Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

PubMed Central

ZIMMERMANN, N E; EDWARDS, T C; MOISEN, G G; FRESCINO, T S; BLACKARD, J A

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. PMID:18642470

[An operational remote sensing algorithm of land surface evapotranspiration based on NOAA PAL dataset].

PubMed

Hou, Ying-Yu; He, Yan-Bo; Wang, Jian-Lin; Tian, Guo-Liang

2009-10-01

Based on the time series 10-day composite NOAA Pathfinder AVHRR Land (PAL) dataset (8 km x 8 km), and by using land surface energy balance equation and "VI-Ts" (vegetation index-land surface temperature) method, a new algorithm of land surface evapotranspiration (ET) was constructed. This new algorithm did not need the support from meteorological observation data, and all of its parameters and variables were directly inversed or derived from remote sensing data. A widely accepted ET model of remote sensing, i. e., SEBS model, was chosen to validate the new algorithm. The validation test showed that both the ET and its seasonal variation trend estimated by SEBS model and our new algorithm accorded well, suggesting that the ET estimated from the new algorithm was reliable, being able to reflect the actual land surface ET. The new ET algorithm of remote sensing was practical and operational, which offered a new approach to study the spatiotemporal variation of ET in continental scale and global scale based on the long-term time series satellite remote sensing images.

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

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

NASA Technical Reports Server (NTRS)

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.

An intercomparison of three remote sensing-based energy balance models using large aperture scintillometer measurements over a wheat-corn production region

USDA-ARS?s Scientific Manuscript database

This paper compares three remote sensing-based models for estimating evapotranspiration (ET), namely the Surface Energy Balance System (SEBS), the Two-Source Energy Balance (TSEB) model, and the surface Temperature-Vegetation index Triangle (TVT). The models used as input MODIS/TERRA products and gr...

Remote sensing sensors and applications in environmental resources mapping and modeling

USGS Publications Warehouse

Melesse, Assefa M.; Weng, Qihao; Thenkabail, Prasad S.; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.

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

NASA Astrophysics Data System (ADS)

Ma, Y.; Liu, S.

2017-12-01

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

Commercial future: making remote sensing a media event

NASA Astrophysics Data System (ADS)

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.

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

NASA Astrophysics Data System (ADS)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

A thermal-based remote sensing modeling system for estimating evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. This paper describes a robust but relatively simple thermal-based energy balance model that parameterizes the key soil/s...

Soil water balance calculation using a two source energy balance model and wireless sensor arrays aboard a center pivot

USDA-ARS?s Scientific Manuscript database

Recent developments in wireless sensor technology and remote sensing algorithms, coupled with increased use of center pivot irrigation systems, have removed several long-standing barriers to adoption of remote sensing for real-time irrigation management. One remote sensing-based algorithm is a two s...

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

Treesearch

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...

Use of high resolution remotely sensed evapotranspiration retrievals for calibration of a process-based hydrologic model in data-poor basins

USDA-ARS?s Scientific Manuscript database

Calibration of process-based hydrologic models is a challenging task in data-poor basins, where monitored hydrologic data are scarce. In this study, we present a novel approach that benefits from remotely sensed evapotranspiration (ET) data to calibrate a complex watershed model, namely the Soil and...

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

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

PubMed

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

2015-11-30

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.

Bushland Evapotranspiration and Agricultural Remote Sensing System (BEARS) software

NASA Astrophysics Data System (ADS)

Gowda, P. H.; Moorhead, J.; Brauer, D. K.

2017-12-01

Evapotranspiration (ET) is a major component of the hydrologic cycle. ET data are used for a variety of water management and research purposes such as irrigation scheduling, water and crop modeling, streamflow, water availability, and many more. Remote sensing products have been widely used to create spatially representative ET data sets which provide important information from field to regional scales. As UAV capabilities increase, remote sensing use is likely to also increase. For that purpose, scientists at the USDA-ARS research laboratory in Bushland, TX developed the Bushland Evapotranspiration and Agricultural Remote Sensing System (BEARS) software. The BEARS software is a Java based software that allows users to process remote sensing data to generate ET outputs using predefined models, or enter custom equations and models. The capability to define new equations and build new models expands the applicability of the BEARS software beyond ET mapping to any remote sensing application. The software also includes an image viewing tool that allows users to visualize outputs, as well as draw an area of interest using various shapes. This software is freely available from the USDA-ARS Conservation and Production Research Laboratory website.

The determinations of remote sensing satellite data delivery service quality: A positivistic case study in Chinese context

NASA Astrophysics Data System (ADS)

Jin, Jiahua; Yan, Xiangbin; Tan, Qiaoqiao; Li, Yijun

2014-03-01

With the development of remote sensing technology, remote-sensing satellite has been widely used in many aspects of national construction. Big data with different standards and massive users with different needs, make the satellite data delivery service to be a complex giant system. How to deliver remote-sensing satellite data efficiently and effectively is a big challenge. Based on customer service theory, this paper proposes a hierarchy conceptual model for examining the determinations of remote-sensing satellite data delivery service quality in the Chinese context. Three main dimensions: service expectation, service perception and service environment, and 8 sub-dimensions are included in the model. Large amount of first-hand data on the remote-sensing satellite data delivery service have been obtained through field research, semi-structured questionnaire and focused interview. A positivist case study is conducted to validate and develop the proposed model, as well as to investigate the service status and related influence mechanisms. Findings from the analysis demonstrate the explanatory validity of the model, and provide potentially helpful insights for future practice.

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

NASA Astrophysics Data System (ADS)

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.

Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

PubMed Central

Melesse, Assefa M.; Weng, Qihao; S.Thenkabail, Prasad; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling. PMID:28903290

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

NASA Astrophysics Data System (ADS)

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 optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

[Study on artificial neural network combined with multispectral remote sensing imagery for forest site evaluation].

PubMed

Gong, Yin-Xi; He, Cheng; Yan, Fei; Feng, Zhong-Ke; Cao, Meng-Lei; Gao, Yuan; Miao, Jie; Zhao, Jin-Long

2013-10-01

Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.

A comparison of operational remote sensing-based models for estimating crop evapotranspiration

USDA-ARS?s Scientific Manuscript database

The integration of remotely sensed data into models of actual evapotranspiration has allowed for the estimation of water consumption across agricultural regions. Two modeling approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface...

Remotely monitoring evaporation rate and soil water status using thermal imaging and "three-temperatures model (3T Model)" under field-scale conditions.

PubMed

Qiu, Guo Yu; Zhao, Ming

2010-03-01

Remote monitoring of soil evaporation and soil water status is necessary for water resource and environment management. Ground based remote sensing can be the bridge between satellite remote sensing and ground-based point measurement. The primary object of this study is to provide an algorithm to estimate evaporation and soil water status by remote sensing and to verify its accuracy. Observations were carried out in a flat field with varied soil water content. High-resolution thermal images were taken with a thermal camera; soil evaporation was measured with a weighing lysimeter; weather data were recorded at a nearby meteorological station. Based on the thermal imaging and the three-temperatures model (3T model), we developed an algorithm to estimate soil evaporation and soil water status. The required parameters of the proposed method were soil surface temperature, air temperature, and solar radiation. By using the proposed method, daily variation in soil evaporation was estimated. Meanwhile, soil water status was remotely monitored by using the soil evaporation transfer coefficient. Results showed that the daily variation trends of measured and estimated evaporation agreed with each other, with a regression line of y = 0.92x and coefficient of determination R(2) = 0.69. The simplicity of the proposed method makes the 3T model a potentially valuable tool for remote sensing.

A thermal-based remote sensing modelling system for estimating crop water use and stress from field to regional scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. A thermal-based scheme, called the Two-Source Energy Balance (TSEB) model, solves for the soil/substrate and canopy temp...

Applying narrowband remote-sensing reflectance models to wideband data.

PubMed

Lee, Zhongping

2009-06-10

Remote sensing of coastal and inland waters requires sensors to have a high spatial resolution to cover the spatial variation of biogeochemical properties in fine scales. High spatial-resolution sensors, however, are usually equipped with spectral bands that are wide in bandwidth (50 nm or wider). In this study, based on numerical simulations of hyperspectral remote-sensing reflectance of optically-deep waters, and using Landsat band specifics as an example, the impact of a wide spectral channel on remote sensing is analyzed. It is found that simple adoption of a narrowband model may result in >20% underestimation in calculated remote-sensing reflectance, and inversely may result in >20% overestimation in inverted absorption coefficients even under perfect conditions, although smaller (approximately 5%) uncertainties are found for higher absorbing waters. These results provide a cautious note, but also a justification for turbid coastal waters, on applying narrowband models to wideband data.

Potential impact of remote sensing data on sea-state analysis and prediction

NASA Technical Reports Server (NTRS)

Cardone, V. J.

1983-01-01

The severe North Atlantic storm which damaged the ocean liner Queen Elizabeth 2 (QE2) was studied to assess the impact of remotely sensed marine surface wind data obtained by SEASAT-A, on sea state specifications and forecasts. Alternate representations of the surface wind field in the QE2 storm were produced from the SEASAT enhanced data base, and from operational analyses based upon conventional data. The wind fields were used to drive a high resolution spectral ocean surface wave prediction model. Results show that sea state analyses would have been vastly improved during the period of storm formation and explosive development had remote sensing wind data been available in real time. A modest improvement in operational 12 to 24 hour wave forecasts would have followed automatically from the improved initial state specification made possible by the remote sensing data in both numerical and sea state prediction models. Significantly improved 24 to 48 hour wave forecasts require in addition to remote sensing data, refinement in the numerical and physical aspects of weather prediction models.

Remote sensing image ship target detection method based on visual attention model

NASA Astrophysics Data System (ADS)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

Effects of 4D-Var data assimilation using remote sensing precipitation products in a WRF over the complex Heihe River Basin

NASA Astrophysics Data System (ADS)

Pan, Xiaoduo; Li, Xin; Cheng, Guodong

2017-04-01

Traditionally, ground-based, in situ observations, remote sensing, and regional climate modeling, individually, cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrain. Data assimilation techniques are often used to assimilate ground observations and remote sensing products into models for dynamic downscaling. In this study, the Weather Research and Forecasting (WRF) model was used to assimilate two satellite precipitation products (TRMM 3B42 and FY-2D) using the 4D-Var data assimilation method. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly for short-term weather forecasting. Future work is proposed to assimilate a suite of remote sensing data, e.g., the combination of precipitation and soil moisture data, into a WRF model to improve 7-8 day forecasts of precipitation and other atmospheric variables.

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review.

PubMed

Lees, K J; Quaife, T; Artz, R R E; Khomik, M; Clark, J M

2018-02-15

Peatlands store large amounts of terrestrial carbon and any changes to their carbon balance could cause large changes in the greenhouse gas (GHG) balance of the Earth's atmosphere. There is still much uncertainty about how the GHG dynamics of peatlands are affected by climate and land use change. Current field-based methods of estimating annual carbon exchange between peatlands and the atmosphere include flux chambers and eddy covariance towers. However, remote sensing has several advantages over these traditional approaches in terms of cost, spatial coverage and accessibility to remote locations. In this paper, we outline the basic principles of using remote sensing to estimate ecosystem carbon fluxes and explain the range of satellite data available for such estimations, considering the indices and models developed to make use of the data. Past studies, which have used remote sensing data in comparison with ground-based calculations of carbon fluxes over Northern peatland landscapes, are discussed, as well as the challenges of working with remote sensing on peatlands. Finally, we suggest areas in need of future work on this topic. We conclude that the application of remote sensing to models of carbon fluxes is a viable research method over Northern peatlands but further work is needed to develop more comprehensive carbon cycle models and to improve the long-term reliability of models, particularly on peatland sites undergoing restoration. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Progress in remote sensing of global land surface heat fluxes and evaporations with a turbulent heat exchange parameterization method

NASA Astrophysics Data System (ADS)

Chen, Xuelong; Su, Bob

2017-04-01

Remote sensing has provided us an opportunity to observe Earth land surface with a much higher resolution than any of GCM simulation. Due to scarcity of information for land surface physical parameters, up-to-date GCMs still have large uncertainties in the coupled land surface process modeling. One critical issue is a large amount of parameters used in their land surface models. Thus remote sensing of land surface spectral information can be used to provide information on these parameters or assimilated to decrease the model uncertainties. Satellite imager could observe the Earth land surface with optical, thermal and microwave bands. Some basic Earth land surface status (land surface temperature, canopy height, canopy leaf area index, soil moisture etc.) has been produced with remote sensing technique, which already help scientists understanding Earth land and atmosphere interaction more precisely. However, there are some challenges when applying remote sensing variables to calculate global land-air heat and water exchange fluxes. Firstly, a global turbulent exchange parameterization scheme needs to be developed and verified, especially for global momentum and heat roughness length calculation with remote sensing information. Secondly, a compromise needs to be innovated to overcome the spatial-temporal gaps in remote sensing variables to make the remote sensing based land surface fluxes applicable for GCM model verification or comparison. A flux network data library (more 200 flux towers) was collected to verify the designed method. Important progress in remote sensing of global land flux and evaporation will be presented and its benefits for GCM models will also be discussed. Some in-situ studies on the Tibetan Plateau and problems of land surface process simulation will also be discussed.

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

PubMed

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.

Remote Sensing based modelling of Annual Surface Mass Balances of Chhota Shigiri Glacier, Western Himalayas, India

NASA Astrophysics Data System (ADS)

Chandrasekharan, Anita; Ramsankaran, Raaj

2017-04-01

The current study aims at modelling glacier mass balances over Chhota Shigiri glacier (32.28o N; 77.58° E) in Himachal Pradesh, India using the Equilibrium Line Altitude (ELA) gradient approach proposed by Rabatel et al. (2005). The model requires yearly ELA, average mass balance and mass balance gradient to estimate annual mass balance of a glacier which can be obtained either through field measurements or remote sensing observations. However, in view of the general scenario of lack of field data for Himalayan glaciers, in this study the model has been applied only using the inputs derived through multi-temporal satellite remote sensing observations thus eliminating the need for any field measurements. Preliminary analysis show that the obtained results are comparable with the observed field mass balance. The results also demonstrate that this approach with remote sensing inputs has potential to be used for glacier mass balance estimations provided good quality multi-temporal remote sensing dataset are available.

Developing the remote sensing-based water environmental model for monitoring alpine river water environment over Plateau cold zone

NASA Astrophysics Data System (ADS)

You, Y.; Wang, S.; Yang, Q.; Shen, M.; Chen, G.

2017-12-01

Alpine river water environment on the Plateau (such as Tibetan Plateau, China) is a key indicator for water security and environmental security in China. Due to the complex terrain and various surface eco-environment, it is a very difficult to monitor the water environment over the complex land surface of the plateau. The increasing availability of remote sensing techniques with appropriate spatiotemporal resolutions, broad coverage and low costs allows for effective monitoring river water environment on the Plateau, particularly in remote and inaccessible areas where are lack of in situ observations. In this study, we propose a remote sense-based monitoring model by using multi-platform remote sensing data for monitoring alpine river environment. In this study some parameterization methodologies based on satellite remote sensing data and field observations have been proposed for monitoring the water environmental parameters (including chlorophyll-a concentration (Chl-a), water turbidity (WT) or water clarity (SD), total nitrogen (TN), total phosphorus (TP), and total organic carbon (TOC)) over the china's southwest highland rivers, such as the Brahmaputra. First, because most sensors do not collect multiple observations of a target in a single pass, data from multiple orbits or acquisition times may be used, and varying atmospheric and irradiance effects must be reconciled. So based on various types of satellite data, at first we developed the techniques of multi-sensor data correction, atmospheric correction. Second, we also built the inversion spectral database derived from long-term remote sensing data and field sampling data. Then we have studied and developed a high-precision inversion model over the southwest highland river backed by inversion spectral database through using the techniques of multi-sensor remote sensing information optimization and collaboration. Third, take the middle reaches of the Brahmaputra river as the study area, we validated the key water environmental parameters and further improved the inversion model. The results indicate that our proposed water environment inversion model can be a good inversion for alpine water environmental parameters, and can improve the monitoring and warning ability for the alpine river water environment in the future.

Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures

USDA-ARS?s Scientific Manuscript database

Operational application of a remote sensing-based two source energy balance model (TSEB) to estimate evaportranspiration (ET) and the components evaporation (E), transpiration (T) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSE...

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

NASA Astrophysics Data System (ADS)

Barker, J. Burdette

Spatially informed irrigation management may improve the optimal use of water resources. Sub-field scale water balance modeling and measurement were studied in the context of irrigation management. A spatial remote-sensing-based evapotranspiration and soil water balance model was modified and validated for use in real-time irrigation management. The modeled ET compared well with eddy covariance data from eastern Nebraska. Placement and quantity of sub-field scale soil water content measurement locations was also studied. Variance reduction factor and temporal stability were used to analyze soil water content data from an eastern Nebraska field. No consistent predictor of soil water temporal stability patterns was identified. At least three monitoring locations were needed per irrigation management zone to adequately quantify the mean soil water content. The remote-sensing-based water balance model was used to manage irrigation in a field experiment. The research included an eastern Nebraska field in 2015 and 2016 and a western Nebraska field in 2016 for a total of 210 plot-years. The response of maize and soybean to irrigation using variations of the model were compared with responses from treatments using soil water content measurement and a rainfed treatment. The remote-sensing-based treatment prescribed more irrigation than the other treatments in all cases. Excessive modeled soil evaporation and insufficient drainage times were suspected causes of the model drift. Modifying evaporation and drainage reduced modeled soil water depletion error. None of the included response variables were significantly different between treatments in western Nebraska. In eastern Nebraska, treatment differences for maize and soybean included evapotranspiration and a combined variable including evapotranspiration and deep percolation. Both variables were greatest for the remote-sensing model when differences were found to be statistically significant. Differences in maize yield in 2015 were attributed to random error. Soybean yield was lowest for the remote-sensing-based treatment and greatest for rainfed, possibly because of overwatering and lodging. The model performed well considering that it did not include soil water content measurements during the season. Future work should improve the soil evaporation and drainage formulations, because of excessive precipitation and include aerial remote sensing imagery and soil water content measurement as model inputs.

The ASPRS Remote Sensing Industry Forecast: Phase II & III - Digital Sensor Compilation

NASA Technical Reports Server (NTRS)

Mondello, Charles

2007-01-01

In August 1999, ASPRS and NASA's (then) Commercial Remote Sensing Program (CRSP) entered into a 5-year Space Act Agreement (SAA), combining resources and expertise to: (a) Baseline the Remote Sensing Industry (RSI) based on GEIA Model; (b) Develop a 10-Year RSI market forecast and attendant processes; and (c) Provide improved information for decision makers.

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

PubMed

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

2017-09-15

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

A new multi-angle remote sensing framework for scaling vegetation properties from tower-based spectro-radiometers to next generation "CubeSat"-satellites.

NASA Astrophysics Data System (ADS)

Hilker, T.; Hall, F. G.; Dyrud, L. P.; Slagowski, S.

2014-12-01

Frequent earth observations are essential for assessing the risks involved with global climate change, its feedbacks on carbon, energy and water cycling and consequences for live on earth. Often, satellite-remote sensing is the only practical way to provide such observations at comprehensive spatial scales, but relationships between land surface parameters and remotely sensed observations are mostly empirical and cannot easily be scaled across larger areas or over longer time intervals. For instance, optically based methods frequently depend on extraneous effects that are unrelated to the surface property of interest, including the sun-server geometry or background reflectance. As an alternative to traditional, mono-angle techniques, multi-angle remote sensing can help overcome some of these limitations by allowing vegetation properties to be derived from comprehensive reflectance models that describe changes in surface parameters based on physical principles and radiative transfer theory. Recent results have shown in theoretical and experimental research that multi-angle techniques can be used to infer and scale the photosynthetic rate of vegetation, its biochemical and structural composition robustly from remote sensing. Multi-angle remote sensing could therefore revolutionize estimates of the terrestrial carbon uptake as scaling of primary productivity may provide a quantum leap in understanding the spatial and temporal complexity of terrestrial earth science. Here, we introduce a framework of next generation tower-based instruments to a novel and unique constellation of nano-satellites (Figure 1) that will allow us to systematically scale vegetation parameters from stand to global levels. We provide technical insights, scientific rationale and present results. We conclude that future earth observation from multi-angle satellite constellations, supported by tower based remote sensing will open new opportunities for earth system science and earth system modeling.

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

PubMed Central

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

2015-01-01

Background 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. Methodology 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. Principal Findings 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. Conclusions/Significance 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-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data. PMID:26587839

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

PubMed

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-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data.

Remote sensing of atmospheric chemistry; Proceedings of the Meeting, Orlando, FL, Apr. 1-3, 1991

NASA Technical Reports Server (NTRS)

Mcelroy, James L. (Editor); Mcneal, Robert J. (Editor)

1991-01-01

The present volume on remote sensing of atmospheric chemistry discusses special remote sensing space observations and field experiments to study chemical change in the atmosphere, network monitoring for detection of stratospheric chemical change, stratospheric chemistry studies, and the combining of model, in situ, and remote sensing in atmospheric chemistry. Attention is given to the measurement of tropospheric carbon monoxide using gas filter radiometers, long-path differential absorption measurements of tropospheric molecules, air quality monitoring with the differential optical absorption spectrometer, and a characterization of tropospheric methane through space-based remote sensing. Topics addressed include microwave limb sounder experiments for UARS and EOS, an overview of the spectroscopy of the atmosphere using an FIR emission experiment, the detection of stratospheric ozone trends by ground-based microwave observations, and a FIR Fabry-Perot spectrometer for OH measurements.

Quantification of terrestrial ecosystem carbon dynamics in the conterminous United States combining a process-based biogeochemical model and MODIS and AmeriFlux data

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing provides continuous temporal and spatial information of terrestrial ecosystems. Using these remote sensing data and eddy flux measurements and biogeochemical models, such as the Terrestrial Ecosystem Model (TEM), should provide a more adequate quantification of carbon dynami...

Interfacing geographic information systems and remote sensing for rural land-use analysis

NASA Technical Reports Server (NTRS)

Nellis, M. Duane; Lulla, Kamlesh; Jensen, John

1990-01-01

Recent advances in computer-based geographic information systems (GISs) are briefly reviewed, with an emphasis on the incorporation of remote-sensing data in GISs for rural applications. Topics addressed include sampling procedures for rural land-use analyses; GIS-based mapping of agricultural land use and productivity; remote sensing of land use and agricultural, forest, rangeland, and water resources; monitoring the dynamics of irrigation agriculture; GIS methods for detecting changes in land use over time; and the development of land-use modeling strategies.

Remote sensing impact on corridor selection and placement

NASA Technical Reports Server (NTRS)

Thomson, F. J.; Sellman, A. N.

1975-01-01

Computer-aided corridor selection techniques, utilizing digitized data bases of socio-economic, census, and cadastral data, and developed for highway corridor routing are considered. Land resource data generated from various remote sensing data sources were successfully merged with the ancillary data files of a corridor selection model and prototype highway corridors were designed using the combined data set. Remote sensing derived information considered useful for highway corridor location, special considerations in geometric correction of remote sensing data to facilitate merging it with ancillary data files, and special interface requirements are briefly discussed.

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

NASA Technical Reports Server (NTRS)

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

1976-01-01

A hydrologic planning model is developed based on remotely sensed inputs. Data from LANDSAT 1 are used to supply the model's quantitative parameters and coefficients. The use of LANDSAT data as information input to all categories of hydrologic models requiring quantitative surface parameters for their effects functioning is also investigated.

A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared (TIR) remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation d...

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

USDA-ARS?s Scientific Manuscript database

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

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks.

PubMed

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-04-26

With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction.

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

NASA Technical Reports Server (NTRS)

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 intelligence based techniques. Conclusions: Remote sensing data relevant to the transmission of vectorborne infectious diseases can be conveniently accessed at NASA and some other websites. These data are useful for vectorborne infectious disease surveillance and modeling.

Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications

USDA-ARS?s Scientific Manuscript database

Remote sensing based evapotranspiration (ET) mapping has become an important tool for water resources management at a regional scale. Accurate hourly climatic data and reference ET are crucial input for successfully implementing remote sensing based ET models such as Mapping ET with internal calibra...

Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications

USDA-ARS?s Scientific Manuscript database

Remote sensing based evapotranspiration (ET) mapping is an important improvement for water resources management. Hourly climatic data and reference ET are crucial for implementing remote sensing based ET models such as METRIC and SEBAL. In Turkey, data on all climatic variables may not be available ...

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Soil Moisture Retrieval Using Convolutional Neural Networks: Application to Passive Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Hu, Z.; Xu, L.; Yu, B.

2018-04-01

A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE)

PubMed Central

Tian, Xin; Li, Zengyuan; Chen, Erxue; Liu, Qinhuo; Yan, Guangjian; Wang, Jindi; Niu, Zheng; Zhao, Shaojie; Li, Xin; Pang, Yong; Su, Zhongbo; van der Tol, Christiaan; Liu, Qingwang; Wu, Chaoyang; Xiao, Qing; Yang, Le; Mu, Xihan; Bo, Yanchen; Qu, Yonghua; Zhou, Hongmin; Gao, Shuai; Chai, Linna; Huang, Huaguo; Fan, Wenjie; Li, Shihua; Bai, Junhua; Jiang, Lingmei; Zhou, Ji

2015-01-01

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques. PMID:26332035

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

PubMed Central

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2018-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions. PMID:29657544

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards.

PubMed

Wright, Daniel B; Mantilla, Ricardo; Peters-Lidard, Christa D

2017-04-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

NASA Technical Reports Server (NTRS)

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2017-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, Rainy Day can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, Rainy Day can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. Rainy Day can be useful for hazard modeling under nonstationary conditions.

Remote-sensing supported monitoring of global biodiversity change

NASA Astrophysics Data System (ADS)

Jetz, W.; Tuanmu, M. N.; W, A.; Melton, F. S.; Parmentier, B.; Amatulli, G.; Guzman, A.

2016-12-01

Remote sensing combined with biodiversity observation offers an unrivalled tool for understanding and predicting species distributions and their changes at the planetary scale. I will illustrate recently developed high-resolution remote-sensing based layers targeted for spatiotemporal biodiversity modeling, addressing climate, environment, topography, and habitat heterogeneity. In particular, I will illustrate the development and use of global MODIS-derived environmental layers for biodiversity assessment and change monitoring. Remote-sensing based capture of these putative predictors of biodiversity dynamics provides more a reliable signal than spatially interpolated layers and avoids inflated spatial autocorrelation. The layers result in more accurate models of species occurrence and are more readily able to address the scale of processes underpinning species distributions, e.g. when combined with emerging hierarchical, cross-scale models. I illustrate the multiple ways in which this type of information, based on continuously collected data, supports the prediction of not just spatial but also temporal variation in biodiversity. Using implementations in the Map of Life infrastructure I will showcase new indicators of species distribution and change that demonstrate these new opportunities.

