and implemented in a GIS platform for a three - dimensional subsurface model evaluation. The study is located at the North part of Peloponnese along the new national road.

  15. Remote Sensing and Imaging Physics

    Science.gov (United States)

    2012-03-07

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

  16. 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...

  17. 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...

  18. A quad-pol radar scattering model for use in remote sensing of lava flow morphology

    Science.gov (United States)

    Campbell, Bruce A.; Zisk, Stanley H.; Mouginis-Mark, Peter J.

    1989-01-01

    Mapping of spatial variations in surface roughness over large regions is required to understand the nature of volcanic terrains. An invertible scattering model for quad-polarization radar data is presented to assist in the remote-sensing analysis of lava flow surface morphology. This model permits separation of the polarized part of the radar echo into quasispecular, dihedral, and small-perturbation scatterin components, based on an assumed surface dielectric constant. Tests are presented for a quad-pol scene of Craters of the Moon National Monument in Idaho, where there are a number of basaltic lava flows of differing surface morphology. Comparison of calculated model components with the observed morphology of the lava flows suggests that this technique may be useful for the remote description of changes in surface roughness. The scattering mechanisms chosen to represent the polarizing behavior of the real surface display correlations which indicate that they are sensitive to the expected scales of roughness.

  19. 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.

  20. Probability theory for 3-layer remote sensing radiative transfer model: univariate case.

    Science.gov (United States)

    Ben-David, Avishai; Davidson, Charles E

    2012-04-23

    A probability model for a 3-layer radiative transfer model (foreground layer, cloud layer, background layer, and an external source at the end of line of sight) has been developed. The 3-layer model is fundamentally important as the primary physical model in passive infrared remote sensing. The probability model is described by the Johnson family of distributions that are used as a fit for theoretically computed moments of the radiative transfer model. From the Johnson family we use the SU distribution that can address a wide range of skewness and kurtosis values (in addition to addressing the first two moments, mean and variance). In the limit, SU can also describe lognormal and normal distributions. With the probability model one can evaluate the potential for detecting a target (vapor cloud layer), the probability of observing thermal contrast, and evaluate performance (receiver operating characteristics curves) in clutter-noise limited scenarios. This is (to our knowledge) the first probability model for the 3-layer remote sensing geometry that treats all parameters as random variables and includes higher-order statistics.

  1. Remote Sensing Information Science Research

    Science.gov (United States)

    Clarke, Keith C.; Scepan, Joseph; Hemphill, Jeffrey; Herold, Martin; Husak, Gregory; Kline, Karen; Knight, Kevin

    2002-01-01

    This document is the final report summarizing research conducted by the Remote Sensing Research Unit, Department of Geography, University of California, Santa Barbara under National Aeronautics and Space Administration Research Grant NAG5-10457. This document describes work performed during the period of 1 March 2001 thorough 30 September 2002. This report includes a survey of research proposed and performed within RSRU and the UCSB Geography Department during the past 25 years. A broad suite of RSRU research conducted under NAG5-10457 is also described under themes of Applied Research Activities and Information Science Research. This research includes: 1. NASA ESA Research Grant Performance Metrics Reporting. 2. Global Data Set Thematic Accuracy Analysis. 3. ISCGM/Global Map Project Support. 4. Cooperative International Activities. 5. User Model Study of Global Environmental Data Sets. 6. Global Spatial Data Infrastructure. 7. CIESIN Collaboration. 8. On the Value of Coordinating Landsat Operations. 10. The California Marine Protected Areas Database: Compilation and Accuracy Issues. 11. Assessing Landslide Hazard Over a 130-Year Period for La Conchita, California Remote Sensing and Spatial Metrics for Applied Urban Area Analysis, including: (1) IKONOS Data Processing for Urban Analysis. (2) Image Segmentation and Object Oriented Classification. (3) Spectral Properties of Urban Materials. (4) Spatial Scale in Urban Mapping. (5) Variable Scale Spatial and Temporal Urban Growth Signatures. (6) Interpretation and Verification of SLEUTH Modeling Results. (7) Spatial Land Cover Pattern Analysis for Representing Urban Land Use and Socioeconomic Structures. 12. Colorado River Flood Plain Remote Sensing Study Support. 13. African Rainfall Modeling and Assessment. 14. Remote Sensing and GIS Integration.

  2. Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases

    Institute of Scientific and Technical Information of China (English)

    GONG; Peng

    2006-01-01

    Similar to species immigration or exotic species invasion, infectious disease transmission is strengthened due to the globalization of human activities. Using schistosomiasis as an example, we propose a conceptual model simulating the spatio-temporal dynamics of infectious diseases. We base the model on the knowledge of the interrelationship among the source, media, and the hosts of the disease. With the endemics data of schistosomiasis in Xichang, China, we demonstrate that the conceptual model is feasible; we introduce how remote sensing and geographic information systems techniques can be used in support of spatio-temporal modeling; we compare the different effects caused to the entire population when selecting different groups of people for schistosomiasis control. Our work illustrates the importance of such a modeling tool in supporting spatial decisions. Our modeling method can be directly applied to such infectious diseases as the plague, lyme disease, and hemorrhagic fever with renal syndrome. The application of remote sensing and geographic information systems can shed light on the modeling of other infectious disease and invasive species studies.

  3. Development of an invasive species distribution model with fine-resolution remote sensing

    Science.gov (United States)

    Diao, Chunyuan; Wang, Le

    2014-08-01

    Saltcedar (Tamarix spp.) is recognized as one of the most aggressively invasive species throughout the Western United States. Mapping its suitable habitat is of paramount importance to effective management, and thus, becomes a high priority for conservation practitioners. In previous studies, species distribution models (SDMs) have been applied to predicting the suitable habitats of saltcedar at national scale, but at coarser spatial resolution (1 km). Although such studies achieved some success, they are lacking of capability to accommodate fine-scale resolution environmental variables, and therefore, fail to uncover detailed spatial pattern of habitats. The objective of this study was to develop a remote sensing driven SDM so as to characterize suitable habitats of saltcedar at very fine spatial scale (30 m). We exploited several fine-scale environmental predictors through remote sensing images, and utilized the logistic regression model to analyze the species-habitat relationship by identifying influential factors with subset selection criteria. We also incorporated the spatial autocorrelation with regression kriging method. Our results indicated that the model incorporating spatial autocorrelation achieved a higher accuracy than that of regression only model. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the harmonic analysis were regarded as the most significant in predicting the invasive potential of saltcedar. We conclude that remote sensing driven SDM has potential to identify the suitable habitat of saltcedar at a fine scale and locate appropriate areas at high risk of saltcedar infestation, which could benefit the early control and proactive management strategies to a large extent.

  4. 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 < 0.44 t/ha, MAPE < 10.8%). Finally, feeding PILOTE with noisy 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

  5. Modeling uncertainties in estimation of canopy LAI from hyperspectral remote sensing data - A Bayesian approach

    Science.gov (United States)

    Varvia, Petri; Rautiainen, Miina; Seppänen, Aku

    2017-04-01

    Hyperspectral remote sensing data carry information on the leaf area index (LAI) of forests, and thus in principle, LAI can be estimated based on the data by inverting a forest reflectance model. However, LAI is usually not the only unknown in a reflectance model; especially, the leaf spectral albedo and understory reflectance are also not known. If the uncertainties of these parameters are not accounted for, the inversion of a forest reflectance model can lead to biased estimates for LAI. In this paper, we study the effects of reflectance model uncertainties on LAI estimates, and further, investigate whether the LAI estimates could recover from these uncertainties with the aid of Bayesian inference. In the proposed approach, the unknown leaf albedo and understory reflectance are estimated simultaneously with LAI from hyperspectral remote sensing data. The feasibility of the approach is tested with numerical simulation studies. The results show that in the presence of unknown parameters, the Bayesian LAI estimates which account for the model uncertainties outperform the conventional estimates that are based on biased model parameters. Moreover, the results demonstrate that the Bayesian inference can also provide feasible measures for the uncertainty of the estimated LAI.

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    Heng Sun

    Full Text Available 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.

  8. Suitability of Lake Erie for bigheaded carps based on bioenergetic models and remote sensing

    Science.gov (United States)

    Anderson, Karl R.; Chapman, Duane C.; Wynne, Timothy; Masagounder, Karthik; Paukert, Craig

    2015-01-01

    Algal blooms in the Great Lakes are a potential food source for silver carp (Hypophthalmichthys molitrix) and bighead carp (H. nobilis; together bigheaded carps). Understanding these blooms thus plays an important role in understanding the invasion potential of bigheaded carps. We used remote sensing imagery, temperatures, and improved species specific bioenergetics models to determine algal concentrations sufficient for adult bigheaded carps. Depending on water temperature we found that bigheaded carp require between 2 and 7 μg/L chlorophyll or between 0.3 and 1.26 × 105cells/mL Microcystis to maintain body weight. Algal concentrations in the western basin and shoreline were found to be commonly several times greater than the concentrations required for weight maintenance. The remote sensing images show that area of sufficient algal foods commonly encompassed several hundred square kilometers to several thousands of square kilometers when blooms form. From 2002 to 2011, mean algal concentrations increased 273%–411%. This indicates Lake Erie provides increasingly adequate planktonic algal food for bigheaded carps. The water temperatures and algal concentrations detected in Lake Erie from 2008 to 2012 support positive growth rates such that a 4 kg silver carp could gain between 19 and 57% of its body weight in a year. A 5 kg bighead carp modeled at the same water temperatures could gain 20–81% of their body weight in the same period. The remote sensing imagery and bioenergetic models suggest that bigheaded carps would not be food limited if they invaded Lake Erie.

  9. 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.

  10. A multitemporal remote sensing approach to parsimonious streamflow modeling in a southcentral Texas watershed, USA

    Directory of Open Access Journals (Sweden)

    B. P. Weissling

    2007-01-01

    Full Text Available Soil moisture condition plays a vital role in a watershed's hydrologic response to a precipitation event and is thus parameterized in most, if not all, rainfall-runoff models. Yet the soil moisture condition antecedent to an event has proven difficult to quantify both spatially and temporally. This study assesses the potential to parameterize a parsimonious streamflow prediction model solely utilizing precipitation records and multi-temporal remotely sensed biophysical variables (i.e.~from Moderate Resolution Imaging Spectroradiometer (MODIS/Terra satellite. This study is conducted on a 1420 km2 rural watershed in the Guadalupe River basin of southcentral Texas, a basin prone to catastrophic flooding from convective precipitation events. A multiple regression model, accounting for 78% of the variance of observed streamflow for calendar year 2004, was developed based on gauged precipitation, land surface temperature, and enhanced vegetation Index (EVI, on an 8-day interval. These results compared favorably with streamflow estimations utilizing the Natural Resources Conservation Service (NRCS curve number method and the 5-day antecedent moisture model. This approach has great potential for developing near real-time predictive models for flood forecasting and can be used as a tool for flood management in any region for which similar remotely sensed data are available.

  11. A multitemporal remote sensing approach to parsimonious streamflow modeling in a southcentral Texas watershed, USA

    Science.gov (United States)

    Weissling, B. P.; Xie, H.; Murray, K. E.

    2007-01-01

    Soil moisture condition plays a vital role in a watershed's hydrologic response to a precipitation event and is thus parameterized in most, if not all, rainfall-runoff models. Yet the soil moisture condition antecedent to an event has proven difficult to quantify both spatially and temporally. This study assesses the potential to parameterize a parsimonious streamflow prediction model solely utilizing precipitation records and multi-temporal remotely sensed biophysical variables (i.e.~from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite). This study is conducted on a 1420 km2 rural watershed in the Guadalupe River basin of southcentral Texas, a basin prone to catastrophic flooding from convective precipitation events. A multiple regression model, accounting for 78% of the variance of observed streamflow for calendar year 2004, was developed based on gauged precipitation, land surface temperature, and enhanced vegetation Index (EVI), on an 8-day interval. These results compared favorably with streamflow estimations utilizing the Natural Resources Conservation Service (NRCS) curve number method and the 5-day antecedent moisture model. This approach has great potential for developing near real-time predictive models for flood forecasting and can be used as a tool for flood management in any region for which similar remotely sensed data are available.

  12. A Remote-Sensing Mission

    Science.gov (United States)

    Hotchkiss, Rose; Dickerson, Daniel

    2008-01-01

    Sponsored by NASA and the JASON Education Foundation, the remote Sensing Earth Science Teacher Education Program (RSESTeP) trains teachers to use state-of-the art remote-sensing technology with the idea that participants bring back what they learn and incorporate it into Earth science lessons using technology. The author's participation in the…

  13. A Remote-Sensing Mission

    Science.gov (United States)

    Hotchkiss, Rose; Dickerson, Daniel

    2008-01-01

    Sponsored by NASA and the JASON Education Foundation, the remote Sensing Earth Science Teacher Education Program (RSESTeP) trains teachers to use state-of-the art remote-sensing technology with the idea that participants bring back what they learn and incorporate it into Earth science lessons using technology. The author's participation in the…

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

    DEFF Research Database (Denmark)

    Desholm, M.

    ,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......-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...

  15. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data

    Science.gov (United States)

    Shimabukuro, Yosio Edemir; Smith, James A.

    1991-01-01

    Constrained-least-squares and weighted-least-squares mixing models for generating fraction images derived from remote sensing multispectral data are presented. An experiment considering three components within the pixels-eucalyptus, soil (understory), and shade-was performed. The generated fraction images for shade (shade image) derived from these two methods were compared by considering the performance and computer time. The derived shade images are related to the observed variation in forest structure, i.e., the fraction of inferred shade in the pixel is related to different eucalyptus ages.

  16. Microwave propagation and remote sensing atmospheric influences with models and applications

    CERN Document Server

    Karmakar, Pranab Kumar

    2011-01-01

    Because prevailing atmospheric/troposcopic conditions greatly influence radio wave propagation above 10 GHz, the unguided propagation of microwaves in the neutral atmosphere can directly impact many vital applications in science and engineering. These include transmission of intelligence, and radar and radiometric applications used to probe the atmosphere, among others. Where most books address either one or the other, Microwave Propagation and Remote Sensing: Atmospheric Influences with Models and Applications melds coverage of these two subjects to help readers develop solutions to the probl

  17. 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.

  18. Ice phase in altocumulus clouds over Leipzig: remote sensing observations and detailed modeling

    Science.gov (United States)

    Simmel, M.; Bühl, J.; Ansmann, A.; Tegen, I.

    2015-09-01

    The present work combines remote sensing observations and detailed cloud modeling to investigate two altocumulus cloud cases observed over Leipzig, Germany. A suite of remote sensing instruments was able to detect primary ice at rather high temperatures of -6 °C. For comparison, a second mixed phase case at about -25 °C is introduced. To further look into the details of cloud microphysical processes, a simple dynamics model of the Asai-Kasahara (AK) type is combined with detailed spectral microphysics (SPECS) forming the model system AK-SPECS. Vertical velocities are prescribed to force the dynamics, as well as main cloud features, to be close to the observations. Subsequently, sensitivity studies with respect to ice microphysical parameters are carried out with the aim to quantify the most important sensitivities for the cases investigated. For the cases selected, the liquid phase is mainly determined by the model dynamics (location and strength of vertical velocity), whereas the ice phase is much more sensitive to the microphysical parameters (ice nucleating particle (INP) number, ice particle shape). The choice of ice particle shape may induce large uncertainties that are on the same order as those for the temperature-dependent INP number distribution.

  19. Ice phase in altocumulus clouds over Leipzig: remote sensing observations and detailed modelling

    Science.gov (United States)

    Simmel, M.; Bühl, J.; Ansmann, A.; Tegen, I.

    2015-01-01

    The present work combines remote sensing observations and detailed cloud modeling to investigate two altocumulus cloud cases observed over Leipzig, Germany. A suite of remote sensing instruments was able to detect primary ice at rather warm temperatures of -6 °C. For comparison, a second mixed phase case at about -25 °C is introduced. To further look into the details of cloud microphysical processes a simple dynamics model of the Asai-Kasahara type is combined with detailed spectral microphysics forming the model system AK-SPECS. Vertical velocities are prescribed to force the dynamics as well as main cloud features to be close to the observations. Subsequently, sensitivity studies with respect to ice microphysical parameters are carried out with the aim to quantify the most important sensitivities for the cases investigated. For the cases selected, the liquid phase is mainly determined by the model dynamics (location and strength of vertical velocity) whereas the ice phase is much more sensitive to the microphysical parameters (ice nuclei (IN) number, ice particle shape). The choice of ice particle shape may induce large uncertainties which are in the same order as those for the temperature-dependent IN number distribution.

  20. 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.

  1. An Optical Model for Estimating the Underwater Light Field from Remote Sensing

    Science.gov (United States)

    Liu, Cheng-Chien; Miller, Richard L.

    2002-01-01

    A model of the wavelength-integrated scalar irradiance for a vertically homogeneous water column is developed. It runs twenty thousand times faster than simulations obtained using full Hydrolight code and limits the percentage error to less than 3.7%. Both the distribution of incident sky radiance and a wind-roughened surface are integrated in the model. Our model removes common limitations of earlier models and can be applied to waters with any composition of the inherent optical properties. Implementation of this new model, as well as the ancillary information required for processing global-scale satellite data, is discussed. This new model is fast, accurate, and flexible and therefore provides important information of the underwater light field from remote sensing.

  2. 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.

  3. Modelling the water balance of a mesoscale catchment basin using remotely sensed land cover data

    Science.gov (United States)

    Montzka, Carsten; Canty, Morton; Kunkel, Ralf; Menz, Gunter; Vereecken, Harry; Wendland, Frank

    2008-05-01

    SummaryHydrological modelling of mesoscale catchments is often adversely affected by a lack of adequate information about specific site conditions. In particular, digital land cover data are available from data sets which were acquired on a European or a national scale. These data sets do not only exhibit a restricted spatial resolution but also a differentiation of crops and impervious areas which is not appropriate to the needs of mesoscale hydrological models. In this paper, the impact of remote sensing data on the reliability of a water balance model is investigated and compared to model results determined on the basis of CORINE (Coordination of Information on the Environment) Land Cover as a reference. The aim is to quantify the improved model performance achieved by an enhanced land cover representation and corresponding model modifications. Making use of medium resolution satellite imagery from SPOT, LANDSAT ETM+ and ASTER, detailed information on land cover, especially agricultural crops and impervious surfaces, was extracted over a 5-year period (2000-2004). Crop-specific evapotranspiration coefficients were derived by using remote sensing data to replace grass reference evapotranspiration necessitated by the use of CORINE land cover for rural areas. For regions classified as settlement or industrial areas, degrees of imperviousness were derived. The data were incorporated into the hydrological model GROWA (large-scale water balance model), which uses an empirical approach combining distributed meteorological data with distributed site parameters to calculate the annual runoff components. Using satellite imagery in combination with runoff data from gauging stations for the years 2000-2004, the actual evapotranspiration calculation in GROWA was methodologically extended by including empirical crop coefficients for actual evapotranspiration calculations. While GROWA originally treated agricultural areas as homogeneous, now a consideration and differentiation

  4. 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%.

  5. Using Remote Sensing Data to Parameterize Ice Jam Modeling for a Northern Inland Delta

    Directory of Open Access Journals (Sweden)

    Fan Zhang

    2017-04-01

    Full Text Available The Slave River is a northern river in Canada, with ice being an important component of its flow regime for at least half of the year. During the spring breakup period, ice jams and ice-jam flooding can occur in the Slave River Delta, which is of benefit for the replenishment of moisture and sediment required to maintain the ecological integrity of the delta. To better understand the ice jam processes that lead to flooding, as well as the replenishment of the delta, the one-dimensional hydraulic river ice model RIVICE was implemented to simulate and explore ice jam formation in the Slave River Delta. Incoming ice volume, a crucial input parameter for RIVICE, was determined by the novel approach of using MODIS space-born remote sensing imagery. Space-borne and air-borne remote sensing data were used to parameterize the upstream ice volume available for ice jamming. Gauged data was used to complement modeling calibration and validation. HEC-RAS, another one-dimensional hydrodynamic model, was used to determine ice volumes required for equilibrium jams and the upper limit of ice volume that a jam can sustain, as well as being used as a threshold for the volumes estimated by the dynamic ice jam simulations using RIVICE. Parameter sensitivity analysis shows that morphological and hydraulic properties have great impacts on the ice jam length and water depth in the Slave River Delta.

  6. 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

  7. 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

  8. 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

  9. 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

    -series of precipitation from 112 stations in the basin. The model was calibrated and validated based on river discharge data from nine stations in the basin for 11 years. Calibration and validation results suggested that the spatial resolution of the input data in parts of the area was not sufficient for a satisfactory...... 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...

  10. 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

  11. 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

  12. Modelling for registration of remotely sensed imagery when reference control points contain error

    Institute of Scientific and Technical Information of China (English)

    GE; Yong; Leung; Yee; MA; Jianghong; WANG; Jinfeng

    2006-01-01

    Reference control points (RCPs) used in establishing the regression model in the registration or geometric correction of remote sensing images are generally assumed to be "perfect". That is, the RCPs, as explanatory variables in the regression equation, are accurate and the coordinates of their locations have no errors. Thus ordinary least squares (OLS) estimator has been applied extensively to the registration or geometric correction of remotely sensed data. However, this assumption is often invalid in practice because RCPs always contain errors. Moreover, the errors are actually one of the main sources which lower the accuracy of geometric correction of an uncorrected image. Under this situation, the OLS estimator is biased. It cannot handle explanatory variables with errors and cannot propagate appropriately errors from the RCPs to the corrected image. Therefore, it is essential to develop new feasible methods to overcome such a problem. This paper introduces a consistent adjusted least squares (CALS) estimator and proposes relaxed consistent adjusted least squares (RCALS) estimator, with the latter being more general and flexible, for geometric correction or registration. These estimators have good capability in correcting errors contained in the RCPs, and in propagating appropriately errors of the RCPs to the corrected image with and without prior information.The objective of the CALS and proposed RCALS estimators is to improve the accuracy of measurement value by weakening the measurement errors. The conceptual arguments are substantiated by a real remotely sensed data. Compared to the OLS estimator, the CALS and RCALS estimators give a superior overall performance in estimating the regression coefficients and variance of measurement errors.

  13. A Simple Model for Estimating Evapotranspiration Based Solely on Remote Sensing: Algorithm and Application

    Science.gov (United States)

    Sun, Z.; Wang, Q.; Matsushita, B.; Fukushima, T.; Ouyang, Z.; Gebremichael, M.

    2009-12-01

    Remote sensing (RS) has been considered as the most promising tool for evapotranspiration (ET) estimations from local, regional to global scales. Many studies have been conducted to estimated ET using RS data, however, most of them are based partially on ground observations. This limits the applications of these algorithms when the necessary data are unavailable. Some other algorithms can generate real-time ET solely using remote sensing data, but lack mechanistic realism. In our study, we developed a new dual-source Simple Remote Sensing EvapoTranspiration model (Sim-ReSET) based only on RS data. One merit of this model is that the calculation of aerodynamic resistance can be avoided by means of a reference dry bare soil and an assumption that wind speed at the upper boundary of atmospheric surface layer is homogenous, but the aerodynamic characters are still considered by means of canopy height. The other merit is that all inputs (net radiation, soil heat flux, canopy height, variables related to land surface temperature) can be potentially obtained from remote sensing data, which allows obtaining regular RS-driven ET product. For the purposes of sensitivity analysis and performance evaluation of the Sim-ReSET model without the effect of potential uncertainties and errors from remote sensing data, the Sim-ReSET model was tested only using intensive ground observations at the Yucheng ecological station in the North China Plain from 2006 to 2008. Results show that the model has a good performance for instantaneous ET estimations with a mean absolute difference (MAD) of 34.27 W/m2 and a root mean square error (RMSE) of 41.84 W/m2 under neutral or near-neutral atmospheric conditions. On 12 cloudless days, the MAD of daily ET accumulated from instantaneous estimations is 0.26 mm/day, and the RMSE is 0.30 mm/day. In our study, we mapped Asian 16-day ET from 2000 to 2009 only using MODIS land data products based on the Sim-ReSET model. Then, the obtained ET product was

  14. Synergistic use of an oil drift model and remote sensing observations for oil spill monitoring.

    Science.gov (United States)

    De Padova, Diana; Mossa, Michele; Adamo, Maria; De Carolis, Giacomo; Pasquariello, Guido

    2017-02-01

    In case of oil spills due to disasters, one of the environmental concerns is the oil trajectories and spatial distribution. To meet these new challenges, spill response plans need to be upgraded. An important component of such a plan would be models able to simulate the behaviour of oil in terms of trajectories and spatial distribution, if accidentally released, in deep water. All these models need to be calibrated with independent observations. The aim of the present paper is to demonstrate that significant support to oil slick monitoring can be obtained by the synergistic use of oil drift models and remote sensing observations. Based on transport properties and weathering processes, oil drift models can indeed predict the fate of spilled oil under the action of water current velocity and wind in terms of oil position, concentration and thickness distribution. The oil spill event that occurred on 31 May 2003 in the Baltic Sea offshore the Swedish and Danish coasts is considered a case study with the aim of producing three-dimensional models of sea circulation and oil contaminant transport. The High-Resolution Limited Area Model (HIRLAM) is used for atmospheric forcing. The results of the numerical modelling of current speed and water surface elevation data are validated by measurements carried out in Kalmarsund, Simrishamn and Kungsholmsfort stations over a period of 18 days and 17 h. The oil spill model uses the current field obtained from a circulation model. Near-infrared (NIR) satellite images were compared with numerical simulations. The simulation was able to predict both the oil spill trajectories of the observed slick and thickness distribution. Therefore, this work shows how oil drift modelling and remotely sensed data can provide the right synergy to reproduce the timing and transport of the oil and to get reliable estimates of thicknesses of spilled oil to prepare an emergency plan and to assess the magnitude of risk involved in case of oil spills due

  15. "One-Stop Shopping" for Ocean Remote-Sensing and Model Data

    Science.gov (United States)

    Li, P. Peggy; Vu, Quoc; Chao, Yi; Li, Zhi-Jin; Choi, Jei-Kook

    2006-01-01

    OurOcean Portal 2.0 (http:// ourocean.jpl.nasa.gov) is a software system designed to enable users to easily gain access to ocean observation data, both remote-sensing and in-situ, configure and run an Ocean Model with observation data assimilated on a remote computer, and visualize both the observation data and the model outputs. At present, the observation data and models focus on the California coastal regions and Prince William Sound in Alaska. This system can be used to perform both real-time and retrospective analyses of remote-sensing data and model outputs. OurOcean Portal 2.0 incorporates state-of-the-art information technologies (IT) such as MySQL database, Java Web Server (Apache/Tomcat), Live Access Server (LAS), interactive graphics with Java Applet at the Client site and MatLab/GMT at the server site, and distributed computing. OurOcean currently serves over 20 real-time or historical ocean data products. The data are served in pre-generated plots or their native data format. For some of the datasets, users can choose different plotting parameters and produce customized graphics. OurOcean also serves 3D Ocean Model outputs generated by ROMS (Regional Ocean Model System) using LAS. The Live Access Server (LAS) software, developed by the Pacific Marine Environmental Laboratory (PMEL) of the National Oceanic and Atmospheric Administration (NOAA), is a configurable Web-server program designed to provide flexible access to geo-referenced scientific data. The model output can be views as plots in horizontal slices, depth profiles or time sequences, or can be downloaded as raw data in different data formats, such as NetCDF, ASCII, Binary, etc. The interactive visualization is provided by graphic software, Ferret, also developed by PMEL. In addition, OurOcean allows users with minimal computing resources to configure and run an Ocean Model with data assimilation on a remote computer. Users may select the forcing input, the data to be assimilated, the

  16. Spatial sensitivity analysis of remote sensing snow cover fraction data in a distributed hydrological model

    Science.gov (United States)

    Berezowski, Tomasz; Chormański, Jarosław; Nossent, Jiri; Batelaan, Okke

    2014-05-01

    Distributed hydrological models enhance the analysis and explanation of environmental processes. As more spatial input data and time series become available, more analysis is required of the sensitivity of the data on the simulations. Most research so far focussed on the sensitivity of precipitation data in distributed hydrological models. However, these results can not be compared until a universal approach to quantify the sensitivity of a model to spatial data is available. The frequently tested and used remote sensing data for distributed models is snow cover. Snow cover fraction (SCF) remote sensing products are easily available from the internet, e.g. MODIS snow cover product MOD10A1 (daily snow cover fraction at 500m spatial resolution). In this work a spatial sensitivity analysis (SA) of remotely sensed SCF from MOD10A1 was conducted with the distributed WetSpa model. The aim is to investigate if the WetSpa model is differently subjected to SCF uncertainty in different areas of the model domain. The analysis was extended to look not only at SA quantities but also to relate them to the physical parameters and processes in the study area. The study area is the Biebrza River catchment, Poland, which is considered semi natural catchment and subject to a spring snow melt regime. Hydrological simulations are performed with the distributed WetSpa model, with a simulation period of 2 hydrological years. For the SA the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm is used, with a set of different response functions in regular 4 x 4 km grid. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different landscape features. Moreover, the spatial patterns of the SA results are related to the WetSpa spatial parameters and to different physical processes. Based on the study results, it is clear that spatial approach of SA can be performed with the proposed algorithm and the MOD10A1 SCF is spatially sensitive in

  17. Combination of multispectral remote sensing, variable rate technology and environmental modeling for citrus pest management.