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Ni; Gu, Lianhong; Black, T. Andrew

Here, soil respiration (R s), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual R s at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual R s estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zonemore » soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites.« less

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

NASA Technical Reports Server (NTRS)

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.

Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

NASA Astrophysics Data System (ADS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie

2014-03-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps.

Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains, northwest China

NASA Astrophysics Data System (ADS)

Wu, X.; Shen, Y.; Wang, N.; Pan, X.; Zhang, W.; He, J.; Wang, G.

2017-12-01

Snowmelt water is an important freshwater resource in the Altay Mountains in northwest China, and it is also crucial for local ecological system, economic and social sustainable development; however, warming climate and rapid spring snowmelt can cause floods that endanger both eco-environment and public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature-index model based on remote sensing coupled with high-resolution meteorological data obtained from NCEP reanalysis fields that were downscaled using Weather Research Forecasting model, then bias-corrected using a statistical downscaled model. Validation of the forcing data revealed that the high-resolution meteorological fields derived from downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of temperature-index model based on remote sensing were calibrated for spring 2014, and model performance was validated using MODIS snow cover and snow observations from spring 2012. The results show that the temperature-index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash-Sutchliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt runoff was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt runoff accounts for 72% of spring runoff and 21% of annual runoff. Snowmelt is the main source of runoff for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt runoff predictions, so as to prevent snowmelt-induced floods, and also provide a generalizable approach that can be applied to other remote locations where high-density, long-term observational data is lacking.

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

NASA Technical Reports Server (NTRS)

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.

Integration of Remote Sensing Data In Operational Flood Forecast In Southwest Germany

NASA Astrophysics Data System (ADS)

Bach, H.; Appel, F.; Schulz, W.; Merkel, U.; Ludwig, R.; Mauser, W.

Methods to accurately assess and forecast flood discharge are mandatory to minimise the impact of hydrological hazards. However, existing rainfall-runoff models rarely accurately consider the spatial characteristics of the watershed, which is essential for a suitable and physics-based description of processes relevant for runoff formation. Spatial information with low temporal variability like elevation, slopes and land use can be mapped or extracted from remote sensing data. However, land surface param- eters of high temporal variability, like soil moisture and snow properties are hardly available and used in operational forecasts. Remote sensing methods can improve flood forecast by providing information on the actual water retention capacities in the watershed and facilitate the regionalisation of hydrological models. To prove and demonstrate this, the project 'InFerno' (Integration of remote sensing data in opera- tional water balance and flood forecast modelling) has been set up, funded by DLR (50EE0053). Within InFerno remote sensing data (optical and microwave) are thor- oughly processed to deliver spatially distributed parameters of snow properties and soil moisture. Especially during the onset of a flood this information is essential to estimate the initial conditions of the model. At the flood forecast centres of 'Baden- Württemberg' and 'Rheinland-Pfalz' (Southwest Germany) the remote sensing based maps on soil moisture and snow properties will be integrated in the continuously op- erated water balance and flood forecast model LARSIM. The concept is to transfer the developed methodology from the Neckar to the Mosel basin. The major challenges lie on the one hand in the implementation of algorithms developed for a multisensoral synergy and the creation of robust, operationally applicable remote sensing products. On the other hand, the operational flood forecast must be adapted to make full use of the new data sources. In the operational phase of the project ESA's ENVISAT satellite, which will be launched in 2002, will serve as remote sensing data source. Until EN- VISAT data is available, algorithm retrieval, software development and product gener- ation is performed using existing sensors with ENVISAT-like specifications. Based on these data sets test cases and demonstration runs are conducted and will be presented to prove the advantages of the approach.

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

NASA Astrophysics Data System (ADS)

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

2014-08-01

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

Remote sensing image segmentation based on Hadoop cloud platform

NASA Astrophysics Data System (ADS)

Li, Jie; Zhu, Lingling; Cao, Fubin

2018-01-01

To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

Integration of remote sensing and hydrologic modeling through multi-disciplinary semiarid field campaigns: Moonsoon 1990, Walnut Gulch 1992, and SALSA-MEX

NASA Technical Reports Server (NTRS)

Moran, M. S.; Goodrich, D. C.; Kustas, W. P.

1994-01-01

A research and modeling strategy is presented for development of distributed hydrologic models given by a combination of remotely sensed and ground based data. In support of this strategy, two experiments Moonsoon'90 and Walnut Gulch'92 were conducted in a semiarid rangeland southeast of Tucson, Arizona, (U.S.) and a third experiment, the SALSA-MEX (Semi Arid Land Surface Atmospheric Mountain Experiment) was proposed. Results from the Moonsoon'90 experiment substantially advanced the understanding of the hydrologic and atmospheric fluxes in an arid environment and provided insight into the use of remote sensing data for hydrologic modeling. The Walnut Gulch'92 experiment addressed the seasonal hydrologic dynamics of the region and the potential of combined optical microwave remote sensing for hydrologic applications. SALSA-MEX will combine measurements and modeling to study hydrologic processes influenced by surrounding mountains, such as enhanced precipitation, snowmelt and recharge to ground water aquifers. The results from these experiments, along with the extensive experimental data bases, should aid the research community in large scale modeling of mass and energy exchanges across the soil-plant-atmosphere interface.

Radiative transfer model for aerosols in infrared wavelengths for passive remote sensing applications.

PubMed

Ben-David, Avishai; Embury, Janon F; Davidson, Charles E

2006-09-10

A comprehensive analytical radiative transfer model for isothermal aerosols and vapors for passive infrared remote sensing applications (ground-based and airborne sensors) has been developed. The theoretical model illustrates the qualitative difference between an aerosol cloud and a chemical vapor cloud. The model is based on two and two/four stream approximations and includes thermal emission-absorption by the aerosols; scattering of diffused sky radiances incident from all sides on the aerosols (downwelling, upwelling, left, and right); and scattering of aerosol thermal emission. The model uses moderate resolution transmittance ambient atmospheric radiances as boundary conditions and provides analytical expressions for the information on the aerosol cloud that is contained in remote sensing measurements by using thermal contrasts between the aerosols and diffused sky radiances. Simulated measurements of a ground-based sensor viewing Bacillus subtilis var. niger bioaerosols and kaolin aerosols are given and discussed to illustrate the differences between a vapor-only model (i.e., only emission-absorption effects) and a complete model that adds aerosol scattering effects.

Comparing near-earth and satellite remote sensing based phenophase estimates: an analysis using multiple webcams and MODIS (Invited)

NASA Astrophysics Data System (ADS)

Hufkens, K.; Richardson, A. D.; Migliavacca, M.; Frolking, S. E.; Braswell, B. H.; Milliman, T.; Friedl, M. A.

2010-12-01

In recent years several studies have used digital cameras and webcams to monitor green leaf phenology. Such "near-surface" remote sensing has been shown to be a cost effective means of accurately capturing phenology. Specifically, it allows for accurate tracking of intra- and inter-annual phenological dynamics at high temporal frequency and over broad spatial scales compared to visual observations or tower-based fAPAR and broadband NDVI measurements. Near surface remote sensing measurements therefore show promise for bridging the gap between traditional in-situ measurements of phenology and satellite remote sensing data. For this work, we examined the relationship between phenophase estimates derived from satellite remote sensing (MODIS) and near-earth remote sensing derived from webcams for a select set of sites with high-quality webcam data. A logistic model was used to characterize phenophases for both the webcam and MODIS data. We documented model fit accuracy, phenophase estimates, and model biases for both data sources. Our results show that different vegetation indices (VI's) derived from MODIS produce significantly different phenophase estimates compared to corresponding estimates derived from webcam data. Different VI's showed markedly different radiometric properties, and as a result, influenced phenophase estimates. The study shows that phenophase estimates are not only highly dependent on the algorithm used but also depend on the VI used by the phenology retrieval algorithm. These results highlight the need for a better understanding of how near-earth and satellite remote data relate to eco-physiological and canopy changes during different parts of the growing season.

Remote sensing of rangeland biodiversity

USDA-ARS?s Scientific Manuscript database

Rangelands are managed based on state and transition models for an ecological site. Transitions to alternative ecological states are indicative of degrading rangelands. Three key variables may be remotely sensed to detect transitions between alternative states: amount of bare soil, presence of inva...

Remote Sensing of Surficial Process Responses to Extreme Meteorological Events

NASA Technical Reports Server (NTRS)

Brakenridge, G. Robert

1997-01-01

Changes in the frequency and magnitude of extreme meteorological events are associated with changing environmental means. Such events are important in human affairs, and can also be investigated by orbital remote sensing. During the course of this project, we applied ERS-1, ERS-2, Radarsat, and an airborne sensor (AIRSAR-TOPSAR) to measure flood extents, flood water surface profiles, and flood depths. We established a World Wide Web site (the Dartmouth Flood Observatory) for publishing remote sensing-based maps of contemporary floods worldwide; this is also an online "active archive" that presently constitutes the only global compilation of extreme flood events. We prepared an article for EOS concerning SAR imaging of the Mississippi Valley flood; an article for the International Journal of Remote Sensing on measurement of a river flood wave using ERS-2, began work on an article (since completed and published) on the Flood Observatory for a Geoscience Information Society Proceedings volume, and presented lectures at several Geol. Soc. of America Natl. Meetings, an Assoc. of Amer. Geographers Natl. Meeting, and a Binghamton Geomorphology Symposium (all on SAR remote sensing of the Mississippi Valley flood). We expanded in-house modeling capabilities by installing the latest version of the Army Corps of Engineers RMA two-dimensional hydraulics software and BYU Engineering Graphics Lab's Surface Water Modeling System (finite elements based pre- and post-processors for RMA work) and also added watershed modeling software. We are presently comparing the results of the 2-d flow models with SAR image data. The grant also supported several important upgrades of pc-based remote sensing infrastructure at Dartmouth. During work on this grant, we collaborated with several workers at the U.S. Army Corps of Engineers, Remote Sensing/GIS laboratory (for flood inundation mapping and modeling; particularly of the Illinois River using the AIRSAR/TOPSAR/ERS-2 combined data), with Dr. Karen Prestegaard at the University of Maryland (geomorphological responses to the extreme 1993 flood along the Raccoon drainage in central Iowa), and with Mr Tim Scrom of the Albany National Weather Service River Forecast Center (initial planning for the use of Radarsat and ERS-2 for flood warning). The work thus initiated with this proposal is continuing.

Remote estimation of a managed pine forest evapotranspiration with geospatial technology

Treesearch

S. Panda; D.M. Amatya; G Sun; A. Bowman

2016-01-01

Remote sensing has increasingly been used to estimate evapotranspiration (ET) and its supporting parameters in a rapid, accurate, and cost-effective manner. The goal of this study was to develop remote sensing-based models for estimating ET and the biophysical parameters canopy conductance (gc), upper-canopy temperature, and soil moisture for a mature loblolly pine...

Remote sensing new model for monitoring the east Asian migratory locust infections based on its breeding circle

NASA Astrophysics Data System (ADS)

Han, Xiuzhen; Ma, Jianwen; Bao, Yuhai

2006-12-01

Currently the function of operational locust monitor system mainly focused on after-hazards monitoring and assessment, and to found the way effectively to perform early warning and prediction has more practical meaning. Through 2001, 2002 two years continuously field sample and statistics for locusts eggs hatching, nymph growth, adults 3 phases observation, sample statistics and calculation, spectral measurements as well as synchronically remote sensing data processing we raise the view point of Remote Sensing three stage monitor the locust hazards. Based on the point of view we designed remote sensing monitor in three stages: (1) during the egg hitching phase remote sensing can retrieve parameters of land surface temperature (LST) and soil moisture; (2) during nymph growth phase locust increases appetite greatly and remote sensing can calculate vegetation index, leaf area index, vegetation cover and analysis changes; (3) during adult phase the locust move and assembly towards ponds and water ditches as well as less than 75% vegetation cover areas and remote sensing combination with field data can monitor and predicts potential areas for adult locusts to assembly. In this way the priority of remote sensing technology is elaborated effectively and it also provides technique support for the locust monitor system. The idea and techniques used in the study can also be used as reference for other plant diseases and insect pests.

Modeling, simulation, and analysis of optical remote sensing systems

NASA Technical Reports Server (NTRS)

Kerekes, John Paul; Landgrebe, David A.

1989-01-01

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

[Review of estimation on oceanic primary productivity by using remote sensing methods.

PubMed

Xu, Hong Yun; Zhou, Wei Feng; Ji, Shi Jian

2016-09-01

Accuracy estimation of oceanic primary productivity is of great significance in the assessment and management of fisheries resources, marine ecology systems, global change and other fields. The traditional measurement and estimation of oceanic primary productivity has to rely on in situ sample data by vessels. Satellite remote sensing has advantages of providing dynamic and eco-environmental parameters of ocean surface at large scale in real time. Thus, satellite remote sensing has increasingly become an important means for oceanic primary productivity estimation on large spatio-temporal scale. Combining with the development of ocean color sensors, the models to estimate the oceanic primary productivity by satellite remote sensing have been developed that could be mainly summarized as chlorophyll-based, carbon-based and phytoplankton absorption-based approach. The flexibility and complexity of the three kinds of models were presented in the paper. On this basis, the current research status for global estimation of oceanic primary productivity was analyzed and evaluated. In view of these, four research fields needed to be strengthened in further stu-dy: 1) Global oceanic primary productivity estimation should be segmented and studied, 2) to dee-pen the research on absorption coefficient of phytoplankton, 3) to enhance the technology of ocea-nic remote sensing, 4) to improve the in situ measurement of primary productivity.

The added value of remote sensing products in constraining hydrological models

NASA Astrophysics Data System (ADS)

Nijzink, Remko C.; Almeida, Susana; Pechlivanidis, Ilias; Capell, René; Gustafsson, David; Arheimer, Berit; Freer, Jim; Han, Dawei; Wagener, Thorsten; Sleziak, Patrik; Parajka, Juraj; Savenije, Hubert; Hrachowitz, Markus

2017-04-01

The calibration of a hydrological model still depends on the availability of streamflow data, even though more additional sources of information (i.e. remote sensed data products) have become more widely available. In this research, the model parameters of four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) were constrained with remotely sensed products. The models were applied over 27 catchments across Europe to cover a wide range of climates, vegetation and landscapes. The fluxes and states of the models were correlated with the relevant products (e.g. MOD10A snow with modelled snow states), after which new a-posteriori parameter distributions were determined based on a weighting procedure using conditional probabilities. Briefly, each parameter was weighted with the coefficient of determination of the relevant regression between modelled states/fluxes and products. In this way, final feasible parameter sets were derived without the use of discharge time series. Initial results show that improvements in model performance, with regard to streamflow simulations, are obtained when the models are constrained with a set of remotely sensed products simultaneously. In addition, we present a more extensive analysis to assess a model's ability to reproduce a set of hydrological signatures, such as rising limb density or peak distribution. Eventually, this research will enhance our understanding and recommendations in the use of remotely sensed products for constraining conceptual hydrological modelling and improving predictive capability, especially for data sparse regions.

Integrating ecosystem sampling, gradient modeling, remote sensing, and ecosystem simulation to create spatially explicit landscape inventories

Treesearch

Robert E. Keane; Matthew G. Rollins; Cecilia H. McNicoll; Russell A. Parsons

2002-01-01

Presented is a prototype of the Landscape Ecosystem Inventory System (LEIS), a system for creating maps of important landscape characteristics for natural resource planning. This system uses gradient-based field inventories coupled with gradient modeling remote sensing, ecosystem simulation, and statistical analyses to derive spatial data layers required for ecosystem...

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

USDA-ARS?s Scientific Manuscript database

This study investigates the effect of land use on the Geomorphological Cascade of unequal Linear Reservoirs (GCUR) model. We use the Normalized Difference Vegetation Index (NDVI) derived from remotely sensed data as a measure of land use. Our approach has two important aspects: (i) it considers the ...

Analysis and modeling of atmospheric turbulence on the high-resolution space optical systems

NASA Astrophysics Data System (ADS)

Lili, Jiang; Chen, Xiaomei; Ni, Guoqiang

2016-09-01

Modeling and simulation of optical remote sensing system plays an unslightable role in remote sensing mission predictions, imaging system design, image quality assessment. It has already become a hot research topic at home and abroad. Atmospheric turbulence influence on optical systems is attached more and more importance to as technologies of remote sensing are developed. In order to study the influence of atmospheric turbulence on earth observation system, the atmospheric structure parameter was calculated by using the weak atmospheric turbulence model; and the relationship of the atmospheric coherence length and high resolution remote sensing optical system was established; then the influence of atmospheric turbulence on the coefficient r0h of optical remote sensing system of ground resolution was derived; finally different orbit height of high resolution optical system imaging quality affected by atmospheric turbulence was analyzed. Results show that the influence of atmospheric turbulence on the high resolution remote sensing optical system, the resolution of which has reached sub meter level meter or even the 0.5m, 0.35m and even 0.15m ultra in recent years, image quality will be quite serious. In the above situation, the influence of the atmospheric turbulence must be corrected. Simulation algorithms of PSF are presented based on the above results. Experiment and analytical results are posted.

Remote sensing of high-latitude ionization profiles by ground-based and spaceborne instrumentation

NASA Technical Reports Server (NTRS)

Vondrak, R. R.

1981-01-01

Ionospheric specification and modeling are now largely based on data provided by active remote sensing with radiowave techniques (ionosondes, incoherent-scatter radars, and satellite beacons). More recently, passive remote sensing techniques have been developed that can be used to monitor quantitatively the spatial distribution of high-latitude E-region ionization. These passive methods depend on the measurement, or inference, of the energy distribution of precipitating kilovolt electrons, the principal source of the nighttime E-region at high latitudes. To validate these techniques, coordinated measurements of the auroral ionosphere have been made with the Chatanika incoherent-scatter radar and a variety of ground-based and spaceborne sensors

International Symposium on Remote Sensing of Environment, Third Thematic Conference: Remote Sensing for Exploration Geology, Colorado Springs, CO, April 16-19, 1984, Proceedings. Volumes 1 & 2

NASA Technical Reports Server (NTRS)

1985-01-01

A photogeologic and remote sensing model of porphyry type mineral sytems is considered along with a Landsat application to development of a tectonic model for hydrocarbon exploration of Devonian shales in west-central Virginia, remote sensing and the funnel philosophy, Landsat-based tectonic and metallogenic synthesis of the southwest United States, and an evolving paradigm for computer vision. Attention is given to the neotectonics of the Tibetan plateau deduced from Landsat MSS image interpretation, remote sensing in northern Arizona, the use of an airborne laser system for vegetation inventories and geobotanical prospecting, an evaluation of Thematic Mapper data for hydrocarbon exploration in low-relief basins, and an evaluation of the information content of high spectral resolution imagery. Other topics explored are related to a major source of new radar data for exploration research, the accuracy of geologic maps produced from Landsat data, and an approach for the geometric rectification of radar imagery.

Proxies for soil organic carbon derived from remote sensing

NASA Astrophysics Data System (ADS)

Rasel, S. M. M.; Groen, T. A.; Hussin, Y. A.; Diti, I. J.

2017-07-01

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

Estimation of Snow Parameters Based on Passive Microwave Remote Sensing and Meteorological Information

NASA Technical Reports Server (NTRS)

Tsang, Leung; Hwang, Jenq-Neng

1996-01-01

A method to incorporate passive microwave remote sensing measurements within a spatially distributed snow hydrology model to provide estimates of the spatial distribution of Snow Water Equivalent (SWE) as a function of time is implemented. The passive microwave remote sensing measurements are at 25 km resolution. However, in mountain regions the spatial variability of SWE over a 25 km footprint is large due to topographic influences. On the other hand, the snow hydrology model has built-in topographic information and the capability to estimate SWE at a 1 km resolution. In our work, the snow hydrology SWE estimates are updated and corrected using SSM/I passive microwave remote sensing measurements. The method is applied to the Upper Rio Grande River Basin in the mountains of Colorado. The change in prediction of SWE from hydrology modeling with and without updating is compared with measurements from two SNOTEL sites in and near the basin. The results indicate that the method incorporating the remote sensing measurements into the hydrology model is able to more closely estimate the temporal evolution of the measured values of SWE as a function of time.

Space-Based Remote Sensing of Atmospheric Aerosols: The Multi-Angle Spectro-Polarimetric Frontier

NASA Technical Reports Server (NTRS)

Kokhanovsky, A. A.; Davis, A. B.; Cairns, B.; Dubovik, O.; Hasekamp, O. P.; Sano, I.; Mukai, S.; Rozanov, V. V.; Litvinov, P.; Lapyonok, T.;

[Modeling continuous scaling of NDVI based on fractal theory].

PubMed

Luan, Hai-Jun; Tian, Qing-Jiu; Yu, Tao; Hu, Xin-Li; Huang, Yan; Du, Ling-Tong; Zhao, Li-Min; Wei, Xi; Han, Jie; Zhang, Zhou-Wei; Li, Shao-Peng

2013-07-01

Scale effect was one of the very important scientific problems of remote sensing. The scale effect of quantitative remote sensing can be used to study retrievals' relationship between different-resolution images, and its research became an effective way to confront the challenges, such as validation of quantitative remote sensing products et al. Traditional up-scaling methods cannot describe scale changing features of retrievals on entire series of scales; meanwhile, they are faced with serious parameters correction issues because of imaging parameters' variation of different sensors, such as geometrical correction, spectral correction, etc. Utilizing single sensor image, fractal methodology was utilized to solve these problems. Taking NDVI (computed by land surface radiance) as example and based on Enhanced Thematic Mapper Plus (ETM+) image, a scheme was proposed to model continuous scaling of retrievals. Then the experimental results indicated that: (a) For NDVI, scale effect existed, and it could be described by fractal model of continuous scaling; (2) The fractal method was suitable for validation of NDVI. All of these proved that fractal was an effective methodology of studying scaling of quantitative remote sensing.

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

NASA Astrophysics Data System (ADS)

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

2007-05-01

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

Filtering method of star control points for geometric correction of remote sensing image based on RANSAC algorithm

NASA Astrophysics Data System (ADS)

Tan, Xiangli; Yang, Jungang; Deng, Xinpu

2018-04-01

In the process of geometric correction of remote sensing image, occasionally, a large number of redundant control points may result in low correction accuracy. In order to solve this problem, a control points filtering algorithm based on RANdom SAmple Consensus (RANSAC) was proposed. The basic idea of the RANSAC algorithm is that using the smallest data set possible to estimate the model parameters and then enlarge this set with consistent data points. In this paper, unlike traditional methods of geometric correction using Ground Control Points (GCPs), the simulation experiments are carried out to correct remote sensing images, which using visible stars as control points. In addition, the accuracy of geometric correction without Star Control Points (SCPs) optimization is also shown. The experimental results show that the SCPs's filtering method based on RANSAC algorithm has a great improvement on the accuracy of remote sensing image correction.

a model based on crowsourcing for detecting natural hazards

NASA Astrophysics Data System (ADS)

Duan, J.; Ma, C.; Zhang, J.; Liu, S.; Liu, J.

2015-12-01

Remote Sensing Technology provides a new method for the detecting,early warning,mitigation and relief of natural hazards. Given the suddenness and the unpredictability of the location of natural hazards as well as the actual demands for hazards work, this article proposes an evaluation model for remote sensing detecting of natural hazards based on crowdsourcing. Firstly, using crowdsourcing model and with the help of the Internet and the power of hundreds of millions of Internet users, this evaluation model provides visual interpretation of high-resolution remote sensing images of hazards area and collects massive valuable disaster data; secondly, this evaluation model adopts the strategy of dynamic voting consistency to evaluate the disaster data provided by the crowdsourcing workers; thirdly, this evaluation model pre-estimates the disaster severity with the disaster pre-evaluation model based on regional buffers; lastly, the evaluation model actuates the corresponding expert system work according to the forecast results. The idea of this model breaks the boundaries between geographic information professionals and the public, makes the public participation and the citizen science eventually be realized, and improves the accuracy and timeliness of hazards assessment results.

Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

NASA Astrophysics Data System (ADS)

Jaafar, H. H.; Ahmad, F. A.

2015-04-01

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.

The NASA CYGNSS mission: a pathfinder for GNSS scatterometry remote sensing applications

NASA Astrophysics Data System (ADS)

Rose, Randy; Gleason, Scott; Ruf, Chris

2014-10-01

Global Navigation Satellite System (GNSS) based scatterometry offers breakthrough opportunities for wave, wind, ice, and soil moisture remote sensing. Recent developments in electronics and nano-satellite technologies combined with modeling techniques developed over the past 20 years are enabling a new class of remote sensing capabilities that present more cost effective solutions to existing problems while opening new applications of Earth remote sensing. Key information about the ocean and global climate is hidden from existing space borne observatories because of the frequency band in which they operate. Using GNSS-based bi-static scatterometry performed by a constellation of microsatellites offers remote sensing of ocean wave, wind, and ice data with unprecedented temporal resolution and spatial coverage across the full dynamic range of ocean wind speeds in all precipitating conditions. The NASA Cyclone Global Navigation Satellite System (CYGNSS) is a space borne mission being developed to study tropical cyclone inner core processes. CYGNSS consists of 8 GPS bi-static radar receivers to be deployed on separate micro-satellites in October 2016. CYGNSS will provide data to address what are thought to be the principle deficiencies with current tropical cyclone intensity forecasts: inadequate observations and modeling of the inner core. The inadequacy in observations results from two causes: 1) Much of the inner core ocean surface is obscured from conventional remote sensing instruments by intense precipitation in the eye wall and inner rain bands. 2) The rapidly evolving (genesis and intensification) stages of the tropical cyclone life cycle are poorly sampled in time by conventional polar-orbiting, wide-swath surface wind imagers. It is anticipated that numerous additional Earth science applications can also benefit from the cost effective high spatial and temporal sampling capabilities of GNSS remote sensing. These applications include monitoring of rough and dangerous sea states, global observations of sea ice cover and extent, meso-scale ocean circulation studies, and near surface soil moisture observations. This presentation provides a primer for GNSS based scatterometry, an overview of NASA's CYGNSS mission and its expected performance, as well as a summary of possible other GNSS based remote sensing applications.

A component-based system for agricultural drought monitoring by remote sensing.

PubMed

Dong, Heng; Li, Jun; Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China's Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring.

A component-based system for agricultural drought monitoring by remote sensing

PubMed Central

Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China’s Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring. PMID:29236700

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

PubMed

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.