    Science.gov (United States)

    Du, Qian; Chang, Ni-Bin; Yang, Chenghai; Srilakshmi, Kanth R

    2008-01-01

    The Lower Rio Grande Valley (LRGV) of south Texas is an agriculturally rich area supporting intensive production of vegetables, fruits, grain sorghum, and cotton. Modern agricultural practices involve the combined use of irrigation with the application of large amounts of agrochemicals to maximize crop yields. Intensive agricultural activities in past decades might have caused potential contamination of soil, surface water, and groundwater due to leaching of pesticides in the vadose zone. In an effort to promote precision farming in citrus production, this paper aims at developing an airborne multispectral technique for identifying tree health problems in a citrus grove that can be combined with variable rate technology (VRT) for required pesticide application and environmental modeling for assessment of pollution prevention. An unsupervised linear unmixing method was applied to classify the image for the grove and quantify the symptom severity for appropriate infection control. The PRZM-3 model was used to estimate environmental impacts that contribute to nonpoint source pollution with and without the use of multispectral remote sensing and VRT. Research findings using site-specific environmental assessment clearly indicate that combination of remote sensing and VRT may result in benefit to the environment by reducing the nonpoint source pollution by 92.15%. Overall, this study demonstrates the potential of precision farming for citrus production in the nexus of industrial ecology and agricultural sustainability.

  18. Modeling spatial and temporal change of soil erosion based on multi-temporal remotely sensed data

    Institute of Scientific and Technical Information of China (English)

    Pei Liu; PeiJun Du; RuiMei Han; Chao Ma; YouFeng Zou

    2015-01-01

    In order to monitor the pattern, distribution, and trend of land use/cover change (LUCC) and its impacts on soil erosion, it is highly appropriate to adopt Remote Sensing (RS) data and Geographic Information System (GIS) to analyze, assess, simulate, and predict the spatial and temporal evolution dynamics. In this paper, multi-temporal Landsat TM/ETM+ re-motely sensed data are used to generate land cover maps by image classification, and the Cellular Automata Markov (CA_Markov) model is employed to simulate the evolution and trend of landscape pattern change. Furthermore, the Re-vised Universal Soil Loss Equation (RUSLE) is used to evaluate the situation of soil erosion in the case study mining area. The trend of soil erosion is analyzed according to total/average amount of soil erosion, and the rainfall (R), cover man-agement (C), and support practice (P) factors in RUSLE relevant to soil erosion are determined. The change trends of soil erosion and the relationship between land cover types and soil erosion amount are analyzed. The results demonstrate that the CA_Markov model is suitable to simulate and predict LUCC trends with good efficiency and accuracy, and RUSLE can calculate the total soil erosion effectively. In the study area, there was minimal erosion grade and this is expected to con-tinue to decline in the next few years, according to our prediction results.

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

    Science.gov (United States)

    Al-Kindi, Khalifa M; Kwan, Paul; R Andrew, Nigel; Welch, Mitchell

    2017-01-01

    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.

  20. 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.

  1. 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

  2. Classification of remotely sensed images

    CSIR Research Space (South Africa)

    Dudeni, N

    2008-10-01

    Full Text Available (s)) is the data vector for a pixel located at s θ(s) is an unknown ground class to which pixel s belongs Objective is to classify the pixel at location s to the one of the k clusters Classification of remotely sensed images N. Dudeni, P. Debba...(s) is an unknown ground class to which pixel s belongs Objective is to classify the pixel at location s to the one of the k clusters Classification of remotely sensed images N. Dudeni, P. Debba Introduction to Remote Sensing Introduction to Image...

  3. Coupling ocean colour remote sensing data into physical-ecosystem models: mapping uncertainty distributions from space.

    Science.gov (United States)

    McKee, David; Twardowski, Mike; Trees, Chuck; Sanjuan Calzado, Violeta

    2014-05-01

    Ocean colour remote sensing (OCRS) has transformed our understanding of complex feedback processes linking physical forcing events to biogeochemical responses. With continuous daily global coverage extending beyond the last decade, OCRS has become established as an essential global climate variable with potential use as a sensitive indicator of regional and global response to changing climate. There is increasing focus on use of OCRS data for validation and assimilation into coupled physical-ecosystem models for both environmental and operational applications. It is therefore essential that OCRS data products are not only optimised for maximum accuracy, but are also provided to end users with appropriate uncertainties. A simple spectral deconvolution model will be presented along with a new bootstrap approach for estimating product uncertainties. This approach can be adapted for both remote sensing and in situ data, opening up the possibility of mapping uncertainty distributions in 3-D for the first time, and can be applied to other established OCRS data products, including the existent historic data set. Ecosystem models seek to reproduce and predict ocean biogeochemical processes, where the models are constrained by physical parameters such as: wind, currents, density and light. The hydrographic aspects of marine ecosystems can generally be defined through ocean circulation models, which are largely independent of the ecosystem itself. The physical optics determining the light environment, on the other hand, are two-way coupled with ecosystem models since light interacts with seawater and suspended constituents. The Optical Physical and Ecosystem Regional Assessment (OPERA) model proposes a more comprehensive and challenging approach, where all optical interactions occurring within the volume of water are taken into account, thus providing a more accurate definition of light dependent processes.

  4. Remote sensing Penman-Monteith model to estimate catchment evapotranspiration considering the vegetation diversity

    Science.gov (United States)

    Li, Fawen; Cao, Runxiang; Zhao, Yong; Mu, Dongjing; Fu, Changfeng; Ping, Feng

    2017-01-01

    A new method for calculating evaporation is proposed, using the Penman-Monteith (P-M) model with remote sensing. This paper achieved the effective estimation to daily evapotranspiration in the Ziya river catchment by using the P-M model based on MODIS remote sensing leaf area index and respectively estimated plant transpiration and soil evaporation by using coefficient of soil evaporation. This model divided catchment into seven different sub-regions which are prairie, meadow, grass, shrub, broad-leaved forest, cultivated vegetation, and coniferous forest through thoroughly considering the vegetation diversity. Furthermore, optimizing and calibrating parameters based on each sub-region and analyzing spatio-temporal variation rules of the model main parameters which are coefficient of soil evaporation f and maximum stomatal conductance g sx . The results indicate that f and g sx calibrated by model are basically consistent with measured data and have obvious spatio-temporal distribution characteristics. The monthly average evapotranspiration value of simulation is 37.96 mm/mon which is close to the measured value with 33.66 mm/mon and the relative error of simulation results in each subregion are within 11 %, which illustrates that simulated values and measured values fit well and the precision of model is high. In addition, plant transpiration and soil evaporation account for about 84.64 and 15.36 % respectively in total evapotranspiration, which means the difference between values of them is large. What is more, this model can effectively estimate the green water resources in basin and provide effective technological support for water resources estimation.

  5. MICROWAVE REMOTE SENSING IN SOIL QUALITY ASSESSMENT

    Directory of Open Access Journals (Sweden)

    S. K. Saha

    2012-08-01

    Full Text Available Information of spatial and temporal variations of soil quality (soil properties is required for various purposes of sustainable agriculture development and management. Traditionally, soil quality characterization is done by in situ point soil sampling and subsequent laboratory analysis. Such methodology has limitation for assessing the spatial variability of soil quality. Various researchers in recent past showed the potential utility of hyperspectral remote sensing technique for spatial estimation of soil properties. However, limited research studies have been carried out showing the potential of microwave remote sensing data for spatial estimation of various soil properties except soil moisture. This paper reviews the status of microwave remote sensing techniques (active and passive for spatial assessment of soil quality parameters such as soil salinity, soil erosion, soil physical properties (soil texture & hydraulic properties; drainage condition; and soil surface roughness. Past and recent research studies showed that both active and passive microwave remote sensing techniques have great potentials for assessment of these soil qualities (soil properties. However, more research studies on use of multi-frequency and full polarimetric microwave remote sensing data and modelling of interaction of multi-frequency and full polarimetric microwave remote sensing data with soil are very much needed for operational use of satellite microwave remote sensing data in soil quality assessment.

  6. 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

  7. 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

  8. 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

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

    Science.gov (United States)

    Potter, Christopher; Klooster, Steven; Crabtree, Robert; Huang, Shengli; Gross, Peggy; Genovese, Vanessa

    2011-08-11

    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. 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 Fire, and Falls Fire areas. Rates of

  10. 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%.

  11. Modeling absorption spectra for detection of the combustion products of jet engines by laser remote sensing.

    Science.gov (United States)

    Voitsekhovskaya, Olga K; Kashirskii, Danila E; Egorov, Oleg V; Shefer, Olga V

    2016-05-10

    The absorption spectra of exhaust gases (H2O, CO, CO2, NO, NO2, and SO2) and aerosol (soot and Al2O3) particles were modeled at different temperatures for the first time and suitable spectral ranges were determined for conducting laser remote sensing of the combustion products of jet engines. The calculations were conducted on the basis of experimental concentrations of the substances and the sizes of the aerosol particles. The temperature and geometric parameters of jet engine exhausts were also taken from the literature. The absorption spectra were obtained via the line-by-line method, making use of the spectral line parameters from the authors' own high-temperature databases (for NO2 and SO2 gases) and the HITEMP 2010 database, and taking into account atmospheric transmission. Finally, the theoretical absorption spectra of the exhaust gases were plotted at temperatures of 400, 700, and 1000 K, and the impact of aerosol particles on the total exhaust spectra was estimated in spectral ranges suitable for remote sensing applications.

  12. Component temperatures inversion for remote sensing pixel based on directional thermal radiation model

    Institute of Scientific and Technical Information of China (English)

    王锦地; 李小文; 孙晓敏; 刘强

    2000-01-01

    When the remote sensing pixel is composed of multiple components and a non-isothermal surface, its directional signature of thermal-infrared radiation is mainly determined by the 3D structure of the pixel. In this paper, we present our simple directional thermal radiation model to describe the relation between the pixel thermal emission and the pixel’s component parameters, and invert the model to get the component temperatures. For the inversion algorithm, we focus on how to use the information of given observations in a more effective way. The information content in data space and parameter space is defined, and the transferring of information content in inversion procedure is studied. Our forward model and inversion method are validated using indoor directional measurement data.

  13. Component temperatures inversion for remote sensing pixel based on directional thermal radiation model

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    When the remote sensing pixel is composed of multiple components and a non-isothermal surface,its directional signature of thermal-infrared radiation is mainly determined by the 3D structure of the pixel.In this paper,we present our simple directional thermal radiation model to describe the relation between the pixel thermal emission and the pixel's component parameters,and invert the model to get the component temperatures.For the inversion algorithm,we focus on how to use the information of given observations in a more effective way.The information content in data space and parameter space is defined,and the transferring of information content in inversion procedure is studied.Our forward model and inversion method are validated using indoor directional measurement data.

  14. Estimating streamflow in the Irrawaddy Basin, Myanmar by integrating hydrological model with remote sensing information

    Science.gov (United States)

    Sun, W.; Yu, J.; Wang, G.; Li, Z.

    2016-12-01

    In this study, a method of calibrating hydrological models using river width derived from remote sensing (synthetic aperture radar) is applied to Irrawaddy Basin in Myanmar, for the purpose of estimating daily streamflow in this data-sparse basin. The at-a-station hydraulic geometry is implemented to facilitate shifting the calibration objective from river discharge to river width. The generalized likelihood uncertainty estimation (GLUE) is applied to model calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. And the posterior distribution of at-a-station hydraulic geometry parameter show single peak distribution, indicating they are strongly constrained by the calibration. The method is potentially valuable in data-sparse region for water resource management.

  15. 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.

  16. Development of a Simple Remote Sensing EvapoTranspiration model (Sim-ReSET): Algorithm and model test

    Science.gov (United States)

    Sun, Zhigang; Wang, Qinxue; Matsushita, Bunkei; Fukushima, Takehiko; Ouyang, Zhu; Watanabe, Masataka

    2009-10-01

    SummaryRemote sensing (RS) has been considered as the most promising tool for evapotranspiration (ET) estimations from local, regional to global scales. Many studies have been conducted to estimated ET using RS data, however, most of them are based partially on ground observations. In this study, we developed a new dual-source Simple Remote Sensing EvapoTranspiration model (Sim-ReSET) based only on RS data. One merit of this model is that the calculation of aerodynamic resistance can be avoided by means of a reference dry bare soil and an assumption that wind speed at the upper boundary of atmospheric surface layer is homogenous, but the aerodynamic characters are still considered by means of canopy height. The other merit is that all inputs (net radiation, soil heat flux, canopy height, variables related to land surface temperature) can be potentially obtained from remote sensing data, which allows obtaining regular RS-driven ET product. For the purposes of sensitivity analysis and performance evaluation of the Sim-ReSET model without the effect of potential uncertainties and errors from remote sensing data, the Sim-ReSET model was tested only using intensive ground observations at the Yucheng ecological station in the North China Plain from 2006 to 2008. Results show that the model has a good performance for instantaneous ET estimations with a mean absolute difference (MAD) of 34.27 W/m 2 and a root mean square error (RMSE) of 41.84 W/m 2 under neutral or near-neutral atmospheric conditions. On 12 cloudless days, the MAD of daily ET accumulated from instantaneous estimations is 0.26 mm/day, and the RMSE is 0.30 mm/day.

  17. Towards modeling hydrology and erosion exclusively with remote sensing data in the central Pamirs, Tajikistan

    Science.gov (United States)

    Pohl, E.; Gloaguen, R.; Andermann, C.

    2012-12-01

    Data scarcity, bad data quality, distribution and availability of measuring stations in remote mountain areas are a burden and hinder the application of models relying on meteorological input data. In this contribution, we present 1) a utilization of various remote sensing and modeled gridded data to run a distributed, conceptual hydrological model in the Tajik Pamirs, 2) derivation of qualitative and quantitative understanding of erosion in space and time, and 3) the linking of the hydrological discharge components to erosion dynamics and sediment transport. While some remote sensing products, such as digital elevation models, land cover classification, and increasingly precipitation products are widely used and accepted in hydrological modeling, holistic approaches are not the case yet. The key feature of the high elevation study area of the Gunt and Shakhdara catchments in the central Pamirs (average elevation of 4300 m a.s.l.) is the Westerlies-dominated precipitation input during winter and spring (two thirds of the annual precipitation of 320 mm/yr). During that time, temperatures are on average far below zero, and hence snowfall dominates the annual precipitation amount and temporarily offsets the river runoff generation. Thus, to model the snow accumulation and snowmelt, the amount of precipitation and its distribution pattern as well as the temperature, determining accumulation and snowmelt, are considered to be the most important parameters. For precipitation, we use two TRMM (Tropical Rainfall Measuring Mission) products and one APHRODITE (Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources) product. As proxy for near ground air temperature we use two MODIS (Moderate Resolution Imaging Spectroradiometer) LST (Land Surface Temperature) products that were calibrated with in-situ air temperature data. Mathematical optimization of the model delivers NSE (Nash-Sutcliffe Efficiencies) between 0.66 and 0

  18. Remote sensing of oil slicks

    Digital Repository Service at National Institute of Oceanography (India)

    Fondekar, S.P.; Rao, L.V.G.

    the drawback of expensive conventional surveying methods. An airborne remote sensing system used for monitoring and surveillance of oil comprises different sensors such as side-looking airborne radar, synthetic aperture radar, infrared/ultraviolet line scanner...

  19. Preface: Remote Sensing of Water Resources

    Directory of Open Access Journals (Sweden)

    Deepak R. Mishra

    2016-02-01

    Full Text Available The Special Issue (SI on “Remote Sensing of Water Resources” presents a diverse range of papers studying remote sensing tools, methods, and models to better monitor water resources which include inland, coastal, and open ocean waters. The SI is comprised of fifteen articles on widely ranging research topics related to water bodies. This preface summarizes each article published in the SI.

  20. 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.

  1. 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.

  2. Radiative Transfer Modeling, Spectral Analysis, and Remote Sensing of Coral Reefs

    Science.gov (United States)

    Guild, L.; Ganapol, B.; Furfaro, R.; Kramer, P.; Armstrong, R.; Gleason, A.; Torres, J.

    2004-12-01

    The calcium carbonate structures of tropical coral reefs protect coastlines from storms, create habitats for the world's greatest marine biodiversity, provide nurseries for many marine species; play essential roles in carbon and CO2 cycles, are major protein sources for many local populations, and are vital for sustainable economies of many societies. The world's reefs are in peril due to climate change and anthropogenic activity caused by rapidly growing populations in coastal zones. An important contribution to coral reef research is improved spectral distinction of reef components indicative of reef condition, including physical and biological degradation. Unfortunately, relatively little is known concerning the spectral properties of coral or how coral architecture reflect/transmit light. New insights into optical processes of corals can lead to improved interpretation of remote sensing data and forecasting of immediate or long-term impacts such as bleaching and disease in coral and algal overgrowth. We are investigating the spatial/spectral properties required to remotely sense changes in reef biological and physical properties by coupling spectral analysis of in situ spectra with a new coral-specific radiative transfer model. The first model development phase (CorMOD) imposes a scattering baseline that is constant regardless of coral condition, and further specifies that coral is optically thick. Evolution of the model is towards a coral-specific radiative transfer model that includes coral biochemical concentrations, specific absorptivities of coral components, and transmission measurements from coral surfaces. We present our field collected in situ spectra and resultant output relative absorption profiles of coral from CorMOD. Further, we will present NASA AVIRIS data and in situ spectra collection of coral and seagrass to support the AVIRIS mission that was collected during August 2004 for Florida Keys and Puerto Rico.

  3. An effort for developing a seamless transport modeling and remote sensing system for air pollutants

    Science.gov (United States)

    Nakajima, T.; Goto, D.; Dai, T.; Misawa, S.; Uchida, J.; Schutgens, N.; Hashimoto, M.; Oikawa, E.; Takenaka, H.; Tsuruta, H.; Inoue, T.; Higurashi, A.

    2015-12-01

    Wide area of the globe, like Asian region, still suffers from a large emission of air pollutants and cause serious impacts on the earth's climate and the public health of the area. Launch of an international initiative, Climate and Clean Air Coalition (CCAC), is an example of efforts to ease the difficulties by reducing Short-Lived Climate Pollutants (SLCPs), i.e., black carbon aerosol, methane and other short-lived atmospheric materials that heat the earth's system, along with long-lived greenhouse gas mitigation. Impact evaluation of the air pollutants, however, has large uncertainties. We like to introduce a recent effort of projects MEXT/SALSA and MOEJ/S-12 to develop a seamless transport model for atmospheric constituents, NICAM-Chem, that is flexible enough to cover global scale to regional scale by the NICAM nonhydrostatic dynamic core (NICAM), coupled with SPRINTARS aerosol model, CHASER atmospheric chemistry model and with their three computational grid systems, i.e. quasi homogeneous grids, stretched grids and diamond grids. A local ensemble transform Kalman filter/smoother with this modeling system was successfully applied to data from MODIS, AERONET, and CALIPSO for global assimilation/inversion and surface SPM and SO2 air pollution monitoring networks for Japanese area assimilation. My talk will be extended to discuss an effective utility of satellite remote sensing of aerosols using Cloud and Aerosol Imager (CAI) on board the GOSAT satellite and Advanced Himawari Imager (AHI) on board the new third generation geostationary satellite, Himawari-8. The CAI has a near-ultraviolet channel of 380nm with 500m spatial resolution and the AHI has high frequency measurement capability of every 10 minutes. These functions are very effective for accurate land aerosol remote sensing, so that a combination with the developed aerosol assimilation system is promising.

  4. A Remote Sensing Based Approach For Modeling and Assessing Glacier Hazards

    Science.gov (United States)

    Huggel, C.; Kääb, A.; Salzmann, N.; Haeberli, W.; Paul, F.

    Glacier-related hazards such as ice avalanches and glacier lake outbursts can pose a significant threat to population and installations in high mountain regions. They are well documented in the Swiss Alps and the high data density is used to build up sys- tematic knowledge of glacier hazard locations and potentials. Experiences from long research activities thereby form an important basis for ongoing hazard monitoring and assessment. However, in the context of environmental changes in general, and the highly dynamic physical environment of glaciers in particular, historical experience may increasingly loose its significance with respect to impact zone of hazardous pro- cesses. On the other hand, in large and remote high mountains such as the Himalayas, exact information on location and potential of glacier hazards is often missing. There- fore, it is crucial to develop hazard monitoring and assessment concepts including area-wide applications. Remote sensing techniques offer a powerful tool to narrow current information gaps. The present contribution proposes an approach structured in (1) detection, (2) evaluation and (3) modeling of glacier hazards. Remote sensing data is used as the main input to (1). Algorithms taking advantage of multispectral, high-resolution data are applied for detecting glaciers and glacier lakes. Digital terrain modeling, and classification and fusion of panchromatic and multispectral satellite im- agery is performed in (2) to evaluate the hazard potential of possible hazard sources detected in (1). The locations found in (1) and (2) are used as input to (3). The models developed in (3) simulate the processes of lake outbursts and ice avalanches based on hydrological flow modeling and empirical values for average trajectory slopes. A probability-related function allows the model to indicate areas with lower and higher risk to be affected by catastrophic events. Application of the models for recent ice avalanches and lake outbursts show

  5. 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

  6. 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

  7. Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling

    Science.gov (United States)

    Ciavatta, Stefano; Torres, Ricardo; Martinez-Vicente, Victor; Smyth, Timothy; Dall'Olmo, Giorgio; Polimene, Luca; Allen, J. Icarus

    2014-09-01

    In this paper we evaluate whether the assimilation of remotely-sensed optical data into a marine ecosystem model improves the simulation of biogeochemistry in a shelf sea. A localized Ensemble Kalman filter was used to assimilate weekly diffuse light attenuation coefficient data, Kd(443) from SeaWiFs, into an ecosystem model of the western English Channel. The spatial distributions of (unassimilated) surface chlorophyll from satellite, and a multivariate time series of eighteen biogeochemical and optical variables measured in situ at one long-term monitoring site were used to evaluate the system performance for the year 2006. Assimilation reduced the root mean square error and improved the correlation with the assimilated Kd(443) observations, for both the analysis and, to a lesser extent, the forecast estimates, when compared to the reference model simulation. Improvements in the simulation of (unassimilated) ocean colour chlorophyll were less evident, and in some parts of the Channel the simulation of this data deteriorated. The estimation errors for the (unassimilated) in situ data were reduced for most variables with some exceptions, e.g. dissolved nitrogen. Importantly, the assimilation adjusted the balance of ecosystem processes by shifting the simulated food web towards the microbial loop, thus improving the estimation of some properties, e.g. total particulate carbon. Assimilation of Kd(443) outperformed a comparative chlorophyll assimilation experiment, in both the estimation of ocean colour data and in the simulation of independent in situ data. These results are related to relatively low error in Kd(443) data, and because it is a bulk optical property of marine ecosystems. Assimilation of remotely-sensed optical properties is a promising approach to improve the simulation of biogeochemical and optical variables that are relevant for ecosystem functioning and climate change studies.

  8. Integration of Process Models and Remote Sensing for Estimating Productivity, Soil Moisture, and Energy Fluxes in a Tallgrass Prairie Ecosystem

    Science.gov (United States)

    We describe a research program aimed at integrating remotely sensed data with an ecosystem model (VELMA) and a soil-vegetation-atmosphere transfer (SVAT) model (SEBS) for generating spatially explicit, regional scale estimates of productivity (biomass) and energy\\mass exchanges i...

  9. Integration of Process Models and Remote Sensing for Estimating Productivity, Soil Moisture, and Energy Fluxes in a Tallgrass Prairie Ecosystem

    Science.gov (United States)

    We describe a research program aimed at integrating remotely sensed data with an ecosystem model (VELMA) and a soil-vegetation-atmosphere transfer (SVAT) model (SEBS) for generating spatially explicit, regional scale estimates of productivity (biomass) and energy\\mass exchanges i...

  10. 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.

  11. Remote sensing models using Landsat satellite data to monitor algal blooms in Lake Champlain.

    Science.gov (United States)

    Trescott, A; Park, M-H

    2013-01-01

    Lake Champlain is significantly impaired by excess phosphorus loading, requiring frequent lake-wide monitoring for eutrophic conditions and algal blooms. Satellite remote sensing provides regular, synoptic coverage of algal production over large areas with better spatial and temporal resolution compared with in situ monitoring. This study developed two algal production models using Landsat Enhanced Thematic Mapper Plus (ETM(+)) satellite imagery: a single band model and a band ratio model. The models predicted chlorophyll a concentrations to estimate algal cell densities throughout Lake Champlain. Each model was calibrated with in situ data compiled from summer 2006 (July 24 to September 10), and then validated with data for individual days in August 2007 and 2008. Validation results for the final single band and band ratio models produced Nash-Sutcliffe efficiency (NSE) coefficients of 0.65 and 0.66, respectively, confirming satisfactory model performance for both models. Because these models have been validated over multiple days and years, they can be applied for continuous monitoring of the lake.

  12. 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.

  13. [Comparison and analysis of hyperspectral remote sensing identifiable models for different vegetation under waterlogging stress].

    Science.gov (United States)

    Jiang, Jin-Bao; Steven, Michael D; He, Ru-Yan; Cai, Qing-Kong

    2013-11-01

    With the global climate warming, flooding disasters frequently occurred and its influence scope constantly increased in China. The objective of the present paper was to study the leaf spectral features of vegetation (maize and beetroot) under waterlogging stress and design a hyperspectral remote sensing model to monitor the flooding disasters through a field simulated experiment. The experiment was carried out in the Sutton Bonington Campus of University of Nottingham (52.8 degrees N, 1. 2 degrees W) from May to August in 2008, and samples were collected one time every week and spectra were measured in the laboratory. The result showed that the reflectance of the maize and beetroot decreased in the 550 and 800-1 300 nm region, and the reflectance slightly increased in the 680 nm region. This paper chose NDVI, SIPI, PRI, SRPI, GNDVI and R800 * R550/R680 to identify the vegetation under waterlogging stress, respectively. The result suggested that the SIPI and R800 * R550/R680 was sensitive for maize under waterlogging stress, and then SIPI and PRI and R800 * R550/R680 was sensitive for beetroot under waterlogging stress. In order to seek the best identifiable model, the normalized distances between means of control and stressed vegetation indices were calculated and analyzed, the result indicated that the distance of R800 * R550/R680 is more than that of indices' in the early stress stage, illustrated that the index identifiable ability for waterlogging stress is better than other indices, then the index has the strong sensitivity and stability. Therefore, the index R800 * R550/R680 could be used to quickly extract flooding disaster area by using hyperspectral remote sensing, and would provide information support for disaster relief decisions.

  14. A framework to utilize turbulent flux measurements for mesoscale models and remote sensing applications

    Directory of Open Access Journals (Sweden)

    W. Babel

    2011-05-01

    Full Text Available Meteorologically measured fluxes of energy and matter between the surface and the atmosphere originate from a source area of certain extent, located in the upwind sector of the device. The spatial representativeness of such measurements is strongly influenced by the heterogeneity of the landscape. The footprint concept is capable of linking observed data with spatial heterogeneity. This study aims at upscaling eddy covariance derived fluxes to a grid size of 1 km edge length, which is typical for mesoscale models or low resolution remote sensing data.

    Here an upscaling strategy is presented, utilizing footprint modelling and SVAT modelling as well as observations from a target land-use area. The general idea of this scheme is to model fluxes from adjacent land-use types and combine them with the measured flux data to yield a grid representative flux according to the land-use distribution within the grid cell. The performance of the upscaling routine is evaluated with real datasets, which are considered to be land-use specific fluxes in a grid cell. The measurements above rye and maize fields stem from the LITFASS experiment 2003 in Lindenberg, Germany and the respective modelled timeseries were derived by the SVAT model SEWAB. Contributions from each land-use type to the observations are estimated using a forward lagrangian stochastic model. A representation error is defined as the error in flux estimates made when accepting the measurements unchanged as grid representative flux and ignoring flux contributions from other land-use types within the respective grid cell.

    Results show that this representation error can be reduced up to 56 % when applying the spatial integration. This shows the potential for further application of this strategy, although the absolute differences between flux observations from rye and maize were so small, that the spatial integration would be rejected in a real situation. Corresponding thresholds for

  15. Remote Sensing of Environmental Pollution

    Science.gov (United States)

    North, G. W.

    1971-01-01

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

  16. 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 K.; 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.

  17. 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.

  18. 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.

  19. An information system design for watershed-wide modeling of water loss to the atmosphere using remote sensing techniques

    Science.gov (United States)

    Khorram, S.

    1977-01-01

    Results are presented of a study intended to develop a general location-specific remote-sensing procedure for watershed-wide estimation of water loss to the atmosphere by evaporation and transpiration. The general approach involves a stepwise sequence of required information definition (input data), appropriate sample design, mathematical modeling, and evaluation of results. More specifically, the remote sensing-aided system developed to evaluate evapotranspiration employs a basic two-stage two-phase sample of three information resolution levels. Based on the discussed design, documentation, and feasibility analysis to yield timely, relatively accurate, and cost-effective evapotranspiration estimates on a watershed or subwatershed basis, work is now proceeding to implement this remote sensing-aided system.

  20. 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

  1. Calibration Of Hydrological Models Based On Remotely Sensed Soil Moisture And Evapotranspiration

    Science.gov (United States)

    Lopez, P.; Strohmeier, S.; Sutanudjaja, E.; Haddad, M.; Karrou, M.; Sterk, G.; Schellekens, J.; Bierkens, M. F.

    2016-12-01

    The increasing water demand over recent decades together with the climate change impacts on water resources, especially in dry areas, may lead to growing problems with water availability. Investigating and developing novel strategies to assess and manage water resources have turned into a key issue, leading to increasing efforts to enhance and improve hydrological models and datasets. Despite campaigns to increase the quality and the temporal and spatial availability of ground-based hydro-meteorological data, many river basins around the world, including the Oum Er Rbia in Morocco, still have a limited number of in-situ observations. This in turn limits the application of hydrological models. Recently developed global earth observation products may unlock a greater capability of basin scale hydrological modeling for advanced water management. This study aims to evaluate the applicability of earth observation products for hydrological model simulation in comparison with in-situ data for water resources management and water allocation of the Moroccan Oum Er Rbia river basin. Two different hydrological models (SWAT and PCR-GLOBWB) were applied to inter-compare various combinations of in-situ and global earth observation data. Global earth observation products were obtained from various sources including meteorological data from the WATCH Forcing Data methodology applied to ERA-Interim reanalysis data and the Multi-Source Weighted-Ensemble Precipitation (MSWEP); the remotely sensed ESA CCI surface soil moisture Soil Water Index combined product and the GLEAM evapotranspiration data from satellite-based observations. The daily data were provided for the time period from 1979 to 2012. Due to insufficient in-situ discharge observations available in the basin, local calibration of both hydrological models was based on global evapotranspiration and soil moisture data, covering additional aspects of the hydrological cycle to further reduce modeling uncertainty. Preliminary

  2. remote sensing data combinations - global AOD maps

    Science.gov (United States)

    Kinne, S.

    2009-04-01

    More accurate and more complete measurement-based data-sets are needed to constrain the freedom of global modeling and raise confidence in model predictions. In remote sensing, different methods and sensors frequently yield estimates for the same (or a strongly related) atmospheric property. For maximum benefit to data-users (e.g. input or evaluation data to modeling) - in the context of differences in sensor capabilities and retrieval limitations - there is a desire to combine the strengths of these individual data sources for superior products. In a demonstration, different multi-annual global monthly maps for aerosol optical depth (AOD) from satellite remote sensing been compared and scored against local quality reference data from ground remote sensing. The regionally best performing satellite data-sets have been combined into global monthly AOD maps. As expected, this satellite composite scores better than any individual satellite retrieval. Further improvements are achieved by merging statistics of ground remote sensing into the composite. The global average mid-visible AOD of this remote sensing composite is near 0.13 annually, with lower values during northern hemispheric fall and winter (0.12) and larger values during northern hemispheric spring and summer (0.14). This measurement based data composite also reveals characteristic deficiencies in global modeling: Modeling tends to overestimates AOD over the northern mid-latitudes and to underestimate AOD over tropical and sub-tropical land regions. Also noteworthy are AOD underestimates by modeling in remote oceanic regions, though only in relative sense as AOD values in that region as small. The AOD remote sensing data composite is far from perfect, but it demonstrates the extra value of data-combinations.

  3. Hydroclimatology of Lake Victoria region using hydrologic model and satellite remote sensing data

    Directory of Open Access Journals (Sweden)

    S. I. Khan

    2011-01-01

    Full Text Available Study of hydro-climatology at a range of temporal scales is important in understanding and ultimately mitigating the potential severe impacts of hydrological extreme events such as floods and droughts. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10- year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia basin. The spatiotemporal variability of the water cycle components were quantified using a hydrologic model, with in-situ and multi-satellite remote sensing datasets. The model is calibrated using daily observed discharge data for the period between 1985 and 1999, for which model performance is estimated with a Nash Sutcliffe Efficiency (NSCE of 0.87 and 0.23% bias. The model validation showed an error metrics with NSCE of 0.65 and 1.04% bias. Moreover, the hydrologic capability of satellite precipitation (TRMM-3B42 V6 is evaluated. In terms of reconstruction of the water cycle components the spatial distribution and time series of modeling results for precipitation and runoff showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to

  4. Remote Sensing and Modeling of Polarimetric Signatures of Solar System Objects

    Science.gov (United States)

    Yanamandra-Fisher, P. A.

    2011-12-01

    polarization of exoplanetary systems can detect exoplanets separate from their parent stars. Biological molecules exhibit an inherent handedness or circular polarization or chirality; search for chiral signatures on exo-Earths would identify astrobiological material. Even as the field of polarimetric observations is maturing as a technique for remote sensing, the modeling of polarimetric observations is not similarly mature. Recent efforts include characterization of light scattering by particles of complex shapes and structures to be calculated; vector radiative transfer equation for optically thick media to be solved; with approximations to model closely packed particulate media or regoliths. The synergy between the new modeling techniques and increased use of polarization as a remote sensing technique provide opportunities to understand our solar system and other planetary systems.

  5. Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing

    Science.gov (United States)

    Edirisinghe, Asoka; Clark, Dave; Waugh, Deanne

    2012-06-01

    Pasture biomass is a vital input for management of dairy systems in New Zealand. An accurate estimate of pasture biomass information is required for the calculation of feed budget, on which decisions are made for farm practices such as conservation, nitrogen use, rotational lengths and supplementary feeding leading to profitability and sustainable use of pasture resources. The traditional field based methods of measuring pasture biomass such as using rising plate metres (RPM) are largely inefficient in providing the timely information at the spatial extent and temporal frequency demanded by commercial environments. In recent times remote sensing has emerged as an alternative tool. In this paper we have examined the Normalised Difference Vegetation Index (NDVI) derived from medium resolution imagery of SPOT-4 and SPOT-5 satellite sensors to predict pasture biomass of intensively grazed dairy pastures. In the space and time domain analysis we have found a significant dependency of time over the season and no dependency of space across the scene at a given time for the relationship between NDVI and field based pasture biomass. We have established a positive correlation (81%) between the two variables in a pixel scale analysis. The application of the model on 2 selected farms over 3 images and aggregation of the predicted biomass to paddock scale has produced paddock average pasture biomass values with a coefficient of determination of 0.71 and a standard error of 260 kg DM ha-1 in the field observed range between 1500 and 3500 kg DM ha-1. This result indicates a high potential for operational use of remotely sensed data to predict pasture biomass of intensively grazed dairy pastures.

  6. Large Catchment Scale Sediment Transport Modelling of SOC Using Environmental Tracers and Remote Sensing

    Science.gov (United States)

    Willgoose, G. R.; Kunkel, V.; Hancock, G. R.; Wells, T.

    2015-12-01

    Soil's potential as a carbon sink for atmospheric CO2 has been widely discussed. Studies of soil organic carbon (SOC) controls, and the subsequent models derived from their findings, have focussed mainly on North American and European regions, and more recently, in regions such as China. In Australia, agricultural practices have led to losses in SOC. This implies that Australian soils have a large potential for increased sequestration of SOC. Building on previous work, here we examine the spatial and temporal variation in soil organic carbon (SOC) and its controlling factors controls across a large catchment of approximately 650 km2 in the Upper Hunter Valley, New South Wales, Australia, using data collected from two sampling campaigns, (April 2006 and June-July 2014). The 2006 data represented a period of long-term drought which effectively ended in 2007 with average and above average subsequent rainfall. In 2007 and 2010 there were a series of extreme rainfall events. 137-Cesium and SOC concentrations were obtained from the sampled soils. Remote sensing using Landsat (30m) and MODIS (250m) NDVI was used to determine if catchment SOC could be predicted using both low and high resolution remote sensing. Relationships between SOC and 137-Cesium for both sampling periods were also quantified. Results indicate that, although moderate resolution (250 m) allows for reasonable prediction of the spatial distribution of SOC, the higher resolution (30 m) improved the strength of the SOC-NDVI relationship. Mean 137-Cesium concentrations were observed to show an increase in deposition at the sample sites over the 8 years between samplings. The relationship between SOC and 137-Cesium, as a surrogate for the erosion and deposition of SOC, suggested that sediment transport and deposition influences the distribution of SOC within the catchment. The increase in 137-Cesium also suggests that increased rainfall and extreme storm events, resulting from climate change, may increase

  7. A hidden state space modeling approach for improving glacier surface velocity estimates using remotely sensed data

    Science.gov (United States)

    Henke, D.; Schubert, A.; Small, D.; Meier, E.; Lüthi, M. P.; Vieli, A.

    2014-12-01

    A new method for glacier surface velocity (GSV) estimates is proposed here which combines ground- and space-based measurements with hidden state space modeling (HSSM). Examples of such a fusion of physical models with remote sensing (RS) observations were described in (Henke & Meier, Hidden State Space Models for Improved Remote Sensing Applications, ITISE 2014, p. 1242-1255) and are currently adapted for GSV estimation. GSV can be estimated using in situ measurements, RS methods or numerical simulations based on ice-flow models. In situ measurements ensure high accuracy but limited coverage and time consuming field work, while RS methods offer regular observations with high spatial coverage generally not possible with in situ methods. In particular, spaceborne Synthetic Aperture Radar (SAR) can obtain useful images independent of daytime and cloud cover. A ground portable radar interferometer (GPRI) is useful for investigating a particular area in more detail than is possible from space, but provides local coverage only. Several processing methods for deriving GSV from radar sensors have been established, including interferometry and offset tracking (Schubert et al, Glacier surface velocity estimation using repeat TerraSAR-X images. ISPRS Journal of P&RS, p. 49-62, 2013). On the other hand, it is also possible to derive glacier parameters from numerical ice-flow modeling alone. Given a well-parameterized model, GSV can in theory be derived and propagated continuously in time. However, uncertainties in the glacier flow dynamics and model errors increase with excessive propagation. All of these methods have been studied independently, but attempts to combine them have only rarely been made. The HSSM we propose recursively estimates the GSV based on 1) a process model making use of temporal and spatial interdependencies between adjacent states, and 2) observations (RS and optional in situ). The in situ and GPRI images currently being processed were acquired in the

  8. Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis

    Science.gov (United States)

    Dillon, Chris

    Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGIS(TM) for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGIS(TM) ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies. Keywords: Seafloor terrain characterization, Benthic Terrain Modeller (BTM), Multi-beam SONAR, Airborne LiDAR Bathymetry, Satellite Derived Bathymetry, ArcGISTM ModelBuilder, Textural analysis, Substrate classification.

  9. 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.

  10. Dynamic drought risk assessment using crop model and remote sensing techniques

    Science.gov (United States)

    Sun, H.; Su, Z.; Lv, J.; Li, L.; Wang, Y.

    2017-02-01

    Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

  11. Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

    Directory of Open Access Journals (Sweden)

    Jouko Kleemola

    2010-09-01

    Full Text Available During 1996–2006, the Ministry of Agriculture and Forestry in Finland (MAFF, MTT Agrifood Research and the Finnish Geodetic Institute performed a joint remote sensing satellite research project. It evaluated the applicability of optical satellite (Landsat, SPOT data for cereal yield estimations in the annual crop inventory program. Four Optical Vegetation Indices models (I: Infrared polynomial, II: NDVI, III: GEMI, IV: PARND/FAPAR were validated to estimate cereal baseline yield levels (yb using solely optical harmonized satellite data (Optical Minimum Dataset. The optimized Model II (NDVI yb level was 4,240 kg/ha (R2 0.73, RMSE 297 kg/ha for wheat and 4390 kg/ha (R2 0.61, RMSE 449 kg/ha for barley and Model I yb was 3,480 kg/ha for oats (R2 0.76, RMSE 258 kg/ha. Optical VGI yield estimates were validated with CropWatN crop model yield estimates using SPOT and NOAA data (mean R2 0.71, RMSE 436 kg/ha and with composite SAR/ASAR and NDVI models (mean R2 0.61, RMSE 402 kg/ha using both reflectance and backscattering data. CropWatN and Composite SAR/ASAR & NDVI model mean yields were 4,754/4,170 kg/ha for wheat, 4,192/3,848 kg/ha for barley and 4,992/2,935 kg/ha for oats.

  12. 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.

  13. SSMiles: Using Models to Teach about Remote Sensing and Image Processing.

    Science.gov (United States)

    Tracy, Dyanne M., Ed.

    1994-01-01

    Presents an introductory lesson on remote sensing and image processing to be used in cooperative groups. Students are asked to solve a problem by gathering information, making inferences, transforming data into other forms, and making and testing hypotheses. Includes four expansions of the lesson and a reproducible student worksheet. (MKR)

  14. Establishing a sea bottom model by applying a multi-sensor acoustic remote sensing approach

    NARCIS (Netherlands)

    Siemes, K.

    2013-01-01

    Detailed information about the oceanic environment is essential for many applications in the field of marine geology, marine biology, coastal engineering, and marine operations. Especially, knowledge of the properties of the sediment body is often required. Acoustic remote sensing techniques have be

  15. Linking ground observations, simulation model output, and remote sensing data to characterize phenology across diverse arid landscapes

    Science.gov (United States)

    We combined long-term data on plant phenology with simulation modeling output and remote sensing data to characterize diverse landscapes at the Jornada Experimental Range in the northern Chihuahuan Desert of southern New Mexico. Phenology of 15 key species in Chihuahuan Desert plant communities have...

  16. Photogrammetry - Remote Sensing and Geoinformation

    Science.gov (United States)

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

    2012-07-01

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

  17. Remotely sensed data assimilation technique to develop machine learning models for use in water management

    Science.gov (United States)

    Zaman, Bushra

    Increasing population and water conflicts are making water management one of the most important issues of the present world. It has become absolutely necessary to find ways to manage water more efficiently. Technological advancement has introduced various techniques for data acquisition and analysis, and these tools can be used to address some of the critical issues that challenge water resource management. This research used learning machine techniques and information acquired through remote sensing, to solve problems related to soil moisture estimation and crop identification on large spatial scales. In this dissertation, solutions were proposed in three problem areas that can be important in the decision making process related to water management in irrigated systems. A data assimilation technique was used to build a learning machine model that generated soil moisture estimates commensurate with the scale of the data. The research was taken further by developing a multivariate machine learning algorithm to predict root zone soil moisture both in space and time. Further, a model was developed for supervised classification of multi-spectral reflectance data using a multi-class machine learning algorithm. The procedure was designed for classifying crops but the model is data dependent and can be used with other datasets and hence can be applied to other landcover classification problems. The dissertation compared the performance of relevance vector and the support vector machines in estimating soil moisture. A multivariate relevance vector machine algorithm was tested in the spatio-temporal prediction of soil moisture, and the multi-class relevance vector machine model was used for classifying different crop types. It was concluded that the classification scheme may uncover important data patterns contributing greatly to knowledge bases, and to scientific and medical research. The results for the soil moisture models would give a rough idea to farmers

  18. Modeling spatial surface energy fluxes of agricultural and riparian vegetation using remote sensing

    Science.gov (United States)

    Geli, Hatim Mohammed Eisa

    Modeling of surface energy fluxes and evapotranspiration (ET ) requires the understanding of the interaction between land and atmosphere as well as the appropriate representation of the associated spatial and temporal variability and heterogeneity. This dissertation provides new methodology showing how to rationally and properly incorporate surface features characteristics/properties, including the leaf area index, fraction of cover, vegetation height, and temperature, using different representations as well as identify the related effects on energy balance flux estimates including ET. The main research objectives were addressed in Chapters 2 through 4 with each presented in a separate paper format with Chapter 1 presenting an introduction and Chapter 5 providing summary and recommendations. Chapter 2 discusses a new approach of incorporating temporal and spatial variability of surface features. We coupled a remote sensing-based energy balance model with a traditional water balance method to provide improved estimates of ET. This approach was tested over rainfed agricultural fields ˜ 10 km by 30 km in Ames, Iowa. Before coupling, we modified the water balance method by incorporating a remote sensing-based estimate for one of its parameters to ameliorate its performance on a spatial basis. Promising results were obtained with indications of improved estimates of ET and soil moisture in the root zone. The effects of surface features heterogeneity on measurements of turbulence were investigated in Chapter 3. Scintillometer-based measurements/estimates of sensible heat flux (H) were obtained over the riparian zone of the Cibola National Wildlife Refuge (CNWR), California. Surface roughness including canopy height (hc), roughness length, and zero-plane displacement height were incorporated in different ways, to improve estimates of H. High resolution, 1-m maps of ground surface digital elevation model and canopy height, hc, were derived from airborne LiDAR sensor data

  19. Remote Sensing of Vegetation Parameters for Modeling Coastal Marsh Response to Sea Level Rise

    Science.gov (United States)

    Byrd, K. B.; Windham-Myers, L.; Warzecha, B.; Crowe, R.; Vasey, M. C.; Ferner, M.

    2014-12-01

    Coastal planners are seeking ways to prepare for the potential impacts of future climate change, particularly sea level rise though management of future risks is complicated by uncertainty in the timing, distribution and extent of these impacts. Sea level rise impacts will be most evident at the regional level where decisions related to climate change adaptation including those related to land use planning and habitat management typically occur. To aid coastal managers with decision-making we are integrating remote sensing data with the marsh equilibrium model (MEM3) to project coastal marsh habitat response to future sea level rise. MEM3 is a 1-dimentional, calibrated Excel-based model that incorporates both physical and biological feedbacks to changing relative elevations. Modeled future elevations are then distributed at the regional scale with LiDAR DEMs to project changes to coastal habitats and dependent wildlife. Because plant biomass and structure influence both organic and inorganic accretion, MEM3 includes multiple vegetation input variables. Deriving these variables, including maximum and minimum elevations of marsh vegetation, peak aboveground biomass, and elevation at peak biomass from remote sensing will enable the model to have spatially variable inputs across sites. We are evaluating 30m Landsat 8 and 2m World View-2 (WV2) satellite data for mapping peak biomass at Rush Ranch, a highly diverse brackish marsh in the San Francisco Bay National Estuarine Research Reserve. The high spatial resolution of WV2 produces greater variability in plant reflectance at the pixel scale than Landsat 8. Initial results show the need for plant community-specific biomass models with WV2 to account for differences in plant structure and canopy architecture. When removing plots dominated by Salicornia pacifica and Lepidium latifolium, peak biomass is best estimated with an NDVI-type vegetation index based on WV2 near infrared bands 7 and 8 (R2 = 0.21, RMSE = 318 g/m2

  20. Multilayer Markov Random Field models for change detection in optical remote sensing images

    Science.gov (United States)

    Benedek, Csaba; Shadaydeh, Maha; Kato, Zoltan; Szirányi, Tamás; Zerubia, Josiane

    2015-09-01

    In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our purposes are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of Ground Truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quantitative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches.

  1. A space-time stochastic model of rainfall for satellite remote-sensing studies

    Science.gov (United States)

    Bell, Thomas L.

    1987-01-01

    A model of the spatial and temporal distribution of rainfall is described that produces random spatial rainfall patterns with these characteristics: (1) the model is defined on a grid with each grid point representing the average rain rate over the surrounding grid box, (2) rain occurs at any one grid point, on average, a specified percentage of the time and has a lognormal probability distribution, (3) spatial correlation of the rainfall can be arbitrarily prescribed, and (4) time stepping is carried out so that large-scale features persist longer than small-scale features. Rain is generated in the model from the portion of a correlated Gaussian random field that exceeds a threshold. The portion of the field above the threshold is rescaled to have a lognormal probability distribution. Sample output of the model designed to mimic radar observations of rainfall during the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), is shown. The model is intended for use in evaluating sampling strategies for satellite remote-sensing of rainfall and for development of algorithms for converting radiant intensity received by an instrument from its field of view into rainfall amount.

  2. 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.

  3. 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

  4. A hydrological model for the Sudd wetland using remotely sensed and ground data

    Science.gov (United States)

    Remondi, Federica; Georgakakos, Aris P.; Castelletti, Andrea

    2013-04-01

    Modeling of wetland hydrology and quantification of water inputs and outputs are requisites to understand flooding dynamics, to determine wetland vulnerability to change, and to better inform water-related decision-making. Located in the Upper Nile river basin in South Sudan, the Sudd wetland is one of the largest floodplain swamps in the world. Its complex system is characterized by a seasonal inundation that is essential to the hydroecological functioning of the Sudd but is also the main cause for intensive water losses (nearly half of the inflow) by evaporation in the Nile river basin. The hydrologically characterization of the area is therefore key to assess and predict the water balance in the region The main difficulties in modeling the system are due to the inaccessibility of the area, to the vast extension, to the complexity of the dynamic behavior throughout the year (permanent and seasonal flooded areas), and to the political and institutional setting. This study integrated hydrologic data and remote sensing techniques to analyze the dynamics and spatial response of the wetlands. A new methodology using MODIS data and MNDWI-Modified Normalized Difference Water Index was designed to profile the area of the wetland throughout the years. In particular, the threshold for the MNDWI values was obtained using average annual land cover data and their temporal trends were analyzed to classify the different types of wetland (permanent, seasonal and non-wetland). A characterization of wetland dynamics was then achieved over the 10-years period Jan 2000-Dec 2009. In the second step of the research, other driving forces of the system were studied: new hydrological models were created for the Torrents and Sobat basins, existing river routing models were computed for the reach of Mongalla and Malakal, and estimates on precipitation and evapotranspiration rates were acquired from different projects based on remotely sensed data. All these information were then used to

  5. Real-time remote sensing driven river basin modelling using radar altimetry

    Directory of Open Access Journals (Sweden)

    S. J. Pereira-Cardenal

    2010-10-01

    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 modelling 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 modelling 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 shared between 4 countries with conflicting water management interests.

    The modelling 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 error of the simulated reservoir water levels from 4.7 to 1.9 m, and overall model RMSE from 10.3 m to 6

  6. Improving evapotranspiration processes in distrubing hydrological models using Remote Sensing derived ET products.

    Science.gov (United States)

    Abitew, T. A.; van Griensven, A.; Bauwens, W.

    2015-12-01

    Evapotranspiration is the main process in hydrology (on average around 60%), though has not received as much attention in the evaluation and calibration of hydrological models. In this study, Remote Sensing (RS) derived Evapotranspiration (ET) is used to improve the spatially distributed processes of ET of SWAT model application in the upper Mara basin (Kenya) and the Blue Nile basin (Ethiopia). The RS derived ET data is obtained from recently compiled global datasets (continuously monthly data at 1 km resolution from MOD16NBI,SSEBop,ALEXI,CMRSET models) and from regionally applied Energy Balance Models (for several cloud free days). The RS-RT data is used in different forms: Method 1) to evaluate spatially distributed evapotransiration model resultsMethod 2) to calibrate the evotranspiration processes in hydrological modelMethod 3) to bias-correct the evapotranpiration in hydrological model during simulation after changing the SWAT codesAn inter-comparison of the RS-ET products shows that at present there is a significant bias, but at the same time an agreement on the spatial variability of ET. The ensemble mean of different ET products seems the most realistic estimation and was further used in this study.The results show that:Method 1) the spatially mapped evapotranspiration of hydrological models shows clear differences when compared to RS derived evapotranspiration (low correlations). Especially evapotranspiration in forested areas is strongly underestimated compared to other land covers.Method 2) Calibration allows to improve the correlations between the RS and hydrological model results to some extent.Method 3) Bias-corrections are efficient in producing (sesonal or annual) evapotranspiration maps from hydrological models which are very similar to the patterns obtained from RS data.Though the bias-correction is very efficient, it is advised to improve the model results by better representing the ET processes by improved plant/crop computations, improved

  7. CONTEXT MODELS FOR CRF-BASED CLASSIFICATION OF MULTITEMPORAL REMOTE SENSING DATA

    Directory of Open Access Journals (Sweden)

    T. Hoberg

    2012-07-01

    Full Text Available The increasing availability of multitemporal satellite remote sensing data offers new potential for land cover analysis. By combining data acquired at different epochs it is possible both to improve the classification accuracy and to analyse land cover changes at a high frequency. A simultaneous classification of images from different epochs that is also capable of detecting changes is achieved by a new classification technique based on Conditional Random Fields (CRF. CRF provide a probabilistic classification framework including local spatial and temporal context. Although context is known to improve image analysis results, so far only little research was carried out on how to model it. Taking into account context is the main benefit of CRF in comparison to many other classification methods. Context can be already considered by the choice of features and in the design of the interaction potentials that model the dependencies of interacting sites in the CRF. In this paper, these aspects are more thoroughly investigated. The impact of the applied features on the classification result as well as different models for the spatial interaction potentials are evaluated and compared to the purely label-based Markov Random Field model.

  8. Integrated Evaluation Model for Eco- Environmental Quality in Mountainous Region Based on Remote Sensing and GIS

    Institute of Scientific and Technical Information of China (English)

    LI Ainong; WANG Angsheng; HE Xiaorong; FENG Wenlan; ZHOU Wancun

    2006-01-01

    Based on Remote Sensing (RS), Geographic Information System (GIS), and combining Principal Component Analysis, this paper designed a numerical integrated evaluation model for mountain eco-environment on the base of grid scale. Using this model, we evaluated the mountain eco-environmental quality in a case study area-the upper reaches of Minjiang River, and achieved a good result, which accorded well with the real condition. The study indicates that, the integrated evaluation model is suitable for multi-layer spatial factor computation, effectively lowing man's subjective influence in the evaluation process; treating the whole river basin as a system, the model shows full respect to the circulation of material and energy, synthetically embodies the determining impact of such natural condition as water-heat and landform, as well as human interference in natural eco-system; the evaluation result not only clearly presents mountainous vertical distribution features of input factors, but also provides a scientific and reliable thought for quantitatively evaluating mountain eco-environment.

  9. 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 (<1) between modeled and field observations. This model was further 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.

  10. Characterization of Ring Wave Spectra for Natural Rain: Measurements and Model for Remote Sensing Applications

    Science.gov (United States)

    Bliven, L.; Sobieski, P.; Craeye, C.

    1998-01-01

    Ring waves generated by natural rains from 1 to 100 mm/hr were measured in a small tank located in a field. Time series were obtained: (a) from a wire capacitance probe that measured surface elevation, (b) from an optical gauge that measured rain rates R, (c) from an anemometer that measured wind speeds and (d) from a 13.5 GHz scatterometer (w polarization, and 30 degree incidence angle). Ring wave frequency spectra are computed from the surface elevation data for each minute of rain. All the spectra have a similar shape, with a maximum near 5 Hz, and with a more rapid decay towards higher frequencies than towards lower frequencies. A log-Gaussian spectral model provides a useful representation of these data and analysis of the model coefficients shows that the peak frequency and bandwidth are approximately constant, but the magnitude increases as R increases, Additionally, the normalized radar cross section from the scatterometer varies approximately linearly with the spectral line corresponding to the Bragg-wavelength, so together the log-Gaussian ring wave model and the Bragg scattering theory should be useful for a broad range of applications. These findings can be used to help interpret remote sensing data during rain events and to guide model development for radar scattering from rain roughened seas.

  11. Experimental Research on Quantitative Inversion Models of Suspended Sediment Concentration Using Remote Sensing Technology

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Research on quantitative models of suspended sediment concentration (SSC) using remote sensing technology is very important to understand the scouring and siltation variation in harbors and water channels. Based on laboratory study of the relationship between different suspended sediment concentrations and reflectance spectra measured synchronously, quantitative inversion models of SSC based on single factor, band ratio and sediment parameter were developed, which provides an effective method to retrieve the SSC from satellite images. Results show that the b1 (430-500nm) and b3 (670-735nm) are the optimal wavelengths for the estimation of lower SSC and the b4 (780-835nm) is the optimal wavelength to estimate the higher SSC. Furthermore the band ratio B2/B3 can be used to simulate the variation of lower SSC better and the B4/B1 to estimate the higher SSC accurately. Also the inversion models developed by sediment parameters of higher and lower SSCs can get a relatively higher accuracy than the single factor and band ratio models.

  12. A Pure Marine Aerosol Model, for Use in Remote Sensing Applications

    Science.gov (United States)

    Sayer, A. M.; Smirnov, A.; Hsu, N. C.; Holben, B. N.