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

PubMed Central

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

2017-01-01

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. PMID:28587299

Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Lixia; Pei, Jihong; Xie, Weixin; Liu, Jinyuan

2018-03-01

Large-scale oceansat remote sensing images cover a big area sea surface, which fluctuation can be considered as a non-stationary process. Short-Time Fourier Transform (STFT) is a suitable analysis tool for the time varying nonstationary signal. In this paper, a novel ship detection method using 2-D STFT sea background statistical modeling for large-scale oceansat remote sensing images is proposed. First, the paper divides the large-scale oceansat remote sensing image into small sub-blocks, and 2-D STFT is applied to each sub-block individually. Second, the 2-D STFT spectrum of sub-blocks is studied and the obvious different characteristic between sea background and non-sea background is found. Finally, the statistical model for all valid frequency points in the STFT spectrum of sea background is given, and the ship detection method based on the 2-D STFT spectrum modeling is proposed. The experimental result shows that the proposed algorithm can detect ship targets with high recall rate and low missing rate.

Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review.

PubMed

Maes, W H; Steppe, K

2012-08-01

As evaporation of water is an energy-demanding process, increasing evapotranspiration rates decrease the surface temperature (Ts) of leaves and plants. Based on this principle, ground-based thermal remote sensing has become one of the most important methods for estimating evapotranspiration and drought stress and for irrigation. This paper reviews its application in agriculture. The review consists of four parts. First, the basics of thermal remote sensing are briefly reviewed. Second, the theoretical relation between Ts and the sensible and latent heat flux is elaborated. A modelling approach was used to evaluate the effect of weather conditions and leaf or vegetation properties on leaf and canopy temperature. Ts increases with increasing air temperature and incoming radiation and with decreasing wind speed and relative humidity. At the leaf level, the leaf angle and leaf dimension have a large influence on Ts; at the vegetation level, Ts is strongly impacted by the roughness length; hence, by canopy height and structure. In the third part, an overview of the different ground-based thermal remote sensing techniques and approaches used to estimate drought stress or evapotranspiration in agriculture is provided. Among other methods, stress time, stress degree day, crop water stress index (CWSI), and stomatal conductance index are discussed. The theoretical models are used to evaluate the performance and sensitivity of the most important methods, corroborating the literature data. In the fourth and final part, a critical view on the future and remaining challenges of ground-based thermal remote sensing is presented.

Predicting relative species composition within mixed conifer forest pixels using zero‐inflated models and Landsat imagery

Treesearch

Shannon L. Savage; Rick L. Lawrence; John R. Squires

2015-01-01

Ecological and land management applications would often benefit from maps of relative canopy cover of each species present within a pixel, instead of traditional remote-sensing based maps of either dominant species or percent canopy cover without regard to species composition. Widely used statistical models for remote sensing, such as randomForest (RF),...

A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States.

PubMed

Beckerman, Bernardo S; Jerrett, Michael; Serre, Marc; Martin, Randall V; Lee, Seung-Jae; van Donkelaar, Aaron; Ross, Zev; Su, Jason; Burnett, Richard T

2013-07-02

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States.

PubMed

Lee, Seung-Jae; Serre, Marc L; van Donkelaar, Aaron; Martin, Randall V; Burnett, Richard T; Jerrett, Michael

2012-12-01

A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation. We developed a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals. The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.

Strategies for using remotely sensed data in hydrologic models

NASA Technical Reports Server (NTRS)

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.

Survey of in-situ and remote sensing methods for soil moisture determination

NASA Technical Reports Server (NTRS)

Schmugge, T. J.; Jackson, T. J.; Mckim, H. L.

1981-01-01

General methods for determining the moisture content in the surface layers of the soil based on in situ or point measurements, soil water models and remote sensing observations are surveyed. In situ methods described include gravimetric techniques, nuclear techniques based on neutron scattering or gamma-ray attenuation, electromagnetic techniques, tensiometric techniques and hygrometric techniques. Soil water models based on column mass balance treat soil moisture contents as a result of meteorological inputs (precipitation, runoff, subsurface flow) and demands (evaporation, transpiration, percolation). The remote sensing approaches are based on measurements of the diurnal range of surface temperature and the crop canopy temperature in the thermal infrared, measurements of the radar backscattering coefficient in the microwave region, and measurements of microwave emission or brightness temperature. Advantages and disadvantages of the various methods are pointed out, and it is concluded that a successful monitoring system must incorporate all of the approaches considered.

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

NASA Technical Reports Server (NTRS)

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 and value of the chlorophyll-specific absorption coefficient. The simulation was tested for a wide range of water types, including waters from Monterey Bay, the West Florida Shelf, and the Mississippi River plume. Using the simulation, the R(sub rs)-derived in-water absorption coefficients were consistent with the values from in-water measurements (r(exp 2) greater than 0.94, slope approximately 1.0). In the remote-sensing applications, a new approach is suggested for the estimation of primary production based on remote sensing. Using this approach, the calculated primary production (PP) values based upon remotely sensed data were very close to the measured values for the euphotic zone (r(exp 2) = 0.95, slope 1.26, and 32% average difference), while traditional, pigment-based PP model provided values only one-third the size of the measured data. This indicates a potential to significantly improve the accuracy of the estimation of primary production based upon remote sensing.

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Methods and Applications

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.

1998-01-01

Thermal Infrared (TIR) remote sensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remote sensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remote sensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remote sensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remote sensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of landscape ecological processes.

An evaluation of the use of remotely sensed parameters for prediction of incidence and risk associated with Vibrio parahaemolyticus in Gulf Coast oysters (Crassostrea virginica).

PubMed

Phillips, A M B; Depaola, A; Bowers, J; Ladner, S; Grimes, D J

2007-04-01

The U.S. Food and Drug Administration recently published a Vibrio parahaemolyticus risk assessment for consumption of raw oysters that predicts V. parahaemolyticus densities at harvest based on water temperature. We retrospectively compared archived remotely sensed measurements (sea surface temperature, chlorophyll, and turbidity) with previously published data from an environmental study of V. parahaemolyticus in Alabama oysters to assess the utility of the former data for predicting V. parahaemolyticus densities in oysters. Remotely sensed sea surface temperature correlated well with previous in situ measurements (R(2) = 0.86) of bottom water temperature, supporting the notion that remotely sensed sea surface temperature data are a sufficiently accurate substitute for direct measurement. Turbidity and chlorophyll levels were not determined in the previous study, but in comparison with the V. parahaemolyticus data, remotely sensed values for these parameters may explain some of the variation in V. parahaemolyticus levels. More accurate determination of these effects and the temporal and spatial variability of these parameters may further improve the accuracy of prediction models. To illustrate the utility of remotely sensed data as a basis for risk management, predictions based on the U.S. Food and Drug Administration V. parahaemolyticus risk assessment model were integrated with remotely sensed sea surface temperature data to display graphically variations in V. parahaemolyticus density in oysters associated with spatial variations in water temperature. We believe images such as these could be posted in near real time, and that the availability of such information in a user-friendly format could be the basis for timely and informed risk management decisions.

Studies on mineral dust using airborne lidar, ground-based remote sensing, and in situ instrumentation

NASA Astrophysics Data System (ADS)

Marenco, Franco; Ryder, Claire; Estellés, Victor; Segura, Sara; Amiridis, Vassilis; Proestakis, Emmanouil; Marinou, Eleni; Tsekeri, Alexandra; Smith, Helen; Ulanowski, Zbigniew; O'Sullivan, Debbie; Brooke, Jennifer; Pradhan, Yaswant; Buxmann, Joelle

2018-04-01

In August 2015, the AER-D campaign made use of the FAAM research aircraft based in Cape Verde, and targeted mineral dust. First results will be shown here. The campaign had multiple objectives: (1) lidar dust mapping for the validation of satellite and model products; (2) validation of sunphotometer remote sensing with airborne measurements; (3) coordinated measurements with the CATS lidar on the ISS; (4) radiative closure studies; and (5) the validation of a new model of dustsonde.

2005 AG20/20 Annual Review

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; McKellip, Rodney D.

2005-01-01

Topics covered include: Implementation and Validation of Sensor-Based Site-Specific Crop Management; Enhanced Management of Agricultural Perennial Systems (EMAPS) Using GIS and Remote Sensing; Validation and Application of Geospatial Information for Early Identification of Stress in Wheat; Adapting and Validating Precision Technologies for Cotton Production in the Mid-Southern United States - 2004 Progress Report; Development of a System to Automatically Geo-Rectify Images; Economics of Precision Agriculture Technologies in Cotton Production-AG 2020 Prescription Farming Automation Algorithms; Field Testing a Sensor-Based Applicator for Nitrogen and Phosphorus Application; Early Detection of Citrus Diseases Using Machine Vision and DGPS; Remote Sensing of Citrus Tree Stress Levels and Factors; Spectral-based Nitrogen Sensing for Citrus; Characterization of Tree Canopies; In-field Sensing of Shallow Water Tables and Hydromorphic Soils with an Electromagnetic Induction Profiler; Maintaining the Competitiveness of Tree Fruit Production Through Precision Agriculture; Modeling and Visualizing Terrain and Remote Sensing Data for Research and Education in Precision Agriculture; Thematic Soil Mapping and Crop-Based Strategies for Site-Specific Management; and Crop-Based Strategies for Site-Specific Management.

Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics.

PubMed

Goddijn-Murphy, Lonneke; Peters, Steef; van Sebille, Erik; James, Neil A; Gibb, Stuart

2018-01-01

There is growing global concern over the chemical, biological and ecological impact of plastics in the ocean. Remote sensing has the potential to provide long-term, global monitoring but for marine plastics it is still in its early stages. Some progress has been made in hyperspectral remote sensing of marine macroplastics in the visible (VIS) to short wave infrared (SWIR) spectrum. We present a reflectance model of sunlight interacting with a sea surface littered with macro plastics, based on geometrical optics and the spectral signatures of plastic and seawater. This is a first step towards the development of a remote sensing algorithm for marine plastic using light reflectance measurements in air. Our model takes the colour, transparency, reflectivity and shape of plastic litter into account. This concept model can aid the design of laboratory, field and Earth observation measurements in the VIS-SWIR spectrum and explain the results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Remotely Sensed Based Lake/Reservoir Routing in Congo River Basin

NASA Astrophysics Data System (ADS)

Raoufi, R.; Beighley, E.; Lee, H.

2017-12-01

Lake and reservoir dynamics can influence local to regional water cycles but are often not well represented in hydrologic models. One challenge that limits their inclusion in models is the need for detailed storage-discharge behavior that can be further complicated in reservoirs where specific operation rules are employed. Here, the Hillslope River Routing (HRR) model is combined with a remotely sensed based Reservoir Routing (RR) method and applied to the Congo River Basin. Given that topographic data are often continuous over the entire terrestrial surface (i.e., does not differentiate between land and open water), the HRR-RR model integrates topographic derived river networks and catchment boundaries (e.g., HydroSHEDs) with water boundary extents (e.g., Global Lakes and Wetlands Database) to develop the computational framework. The catchments bordering lakes and reservoirs are partitioned into water and land portions, where representative flowpath characteristics are determined and vertical water balance and lateral routings is performed separately on each partition based on applicable process models (e.g., open water evaporation vs. evapotranspiration). To enable reservoir routing, remotely sensed water surface elevations and extents are combined to determine the storage change time series. Based on the available time series, representative storage change patterns are determined. Lake/reservoir routing is performed by combining inflows from the HRR-RR model and the representative storage change patterns to determine outflows. In this study, a suite of storage change patterns derived from remotely sensed measurements are determined representative patterns for wet, dry and average conditions. The HRR-RR model dynamically selects and uses the optimal storage change pattern for the routing process based on these hydrologic conditions. The HRR-RR model results are presented to highlight the importance of lake attenuation/routing in the Congo Basin.

HABSEED: a Simple Spatially Explicit Meta-Populations Model Using Remote Sensing Derived Habitat Quality Data

NASA Astrophysics Data System (ADS)

Heumann, B. W.; Guichard, F.; Seaquist, J. W.

2005-05-01

The HABSEED model uses remote sensing derived NPP as a surrogate for habitat quality as the driving mechanism for population growth and local seed dispersal. The model has been applied to the Sahel region of Africa. Results show that the functional response of plants to habitat quality alters population distribution. Plants more tolerant of medium quality habitat have greater distributions to the North while plants requiring only the best habitat are limited to the South. For all functional response types, increased seed production results in diminishing returns. Functional response types have been related to life history tradeoffs and r-K strategies based on the results. Results are compared to remote sensing derived vegetation land cover.

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

Treesearch

Steven H. Ackers; Raymond J. Davis; Keith A. Olsen; Katie M. Dugger

2015-01-01

Wildlife habitat mapping has evolved at a rapid pace over the last fewdecades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often...

A parallel method of atmospheric correction for multispectral high spatial resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhao, Shaoshuai; Ni, Chen; Cao, Jing; Li, Zhengqiang; Chen, Xingfeng; Ma, Yan; Yang, Leiku; Hou, Weizhen; Qie, Lili; Ge, Bangyu; Liu, Li; Xing, Jin

2018-03-01

The remote sensing image is usually polluted by atmosphere components especially like aerosol particles. For the quantitative remote sensing applications, the radiative transfer model based atmospheric correction is used to get the reflectance with decoupling the atmosphere and surface by consuming a long computational time. The parallel computing is a solution method for the temporal acceleration. The parallel strategy which uses multi-CPU to work simultaneously is designed to do atmospheric correction for a multispectral remote sensing image. The parallel framework's flow and the main parallel body of atmospheric correction are described. Then, the multispectral remote sensing image of the Chinese Gaofen-2 satellite is used to test the acceleration efficiency. When the CPU number is increasing from 1 to 8, the computational speed is also increasing. The biggest acceleration rate is 6.5. Under the 8 CPU working mode, the whole image atmospheric correction costs 4 minutes.

Use of remote sensing technology for inventorying and planning utilization of land resources in South Dakota

NASA Technical Reports Server (NTRS)

1974-01-01

A comprehensive land use planning process model is being developed in Meade County, South Dakota, using remote sensing technology. The proper role of remote sensing in the land use planning process is being determined by interaction of remote sensing specialists with local land use planners. The data that were collected by remote sensing techniques are as follows: (1) level I land use data interpreted at a scale of 1:250,000 from false color enlargement prints of ERTS-1 color composite transparencies; (2) detailed land use data interpreted at a scale of 1:24,000 from enlargement color prints of high altitude RB-57 photography; and (3) general soils map interpreted at a scale of 1:250,000 from false color enlargement prints of ERTS-1 color composite transparencies. In addition to use of imagery as an interpretation aid, the utility of using photographs as base maps was demonstrated.

Regional yield predictions of malting barley by remote sensing and ancillary data

NASA Astrophysics Data System (ADS)

Weissteiner, Christof J.; Braun, Matthias; Kuehbauch, Walter

2004-02-01

Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Information Extraction of Tourist Geological Resources Based on 3d Visualization Remote Sensing Image

NASA Astrophysics Data System (ADS)

Wang, X.

2018-04-01

Tourism geological resources are of high value in admiration, scientific research and universal education, which need to be protected and rationally utilized. In the past, most of the remote sensing investigations of tourism geological resources used two-dimensional remote sensing interpretation method, which made it difficult for some geological heritages to be interpreted and led to the omission of some information. This aim of this paper is to assess the value of a method using the three-dimensional visual remote sensing image to extract information of geological heritages. skyline software system is applied to fuse the 0.36 m aerial images and 5m interval DEM to establish the digital earth model. Based on the three-dimensional shape, color tone, shadow, texture and other image features, the distribution of tourism geological resources in Shandong Province and the location of geological heritage sites were obtained, such as geological structure, DaiGu landform, granite landform, Volcanic landform, sandy landform, Waterscapes, etc. The results show that using this method for remote sensing interpretation is highly recognizable, making the interpretation more accurate and comprehensive.

An analytical model for subsurface irradiance and remote sensing reflectance in deep and shallow case-2 waters.

PubMed

Albert, A; Mobley, C

2003-11-03

Subsurface remote sensing signals, represented by the irradiance re fl ectance and the remote sensing re fl ectance, were investigated. The present study is based on simulations with the radiative transfer program Hydrolight using optical properties of Lake Constance (German: Bodensee) based on in-situ measurements of the water constituents and the bottom characteristics. Analytical equations are derived for the irradiance re fl ectance and remote sensing re fl ectance for deep and shallow water applications. The input of the parameterization are the inherent optical properties of the water - absorption a(lambda) and backscattering bb(lambda). Additionally, the solar zenith angle thetas, the viewing angle thetav , and the surface wind speed u are considered. For shallow water applications the bottom albedo RB and the bottom depth zB are included into the parameterizations. The result is a complete set of analytical equations for the remote sensing signals R and Rrs in deep and shallow waters with an accuracy better than 4%. In addition, parameterizations of apparent optical properties were derived for the upward and downward diffuse attenuation coefficients Ku and Kd.

Online Remote Sensing Interface

NASA Technical Reports Server (NTRS)

Lawhead, Joel

2007-01-01

BasinTools Module 1 processes remotely sensed raster data, including multi- and hyper-spectral data products, via a Web site with no downloads and no plug-ins required. The interface provides standardized algorithms designed so that a user with little or no remote-sensing experience can use the site. This Web-based approach reduces the amount of software, hardware, and computing power necessary to perform the specified analyses. Access to imagery and derived products is enterprise-level and controlled. Because the user never takes possession of the imagery, the licensing of the data is greatly simplified. BasinTools takes the "just-in-time" inventory control model from commercial manufacturing and applies it to remotely-sensed data. Products are created and delivered on-the-fly with no human intervention, even for casual users. Well-defined procedures can be combined in different ways to extend verified and validated methods in order to derive new remote-sensing products, which improves efficiency in any well-defined geospatial domain. Remote-sensing products produced in BasinTools are self-documenting, allowing procedures to be independently verified or peer-reviewed. The software can be used enterprise-wide to conduct low-level remote sensing, viewing, sharing, and manipulating of image data without the need for desktop applications.

Disaster Emergency Rapid Assessment Based on Remote Sensing and Background Data

NASA Astrophysics Data System (ADS)

Han, X.; Wu, J.

2018-04-01

The period from starting to the stable conditions is an important stage of disaster development. In addition to collecting and reporting information on disaster situations, remote sensing images by satellites and drones and monitoring results from disaster-stricken areas should be obtained. Fusion of multi-source background data such as population, geography and topography, and remote sensing monitoring information can be used in geographic information system analysis to quickly and objectively assess the disaster information. According to the characteristics of different hazards, the models and methods driven by the rapid assessment of mission requirements are tested and screened. Based on remote sensing images, the features of exposures quickly determine disaster-affected areas and intensity levels, and extract key disaster information about affected hospitals and schools as well as cultivated land and crops, and make decisions after emergency response with visual assessment results.

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

NASA Astrophysics Data System (ADS)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

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

PubMed

Qin, Changbo; Jia, Yangwen; Su, Z; Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen

2008-07-29

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

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

PubMed Central

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

2008-01-01

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

A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

NASA Astrophysics Data System (ADS)

Mui, Amy B.

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

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

PubMed Central

Zheng, Guang; Moskal, L. Monika

2009-01-01

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels. PMID:22574042

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors.

PubMed

Zheng, Guang; Moskal, L Monika

2009-01-01

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.

Risk assessment of storm surge disaster based on numerical models and remote sensing

NASA Astrophysics Data System (ADS)

Liu, Qingrong; Ruan, Chengqing; Zhong, Shan; Li, Jian; Yin, Zhonghui; Lian, Xihu

2018-06-01

Storm surge is one of the most serious ocean disasters in the world. Risk assessment of storm surge disaster for coastal areas has important implications for planning economic development and reducing disaster losses. Based on risk assessment theory, this paper uses coastal hydrological observations, a numerical storm surge model and multi-source remote sensing data, proposes methods for valuing hazard and vulnerability for storm surge and builds a storm surge risk assessment model. Storm surges in different recurrence periods are simulated in numerical models and the flooding areas and depth are calculated, which are used for assessing the hazard of storm surge; remote sensing data and GIS technology are used for extraction of coastal key objects and classification of coastal land use are identified, which is used for vulnerability assessment of storm surge disaster. The storm surge risk assessment model is applied for a typical coastal city, and the result shows the reliability and validity of the risk assessment model. The building and application of storm surge risk assessment model provides some basis reference for the city development plan and strengthens disaster prevention and mitigation.

Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management

USDA-ARS?s Scientific Manuscript database

Unmanned aerial vehicles (UAV) provide a unique platform for remote sensing to monitor crop fields that complements remote sensing from satellite, aircraft and ground-based platforms. The UAV-based remote sensing is versatile at ultra-low altitude to be able to provide an ultra-high-resolution imag...

Near-earth orbital guidance and remote sensing

NASA Technical Reports Server (NTRS)

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.

A study of remote sensing as applied to regional and small watersheds. Volume 1: Summary report

NASA Technical Reports Server (NTRS)

Ambaruch, R.

1974-01-01

The accuracy of remotely sensed measurements to provide inputs to hydrologic models of watersheds is studied. A series of sensitivity analyses on continuous simulation models of three watersheds determined: (1)Optimal values and permissible tolerances of inputs to achieve accurate simulation of streamflow from the watersheds; (2) Which model inputs can be quantified from remote sensing, directly, indirectly or by inference; and (3) How accurate remotely sensed measurements (from spacecraft or aircraft) must be to provide a basis for quantifying model inputs within permissible tolerances.

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

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

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

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

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

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

Model for mapping settlements

DOEpatents

Vatsavai, Ranga Raju; Graesser, Jordan B.; Bhaduri, Budhendra L.

2016-07-05

A programmable media includes a graphical processing unit in communication with a memory element. The graphical processing unit is configured to detect one or more settlement regions from a high resolution remote sensed image based on the execution of programming code. The graphical processing unit identifies one or more settlements through the execution of the programming code that executes a multi-instance learning algorithm that models portions of the high resolution remote sensed image. The identification is based on spectral bands transmitted by a satellite and on selected designations of the image patches.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

UAV remote sensing atmospheric degradation image restoration based on multiple scattering APSF estimation

NASA Astrophysics Data System (ADS)

Qiu, Xiang; Dai, Ming; Yin, Chuan-li

2017-09-01

Unmanned aerial vehicle (UAV) remote imaging is affected by the bad weather, and the obtained images have the disadvantages of low contrast, complex texture and blurring. In this paper, we propose a blind deconvolution model based on multiple scattering atmosphere point spread function (APSF) estimation to recovery the remote sensing image. According to Narasimhan analytical theory, a new multiple scattering restoration model is established based on the improved dichromatic model. Then using the L0 norm sparse priors of gradient and dark channel to estimate APSF blur kernel, the fast Fourier transform is used to recover the original clear image by Wiener filtering. By comparing with other state-of-the-art methods, the proposed method can correctly estimate blur kernel, effectively remove the atmospheric degradation phenomena, preserve image detail information and increase the quality evaluation indexes.

Ten ways remote sensing can contribute to conservation

USGS Publications Warehouse

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

2014-01-01

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

Ten ways remote sensing can contribute to conservation.

PubMed

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 monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions? © 2014 Society for Conservation Biology.

Intelligent Detection of Structure from Remote Sensing Images Based on Deep Learning Method

NASA Astrophysics Data System (ADS)

Xin, L.

2018-04-01

Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.

Using Remote Sensing to Estimate Crop Water Use to Improve Irrigation Water Management

NASA Astrophysics Data System (ADS)

Reyes-Gonzalez, Arturo

Irrigation water is scarce. Hence, accurate estimation of crop water use is necessary for proper irrigation managements and water conservation. Satellite-based remote sensing is a tool that can estimate crop water use efficiently. Several models have been developed to estimate crop water requirement or actual evapotranspiration (ETa) using remote sensing. One of them is the Mapping EvapoTranspiration at High Resolution using Internalized Calibration (METRIC) model. This model has been compared with other methods for ET estimations including weighing lysimeters, pan evaporation, Bowen Ratio Energy Balance System (BREBS), Eddy Covariance (EC), and sap flow. However, comparison of METRIC model outputs to an atmometer for ETa estimation has not yet been attempted in eastern South Dakota. The results showed a good relationship between ETa estimated by the METRIC model and estimated with atmometer (r2 = 0.87 and RMSE = 0.65 mm day-1). However, ETa values from atmometer were consistently lower than ET a values from METRIC. The verification of remotely sensed estimates of surface variables is essential for any remote-sensing study. The relationships between LAI, Ts, and ETa estimated using the remote sensing-based METRIC model and in-situ measurements were established. The results showed good agreement between the variables measured in situ and estimated by the METRIC model. LAI showed r2 = 0.76, and RMSE = 0.59 m2 m -2, Ts had r2 = 0.87 and RMSE 1.24 °C and ETa presented r2= 0.89 and RMSE = 0.71 mm day -1. Estimation of ETa using energy balance method can be challenging and time consuming. Thus, there is a need to develop a simple and fast method to estimate ETa using minimum input parameters. Two methods were used, namely 1) an energy balance method (EB method) that used input parameters of the Landsat image, weather data, a digital elevation map, and a land cover map and 2) a Kc-NDVI method that use two input parameters: the Landsat image and weather data. A strong relationship was found between the two methods with r2 of 0.97 and RMSE of 0.37 mm day -1. Hence, the Kc-NDVI method performed well for ET a estimations, indicating that Kc-NDVI method can be a robust and reliable method to estimate ETa in a short period of time. Estimation of crop evapotranspiration (ETc) using satellite remote sensing-based vegetation index such as the Normalized Difference Vegetation Index (NDVI). The NDVI was calculated using near-infrared and red wavebands. The relationship between NDVI and tabulated Kc's was used to generate Kc maps. ETc maps were developed as an output of Kc maps multiplied by reference evapotranspiration (ETr). Daily ETc maps helped to explain the variability of crop water use during the growing season. Based on the results we can conclude that ETc maps developed from remotely sensed multispectral vegetation indices are a useful tool for quantifying crop water use at regional and field scales.

Remote-sensing based approach to forecast habitat quality under climate change scenarios.

PubMed

Requena-Mullor, Juan M; López, Enrique; Castro, Antonio J; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Remote-sensing based approach to forecast habitat quality under climate change scenarios

PubMed Central

Requena-Mullor, Juan M.; López, Enrique; Castro, Antonio J.; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios. PMID:28257501

Monitoring spatial variations in soil organic carbon using remote sensing and geographic information systems

NASA Astrophysics Data System (ADS)

Jaber, Salahuddin M.

Soil organic carbon (SOC) sequestration is a component of larger strategies to control the accumulation of greenhouse gases that may be causing global warming. To implement this approach, it is necessary to improve the methods of measuring SOC content. Among these methods are indirect remote sensing and geographic information systems (GIS) techniques that are required to provide non-intrusive, low cost, and spatially continuous information that cover large areas on a repetitive basis. The main goal of this study is to evaluate the effects of using Hyperion hyperspectral data on improving the existing remote sensing and GIS-based methodologies for rapidly, efficiently, and accurately measuring SOC content on farmland. The study area is Big Creek Watershed (BCW) in Southern Illinois. The methodology consists of compiling a GIS database (consisting of remote sensing and soil variables) for 303 composite soil samples collected from representative pixels along the Hyperion coverage area of the watershed. Stepwise procedures were used to calibrate and validate linear multiple regression models where SOC was regarded as the response and the other remote sensing and soil variables as the predictors. Two models were selected. The first was the best all variables model and the second was the best only raster variables model. Map algebra was implemented to extrapolate the best only raster variables model and produce a SOC map for the BGW. This study concluded that Hyperion data marginally improved the predictability of the existing SOC statistical models based on multispectral satellite remote sensing sensors with correlation coefficient of 0.37 and root mean square error of 3.19 metric tons/hectare to a 15-cm depth. The total SOC pool of the study area is about 225,232 metric tons to 15-cm depth. The nonforested wetlands contained the highest SOC density (34.3 metric tons/hectare/15cm) with total SOC content of about 2,003.5 metric tons to 15-cm depth, where croplands had the lowest SOC density (21.6 metric tons/hectare/15cm) with total SOC content of about 44,571.2 metric tons to 15-cm depth.

Using NASA Remote Sensing Data to Reduce Uncertainty of Land-use Transitions in Global Carbon-Climate Models

NASA Astrophysics Data System (ADS)

Chini, L. P.; Hurtt, G. C.; Frolking, S. E.; Sahajpal, R.; Potapov, P.; Hansen, M.; Fisk, J.

2016-12-01

For the 5th IPCC Assessment almost all Earth System Models (ESMs) incorporated new gridded products of land-use and land-use change that were harmonized to ensure a continuous transition from historical to future data in a consistent format for all models. However, these Land-Use Harmonization (LUH) data products are estimates, constrained with data where available, and with modeling assumptions, and the remaining challenge is to quantify, and reduce, the uncertainty in these products. At the same time, satellite remote sensing of the terrestrial biosphere has also evolved. Global-scale land cover extent and change monitoring is now possible given systematically acquired earth observation data sets, advanced characterization algorithms and data intensive computing capabilities. Here we consider: how can satellite remote sensing products be used to generate (and reduce uncertainty in) new gridded maps of land-use transitions for use in coupled carbon-climate simulations? As part of the international effort to develop the next generation of land-use datasets (LUH2), new NASA remote-sensing-based maps of global forest extent and change (Hansen et al. 2013) were used as both an added constraint and diagnostic in the LUH process. Harmonizing this remote sensing data with the LUH data was a major computational challenge involving 143 billion 30m Landsat pixels, and the simulation of over 20 billion LUH unknowns. Our approach involved first harmonizing the definitions of forest loss between the observed and simulated data for the years 2000-2012. Next, new spatial patterns of historical wood harvest were calculated to match the observed forest loss transitions while simultaneously meeting all other constraints of the model, and ensuring consistency throughout the historical time-period. After reconciling definitions and developing new wood harvest patterns the LUH2 global forest loss for the period 2000-2012 was reduced from over 8.3 million km2 to 1.78 million km2 (compared with the remote-sensing-based forest loss of 2.03 million km2). Next steps are to evaluate the ability of these land-use transitions to improve the representation of land-use-related climate forcings in ESM experiments, and to then build upon the LUH framework to incorporate additional remote-sensing data constraints.

Research on the remote sensing methods of drought monitoring in Chongqing

NASA Astrophysics Data System (ADS)

Yang, Shiqi; Tang, Yunhui; Gao, Yanghua; Xu, Yongjin

2011-12-01

There are regional and periodic droughts in Chongqing, which impacted seriously on agricultural production and people's lives. This study attempted to monitor the drought in Chongqing with complex terrain using MODIS data. First, we analyzed and compared three remote sensing methods for drought monitoring (time series of vegetation index, temperature vegetation dryness index (TVDI), and vegetation supply water index (VSWI)) for the severe drought in 2006. Then we developed a remote sensing based drought monitoring model for Chongqing by combining soil moisture data and meteorological data. The results showed that the three remote sensing based drought monitoring models performed well in detecting the occurrence of drought in Chongqing on a certain extent. However, Time Series of Vegetation Index has stronger sensitivity in time pattern but weaker in spatial pattern; although TVDI and VSWI can reflect inverse the whole process of severe drought in 2006 summer from drought occurred - increased - relieved - increased again - complete remission in spatial domain, but TVDI requires the situation of extreme drought and extreme moist both exist in study area which it is more difficult in Chongqing; VSWI is simple and practicable, which the correlation coefficient between VSWI and soil moisture data reaches significant levels. In summary, VSWI is the best model for summer drought monitoring in Chongqing.

A stochastic atmospheric model for remote sensing applications

NASA Technical Reports Server (NTRS)

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.

Remote sensing and GIS-based prediction and assessment of copper-gold resources in Thailand

NASA Astrophysics Data System (ADS)

Yang, Shasha; Wang, Gongwen; Du, Wenhui; Huang, Luxiong

2014-03-01

Quantitative integration of geological information is a frontier and hotspot of prospecting decision research in the world. The forming process of large scale Cu-Au deposits is influenced by complicated geological events and restricted by various geological factors (stratum, structure and alteration). In this paper, using Thailand's copper-gold deposit district as a case study, geological anomaly theory is used along with the typical copper and gold metallogenic model, ETM+ remote sensing images, geological maps and mineral geology database in study area are combined with GIS technique. These techniques create ore-forming information such as geological information (strata, line-ring faults, intrusion), remote sensing information (hydroxyl alteration, iron alteration, linear-ring structure) and the Cu-Au prospect targets. These targets were identified using weights of evidence model. The research results show that the remote sensing and geological data can be combined to quickly predict and assess for exploration of mineral resources in a regional metallogenic belt.

Viewing marine bacteria, their activity and response to environmental drivers from orbit: satellite remote sensing of bacteria.

PubMed

Grimes, D Jay; Ford, Tim E; Colwell, Rita R; Baker-Austin, Craig; Martinez-Urtaza, Jaime; Subramaniam, Ajit; Capone, Douglas G

2014-04-01

Satellite-based remote sensing of marine microorganisms has become a useful tool in predicting human health risks associated with these microscopic targets. Early applications were focused on harmful algal blooms, but more recently methods have been developed to interrogate the ocean for bacteria. As satellite-based sensors have become more sophisticated and our ability to interpret information derived from these sensors has advanced, we have progressed from merely making fascinating pictures from space to developing process models with predictive capability. Our understanding of the role of marine microorganisms in primary production and global elemental cycles has been vastly improved as has our ability to use the combination of remote sensing data and models to provide early warning systems for disease outbreaks. This manuscript will discuss current approaches to monitoring cyanobacteria and vibrios, their activity and response to environmental drivers, and will also suggest future directions.

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

NASA Astrophysics Data System (ADS)

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

2009-05-01

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

A revised surface resistance parameterisation for estimating latent heat flux from remotely sensed data

NASA Astrophysics Data System (ADS)

Song, Yi; Wang, Jiemin; Yang, Kun; Ma, Mingguo; Li, Xin; Zhang, Zhihui; Wang, Xufeng

2012-07-01

Estimating evapotranspiration (ET) is required for many environmental studies. Remote sensing provides the ability to spatially map latent heat flux. Many studies have developed approaches to derive spatially distributed surface energy fluxes from various satellite sensors with the help of field observations. In this study, remote-sensing-based λE mapping was conducted using a Landsat Thematic Mapper (TM) image and an Enhanced Thematic Mapper Plus (ETM+) image. The remotely sensed data and field observations employed in this study were obtained from Watershed Allied Telemetry Experimental Research (WATER). A biophysics-based surface resistance model was revised to account for water stress and temperature constraints. The precision of the results was validated using 'ground truth' data obtained by eddy covariance (EC) system. Scale effects play an important role, especially for parameter optimisation and validation of the latent heat flux (λE). After considering the footprint of EC, the λE derived from the remote sensing data was comparable to the EC measured value during the satellite's passage. The results showed that the revised surface resistance parameterisation scheme was useful for estimating the latent heat flux over cropland in arid regions.

Modeling multi-layer effects in passive microwave remote sensing of dry snow using Dense Media Radiative Transfer Theory (DMRT) based on quasicrystalline approximation

USGS Publications Warehouse

Liang, D.; Xu, X.; Tsang, L.; Andreadis, K.M.; Josberger, E.G.

2008-01-01

The Dense Media Radiative Transfer theory (DMRT) of Quasicrystalline Approximation of Mie scattering by sticky particles is used to study the multiple scattering effects in layered snow in microwave remote sensing. Results are illustrated for various snow profile characteristics. Polarization differences and frequency dependences of multilayer snow model are significantly different from that of the single-layer snow model. Comparisons are also made with CLPX data using snow parameters as given by the VIC model. ?? 2007 IEEE.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

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

NASA Astrophysics Data System (ADS)

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.

Remote sensing information sciences research group

NASA Technical Reports Server (NTRS)

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.

The Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX): a multidisciplinary project to develop a robust remote sensing-based ET modeling tool for vineyards

USDA-ARS?s Scientific Manuscript database

The recent drought in much of California, particularly in the Central Valley region, has caused severe reduction in water reservoir levels and a major depletion of ground water by agriculture. Dramatic improvements in water and irrigation management practices are critical for agriculture to remain s...

Use of Landsat and environmental satellite data in evapotranspiration estimation from a wildland area

NASA Technical Reports Server (NTRS)

Khorram, S.; Smith, H. G.

1979-01-01

A remote sensing-aided procedure was applied to the watershed-wide estimation of water loss to the atmosphere (evapotranspiration, ET). The approach involved a spatially referenced databank based on both remotely sensed and ground-acquired information. Physical models for both estimation of ET and quantification of input parameters are specified, and results of the investigation are outlined.

Improving simulations of snow water equivalent and total water storage changes over the Upper Yangtze River basin using multi-source remote sensing data

NASA Astrophysics Data System (ADS)

Han, P.; Long, D.

2017-12-01

Snow water equivalent (SWE) and total water storage (TWS) changes are important hydrological state variables over cryospheric regions, such as China's Upper Yangtze River (UYR) basin. Accurate simulation of these two state variables plays a critical role in understanding hydrological processes over this region and, in turn, benefits water resource management, hydropower development, and ecological integrity over the lower reaches of the Yangtze River, one of the largest rivers globally. In this study, an improved CREST model coupled with a snow and glacier melting module was used to simulate SWE and TWS changes over the UYR, and to quantify contributions of snow and glacier meltwater to the total runoff. Forcing, calibration, and validation data are mainly from multi-source remote sensing observations, including satellite-based precipitation estimates, passive microwave remote sensing-based SWE, and GRACE-derived TWS changes, along with streamflow measurements at the Zhimenda gauging station. Results show that multi-source remote sensing information can be extremely valuable in model forcing, calibration, and validation over the poorly gauged region. The simulated SWE and TWS changes and the observed counterparts are highly consistent, showing NSE coefficients higher than 0.8. The results also show that the contributions of snow and glacier meltwater to the total runoff are 8% and 6%, respectively, during the period 2003‒2014, which is an important source of runoff. Moreover, from this study, the TWS is found to increase at a rate of 5 mm/a ( 0.72 Gt/a) for the period 2003‒2014. The snow melting module may overestimate SWE for high precipitation events and was improved in this study. Key words: CREST model; Remote Sensing; Melting model; Source Region of the Yangtze River

[Crop geometry identification based on inversion of semiempirical BRDF models].

PubMed

Zhao, Chun-jiang; Huang, Wen-jiang; Mu, Xu-han; Wang, Jin-diz; Wang, Ji-hua

2009-09-01

With the rapid development of remote sensing technology, the application of remote sensing has extended from single view angle to multi-view angles. It was studied for the qualitative and quantitative effect of average leaf angle (ALA) on crop canopy reflected spectrum. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of leaf area index (LAI) and monitoring of crop growth condition by remote sensing technology. Investigations of the effect of erective and horizontal varieties were conducted by bidirectional canopy reflected spectrum and semiempirical bidirectional reflectance distribution function (BRDF) models. The sensitive analysis was done based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso) at red band (680 nm) and near infrared band (800 nm). By combining the weights of the red and near-infrared bands, the semiempirical models can obtain structural information by retrieving biophysical parameters from the physical BRDF model and a number of bidirectional observations. So, it will allow an on-site and non-sampling mode of crop ALA identification, which is useful for using remote sensing for crop growth monitoring and for improving the LAI inversion accuracy, and it will help the farmers in guiding the fertilizer and irrigation management in the farmland without a priori knowledge.

Ground-based remote sensing of tropospheric water vapour isotopologues within the project MUSICA

NASA Astrophysics Data System (ADS)

Schneider, M.; Barthlott, S.; Hase, F.; González, Y.; Yoshimura, K.; García, O. E.; Sepúlveda, E.; Gomez-Pelaez, A.; Gisi, M.; Kohlhepp, R.; Dohe, S.; Blumenstock, T.; Wiegele, A.; Christner, E.; Strong, K.; Weaver, D.; Palm, M.; Deutscher, N. M.; Warneke, T.; Notholt, J.; Lejeune, B.; Demoulin, P.; Jones, N.; Griffith, D. W. T.; Smale, D.; Robinson, J.

2012-12-01

Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), long-term tropospheric water vapour isotopologue data records are provided for ten globally distributed ground-based mid-infrared remote sensing stations of the NDACC (Network for the Detection of Atmospheric Composition Change). We present a new method allowing for an extensive and straightforward characterisation of the complex nature of such isotopologue remote sensing datasets. We demonstrate that the MUSICA humidity profiles are representative for most of the troposphere with a vertical resolution ranging from about 2 km (in the lower troposphere) to 8 km (in the upper troposphere) and with an estimated precision of better than 10%. We find that the sensitivity with respect to the isotopologue composition is limited to the lower and middle troposphere, whereby we estimate a precision of about 30‰ for the ratio between the two isotopologues HD16O and H216O. The measurement noise, the applied atmospheric temperature profiles, the uncertainty in the spectral baseline, and the cross-dependence on humidity are the leading error sources. We introduce an a posteriori correction method of the cross-dependence on humidity, and we recommend applying it to isotopologue ratio remote sensing datasets in general. In addition, we present mid-infrared CO2 retrievals and use them for demonstrating the MUSICA network-wide data consistency. In order to indicate the potential of long-term isotopologue remote sensing data if provided with a well-documented quality, we present a climatology and compare it to simulations of an isotope incorporated AGCM (Atmospheric General Circulation Model). We identify differences in the multi-year mean and seasonal cycles that significantly exceed the estimated errors, thereby indicating deficits in the modeled atmospheric water cycle.

Ground-based remote sensing of tropospheric water vapour isotopologues within the project MUSICA

NASA Astrophysics Data System (ADS)

Schneider, M.; Barthlott, S.; Hase, F.; González, Y.; Yoshimura, K.; García, O. E.; Sepúlveda, E.; Gomez-Pelaez, A.; Gisi, M.; Kohlhepp, R.; Dohe, S.; Blumenstock, T.; Strong, K.; Weaver, D.; Palm, M.; Deutscher, N. M.; Warneke, T.; Notholt, J.; Lejeune, B.; Demoulin, P.; Jones, N.; Griffith, D. W. T.; Smale, D.; Robinson, J.

2012-08-01

Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), long-term tropospheric water vapour isotopologues data records are provided for ten globally distributed ground-based mid-infrared remote sensing stations of the NDACC (Network for the Detection of Atmospheric Composition Change). We present a new method allowing for an extensive and straightforward characterisation of the complex nature of such isotopologue remote sensing datasets. We demonstrate that the MUSICA humidity profiles are representative for most of the troposphere with a vertical resolution ranging from about 2 km (in the lower troposphere) to 8 km (in the upper troposphere) and with an estimated precision of better than 10%. We find that the sensitivity with respect to the isotopologue composition is limited to the lower and middle troposphere, whereby we estimate a precision of about 30‰ for the ratio between the two isotopologues HD16O and H216O. The measurement noise, the applied atmospheric temperature profiles, the uncertainty in the spectral baseline, and interferences from humidity are the leading error sources. We introduce an a posteriori correction method of the humidity interference error and we recommend applying it for isotopologue ratio remote sensing datasets in general. In addition, we present mid-infrared CO2 retrievals and use them for demonstrating the MUSICA network-wide data consistency. In order to indicate the potential of long-term isotopologue remote sensing data if provided with a well-documented quality, we present a climatology and compare it to simulations of an isotope incorporated AGCM (Atmospheric General Circulation Model). We identify differences in the multi-year mean and seasonal cycles that significantly exceed the estimated errors, thereby indicating deficits in the modeled atmospheric water cycle.

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

NASA Astrophysics Data System (ADS)

Lebed, L.; Qi, J.; Heilman, P.

2012-06-01

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

Offshore Wind Resource Characterization | Wind | NREL

Science.gov Websites

identify critical data needed. Remote Sensing and Modeling Photo of the SeaZephIR Prototype at sea. 2009 techniques such as remote sensing and modeling to provide data on design conditions. Research includes comparing the data provided by remote sensing devices and models to data collected by traditional methods

[Progress in inversion of vegetation nitrogen concentration by hyperspectral remote sensing].

PubMed

Wang, Li-Wen; Wei, Ya-Xing

2013-10-01

Nitrogen is the necessary element in life activity of vegetation, which takes important function in biosynthesis of protein, nucleic acid, chlorophyll, and enzyme etc, and plays a key role in vegetation photosynthesis. The technology about inversion of vegetation nitrogen concentration by hyperspectral remote sensing has been the research hotspot since the 70s of last century. With the development of hyperspectral remote sensing technology in recent years, the advantage of spectral bands subdivision in a certain spectral region provides the powerful technology measure for correlative spectral characteristic research on vegetation nitrogen. In the present paper, combined with the newest research production about monitoring vegetation nitrogen concentration by hyperspectral remote sensing published in main geography science literature in recent several years, the principle and correlated problem about monitoring vegetation nitrogen concentration by hyperspectral remote sensing were introduced. From four aspects including vegetation nitrogen spectral index, vegetation nitrogen content inversion based on chlorophyll index, regression model, and eliminating influence factors to inversion of vegetation nitrogen concentration, main technology methods about inversion of vegetation nitrogen concentration by hyperspectral remote sensing were detailedly introduced. Correlative research conclusions were summarized and analyzed, and research development trend was discussed.

Estimation of Carbon Flux of Forest Ecosystem over Qilian Mountains by BIOME-BGC Model

NASA Astrophysics Data System (ADS)

Yan, Min; Tian, Xin; Li, Zengyuan; Chen, Erxue; Li, Chunmei

2014-11-01

The gross primary production (GPP) and net ecosystem exchange (NEE) are important indicators for carbon fluxes. This study aims at evaluating the forest GPP and NEE over the Qilian Mountains using meteorological, remotely sensed and other ancillary data at large scale. To realize this, the widely used ecological-process-based model, Biome-BGC, and remote-sensing-based model, MODIS GPP algorithm, were selected for the simulation of the forest carbon fluxes. The combination of these two models was based on calibrating the Biome-BGC by the optimized MODIS GPP algorithm. The simulated GPP and NEE values were evaluated against the eddy covariance observed GPPs and NEEs, and the well agreements have been reached, with R2=0.76, 0.67 respectively.

Estimation of Carbon Flux of Forest Ecosystem over Qilian Mountains by BIOME-BGC Model

NASA Astrophysics Data System (ADS)

Yan, Min; Tian, Xin; Li, Zengyuan; Chen, Erxue; Li, Chunmei

2014-11-01

The gross primary production (GPP) and net ecosystem exchange (NEE) are important indicators for carbon fluxes. This study aims at evaluating the forest GPP and NEE over the Qilian Mountains using meteorological, remotely sensed and other ancillary data at large scale. To realize this, the widely used ecological-process- based model, Biome-BGC, and remote-sensing-based model, MODIS GPP algorithm, were selected for the simulation of the forest carbon fluxes. The combination of these two models was based on calibrating the Biome-BGC by the optimized MODIS GPP algorithm. The simulated GPP and NEE values were evaluated against the eddy covariance observed GPPs and NEEs, and the well agreements have been reached, with R2=0.76, 0.67 respectively.

[Maximum entropy model versus remote sensing-based methods for extracting Oncomelania hupensis snail habitats].

PubMed

Cong-Cong, Xia; Cheng-Fang, Lu; Si, Li; Tie-Jun, Zhang; Sui-Heng, Lin; Yi, Hu; Ying, Liu; Zhi-Jie, Zhang

2016-12-02

To explore the technique of maximum entropy model for extracting Oncomelania hupensis snail habitats in Poyang Lake zone. The information of snail habitats and related environment factors collected in Poyang Lake zone were integrated to set up the maximum entropy based species model and generate snail habitats distribution map. Two Landsat 7 ETM+ remote sensing images of both wet and drought seasons in Poyang Lake zone were obtained, where the two indices of modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI) were applied to extract snail habitats. The ROC curve, sensitivities and specificities were applied to assess their results. Furthermore, the importance of the variables for snail habitats was analyzed by using Jackknife approach. The evaluation results showed that the area under receiver operating characteristic curve (AUC) of testing data by the remote sensing-based method was only 0.56, and the sensitivity and specificity were 0.23 and 0.89 respectively. Nevertheless, those indices above-mentioned of maximum entropy model were 0.876, 0.89 and 0.74 respectively. The main concentration of snail habitats in Poyang Lake zone covered the northeast part of Yongxiu County, northwest of Yugan County, southwest of Poyang County and middle of Xinjian County, and the elevation was the most important environment variable affecting the distribution of snails, and the next was land surface temperature (LST). The maximum entropy model is more reliable and accurate than the remote sensing-based method for the sake of extracting snail habitats, which has certain guiding significance for the relevant departments to carry out measures to prevent and control high-risk snail habitats.

Assimilation of Remotely-Sensed Snow information to improve streamflow predictions in the Southwestern US

NASA Astrophysics Data System (ADS)

López-Burgos, V.; Rajagopal, S.; Martinez Baquero, G. F.; Gupta, H. V.

2009-12-01

Rapidly growing population in the southwestern US is leading to increasing demand and decreasing availability of water, requiring a detailed quantification of hydrological processes. The integration of detailed spatial information of water fluxes from remote sensing platforms, and hydrological models coupled with ground based data is an important step towards this goal. This project is exploring the use of Snow Water Equivalent (SWE) estimates to update the snow component of the Variable Infiltration Capacity model (VIC). SWE estimates are obtained by combining SNOTEL data with MODIS Snow Cover Area (SCA) information. Because, cloud cover corrupts the estimates of SCA, a rule-based method is used to clean up the remotely sensed images. The rules include a time interpolation method, and the probability of a pixel for been covered with snow based on the relationships between elevation, temperature, lapse rate, aspect and topographic shading. The approach is used to improve streamflow predictions on two rivers managed by the Salt River Project, a water and energy supplier in central Arizona. This solution will help improve the management of reservoirs in the Salt and Verde River in Phoenix, Arizona (tributaries of the lower Colorado River basin), by incorporating physically based distributed models and remote sensing observations into their Decision Support Tools and planning tools. This research seeks to increase the knowledge base used to manage reservoirs and groundwater resources in a region affected by a long-term drought. It will be applicable and relevant for other water utility companies facing the challenges of climate change and decreasing water resources.

First results of ground-based LWIR hyperspectral imaging remote gas detection

NASA Astrophysics Data System (ADS)

Zheng, Wei-jian; Lei, Zheng-gang; Yu, Chun-chao; Wang, Hai-yang; Fu, Yan-peng; Liao, Ning-fang; Su, Jun-hong

2014-11-01

The new progress of ground-based long-wave infrared remote sensing is presented. The LWIR hyperspectral imaging by using the windowing spatial and temporal modulation Fourier spectroscopy, and the results of outdoor ether gas detection, verify the features of LWIR hyperspectral imaging remote sensing and technical approach. It provides a new technical means for ground-based gas remote sensing.

Assimilation of Remotely Sensed Evaporative Fraction for Improved Agricultural Irrigation Water Management

NASA Astrophysics Data System (ADS)

Lei, F.; Crow, W. T.; Kustas, W. P.; Yang, Y.; Anderson, M. C.

2017-12-01

Improving the water usage efficiency and maintaining water use sustainability is challenging under rapidly changed natural environments. For decades, extensive field investigations and conceptual/physical numerical modeling have been developed to quantify and track surface water and energy fluxes at different spatial and temporal scales. Meanwhile, with the development of satellite-based sensors, land surface eco-hydrological parameters can be retrieved remotely to supplement ground-based observations. However, both models and remote sensing retrievals contain various sources of errors and an accurate and spatio-temporally continuous simulation and forecasting system at the field-scale is crucial for the efficient water management in agriculture. Specifically, data assimilation technique can optimally integrate measurements acquired from various sources (including in-situ and remotely-sensed data) with numerical models through consideration of different types of uncertainties. In this presentation, we will focus on improving the estimation of water and energy fluxes over a vineyard in California, U.S. A high-resolution remotely-sensed Evaporative Fraction (EF) product from the Atmosphere-Land Exchange Inverse (ALEXI) model will be incorporated into a Soil Vegetation Atmosphere Transfer (SVAT) model via a 2-D data assimilation method. The results will show that both the accuracy and spatial variability of soil water content and evapotranspiration in SVAT model can be enhanced through the assimilation of EF data. Furthermore, we will demonstrate that by taking the optimized soil water flux as initial condition and combining it with weather forecasts, future field water status can be predicted under different irrigation scenarios. Finally, we will discuss the practical potential of these advances by leveraging our numerical experiment for the design of new irrigation strategies and water management techniques.

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks

PubMed Central

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-01-01

Simple Summary The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have been widely used to track animal movements. The aim of the study was to generate suitability maps of bar-head geese using movement data coupled with environmental parameters, such as remote sensing images and temperature data. Therefore, we modified a deep convolutional neural network for the multi-scale inputs. The results indicate that the proposed method can identify the areas with the dense goose species around Qinghai Lake. In addition, this approach might also be interesting for implementation in other species with different niche factors or in areas where biological survey data are scarce. Abstract With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction. PMID:29701686

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

Treesearch

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

2016-01-01

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

Remote Sensing and Reflectance Profiling in Entomology.

PubMed

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.