    2011-01-01

    Retrievals of aerosol optical depth (AOD) and related parameters from satellite measurements typically involve prescribed models of aerosol size and composition, and are therefore dependent on how well these models are able to represent the radiative behaviour of real aerosols, This study uses aerosol volume size distributions retrieved from Sun-photometer measurements at 11 Aerosol Robotic Network (AERONET) island sites, spread throughout the world's oceans, as a basis to define such a model for unpolluted maritime aerosols. Size distributions are observed to be bimodal and approximately lognormal, although the coarse mode is skewed with a long tail on the low-radius end, The relationship of AOD and size distribution parameters to meteorological conditions is also examined, As wind speed increases, so do coarse-mode volume and radius, The AOD and Angstrom exponent (alpha) show linear relationships with wind speed, although there is considerable scatter in all these relationships, limiting their predictive power. Links between aerosol properties and near-surface relative humidity, columnar water vapor, and sea surface temperature are also explored. A recommended bimodal maritime model, which is able to reconstruct the AERONET AOD with accuracy of order 0.01-0.02, is presented for use in aerosol remote sensing applications. This accuracy holds at most sites and for wavelengths between 340 nm and 1020 nm. Calculated lidar ratios are also provided, and differ significantly from those currently used in Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) processing.

  13. A Remote Sensing Model to Estimate Sunshine Duration in the Ningxia Hui Autonomous Region, China

    Institute of Scientific and Technical Information of China (English)

    朱晓晨; 邱新法; 曾燕; 高佳琦; 何永健

    2015-01-01

    Sunshine duration (SD) is strongly correlated with solar radiation, and is most widely used to estimate the latter. This study builds a remote sensing model on a 100 m × 100 m spatial resolution to estimate SD for the Ningxia Hui Autonomous Region, China. Digital elevation model (DEM) data are employed to refl ect topography, and moderate-resolution imaging spectroradiometer (MODIS) cloud products (Aqua MYD06−L2 and Terra MOD06−L2) are used to estimate sunshine percentage. Based on the terrain (e.g., slope, aspect, and terrain shadowing degree) and the atmospheric conditions (e.g., air molecules, aerosols, moisture, cloud cover, and cloud types), observation data from weather stations are also incorporated into the model. Verifi cation results indicate that the model simulations match reasonably with the observations, with the average relative error of the total daily SD being 2.21%. Further data analysis reveals that the variation of the estimated SD is consistent with that of the maximum possible SD; its spatial variation is so substantial that the estimated SD diff ers signifi cantly between the south-facing and north-facing slopes, and its seasonal variation is also large throughout the year.

  14. 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 compendium, and we also acknowledge all our colleagues in the Meteorology and Test and Measurements Programs from the Wind Energy Division at Risø DTU 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 compendium available for people involved in Remote Sensing in Wind Energy....

  15. 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....

  16. 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...... in Wind Energy....

  17. 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...... 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....

  18. 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.

  19. Deriving remote sensing reflectance from turbid Case II waters using green-shortwave infrared bands based model

    Science.gov (United States)

    Chen, Jun; Yin, Shoujing; Xiao, Rulin; Xu, Qianxiang; Lin, Changsong

    2014-04-01

    The objectives of this study are to validate the applicability of a shortwave infrared atmospheric correction model (SWIR-based model) in deriving remote sensing reflectance in turbid Case II waters, and to improve that model using a proposed green-shortwave infrared model (GSWIR-based model). In a GSWIR-based model, the aerosol type is determined by a SWIR-based model and the reflectance due to aerosol scattering is calculated using spectral slope technology. In this study, field measurements collected from three independent cruises from two different Case II waters were used to compare models. The results indicate that both SWIR- and GSWIR-based models can be used to derive the remote sensing reflectance at visible wavelengths in turbid Case II waters, but GSWIR-based models are superior to SWIR-based models. Using the GSWIR-based model decreases uncertainty in remote sensing reflectance retrievals in turbid Case II waters by 2.6-12.1%. In addition, GSWIR-based model’s sensitivity to user-supplied parameters was determined using the numerical method, which indicated that the GSWIR-based model is more sensitive to the uncertainty of spectral slope technology than to that of aerosol type retrieval methodology. Due to much lower noise tolerance of GSWIR-based model in the blue and near-infrared regions, the GSWIR-based model performs poorly in determining remote sensing reflectance at these wavelengths, which is consistent with the GSWIR-based model’s accuracy evaluation results.

  20. Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

    Directory of Open Access Journals (Sweden)

    Wooyeon Sunwoo

    2017-01-01

    Full Text Available Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall–runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC prior to a rainfall event. Soil moisture is one of the most important variables in rainfall–runoff modeling, and remotely sensed soil moisture is recognized as an effective way to improve the accuracy of runoff prediction. In this study, the IWC was evaluated based on remotely sensed soil moisture by using the Soil Conservation Service-Curve Number (SCS-CN method, which is one of the representative event-based models used for reducing the uncertainty of runoff prediction. Four proxy variables for the IWC were determined from the measurements of total rainfall depth (API5, ground-based soil moisture (SSMinsitu, remotely sensed surface soil moisture (SSM, and soil water index (SWI provided by the advanced scatterometer (ASCAT. To obtain a robust IWC framework, this study consists of two main parts: the validation of remotely sensed soil moisture, and the evaluation of runoff prediction using four proxy variables with a set of rainfall–runoff events in the East Asian monsoon region. The results showed an acceptable agreement between remotely sensed soil moisture (SSM and SWI and ground based soil moisture data (SSMinsitu. In the proxy variable analysis, the SWI indicated the optimal value among the proposed proxy variables. In the runoff prediction analysis considering various infiltration conditions, the SSM and SWI proxy variables significantly reduced the runoff prediction error as compared with API5 by 60% and 66%, respectively. Moreover, the proposed IWC framework with

  1. Monitoring and modeling of wetland environment using time-series bi-sensor remotely sensed data

    Science.gov (United States)

    Michishita, Ryo

    More than half of the wetlands in the world have been lost in the last century mainly due to human activities. Since natural wetlands receive a significant amount of untreated runoff from urban and agricultural areas, it is necessary to account for other landscapes adjacent to wetlands, such as water bodies, agricultural areas, and urban areas, in the protection and restoration of the wetlands. The goal of this dissertation is to monitor and model land cover changes using the time-series Landsat-5 TM and Terra MODIS data in the Poyang Lake area of China from two perspectives: wetland cover changes and urbanization. A bi-scale monitoring approach was adopted in the monitoring and modeling of wetland cover changes to examine the similarities and differences derived from remotely sensed imagery with different spatial resolutions. The effect of different modeling settings of multiple endmember spectral mixture analysis (MESMA) were examined utilizing a single pair of TM and MODIS scenes. MESMA applied to nine pairs of TM and MODIS scenes acquired from July 2004 to October 2005 captured phenological and hydrological trends of land cover fractions (LCFs) and LCF agreement between the image pairs. Ground surface reflectance, rather than LCFs, was chosen as the key parameter in the blending of bi-scale remotely sensed data that utilized the spatial details of one data type and temporal details of the other. This research customized an existing fusion model to overcome the problem with the unobserved pixels in MODIS data acquired on TM data acquisition dates. It is interesting that the input data combination considering water level change achieved higher accuracy. In the monitoring of urbanization, this research investigated the relationship between urban land cover and human activities, and detected the areas of new urban development and redevelopment of built-up areas. Different urbanization processes largely influenced by the economic reforms of China were demonstrated

  2. The Utility of Remotely-Sensed Land Surface Temperature from Multiple Platforms For Testing Distributed Hydrologic Models over Complex Terrain

    Science.gov (United States)

    Xiang, T.; Vivoni, E. R.; Gochis, D. J.

    2011-12-01

    Land surface temperature (LST) is a key parameter in watershed energy and water budgets that is relatively unexplored as a validation metric for distributed hydrologic models. Ground-based or remotely-sensed LST datasets can provide insights into a model's ability in reproducing water and energy fluxes across a large range of terrain, vegetation, soil and meteorological conditions. As a result, spatiotemporal LST observations can serve as a strong constraint for distributed simulations and can augment other available in-situ data. LST fields are particular useful in mountainous areas where temperature varies with terrain properties and time-variable surface conditions. In this study, we collect and process remotely-sensed fields from several satellite platforms - Landsat 5/7, MODIS and ASTER - to capture spatiotemporal LST dynamics at multiple resolutions and with frequent repeat visits. We focus our analysis of these fields over the Sierra Los Locos basin (~100 km2) in Sonora, Mexico, for a period encompassing the Soil Moisture Experiment in 2004 and the North American Monsoon Experiment (SMEX04-NAME). Satellite observations are verified using a limited set of ground data from manual sampling at 30 locations and continuous measurements at 2 sites. First, we utilize the remotely-sensed fields to understand the summer seasonal evolution of LST in the basin in response to the arrival of summer storms and the vigorous ecosystem greening organized along elevation bands. Then, we utilize the ground and remote-sensing datasets to test the distributed predictions of the TIN-based Real-time Integrated Basin Simulator (tRIBS) under conditions accounting static and dynamic vegetation patterns. Basin-averaged and distributed comparisons are carried out for two different terrain products (INEGI aerial photogrammetry and ASTER stereo processing) used to derive the distributed model domain. Results from the comparisons are discussed in light of the utility of remotely-sensed LST

  3. A Unidirectional Total Variation and Second-Order Total Variation Model for Destriping of Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available Remote sensing images often suffer from stripe noise, which greatly degrades the image quality. Destriping of remote sensing images is to recover a good image from the image containing stripe noise. Since the stripes in remote sensing images have a directional characteristic (horizontal or vertical, the unidirectional total variation has been used to consider the directional information and preserve the edges. The remote sensing image contaminated by heavy stripe noise always has large width stripes and the pixels in the stripes have low correlations with the true pixels. On this occasion, the destriping process can be viewed as inpainting the wide stripe domains. In many works, high-order total variation has been proved to be a powerful tool to inpainting wide domains. Therefore, in this paper, we propose a variational destriping model that combines unidirectional total variation and second-order total variation regularization to employ the directional information and handle the wide stripes. In particular, the split Bregman iteration method is employed to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed method.

  4. Fundamentals of polarimetric remote sensing

    CERN Document Server

    Schott, John R

    2009-01-01

    This text is for those who need an introduction to polarimetric signals to begin working in the field of polarimetric remote sensing, particularly where the contrast between manmade objects and natural backgrounds are the subjects of interest. The book takes a systems approach to the physical processes involved with formation, collection, and analysis of polarimetric remote sensing data in the visible through longwave infrared. Beginning with a brief review of the polarized nature of electromagnetic energy and radiometry, Dr. Schott then introduces ways to characterize a beam of polarized ene

  5. WATERSHED MODELING OF KRISHNA DELTA, ANDHRA PRADESH, USING GIS AND REMOTE SENSING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    V. MALLIKARJUNA

    2012-11-01

    Full Text Available Water is the vital natural resource essential for the survival of mankind. Rainfall is the main source of water which is unevenly distributed spatially and temporally. Unprecedented increase in population, urbanization, agricultural expansion and industrialization leads to higher levels of human activities. As waterdemand increases, issues on water availability and demand become critical. This makes the management of water resources, such as assessing, managing and planning of water resources for sustainable use, a complex task. Therefore, it is essential to make measurement of factors such as size, slope, soil type and land use, vegetation and flow capacity of the channel. The drainage area, length of the water courses and mainstream are the most significant variables for prediction of run-off. Remote Sensing integrated with GeographicalInformation System has been efficiently used in generating input parameters of hydrological models. In the present investigation, an attempt has been made to develop a GIS based Watershed Model for theassessment of spatial distribution of runoff for the Krishna Delta in Andhra Pradesh, India. The GIS layers namely, contours, stream network were prepared including watershed boundary. A Digital Elevation Model (DEM of the study area was also generated in ArcGIS using Contours and Stream layers. Subsequently Slope and Aspect maps were generated for the study area.

  6. 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.

  7. A Model with Ellipsoidal Scatterers for Polarimetric Remote Sensing of Anisotropic Layered Media

    Science.gov (United States)

    Nghiem, S. V.; Kwok, R.; Kong, J. A.; Shin, R. T.

    1993-01-01

    This paper presents a model with ellipsoidal scatterers for applications to polarimetric remote sensing of anisotropic layered media at microwave frequencies. The physical configuration includes an isotropic layer covering an anisotropic layer above a homogeneous half space. The isotropic layer consists of randomly oriented spheroids. The anisotropic layer contains ellipsoidal scatterers with a preferential vertical alignment and random azimuthal orientations. Effective permittivities of the scattering media are calculated with the strong fluctuation theory extended to account for the nonspherical shapes and the scatterer orientation distributions. On the basis of the analytic wave theory, dyadic Green's functions for layered media are used to derive polarimetric backscattering coefficients under the distorted Born approximation. The ellipsoidal shape of the scatterers gives rise to nonzero cross-polarized returns from the untilted anisotropic medium in the first-order approximation. Effects of rough interfaces are estimated by an incoherent addition method. Theoretical results and experimental data are matched at 9 GHz for thick first-year sea ice with a bare surface and with a snow cover at Point Barrow, Alaska. The model is then used to study the sensitivity of polarimetric backscattering coefficients with respect to correlation lengths representing the geometry of brine inclusions. Polarimetric signatures of bare and snow-covered sea ice are also simulated based on the model to investigate effects of different scattering mechanisms.

  8. 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.

  9. Modeling rating curves using remotely-sensed LiDAR data

    Science.gov (United States)

    Nathanson, M.; Lyon, S. W.; Kean, J. W.; Grabs, T. J.; Seibert, J.; Laudon, H.

    2010-12-01

    surveys and LiDAR scans or various combinations of LiDAR scans (e.g., green LiDAR that can detect topographical variations through water) could be useful in generating the data needed to run such a fluid mechanically based model for generating rating curves. This opens a realm of possibility to remotely sense and monitor stream flows in natural channels in remote locations.

  10. Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

    Science.gov (United States)

    Beck, L. R.; Rodriguez, M. H.; Dister, S. W.; Rodriguez, A. D.; Washino, R. K.; Roberts, D. R.; Spanner, M. A.

    1997-01-01

    A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

  11. 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 (Kex) 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 Kex 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.

  12. Multiscale and Multitemporal Urban Remote Sensing

    Science.gov (United States)

    Mesev, V.

    2012-07-01

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

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

    Science.gov (United States)

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

    2013-12-01

    Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system that is not directly linked to discharge, in particular the unsaturated zone, remains uncalibrated, or might be modified unrealistically. Soil moisture observations from satellites have the potential to fill this gap, as these provide the closest thing to a direct measurement of the state of the unsaturated zone, and thus are potentially useful in calibrating unsaturated zone model parameters. This is expected to result in a better identification of the complete hydrological system, potentially leading to improved forecasts of the hydrograph as well. Here we evaluate this 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 approaches that calibrate only with discharge, such that this leads to improved forecasts of soil moisture content and discharge as well? To answer these questions we use a dual state and parameter ensemble Kalman filter to calibrate the hydrological model LISFLOOD for the Upper Danube area. Calibration is done with discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS and ASCAT. Four scenarios are studied: no calibration (expert knowledge), calibration on discharge, calibration on remote sensing data (three satellites) and calibration on both discharge and remote sensing data. Using a split-sample approach, the model is calibrated for a period of 2 years and validated for the calibrated model parameters on a validation period of 10 years. Results show that calibration with discharge data improves the estimation of groundwater parameters (e.g., groundwater reservoir constant) and

  14. River Water Quality Model Based on Remote Sensing Information Methods--A Case Study of Lijing River in Guilin City

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    River water quality models based on remote sensing information models are superior to pure water quality models because they combine the inevitability and risk of geographical phenomena and can take complex geographical characteristics into account. A water quality model for forecasting COD has been established with remote sensing information modeling methods by monitoring and analyzing water quantity and water quality of the Lijing River reach which flows through a complicated Karst mountain area. This model provides a good tool to predict water quality of complex rivers. It is validated by simulating contaminant concentrations of the study area. The results show that remote sensing information models are suitable for complex geography. It is not only a combined model of inevitability and risk of the geographical phenomena, but also a semi-theoretical and semi-empirical formula, providing a good tool to study organic contaminants in complicated rivers. The coefficients and indices obtained have limited value and the model is not suitable for all situations. Some improvements are required.

  15. 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)

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

    Science.gov (United States)

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

    2015-01-01

    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 km(2)) 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 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.

  17. Beyond Flood Hazard Maps: Detailed Flood Characterization with Remote Sensing, GIS and 2d Modelling

    Science.gov (United States)

    Santillan, J. R.; Marqueso, J. T.; Makinano-Santillan, M.; Serviano, J. L.

    2016-09-01

    Flooding is considered to be one of the most destructive among many natural disasters such that understanding floods and assessing the risks associated to it are becoming more important nowadays. In the Philippines, Remote Sensing (RS) and Geographic Information System (GIS) are two main technologies used in the nationwide modelling and mapping of flood hazards. Although the currently available high resolution flood hazard maps have become very valuable, their use for flood preparedness and mitigation can be maximized by enhancing the layers of information these maps portrays. In this paper, we present an approach based on RS, GIS and two-dimensional (2D) flood modelling to generate new flood layers (in addition to the usual flood depths and hazard layers) that are also very useful in flood disaster management such as flood arrival times, flood velocities, flood duration, flood recession times, and the percentage within a given flood event period a particular location is inundated. The availability of these new layers of flood information are crucial for better decision making before, during, and after occurrence of a flood disaster. The generation of these new flood characteristic layers is illustrated using the Cabadbaran River Basin in Mindanao, Philippines as case study area. It is envisioned that these detailed maps can be considered as additional inputs in flood disaster risk reduction and management in the Philippines.

  18. BEYOND FLOOD HAZARD MAPS: DETAILED FLOOD CHARACTERIZATION WITH REMOTE SENSING, GIS AND 2D MODELLING

    Directory of Open Access Journals (Sweden)

    J. R. Santillan

    2016-09-01

    Full Text Available Flooding is considered to be one of the most destructive among many natural disasters such that understanding floods and assessing the risks associated to it are becoming more important nowadays. In the Philippines, Remote Sensing (RS and Geographic Information System (GIS are two main technologies used in the nationwide modelling and mapping of flood hazards. Although the currently available high resolution flood hazard maps have become very valuable, their use for flood preparedness and mitigation can be maximized by enhancing the layers of information these maps portrays. In this paper, we present an approach based on RS, GIS and two-dimensional (2D flood modelling to generate new flood layers (in addition to the usual flood depths and hazard layers that are also very useful in flood disaster management such as flood arrival times, flood velocities, flood duration, flood recession times, and the percentage within a given flood event period a particular location is inundated. The availability of these new layers of flood information are crucial for better decision making before, during, and after occurrence of a flood disaster. The generation of these new flood characteristic layers is illustrated using the Cabadbaran River Basin in Mindanao, Philippines as case study area. It is envisioned that these detailed maps can be considered as additional inputs in flood disaster risk reduction and management in the Philippines.

  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. 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-errormodelling. The RS irrigated areas and actual evapotranspiration can be used to: (i) understand irrigation dynamics, (ii) constrain irrigation models in data scarce regions, as well as (iii) pinpointing areas that require better ground

  1. 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.

  2. 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...

  3. [Estimation models of vegetation fractional coverage (VFC) based on remote sensing image at different radiometric correction levels].

    Science.gov (United States)

    Gu, Zhu-Jun; Zeng, Zhi-Yuan; Shi, Xue-Zheng; Yu, Dong-Sheng; Zheng, Wei; Zhang, Zhen-Long; Hu, Zi-Fu

    2008-06-01

    The images of post atmospheric correction reflectance (PAC), top of atmosphere reflectance (TOA), and digital number (DN) of a SPOT5 HRG remote sensing image of Nanjing, China were used to derive four vegetation indices (VIs), i. e., normalized difference vegetation index (NDVI), transformed vegetation index (TVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and 36 VI-VFC relationship models were established based on these VIs and the VFC data obtained from ground measurement. The results showed that among the models established, the cubic polynomial models based on NDVI and TVI from PAC were the best, followed by those based on SAVI and MSAVI from DN, with the accuracy being slightly higher than that of the former two models when VFC > 0.8. The accuracy of these four models was higher in middle-densely vegetated areas (VFC = 0.4-0.8) than in sparsely vegetated areas (VFC = 0-0.4). All the established models could be used in other places via the introduction of calibration models. In VI-VFC modeling, using VIs derived from different radiometric correction levels of remote sensing image could help mining valuable information from remote sensing image, and thus, improving the accuracy of VFC estimation.

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

    Directory of Open Access Journals (Sweden)

    H. C. Winsemius

    2008-08-01

    Full Text Available In this study, land surface related parameter distributions of a conceptual semi-distributed hydrological model are estimated by employing time series of satellite-based evaporation estimates during the dry season as explanatory information. A key application for this approach is to identify part of the parameter distribution space in ungauged river basins without the need for ground data. The information, contained in the evaporation estimates implicitly imposes compliance of the model with the largest water balance term, evaporation, and a spatially and temporally realistic depletion of soil moisture within the dry season. Furthermore, the model results can provide a better understanding of the information density of remotely sensed evaporation.

    The approach has been applied to the ungauged Luangwa river basin (150 000 (km2 in Zambia. Model units were delineated on the basis of similar land cover. For each model unit, model parameters for which evaporation is sensitive, have been conditioned on the evaporation estimates by means of Monte-Carlo sampling. 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 a relatively small unsaturated zone (due to the shallow rooting depth of the vegetation and moisture stressed vegetation. The forested areas and evergreen highlands show parameter ranges that indicate a much deeper root zone and drought resistance.

    Unrealistic parameter ranges, found for instance in the high optimal field capacity values in the highlands may indicate model structural deficiencies. We believe that in these areas, groundwater uptake into the root zone and lateral movement of groundwater should be included in the model structure. Furthermore, a less distinct

  5. Remote sensing in soil science.

    NARCIS (Netherlands)

    Mulders, M.A.

    1987-01-01

    This book provides coverage of remote sensing techniques and their application in soil science. A clear, step-by-step approach to the various aspects ensures that the reader will gain a good grasp of the subject so that he can apply the techniques to his own field of study. The book opens with an in

  6. 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.

  7. Satellite Microwave Remote Sensing for Environmental Modeling of Mosquito Population Dynamics

    Science.gov (United States)

    Chuang, Ting-Wu; Henebry, Geoffrey M.; Kimball, John S.; VanRoekel-Patton, Denise L.; Hildreth, Michael B.; Wimberly, Michael C.

    2012-01-01

    Environmental variability has important influences on mosquito life cycles and understanding the spatial and temporal patterns of mosquito populations is critical for mosquito control and vector-borne disease prevention. Meteorological data used for model-based predictions of mosquito abundance and life cycle dynamics are typically acquired from ground-based weather stations; however, data availability and completeness are often limited by sparse networks and resource availability. In contrast, environmental measurements from satellite remote sensing are more spatially continuous and can be retrieved automatically. This study compared environmental measurements from the NASA Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and in situ weather station data to examine their ability to predict the abundance of two important mosquito species (Aedes vexans and Culex tarsalis) in Sioux Falls, South Dakota, USA from 2005 to 2010. The AMSR-E land parameters included daily surface water inundation fraction, surface air temperature, soil moisture, and microwave vegetation opacity. The AMSR-E derived models had better fits and higher forecasting accuracy than models based on weather station data despite the relatively coarse (25-km) spatial resolution of the satellite data. In the AMSR-E models, air temperature and surface water fraction were the best predictors of Aedes vexans, whereas air temperature and vegetation opacity were the best predictors of Cx. tarsalis abundance. The models were used to extrapolate spatial, seasonal, and interannual patterns of climatic suitability for mosquitoes across eastern South Dakota. Our findings demonstrate that environmental metrics derived from satellite passive microwave radiometry are suitable for predicting mosquito population dynamics and can potentially improve the effectiveness of mosquito-borne disease early warning systems. PMID:23049143

  8. Satellite remote sensing of water turbidity in Alqueva reservoir and implications on lake modelling

    Directory of Open Access Journals (Sweden)

    M. Potes

    2012-06-01

    Full Text Available The quality control and monitoring of surface freshwaters is crucial, since some of these water masses constitute essential renewable water resources for a variety of purposes. In addition, changes in the surface water composition may affect the physical properties of lake water, such as temperature, which in turn may impact the interactions of the water surface with the lower atmosphere.

    The use of satellite remote sensing to estimate the water turbidity of Alqueva reservoir, located in the south of Portugal, is explored. A validation study of the satellite derived water leaving spectral reflectance is firstly presented, using data taken during three field campaigns carried out during 2010 and early 2011. Secondly, an empirical algorithm to estimate lake water surface turbidity from the combination of in situ and satellite measurements is proposed. Finally, the importance of water turbidity on the surface energy balance is tested in the form of a study of the sensitivity of a lake model to the extinction coefficient of water (estimated from turbidity, showing that this is an important parameter that affects the lake surface temperature.

  9. Digital imaging and remote sensing image generator (DIRSIG) as applied to NVESD sensor performance modeling

    Science.gov (United States)

    Kolb, Kimberly E.; Choi, Hee-sue S.; Kaur, Balvinder; Olson, Jeffrey T.; Hill, Clayton F.; Hutchinson, James A.

    2016-05-01

    The US Army's Communications Electronics Research, Development and Engineering Center (CERDEC) Night Vision and Electronic Sensors Directorate (referred to as NVESD) is developing a virtual detection, recognition, and identification (DRI) testing methodology using simulated imagery as a means of augmenting the field testing component of sensor performance evaluation, which is expensive, resource intensive, time consuming, and limited to the available target(s) and existing atmospheric visibility and environmental conditions at the time of testing. Existing simulation capabilities such as the Digital Imaging Remote Sensing Image Generator (DIRSIG) and NVESD's Integrated Performance Model Image Generator (NVIPM-IG) can be combined with existing detection algorithms to reduce cost/time, minimize testing risk, and allow virtual/simulated testing using full spectral and thermal object signatures, as well as those collected in the field. NVESD has developed an end-to-end capability to demonstrate the feasibility of this approach. Simple detection algorithms have been used on the degraded images generated by NVIPM-IG to determine the relative performance of the algorithms on both DIRSIG-simulated and collected images. Evaluating the degree to which the algorithm performance agrees between simulated versus field collected imagery is the first step in validating the simulated imagery procedure.

  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. Radiative transfer model for satellite remote sensing of ocean color in coastal zones

    Science.gov (United States)

    Kobayashi, Hiroshi; Ohta, Sachio; Murao, Naoto; Tachibana, Harukuni; Yamagata, Sadamu

    2001-01-01

    A radiative transfer model for a coupled atmosphere-ocean system was developed for satellite remote sensing of costal pollution to estimate water-leaving radiance from polluted sea surfaces. The optical properties of suspended substances in the ocean such as phytoplankton (Skeletonema costatum and Heterosigma akashiwo), detritus, submicron particles, and inorganic particles were measured or estimated. The equation of radiative transfer in the coupled atmosphere-ocean system was solved by using the invariance imbedding method. The water-leaving radiance in clear and Case II waters, turbid waters with soil particles, and red tide waters, were calculated. It was possible to estimate the soil particle concentration of water by using the ratio of the upward radiance at different wavelengths with a high resolution sensor for the land like the Landsat TM. However, estimating the red tide phytoplankton concentration using Landsat TM was difficult, because the water-leaving radiance varies little with phytoplankton concentration, and is affected by assumed amounts of detritus.

  12. Remote sensing image fusion based on Gaussian mixture model and multiresolution analysis

    Science.gov (United States)

    Xiao, Moyan; He, Zhibiao

    2013-10-01

    A novel image fusion algorithm based on region segmentation and multiresolution analysis(MRA) is proposed to make full use of advantages of different multiscale transform. Nonsubsampled contourlet transform(NSCT) processes edges better than wavelet transform does. While wavelet transform handles smooth area and singularities better than NSCT does. As an image often includes more than one feature, the proposed method is conducted on the basis of Gaussian mixture model(GMM) based region segmentation. Firstly, transform the multispectral(MS) image into intensity, hue and saturation component. Secondly, segment intensity component into dense contour and smooth regions according to GMM and NSCT. And then gain new intensity component by fusing intensity component and high resolution image with Àtrous wavelet transform(ATWT) fusion in smooth areas and NSCT fusion in dense contour areas. Finally transform the new intensity together with hue component, saturation component back into RGB space and obtain the fused image. Multisource remote sensing images are tested to assess this proposed algorithm. Visual evaluation and statistics analysis are employed to evaluate the quality of fused images of different methods. The proposed improved algorithm demonstrates excellent spectrum information and high resolution. Experiment results show that the new proposed fusion algorithm incorporating with region segmentation based improved GMM and MRA outperforms those algorithms based on single multiscale transform.

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

    Science.gov (United States)

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

    2014-12-01

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

  14. Ice formation in altocumulus clouds over Leipzig: Remote sensing measurements and detailed model simulations

    Science.gov (United States)

    Simmel, Martin; Bühl, Johannes; Ansmann, Albert; Tegen, Ina

    2014-05-01

    Over Leipzig, altocumulus clouds are frequently observed using a suite of remote sensing instruments. These observations cover a wide range of heights, temperatures, and microphysical properties of the clouds ranging from purely liquid to heavily frozen. For the current study, two cases were chosen to test the sensitivity of these clouds with respect to several microphysical and dynamical parameters such as aerosol properties (CCN, IN), ice particle shape as well as turbulence. The mixed-phase spectral microphysical model SPECS was coupled to a dynamical model of the Asai-Kasahara type resulting in the model system AK-SPECS. The relatively simple dynamics allows for a fine vertical resolution needed for the rather shallow cloud layers observed. Additionally, the proper description of hydrometeor sedimentation is important especially for the fast growing ice crystals to realistically capture their interaction with the vapour and liquid phase (Bergeron-Findeisen process). Since the focus is on the cloud microphysics, the dynamics in terms of vertical velocity profile is prescribed for the model runs and the feedback of the microphysics on dynamics by release or consumption of latent heat due to phase transfer is not taken into account. The microphysics focuses on (1) ice particle shape allowing hexagonal plates and columns with size-dependant axis ratios and (2) the ice nuclei (IN) budget realized with a prognostic temperature resolved field of potential IN allowing immersion freezing only when active IN and supercooled drops above a certain size threshold are present within a grid cell. Sensitivity studies show for both cases that ice particle shape seems to have the major influence on ice mass formation under otherwise identical conditions. This is due to the effect (1) on terminal fall velocity of the individual ice particle allowing for longer presence times in conditions supersaturated with respect to ice and (2) on water vapour deposition which is enhanced due

  15. 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.

  16. Remote Sensing Best Paper Award 2013

    OpenAIRE

    Prasad Thenkabail

    2013-01-01

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

  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. Estimation of soil organic carbon based on remote sensing and process model

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The estimation of the soil organic carbon content (SOC) is one of the important issues in the research of the global carbon cycle.However,there are great differences among different scientists regarding the estimated magnitude of SOC.There are two commonly used methods for the estimation of SOC,with each method having both advantages and disadvantages.One method is the so called direct method,which is based on the samples of measured SOC and maps of soil or vegetation types.The other method is the so called indirect method,which is based on the ecosystem process model of the carbon cycle.The disadvantage of the direct method is that it mainly discloses the difference of the SOC among different soil or vegetation types.It can hardly distinguish the difference of the SOC in the same type of soil or vegetation.The indirect method,a process-based method,is based on the mechanics of carbon transfer in the ecosystem and can potentially improve the spatial resolution of the SOC estimation if the input variables have a high spatial resolution.However,due to the complexity of the process-based model,the model usually simplifies some key model parameters that have spatial heterogeneity with constants.This simplification will produce a great deal of uncertainties in the estimation of the SOC,especially on the spatial precision.In this paper,we combined the process-based model (CASA model) with the measured SOC,in which the remote sensing data (AVHRR NDIV) was incorporated into the model to enhance the spatial resolution.To model the soil base respiration,the Van't Hoff model was used to combine with the CASA model.The results show that this method could significantly improve the spatial precision (8 km spatial resolution).The results also show that there is a relationship between soil base respiration and the SOC as the influence of environmental factors,i.e.,temperature and moisture,had been removed from soil respiration which makes the SOC the most important factor of soil

  20. 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.

  1. Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery

    Directory of Open Access Journals (Sweden)

    Xiong Xu

    2016-12-01

    Full Text Available Spectral unmixing and sub-pixel mapping have been used to estimate the proportion and spatial distribution of the different land-cover classes in mixed pixels at a sub-pixel scale. In the past decades, several algorithms were proposed in both categories; however, these two techniques are generally regarded as independent procedures, with most sub-pixel mapping methods using abundance maps generated by spectral unmixing techniques. It should be noted that the utilized abundance map has a strong impact on the performance of the subsequent sub-pixel mapping process. Recently, we built a novel sub-pixel mapping model in combination with the linear spectral mixture model. Therefore, a joint sub-pixel mapping model was established that connects an original (coarser resolution remotely sensed image with the final sub-pixel result directly. However, this approach focuses on incorporating the spectral information contained in the original image without addressing the spectral endmember variability resulting from variable illumination and environmental conditions. To address this important issue, in this paper we designed a new joint sparse sub-pixel mapping method under the assumption that various representative spectra for each endmember are known a priori and available in a library. In addition, the total variation (TV regularization was also adopted to exploit the spatial information. The proposed approach was experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method can achieve better results by considering the impact of endmember variability when compared with other sub-pixel mapping methods.

  2. 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.

  3. Combining modelled and remote sensing soil moisture anomalies for an operational global drought monitoring

    Science.gov (United States)

    Cammalleri, Carmelo; Vogt, Jürgen

    2017-04-01

    Soil moisture anomalies (i.e., deviations from the climatology) are often seen as a reliable tool to monitor and quantify the occurrence of drought events and their potential impacts, especially in agricultural and naturally vegetated lands. Soil moisture datasets (or their proxy) can be derived from a variety of sources, including land-surface models and thermal and microwave satellite remote sensing images. However, each data source has different advantages and drawbacks that prevent to unequivocally prefer one dataset over the others, especially in global applications that encompass a wide range of soil moisture regimes. The analysis of the spatial reliability of the different datasets at global scale is further complicated by the lack of reliable long-term soil moisture records for a ground validation over most regions. To overcome this limitation, in recent years the Triple Collocation (TC) technique has been deployed in order to quantify the likely errors associated to three mutually-independent datasets without assuming that one of them represents the "truth". In this study, three global datasets of soil moisture anomalies are investigated: the first one derived from the runs of the Lisflood hydrological model, the second one obtained from the combined active/passive microwave dataset produced in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI), and the last one derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) observations. A preliminary analysis of the three datasets aimed at detecting the areas where the TC technique can be successfully applied, hence the spatial distribution of the random error variance for each model is evaluated. This study allows providing useful advises for a robust combination of the three datasets into a single product for a more reliable global drought monitoring.

  4. Application of Satellite remote sensing for detailed landslide inventories using Frequency ratio model and GIS

    Directory of Open Access Journals (Sweden)

    Himan Shahabi

    2012-07-01

    Full Text Available This paper presents landslide susceptibility analysis in central Zab basin in the southwest mountainsides of West-Azerbaijan province in Iran using remotely sensed data and Geographic Information System. Landslide database was generated using satellite imagery and aerial photographs accompanied by field investigations using Differential Global Positioning System to generate a landslide inventory map. Digital elevation model (DEM was first constructed using GIS software. Nine landslide inducing factors were used for landslide vulnerability analysis: slope, slope aspect, distance to road, distance to drainage network, distance to fault, land use, Precipitation, Elevation, and geological factors. This study demonstrates the synergistic use of medium resolution, multitemporal Satellite pour lObservation de la Terre (SPOT, for prepare of landslide-inventory map and Landsat ETM+ for prepare of Land use. The post-classification comparison method using the Maximum Likelihood classifier with SPOT images was able to detect approximately 70% of landslides. Frequency ratio of each factor was computed using the above thematic factors with past landslide locations. It employs the landslide events as dependant variable and data layers as independent variable, and makes use of the correlation between these two factors in landslide zonation. Given the employed model and the variables, signification tests were implemented on each independent variable, and the degree of fitness of zonation map was estimated Landslide susceptibility map was produced using raster analysis. The landslide susceptibility map was classified into four classes: low, moderate, high and very high. The model is validated using the Relative landslide density index (R-index method. The final, landslide low hazard susceptibility map was drawn using frequency ratio. As a result, showed that the identified landslides were located in the class (51.37%, moderate (29.35%, high (11.10% and very high

  5. Comparison Between Linear and Nonlinear Models of Mixed Pixels in Remote Sensing Satellite Images Based on Cierniewski Surface BRDF Model by Means of Monte Carlo Ray Tracing Simulation

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2013-04-01

    Full Text Available Comparative study on linear and nonlinear mixed pixel models of which pixels in remote sensing satellite images is composed with plural ground cover materials mixed together, is conducted for remote sensing satellite image analysis. The mixed pixel models are based on Cierniewski of ground surface reflectance model. The comparative study is conducted by using of Monte Carlo Ray Tracing: MCRT simulations. Through simulation study, the difference between linear and nonlinear mixed pixel models is clarified. Also it is found that the simulation model is validated.

  6. Spatialized Application of Remotely Sensed Data Assimilation Methods for Farmland Drought Monitoring Using Two Different Crop Models

    Science.gov (United States)

    Silvestro, Paolo Cosmo; Casa, Raffaele; Pignatti, Stefano; Castaldi, Fabio; Yang, Hao; Guijun, Yang

    2016-08-01

    The aim of this work was to develop a tool to evaluate the effect of water stress on yield losses at the farmland and regional scale, by assimilating remotely sensed biophysical variables into crop growth models. Biophysical variables were retrieved from HJ1A, HJ1B and Landsat 8 images, using an algorithm based on the training of artificial neural networks on PROSAIL.For the assimilation, two crop models of differing degree of complexity were used: Aquacrop and SAFY. For Aquacrop, an optimization procedure to reduce the difference between the remotely sensed and simulated CC was developed. For the modified version of SAFY, the assimilation procedure was based on the Ensemble Kalman Filter.These procedures were tested in a spatialized application, by using data collected in the rural area of Yangling (Shaanxi Province) between 2013 and 2015Results were validated by utilizing yield data both from ground measurements and statistical survey.

  7. 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.

  8. Uncertainty reduction and parameters estimation of a~distributed hydrological model with ground and remote sensing data

    Science.gov (United States)

    Silvestro, F.; Gabellani, S.; Rudari, R.; Delogu, F.; Laiolo, P.; Boni, G.

    2014-06-01

    During the last decade the opportunity and usefulness of using remote sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite based products often provide the advantage of observing hydrologic variables in a distributed way while offering a different view that can help to understand and model the hydrological cycle. Moreover, remote sensing data are fundamental in scarce data environments. The use of satellite derived DTM, which are globally available (e.g. from SRTM as used in this work), have become standard practice in hydrologic model implementation, but other types of satellite derived data are still underutilized. In this work, Meteosat Second Generation Land Surface Temperature (LST) estimates and Surface Soil Moisture (SSM) available from EUMETSAT H-SAF are used to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. This work aims at proving that satellite observations dramatically reduce uncertainties in parameters calibration by reducing their equifinality. Two parameter estimation strategies are implemented and tested: a multi-objective approach that includes ground observations and one solely based on remotely sensed data. Two Italian catchments are used as the test bed to verify the model capability in reproducing long-term (multi-year) simulations.

  9. 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

  10. Remote sensing information sciences research group

    Science.gov (United States)

    Estes, John E.; Smith, Terence; Star, Jeffrey L.

    1988-01-01

    Research conducted under this grant was used to extend and expand existing remote sensing activities at the University of California, Santa Barbara in the areas of georeferenced information systems, matching assisted information extraction from image data and large spatial data bases, artificial intelligence, and vegetation analysis and modeling. The research thrusts during the past year are summarized. The projects are discussed in some detail.

  11. Remote Sensing of Open Water in Northern High Latitudes for use in Hydrologic Modeling

    Science.gov (United States)

    Podest, E.; McDonald, K. C.; Kimball, J.; Maumenee, N.; Bohn, T.; Lettenmaier, D.; Bowling, L.

    2007-12-01

    In the northern high latitudes open water bodies are common landscape features, having a large influence on hydrologic processes as well as surface-atmosphere carbon exchange and associated impacts on global climate. It is therefore of great importance to assess their spatial extent and temporal character in order to improve hydrologic and ecosystem process modeling. Spaceborne synthetic aperture radar (SAR) is an effective tool for this purpose since it is particularly sensitive to surface water and it can monitor large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We employ multi-temporal L-band SAR data from the Japanese Earth Remote Sensing Satellite (JERS-1) and ALOS PALSAR to map open water bodies across Alaska and Eurasia. A supervised decision tree-based classification approach was used to generate open water maps. For Alaska, we assembled regional-scale monthly JERS-1 SAR mosaics from data acquired during 1998. Digital elevation model (DEM) terrain and slope information were also employed in the decision tree classifier. These supplementary data aided significantly in improving classification performance in topographically complex regions where radar shadowing was prevalent. For study regions in Eurasia, PALSAR data was used in conjunction with JERS-1 imagery to map spatial patterns and seasonal variability in open water characteristics over selected study basins. These results were examined in relation to regional topographic and land cover characteristics. Classification results were also evaluated relative to other open water and land cover classification maps derived from Landsat, AVHRR, MODIS and SRTM. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology; at the University of Montana; at the University of Washington; and at Purdue University under contract with the National Aeronautics and Space Administration.

  12. 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)

  13. Remote Sensing-based Estimates of Potential Evapotranspiration for Hydrologic Modeling in the Upper Colorado River Basin Region

    OpenAIRE

    Barik, Muhammad Ghulam

    2014-01-01

    Potential Evapotranspiration (PET) is used as a common input to calculate evaporative demand in hydrological, ecological and biological modeling. Dynamic and distributed measurement of PET is important for improved hydrologic predictions at the watershed scale since PET varies with time and space. In this work, an advanced dynamic PET estimation is proposed by integrating geostationary satellite products into a currently existing remote sensing-based PET algorithm and evaluated in the framewo...

  14. 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

  15. Snowpack Microstructure Characterization and Partial Coherent and Fully Coherent Forward Scattering Models in Microwave Remote Sensing

    Science.gov (United States)

    Tan, S.; Tsang, L.; Xu, X.; Ding, K. H.

    2015-12-01

    In this paper we describe partial coherent model and fully coherent snowpack scattering model based on numerical simulation of Maxwell's equation. In medium characterization, we derive the correlation functions from the pair distribution functions of sticky spheres and multiple-size spheres used in QCA. We show that both the Percus-Yevick pair functions and the bicontinuous model have tails in the correlation functions that are distinctly different from the traditional exponential correlation functions. The methodologies of using ground measurements of grain size distributions and correlation functions to obtain model parameters are addressed. The DMRT theory has been extended to model the backscattering enhancement. We developed the methodology of cyclical corrections beyond first order to all orders of multiple scattering. This enables the physical modeling of combined active and passive microwave remote sensing of snow over the same scene. The bicontinuous /DMRT is applied to compare with data acquired in the NoSREx campaign, and the model results are validated against coincidental active and passive measurements using the same set of physical parameters of snow in all frequency and polarization channels. The DMRT is a partially coherent approach that one accounts for the coherent wave interaction only within few wavelengths as represented by phase matrix. However, the phase information of field is lost in propagating the specific intensity via RT and this hinders the use of DMRT in coherent synthetic aperture radar (SAR) analysis, including InSAR, PolInSAR and Tomo-SAR. One can alternatively calculate the scattering matrix of the terrestrial snowpack above ground by solving the volume integral equations directly with half space Green's function. The scattering matrix of the snowpack is computed for each realization giving rise to the speckle statistics. The resulting bistatic scattering automatically includes the backscattering enhancement effects. Tomograms of

  16. 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

  17. 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...

  18. Estimating national forest carbon stocks and dynamics: combining models and remotely sensed information

    Science.gov (United States)

    Smallman, Luke; Williams, Mathew

    2016-04-01

    Forests are a critical component of the global carbon cycle, storing significant amounts of carbon, split between living biomass and dead organic matter. The carbon budget of forests is the most uncertain component of the global carbon cycle - it is currently impossible to quantify accurately the carbon source/sink strength of forest biomes due to their heterogeneity and complex dynamics. It has been a major challenge to generate robust carbon budgets across landscapes due to data scarcity. Models have been used but outputs have lacked an assessment of uncertainty, making a robust assessment of their reliability and accuracy challenging. Here a Metropolis Hastings - Markov Chain Monte Carlo (MH-MCMC) data assimilation framework has been used to combine remotely sensed leaf area index (MODIS), biomass (where available) and deforestation estimates, in addition to forest planting and clear-felling information from the UK's national forest inventory, an estimate of soil carbon from the Harmonized World Database (HWSD) and plant trait information with a process model (DALEC) to produce a constrained analysis with a robust estimate of uncertainty of the UK forestry carbon budget between 2000 and 2010. Our analysis estimates the mean annual UK forest carbon sink at -3.9 MgC ha-1yr-1 with a 95 % confidence interval between -4.0 and -3.1 MgC ha-1 yr-1. The UK national forest inventory (NFI) estimates the mean UK forest carbon sink to be between -1.4 and -5.5 MgC ha-1 yr-1. The analysis estimate for total forest biomass stock in 2010 is estimated at 229 (177/232) TgC, while the NFI an estimated total forest biomass carbon stock of 216 TgC. Leaf carbon area (LCA) is a key plant trait which we are able to estimate using our analysis. Comparison of median estimates for LCA retrieved from the analysis and a UK land cover map show higher and lower values for LCA are estimated areas dominated by needle leaf and broad leaf forests forest respectively, consistent with ecological

  19. Back to the Future: Have Remotely Sensed Digital Elevation Models Improved Hydrological Parameter Extraction?

    Science.gov (United States)

    Jarihani, B.

    2015-12-01

    Digital Elevation Models (DEMs) that accurately replicate both landscape form and processes are critical to support modeling of environmental processes. Pre-processing analysis of DEMs and extracting characteristics of the watershed (e.g., stream networks, catchment delineation, surface and subsurface flow paths) is essential for hydrological and geomorphic analysis and sediment transport. This study investigates the status of the current remotely-sensed DEMs in providing advanced morphometric information of drainage basins particularly in data sparse regions. Here we assess the accuracy of three available DEMs: (i) hydrologically corrected "H-DEM" of Geoscience Australia derived from the Shuttle Radar Topography Mission (SRTM) data; (ii) the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) version2 1-arc-second (~30 m) data; and (iii) the 9-arc-second national GEODATA DEM-9S ver3 from Geoscience Australia and the Australian National University. We used ESRI's geospatial data model, Arc Hydro and HEC-GeoHMS, designed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. A coastal catchment in northeast Australia was selected as the study site where very high resolution LiDAR data are available for parts of the area as reference data to assess the accuracy of other lower resolution datasets. This study provides morphometric information for drainage basins as part of the broad research on sediment flux from coastal basins to Great Barrier Reef, Australia. After applying geo-referencing and elevation corrections, stream and sub basins were delineated for each DEM. Then physical characteristics for streams (i.e., length, upstream and downstream elevation, and slope) and sub-basins (i.e., longest flow lengths, area, relief and slopes) were extracted and compared with reference datasets from LiDAR. Results showed that

  20. Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

    Science.gov (United States)

    Tedrow, Christine Atkins

    The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and

  1. Remote Sensing Geological Exploration Model for Copper and Gold Deposits in the East Tianshan, Xinjiang

    Institute of Scientific and Technical Information of China (English)

    ZHANG Shoulin; Fu Shuixing; LI Chunxia

    2004-01-01

    Based on the identification and enhancive processing of information about strata, structure, magmatite, and alteration in ore-concentrated area in the eastern Tianshan, an exploration mode of remote sensing geology is established.The mode covers basic images composed of TM (7, 4, 1), Munsell space transformation for recognizing rock type,directional matched filtering for enhancing structures, multi-layer separating and extracting weak alteration information. It will provide a rapid and effective method for geological mapping and metallogenic prediction in this region.

  2. 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.

  3. Remote sensing in biological oceanography

    Science.gov (United States)

    Esaias, W. E.

    1981-01-01

    The main attribute of remote sensing is seen as its ability to measure distributions over large areas on a synoptic basis and to repeat this coverage at required time periods. The way in which the Coastal Zone Color Scanner, by showing the distribution of chlorophyll a, can locate areas productive in both phytoplankton and fishes is described. Lidar techniques are discussed, and it is pointed out that lidar will increase the depth range for observations.

  4. 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.

  5. Landslide Mapping and Modeling Using Remote Sensing, GIS and Statistical Analysis of District Muzaffarabad, Pakistan

    Science.gov (United States)

    Khalid, Nimrah; Mushtaq, Saman

    2016-07-01

    Occurrence factors of Landslide hazard can be natural such as high slopes, geological conditions and lineaments, faults, rain, and river cutting. Man-made factors such as road cuttings, deforestation or development can also contribute to the landsliding. The focus of this study was to model those landslides susceptible prone to hazard areas which in turn can help for the development, urbanization and for setting up rules or regulations to save nature and environment of the area. The focal of the current research work was the Earthquake of October, 2005 also known as Kashmir Earthquake, the epicenter location of the earthquake 34°29'35″N 73°37'44″E at height of ~2000 from mean sea level and ~20 Km North-East from Muzaffarabad city, Azad Jammu & Kashmir, at the scale of 1:50000 Geological map of 43-F/11, tehsil Nauseri area. The techniques used in this research is based on theorem of Bayes's bivariat statistic (weight of evidence) which predicts the events geographically and on input layers and the relationship of event. A relationship between event of landslide and factors was studied and analyzed using this method. Subsequently a prediction of the occurrence of the spatial location of the landslide event was established successfully. The relationship of distribution of landslide and factors layers was calculated using the statistical methods which enabled to predict the landslides zones in different areas. The methodology applied proved that the success rate was 80% landslide occurred in 18% area and prediction rate was 70% of landslides occurred in 70% of area. The use satellite remote sensing data, and GIS with the integration of statistical method are definitely an effective tool for predicting the future landslide prone areas.

  6. 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.

  7. A robust interpolation method for constructing digital elevation models from remote sensing data

    Science.gov (United States)

    Chen, Chuanfa; Liu, Fengying; Li, Yanyan; Yan, Changqing; Liu, Guolin

    2016-09-01

    A digital elevation model (DEM) derived from remote sensing data often suffers from outliers due to various reasons such as the physical limitation of sensors and low contrast of terrain textures. In order to reduce the effect of outliers on DEM construction, a robust algorithm of multiquadric (MQ) methodology based on M-estimators (MQ-M) was proposed. MQ-M adopts an adaptive weight function with three-parts. The weight function is null for large errors, one for small errors and quadric for others. A mathematical surface was employed to comparatively analyze the robustness of MQ-M, and its performance was compared with those of the classical MQ and a recently developed robust MQ method based on least absolute deviation (MQ-L). Numerical tests show that MQ-M is comparative to the classical MQ and superior to MQ-L when sample points follow normal and Laplace distributions, and under the presence of outliers the former is more accurate than the latter. A real-world example of DEM construction using stereo images indicates that compared with the classical interpolation methods, such as natural neighbor (NN), ordinary kriging (OK), ANUDEM, MQ-L and MQ, MQ-M has a better ability of preserving subtle terrain features. MQ-M replaces thin plate spline for reference DEM construction to assess the contribution to our recently developed multiresolution hierarchical classification method (MHC). Classifying the 15 groups of benchmark datasets provided by the ISPRS Commission demonstrates that MQ-M-based MHC is more accurate than MQ-L-based and TPS-based MHCs. MQ-M has high potential for DEM construction.

  8. 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.

  9. Basic research in the field of thermal infrared remote sensing

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    This overview paper points out that one of the problems impeding further development of remote sensing is that not much attention has been paid to basic research.Key contents of basic research in remote sensing,including modeling,inversion,scaling and scientific experiments,are reviewed.Significance of basic research is demonstrated through summarizing the intentions and progress of the project "Quantitative Remote Sensing Research on Land Surface Energy Exchange".

  10. Basic research in the field of thermal infrared remote sensing

    Institute of Scientific and Technical Information of China (English)

    徐冠华

    2000-01-01

    This overview paper points out that one of the problems impeding further development of remote sensing is that not much attention has been paid to basic research. Key contents of basic research in remote sensing, including modeling, inversion, scaling and scientific experiments, are reviewed. Significance of basic research is demonstrated through summarizing the intentions and progress of the project "Quantitative Remote Sensing Research on Land Surface Energy Exchange".

  11. Remote sensing for wind energy

    Energy Technology Data Exchange (ETDEWEB)

    Pena, A.; Bay Hasager, C.; Lange, J. [Technical Univ. of Denmark. DTU Wind Energy, DTU Risoe Campus, Roskilde (Denmark) (and others

    2013-06-15

    The Remote Sensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risoe) during the first PhD Summer School: Remote Sensing in Wind Energy. Thus it is closely linked to the PhD Summer Schools where state-of-the-art is presented during the lecture sessions. The advantage of the report is to supplement with in-depth, article style information. Thus we strive to provide link from the lectures, field demonstrations, and hands-on exercises to theory. The report will allow alumni to trace back details after the course and benefit from the collection of information. This is the third edition of the report (first externally available), after very successful and demanded first two, and we warmly acknowledge all the contributing authors for their work in the writing of the chapters, and we also acknowledge all our colleagues in the Meteorology and Test and Measurements Sections from DTU Wind Energy in the PhD Summer Schools. We hope to continue adding more topics in future editions and to update and improve as necessary, to provide a truly state-of-the-art 'guideline' available for people involved in Remote Sensing in Wind Energy. (Author)

  12. An overview of GNSS remote sensing

    Science.gov (United States)

    Yu, Kegen; Rizos, Chris; Burrage, Derek; Dempster, Andrew G.; Zhang, Kefei; Markgraf, Markus

    2014-12-01

    The Global Navigation Satellite System (GNSS) signals are always available, globally, and the signal structures are well known, except for those dedicated to military use. They also have some distinctive characteristics, including the use of L-band frequencies, which are particularly suited for remote sensing purposes. The idea of using GNSS signals for remote sensing - the atmosphere, oceans or Earth surface - was first proposed more than two decades ago. Since then, GNSS remote sensing has been intensively investigated in terms of proof of concept studies, signal processing methodologies, theory and algorithm development, and various satellite-borne, airborne and ground-based experiments. It has been demonstrated that GNSS remote sensing can be used as an alternative passive remote sensing technology. Space agencies such as NASA, NOAA, EUMETSAT and ESA have already funded, or will fund in the future, a number of projects/missions which focus on a variety of GNSS remote sensing applications. It is envisaged that GNSS remote sensing can be either exploited to perform remote sensing tasks on an independent basis or combined with other techniques to address more complex applications. This paper provides an overview of the state of the art of this relatively new and, in some respects, underutilised remote sensing technique. Also addressed are relevant challenging issues associated with GNSS remote sensing services and the performance enhancement of GNSS remote sensing to accurately and reliably retrieve a range of geophysical parameters.

  13. 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

    2011-01-01

    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 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 (TERRA/AQUA MODIS and Landsat-5 TM/Landsat-7 ETM+ and combining three different approaches to calculate the B parameter. 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, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30%. The poor agreement obtained using MODIS data reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue.

  14. 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.

  15. 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

  16. Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services

    Science.gov (United States)

    Wu, Changshan

    Public transit service is a promising transportation mode because of its potential to address urban sustainability. Current ridership of public transit, however, is very low in most urban regions, particularly those in the United States. This woeful transit ridership can be attributed to many factors, among which poor service quality is key. Given this, there is a need for transit planning and analysis to improve service quality. Traditionally, spatially aggregate data are utilized in transit analysis and planning. Examples include data associated with the census, zip codes, states, etc. Few studies, however, address the influences of spatially aggregate data on transit planning results. In this research, previous studies in transit planning that use spatially aggregate data are reviewed. Next, problems associated with the utilization of aggregate data, the so-called modifiable areal unit problem (MAUP), are detailed and the need for fine resolution data to support public transit planning is argued. Fine resolution data is generated using intelligent interpolation techniques with the help of remote sensing imagery. In particular, impervious surface fraction, an important socio-economic indicator, is estimated through a fully constrained linear spectral mixture model using Landsat Enhanced Thematic Mapper Plus (ETM+) data within the metropolitan area of Columbus, Ohio in the United States. Four endmembers, low albedo, high albedo, vegetation, and soil are selected to model heterogeneous urban land cover. Impervious surface fraction is estimated by analyzing low and high albedo endmembers. With the derived impervious surface fraction, three spatial interpolation methods, spatial regression, dasymetric mapping, and cokriging, are developed to interpolate detailed population density. Results suggest that cokriging applied to impervious surface is a better alternative for estimating fine resolution population density. With the derived fine resolution data, a multiple

  17. Estimation of effective soil hydraulic parameters for water management studies in semi-arid zones. Integral use of modelling, remote sensing and parameter estimation

    NARCIS (Netherlands)

    Jhorar, R.K.

    2002-01-01

    Key words: evapotranspiration, effective soil hydraulic parameters, remote sensing, regional water management, groundwater use, Bhakra Irrigation System, India.The meaningful application of water management simulation models at regional scale for the analysis of alternate water manage

  18. Red tide optical index: in situ optics and remote sensing models

    Science.gov (United States)

    Cetinic, I.; Karp-Boss, L.; Boss, E.; Ragan, M. A.; Jones, B. H.