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

PubMed Central

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

2017-01-01

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. PMID:28640236

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

Knowledge on hydrological regimes of river basins is crucial for water management. However, data requirements often limit the applicability of hydrological models in basins with scarce in-situ data. Remote sensing provides a unique possibility to acquire information on hydrological variables in these basins. This study explores how multi-mission remote sensing data can inform a hydrological model. The Ogooué basin in Gabon is used as study area. No previous modelling efforts have been conducted for the basin and only historical flow and precipitation observations are available. Publicly available remote sensing observations are used to parametrize, force, calibrate and validate a hydrological model of the Ogooué. The modelling framework used in the study, is a lumped conceptual rainfall-runoff model based on the Budyko framework coupled to a Muskingum routing scheme. Precipitation is a crucial driver of the land-surface water balance, therefore two satellite-based rainfall estimates, Tropical Rainfall Measuring Mission (TRMM) product 3B42 version 7 and Famine Early Warning System - Rainfall Estimate (FEWS-RFE), are compared. The comparison shows good seasonal and spatial agreement between the products; however, TRMM consistently predicts significantly more precipitation: 1726 mm on average per year against 1556 mm for FEWS-RFE. Best modeling results are obtained with the TRMM precipitation forcing. Model calibration combines historical in-situ flow observations and GRACE total water storage observations using the Jet Propulsion Laboratory (JPL) mascon solution in a multi-objective approach. The two models are calibrated using flow duration curves and climatology benchmarks to overcome the lack of simultaneity between simulated and observed discharge. The objectives are aggregated into a global objective function, and the models are calibrated using the Shuffled Complex Evolution Algorithm. Water height observations from drifting orbit altimetry missions are extracted along the river line, using a detailed water mask based on Sentinel-1 SAR imagery. 1399 single CryoSat-2 altimetry observations and 48 ICESat observations are acquired. Additionally, water heights have been measured by the repeat-orbit satellite missions Envisat and Jason-2 at 12 virtual stations along the river. The four missions show generally good agreement in terms of mean annual water height amplitudes. The altimetry observations are used to validate the hydrological model of the Ogooué River. By combining hydrological modelling and remote sensing, new information on an otherwise unstudied basin is obtained. The study shows the potential of using remote sensing observations to parameterize, force, calibrate and validate models of poorly gauged river basins. Specifically, the study shows how Sentinel-1 SAR imagery supports the extraction of satellite altimetry data over rivers. The model can be used to assess climate change scenarios, evaluate hydraulic infrastructure development projects and predict the impact of irrigation diversions.

Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

NASA Technical Reports Server (NTRS)

Zhou, Yuyu; Weng, Qihao; Gurney, Kevin R.; Shuai, Yanmin; Hu, Xuefei

2012-01-01

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.

Feasibility of remote sensing for detecting thermal pollution. Part 1: Feasibility study. Part 2: Implementation plan. [coastal ecology

NASA Technical Reports Server (NTRS)

Veziroglu, T. N.; Lee, S. S.

1973-01-01

A feasibility study for the development of a three-dimensional generalized, predictive, analytical model involving remote sensing, in-situ measurements, and an active system to remotely measure turbidity is presented. An implementation plan for the development of the three-dimensional model and for the application of remote sensing of temperature and turbidity measurements is outlined.

Remote sensing of suspended sediment water research: principles, methods, and progress

NASA Astrophysics Data System (ADS)

Shen, Ping; Zhang, Jing

2011-12-01

In this paper, we reviewed the principle, data, methods and steps in suspended sediment research by using remote sensing, summed up some representative models and methods, and analyzes the deficiencies of existing methods. Combined with the recent progress of remote sensing theory and application in water suspended sediment research, we introduced in some data processing methods such as atmospheric correction method, adjacent effect correction, and some intelligence algorithms such as neural networks, genetic algorithms, support vector machines into the suspended sediment inversion research, combined with other geographic information, based on Bayesian theory, we improved the suspended sediment inversion precision, and aim to give references to the related researchers.

An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.; Boyte, Stephen; Picotte, Joshua J.; Howard, Danny; Smith, Kelcy; Nelson, Kurtis

2016-01-01

Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.

Geotechnical applications of remote sensing and remote data transmission; Proceedings of the Symposium, Cocoa Beach, FL, Jan. 31-Feb. 1, 1986

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, A.I.; Pettersson, C.B.

1988-01-01

Papers and discussions concerning the geotechnical applications of remote sensing and remote data transmission, sources of remotely sensed data, and glossaries of remote sensing and remote data transmission terms, acronyms, and abbreviations are presented. Aspects of remote sensing use covered include the significance of lineaments and their effects on ground-water systems, waste-site use and geotechnical characterization, the estimation of reservoir submerging losses using CIR aerial photographs, and satellite-based investigation of the significance of surficial deposits for surface mining operations. Other topics presented include the location of potential ground subsidence and collapse features in soluble carbonate rock, optical Fourier analysis ofmore » surface features of interest in geotechnical engineering, geotechnical applications of U.S. Government remote sensing programs, updating the data base for a Geographic Information System, the joint NASA/Geosat Test Case Project, the selection of remote data telemetry methods for geotechnical applications, the standardization of remote sensing data collection and transmission, and a comparison of airborne Goodyear electronic mapping system/SAR with satelliteborne Seasat/SAR radar imagery.« less

Exploring Models and Data for Remote Sensing Image Caption Generation

NASA Astrophysics Data System (ADS)

Lu, Xiaoqiang; Wang, Binqiang; Zheng, Xiangtao; Li, Xuelong

2018-04-01

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal

Rapid-response tools and datasets for post-fire remediation: Linking remote sensing and process-based hydrological models

Treesearch

M. E. Miller; William Elliot; M. Billmire; Pete Robichaud; K. A. Endsley

2016-01-01

Post-wildfire flooding and erosion can threaten lives, property and natural resources. Increased peak flows and sediment delivery due to the loss of surface vegetation cover and fire-induced changes in soil properties are of great concern to public safety. Burn severity maps derived from remote sensing data reflect fire-induced changes in vegetative cover and soil...

[Analysis of the effect of detector's operating temperature on SNR in space-based remote sensor].

PubMed

Li, Zhan-feng; Wang, Shu-rong; Huang, Yu

2012-03-01

Limb viewing is a new viewing geometry for space-based atmospheric remote sensing, but the spectral radiance of atmosphere scattering reduces rapidly with limb height. So the signal-noise-ratio (SNR) is a key performance parameter of limb remote sensor. A SNR model varying with detector's temperature is proposed, based on analysis of spectral radiative transfer and noise' source in representative instruments. The SNR at limb height 70 km under space conditions was validated by simulation experiment on limb remote sensing spectrometer prototype. Theoretic analysis and experiment's results indicate congruously that when detector's temperature reduces to some extent, a maximum SNR will be reached. After considering the power consumption, thermal conductivity and other issues, optimal operating temperature of detector can be decided.

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

NASA Astrophysics Data System (ADS)

Huang, Ni; Gu, Lianhong; Black, T. Andrew; Wang, Li; Niu, Zheng

2015-11-01

Soil respiration (Rs), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual Rs at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual Rs estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zone soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites. In addition, a multicollinearity problem among LST-night, root-zone soil moisture, and plant photosynthesis factor was effectively avoided by selecting the LST-night-driven model. Cross validation showed that temporal variation in Rs was captured by the LST-night-driven model with a mean absolute error below 1 µmol CO2 m-2 s-1 at both forest sites. An obvious overestimation that occurred in 2005 and 2007 at the Missouri Ozark site reduced the evaluation accuracy of cross validation because of summer drought. However, no significant difference was found between the Arrhenius-type function driven by LST-night and the function considering LST-night and root-zone soil moisture. This finding indicated that the contribution of soil moisture to Rs was relatively small at our multiyear data set. To predict intersite Rs, maximum leaf area index (LAImax) was used as an upscaling factor to calibrate the site-specific reference respiration rates. Independent validation demonstrated that the model incorporating LST-night and LAImax efficiently predicted the spatial and temporal variabilities of Rs. Based on the Arrhenius-type function using LST-night as an input parameter, the rates of annual C release from Rs were 894-1027 g C m-2 yr-1 at the BC-Campbell River 1949 Douglas-fir site and 818-943 g C m-2 yr-1 at the Missouri Ozark site. The ratio between annual Rs estimates based on remotely sensed data and the total annual ecosystem respiration from eddy covariance measurements fell within the range reported in previous studies. Our results demonstrated that estimating annual Rs based on remote sensing data products was possible at deciduous and evergreen forest sites.

A remote-sensing, GIS-based approach to identify, characterize, and model spawning habitat for fall-run chum salmon in a sub-arctic, glacially fed river

USGS Publications Warehouse

Wirth, Lisa; Rosenberger, Amanda; Prakash, Anupma; Gens, Rudiger; Margraf, F. Joseph; Hamazaki, Toshihide

2012-01-01

At northern limits of a species’ distribution, fish habitat requirements are often linked to thermal preferences, and the presence of overwintering habitat. However, logistical challenges and hydrologic processes typical of glacial systems could compromize the identification of these habitats, particularly in large river environments. Our goal was to identify and characterize spawning habitat for fall-run chum salmon Oncorhynchus keta and model habitat selection from spatial distributions of tagged individuals in the Tanana River, Alaska using an approach that combined ground surveys with remote sensing. Models included braiding, sinuosity, ice-free water surface area (indicating groundwater influence), and persistent ice-free water (i.e., consistent presence of ice-free water for a 12-year period according to satellite imagery). Candidate models containing persistent ice-free water were selected as most likely, highlighting the utility of remote sensing for monitoring and identifying salmon habitat in remote areas. A combination of ground and remote surveys revealed spatial and temporal thermal characteristics of these habitats that could have strong biological implications. Persistent ice-free sites identified using synthetic aperture radar appear to serve as core areas for spawning fall chum salmon, and the importance of stability through time suggests a legacy of successful reproductive effort for this homing species. These features would not be captured with a one-visit traditional survey but rather required remote-sensing monitoring of the sites through time.

Evaluating high temporal and spatial resolution vegetation index for crop yield prediction

USDA-ARS?s Scientific Manuscript database

Remote sensing data have been widely used in estimating crop yield. Remote sensing derived parameters such as Vegetation Index (VI) were used either directly in building empirical models or by assimilating with crop growth models to predict crop yield. The abilities of remote sensing VI in crop yiel...

Bridging the Scales from Field to Region with Practical Tools to Couple Time- and Space-Synchronized Data from Flux Towers and Networks with Proximal and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Burba, G. G.; Avenson, T.; Burkart, A.; Gamon, J. A.; Guan, K.; Julitta, T.; Pastorello, G.; Sakowska, K.

2017-12-01

Many hundreds of flux towers are presently operational as standalone projects and as parts of regional networks. However, the vast majority of these towers do not allow straightforward coupling with remote sensing (drone, aircraft, satellite, etc.) data, and even fewer have optical sensors for validation of remote sensing products, and upscaling from field to regional levels. In 2016-2017, new tools to collect, process, and share time-synchronized flux data from multiple towers were developed and deployed globally. Originally designed to automate site and data management, and to streamline flux data analysis, these tools allow relatively easy matching of tower data with remote sensing data: GPS-driven PTP time protocol synchronizes instrumentation within the station, different stations with each other, and all of these to remote sensing data to precisely align remote sensing and flux data in time Footprint size and coordinates computed and stored with flux data help correctly align tower flux footprints and drone, aircraft or satellite motion to precisely align optical and flux data in space Full snapshot of the remote sensing pixel can then be constructed, including leaf-level, ground optical sensor, and flux tower measurements from the same footprint area, closely coupled with the remote sensing measurements to help interpret remote sensing data, validate models, and improve upscaling Additionally, current flux towers can be augmented with advanced ground optical sensors and can use standard routines to deliver continuous products (e.g. SIF, PRI, NDVI, etc.) based on automated field spectrometers (e.g., FloX and RoX, etc.) and other optical systems. Several dozens of new towers already operational globally can be readily used for the proposed workflow. Over 500 active traditional flux towers can be updated to synchronize their data with remote sensing measurements. This presentation will show how the new tools are used by major networks, and describe how this approach can be utilized for matching remote sensing and tower data to aid in ground truthing, improve scientific interactions, and promote joint grant writing and other forms of collaboration between the flux and remote sensing communities.

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

NASA Astrophysics Data System (ADS)

Necsoiu, Dorel Marius

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

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

NASA Astrophysics Data System (ADS)

Li, Nana; Jia, Li; Lu, Jing; Menenti, Massimo; Zhou, Jie

2017-01-01

The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as "HM model") and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situ G0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from -7 to -0.5 K in LST amplitude and from -300 to 300 J m-2 K-1 s-0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

Remote sensing and reflectance profiling in entomology

USDA-ARS?s Scientific Manuscript database

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...

Estimating Vegetation Rainfall Interception Using Remote Sensing Observations at Very High Resolution

NASA Astrophysics Data System (ADS)

Cui, Y.; Zhao, P.; Hong, Y.; Fan, W.; Yan, B.; Xie, H.

2017-12-01

Abstract: As an important compont of evapotranspiration, vegetation rainfall interception is the proportion of gross rainfall that is intercepted, stored and subsequently evaporated from all parts of vegetation during or following rainfall. Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model's strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception loss and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study. Keywords: rainfall interception; remote sensing; RS-Gash analytical model; high resolution

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

NASA Astrophysics Data System (ADS)

Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

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

PubMed

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.

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

PubMed Central

Lu, Xiaoman; Zheng, Guang; Miller, Colton

2017-01-01

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. PMID:28885556

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-01

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Study on paddy rice yield estimation based on multisource data and the Grey system theory

NASA Astrophysics Data System (ADS)

Deng, Wensheng; Wang, Wei; Liu, Hai; Li, Chen; Ge, Yimin; Zheng, Xianghua

2009-10-01

The paddy rice is our important crops. In study of the paddy rice yield estimation, compared with the scholars who usually only take the remote sensing data or meteorology as the influence factors, we combine the remote sensing and the meteorological data to make the monitoring result closer reality. Although the gray system theory has used in many aspects, it is applied very little in paddy rice yield estimation. This study introduces it to the paddy rice yield estimation, and makes the yield estimation model. This can resolve small data sets problem that can not be solved by deterministic model. It selects some regions in Jianghan plain for the study area. The data includes multi-temporal remote sensing image, meteorological and statistic data. The remote sensing data is the 16-day composite images (250-m spatial resolution) of MODIS. The meteorological data includes monthly average temperature, sunshine duration and rain fall amount. The statistical data is the long-term paddy rice yield of the study area. Firstly, it extracts the paddy rice planting area from the multi-temporal MODIS images with the help of GIS and RS. Then taking the paddy rice yield as the reference sequence, MODIS data and meteorological data as the comparative sequence, computing the gray correlative coefficient, it selects the yield estimation factor based on the grey system theory. Finally, using the factors, it establishes the yield estimation model and does the result test. The result indicated that the method is feasible and the conclusion is credible. It can provide the scientific method and reference value to carry on the region paddy rice remote sensing estimation.

Ground-Based Remote Sensing of Water-Stressed Crops: Thermal and Multispectral Imaging

USDA-ARS?s Scientific Manuscript database

Ground-based methods of remote sensing can be used as ground-truthing for satellite-based remote sensing, and in some cases may be a more affordable means of obtaining such data. Plant canopy temperature has been used to indicate and quantify plant water stress. A field research study was conducted ...

Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar

NASA Astrophysics Data System (ADS)

Chen, Qi

2015-08-01

Estimating tree aboveground biomass (AGB) and carbon (C) stocks using remote sensing is a critical component for understanding the global C cycle and mitigating climate change. However, the importance of allometry for remote sensing of AGB has not been recognized until recently. The overarching goals of this study are to understand the differences and relationships among three national-scale allometric methods (CRM, Jenkins, and the regional models) of the Forest Inventory and Analysis (FIA) program in the U.S. and to examine the impacts of using alternative allometry on the fitting statistics of remote sensing-based woody AGB models. Airborne lidar data from three study sites in the Pacific Northwest, USA were used to predict woody AGB estimated from the different allometric methods. It was found that the CRM and Jenkins estimates of woody AGB are related via the CRM adjustment factor. In terms of lidar-biomass modeling, CRM had the smallest model errors, while the Jenkins method had the largest ones and the regional method was between. The best model fitting from CRM is attributed to its inclusion of tree height in calculating merchantable stem volume and the strong dependence of non-merchantable stem biomass on merchantable stem biomass. This study also argues that it is important to characterize the allometric model errors for gaining a complete understanding of the remotely-sensed AGB prediction errors.

Developing a Data Driven Process-Based Model for Remote Sensing of Ecosystem Production

NASA Astrophysics Data System (ADS)

Elmasri, B.; Rahman, A. F.

2010-12-01

Estimating ecosystem carbon fluxes at various spatial and temporal scales is essential for quantifying the global carbon cycle. Numerous models have been developed for this purpose using several environmental variables as well as vegetation indices derived from remotely sensed data. Here we present a data driven modeling approach for gross primary production (GPP) that is based on a process based model BIOME-BGC. The proposed model was run using available remote sensing data and it does not depend on look-up tables. Furthermore, this approach combines the merits of both empirical and process models, and empirical models were used to estimate certain input variables such as light use efficiency (LUE). This was achieved by using remotely sensed data to the mathematical equations that represent biophysical photosynthesis processes in the BIOME-BGC model. Moreover, a new spectral index for estimating maximum photosynthetic activity, maximum photosynthetic rate index (MPRI), is also developed and presented here. This new index is based on the ratio between the near infrared and the green bands (ρ858.5/ρ555). The model was tested and validated against MODIS GPP product and flux measurements from two eddy covariance flux towers located at Morgan Monroe State Forest (MMSF) in Indiana and Harvard Forest in Massachusetts. Satellite data acquired by the Advanced Microwave Scanning Radiometer (AMSR-E) and MODIS were used. The data driven model showed a strong correlation between the predicted and measured GPP at the two eddy covariance flux towers sites. This methodology produced better predictions of GPP than did the MODIS GPP product. Moreover, the proportion of error in the predicted GPP for MMSF and Harvard forest was dominated by unsystematic errors suggesting that the results are unbiased. The analysis indicated that maintenance respiration is one of the main factors that dominate the overall model outcome errors and improvement in maintenance respiration estimation will result in improved GPP predictions. Although there might be a room for improvements in our model outcomes through improved parameterization, our results suggest that such a methodology for running BIOME-BGC model based entirely on routinely available data can produce good predictions of GPP.

American Society for Photogrammetry and Remote Sensing and ACSM, Fall Convention, Reno, NV, Oct. 4-9, 1987, ASPRS Technical Papers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1987-01-01

Recent advances in remote-sensing technology and applications are examined in reviews and reports. Topics addressed include the use of Landsat TM data to assess suspended-sediment dispersion in a coastal lagoon, the use of sun incidence angle and IR reflectance levels in mapping old-growth coniferous forests, information-management systems, Large-Format-Camera soil mapping, and the economic potential of Landsat TM winter-wheat crop-condition assessment. Consideration is given to measurement of ephemeral gully erosion by airborne laser ranging, the creation of a multipurpose cadaster, high-resolution remote sensing and the news media, the role of vegetation in the global carbon cycle, PC applications in analytical photogrammetry,more » multispectral geological remote sensing of a suspected impact crater, fractional calculus in digital terrain modeling, and automated mapping using GP-based survey data.« less

Research on Land Use Changes in Panjin City Basing on Remote Sensing Data

NASA Astrophysics Data System (ADS)

Ding, Hua; Li, Ru Ren; Shuang Sun, Li; Wang, Xin; Liu, Yu Mei

2018-05-01

Taking Landsat remote sensing image as the main data source, the research on land use changes in Panjin City in 2005 to 2015 is made with the support of remote sensing platform and GIS platform in this paper; the range of land use changes and change rate are analyzed through the classification of remote sensing image; the dynamic analysis on land changes is made with the help of transfer matrix of land use type; the quantitative calculation on all kinds of dynamic change features of land changes is made by utilizing mathematical model; and the analysis on driving factors of land changes of image is made at last. The research results show that, in recent ten years, the area of cultivated land in Panjin City decreased, the area of vegetation increased, and meanwhile the area of road increased drastically, the settlement place decreased than ever, and water area changed slightly.

Separating vegetation and soil temperature using airborne multiangular remote sensing image data

NASA Astrophysics Data System (ADS)

Liu, Qiang; Yan, Chunyan; Xiao, Qing; Yan, Guangjian; Fang, Li

2012-07-01

Land surface temperature (LST) is a key parameter in land process research. Many research efforts have been devoted to increase the accuracy of LST retrieval from remote sensing. However, because natural land surface is non-isothermal, component temperature is also required in applications such as evapo-transpiration (ET) modeling. This paper proposes a new algorithm to separately retrieve vegetation temperature and soil background temperature from multiangular thermal infrared (TIR) remote sensing data. The algorithm is based on the localized correlation between the visible/near-infrared (VNIR) bands and the TIR band. This method was tested on the airborne image data acquired during the Watershed Allied Telemetry Experimental Research (WATER) campaign. Preliminary validation indicates that the remote sensing-retrieved results can reflect the spatial and temporal trend of component temperatures. The accuracy is within three degrees while the difference between vegetation and soil temperature can be as large as twenty degrees.

The Use of Satellite Remote Sensing in Epidemiological Studies

PubMed Central

Sorek-Hamer, Meytar; Just, Allan C.; Kloog, Itai

2016-01-01

Purpose of review Particulate matter (PM) air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite based remote sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies. Recent findings Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level PM can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for PM model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. Summary It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies. PMID:26859287

Satellite remote sensing in epidemiological studies.

PubMed

Sorek-Hamer, Meytar; Just, Allan C; Kloog, Itai

2016-04-01

Particulate matter air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite-based remote-sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies. Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level particulate matter can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for particulate matter model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies.

[A review on research of land surface water and heat fluxes].

PubMed

Sun, Rui; Liu, Changming

2003-03-01

Many field experiments were done, and soil-vegetation-atmosphere transfer(SVAT) models were stablished to estimate land surface heat fluxes. In this paper, the processes of experimental research on land surface water and heat fluxes are reviewed, and three kinds of SVAT model(single layer model, two layer model and multi-layer model) are analyzed. Remote sensing data are widely used to estimate land surface heat fluxes. Based on remote sensing and energy balance equation, different models such as simplified model, single layer model, extra resistance model, crop water stress index model and two source resistance model are developed to estimate land surface heat fluxes and evapotranspiration. These models are also analyzed in this paper.

Ontology-based classification of remote sensing images using spectral rules

NASA Astrophysics Data System (ADS)

Andrés, Samuel; Arvor, Damien; Mougenot, Isabelle; Libourel, Thérèse; Durieux, Laurent

2017-05-01

Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for "domain" experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.

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

NASA Astrophysics Data System (ADS)

Jones, B. K.

2014-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook.

PubMed

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Raso, Giovanna; Utzinger, Jürg

2015-03-17

Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions. We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised. We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from. Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.

Using remote sensing in support of environmental management: A framework for selecting products, algorithms and methods.

PubMed

de Klerk, Helen M; Gilbertson, Jason; Lück-Vogel, Melanie; Kemp, Jaco; Munch, Zahn

2016-11-01

Traditionally, to map environmental features using remote sensing, practitioners will use training data to develop models on various satellite data sets using a number of classification approaches and use test data to select a single 'best performer' from which the final map is made. We use a combination of an omission/commission plot to evaluate various results and compile a probability map based on consistently strong performing models across a range of standard accuracy measures. We suggest that this easy-to-use approach can be applied in any study using remote sensing to map natural features for management action. We demonstrate this approach using optical remote sensing products of different spatial and spectral resolution to map the endemic and threatened flora of quartz patches in the Knersvlakte, South Africa. Quartz patches can be mapped using either SPOT 5 (used due to its relatively fine spatial resolution) or Landsat8 imagery (used because it is freely accessible and has higher spectral resolution). Of the variety of classification algorithms available, we tested maximum likelihood and support vector machine, and applied these to raw spectral data, the first three PCA summaries of the data, and the standard normalised difference vegetation index. We found that there is no 'one size fits all' solution to the choice of a 'best fit' model (i.e. combination of classification algorithm or data sets), which is in agreement with the literature that classifier performance will vary with data properties. We feel this lends support to our suggestion that rather than the identification of a 'single best' model and a map based on this result alone, a probability map based on the range of consistently top performing models provides a rigorous solution to environmental mapping. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysing and correcting the differences between multi-source and multi-scale spatial remote sensing observations.

PubMed

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

PubMed Central

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation. PMID:25405760

Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields

NASA Astrophysics Data System (ADS)

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

2017-01-01

Soil prediction models based on spectral indices from some multispectral images are too coarse to characterize spatial pattern of soil properties in small and heterogeneous agricultural lands. Image pan-sharpening has seldom been utilized in Digital Soil Mapping research before. This research aimed to analyze the effects of pan-sharpened (PAN) remote sensing spectral indices on soil prediction models in smallholder farm settings. This research fused the panchromatic band and multispectral (MS) bands of WorldView-2, GeoEye-1, and Landsat 8 images in a village in Southern India by Brovey, Gram-Schmidt and Intensity-Hue-Saturation methods. Random Forest was utilized to develop soil total nitrogen (TN) and soil exchangeable potassium (Kex) prediction models by incorporating multiple spectral indices from the PAN and MS images. Overall, our results showed that PAN remote sensing spectral indices have similar spectral characteristics with soil TN and Kex as MS remote sensing spectral indices. There is no soil prediction model incorporating the specific type of pan-sharpened spectral indices always had the strongest prediction capability of soil TN and Kex. The incorporation of pan-sharpened remote sensing spectral data not only increased the spatial resolution of the soil prediction maps, but also enhanced the prediction accuracy of soil prediction models. Small farms with limited footprint, fragmented ownership and diverse crop cycle should benefit greatly from the pan-sharpened high spatial resolution imagery for soil property mapping. Our results show that multiple high and medium resolution images can be used to map soil properties suggesting the possibility of an improvement in the maps' update frequency. Additionally, the results should benefit the large agricultural community through the reduction of routine soil sampling cost and improved prediction accuracy.

Remote sensing of natural resources: Quarterly literature review

NASA Technical Reports Server (NTRS)

1976-01-01

A quarterly review of technical literature concerning remote sensing techniques is presented. The format contains indexed and abstracted materials with emphasis on data gathering techniques performed or obtained remotely from space, aircraft, or ground-based stations. Remote sensor applications including the remote sensing of natural resources are presented.

Hydrological Relevant Parameters from Remote Sensing - Spatial Modelling Input and Validation Basis

NASA Astrophysics Data System (ADS)

Hochschild, V.