    2007-05-01

    Harmful Algal Blooms (HABs) are recurring events in the coastal ocean, and local economies that depend on beach and coastal use are often adversely affected by these events. Inherent optical properties (absorption and backscattering) of the HAB dinoflagellate Lingulodinium polyedrum were measured in order to develop specific index that would enable easier detection of this HAB organism in the field. It has been noticed that red to blue and red to green ratio of absorption in this species is much lower then other measured species. A red tide ratio was tested in the field during a red tide episode in the San Pedro Channel, using a Wetlabs acS flow-through system. The red tide index gave a distinguishable signal in areas where L.polyedrum was present. Remote sensing reflectance was calculated from field and laboratory IOP measurements, using reverse Quasi-Analythical Alghoritm and Hydrolight to evaluate if the red tide index can be detected in the remote sensing ocean color measurements.

  19. 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.

  20. 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.

  1. Modeling of Percentage of Canopy in Merawu Catchment Derived From Various Vegetation Indices of Remotely Sensed Data

    Directory of Open Access Journals (Sweden)

    Bambang Sulistyo

    2013-07-01

    Full Text Available The research was aimed at studying Percentage of Canopy mapping derived from various vegetation indices of remotely-sensed data int Merawu Catchment. Methodology applied was by analyzing remote sensing data of Landsat 7 ETM+ image to obtain various vegetation indices for correlation analysis with Percentage of Canopy measured directly on the field (PTactual at 48 locations. These research used 11 (eleven vegetation indices of remotely-sensed data, namely ARVI, MSAVI, TVI, VIF, NDVI, TSAVI, SAVI, EVI, RVI, DVI and PVI. The analysis resulted models (PTmodel for Percentage of Canopy mapping. The vegetation indices selected are those having high coefficient of correlation (>=0.80 to PTactual. Percentage of Canopy maps were validated using 39 locations on the field to know their accuracies. Percentage of Canopy map (PTmodel is said to be accurate when its coefficient of correlation value to PTactual is high (>=0.80. The research result in Merawu Catchment showed that from 11 vegetation indices under studied, there were 6 vegetation indices resulted high accuracy of Percentage of Canopy maps (as shown in the value of coefficient of correlation as >=0.80, i.e. TVI, VIF, NDVI, TSAVI, RVI dan SAVI, while the rest, namely ARVI, PVI, DVI, EVI and MSAVI, have r values of < 0.80.

  2. DUE PERMAFROST: A Circumpolar Remote Sensing Service for Permafrost - Evaluation Case Studies and Intercomparison with Regional Climate Model Simulations

    Science.gov (United States)

    Heim, B.; Bartsch, A.; Elger, K. K.; Rinke, A.; Matthes, H.; Zhou, X.; Klehmet, K.; Buchhorn, M.; Soliman, A. S.; Duguay, C. R.

    2013-12-01

    The objective of the ESA Data User Element DUE Permafrost project (https://www.ipf.tuwien.ac.at/permafrost/) was to establish a Remote Sensing Service for permafrost applications. Permafrost has been addressed as one of the Essential Climate Variables (ECVs) in the Global Climate Observing System (GCOS). Permafrost is a subground phenomenon but Earth Observation can provide permafrost-related indicators and geophysical parameters used in modelling and monitoring. Climate and permafrost modelers as well as field investigators are associated users including the International Permafrost Association (IPA). http://www.page21.eu/ The ESA DUE Permafrost project (2009-2012) developed a suite of remote sensing products indicative for the subsurface phenomenon permafrost: Land Surface Temperature (LST), Surface Soil Moisture (SSM), Surface Frozen and Thawed State (Freeze/Thaw), Terrain, Land Cover, and Surface Water. Snow parameters (Snow Extent and Snow Water Equivalent) are being developed through the DUE GlobSnow project (Global Snow Monitoring for Climate Research, 2008-2011). The final DUE Permafrost remote sensing products cover the years 2007 to 2011 with a circumpolar coverage (north of 50°N). The products were released in 2012, to be used to analyze the temporal dynamics and map the spatial patterns of permafrost indicators. Further information is available at www.ipf.tuwien.ac.at/ permafrost. The remote sensing service also supports the FP7 funded project PAGE21 - Changing Permafrost in the Arctic and its Global Effects in the 21st Century, http://www.page21.eu/. The primary programme providing various ground data for the evaluation is the Global Terrestrial Network for Permafrost (GTN-P) initiated by the International Permafrost Association (IPA). Ground data ranges from active layer- and snow depths, to air-, ground-, and borehole temperature data as well as soil moisture measurements and the description of landform and vegetation. The involvement of scientific

  3. Evaluation of Surface Energy Balance models for mapping evapotranspiration using very high resolution airborne remote sensing data

    Science.gov (United States)

    Paul, George

    Agriculture is the largest (90%) consumer of all fresh water in the world. The consumptive use of water by vegetation represented by the process evapotranspiration (ET) has a vital role in the dynamics of water, carbon and energy fluxes of the biosphere. Consequently, mapping ET is essential for making water a sustainable resource and also for monitoring ecosystem response to water stress and changing climate. Over the past three decades, numerous thermal remote sensing based ET mapping algorithms were developed and these have brought a significant theoretical and technical advancement in the spatial modeling of ET. Though these algorithms provided a robust, economical, and efficient tool for ET estimations at field and regional scales, yet the uncertainties in flux estimations were large, making evaluation a difficult task. The main objective of this study was to evaluate and improve the performance of widely used remote sensing based energy balance models, namely: the Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution and with Internalized Calibration (METRIC), and Surface Energy Balance System (SEBS). Data used in this study was collected as part of a multi-disciplinary and multi-institutional field campaign BEAREX (Bushland Evapotranspiration and Agricultural Remote Sensing Experiment) that was conducted during 2007 and 2008 summer cropping seasons at the USDA-ARS Conservation and Production Research Laboratory (CPRL) in Bushland, Texas. Seventeen high resolution remote sensing images taken from multispectral sensors onboard aircraft and field measurements of the agro-meteorological variables from the campaign were used for model evaluation and improvement. Overall relative error measured in terms of mean absolute percent difference (MAPD) for instantaneous ET (mm h -1) were 22.7%, 23.2%, and 12.6% for SEBAL, METRIC, and SEBS, respectively. SEBAL and METRIC performances for irrigated fields representing higher ET

  4. Quantitative Application Study on Remote Sensing of Suspended Sediment

    Institute of Scientific and Technical Information of China (English)

    CHEN Yi-mei; XU Su-dong; LIN Qiang

    2012-01-01

    Quantitative application on remote sensing of suspended sediment is an important aspect of the engineering application of remote sensing study.In this paper,the Xiamen Bay is chosen as the study area.Eleven different phases of the remote sensing data are selected to establish a quantitative remote sensing model to map suspended sediment by using remote sensing images and the quasi-synchronous measured sediment data.Based on empirical statistics developed are the conversion models between instantaneous suspended sediment concentration and tidally-averaged suspended sediment concentration as well as the conversion models between surface layer suspended sediment concentration and the depth-averaged suspended sediment concentration.On this basis,the quantitative application integrated model on remote sensing of suspended sediment is developed.By using this model as well as multi-temporal remote sensing images,multi-year averaged suspended sediment concentration of the Xiamen Bay are predicted.The comparison between model prediction and observed data shows that the multi-year averaged suspended sediment concentration of studied sites as well as the concentration difference of neighboring sites can be well predicted by the remote sensing model with an error rate of 21.61% or less,which can satisfy the engineering requirements of channel deposition calculation.

  5. Geospatial environmental data modelling applications using remote sensing, GIS and spatial statistics

    Energy Technology Data Exchange (ETDEWEB)

    Siljander, M.

    2010-07-01

    This thesis presents novel modelling applications for environmental geospatial data using remote sensing, GIS and statistical modelling techniques. The studied themes can be classified into four main themes: (i) to develop advanced geospatial databases. Paper (I) demonstrates the creation of a geospatial database for the Glanville fritillary butterfly (Melitaea cinxia) in the Aaland Islands, south-western Finland; (ii) to analyse species diversity and distribution using GIS techniques. Paper (II) presents a diversity and geographical distribution analysis for Scopulini moths at a world-wide scale; (iii) to study spatiotemporal forest cover change. Paper (III) presents a study of exotic and indigenous tree cover change detection in Taita Hills Kenya using airborne imagery and GIS analysis techniques; (iv) to explore predictive modelling techniques using geospatial data. In Paper (IV) human population occurrence and abundance in the Taita Hills highlands was predicted using the generalized additive modelling (GAM) technique. Paper (V) presents techniques to enhance fire prediction and burned area estimation at a regional scale in East Caprivi Namibia. Paper (VI) compares eight state-of-the-art predictive modelling methods to improve fire prediction, burned area estimation and fire risk mapping in East Caprivi Namibia. The results in Paper (I) showed that geospatial data can be managed effectively using advanced relational database management systems. Metapopulation data for Melitaea cinxia butterfly was successfully combined with GPS-delimited habitat patch information and climatic data. Using the geospatial database, spatial analyses were successfully conducted at habitat patch level or at more coarse analysis scales. Moreover, this study showed it appears evident that at a large-scale spatially correlated weather conditions are one of the primary causes of spatially correlated changes in Melitaea cinxia population sizes. In Paper (II) spatiotemporal characteristics

  6. 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

  7. Flood susceptible analysis at Kelantan river basin using remote sensing and logistic regression model

    Science.gov (United States)

    Pradhan, Biswajeet

    Recently, in 2006 and 2007 heavy monsoons rainfall have triggered floods along Malaysia's east coast as well as in southern state of Johor. The hardest hit areas are along the east coast of peninsular Malaysia in the states of Kelantan, Terengganu and Pahang. The city of Johor was particularly hard hit in southern side. The flood cost nearly billion ringgit of property and many lives. The extent of damage could have been reduced or minimized if an early warning system would have been in place. This paper deals with flood susceptibility analysis using logistic regression model. We have evaluated the flood susceptibility and the effect of flood-related factors along the Kelantan river basin using the Geographic Information System (GIS) and remote sensing data. Previous flooded areas were extracted from archived radarsat images using image processing tools. Flood susceptibility mapping was conducted in the study area along the Kelantan River using radarsat imagery and then enlarged to 1:25,000 scales. Topographical, hydrological, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence flood occurrence were: topographic slope, topographic aspect, topographic curvature, DEM and distance from river drainage, all from the topographic database; flow direction, flow accumulation, extracted from hydrological database; geology and distance from lineament, taken from the geologic database; land use from SPOT satellite images; soil texture from soil database; and the vegetation index value from SPOT satellite images. Flood susceptible areas were analyzed and mapped using the probability-logistic regression model. Results indicate that flood prone areas can be performed at 1:25,000 which is comparable to some conventional flood hazard map scales. The flood prone areas delineated on these maps correspond to areas that would be inundated by significant flooding

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    Fukun Bi

    2017-06-01

    Full Text Available 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.

  10. Spaceborne Remote Sensing of Aerosol Type: Global Distribution, Model Evaluation and Translation into Chemical Speciation

    Science.gov (United States)

    Kacenelenbogen, M. S.; Tan, Q.; Johnson, M. S.; Burton, S. P.; Redemann, J.; Hasekamp, O. P.; Dawson, K. W.; Hair, J. W.; Ferrare, R. A.; Butler, C. F.; Holben, B. N.; Beyersdorf, A. J.; Ziemba, L. D.; Froyd, K. D.; Dibb, J. E.; Shingler, T.; Sorooshian, A.; Jimenez, J. L.; Campuzano Jost, P.; Jacob, D.; Kim, P. S.; Travis, K.; Lacagnina, C.

    2016-12-01

    It is essential to evaluate and refine aerosol classification methods applied to passive satellite remote sensing. We have developed an aerosol classification algorithm (called Specified Clustering and Mahalanobis Classification, SCMC) that assigns an aerosol type to multi-parameter retrievals by spaceborne, airborne or ground-based passive remote sensing instruments [1]. The aerosol types identified by our scheme are pure dust, polluted dust, urban-industrial/developed economy, urban-industrial/developing economy, dark biomass smoke, light biomass smoke and pure marine. We apply the SCMC method to inversions from the ground-based AErosol RObotic NETwork (AERONET [2]) and retrievals from the space-borne Polarization and Directionality of Earth's Reflectances instrument (POLDER, [3]). The POLDER retrievals that we use differ from the standard POLDER retrievals [4] as they make full use of multi-angle, multispectral polarimetric data [5]. We analyze agreement in the aerosol types inferred from both AERONET and POLDER and evaluate GEOS-Chem [6] simulations over the globe. Finally, we use in-situ observations from the SEAC4RS airborne field experiment to bridge the gap between remote sensing-inferred qualitative SCMC aerosol types and their corresponding quantitative chemical speciation. We apply the SCMC method to airborne in-situ observations from the NASA Langley Aerosol Research Group Experiment (LARGE, [7]) and the Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP, [8]) instruments; we then relate each coarsely defined SCMC type to a sum of percentage of individual aerosol species, using in-situ observations from the Particle Analysis by Laser Mass Spectrometry (PALMS, [9]), the Soluble Acidic Gases and Aerosol (SAGA, [10]), and the High - Resolution Time - of - Flight Aerosol Mass Spectrometer (HR ToF AMS, [11]). [1] Russell P. B., et al., JGR, 119.16 (2014) [2] Holben B. N., et al., RSE, 66.1 (1998) [3] Tanré D., et al., AMT, 4.7 (2011

  11. Optimal decision-making model of spatial sampling for survey of China's land with remotely sensed data

    Institute of Scientific and Technical Information of China (English)

    LI Lianfa; WANG Jinfeng; LIU Jiyuan

    2005-01-01

    Abstract In the remote sensing survey of the country land, cost and accuracy are a pair of conflicts, for which spatial sampling is a preferable solution with the aim of an optimal balance between economic input and accuracy of results, or in other words, acquirement of higher accuracy at less cost. Counter to drawbacks of previous application models, e.g. lack of comprehensive and quantitative-comparison, the optimal decision-making model of spatial sampling is proposed. This model first acquires the possible accuracy-cost diagrams of multiple schemes through initial spatial exploration, then regresses them and standardizes them into a unified reference frame, and finally produces the relatively optimal sampling scheme by using the discrete decision-making function (built by this paper) and comparing them in combination with the diagrams. According to the test result in the survey of the arable land using remotely sensed data, the Sandwich model, while applied in the survey of the thin-feature and cultivated land areas with aerial photos, can better realize the goal of the best balance between investment and accuracy. With this case and other cases, it is shown that the optimal decision-making model of spatial sampling is a good choice in the survey of the farm areas using remote sensing, with its distinguished benefit of higher precision at less cost or vice versa. In order to extensively apply the model in the surveys of natural resources, including arable farm areas, this paper proposes the prototype of development using the component technology, that could considerably improve the analysis efficiency by insetting program components within the software environment of GIS and RS.

  12. On the use of integrating FLUXNET eddy covariance and remote sensing data for model evaluation

    Science.gov (United States)

    Reichstein, Markus; Jung, Martin; Beer, Christian; Carvalhais, Nuno; Tomelleri, Enrico; Lasslop, Gitta; Baldocchi, Dennis; Papale, Dario

    2010-05-01

    The current FLUXNET database (www.fluxdata.org) of CO2, water and energy exchange between the terrestrial biosphere and the atmosphere contains almost 1000 site-years with data from more than 250 sites, encompassing all major biomes of the world and being processed in a standardized way (1-3). In this presentation we show that the information in the data is sufficient to derive generalized empirical relationships between vegetation/respective remote sensing information, climate and the biosphere-atmosphere exchanges across global biomes. These empirical patterns are used to generate global grids of the respective fluxes and derived properties (e.g. radiation and water-use efficiencies or climate sensitivities in general, bowen-ratio, AET/PET ratio). For example we revisit global 'text-book' numbers such as global Gross Primary Productivity (GPP) estimated since the 70's as ca. 120PgC (4), or global evapotranspiration (ET) estimated at 65km3/yr-1 (5) - for the first time with a more solid and direct empirical basis. Evaluation against independent data at regional to global scale (e.g. atmospheric CO2 inversions, runoff data) lends support to the validity of our almost purely empirical up-scaling approaches. Moreover climate factors such as radiation, temperature and water balance are identified as driving factors for variations and trends of carbon and water fluxes, with distinctly different sensitivities between different vegetation types. Hence, these global fields of biosphere-atmosphere exchange and the inferred relations between climate, vegetation type and fluxes should be used for evaluation or benchmarking of climate models or their land-surface components, while overcoming scale-issues with classical point-to-grid-cell comparisons. 1. M. Reichstein et al., Global Change Biology 11, 1424 (2005). 2. D. Baldocchi, Australian Journal of Botany 56, 1 (2008). 3. D. Papale et al., Biogeosciences 3, 571 (2006). 4. D. E. Alexander, R. W. Fairbridge, Encyclopedia of

  13. Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data

    Science.gov (United States)

    Silvestro, F.; Gabellani, S.; Rudari, R.; Delogu, F.; Laiolo, P.; Boni, G.

    2015-04-01

    During the last decade the opportunity and usefulness of using remote-sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite-based products often allow for the advantage of observing hydrologic variables in a distributed way, offering a different view with respect to traditional observations that can help with understanding and modeling the hydrological cycle. Moreover, remote-sensing data are fundamental in scarce data environments. The use of satellite-derived digital elevation models (DEMs), which are now globally available at 30 m resolution (e.g., from Shuttle Radar Topographic Mission, SRTM), have become standard practice in hydrologic model implementation, but other types of satellite-derived data are still underutilized. As a consequence there is the need for developing and testing techniques that allow the opportunities given by remote-sensing data to be exploited, parameterizing hydrological models and improving their calibration. In this work, Meteosat Second Generation land-surface temperature (LST) estimates and surface soil moisture (SSM), available from European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) H-SAF, are used together with streamflow observations (S. N.) to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. The first part of the work aims at proving that satellite observations can be exploited to reduce uncertainties in parameter calibration by reducing the parameter equifinality that can become an issue in forecast mode. In the second part, four parameter estimation strategies are implemented and tested in a comparative mode: (i) a multi-objective approach that includes both satellite and ground observations which is an attempt to use different sources of data to add constraints to the parameters; (ii and iii) two approaches solely based on remotely sensed data that reproduce the case of a scarce data

  14. 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.

  15. Modelling Rift Valley fever (RVF) disease vector habitats using active and passive remote sensing systems

    Science.gov (United States)

    Ambrosia, Vincent G.; Linthicum, K. G.; Bailey, C. L.; Sebesta, P.

    1989-01-01

    The NASA Ames Ecosystem Science and Technology Branch and the U.S. Army Medical Research Institute of Infectious Diseases are conducting research to detect Rift Valley fever (RVF) vector habitats in eastern Africa using active and passive remote-sensing. The normalized difference vegetation index (NDVI) calculated from Landsat TM and SPOT data is used to characterize the vegetation common to the Aedes mosquito. Relationships have been found between the highest NDVI and the 'dambo' habitat areas near Riuru, Kenya on both wet and dry data. High NDVI values, when combined with the vegetation classifications, are clearly related to the areas of vector habitats. SAR data have been proposed for use during the rainy season when optical systems are of minimal use and the short frequency and duration of the optimum RVF mosquito habitat conditions necessitate rapid evaluation of the vegetation/moisture conditions; only then can disease potential be stemmed and eradication efforts initiated.

  16. 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.

  17. A Modified Gash Model for Estimating Rainfall Interception Loss of Forest Using Remote Sensing Observations at Regional Scale

    Directory of Open Access Journals (Sweden)

    Yaokui Cui

    2014-04-01

    Full Text Available Rainfall interception loss of forest is an important component of water balance in a forested ecosystem. The Gash analytical model has been widely used to estimate the forest interception loss at field scale. In this study, we proposed a simple model to estimate rainfall interception loss of heterogeneous forest at regional scale with several reasonable assumptions using remote sensing observations. The model is a modified Gash analytical model using easily measured parameters of forest structure from satellite data and extends the original Gash model from point-scale to the regional scale. Preliminary results, using remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS products, field measured rainfall data, and meteorological data of the Automatic Weather Station (AWS over a picea crassifolia forest in the upper reaches of the Heihe River Basin in northwestern China, showed reasonable accuracy in estimating rainfall interception loss at both the Dayekou experimental site (R2 = 0.91, RMSE = 0.34 mm∙d −1 and the Pailugou experimental site (R2 = 0.82, RMSE = 0.6 mm∙d −1, compared with ground measurements based on per unit area of forest. The interception loss map of the study area was shown to be strongly heterogeneous. The modified model has robust physics and is insensitive to the input parameters, according to the sensitivity analysis using numerical simulations. The modified model appears to be stable and easy to be applied for operational estimation of interception loss over large areas.

  18. Biogeochemical cycling and remote sensing

    Science.gov (United States)

    Peterson, D. L.

    1985-01-01

    Research is underway at the NASA Ames Research Center that is concerned with aspects of the nitrogen cycle in terrestrial ecosystems. An interdisciplinary research group is attempting to correlate nitrogen transformations, processes, and productivity with variables that can be remotely sensed. Recent NASA and other publications concerning biogeochemical cycling at global scales identify attributes of vegetation that could be related or explain the spatial variation in biologically functional variables. These functional variables include net primary productivity, annual nitrogen mineralization, and possibly the emission rate of nitrous oxide from soils.

  19. Remote Sensing Wind and Wind Shear System.

    Science.gov (United States)

    Contents: Remote sensing of wind shear and the theory and development of acoustic doppler; Wind studies; A comparison of methods for the remote detection of winds in the airport environment; Acoustic doppler system development; System calibration; Airport operational tests.

  20. 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.

  1. Advances in the Two Source Energy Balance (TSEB) model using very high resolution remote sensing data in vineyards

    Science.gov (United States)

    Nieto Solana, H.; Kustas, W. P.; Torres-Rua, A. F.; ELarab, M.; Song, L.; Alfieri, J. G.; Prueger, J. H.; McKee, L.; Anderson, M. C.; Alsina, M. M.; Jensen, A.; McKee, M.

    2015-12-01

    The thermal-based Two Source Energy Balance (TSEB) model partitions the water and energy fluxes from vegetation and soil components providing thus the ability for estimating soil evaporation (E) and canopy transpiration (T) separately. However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures as well as the net radiation partitioning (ΔRn), as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in agricultural areas, with vegetation clumped along rows and hence only partially covering the soil surface for much of the growing season. The effects on radiation and temperature partitioning is extreme for vineyards and orchards, where there is often significant separation between plants, resulting in strongly clumped vegetation with significant fraction of bare soil/substrate. To better understand the effects of strongly clumped vegetation on radiation and Land Surface Temperature (LST) partitioning very high spatial resolution remote sensing data acquired from an Unmanned Aerial System (UAS) were collected over vineyards in Califronia, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX).The multi-temporal observations from the UAS and very high pixel resolution permitted the estimation of reliable soil and leaf temperatures using a contextual algorithm based on the inverse relationship between LST and a vegetation index. An improvement in the algorithm estimating the effective leaf area index explicitly developed for vine rows and ΔRn using the 4SAIL Radiative Transfer Model is as well developed. The revisions to the TSEB model are evaluated with in situ measurements of energy fluxes and transmitted solar radiation. Results show that the modifications to the TSEB resulted in closer agreement with the flux tower measurements compared to the original TSEB model formulations. The

  2. 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.

  3. Model for water pollution remote sensing based on double scattering and its application in the Zhujiang River outfall

    Institute of Scientific and Technical Information of China (English)

    DENG Ruru; LIU Qinhuo; KE Ruiping; CHENG Lei; LIU Xiaoping

    2004-01-01

    It is a valid route for quantitatively remote sensing on water pollution to build a model according to the physical mechanisms of scattering and absorbing of suspended substance, pollutant, and molecules of water. Remote sensing model for water pollution based on single scattering is simple and easy to be used, but the precision is affected by turbidity of water. The characteristics of the energy composition of multiple scattering, are analyzed and it is proposed that, based on the model of single scattering, ifthe flux of the second scattering is considered additionally, the precision of the modelwill be remarkably improved and the calculation is still very simple. The factor of the second scattering is deduced to build a double scattering model, and the practical arithmetic for the calculation of the model is put forward. The result of applying this model in the water area around the Zhujiang(Pearl) River outfall shows that the precision is obviously improved. The result also shows that the seriously polluted water area is distributed in the northeast of Lingding Sea, the Victoria Bay of Hong Kong, and the Shengzhen Bay.

  4. 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.

  5. LWIR Microgrid Polarimeter for Remote Sensing Studies

    Science.gov (United States)

    2010-02-28

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

  6. Measurement Strategies for Remote Sensing Applications

    Energy Technology Data Exchange (ETDEWEB)

    Weber, P.G.; Theiler, J.; Smith, B.; Love, S.P.; LaDelfe, P.C.; Cooke, B.J.; Clodius, W.B.; Borel, C.C.; Bender, S.C.

    1999-03-06

    Remote sensing has grown to encompass many instruments and observations, with concomitant data from a huge number of targets. As evidenced by the impressive growth in the number of published papers and presentations in this field, there is a great deal of interest in applying these capabilities. The true challenge is to transition from directly observed data sets to obtaining meaningful and robust information about remotely sensed targets. We use physics-based end-to-end modeling and analysis techniques as a framework for such a transition. Our technique starts with quantified observables and signatures of a target. The signatures are propagated through representative atmospheres to realistically modeled sensors. Simulated data are then propagated through analysis routines, yielding measurements that are directly compared to the original target attributes. We use this approach to develop measurement strategies which ensure that our efforts provide a balanced approach to obtaining substantive information on our targets.

  7. Modeling dynamic assessment of ecosystem services based on remote sensing technology:A sampling of the Gansu grassland ecosystem

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The ecosystem is important because it is the life sustaining system for human survival.Three ecosystem characteristics are:regional particularities,ecosystem complexity and conventional cultural particularities.This paper develops a remote sensing based dynamic model to assess grassland ecosystem service values involving multidisciplinary knowledge.The ecological value of grassland ecosystems is focused on using a remote sensing technique in the model,and setting up the framework for a dynamic assessing model.The grassland ecological services condition and value in 1985 is used as the benchmark.The dynamic model has two adjusting indicators:biomass and price index.The biomass is simulated using the CASA(Carnegie-Ames-Stanford Approach) model.The price index was obtained from statistics data published by the statistical bureau.Results show that the grassland ecosystem value in Gansu Province was 28.36 billion Chinese Yuan in 1985,140.37 billion in 1999 and 130.86 billion in 2002.

  8. 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

  9. 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

  10. 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.

  11. Use of remote sensing in agriculture

    Science.gov (United States)

    Pettry, D. E.; Powell, N. L.; Newhouse, M. E.

    1974-01-01

    Remote sensing studies in Virginia and Chesapeake Bay areas to investigate soil and plant conditions via remote sensing technology are reported ant the results given. Remote sensing techniques and interactions are also discussed. Specific studies on the effects of soil moisture and organic matter on energy reflection of extensively occurring Sassafras soils are discussed. Greenhouse and field studies investigating the effects of chlorophyll content of Irish potatoes on infrared reflection are presented. Selected ground truth and environmental monitoring data are shown in summary form. Practical demonstrations of remote sensing technology in agriculture are depicted and future use areas are delineated.

  12. Processing Remote Sensing Data with Python

    OpenAIRE

    Dillon, Ryan J., 1984-

    2013-01-01

    With public access available for numerous satellite imaging products, modelling in atmospheric and oceanographic applications has become increasingly more prevalent. Though there are numerous tools available for geospatial development, their use is more commonly applied towards mapping applications. With this being the case, there are a number of valuable texts for using these tools in such mapping applications; though, documentation for processing of remote sensing datasets is limited to ...

  13. Regional adaptation of a dynamic global vegetation model using a remote sensing data derived land cover map of Russia

    Science.gov (United States)

    Khvostikov, S.; Venevsky, S.; Bartalev, S.

    2015-12-01

    The dynamic global vegetation model (DGVM) SEVER has been regionally adapted using a remote sensing data-derived land cover map in order to improve the reconstruction conformity of the distribution of vegetation functional types over Russia. The SEVER model was modified to address noticeable divergences between modelling results and the land cover map. The model modification included a light competition method elaboration and the introduction of a tundra class into the model. The rigorous optimisation of key model parameters was performed using a two-step procedure. First, an approximate global optimum was found using the efficient global optimisation (EGO) algorithm, and afterwards a local search in the vicinity of the approximate optimum was performed using the quasi-Newton algorithm BFGS. The regionally adapted model shows a significant improvement of the vegetation distribution reconstruction over Russia with better matching with the satellite-derived land cover map, which was confirmed by both a visual comparison and a formal conformity criterion.

  14. Remote sensing model and dynamic mechanism for seasonal changes of the euphotic depth in the East China Sea

    Institute of Scientific and Technical Information of China (English)

    LI Guosheng; YANG Shilin; LIANG Qiang

    2003-01-01

    Based on remote sensing data and models, spatial distribution of the monthly euphotic depth in the East China Sea in 1998 has been obtained. The character of the seasonal changes of the euphotic depth is summarized, and the dynamic mechanism of the key influencing factors is analyzed. The results indicate that the controlling factors of the seasonal changes of euphotic depth in the East China Sea are the seasonal changes of temperature, diluted water from the Yangtze River, the ocean currents and the front process of different water masses.

  15. Description of algorithms for co-locating and comparing gridded model data with remote-sensing observations

    Directory of Open Access Journals (Sweden)

    B. Langerock

    2014-11-01

    Full Text Available MACC-II,III, Monitoring Atmospheric Composition and Climate, is the current pre-operational Copernicus Atmosphere Monitoring Service (CAMS. It provides data records on atmospheric composition for recent years, present conditions and forecasts for a few days ahead. To support the quality assessment of the CAMS products, the EU FP7 project NORS created a server to validate the gridded MACC-II,III/CAMS model data against remote-sensing observations from the Network for the Detection of Atmospheric Composition Change (NDACC, for a selected set of target species and pilot stations. This paper describes in detail the algorithms used in this validation server.

  16. Development of a computer model to predict platform station keeping requirements in the Gulf of Mexico using remote sensing data

    Science.gov (United States)

    Barber, Bryan; Kahn, Laura; Wong, David

    1990-01-01

    Offshore operations such as oil drilling and radar monitoring require semisubmersible platforms to remain stationary at specific locations in the Gulf of Mexico. Ocean currents, wind, and waves in the Gulf of Mexico tend to move platforms away from their desired locations. A computer model was created to predict the station keeping requirements of a platform. The computer simulation uses remote sensing data from satellites and buoys as input. A background of the project, alternate approaches to the project, and the details of the simulation are presented.

  17. Estimating national forest carbon stocks and dynamics: combining models and remotely sensed information

    Science.gov (United States)

    Smallman, Thomas Luke; Exbrayat, Jean-François; Bloom, Anthony; Williams, Mathew

    2017-04-01

    Forests are a critical component of the global carbon cycle, storing significant amounts of carbon, split between living biomass and dead organic matter. The carbon budget of forests is the most uncertain component of the global carbon cycle - it is currently impossible to quantify accurately the carbon source/sink strength of forest biomes due to their heterogeneity and complex dynamics. It has been a major challenge to generate robust carbon budgets across landscapes due to data scarcity. Models have been used for estimating carbon budgets, but outputs have lacked an assessment of uncertainty, making a robust assessment of their reliability and accuracy challenging. Here a Metropolis Hastings - Markov Chain Monte Carlo (MH-MCMC) data assimilation framework has been used to combine remotely sensed leaf area index (MODIS), biomass (where available) and deforestation estimates, in addition to forest planting information from the UK's national forest inventory, an estimate of soil carbon from the Harmonized World Database (HWSD) and plant trait information with a process model (DALEC) to produce a constrained analysis with a robust estimate of uncertainty of the UK forestry carbon budget between 2000 and 2010. Our analysis estimates the mean annual UK forest carbon sink at -3.9 MgC ha-1 yr-1 with a 95 % confidence interval between -4.0 and -3.1 MgC ha-1yr-1. The UK national forest inventory (NFI) estimates the mean UK forest carbon sink to be between -1.4 and -5.5 MgC ha-1 yr-1. The analysis estimate for total forest biomass stock in 2010 is estimated at 229 (177/232) TgC, while the NFI an estimated total forest biomass carbon stock of 216 TgC. Leaf carbon area (LCA) is a key plant trait which we are able to estimate using our analysis. Comparison of median estimates for (LCA) retrieved from the analysis and a UK land cover map show higher and lower values for LCA are estimated areas dominated by needle leaf and broad leaf forests forest respectively, consistent with

  18. Subpixel mapping on remote sensing imagery using a prediction model combining wavelet transform and radial basis function neural network

    Science.gov (United States)

    Dai, Xiaoyan; Guo, Zhongyang; Zhang, Liquan; Xu, Wencheng

    2009-12-01

    Soft classification methods can be used for mixed-pixel classification on remote sensing imagery by estimating different land cover class fractions of every pixel. However, the spatial distribution and location of these class components within the pixel remain unknown. To map land cover at subpixel scale and increase the spatial resolution of land cover classification maps, in this paper, a prediction model combining wavelet transform and Radial Basis Functions (RBF) neural network, abbreviated as Wavelet-RBFNN, is constructed by predicting high-frequency wavelet coefficients from low-frequency coefficients at the same resolution with RBF network and taking wavelet coefficients at coarser resolution as training samples. According to different land cover class fraction images obtained from mixed-pixel classification, based on the assumption of neighborhood dependence of wavelet coefficients, subpixel mapping on remote sensing imagery can be accomplished through two steps, i.e., prediction of land cover class compositions within subpixels and hard classification. The experimental results obtained with artificial images, QuickBird image and Landsat 7 ETM+ image indicate that the subpixel mapping method proposed in this paper can successfully produce super-resolution land cover classification maps from remote sensing imagery, outperforming cubic B-spline and Kriging interpolation method in visual effect and prediction accuracy. The Wavelet-RBFNN model can also be applied to simulate higher spatial resolution image, and automatically identify and locate land cover targets at the subpixel scales, when the cost and availability of high resolution imagery prohibit its use in many areas of work.

  19. Validation of a remote sensing model to identify Simulium damnosum s.l. breeding sites in Sub-Saharan Africa.

    Directory of Open Access Journals (Sweden)

    Benjamin G Jacob

    Full Text Available BACKGROUND: Recently, most onchocerciasis control programs have begun to focus on elimination. Developing an effective elimination strategy relies upon accurately mapping the extent of endemic foci. In areas of Africa that suffer from a lack of infrastructure and/or political instability, developing such accurate maps has been difficult. Onchocerciasis foci are localized near breeding sites for the black fly vectors of the infection. The goal of this study was to conduct ground validation studies to evaluate the sensitivity and specificity of a remote sensing model developed to predict S. damnosum s.l. breeding sites. METHODOLOGY/PRINCIPAL FINDINGS: Remote sensing images from Togo were analyzed to identify areas containing signature characteristics of S. damnosum s.l. breeding habitat. All 30 sites with the spectral signature were found to contain S. damnosum larvae, while 0/52 other sites judged as likely to contain larvae were found to contain larvae. The model was then used to predict breeding sites in Northern Uganda. This area is hyper-endemic for onchocerciasis, but political instability had precluded mass distribution of ivermectin until 2009. Ground validation revealed that 23/25 sites with the signature contained S. damnosum larvae, while 8/10 sites examined lacking the signature were larvae free. Sites predicted to have larvae contained significantly more larvae than those that lacked the signature. CONCLUSIONS/SIGNIFICANCE: This study suggests that a signature extracted from remote sensing images may be used to predict the location of S. damnosum s.l. breeding sites with a high degree of accuracy. This method should be of assistance in predicting communities at risk for onchocerciasis in areas of Africa where ground-based epidemiological surveys are difficult to implement.

  20. Validation of a remote sensing model to identify Simulium damnosum s.l. breeding sites in Sub-Saharan Africa.

    Science.gov (United States)

    Jacob, Benjamin G; Novak, Robert J; Toe, Laurent D; Sanfo, Moussa; Griffith, Daniel A; Lakwo, Thomson L; Habomugisha, Peace; Katabarwa, Moses N; Unnasch, Thomas R

    2013-01-01

    Recently, most onchocerciasis control programs have begun to focus on elimination. Developing an effective elimination strategy relies upon accurately mapping the extent of endemic foci. In areas of Africa that suffer from a lack of infrastructure and/or political instability, developing such accurate maps has been difficult. Onchocerciasis foci are localized near breeding sites for the black fly vectors of the infection. The goal of this study was to conduct ground validation studies to evaluate the sensitivity and specificity of a remote sensing model developed to predict S. damnosum s.l. breeding sites. Remote sensing images from Togo were analyzed to identify areas containing signature characteristics of S. damnosum s.l. breeding habitat. All 30 sites with the spectral signature were found to contain S. damnosum larvae, while 0/52 other sites judged as likely to contain larvae were found to contain larvae. The model was then used to predict breeding sites in Northern Uganda. This area is hyper-endemic for onchocerciasis, but political instability had precluded mass distribution of ivermectin until 2009. Ground validation revealed that 23/25 sites with the signature contained S. damnosum larvae, while 8/10 sites examined lacking the signature were larvae free. Sites predicted to have larvae contained significantly more larvae than those that lacked the signature. This study suggests that a signature extracted from remote sensing images may be used to predict the location of S. damnosum s.l. breeding sites with a high degree of accuracy. This method should be of assistance in predicting communities at risk for onchocerciasis in areas of Africa where ground-based epidemiological surveys are difficult to implement.

  1. 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

  2. A Study on Data Storage and Management for Massive Remote Sensing Data Based on Multi-level Grid Model

    Directory of Open Access Journals (Sweden)

    LI Shuang

    2016-12-01

    Full Text Available With the rapid development of remote sensing technology, spatial information is exploding. For current remote sensing data storage management system, their data volume, rich data sources, query retrieves slow and other issues are problems to be solved. This paper then proposed a remote sensing data organization scheme based on GeoSOT. By firstly adding a GeoSOT code column which is array format in relational database, spatial information in the metadata can be stored and logically subdivided, in order to achieve unified storage and retrieval of image data space area. We compare our method with Oracle platform by simulating worldwide image data. Experimental results show that the retrieval efficiency of this article has obvious advantages and can effectively improve the integration of remote sensing data, retrieval efficiency. Our approach also offers a more effective storage management program for existing storage center or management system.

  3. Linking Satellite Remote Sensing Based Environmental Predictors to Disease: AN Application to the Spatiotemporal Modelling of Schistosomiasis in Ghana

    Science.gov (United States)

    Wrable, M.; Liss, A.; Kulinkina, A.; Koch, M.; Biritwum, N. K.; Ofosu, A.; Kosinski, K. C.; Gute, D. M.; Naumova, E. N.

    2016-06-01

    90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.

  4. Study of a model for correcting the effects of horizontal advection on surface fluxes measurement based on remote sensing

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    <正>As well known, the methods of remote sensing and Bowen Ratio for retrieving surface flux are based on energy balance closure; however, in most cases, surface energy observed in experiment is lack of closure. There are two main causes for this: one is from the errors of the observation devices and the differences of their observational scale; the other lies in the effect of horizontal advection on the surface flux measurement. Therefore, it is very important to estimate the effects of horizontal advection quantitatively. Based on the local advection theory and the surface experiment, a model has been proposed for correcting the effect of horizontal advection on surface flux measurement, in which the relationship between the fetch of the measurement and pixel size for remote sensed data was considered. By means of numerical simulations, the sensitivities of the main parameters in the model and the scaling problems of horizontal advection were analyzed. At last, by using the observational data acquired in agricultural field with relatively homogeneous surface, the model was validated.

  5. LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA

    Directory of Open Access Journals (Sweden)

    M. Wrable

    2016-06-01

    Full Text Available 90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.

  6. 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

  7. 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...

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

    Science.gov (United States)

    Calera, Alfonso; Campos, Isidro; Osann, Anna; D'Urso, Guido; Menenti, Massimo

    2017-05-11

    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.

  9. Integrating remote sensing, geographic information system and modeling for estimating crop yield

    Science.gov (United States)

    Salazar, Luis Alonso

    This thesis explores various aspects of the use of remote sensing, geographic information system and digital signal processing technologies for broad-scale estimation of crop yield in Kansas. Recent dry and drought years in the Great Plains have emphasized the need for new sources of timely, objective and quantitative information on crop conditions. Crop growth monitoring and yield estimation can provide important information for government agencies, commodity traders and producers in planning harvest, storage, transportation and marketing activities. The sooner this information is available the lower the economic risk translating into greater efficiency and increased return on investments. Weather data is normally used when crop yield is forecasted. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop assessment systems to be economical, more efficient methods for data collection and analysis are necessary. The purpose of this research is to use satellite data which provides 50 times more spatial information about the environment than the weather station network in a short amount of time at a relatively low cost. Specifically, we are going to use Advanced Very High Resolution Radiometer (AVHRR) based vegetation health (VH) indices as proxies for characterization of weather conditions.

  10. 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.

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

    Science.gov (United States)

    Calera, Alfonso; Campos, Isidro; Osann, Anna; D’Urso, Guido; Menenti, Massimo

    2017-01-01

    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. PMID:28492515

  12. 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.

  13. Geological remote sensing in Africa

    Science.gov (United States)

    Sabins, Floyd F., Jr.; Bailey, G. Bryan; Abrams, Michael J.

    1987-01-01

    Programs using remote sensing to obtain geologic information in Africa are reviewed. Studies include the use of Landsat MSS data to evaluate petroleum resources in sedimentary rock terrains in Kenya and Sudan and the use of Landsat TM 30-m resolution data to search for mineral deposits in an ophiolite complex in Oman. Digitally enhanced multispectral SPOT data at a scale of 1:62,000 were used to map folds, faults, diapirs, bedding attitudes, and stratigraphic units in the Atlas Mountains in northern Algeria. In another study, SIR-A data over a vegetated and faulted area of Sierra Leone were compared with data collected by the Landsat MSS and TM systems. It was found that the lineaments on the SIR-A data were more easily detected.

  14. 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

  15. 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…

  16. Remote sensing and reflectance profiling in entomology

    Science.gov (United States)

    Remote sensing is about characterizing the status of objects and/or classifies their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be ground-based, and therefore acquired at a high spatial resolutio...

  17. Planning and Implementation of Remote Sensing Experiments.

    Science.gov (United States)

    Contents: TEKTITE II experiment-upwelling detection (NASA Mx 138); Design of oceanographic experiments (Gulf of Mexico, Mx 159); Design of oceanographic experiments (Gulf of Mexico, Mx 165); Experiments on thermal pollution; Remote sensing newsletter; Symposium on remote sensing in marine biology and fishery resources.

  18. Technology Progress Report for Microwave Remote Sensing

    Institute of Scientific and Technical Information of China (English)

    JIANG Jingshan; DONG Xiaolong; LIU Heguang

    2004-01-01

    In this presentation, technological progress for China's microwave remote sensing is introduced. New developments of the microwave remote sensing instruments for China's lunar exploration satellite (Chang'E-1), meteorological satellite FY-3 and ocean dynamic measurement satellite (HY-2) are reported.

  19. 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

  20. APPLICATION OF REMOTE SENSING TECHNOLOGY TO POPULATION ESTIMATION

    Institute of Scientific and Technical Information of China (English)

    ZHANG Bao-guang

    2003-01-01

    This paper attempts to explore a new avenue of urban small-regional population estimation by remote sensing technology, creatively and comprehensively for the first time using a residence count method, area (density) method and model method, incorporating the application experience of American scholars in the light of the state of our country. Firstly, the author proposes theoretical basis for population estimation by remote sensing, on the basis of analysing and evaluating the history and state quo of application of methods of population estimation by remote sens-ing. Secondly, two original types of mathematical models of population estimation are developed on the basis of remote sensing data, taking Tianjin City as an example. By both of the mathematical models the regional population may be estimated from remote sensing variable values with high accuracy. The number of the independent variables in the lat-ter model is somewhat smaller and the collection of remote sensing data is somewhat easier, but the deviation is a little larger. Finally, some viewpoints on the principled problems about the practical application of remote sensing to popu-lation estimation are put forward.

  1. 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

  2. An overview of GNSS remote sensing

    OpenAIRE

    Kegen, Yu; Rizos, Chris; Burrage, Derek; Dempster, Andrew; Zhang, Kefei; Markgraf, Markus

    2014-01-01

    The Global Navigation Satellite System (GNSS) signals are always available, globally, and the signal structures are well known, except for those dedicated to military use. They also have some distinctive characteristics, including the use of L-band frequencies, which are particularly suited for remote sensing purposes. The idea of using GNSS signals for remote sensing - the atmosphere, oceans or Earth surface - was first proposed more than two decades ago. Since then, GNSS remote ...

  3. 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.

  4. Use of IRI to Model the Effect of Ionosphere Emission on Earth Remote Sensing at L-Band

    Science.gov (United States)

    Abraham, Saji; LeVine, David M.

    2004-01-01

    Microwave remote sensing in the window at 1.413 GHz (L-band) set aside for passive use only is important for monitoring sea surface salinity and soil moisture. These parameters are important for understanding ocean dynamics and energy exchange between the surface and atmosphere, and both NASA and ESA plan to launch satellite sensors to monitor these parameters at L-band (Aquarius, Hydros and SMOS). The ionosphere is an important source of error for passive remote sensing at this frequency. In addition to Faraday rotation, emission from the ionosphere is also a potential source of error at L-band. As an aid for correcting for emission, a regression model is presented that relates ionosphere emission to the integrated electron density (TEC). The goal is to use TEC from sources such as TOPEX, JASON or GPS to obtain estimates of emission over the oceans where the electron density profiles needed to compute emission are not available. In addition, data will also be presented to evaluate the use of the IRI for computing emission over the ocean.

  5. Forest and land use mapping using Remote Sensing and Geographical Information System: A case study on model system

    Directory of Open Access Journals (Sweden)

    Prabhat Kumar Rai

    2013-09-01

    Full Text Available Remote sensing and geospatial technologies find tremendous application in rapid spatial and temporal monitoring as well as assessment of tropical forest resources and hence in formulation of concrete policy frameworks for their sustainable management. Present paper provides an overview on application of remote sensing in forestry and ecology with a case study which may be further extrapolated in other Indian Himalayan regions of North-East India. The case study used an IKONOS (2001 image, Arc View ver. 3.2, and ERDAS IMAGINE ver. 9.1 in order to investigate the forest/vegetation types/land cover mapping of Forest Research Institute campus (FRI, Dehradun, India (as model system through visual image interpretation. In the present case study, Chir pine was the dominant vegetation type covering major area of plantation inside FRI campus followed by Sal, Teak, Cassia, Cupressus and mixed vegetation with intermittent built up areas. Since FRI consists of huge plantations, separated in a segmented way, the site was feasible for learners of vegetation or forest mapping in an effective and systematic way. In nutshell, vegetation type/land use mapping through visual interpretation may be a valuable tool in monitoring, assessment and conservation planning of forests.

  6. [Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data: a review].

    Science.gov (United States)

    Wu, Chang-Guang; Li, Sheng; Ren, Hua-Dong; Yao, Xiao-Hua; Huang, Zi-Jie

    2012-06-01

    Soil loss prediction models such as universal soil loss equation (USLE) and its revised universal soil loss equation (RUSLE) are the useful tools for risk assessment of soil erosion and planning of soil conservation at regional scale. To make a rational estimation of vegetation cover and management factor, the most important parameters in USLE or RUSLE, is particularly important for the accurate prediction of soil erosion. The traditional estimation based on field survey and measurement is time-consuming, laborious, and costly, and cannot rapidly extract the vegetation cover and management factor at macro-scale. In recent years, the development of remote sensing technology has provided both data and methods for the estimation of vegetation cover and management factor over broad geographic areas. This paper summarized the research findings on the quantitative estimation of vegetation cover and management factor by using remote sensing data, and analyzed the advantages and the disadvantages of various methods, aimed to provide reference for the further research and quantitative estimation of vegetation cover and management factor at large scale.

  7. Quantitative interpretation of Great Lakes remote sensing data

    Science.gov (United States)

    Shook, D. F.; Salzman, J.; Svehla, R. A.; Gedney, R. T.

    1980-01-01

    The paper discusses the quantitative interpretation of Great Lakes remote sensing water quality data. Remote sensing using color information must take into account (1) the existence of many different organic and inorganic species throughout the Great Lakes, (2) the occurrence of a mixture of species in most locations, and (3) spatial variations in types and concentration of species. The radiative transfer model provides a potential method for an orderly analysis of remote sensing data and a physical basis for developing quantitative algorithms. Predictions and field measurements of volume reflectances are presented which show the advantage of using a radiative transfer model. Spectral absorptance and backscattering coefficients for two inorganic sediments are reported.

  8. Applications of Satellite Remote Sensing Products to Enhance and Evaluate the AIRPACT Regional Air Quality Modeling System

    Science.gov (United States)

    Herron-Thorpe, F. L.; Mount, G. H.; Emmons, L. K.; Lamb, B. K.; Jaffe, D. A.; Wigder, N. L.; Chung, S. H.; Zhang, R.; Woelfle, M.; Vaughan, J. K.; Leung, F. T.

    2013-12-01

    The WSU AIRPACT air quality modeling system for the Pacific Northwest forecasts hourly levels of aerosols and atmospheric trace gases for use in determining potential health and ecosystem impacts by air quality managers. AIRPACT uses the WRF/SMOKE/CMAQ modeling framework, derives dynamic boundary conditions from MOZART-4 forecast simulations with assimilated MOPITT CO, and uses the BlueSky framework to derive fire emissions. A suite of surface measurements and satellite-based remote sensing data products across the AIRPACT domain are used to evaluate and improve model performance. Specific investigations include anthropogenic emissions, wildfire simulations, and the effects of long-range transport on surface ozone. In this work we synthesize results for multiple comparisons of AIRPACT with satellite products such as IASI ammonia, AIRS carbon monoxide, MODIS AOD, OMI tropospheric ozone and nitrogen dioxide, and MISR plume height. Features and benefits of the newest version of AIRPACT's web-interface are also presented.

  9. Multi-objective assessment of three remote sensing vegetation products for streamflow prediction in a conceptual ecohydrological model

    Science.gov (United States)

    Naseem, Bushra; Ajami, Hoori; Liu, Yi; Cordery, Ian; Sharma, Ashish

    2016-12-01

    This study assesses the implications of using three alternate remote sensing vegetation products in the simulation of streamflow using a conceptual ecohydrologic model. Vegetation is represented as a dynamic component in this model which simulates two response variables, streamflow and one of the following three vegetation attributes: Gross Primary Productivity (GPP), Leaf Area Index (LAI) or Vegetation Optical Depth (VOD). Model simulations are performed across 50 catchments with areas ranging between 50 and 1600 km2 in the Murray-Darling Basin in Australia. Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and GPP products, passive microwave observations of VOD and streamflow are used for model calibration and/or validation. Single-objective model calibration based on one of the vegetation products (GPP, LAI and VOD) shows that GPP is the best vegetation simulating product. On the contrary, LAI produces the best streamflow during validation when the optimized parameters are applied for streamflow estimation. To obtain the best compromise solution for simultaneous simulation of streamflow and a vegetation product, a multi-objective optimization is applied on GPP and streamflow, VOD and streamflow and LAI and streamflow. Results show that LAI and then VOD are the two best products in simulating streamflow across these catchments. Improved simulation of VOD and LAI in a multi-objective setting is partly related to the higher temporal resolution of these datasets and inclusion of processes for converting GPP to net primary productivity and biomass. It is suggested that further development of these remote sensing products at finer spatial and temporal resolutions may lead to improved streamflow prediction, as well as a better simulation capability of the ecohydrological system being modeled.

  10. 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.

  11. Pollution Prevention study using Remote Sensing and GIS: A model study from visakhapatnam district, Andhra Pradesh

    Directory of Open Access Journals (Sweden)

    Ss. Asadi ,

    2011-06-01

    Full Text Available The present Studies Involves to develop a base line data and existing environmental conditions to the GIS database to holistically assess and manage environmental and non-environmental resources used byIndustrial investors and other land and water users. The implementation of the Geographical Information System (GIS will bring positive benefits through the generation of information and creation of digital databases with information on Air, Water Quality, Health and Hygiene and streamlines the decision making process. IRSP6,LISS-IV geo coded Remote sensing Satellite data and top sheets from Survey of India (SOI are acquired for primary analysis. The present study has been carried out in nine (9 mandals namely Nakkapalli ,Elamanchilli,S. Rayavaram, Achchutapuram, Rambilli, Anakapalle, Munagapaka, Kasimkota, Paravada of Visakhapatnam District, covering an area of 1355 Sq.km. The study area is located between north latitudes 17° 19’ and 17° 46’”and east longitudes 82°35’ and 83°10’ and is covered in the survey of India topographical map numbers56H65 K/10,11,13,14,15M 65 O/1 and 2. Using Visual Interpretation technique different thematic maps are prepared like land use/land cover, base map, village information, drainage maps These thematic maps were scanned and digitized using AutoCAD and converted into GIS. Topology is created by linking the spatial data file and attribute data file. GIS overlay analysis derived maps like surface water Table, surface water use, surface water quality, was carried out to find out the above parameters pollution lodes in the study area, Finally integrating of the all the above maps sensitive zone maps has developed. This kind of studies is very useful for Pollution Prevention in industrial areas and also useful for the planners decision makers for management and monitoring of industrial areas.

  12. Commercial future: making remote sensing a media event

    Science.gov (United States)

    Lurie, Ian

    1999-12-01

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

  13. 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

  14. A component decomposition model for evaluating atmospheric effects in remote sensing

    Science.gov (United States)

    Li, S.; Wan, Z. M.; Dozier, J.

    1987-01-01

    A radiance value of a target pixel recorded by a remote sensor can be decomposed into three components: (1) attenuated target signature, (2) pure atmospheric radiation, and (3) the contribution made by the ground through the atmospheric scattering process. Given the meteorological and optical parameters of a layer-structured atmosphere, its transmittance and radiance distribution can be accurately calculated with a plane-parallel radiative transfer model. For a uniform surface, the ground contribution can be obtained by comparing radiances for an atmosphere over a black but nonemitting surface and the same atmosphere with an underlying ground of given albedo or temperature. For an inhomogeneous surface, the first two components remain the same as long as the surface is a plane. The third may be estimated using the locally averaged top-of-atmosphere radiance. An atmospheric point spread function is calculated by a Monte Carlo approach and is used for retrieving the ground signature through a deconvolution procedure.

  15. Observing and modeling dynamics in terrestrial gross primary productivity and phenology from remote sensing: An assessment using in-situ measurements

    Science.gov (United States)

    Verma, Manish K.

    Terrestrial gross primary productivity (GPP) is the largest and most variable component of the carbon cycle and is strongly influenced by phenology. Realistic characterization of spatio-temporal variation in GPP and phenology is therefore crucial for understanding dynamics in the global carbon cycle. In the last two decades, remote sensing has become a widely-used tool for this purpose. However, no study has comprehensively examined how well remote sensing models capture spatiotemporal patterns in GPP, and validation of remote sensing-based phenology models is limited. Using in-situ data from 144 eddy covariance towers located in all major biomes, I assessed the ability of 10 remote sensing-based methods to capture spatio-temporal variation in GPP at annual and seasonal scales. The models are based on different hypotheses regarding ecophysiological controls on GPP and span a range of structural and computational complexity. The results lead to four main conclusions: (i) at annual time scale, models were more successful capturing spatial variability than temporal variability; (ii) at seasonal scale, models were more successful in capturing average seasonal variability than interannual variability; (iii) simpler models performed as well or better than complex models; and (iv) models that were best at explaining seasonal variability in GPP were different from those that were best able to explain variability in annual scale GPP. Seasonal phenology of vegetation follows bounded growth and decay, and is widely modeled using growth functions. However, the specific form of the growth function affects how phenological dynamics are represented in ecosystem and remote sensing-base models. To examine this, four different growth functions (the logistic, Gompertz, Mirror-Gompertz and Richards function) were assessed using remotely sensed and in-situ data collected at several deciduous forest sites. All of the growth functions provided good statistical representation of in

  16. Shape saliency for remote sensing image

    Science.gov (United States)

    Xu, Sheng; Hong, Huo; Fang, Tao; Li, Deren

    2007-11-01

    In this paper, a shape saliency measure for only shape feature of each object in the image is described. Instead biologically-inspired bottom-up Itti model, the dissimilarity is measured by the shape feature. And, Fourier descriptor is used for measuring dissimilarity in this paper. In the model, the object is determined as a salient region, when it is far different from others. Different value of the saliency is ranged to generate a saliency map. It is shown that the attention shift processing can be recorded. Some results from psychological images and remote sensing images are shown and discussed in the paper.