2012-12-01

This keynote paper will demonstrate how multisensoral remote sensing data is used as spatial input for mesoscale hydrological modeling as well as for sophisticated validation purposes. The tasks of Water Resources Management are subject as well as the role of remote sensing in regional catchment modeling. Parameters derived from remote sensing discussed in this presentation will be land cover, topographical information from digital elevation models, biophysical vegetation parameters, surface soil moisture, evapotranspiration estimations, lake level measurements, determination of snow covered area, lake ice cycles, soil erosion type, mass wasting monitoring, sealed area, flash flood estimation. The actual possibilities of recent satellite and airborne systems are discussed, as well as the data integration into GIS and hydrological modeling, scaling issues and quality assessment will be mentioned. The presentation will provide an overview of own research examples from Germany, Tibet and Africa (Ethiopia, South Africa) as well as other international research activities. Finally the paper gives an outlook on upcoming sensors and concludes the possibilities of remote sensing in hydrology.

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

PubMed

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

2015-05-01

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

An investigation of vegetation and other Earth resource/feature parameters using LANDSAT and other remote sensing data. 1: LANDSAT. 2: Remote sensing of volcanic emissions. [New England forest and emissions from Mt. St. Helens and Central American volcanoes

NASA Technical Reports Server (NTRS)

Birnie, R. W.; Stoiber, R. E. (Principal Investigator)

1981-01-01

A fanning technique based on a simplistic physical model provided a classification algorithm for mixture landscapes. Results of applications to LANDSAT inventory of 1.5 million acres of forest land in Northern Maine are presented. Signatures for potential deer year habitat in New Hampshire were developed. Volcanic activity was monitored in Nicaragua, El Salvador, and Guatemala along with the Mt. St. Helens eruption. Emphasis in the monitoring was placed on the remote sensing of SO2 concentrations in the plumes of the volcanoes.

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

NASA Technical Reports Server (NTRS)

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.

Remote sensing applications to hydrologic modeling

NASA Technical Reports Server (NTRS)

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.

Assessment of wetland productive capacity from a remote-sensing-based model - A NASA/NMFS joint research project

NASA Technical Reports Server (NTRS)

Butera, M. K.; Frick, A. L.; Browder, J.

1983-01-01

NASA and the U.S. National Marine Fisheries Service have undertaken the development of Landsat Thematic Mapper (TM) technology for the evaluation of the usefulness of wetlands to estuarine fish and shellfish production. Toward this end, a remote sensing-based Productive Capacity model has been developed which characterizes the biological and hydrographic features of a Gulf Coast Marsh to predict detrital export. Regression analyses of TM simulator data for wetland plant production estimation are noted to more accurately estimate the percent of total vegetative cover than biomass, indicating that a nonlinear relationship may be involved.

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER... electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER...electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Basic Remote Sensing Investigations for Beach Reconnaissance.

DTIC Science & Technology

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

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

PubMed

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.

Inquiry-Based Learning in Remote Sensing: A Space Balloon Educational Experiment

ERIC Educational Resources Information Center

Mountrakis, Giorgos; Triantakonstantis, Dimitrios

2012-01-01

Teaching remote sensing in higher education has been traditionally restricted in lecture and computer-aided laboratory activities. This paper presents and evaluates an engaging inquiry-based educational experiment. The experiment was incorporated in an introductory remote sensing undergraduate course to bridge the gap between theory and…

Assessment of Wildfire Risk in Southern California with Live Fuel Moisture Measurement and Remotely Sensed Vegetation Water Content Proxies

NASA Astrophysics Data System (ADS)

Jia, S.; Kim, S. H.; Nghiem, S. V.; Kafatos, M.

2017-12-01

Live fuel moisture (LFM) is the water content of live herbaceous plants expressed as a percentage of the oven-dry weight of plant. It is a critical parameter in fire ignition in Mediterranean climate and routinely measured in sites selected by fire agencies across the U.S. Vegetation growing cycle, meteorological metrics, soil type, and topography all contribute to the seasonal and inter-annual variation of LFM, and therefore, the risk of wildfire. The optical remote sensing-based vegetation indices (VIs) have been used to estimate the LFM. Comparing to the VIs, microwave remote sensing products have advantages like less saturation effect in greenness and representing the water content of the vegetation cover. In this study, we established three models to evaluate the predictability of LFM in Southern California using MODIS NDVI, vegetation temperature condition index (VTCI) from downscaled Soil Moisture Active Passive (SMAP) products, and vegetation optical depth (VOD) derived by Land Parameter Retrieval Model. Other ancillary variables, such as topographic factors (aspects and slope) and meteorological metrics (air temperature, precipitation, and relative humidity), are also considered in the models. The model results revealed an improvement of LFM estimation from SMAP products and VOD, despite the uncertainties introduced in the downscaling and parameter retrieval. The estimation of LFM using remote sensing data can provide an assessment of wildfire danger better than current methods using NDVI-based growing seasonal index. Future study will test the VOD estimation from SMAP data using the multi-temporal dual channel algorithm (MT-DCA) and extend the LFM modeling to a regional scale.

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

NASA Astrophysics Data System (ADS)

Friedrich, J.; Karslioglu, M. O.

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

Using ontological inference and hierarchical matchmaking to overcome semantic heterogeneity in remote sensing-based biodiversity monitoring

NASA Astrophysics Data System (ADS)

Nieland, Simon; Kleinschmit, Birgit; Förster, Michael

2015-05-01

Ontology-based applications hold promise in improving spatial data interoperability. In this work we use remote sensing-based biodiversity information and apply semantic formalisation and ontological inference to show improvements in data interoperability/comparability. The proposed methodology includes an observation-based, "bottom-up" engineering approach for remote sensing applications and gives a practical example of semantic mediation of geospatial products. We apply the methodology to three different nomenclatures used for remote sensing-based classification of two heathland nature conservation areas in Belgium and Germany. We analysed sensor nomenclatures with respect to their semantic formalisation and their bio-geographical differences. The results indicate that a hierarchical and transparent nomenclature is far more important for transferability than the sensor or study area. The inclusion of additional information, not necessarily belonging to a vegetation class description, is a key factor for the future success of using semantics for interoperability in remote sensing.

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

PubMed Central

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

2014-01-01

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. Key Points Remote sensing of AOT is very useful in validation of climate models PMID:25821664

A remote sensing based vegetation classification logic for global land cover analysis

USGS Publications Warehouse

Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond

1995-01-01

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

An Approach of Registration between Remote Sensing Image and Electronic Chart Based on Coastal Line

NASA Astrophysics Data System (ADS)

Li, Ying; Yu, Shuiming; Li, Chuanlong

Remote sensing plays an important role marine oil spill emergency. In order to implement a timely and effective countermeasure, it is important to provide exact position of oil spills. Therefore it is necessary to match remote sensing image and electronic chart properly. Variance ordinarily exists between oil spill image and electronic chart, although geometric correction is applied to remote sensing image. It is difficult to find the steady control points on sea to make exact rectification of remote sensing image. An improved relaxation algorithm was developed for finding the control points along the coastline since oil spills occurs generally near the coast. A conversion function is created with the least square, and remote sensing image can be registered with the vector map based on this function. SAR image was used as the remote sensing data and shape format map as the electronic chart data. The results show that this approach can guarantee the precision of the registration, which is essential for oil spill monitoring.

Remote Sensing and the Environment.

ERIC Educational Resources Information Center

Osmers, Karl

1991-01-01

Suggests using remote sensing technology to help students make sense of the natural world. Explains that satellite information allows observation of environmental changes over time. Identifies possible student projects based on remotely sensed data. Recommends obtaining the assistance of experts and seeking funding through effective project…

The promise of remote sensing in the atmospheric sciences

NASA Technical Reports Server (NTRS)

Atlas, D.

1981-01-01

The applications and advances in remote sensing technology for weather prediction, mesoscale meteorology, severe storms, and climate studies are discussed. Doppler radar permits tracking of the three-dimensional field of motion within storms, thereby increasing the accuracy of convective storm modeling. Single Doppler units are also employed for detecting mesoscale storm vortices and tornado vortex signatures with lead times of 30 min. Clear air radar in pulsed and high resolution FM-CW forms reveals boundary layer convection, Kelvin-Helmoltz waves, shear layer turbulence, and wave motions. Lidar is successfully employed for stratospheric aerosol measurements, while Doppler lidar provides data on winds from the ground and can be based in space. Sodar is useful for determining the structure of the PBL. Details and techniques of satellite-based remote sensing are presented, and results from the GWE and FGGE experiments are discussed.

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

NASA Technical Reports Server (NTRS)

Balter, B. M.

1979-01-01

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

International Models and Methods of Remote Sensing Education and Training.

ERIC Educational Resources Information Center

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…

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa

PubMed Central

2012-01-01

Introduction The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping. Methods A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST). Results The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points. Conclusions Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming. PMID:22443452

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa.

PubMed

Dambach, Peter; Machault, Vanessa; Lacaux, Jean-Pierre; Vignolles, Cécile; Sié, Ali; Sauerborn, Rainer

2012-03-23

The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping. A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST). The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points. Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming. © 2012 Dambach et al; licensee BioMed Central Ltd.

Scaling from instantaneous remote-sensing-based latent heat flux to daytime integrated value with the help of SiB2

NASA Astrophysics Data System (ADS)

Song, Yi; Ma, Mingguo; Li, Xin; Wang, Xufeng

2011-11-01

This research dealt with a daytime integration method with the help of Simple Biosphere Model, Version 2 (SiB2). The field observations employed in this study were obtained at the Yingke (YK) oasis super-station, which includes an Automatic Meteorological Station (AMS), an eddy covariance (EC) system and a Soil Moisture and Temperature Measuring System (SMTMS). This station is located in the Heihe River Basin, the second largest inland river basin in China. The remotely sensed data and field observations employed in this study were derived from Watershed Allied Telemetry Experimental Research (WATER). Daily variations of EF in temporal and spatial scale would be detected by using SiB2. An instantaneous midday EF was calculated based on a remote-sensing-based estimation of surface energy budget. The invariance of daytime EF was examined using the instantaneous midday EF calculated from a remote-sensing-based estimation. The integration was carried out using the constant EF method in the intervals with a steady EF. Intervals with an inconsistent EF were picked up and ET in these intervals was integrated separately. The truth validation of land Surface ET at satellite pixel scale was carried out using the measurement of eddy covariance (EC) system.

A Comparative Distributed Evaluation of the NWS-RDHM using Shape Matching and Traditional Measures with In Situ and Remotely Sensed Information

NASA Astrophysics Data System (ADS)

KIM, J.; Bastidas, L. A.

2011-12-01

We evaluate, calibrate and diagnose the performance of National Weather Service RDHM distributed model over the Durango River Basin in Colorado using simultaneously in situ and remotely sensed information from different discharge gaging stations (USGS), information about snow cover (SCV) and snow water equivalent (SWE) in situ from several SNOTEL sites and snow information distributed over the catchment from remotely sensed information (NOAA-NASA). In the process of evaluation we attempt to establish the optimal degree of parameter distribution over the catchment by calibration. A multi-criteria approach based on traditional measures (RMSE) and similarity based pattern comparisons using the Hausdorff and Earth Movers Distance approaches is used for the overall evaluation of the model performance. These pattern based approaches (shape matching) are found to be extremely relevant to account for the relatively large degree of inaccuracy in the remotely sensed SWE (judged inaccurate in terms of the value but reliable in terms of the distribution pattern) and the high reliability of the SCV (yes/no situation) while at the same time allow for an evaluation that quantifies the accuracy of the model over the entire catchment considering the different types of observations. The Hausdorff norm, due to its intrinsically multi-dimensional nature, allows for the incorporation of variables such as the terrain elevation as one of the variables for evaluation. The EMD, because of its extremely high computational overburden, requires the mapping of the set of evaluation variables into a two dimensional matrix for computation.

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

NASA Technical Reports Server (NTRS)

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.

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost

PubMed Central

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited. PMID:26528811

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.

PubMed

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.

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

NASA Astrophysics Data System (ADS)

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.

Data needs and data bases for climate studies

NASA Technical Reports Server (NTRS)

Matthews, Elaine

1986-01-01

Two complementary global digital data bases of vegetation and land use, compiled at 1 deg resolution from published sources for use in climate studies, are discussed. The data bases were implemented, in several individually tailored formulations, in a series of climate related applications including: land-surface prescriptions in three-dimensional general circulation models, global biogeochemical cycles (CO2, methane), critical-area mapping for satellite monitoring of land-cover change, and large-scale remote sensing of surface reflectance. The climate applications are discussed with reference to data needs, and data availability from traditional and remote sensing sources.

Environmental mapping and monitoring of Iceland by remote sensing (EMMIRS)

NASA Astrophysics Data System (ADS)

Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.

2016-04-01

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remote sensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remote sensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remote sensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remote sensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project investigates the complex landscape dynamics between geological and ecological processes. This is done through cross-correlation of mapping results and implementation of modelling techniques that simulate geological and ecological processes in order to extrapolate the landscape evolution

Remote sensing of ecosystem health: opportunities, challenges, and future perspectives.

PubMed

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-11-07

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

A Prototype Hydrologic Observatory for the Neuse River Basin Using Remote Sensing Data as a Part of the CUAHSI-HIS Effort

NASA Astrophysics Data System (ADS)

Kanwar, R.; Narayan, U.; Lakshmi, V.

2005-12-01

Remote sensing has the potential to immensely advance the science and application of hydrology as it provides multi-scale and multi-temporal measurements of several hydrologic parameters. There is a wide variety of remote sensing data sources available to a hydrologist with a myriad of data formats, access techniques, data quality issues and temporal and spatial extents. It is very important to make data availability and its usage as convenient as possible for potential users. The CUAHSI Hydrologic Information System (HIS) initiative addresses this issue of better data access and management for hydrologists with a focus on in-situ data, that is point measurements of water and energy fluxes which make up the 'more conventional' sources of hydrologic data. This paper explores various sources of remotely sensed hydrologic data available, their data formats and volumes, current modes of data acquisition by end users, metadata associated with data itself, and requirements from potential data models that would allow a seamless integration of remotely sensed hydrologic observations into the Hydrologic Information System. Further, a prototype hydrologic observatory (HO) for the Neuse River Basin is developed using surface temperature, vegetation indices and soil moisture estimates available from remote sensing. The prototype (HO) uses the CUAHSI digital library system (DLS) on the back (server) end. On the front (client) end, a rich visual environment has been developed in order to provide better decision making tools in order to make an optimal choice in the selection of remote sensing data for a particular application. An easy point and click interface to the remote sensing data is also implemented for common users who are just interested in location based query of hydrologic variable values.

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

NASA Astrophysics Data System (ADS)

Zhou, Lu; Xu, Shiming; Liu, Jiping; Wang, Bin

2018-03-01

The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

The application analysis of the multi-angle polarization technique for ocean color remote sensing

NASA Astrophysics Data System (ADS)

Zhang, Yongchao; Zhu, Jun; Yin, Huan; Zhang, Keli

2017-02-01

The multi-angle polarization technique, which uses the intensity of polarized radiation as the observed quantity, is a new remote sensing means for earth observation. With this method, not only can the multi-angle light intensity data be provided, but also the multi-angle information of polarized radiation can be obtained. So, the technique may solve the problems, those could not be solved with the traditional remote sensing methods. Nowadays, the multi-angle polarization technique has become one of the hot topics in the field of the international quantitative research on remote sensing. In this paper, we firstly introduce the principles of the multi-angle polarization technique, then the situations of basic research and engineering applications are particularly summarized and analysed in 1) the peeled-off method of sun glitter based on polarization, 2) the ocean color remote sensing based on polarization, 3) oil spill detection using polarization technique, 4) the ocean aerosol monitoring based on polarization. Finally, based on the previous work, we briefly present the problems and prospects of the multi-angle polarization technique used in China's ocean color remote sensing.

Discrimination of soil hydraulic properties by combined thermal infrared and microwave remote sensing

NASA Technical Reports Server (NTRS)

Vandegriend, A. A.; Oneill, P. E.

1986-01-01

Using the De Vries models for thermal conductivity and heat capacity, thermal inertia was determined as a function of soil moisture for 12 classes of soil types ranging from sand to clay. A coupled heat and moisture balance model was used to describe the thermal behavior of the top soil, while microwave remote sensing was used to estimate the soil moisture content of the same top soil. Soil hydraulic parameters are found to be very highly correlated with the combination of soil moisture content and thermal inertia at the same moisture content. Therefore, a remotely sensed estimate of the thermal behavior of the soil from diurnal soil temperature observations and an independent remotely sensed estimate of soil moisture content gives the possibility of estimating soil hydraulic properties by remote sensing.

Remote Sensing of Supercooled Cloud Layers in Cold Climate Using Ground Based Integrated Sensors System and Comparison with Pilot Reports and model forecasts

NASA Astrophysics Data System (ADS)

Boudala, Faisal; Wu, Di; Gultepe, Ismail; Anderson, Martha; turcotte, marie-france

2017-04-01

In-flight aircraft icing is one of the major weather hazards to aviation . It occurs when an aircraft passes through a cloud layer containing supercooled drops (SD). The SD in contact with the airframe freezes on the surface which degrades the performance of the aircraft.. Prediction of in-flight icing requires accurate prediction of SD sizes, liquid water content (LWC), and temperature. The current numerical weather predicting (NWP) models are not capable of making accurate prediction of SD sizes and associated LWC. Aircraft icing environment is normally studied by flying research aircraft, which is quite expensive. Thus, developing a ground based remote sensing system for detection of supercooled liquid clouds and characterization of their impact on severity of aircraft icing one of the important tasks for improving the NWPs based predictions and validations. In this respect, Environment and Climate Change Canada (ECCC) in cooperation with the Department of National Defense (DND) installed a number of specialized ground based remote sensing platforms and present weather sensors at Cold Lake, Alberta that includes a multi-channel microwave radiometer (MWR), K-band Micro Rain radar (MRR), Ceilometer, Parsivel distrometer and Vaisala PWD22 present weather sensor. In this study, a number of pilot reports confirming icing events and freezing precipitation that occurred at Cold Lake during the 2014-2016 winter periods and associated observation data for the same period are examined. The icing events are also examined using aircraft icing intensity estimated using ice accumulation model which is based on a cylindrical shape approximation of airfoil and the Canadian High Resolution Regional Deterministic Prediction System (HRDPS) model predicted LWC, median volume diameter and temperature. The results related to vertical atmospheric profiling conditions, surface observations, and the Canadian High Resolution Regional Deterministic Prediction System (HRDPS) model predictions are given. Preliminary results suggest that remote sensing and present weather sensors based observations of cloud SD regions can be used to describe micro and macro physical characteristics of the icing conditions. The model based icing intensity prediction reasonably agreed with the PIREPs and MWR observations.

Utility of an image-based canopy reflectance modeling tool for remote estimation and LAI and leaf chlorophyll content at regional scales

USDA-ARS?s Scientific Manuscript database

Radiance data recorded by remote sensors function as a unique source for monitoring the terrestrial biosphere and vegetation dynamics at a range of spatial and temporal scales. A key challenge is to relate the remote sensing signal to critical variables describing land surface vegetation canopies su...

Crop biomass and evapotranspiration estimation using SPOT and Formosat-2 Data

NASA Astrophysics Data System (ADS)

Veloso, Amanda; Demarez, Valérie; Ceschia, Eric; Claverie, Martin

2013-04-01

The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the assimilation of the remotely sensed GAI time series. The calibration process led to accurate spatial estimates of GAI, ETR as well as of biomass and yield over the study area (24 km x 24 km window). The results highlight the interest of using a combined approach (crop model coupled with high spatial and temporal resolution remote sensing data) for the estimation of agronomical variables. At local scale, the model reproduced correctly the biomass production and ETR for summer crops (with relative RMSE of 29% and 35%, respectively). At regional scale, estimated yield and water requirement for irrigation were compared to regional statistics of yield and irrigation inventories provided by the local water agency. Results showed good agreements for inter-annual dynamics of yield estimates. Differences between water requirement for irrigation and actual supply were lower than 10% and inter-annual variability was well represented as well. The work, initially focused on summer crops, is being adapted to winter crops.

Remote Sensing Terminology in a Global and Knowledge-Based World

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana

The paper is devoted to terminology issues related to all aspects of remote sensing research and applications. Terminology is the basis for a better understanding among people. It is crucial to keep up with the latest developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remote sensing and geoinformatics which develop rapidly and have ever extending applications in various domains of science and human activities. Remote sensing terminology issues are directly relevant to the contemporary worldwide policies on information accessibility, dissemination and utilization of research results in support of solutions to global environmental challenges and sustainable development goals. Remote sensing and spatial information technologies are an integral part of the international strategies for cooperation in scientific, research and application areas with a particular accent on environmental monitoring, ecological problems natural resources management, climate modeling, weather forecasts, disaster mitigation and many others to which remote sensing data can be put. Remote sensing researchers, professionals, students and decision makers of different counties and nationalities should fully understand, interpret and translate into their native language any term, definition or acronym found in papers, books, proceedings, specifications, documentation, and etc. The importance of the correct use, precise definition and unification of remote sensing terms refers not only to people working in this field but also to experts in a variety of disciplines who handle remote sensing data and information products. In this paper, we draw the attention on the specifics, peculiarities and recent needs of compiling specialized dictionaries in the area of remote sensing focusing on Earth observations and the integration of remote sensing with other geoinformation technologies such as photogrammetry, geodesy, GIS, etc. Our belief is that the elaboration of bilingual and multilingual dictionaries and glossaries in this spreading, most technically advanced and promising field of human expertise is of great practical importance. The work on an English-Bulgarian Dictionary of Remote Sensing Terms is described including considerations on its scope, structure, information content, sellection of terms, and etc. The vision builds upon previous national and international experience and makes use of ongoing activities on the subject. Any interest in cooperation and initiating suchlike collaborative projects is welcome and highly appreciated.

Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

NASA Astrophysics Data System (ADS)

Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

2017-09-01

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

Assessing UAVs in Monitoring Crop Evapotranspiration within a Heterogeneous Soil

NASA Astrophysics Data System (ADS)

Rouze, G.; Neely, H.; Morgan, C.; Kustas, W. P.; McKee, L.; Prueger, J. H.; Cope, D.; Yang, C.; Thomasson, A.; Jung, J.

2017-12-01

Airborne and satellite remote sensing methods have been developed to provide ET estimates across entire management fields. However, airborne-based ET is not particularly cost-effective and satellite-based ET provides insufficient spatial/temporal information. ET estimations through remote sensing are also problematic where soils are highly variable within a given management field. Unlike airborne/satellite-based ET, Unmanned Aerial Vehicle (UAV)-based ET has the potential to increase the spatial and temporal detail of these measurements, particularly within a heterogeneous soil landscape. However, it is unclear to what extent UAVs can model ET. The overall goal of this project was to assess the capability of UAVs in modeling ET across a heterogeneous landscape. Within a 20-ha irrigated cotton field in Central Texas, low-altitude UAV surveys were conducted throughout the growing season over two soil types. UAVs were equipped with thermal and multispectral cameras to obtain canopy temperature and NDVI, respectively. UAV data were supplemented simultaneously with ground-truth measurements such as Leaf Area Index (LAI) and plant height. Both remote sensing and ground-truth parameters were used to model ET using a Two-Source Energy Balance (TSEB) model. UAV-based estimations of ET and other energy balance components were validated against energy balance measurements obtained from nearby eddy covariance towers that were installed within each soil type. UAV-based ET fluxes were also compared with airborne and satellite (Landsat 8)-based ET fluxes collected near the time of the UAV survey.

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

PubMed

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

2018-03-24

Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections. © 2018 by the Ecological Society of America.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Methods of training the graduate level and professional geologist in remote sensing technology

NASA Technical Reports Server (NTRS)

Kolm, K. E.

1981-01-01

Requirements for a basic course in remote sensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remote sensing theory, the theory and data types relating to different remote sensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remote sensing in a given study, the integration of other data bases (geophysical and geochemical) into a remote sensing study, and geological remote sensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotely sensed data.

Current NASA Earth Remote Sensing Observations

NASA Technical Reports Server (NTRS)

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

Applications of remote sensing to watershed management

NASA Technical Reports Server (NTRS)

Rango, A.

1975-01-01

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

Simple and Multiple Endmember Mixture Analysis in the Boreal Forest

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Gamon, John A.; Qiu, Hong-Lie

2000-01-01

A key scientific objective of the original Boreal Ecosystem-Atmospheric Study (BOREAS) field campaign (1993-1996) was to obtain the baseline data required for modeling and predicting fluxes of energy, mass, and trace gases in the boreal forest biome. These data sets are necessary to determine the sensitivity of the boreal forest biome to potential climatic changes and potential biophysical feedbacks on climate. A considerable volume of remotely sensed and supporting field data were acquired by numerous researchers to meet this objective. By design, remote sensing and modeling were considered critical components for scaling efforts, extending point measurements from flux towers and field sites over larger spatial and longer temporal scales. A major focus of the BOREAS Follow-on program was concerned with integrating the diverse remotely sensed and ground-based data sets to address specific questions such as carbon dynamics at local to regional scales.

Robust feature matching via support-line voting and affine-invariant ratios

NASA Astrophysics Data System (ADS)

Li, Jiayuan; Hu, Qingwu; Ai, Mingyao; Zhong, Ruofei

2017-10-01

Robust image matching is crucial for many applications of remote sensing and photogrammetry, such as image fusion, image registration, and change detection. In this paper, we propose a robust feature matching method based on support-line voting and affine-invariant ratios. We first use popular feature matching algorithms, such as SIFT, to obtain a set of initial matches. A support-line descriptor based on multiple adaptive binning gradient histograms is subsequently applied in the support-line voting stage to filter outliers. In addition, we use affine-invariant ratios computed by a two-line structure to refine the matching results and estimate the local affine transformation. The local affine model is more robust to distortions caused by elevation differences than the global affine transformation, especially for high-resolution remote sensing images and UAV images. Thus, the proposed method is suitable for both rigid and non-rigid image matching problems. Finally, we extract as many high-precision correspondences as possible based on the local affine extension and build a grid-wise affine model for remote sensing image registration. We compare the proposed method with six state-of-the-art algorithms on several data sets and show that our method significantly outperforms the other methods. The proposed method achieves 94.46% average precision on 15 challenging remote sensing image pairs, while the second-best method, RANSAC, only achieves 70.3%. In addition, the number of detected correct matches of the proposed method is approximately four times the number of initial SIFT matches.

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with...results compared with those from remote - sensing models and from direct measurements. The agreement from different determinations suggests that...reasonable results for remote sensing reflectance of clear blue water to turbid brown water are obtainable from above-surface measurements, even under conditions of high waves.