  17. Remote Sensing-based Estimates of Potential Evapotranspiration for Hydrologic Modeling in the Upper Colorado River Basin Region

    Science.gov (United States)

    Barik, Muhammad Ghulam

    Potential Evapotranspiration (PET) is used as a common input to calculate evaporative demand in hydrological, ecological and biological modeling. Dynamic and distributed measurement of PET is important for improved hydrologic predictions at the watershed scale since PET varies with time and space. In this work, an advanced dynamic PET estimation is proposed by integrating geostationary satellite products into a currently existing remote sensing-based PET algorithm and evaluated in the framework of operational hydrologic forecasting modeling. The development work is approached through a series of studies. At first, a previously developed Moderate Resolution Imaging Spectroradiometer (MODIS) based PET (MODIS-PET) product applied over several flux towers and basins in the Upper Colorado River Basin (UCRB) to determine its applicability and predictive ability in comparison to other ground based distributed PET methods. Results from this primary study indicate the MODIS-PET is an improved PET estimation method compared to the other two contemporary distributed PET products that were tested over this geographically complex study region. In addition to elevation and cloud cover, uncertainties are associated with the MODIS-PET algorithm pertaining from three model variables; land surface temperature, air temperature and surface emissivity. The crude hypothetical sinusoidal curve considered in the conversion of instantaneous MODIS-PET to the daily PET estimation can potentially be replaced with satellite data with improved temporal resolution. Hence, integration of Geostationary Operational Environmental Satellites (GOES), a series of geostationary satellites with frequent observations, data in the MODIS-PET algorithm is performed in the second part. The coupling of GOES within the MODIS-PET algorithm shows significant improvement over the previously developed stand-alone MODIS-PET product, especially for cloudy days and high temperature pixels. Finally, evaluation of these

  18. Lidar Remote Sensing for Forest Canopy Studies 2014

    Science.gov (United States)

    Remote sensing has facilitated extraordinary advances in modeling, mapping, and the understanding of ecosystems. Conventional sensors have significant limitations for ecological and forest applications. The sensitivity and accuracy of these devices have repeatedly been shown to fall with increasing ...

  19. [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.

  20. [Monitoring the thermal plume from coastal nuclear power plant using satellite remote sensing data: modeling, and validation].

    Science.gov (United States)

    Zhu, Li; Zhao, Li-Min; Wang, Qiao; Zhang, Ai-Ling; Wu, Chuan-Qing; Li, Jia-Guo; Shi, Ji-Xiang

    2014-11-01

    Thermal plume from coastal nuclear power plant is a small-scale human activity, mornitoring of which requires high-frequency and high-spatial remote sensing data. The infrared scanner (IRS), on board of HJ-1B, has an infrared channel IRS4 with 300 m and 4-days as its spatial and temporal resolution. Remote sensing data aquired using IRS4 is an available source for mornitoring thermal plume. Retrieval pattern for coastal sea surface temperature (SST) was built to monitor the thermal plume from nuclear power plant. The research area is located near Guangdong Daya Bay Nuclear Power Station (GNPS), where synchronized validations were also implemented. The National Centers for Environmental Prediction (NCEP) data was interpolated spatially and temporally. The interpolated data as well as surface weather conditions were subsequently employed into radiative transfer model for the atmospheric correction of IRS4 thermal image. A look-up-table (LUT) was built for the inversion between IRS4 channel radiance and radiometric temperature, and a fitted function was also built from the LUT data for the same purpose. The SST was finally retrieved based on those preprocessing procedures mentioned above. The bulk temperature (BT) of 84 samples distributed near GNPS was shipboard collected synchronically using salinity-temperature-deepness (CTD) instruments. The discrete sample data was surface interpolated and compared with the satellite retrieved SST. Results show that the average BT over the study area is 0.47 degrees C higher than the retrieved skin temperature (ST). For areas far away from outfall, the ST is higher than BT, with differences less than 1.0 degrees C. The main driving force for temperature variations in these regions is solar radiation. For areas near outfall, on the contrary, the retrieved ST is lower than BT, and greater differences between the two (meaning > 1.0 degrees C) happen when it gets closer to the outfall. Unlike the former case, the convective heat

  1. 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 < 0.05, RMSE = 2.20 kg/m²) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m²) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the 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.

  2. 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.

  3. Ten ways remote sensing can contribute to conservation.

    Science.gov (United States)

    Rose, Robert A; Byler, Dirck; Eastman, J Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A; Laporte, Nadine; Leidner, Allison; 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

  4. Hyperspectral remote sensing for terrestrial applications

    Science.gov (United States)

    Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G.; Murali Krishna Gumma,; Venkateswarlu Dheeravath,

    2015-01-01

    Remote sensing data are considered hyperspectral when the data are gathered from numerous wavebands, contiguously over an entire range of the spectrum (e.g., 400–2500 nm). Goetz (1992) defines hyperspectral remote sensing as “The acquisition of images in hundreds of registered, contiguous spectral bands such that for each picture element of an image it is possible to derive a complete reflectance spectrum.” However, Jensen (2004) defines hyperspectral remote sensing as “The simultaneous acquisition of images in many relatively narrow, contiguous and/or non contiguous spectral bands throughout the ultraviolet, visible, and infrared portions of the electromagnetic spectrum.

  5. An international organization for remote sensing

    Science.gov (United States)

    Helm, Neil R.; Edelson, Burton I.

    1991-01-01

    A recommendation is presented for the formation of a new commercially oriented international organization to acquire or develop, coordinate or manage, the space and ground segments for a global operational satellite system to furnish the basic data for remote sensing and meteorological, land, and sea resource applications. The growing numbers of remote sensing programs are examined and possible ways of reducing redundant efforts and improving the coordination and distribution of these global efforts are discussed. This proposed remote sensing organization could play an important role in international cooperation and the distribution of scientific, commercial, and public good data.

  6. Remote sensing and urban public health

    Science.gov (United States)

    Rush, M.; Vernon, S.

    1975-01-01

    The applicability of remote sensing in the form of aerial photography to urban public health problems is examined. Environmental characteristics are analyzed to determine if health differences among areas could be predicted from the visual expression of remote sensing data. The analysis is carried out on a socioeconomic cross-sectional sample of census block groups. Six morbidity and mortality rates are the independent variables while environmental measures from aerial photographs and from the census constitute the two independent variable sets. It is found that environmental data collected by remote sensing are as good as census data in evaluating rates of health outcomes.

  7. PROMSAR: a multiple scattering atmospheric model for the analysis of DOAS remote sensing measurements

    Science.gov (United States)

    Palazzi, E.; Premuda, M.; Petritoli, A.; Giovanelli, G.; Kostadinov, I.; Ravegnani, F.; Bortoli, D.

    A correct interpretation of diffuse solar radiation measurements made by DOAS (Differential Optical Absorption Spectroscopy) remote sensors, requires the use of radiative transfer models of the atmosphere. The simplest models, the geometrical ones, consider radiation scattering in the atmosphere as a single scattering process. This means that the photons collected by the receiver have changed their direction from the sun only once. More realistic atmospheric models are those which consider multiple scattering: their application is useful and essential for the analysis of zenith and off-axis measurements regarding the lowest layers of the atmosphere, characterized by the highest values of air density and quantities of particles and aerosols acting as scattering nuclei. A new atmospheric model, called PROMSAR (PROcessing of Multi-Scattered Atmospheric Radiation), including multiple Rayleigh and Mie scattering, has recently been developed at the ISAC-CNR institute. It is based on a backward Monte Carlo technique, very suitable for studying the various interactions taking place in a complex and non-homogeneous system like the terrestrial atmosphere. PROMSAR code calculates the mean path of the radiation within each layer into which the atmosphere is sub-divided, taking into account the large variety of processes which solar radiation undergoes during propagation through the atmosphere. This quantity is then employed to work out the Air Mass Factor (AMF) of several trace gases, to simulate, both in zenith and off-axis configurations, their slant column amounts and to calculate the weighting functions from which information about the gas vertical distribution is obtained using inversion methods. Results from the model, simulations and comparisons with slant column measurements are presented and discussed.

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

    Science.gov (United States)

    Ebhuoma, Osadolor; Gebreslasie, Michael

    2016-01-01

    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 KnowledgeSM 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

  9. 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

  10. Suntracker for atmospheric remote sensing

    Science.gov (United States)

    Hawat, Toufic-Michel; Camy-Peyret, Claude; Torguet, Roger J.

    1998-05-01

    A heliostat is designed and built to track the sun for optical remote sensing of the stratosphere from a balloon- borne pointed gondola. The tracking mechanism is controlled by two direct torque motors used to drive a single flat acquisition mirror. A horizontal turntable, rigidly attached to the azimuth drive, supports the elevation assembly. A position sensor receiving a small part of the solar beam reflected off the main acquisition mirror is used for the fine servo control. Using a CCD camera prepointing of the acquisition mirror is achieved when the sun is in the field of view of the heliostat. This system is coupled with a high-resolution (0.02-cm-1) Fourier transform IR spectrometer to retrieve stratospheric trace species concentration profiles. The suntracker directs the solar radiation in a stable direction along the spectrometer optical axis. The pointing precision is 1 arcmin from a stratospheric gondola, which has static and dynamic angular excursions up to 6 deg. The heliostat coupled to the Limb Profile Monitor of the Atmosphere instrument performs successfully on several balloon flights. The description, ground tests, and balloon flight results of the suntracker are presented.

  11. 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...

  12. Quantifying the spatio-temporal dynamics of woody plant encroachment using an integrative remote sensing, GIS, and spatial modeling approach

    Science.gov (United States)

    Buenemann, Michaela

    Despite a longstanding universal concern about and intensive research into woody plant encroachment (WPE)---the replacement of grasslands by shrub- and woodlands---our accumulated understanding of the process has either not been translated into sustainable rangeland management strategies or with only limited success. In order to increase our scientific insights into WPE, move us one step closer toward the sustainable management of rangelands affected by or vulnerable to the process, and identify needs for a future global research agenda, this dissertation presents an unprecedented critical, qualitative and quantitative assessment of the existing literature on the topic and evaluates the utility of an integrative remote sensing, GIS, and spatial modeling approach for quantifying the spatio-temporal dynamics of WPE. Findings from this research suggest that gaps in our current understanding of WPE and difficulties in devising sustainable rangeland management strategies are in part due to the complex spatio-temporal web of interactions between geoecological and anthropogenic variables involved in the process as well as limitations of presently available data and techniques. However, an in-depth analysis of the published literature also reveals that aforementioned problems are caused by two further crucial factors: the absence of information acquisition and reporting standards and the relative lack of long-term, large-scale, multi-disciplinary research efforts. The methodological framework proposed in this dissertation yields data that are easily standardized according to various criteria and facilitates the integration of spatially explicit data generated by a variety of studies. This framework may thus provide one common ground for scientists from a diversity of fields. Also, it has utility for both research and management. Specifically, this research demonstrates that the application of cutting-edge remote sensing techniques (Multiple Endmember Spectral Mixture

  13. Image quality modeling and characterization of Nyquist-sampled framing sensors with operational considerations for remote sensing

    Science.gov (United States)

    Garma, Rey Jan D.; Schott, John R.; Fiete, Robert D.; Qiao, Jie; McKeown, Donald

    2017-01-01

    The effect of increasing Q on image interpretability is explored, and the fidelity of general image quality equation (GIQE) predictions is assessed for Nyquist-sampled (Q=2) imagery at low signal-to-noise ratio. A digital image chain simulation is developed and validated against a laboratory test bed using objective and subjective assessments. Using the validated model, additional test cases are simulated to study the effects of increased detector sampling on image quality with operational considerations for space-based remote sensing. Variants of the GIQE are evaluated against subject-provided ratings, and modifications that increase prediction accuracy for Q=2 imagery are proposed. Finally, using the validated simulation and modified image quality equation, trades are conducted to ascertain the feasibility of implementing Q=2 designs in future electro-optical systems.

  14. Coupled atmosphere/canopy model for remote sensing of plant reflectance features

    Science.gov (United States)

    Gerstl, S. A.; Zardecki, A.

    1985-01-01

    Solar radiative transfer through a coupled system of atmosphere and plant canopy is modeled as a multiple-scattering problem through a layered medium of random scatterers. The radiative transfer equation is solved by the discrete-ordinates finite-element method. Analytic expressions are derived that allow the calculation of scattering and absorption cross sections for any plant canopy layer form measurable biophysical parameters such as the leaf area index, leaf angle distribution, and individual leaf reflectance and transmittance data. An expression for a canopy scattering phase function is also given. Computational results are in good agreement with spectral reflectance measurements directly above a soybean canopy, and the concept of greenness- and brightness-transforms of Landsat MSS data is reconfirmed with the computed results. A sensitivity analysis with the coupled atmosphere/canopy model quantifies how satellite-sensed spectral radiances are affected by increased atmospheric aerosols, by varying leaf area index, by anisotropic leaf scattering, and by non-Lambertian soil boundary conditions. Possible extensions to a 2-D model are also discussed.

  15. Coupled atmosphere/canopy model for remote sensing of plant reflectance features

    Science.gov (United States)

    Gerstl, S. A.; Zardecki, A.

    1985-01-01

    Solar radiative transfer through a coupled system of atmosphere and plant canopy is modeled as a multiple-scattering problem through a layered medium of random scatterers. The radiative transfer equation is solved by the discrete-ordinates finite-element method. Analytic expressions are derived that allow the calculation of scattering and absorption cross sections for any plant canopy layer form measurable biophysical parameters such as the leaf area index, leaf angle distribution, and individual leaf reflectance and transmittance data. An expression for a canopy scattering phase function is also given. Computational results are in good agreement with spectral reflectance measurements directly above a soybean canopy, and the concept of greenness- and brightness-transforms of Landsat MSS data is reconfirmed with the computed results. A sensitivity analysis with the coupled atmosphere/canopy model quantifies how satellite-sensed spectral radiances are affected by increased atmospheric aerosols, by varying leaf area index, by anisotropic leaf scattering, and by non-Lambertian soil boundary conditions. Possible extensions to a 2-D model are also discussed.

  16. 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

  17. 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.

  18. Application of ensemble kalman filter to geophysical parameters retrieval in remote sensing: A case study of kernel-driven BRDF model inversion

    Institute of Scientific and Technical Information of China (English)

    QIN Jun; YAN Guangjian; LIU Shaomin; LIANG Shunlin; ZHANG Hao; WANG Jindi; LI Xiaowen

    2006-01-01

    The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insufficient observations. Common optimization algorithms have difficulties in providing posterior distribution and thus cannot directly acquire uncertainties in inversion results, which is of no benefit to remote sensing application. In this article, ensemble Kalman filter (EnKF) has been introduced to retrieve surface geophysical parameters from remote sensing observations, which has the capability of not merely obtaining inversion results but also giving its posterior distribution. To show the advantage of EnKF, it is compared to standard MODIS AMBRALS algorithm and highly efficient global optimization method SCE-UA. The inversion abilities of kernel-driven BRDF models with different kernel combinations at several main cover types are emphatically discussed when observations are deficient and a priori knowledge is introduced into inversion.

  19. 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.

  20. Spatially explicit modelling of forest structure and function using airborne lidar and hyperspectral remote sensing data combined with micrometeorological measurements

    Science.gov (United States)

    Thomas, Valerie Anne

    This research models canopy-scale photosynthesis at the Groundhog River Flux Site through the integration of high-resolution airborne remote sensing data and micrometeorological measurements collected from a flux tower. Light detection and ranging (lidar) data are analysed to derive models of tree structure, including: canopy height, basal area, crown closure, and average aboveground biomass. Lidar and hyperspectral remote sensing data are used to model canopy chlorophyll (Chl) and carotenoid concentrations (known to be good indicators of photosynthesis). The integration of lidar and hyperspectral data is applied to derive spatially explicit models of the fraction of photosynthetically active radiation (fPAR) absorbed by the canopy as well as a species classification for the site. These products are integrated with flux tower meteorological measurements (i.e., air temperature and global solar radiation) collected on a continuous basis over 2004 to apply the C-Fix model of carbon exchange to the site. Results demonstrate that high resolution lidar and lidar-hyperspectral integration techniques perform well in the boreal mixedwood environment. Lidar models are well correlated with forest structure, despite the complexities introduced in the mixedwood case (e.g., r2=0.84, 0.89, 0.60, and 0.91, for mean dominant height, basal area, crown closure, and average aboveground biomass). Strong relationships are also shown for canopy scale chlorophyll/carotenoid concentration analysis using integrated lidar-hyperspectral techniques (e.g., r2=0.84, 0.84, and 0.82 for Chl(a), Chl(a+b), and Chl(b)). Examination of the spatially explicit models of fPAR reveal distinct spatial patterns which become increasingly apparent throughout the season due to the variation in species groupings (and canopy chlorophyll concentration) within the 1 km radius surrounding the flux tower. Comparison of results from the modified local-scale version of the C-Fix model to tower gross ecosystem

  1. Hyperspectral remote sensing for light pollution monitoring

    Directory of Open Access Journals (Sweden)

    P. Marcoionni

    2006-06-01

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

  2. Application of Spaceborne Remote Sensing to Archaeology

    Science.gov (United States)

    Crippen, Robert E.

    1997-01-01

    Spaceborne remote sensing data have been underutilized in archaeology for a variety of seasons that are slowly but surely being overcome. Difficulties have included cost/availability of data, inadequate resolution, and data processing issues.

  3. 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.

  4. 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...

  5. 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.

  6. Integrating spatial statistics and remote sensing.

    NARCIS (Netherlands)

    Stein, A.; Bastiaanssen, W.G.M.; Bruin, de S.; Cracknell, A.P.; Curran, P.J.; Fabbri, A.G.; Gorte, B.G.H.; Groenigen, van J.W.; Meer, van der F.D.; Saldana, A.

    1998-01-01

    This paper presents an integrated approach towards spatial statistics for remote sensing. Using the layer concept in Geographical Information Systems we treat successively elements of spatial statistics, scale, classification, sampling and decision support. The layer concept allows to combine contin

  7. Remote Sensing Information Sciences Research Group, year four

    Science.gov (United States)

    Estes, John E.; Smith, Terence; Star, Jeffrey L.

    1987-01-01

    The needs of the remote sensing research and application community which will be served by the Earth Observing System (EOS) and space station, including associated polar and co-orbiting platforms are examined. Research conducted was used to extend and expand existing remote sensing research activities in the areas of georeferenced information systems, machine assisted information extraction from image data, artificial intelligence, and vegetation analysis and modeling. Projects are discussed in detail.

  8. Environmental impact prediction using remote sensing images

    Institute of Scientific and Technical Information of China (English)

    Pezhman ROUDGARMI; Masoud MONAVARI; Jahangir FEGHHI; Jafar NOURI; Nematollah KHORASANI

    2008-01-01

    Environmental impact prediction is an important step in many environmental studies. Awide variety of methods have been developed in this concern. During this study, remote sensing images were used for environmental impact prediction in Robatkarim area, Iran, during the years of 2005~2007. It was assumed that environmental impact could be predicted using time series satellite imageries. Natural vegetation cover was chosen as a main environmental element and a case study. Environmental impacts of the regional development on natural vegetation of the area were investigated considering the changes occurred on the extent of natural vegetation cover and the amount of biomass. Vegetation data, land use and land cover classes (as activity factors) within several years were prepared using satellite images. The amount ofbiomass was measured by Soil-adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) based on satellite images. The resulted biomass estimates were tested by the paired samples t-test method. No significant difference was observed between the average biomass of estimated and control samples at the 5% significance level. Finally, regression models were used for the environmental impacts prediction. All obtained regression models for prediction of impacts on natural vegetation cover show values over 0.9 for both correlation coefficient and R-squared. According to the resulted methodology, the prediction models of projects and plans impacts can also be developed for other environmental elements which may be derived using time series remote sensing images.

  9. 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.

  10. 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.

  11. Talisman-Saber 2009 Remote Sensing Experiment

    Science.gov (United States)

    2012-03-30

    Naval Research Laboratory Washington, DC 20375-5320 NRL/MR/7230--12-9404 Talisman -Saber 2009 Remote Sensing Experiment March 30, 2012 Approved for... Talisman -Saber 2009 Remote Sensing Experiment Charles M. Bachmann, Robert A. Fusina, Marcos J. Montes, Rong-Rong Li, Carl Gross, C. Reid Nichols,* John C...sensor were used to build shallow water bathymetric charts and trafficability maps that were provided to military planners during Exercise Talisman

  12. Remote sensing of coastal and ocean studies

    Digital Repository Service at National Institute of Oceanography (India)

    Sathe, P.V.

    the sensors on board 2 satellites or aircrafts (and vice versa). Hence, they cannot be used in remote sensing. Similarly, long waves like radio waves are also not used in remote sensing because of their poor information carrying capacity. Only visible, infra..., infra-red radiation is also affected by clouds (though less significantly). This requires atmospheric corrections to be applied to such data. At present, sea surface temperatures are routinely being retrieved from the sensor called AVBRR (Advanced Vary...

  13. A Semi-Analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China

    Directory of Open Access Journals (Sweden)

    Jin-Ling Kong

    2015-04-01

    Full Text Available Suspended sediment concentration (SSC is one of the most critical parameters in ocean ecological environment evaluation and it can be determined using ocean color remote sensing (RS. The purpose of this study is to develop a model that provides a reliable and sensitive evaluation of SSC retrieval using RS data. Data were acquired for and gathered from the Gulf of Bohai where SSC levels are relatively low with an average value below 30 mg·L−1. The study indicates that the most sensitive band to SSC levels in the study area is the NIR band of Landsat5 TM images. A quadratic polynomial semi-analytical model appears to be the best retrieval model based on the relationship between the inherent optical properties (IOPs and apparent optical properties (AOPs of water as described by the quasi-analytical algorithm (QAA. The model has a higher precision and effectiveness for SSC retrieval than data-driven statistical models, especially when SSC level is relatively high. The average relative error and the root mean square error (RMSE are 12.32% and 4.53 mg·L−1, respectively, while the correlation coefficient between observed and estimated SSC by the model is 0.95. Using the proposed retrieval model and TM data, SSC levels of the entire study region in the Gulf of Bohai were estimated. These estimates can serve as the baseline for efficient monitoring of the ocean environment in the future.

  14. Image based remote sensing method for modeling black-eyed beans (Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

    Science.gov (United States)

    Papadavid, Giorgos; Fasoula, Dionysia; Hadjimitsis, Michael; Skevi Perdikou, P.; Hadjimitsis, Diofantos

    2013-03-01

    In this paper, Leaf Area Index (LAI) and Crop Height (CH) are modeled to the most known spectral vegetation index — NDVI — using remotely sensed data. This approach has advantages compared to the classic approaches based on a theoretical background. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data for estimating a spectral vegetation index (NDVI), for establishing a semiempirical relationship between black-eyed beans' canopy factors and remotely sensed data. Such semi-empirical models can be used then for agricultural and environmental studies. A field campaign was undertaken with measurements of LAI and CH using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric (GER1500) measurements between May and June of 2010. Field spectroscopy and remotely sensed imagery have been combined and used in order to retrieve and validate the results of this study. The results showed that there are strong statistical relationships between LAI or CH and NDVI which can be used for modeling crop canopy factors (LAI, CH) to remotely sensed data. The model for each case was verified by the factor of determination. Specifically, these models assist to avoid direct measurements of the LAI and CH for all the dates for which satellite images are available and support future users or future studies regarding crop canopy parameters.

  15. Hyperspectral remote sensing of crop leaf chlorophyll content using reflectance simulation model and field data in open canopies

    Science.gov (United States)

    Jiao, Quanjun; Wu, Yanhong; Liu, Liangyun; Zhang, Bing

    2015-04-01

    Leaf chlorophyll content -a and -b content (Cab) is an indicator for crop nutrition status and photosynthetic capacity. Remote sensing of Cab plays an important role in crop growth monitoring, pest and disease diagnosis, and crop yield assessment, yet the feasibility and stability of such estimation has not been assessed thoroughly for mixed pixels when crop canopies are not closed. This study analyzes the influence of spectral mixing on leaf chlorophyll content estimation using canopy spectra simulated by the PROSAIL reflectance model and the spectral linear mixture concept. It is observed that the accuracy of leaf chlorophyll content estimation would be degraded for mixed pixels using the well accepted approach of the combination of TCARI and OSAVI. A two-step method was thus developed for winter wheat chlorophyll content estimation by taking into consideration the fractional vegetation cover using a look-up table approach. The two methods were validated using ground spectra, airborne hyperspectral data and leaf chlorophyll content measured the same time over experimental winter wheat fields. Using the two-step method, the leaf chlorophyll content of the open canopy was estimated from the airborne hyperspectral imagery with a root mean square error of 5.18 μg cm-2, which is an improvement of about 8.9% relative to the accuracy obtained using the TCARI/OSAVI ratio directly. This implies that the method proposed in this study has great potential for hyperspectral applications in agricultural management, particularly for applications before crop canopy closure. This study, therefore, offers a feasible technique that might be applied to crop chlorophyll content estimation using large-scale remote sensing data.

  16. Soil vulnerability to erosion assessed with remote sensing, digital elevation models and a fuzzy logic Multi-Criteria Evaluation

    Science.gov (United States)

    Melendez-Pastor, I.; Navarro-Pedreño, J.; Gómez, I.; Koch, M.

    2009-04-01

    Soil vulnerability is the capacity of one or more of the ecological functions of the soil system to be harmed. Soil vulnerability is related with the sensitivity of the soil system to degradation processes like erosion, desertification or salinization. Vegetation plays a crucial role in soil vulnerability because is a source of organic matter and a protection against rain, wind and other erosive agents. A soil covered by a dense and vigorous vegetation is more resistant against erosion. Another important factor that determines soil vulnerability is the topography. Slope and aspect have a great influence on vegetation distribution and losses of soil due to erosive processes. A key problem with traditional erosion models (USLE; RUSLE, etc.) is that input parameters are obtained locally or with large intervals of time. This technical problem greatly limits the update of soil erosion maps and their modification according to landscape changes (land use change, forest fires, etc.). To solve this technical difficulties, remote sensing and GIS techniques has been employed to compute input parameters of erosion models or develop new methodological approaches for soil vulnerability and erosion assessment. This work presents a methodological approach to assess soil vulnerability using remote sensing and GIS techniques to estimate input variables and to develop calculations in a spatial basis. Input variables include information about vegetation status and topography. The main advantage of this approach is that input variables can be updated fast to reflect landscape changes and the phenological status of vegetation that substantially could affect soil vulnerability. Soil vulnerability is assessed with a fuzzy logic model. Fuzzy logic emanates from Fuzzy Sets theory developed by Zadeh (1965) as a way to express and operate with membership degrees of the elements in a set. Fuzzy logic works well with continuous variables and with data uncertainties, and thus is very suitable to

  17. Remote sensing for land management and planning

    Science.gov (United States)

    Woodcock, Curtis E.; Strahler, Alan H.; Franklin, Janet

    1983-05-01

    The primary role of remote sensing in land management and planning has been to provide information concerning the physical characteristics of the land which influence the management of individual land parcels or the allocation of lands to various uses These physical characteristics have typically been assessed through aerial photography, which is used to develop resource maps and to monitor changing environmental conditions These uses are well developed and currently well integrated into the planning infrastructure at local, state, and federal levels in the United States. Many newly emerging uses of remote sensing involve digital images which are collected, stored, and processed automatically by electromechanical scanning devices and electronic computers Some scanning devices operate from aircraft or spacecraft to scan ground scenes directly; others scan conventional aerial transparencies to yield digital images. Digital imagery offers the potential for computer-based automated map production, a process that can significantly increase the amount and timeliness of information available to land managers and planners. Future uses of remote sensing in land planning and management will involve geographic information systems, which store resource information in a geocoded format. Geographic information systems allow the automated integration of disparate types of resource data through various types of spatial models so that with accompanying sample ground data, information in the form of thematic maps and/ or aerially aggregated statistics can be produced Key issues confronting the development and integration of geographic information systems into planning pathways are restoration and rectification of digital images, automated techniques for combining both quantitative and qualitative types of data in information-extracting procedures, and the compatibility of alternative data storage modes

  18. 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...

  19. Crop Yield and Area can be Reliably Estimated Using Farmer Supplied Yield Data, Remote Sensing and Crop Models in Australia.

    Science.gov (United States)

    Lawes, R.

    2016-12-01

    The Australian grain growing region is vast and occupies where some 25 million tonnes of wheat is produced from latitudes -27 to -42, where soils, crops and climates vary considerably. Predicting the area of individual crops is time consuming and currently conducted by survey, while yield estimates are derived from these areas and from information about grain receivables with little pre-harvest information available to industry. The existing approach fails to provide reliable, timely, small scale information about production. Similarly, previous attempts to predict yield using satellite derived information rely on information collected using the existing systems to calibrate models. We have developed a crop productivity and yield model - called C-Store Crop - that uses remotely sensed vegetation indices, along with site based rainfall, radiation and temperature information. Model calibration using 3000 points derived from farmer supplied yield maps for wheat, barley, canola and chickpea showed strong relationships (>70%) between modelled plant mass and observed crop yield at the paddock scale. C-Store Crop is being applied at 250m and 25m grid resolution. Farmer supplied yield data was also used to train a combination of Radar and Landsat images collected whilst the crop is growing to discriminate between crop types. Landsat information alone was unable to discriminate legume and cereal crops. Problems such as cloud prevented accessing appropriate scenes. Inclusion of Radar information reduced errors of commission and omission. By combining the C-Store Crop model with remote estimates of crop type, we anticipate predicting crop type and crop yield with uncertainty estimates across the Australian continent.

  20. A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

    Science.gov (United States)

    Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

    2011-12-01

    The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optica