Water Quality Variable Estimation using Partial Least Squares Regression and Multi-Scale Remote Sensing.

NASA Astrophysics Data System (ADS)

Peterson, K. T.; Wulamu, A.

2017-12-01

Water, essential to all living organisms, is one of the Earth's most precious resources. Remote sensing offers an ideal approach to monitor water quality over traditional in-situ techniques that are highly time and resource consuming. Utilizing a multi-scale approach, incorporating data from handheld spectroscopy, UAS based hyperspectal, and satellite multispectral images were collected in coordination with in-situ water quality samples for the two midwestern watersheds. The remote sensing data was modeled and correlated to the in-situ water quality variables including chlorophyll content (Chl), turbidity, and total dissolved solids (TDS) using Normalized Difference Spectral Indices (NDSI) and Partial Least Squares Regression (PLSR). The results of the study supported the original hypothesis that correlating water quality variables with remotely sensed data benefits greatly from the use of more complex modeling and regression techniques such as PLSR. The final results generated from the PLSR analysis resulted in much higher R2 values for all variables when compared to NDSI. The combination of NDSI and PLSR analysis also identified key wavelengths for identification that aligned with previous study's findings. This research displays the advantages and future for complex modeling and machine learning techniques to improve water quality variable estimation from spectral data.

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

NASA Astrophysics Data System (ADS)

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.

Remote monitoring of a thermal plume

NASA Technical Reports Server (NTRS)

Kuo, C. Y.; Talay, T. A.

1979-01-01

A remote-sensing experiment conducted on May 17, 1977, over the Surry nuclear power station on the James River, Virginia is discussed. Isotherms of the thermal plume from the power station were derived from remotely sensed data and compared with in situ water temperature measurements provided by the Virginia Electric and Power Company, VEPCO. The results of this study were also qualitatively compared with those from other previous studies under comparable conditions of the power station's operation and the ambient flow. These studies included hydraulic model predictions carried out by Pritchard and Carpenter and a 5-year in situ monitoring program based on boat surveys.

Random-Forest Classification of High-Resolution Remote Sensing Images and Ndsm Over Urban Areas

NASA Astrophysics Data System (ADS)

Sun, X. F.; Lin, X. G.

2017-09-01

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample's category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

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

PubMed

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.

A DNA-Based Semantic Fusion Model for Remote Sensing Data

PubMed Central

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. PMID:24116207

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

NASA Astrophysics Data System (ADS)

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 improve the hydrological model through higher resolution products and parameterization of variables that have previously been largely unknown.

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

NASA Technical Reports Server (NTRS)

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.

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

NASA Technical Reports Server (NTRS)

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.

Observations in the solar spectrum interest for remote sensing purposes

NASA Technical Reports Server (NTRS)

Herman, M.; Vanderbilt, V.

1994-01-01

The polarization of the sunlight scattered by atmospheric aerosols or cloud droplets and reflected from ground surfaces or plant canopies may convey much information when used for remote sensing purposes. The typical polarization features of aerosols, cloud droplets, and plant canopies, as observed by ground based and airborne sensors, are investigated, looking especially for those invariant properties amenable to description by simple models when possible. The question of polarization measurements from space is addressed. The interest of such measurements for remote sensing purposes is investigated, and their feasibility is tested by using results obtained during field campaigns of the airborne POLDER instrument, a radiometer designed to measure the directionality and polarization of the sunlight scattered by the ground atmosphere system.

The application of remote sensing to resource management and environmental quality programs in Kansas

NASA Technical Reports Server (NTRS)

Barr, B. G.; Martinko, E. A. (Principal Investigator)

1982-01-01

Capabilities to process data for state agencies in Kansas were upgraded through the vehicle of a low cost data processing system. Short term projects in which agencies identified areas of immediate needs, and longer terms projects, continued from previous years, are described including studies of Arkansas River irrigation; evaluation of rangeland in the Cimarron National Grassland; a model of the Walnut Creek watershed groundwater; selection of a pronghorn antelope release site; the establishment of a geographical data base for tax reassessment in Finney County; a land use/land cover inventory and hazards assessment; and applied R & D in agricultural remote sensing. The topics discussed at a short course in remote sensing and publications are listed.

Remote sensing of forest dynamics and land use in Amazonia

NASA Astrophysics Data System (ADS)

Toomey, Michael Paul

The rich, vast Amazonian ecosystem is directly and indirectly threatened by human activities; remote sensing serves as an essential tool for monitoring, understanding and mitigating these threats. A multi-faceted body of work is described here, addressing three major issues that employ and advance remote sensing techniques for the study of Amazonia and other tropical rainforest regions. In Chapter 2, canopy reflectance modeling and satellite observations were used to quantify the effect of epiphylls on remote sensing of humid forests. Modeling simulations demonstrated sensitivity of canopy-level near infrared and green reflectance to epiphylls on leaves. Time series of Moderate Resolution Imaging Spectrometer (MODIS) data corroborated the modeling results, suggesting a degree of coupling between epiphyll cover and vegetation indices which must be accounted for when using optical remote sensing in humid forests. In Chapter 4, 11 years (2000--2010) of MODIS land surface temperature (LST) data covering the entire Amazon basin were used to ascertain the role of heat stress during droughts in 2005 and 2010. Preliminary accuracy assessments showed that LST data provided reasonably accurate estimates of daytime air temperatures (RMSE = 1.45°C; Chapter 3). There were moderate to strong correlations between LST-based air temperature estimates and tower measurements (mean r = 0.64), illustrating a sensitivity to temporal variability. During both droughts, MODIS LST data detected anomalously high daytime and nighttime canopy temperatures throughout drought-affected regions. Multivariate linear models of LST and precipitation anomalies explained 65.1% of the variability in forest biomass losses, as determined from a wide network of forest inventory plots. These results suggest that models should incorporate both heat and moisture to predict drought effects on tropical forests. In Chapter 5, I performed high spatial and temporal resolution modeling of carbon stocks and fluxes in the state of Rondonia, Brazil for the period 1985--2009. Based on this analysis, Rondonia contributed ˜4% of pan-tropical humid forest deforestation emissions while carbon uptake by secondary forest was negligible due to limited spatial extent and high turnover rates. Spatial analysis of land cover change demonstrated the necessity for fine resolution carbon monitoring in tropical regions dominated by non-mechanized, smallholder land uses.

Opportunities and problems in introducing or expanding the teaching of remote sensing in universities

NASA Technical Reports Server (NTRS)

Maxwell, E. L.

1980-01-01

The need for degree programs in remote sensing is considered. Any education program which claims to train remote sensing specialists must include expertise in the physical principles upon which remote sensing is based. These principles dictate the limits of engineering and design, computer analysis, photogrammetry, and photointerpretation. Faculty members must be hired to provide emphasis in those five areas.

Discharge prediction in the Upper Senegal River using remote sensing data

NASA Astrophysics Data System (ADS)

Ceccarini, Iacopo; Raso, Luciano; Steele-Dunne, Susan; Hrachowitz, Markus; Nijzink, Remko; Bodian, Ansoumana; Claps, Pierluigi

2017-04-01

The Upper Senegal River, West Africa, is a poorly gauged basin. Nevertheless, discharge predictions are required in this river for the optimal operation of the downstream Manantali reservoir, flood forecasting, development plans for the entire basin and studies for adaptation to climate change. Despite the need for reliable discharge predictions, currently available rainfall-runoff models for this basin provide only poor performances, particularly during extreme regimes, both low-flow and high-flow. In this research we develop a rainfall-runoff model that combines remote-sensing input data and a-priori knowledge on catchment physical characteristics. This semi-distributed model, is based on conceptual numerical descriptions of hydrological processes at the catchment scale. Because of the lack of reliable input data from ground observations, we use the Tropical Rainfall Measuring Mission (TRMM) remote-sensing data for precipitation and the Global Land Evaporation Amsterdam Model (GLEAM) for the terrestrial potential evaporation. The model parameters are selected by a combination of calibration, by match of observed output and considering a large set of hydrological signatures, as well as a-priori knowledge on the catchment. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to choose the most likely range in which the parameter sets belong. Analysis of different experiments enhances our understanding on the added value of distributed remote-sensing data and a-priori information in rainfall-runoff modelling. Results of this research will be used for decision making at different scales, contributing to a rational use of water resources in this river.

Scripps Ocean Modeling and Remote Sensing (SOMARS)

DTIC Science & Technology

1988-09-20

Topics in this brief reports include: Kalman filtering of oceanographic data; Remote sensing of sea surface temperature; Altimetry and Surface heat fluxes; Ocean models of the marine mixed layer; Radar altimetry; Mathematical model of California current eddies.

Remote sensing strategic exploration of large or superlarge gold ore deposits

NASA Astrophysics Data System (ADS)

Yan, Shouxun; Liu, Qingsheng; Wang, Hongmei; Wang, Zhigang; Liu, Suhong

1998-08-01

To prospect large or superlarge gold ore deposits, blending of remote sensing techniques and modern metallogenitic theories is one of the effective measures. The theory of metallogeny plays a director role before and during remote sensing technique applications. The remote sensing data with different platforms and different resolutions can be respectively applied to detect direct or indirect metallogenic information, and to identify the ore-controlling structure, especially, the ore-controlling structural assemblage, which, conversely, usually are the new conditions to study and to modify the metallogenic model, and to further develop the exploration model of large or superlarge ore deposits. Guidance by an academic idea of 'adjustment structure' which is the conceptual model of transverse structure, an obscured ore- controlling transverse structure has been identified on the refined TM imagery in the Hadamengou gold ore deposit, Setai Hyperspectral Geological Remote Sensing Testing Site (SHGRSTS), Wulashan mountains, Inner Mongolia, China. Meanwhile, The MAIS data has been applied to quickly identify the auriferous alteration rocks with Correspondence Analysis method and Spectral Angle Mapping (SAM) technique. The theoretical system and technical method of remote sensing strategic exploration of large or superlarge gold ore deposits have been demonstrated by the practices in the SHGRSTS.

How robust are burn severity indices when applied in a new region? Evaluation of alternate field-based and remote-sensing methods

Treesearch

C. Alina Cansler; Donald McKenzie

2012-01-01

Remotely sensed indices of burn severity are now commonly used by researchers and land managers to assess fire effects, but their relationship to field-based assessments of burn severity has been evaluated only in a few ecosystems. This analysis illustrates two cases in which methodological refinements to field-based and remotely sensed indices of burn severity...

Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications.

USGS Publications Warehouse

Clark, R.N.; Roush, T.L.

1984-01-01

Several methods for the analysis of remotely sensed reflectance data are compared, including empirical methods and scattering theories, both of which are important for solving remote sensing problems. The concept of the photon mean path length and the implications for use in modeling reflectance spectra are presented.-from Authors

Remote sensing of earth terrain

NASA Technical Reports Server (NTRS)

Kong, Jin AU; Yueh, Herng-Aung; Shin, Robert T.

1991-01-01

Abstracts from 46 refereed journal and conference papers are presented for research on remote sensing of earth terrain. The topics covered related to remote sensing include the following: mathematical models, vegetation cover, sea ice, finite difference theory, electromagnetic waves, polarimetry, neural networks, random media, synthetic aperture radar, electromagnetic bias, and others.

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

NASA Technical Reports Server (NTRS)

Olson, R. J.; Scurlock, J. M. O.; Turner, R. S.; Jennings, S. V.

1995-01-01

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 Program'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.

Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives

PubMed Central

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-01-01

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges. PMID:25386759

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Lin, Daoyu; Fu, Kun; Wang, Yang; Xu, Guangluan; Sun, Xian

2017-11-01

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.

Estimation of Transmittance of Solar Radiation in the Visible Domain Based on Remote Sensing: Evaluation of Models Using In Situ Data

NASA Astrophysics Data System (ADS)

Zoffoli, M. Laura; Lee, Zhongping; Ondrusek, Michael; Lin, Junfang; Kovach, Charles; Wei, Jianwei; Lewis, Marlon

2017-11-01

The transmittance of solar radiation in the oceanic water column plays an important role in heat transfer and photosynthesis, with implications for the global carbon cycle, global circulation, and climate. Globally, the transmittance of solar radiation in the visible domain (˜400-700 nm) (TRVIS) through the water column, which determines the vertical distribution of visible light, has to be based on remote sensing products. There are models centered on chlorophyll-a (Chl) concentration or Inherent Optical Properties (IOPs) as both can be derived from ocean color measurements. We present evaluations of both schemes with field data from clear oceanic and from coastal waters. Here five models were evaluated: (1) Morel and Antoine (1994) (MA94), (2) Ohlmann and Siegel (2000) (OS00), (3) Murtugudde et al. (2002) (MU02), (4) Manizza et al. (2005) (MA05), and (5) Lee et al. ([Lee, Z., 2005]) (IOPs05), where the first four are Chl-based and the last one is IOPs-based, with all inputs derived from remote sensing reflectance. It is found that the best performing model is the IOPs05, with Unbiased Absolute Percent Difference (UAPD) ˜23%, while Chl-based models show higher uncertainties (UAPD for MA94: ˜54%, OS00: ˜133%, MU02: ˜56%, and MA05: ˜39%). The IOPs-based model was insensitive to the type of water, allowing it to be applied in most marine environments; whereas some of the Chl-based models (MU02 and MA05) show much higher sensitivities in coastal turbid waters (higher Chl waters). These results highlight the applicablity of using IOPs products for such applications.

A stereo remote sensing feature selection method based on artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

Assessing indicators of rangeland health with remote sensing in southeast Arizona

Treesearch

Jared Buono; Philip Heilman; David Williams; Phillip Guertin

2005-01-01

The goal of this study was to scale up ground-based range assessments to ranch and landscape scales in southeast Arizona using remote sensing and minimum amount of field data collection. Remotely sensed metrics of canopy cover, biomass, and mesquite composition were used to assess soil and site stability and biotic integrity. Ground-based assessments were conducted on...

Multi-Year Estimates of Regional Alaskan Net CO2 Exchange: Constraining a Remote-Sensing Based Model with Aircraft Observations

NASA Astrophysics Data System (ADS)

Lindaas, J.; Commane, R.; Luus, K. A.; Chang, R. Y. W.; Miller, C. E.; Dinardo, S. J.; Henderson, J.; Mountain, M. E.; Karion, A.; Sweeney, C.; Miller, J. B.; Lin, J. C.; Daube, B. C.; Pittman, J. V.; Wofsy, S. C.

2014-12-01

The Alaskan region has historically been a sink of atmospheric CO2, but permafrost currently stores large amounts of carbon that are vulnerable to release to the atmosphere as northern high-latitudes continue to warm faster than the global average. We use aircraft CO2 data with a remote-sensing based model driven by MODIS satellite products and validated by CO2 flux tower data to calculate average daily CO2 fluxes for the region of Alaska during the growing seasons of 2012 and 2013. Atmospheric trace gases were measured during CARVE (Carbon in Arctic Reservoirs Vulnerability Experiment) aboard the NASA Sherpa C-23 aircraft. For profiles along the flight track, we couple the Weather Research and Forecasting (WRF) model with the Stochastic Time-Inverted Lagrangian Transport (STILT) model, and convolve these footprints of surface influence with our remote-sensing based model, the Polar Vegetation Photosynthesis Respiration Model (PolarVPRM). We are able to calculate average regional fluxes for each month by minimizing the difference between the data and model column integrals. Our results provide a snapshot of the current state of regional Alaskan growing season net ecosystem exchange (NEE). We are able to begin characterizing the interannual variation in Alaskan NEE and to inform future refinements in process-based modeling that will produce better estimates of past, present, and future pan-Arctic NEE. Understanding if/when/how the Alaskan region transitions from a sink to a source of CO2 is crucial to predicting the trajectory of future climate change.

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

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

Region of interest extraction based on multiscale visual saliency analysis for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yinggang; Zhang, Libao; Yu, Xianchuan

2015-01-01

Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.

Adequacy of satellite derived rainfall data for stream flow modeling

USGS Publications Warehouse

Artan, G.; Gadain, Hussein; Smith, Jodie; Asante, Kwasi; Bandaragoda, C.J.; Verdin, J.P.

2007-01-01

Floods are the most common and widespread climate-related hazard on Earth. Flood forecasting can reduce the death toll associated with floods. Satellites offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions. However, satellite-based rainfall estimates have had limited use in flood forecasting and hydrologic stream flow modeling because the rainfall estimates were considered to be unreliable. In this study we present the calibration and validation results from a spatially distributed hydrologic model driven by daily satellite-based estimates of rainfall for sub-basins of the Nile and Mekong Rivers. The results demonstrate the usefulness of remotely sensed precipitation data for hydrologic modeling when the hydrologic model is calibrated with such data. However, the remotely sensed rainfall estimates cannot be used confidently with hydrologic models that are calibrated with rain gauge measured rainfall, unless the model is recalibrated. ?? Springer Science+Business Media, Inc. 2007.

Spatial Statistical Data Fusion (SSDF)

NASA Technical Reports Server (NTRS)

Braverman, Amy J.; Nguyen, Hai M.; Cressie, Noel

2013-01-01

As remote sensing for scientific purposes has transitioned from an experimental technology to an operational one, the selection of instruments has become more coordinated, so that the scientific community can exploit complementary measurements. However, tech nological and scientific heterogeneity across devices means that the statistical characteristics of the data they collect are different. The challenge addressed here is how to combine heterogeneous remote sensing data sets in a way that yields optimal statistical estimates of the underlying geophysical field, and provides rigorous uncertainty measures for those estimates. Different remote sensing data sets may have different spatial resolutions, different measurement error biases and variances, and other disparate characteristics. A state-of-the-art spatial statistical model was used to relate the true, but not directly observed, geophysical field to noisy, spatial aggregates observed by remote sensing instruments. The spatial covariances of the true field and the covariances of the true field with the observations were modeled. The observations are spatial averages of the true field values, over pixels, with different measurement noise superimposed. A kriging framework is used to infer optimal (minimum mean squared error and unbiased) estimates of the true field at point locations from pixel-level, noisy observations. A key feature of the spatial statistical model is the spatial mixed effects model that underlies it. The approach models the spatial covariance function of the underlying field using linear combinations of basis functions of fixed size. Approaches based on kriging require the inversion of very large spatial covariance matrices, and this is usually done by making simplifying assumptions about spatial covariance structure that simply do not hold for geophysical variables. In contrast, this method does not require these assumptions, and is also computationally much faster. This method is fundamentally different than other approaches to data fusion for remote sensing data because it is inferential rather than merely descriptive. All approaches combine data in a way that minimizes some specified loss function. Most of these are more or less ad hoc criteria based on what looks good to the eye, or some criteria that relate only to the data at hand.

Multi-scale remote sensing of coral reefs

USGS Publications Warehouse

Andréfouët, Serge; Hochberg, E.J.; Chevillon, Christophe; Muller-Karger, Frank E.; Brock, John C.; Hu, Chuanmin

2005-01-01

In this chapter we present how both direct and indirect remote sensing can be integrated to address two major coral reef applications - coral bleaching and assessment of biodiversity. This approach reflects the current non-linear integration of remote sensing for environmental assessment of coral reefs, resulting from a rapid increase in available sensors, processing methods and interdisciplinary collaborations (Andréfouët and Riegl, 2004). Moreover, this approach has greatly benefited from recent collaborations of once independent investigations (e.g., benthic ecology, remote sensing, and numerical modeling).

Mapping multi-scale vascular plant richness in a forest landscape with integrated LiDAR and hyperspectral remote-sensing.

PubMed

Hakkenberg, C R; Zhu, K; Peet, R K; Song, C

2018-02-01

The central role of floristic diversity in maintaining habitat integrity and ecosystem function has propelled efforts to map and monitor its distribution across forest landscapes. While biodiversity studies have traditionally relied largely on ground-based observations, the immensity of the task of generating accurate, repeatable, and spatially-continuous data on biodiversity patterns at large scales has stimulated the development of remote-sensing methods for scaling up from field plot measurements. One such approach is through integrated LiDAR and hyperspectral remote-sensing. However, despite their efficiencies in cost and effort, LiDAR-hyperspectral sensors are still highly constrained in structurally- and taxonomically-heterogeneous forests - especially when species' cover is smaller than the image resolution, intertwined with neighboring taxa, or otherwise obscured by overlapping canopy strata. In light of these challenges, this study goes beyond the remote characterization of upper canopy diversity to instead model total vascular plant species richness in a continuous-cover North Carolina Piedmont forest landscape. We focus on two related, but parallel, tasks. First, we demonstrate an application of predictive biodiversity mapping, using nonparametric models trained with spatially-nested field plots and aerial LiDAR-hyperspectral data, to predict spatially-explicit landscape patterns in floristic diversity across seven spatial scales between 0.01-900 m 2 . Second, we employ bivariate parametric models to test the significance of individual, remotely-sensed predictors of plant richness to determine how parameter estimates vary with scale. Cross-validated results indicate that predictive models were able to account for 15-70% of variance in plant richness, with LiDAR-derived estimates of topography and forest structural complexity, as well as spectral variance in hyperspectral imagery explaining the largest portion of variance in diversity levels. Importantly, bivariate tests provide evidence of scale-dependence among predictors, such that remotely-sensed variables significantly predict plant richness only at spatial scales that sufficiently subsume geolocational imprecision between remotely-sensed and field data, and best align with stand components including plant size and density, as well as canopy gaps and understory growth patterns. Beyond their insights into the scale-dependent patterns and drivers of plant diversity in Piedmont forests, these results highlight the potential of remotely-sensible essential biodiversity variables for mapping and monitoring landscape floristic diversity from air- and space-borne platforms. © 2017 by the Ecological Society of America.

An Improved Unsupervised Image Segmentation Evaluation Approach Based on - and Over-Segmentation Aware

NASA Astrophysics Data System (ADS)

Su, Tengfei

2018-04-01

In this paper, an unsupervised evaluation scheme for remote sensing image segmentation is developed. Based on a method called under- and over-segmentation aware (UOA), the new approach is improved by overcoming the defect in the part of estimating over-segmentation error. Two cases of such error-prone defect are listed, and edge strength is employed to devise a solution to this issue. Two subsets of high resolution remote sensing images were used to test the proposed algorithm, and the experimental results indicate its superior performance, which is attributed to its improved OSE detection model.

Assimilation of remote sensing observations into a continuous distributed hydrological model: impacts on the hydrologic cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Campo, Lorenzo; Cenci, Luca; Silvestro, Francesco; Delogu, Fabio; Boni, Giorgio; Rudari, Roberto

2015-04-01

The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce in-situ data. This work investigates the impact of the assimilation of different remote sensing products on the hydrological cycle by using a continuous physically based distributed hydrological model. Three soil moisture products derived by ASCAT (Advanced SCATterometer) are used to update the model state variables. The satellite-derived products are assimilated into the hydrological model using different assimilation techniques: a simple nudging and the Ensemble Kalman Filter. Moreover two assimilation strategies are evaluated to assess the impact of assimilating the satellite products at model spatial resolution or at the satellite scale. The experiments are carried out for three Italian catchments on multi year period. The benefits on the model predictions of discharge, LST, evapotranspiration and soil moisture dynamics are tested and discussed.

Evaporation estimation of rift valley lakes: comparison of models.

PubMed

Melesse, Assefa M; Abtew, Wossenu; Dessalegne, Tibebe

2009-01-01

Evapotranspiration (ET) accounts for a substantial amount of the water flux in the arid and semi-arid regions of the World. Accurate estimation of ET has been a challenge for hydrologists, mainly because of the spatiotemporal variability of the environmental and physical parameters governing the latent heat flux. In addition, most available ET models depend on intensive meteorological information for ET estimation. Such data are not available at the desired spatial and temporal scales in less developed and remote parts of the world. This limitation has necessitated the development of simple models that are less data intensive and provide ET estimates with acceptable level of accuracy. Remote sensing approach can also be applied to large areas where meteorological data are not available and field scale data collection is costly, time consuming and difficult. In areas like the Rift Valley regions of Ethiopia, the applicability of the Simple Method (Abtew Method) of lake evaporation estimation and surface energy balance approach using remote sensing was studied. The Simple Method and a remote sensing-based lake evaporation estimates were compared to the Penman, Energy balance, Pan, Radiation and Complementary Relationship Lake Evaporation (CRLE) methods applied in the region. Results indicate a good correspondence of the models outputs to that of the above methods. Comparison of the 1986 and 2000 monthly lake ET from the Landsat images to the Simple and Penman Methods show that the remote sensing and surface energy balance approach is promising for large scale applications to understand the spatial variation of the latent heat flux.

Identification of Terrestrial Reflectance From Remote Sensing

NASA Technical Reports Server (NTRS)

Alter-Gartenberg, Rachel; Nolf, Scott R.; Stacy, Kathryn (Technical Monitor)

2000-01-01

Correcting for atmospheric effects is an essential part of surface-reflectance recovery from radiance measurements. Model-based atmospheric correction techniques enable an accurate identification and classification of terrestrial reflectances from multi-spectral imagery. Successful and efficient removal of atmospheric effects from remote-sensing data is a key factor in the success of Earth observation missions. This report assesses the performance, robustness and sensitivity of two atmospheric-correction and reflectance-recovery techniques as part of an end-to-end simulation of hyper-spectral acquisition, identification and classification.

A Multiscale Random Field Model for Bayesian Image Segmentation

DTIC Science & Technology

1994-06-01

ATrN: Natural Resources Branch ATTN G ieCN-C3 D-E Aberden Povig Ground . MD 21005 At Aii-DI (2)AWN IS-TEOMAMr: ATZHI-DtE (2) ATTN: ISH-BECOM Fort...based remotely-sensed data and ground -level data for natural resource inventory and evaluation. Coupling remotely sensed digital data with traditional...ecological ground data could help Army land managers inventory and monitor natural resources. This study used LCTA data sets to D T IC test image

Object-oriented structures supporting remote sensing databases

NASA Technical Reports Server (NTRS)

Wichmann, Keith; Cromp, Robert F.

1995-01-01

Object-oriented databases show promise for modeling the complex interrelationships pervasive in scientific domains. To examine the utility of this approach, we have developed an Intelligent Information Fusion System based on this technology, and applied it to the problem of managing an active repository of remotely-sensed satellite scenes. The design and implementation of the system is compared and contrasted with conventional relational database techniques, followed by a presentation of the underlying object-oriented data structures used to enable fast indexing into the data holdings.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Landsat's role in ecological applications of remote sensing.

Treesearch

Warren B. Cohen; Samuel N. Goward

2004-01-01

Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotely sensed data, those acquired by landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on...

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

USGS Publications Warehouse

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

2016-01-01

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

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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 routing parameters. Calibration with only remotely sensed soil moisture results in an accurate calibration of parameters related to land surface process (e.g., the saturated conductivity of the soil), which is not possible when calibrating on discharge alone. For the upstream area up to 40000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30 % in the RMSE for discharge simulations, compared to calibration on discharge alone. For discharge in the downstream area, the model performance due to assimilation of remotely sensed soil moisture is not increased or slightly decreased, most probably due to the longer relative importance of the routing and contribution of groundwater in downstream areas. When microwave soil moisture is used for calibration the RMSE of soil moisture simulations decreases from 0.072 m3m-3 to 0.062 m3m-3. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models leading to a better simulation of soil moisture content throughout and a better simulation of discharge in upstream areas, particularly if discharge observations are sparse.

a Hadoop-Based Distributed Framework for Efficient Managing and Processing Big Remote Sensing Images

NASA Astrophysics Data System (ADS)

Wang, C.; Hu, F.; Hu, X.; Zhao, S.; Wen, W.; Yang, C.

2015-07-01

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

Preliminary results of fisheries investigation associated with Skylab-3. [remotely sensed distribution and abundance of gamefish in Gulf of Mexico

NASA Technical Reports Server (NTRS)

Savastano, K. J. (Principal Investigator); Pastula, E. J., Jr.; Woods, G.; Faller, K.

1974-01-01

The author has identified the following significant results. This investigation is to establish the feasibility of utilizing remotely sensed data acquired from aircraft and satellite platforms to provide information concerning the distribution and abundance of oceanic gamefish. Data from the test area in the northeastern Gulf of Mexico has made possible the identification of fisheries significant environmental parameters for white marlin. Predictive models based on catch data and surface truth information have been developed and have demonstrated potential for reducing search significantly by identifying areas which have a high probability of being productive. Three of the parameters utilized by the model, chlorophyll-a, sea surface temperature, and turbidity have been inferred from aircraft sensor data. Cloud cover and delayed receipt have inhibited the use of Skylab data. The first step toward establishing the feasibility of utilizing remotely sensed data to assess amd monitor the distribution of ocean gamefish has been taken with the successful identification of fisheries significant oceanographic parameters and the demonstration of the capability of measuring most of these parameters remotely.

The feasibility of utilizing remotely sensed data to assess and monitor oceanic gamefish

NASA Technical Reports Server (NTRS)

Savastano, K. J.; Leming, T. D.

1975-01-01

An investigation was conducted to establish the feasibility of utilizing remotely sensed data acquired from aircraft and satellite platforms to provide information concerning the distribution and abundance of oceanic gamefish. The data from the test area was jointly acquired by NASA, the Navy, the Air Force and NOAA/NMFS elements and private and professional fishermen in the northeastern Gulf of Mexico. The data collected has made it possible to identify fisheries significant environmental parameters for white marlin. Prediction models, based on catch data and surface truth information, were developed and demonstrated a potential for significantly reducing search by identifying areas that have a high probability of productivity. Three of the parameters utilized by the models, chlorophyll-a, sea surface temperature, and turbidity were inferred from aircraft sensor data and were tested. Effective use of Skylab data was inhibited by cloud cover and delayed delivery. Initial efforts toward establishing the feasibility of utilizing remotely sensed data to assess and monitor the distribution of oceanic gamefish has successfully identified fisheries significant oceanographic parameters and demonstrated the capability of remotely measuring most of the parameters.

[A review on polarization information in the remote sensing detection].

PubMed

Gong, Jie-Qiong; Zhan, Hai-Gang; Liu, Da-Zhao

2010-04-01

Polarization is one of the inherent characteristics. Because the surface of the target structure, internal structure, and the angle of incident light are different, the earth's surface and any target in atmosphere under optical interaction process will have their own characteristic nature of polarization. Polarimetric characteristics of radiation energy from the targets are used in polarization remote sensing detection as detective information. Polarization remote sensing detection can get the seven-dimensional information of targets in complicated backgrounds, detect well-resolved outline of targets and low-reflectance region of objectives, and resolve the problems of atmospheric detection and identification camouflage detection which the traditional remote sensing detection can not solve, having good foreground in applications. This paper introduces the development of polarization information in the remote sensing detection from the following four aspects. The rationale of polarization remote sensing detection is the base of polarization remote sensing detection, so it is firstly introduced. Secondly, the present researches on equipments that are used in polarization remote sensing detection are particularly and completely expatiated. Thirdly, the present exploration of theoretical simulation of polarization remote sensing detection is well detailed. Finally, the authors present the applications research home and abroad of the polarization remote sensing detection technique in the fields of remote sensing, atmospheric sounding, sea surface and underwater detection, biology and medical diagnosis, astronomical observation and military, summing up the current problems in polarization remote sensing detection. The development trend of polarization remote sensing detection technology in the future is pointed out in order to provide a reference for similar studies.

Quantitative interpretation of Great Lakes remote sensing data

NASA Technical Reports Server (NTRS)

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.

The application of remote sensing techniques to selected inter and intra urban data acquisition problems

NASA Technical Reports Server (NTRS)

Horton, F. E.

1970-01-01

The utility of remote sensing techniques to urban data acquisition problems in several distinct areas was identified. This endeavor included a comparison of remote sensing systems for urban data collection, the extraction of housing quality data from aerial photography, utilization of photographic sensors in urban transportation studies, urban change detection, space photography utilization, and an application of remote sensing techniques to the acquisition of data concerning intra-urban commercial centers. The systematic evaluation of variable extraction for urban modeling and planning at several different scales, and the model derivation for identifying and predicting economic growth and change within a regional system of cities are also studied.

Remotely sensed vegetation indices for seasonal crop yields predictions in the Czech Republic

NASA Astrophysics Data System (ADS)

Hlavinka, Petr; Semerádová, Daniela; Balek, Jan; Bohovic, Roman; Žalud, Zdeněk; Trnka, Miroslav

2015-04-01

Remotely sensed vegetation indices by satellites are valuable tool for vegetation conditions assessment also in the case of field crops. This study is based on the use of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard Terra satellite. Data available from the year 2000 were analyzed and tested for seasonal yields predictions within selected districts of the Czech Republic (Central Europe). Namely the yields of spring barley, winter wheat and oilseed winter rape during the period from 2000 to 2014 were assessed. Observed yields from 14 districts (NUTS 4) were collected and thus 210 seasons were included. Selected districts differ considerably in their soil fertility and terrain configuration and represent transect across various agroclimatic conditions (from warm and dry to relative cool and wet regions). Two approaches were tested: 1) using of composite remotely sensed data (available in 16 day time step) provided by the USGS (https://lpdaac.usgs.gov/); 2) using daily remotely sensed data in combination with originally developed smoothing method. The yields were successfully predicted based on established regression models (remotely sensed data used as independent parameter). Besides others the impact of severe drought episodes within vegetation were identified and yield reductions at district level predicted (even before harvest). As a result the periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above normal yields of field crops could be predicted by proposed method within study region up to 30 days prior to the harvest. It could be concluded that remotely sensed vegetation conditions assessment should be important part of early warning systems focused on drought. Such information should be widely available for various users (decision makers, farmers, etc.) in order to improve planning, business strategies but also to target the drought relief in case of major drought event. This study was funded by project "Building up a multidisciplinary scientific team focused on drought" No. CZ.1.07/2.3.00/20.0248, project supported by Czech National Agency of Agricultural Research No. QJ1310123 "Crop modelling as a tool for increasing the production potential and food security of the Czech Republic under Climate Change".

Coastal water optical properties from four Southeast Asian coastal environments ranging from relatively pristine to heavily impacted

NASA Astrophysics Data System (ADS)

Osburn, C. L.; Boyd, T. J.; Anastasiou, C. J.; Thao, P. T. P.; Reid, J. S.

2016-02-01

Optical measurements (absorbance, EEM fluorescence, remote sensing reflectance) and concurrently-collected sensor-based data (CDOM, chlorophyll-a, salinity, turbidity, and temperature) were used to link optical properties to water mass characteristics. Data and samples were collected during four field events in the Philippines (SEP2011, SEP2012 - transects from Manila to Palawan Island), Thailand (MAR2012 - Pattaya Beach area) and Vietnam (MAR2012 - Nha Trang and Ha Long Bay). EEM fluorescence spectra from each site were modeled using PARAFAC to identify representative fluorophores. Remote sensing reflectance was modeled using PCA, determining spectral loadings showing variation in samples from each site. These synthesized model data and sensor-based measurements were collated and ordinated using PCA to determine if optical properties could be linked to water quality and biogeochemical measures. PCA models at each site showed stations nearest to the coastline falling near or outside 95% confidence regions. Initial results indicate protein-like fluorophores were found in lower salinity waters and more heavily-impacted regions (Manila Bay - Philippines, Nha Trang River - Vietnam, Bang Pakong River - Thailand). Spectral slope and an component loading from remote sensing reflectance appeared to co-vary with sensor-derived CDOM fluorescence. Results from intra- and inter-site comparisons and linkages to biogeochemical parameters will be presented.

Assimilation of remote sensing data into a process-based ecosystem model for monitoring changes of soil water content in croplands

NASA Astrophysics Data System (ADS)

Ju, Weimin; Gao, Ping; Wang, Jun; Li, Xianfeng; Chen, Shu

2008-10-01

Soil water content (SWC) is an important factor affecting photosynthesis, growth, and final yields of crops. The information on SWC is of importance for mitigating the reduction of crop yields caused by drought through proper agricultural water management. A variety of methodologies have been developed to estimate SWC at local and regional scales, including field sampling, remote sensing monitoring and model simulations. The reliability of regional SWC simulation depends largely on the accuracy of spatial input datasets, including vegetation parameters, soil and meteorological data. Remote sensing has been proved to be an effective technique for controlling uncertainties in vegetation parameters. In this study, the vegetation parameters (leaf area index and land cover type) derived from the Moderate Resolution Imaging Spectrometer (MODIS) were assimilated into a process-based ecosystem model BEPS for simulating the variations of SWC in croplands of Jiangsu province, China. Validation shows that the BEPS model is able to capture 81% and 83% of across-site variations of SWC at 10 and 20 cm depths during the period from September to December, 2006 when a serous autumn drought occurred. The simulated SWC responded the events of rainfall well at regional scale, demonstrating the usefulness of our methodology for SWC and practical agricultural water management at large scales.

Remote sensing programs and courses in engineering and water resources

NASA Technical Reports Server (NTRS)

Kiefer, R. W.

1981-01-01

The content of typical basic and advanced remote sensing and image interpretation courses are described and typical remote sensing graduate programs of study in civil engineering and in interdisciplinary environmental remote sensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remote sensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remote sensing upon which the more specialized advanced remote sensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.

Remote Sensing Technologies and Geospatial Modelling Hierarchy for Smart City Support

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Simple algorithms for remote determination of mineral abundances and particle sizes from reflectance spectra

NASA Technical Reports Server (NTRS)

Johnson, Paul E.; Smith, Milton O.; Adams, John B.

1992-01-01

Algorithms were developed, based on Hapke's (1981) equations, for remote determinations of mineral abundances and particle sizes from reflectance spectra. In this method, spectra are modeled as a function of end-member abundances and illumination/viewing geometry. The method was tested on a laboratory data set. It is emphasized that, although there exist more sophisticated models, the present algorithms are particularly suited for remotely sensed data, where little opportunity exists to independently measure reflectance versus article size and phase function.

Potential of high resolution satellite imagery, remote weather data and 1D hydraulic modeling to evaluate flood areas in Gonaives, Haiti

NASA Astrophysics Data System (ADS)

Bozza, Andrea; Durand, Arnaud; Allenbach, Bernard; Confortola, Gabriele; Bocchiola, Daniele

2013-04-01

We present a feasibility study to explore potential of high-resolution imagery, coupled with hydraulic flood modeling to predict flooding risks, applied to the case study of Gonaives basins (585 km²), Haiti. We propose a methodology working at different scales, providing accurate results and a faster intervention during extreme flood events. The 'Hispaniola' island, in the Caribbean tropical zone, is often affected by extreme floods events. Floods are caused by tropical springs and hurricanes, and may lead to several damages, including cholera epidemics, as recently occurred, in the wake of the earthquake upon January 12th 2010 (magnitude 7.0). Floods studies based upon hydrological and hydraulic modeling are hampered by almost complete lack of ground data. Thenceforth, and given the noticeable cost involved in the organization of field measurement campaigns, the need for exploitation of remote sensing images data. HEC-RAS 1D modeling is carried out under different scenarios of available Digital Elevation Models. The DEMs are generated using optical remote sensing satellite (WorldView-1) and SRTM, combined with information from an open source database (Open Street Map). We study two recent flood episodes, where flood maps from remote sensing were available. Flood extent and land use have been assessed by way of data from SPOT-5 satellite, after hurricane Jeanne in 2004 and hurricane Hanna in 2008. A semi-distributed, DEM based hydrological model is used to simulate flood flows during the hurricanes. Precipitation input is taken from daily rainfall data derived from TRMM satellite, plus proper downscaling. The hydraulic model is calibrated using floodplain friction as tuning parameters against the observed flooded area. We compare different scenarios of flood simulation, and the predictive power of model calibration. The method provide acceptable results in depicting flooded areas, especially considering the tremendous lack of ground data, and show the potential of remote sensing information in prediction of flood events in this area, for the purpose of risk assessment and land use planning, and possibly for flood forecast during extreme events.

Remote Distributed Vibration Sensing Through Opaque Media Using Permanent Magnets

DOE PAGES

Chen, Yi; Mazumdar, Anirban; Brooks, Carlton F.; ...

2018-04-05

Vibration sensing is critical for a variety of applications from structural fatigue monitoring to understanding the modes of airplane wings. In particular, remote sensing techniques are needed for measuring the vibrations of multiple points simultaneously, assessing vibrations inside opaque metal vessels, and sensing through smoke clouds and other optically challenging environments. Here, in this paper, we propose a method which measures high-frequency displacements remotely using changes in the magnetic field generated by permanent magnets. We leverage the unique nature of vibration tracking and use a calibrated local model technique developed specifically to improve the frequency-domain estimation accuracy. The results showmore » that two-dimensional local models surpass the dipole model in tracking high-frequency motions. A theoretical basis for understanding the effects of electronic noise and error due to correlated variables is generated in order to predict the performance of experiments prior to implementation. Simultaneous measurements of up to three independent vibrating components are shown. The relative accuracy of the magnet-based displacement tracking with respect to the video tracking ranges from 40 to 190 μm when the maximum displacements approach ±5 mm and when sensor-to-magnet distances vary from 25 to 36 mm. Finally, vibration sensing inside an opaque metal vessel and mode shape changes due to damage on an aluminum beam are also studied using the wireless permanent-magnet vibration sensing scheme.« less

Remote Distributed Vibration Sensing Through Opaque Media Using Permanent Magnets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yi; Mazumdar, Anirban; Brooks, Carlton F.

Vibration sensing is critical for a variety of applications from structural fatigue monitoring to understanding the modes of airplane wings. In particular, remote sensing techniques are needed for measuring the vibrations of multiple points simultaneously, assessing vibrations inside opaque metal vessels, and sensing through smoke clouds and other optically challenging environments. Here, in this paper, we propose a method which measures high-frequency displacements remotely using changes in the magnetic field generated by permanent magnets. We leverage the unique nature of vibration tracking and use a calibrated local model technique developed specifically to improve the frequency-domain estimation accuracy. The results showmore » that two-dimensional local models surpass the dipole model in tracking high-frequency motions. A theoretical basis for understanding the effects of electronic noise and error due to correlated variables is generated in order to predict the performance of experiments prior to implementation. Simultaneous measurements of up to three independent vibrating components are shown. The relative accuracy of the magnet-based displacement tracking with respect to the video tracking ranges from 40 to 190 μm when the maximum displacements approach ±5 mm and when sensor-to-magnet distances vary from 25 to 36 mm. Finally, vibration sensing inside an opaque metal vessel and mode shape changes due to damage on an aluminum beam are also studied using the wireless permanent-magnet vibration sensing scheme.« less

Application of an imputation method for geospatial inventory of forest structural attributes across multiple spatial scales in the Lake States, U.S.A

NASA Astrophysics Data System (ADS)

Deo, Ram K.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Overview of the Bushland Evapotranspiration and Agricultural Remote sensing EXperiment 2008 (BEAREX08): A field experiment evaluating methods for quantifying ET at multiple scales

NASA Astrophysics Data System (ADS)

Evett, Steven R.; Kustas, William P.; Gowda, Prasanna H.; Anderson, Martha C.; Prueger, John H.; Howell, Terry A.

2012-12-01

In 2008, scientists from seven federal and state institutions worked together to investigate temporal and spatial variations of evapotranspiration (ET) and surface energy balance in a semi-arid irrigated and dryland agricultural region of the Southern High Plains in the Texas Panhandle. This Bushland Evapotranspiration and Agricultural Remote sensing EXperiment 2008 (BEAREX08) involved determination of micrometeorological fluxes (surface energy balance) in four weighing lysimeter fields (each 4.7 ha) containing irrigated and dryland cotton and in nearby bare soil, wheat stubble and rangeland fields using nine eddy covariance stations, three large aperture scintillometers, and three Bowen ratio systems. In coordination with satellite overpasses, flux and remote sensing aircraft flew transects over the surrounding fields and region encompassing an area contributing fluxes from 10 to 30 km upwind of the USDA-ARS lysimeter site. Tethered balloon soundings were conducted over the irrigated fields to investigate the effect of advection on local boundary layer development. Local ET was measured using four large weighing lysimeters, while field scale estimates were made by soil water balance with a network of neutron probe profile water sites and from the stationary flux systems. Aircraft and satellite imagery were obtained at different spatial and temporal resolutions. Plot-scale experiments dealt with row orientation and crop height effects on spatial and temporal patterns of soil surface temperature, soil water content, soil heat flux, evaporation from soil in the interrow, plant transpiration and canopy and soil radiation fluxes. The BEAREX08 field experiment was unique in its assessment of ET fluxes over a broad range in spatial scales; comparing direct and indirect methods at local scales with remote sensing based methods and models using aircraft and satellite imagery at local to regional scales, and comparing mass balance-based ET ground truth with eddy covariance and remote sensing-based methods. Here we present an overview of the experiment and a summary of preliminary findings described in this special issue of AWR. Our understanding of the role of advection in the measurement and modeling of ET is advanced by these papers integrating measurements and model estimates.

Improved Mapping of Carbon, Water and Energy Land-Surface Fluxes Using Remotely Sensed Indicators of Canopy Light Use Efficiency

NASA Astrophysics Data System (ADS)

Schull, M. A.; Anderson, M. C.; Kustas, W.; Cammalleri, C.; Houborg, R.

2012-12-01

A light-use-efficiency (LUE) based model of canopy resistance has been embedded into a thermal-based Two-Source Energy Balance (TSEB) model to facilitate coupled simulations of transpiration and carbon assimilation. The model assumes that deviations of the observed canopy LUE from a nominal stand-level value (LUEn - typically indexed by vegetation class) are due to varying conditions of light, humidity, CO2 concentration and leaf temperature. The deviations are accommodated by adjusting an effective LUE that responds to the varying conditions. The challenge to monitoring fluxes on a larger scale is to capture the physiological responses due to changing conditions. This challenge can be met using remotely sensed leaf chlorophyll (Cab). Since Cab is a vital pigment for absorbing light for use in photosynthesis, it has been recognized as a key parameter for quantifying photosynthetic functioning that are sensitive to these conditions. Recent studies have shown that it is sensitive to changes in LUE, which defines how efficiently a plant can assimilate carbon dioxide (CO2) given the absorbed Photosynthetically Active Radiation (PAR) and is therefore useful for monitoring carbon fluxes. We investigate the feasibility of leaf chlorophyll to capture these variations in LUEn using remotely sensed data. To retrieve Cab from remotely sensed data we use REGFLEC, a physically based tool that translates at-sensor radiances in the green, red and NIR spectral regions from multiple satellite sensors into realistic maps of LAI and Cab. Initial results show that Cab is exponentially correlated to light use efficiency. Incorporating nominal light use efficiency estimated from Cab is shown to improve fluxes of carbon, water and energy most notably in times of stressed vegetation. The result illustrates that Cab is sensitive to changes in plant physiology and can capture plant stress needed for improved estimation of fluxes. The observed relationship and initial results demonstrate the need for integrating remotely sensed Cab to facilitate improved mapping of coupled carbon, water, and energy fluxes across vegetated landscapes.

Flood Prediction for the Tam Nong District in Mekong Delta Using Hydrological Modelling and Hydrologic Remote Sensing Technique

NASA Astrophysics Data System (ADS)

Kappas, Martin; Nguyen Hong, Quang; Thanh, Nga Pham Thi; Thu, Hang Le Thi; Nguyen Vu, Giang; Degener, Jan; Rafiei Emam, Ammar

2017-04-01

There has been an increasing attention to the large trans-boundary Mekong river basin due to various problems related to water management and flood control, for instance. Vietnam Mekong delta is located at the downstream of the river basin where is affected most by this human-induced reduction in flows from the upstream. On the other hand, the flood plain of nine anastomosing channels is increasingly effected by the seawater intrusion due to sea level rising of climate change. This results in negative impacts of salinization, drought, and floods, while formerly flooding had frequently brought positive natural gain of irrigation water and alluvial aggradation. In this research, our aim is to predict flooding for the better water management adaptation and control. We applied the model HEC-SSP 2.1 to analyze flood flow frequency, two-dimensional unsteady flow calculations in HEC-RAS 5.0 for simulating a floodplain inundation. Remote sensing-based water level (Jason-2) and inundation map were used for validation and comparison with the model simulations. The results revealed a reduction of water level at all the monitoring stations, particularly in the last decade. In addition, a trend of the inundation extension gradually declined, but in some periods it remained severe due to water release from upstream reservoirs during the rainy season (October-November). We found an acceptable agreement between the HEC-RAS and remote sensing flooding maps (around 70%). Based on the flood routine analysis, we could conclude that the water level will continue lower and lead to a trend of drought and salinization harsher in the near future. Keywords: Mekong delta, flood control, inundation, water management, hydrological modelling, remote sensing

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Accommodating Student Diversity in Remote Sensing Instruction.

ERIC Educational Resources Information Center

Hammen, John L., III.

1992-01-01

Discusses the difficulty of teaching computer-based remote sensing to students of varying levels of computer literacy. Suggests an instructional method that accommodates all levels of technical expertise through the use of microcomputers. Presents a curriculum that includes an introduction to remote sensing, digital image processing, and…

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

NASA Astrophysics Data System (ADS)

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

2013-04-01

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

Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling

USGS Publications Warehouse

Van Linn, Peter F.; Nussear, Kenneth E.; Esque, Todd C.; DeFalco, Lesley A.; Inman, Richard D.; Abella, Scott R.

2013-01-01

Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.

Development of a data driven process-based model for remote sensing of terrestrial ecosystem productivity, evapotranspiration, and above-ground biomass

NASA Astrophysics Data System (ADS)

El Masri, Bassil

2011-12-01

Modeling terrestrial ecosystem functions and structure has been a subject of increasing interest because of the importance of the terrestrial carbon cycle in global carbon budget and climate change. In this study, satellite data were used to estimate gross primary production (GPP), evapotranspiration (ET) for two deciduous forests: Morgan Monroe State forest (MMSF) in Indiana and Harvard forest in Massachusetts. Also, above-ground biomass (AGB) was estimated for the MMSF and the Howland forest (mixed forest) in Maine. Surface reflectance and temperature, vegetation indices, soil moisture, tree height and canopy area derived from the Moderate Resolution Imagining Spectroradiometer (MODIS), the Advanced Microwave Scanning Radiometer (AMRS-E), LIDAR, and aerial imagery respectively, were used for this purpose. These variables along with others derived from remotely sensed data were used as inputs variables to process-based models which estimated GPP and ET and to a regression model which estimated AGB. The process-based models were BIOME-BGC and the Penman-Monteith equation. Measured values for the carbon and water fluxes obtained from the Eddy covariance flux tower were compared to the modeled GPP and ET. The data driven methods produced good estimation of GPP and ET with an average root mean square error (RMSE) of 0.17 molC/m2 and 0.40 mm/day, respectively for the MMSF and the Harvard forest. In addition, allometric data for the MMSF were used to develop the regression model relating AGB with stem volume. The performance of the AGB regression model was compared to site measurements using remotely sensed data for the MMSF and the Howland forest where the model AGB RMSE ranged between 2.92--3.30 Kg C/m2. Sensitivity analysis revealed that improvement in maintenance respiration estimation and remotely sensed maximum photosynthetic activity as well as accurate estimate of canopy resistance will result in improved GPP and ET predictions. Moreover, AGB estimates were found to decrease as large grid size is used in rasterizing LIDAR return points. The analysis suggested that this methodology could be used as an operational procedure for monitoring changes in terrestrial ecosystem functions and structure brought by environmental changes.

Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study

PubMed Central

2014-01-01

Background Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Methods Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. Results During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. Conclusions In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season. PMID:24927747

Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study.

PubMed

Nygren, David; Stoyanov, Cristina; Lewold, Clemens; Månsson, Fredrik; Miller, John; Kamanga, Aniset; Shiff, Clive J

2014-06-13

Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season.

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

[Fluorescence peak shift corresponding to high chlorophyll concentrations in inland water].

PubMed

Duan, Hong-Tao; Ma, Rong-Hua; Zhang, Yuan-Zhi; Zhang, Bai

2009-01-01

Hyperspectral remote sensing offers the potential to detect water quality variables such as Chl-a by using narrow spectral channels of less than 10 nm, which could otherwise be masked by broadband satellites such as Landsat TM. Fluorescence peak of the red region is very important for the remote sensing of inland and coastal waters, which is unique to phytoplankton Chl-a that takes place in this region. Based on in situ water sampling and field spectral measurement from 2004 to 2006 in Nanhu Lake, the features of the spectral reflectance were analyzed in detail with peak position shift. The results showed: An exponential fitting model, peak position = a(Chl-a)b, was developed between chlorophyll-a concentration and fluorescence peak shift, where a varies between 686.11 and 686.29, while b between 0.0062 and 0.0065. It was found that the better the spectral resolution, the higher the precision of the model. Except that, the average of peak shift showed a high correlation with the average of different Chl-a grades, and the determination coefficient (R2) was higher than 0.81. It contributed significantly to the increase in the accuracy of the derivation of chlorophyll values from remote sensing data in Nanhu Lake. There is satisfactory correspondence between hyperspectral models and chl-a concentration, therefore, it is possible to monitor the water quality of Nanhu lake throngh the hyperspetral remote sensing data.

Validating Remotely Sensed Land Surface Evapotranspiration Based on Multi-scale Field Measurements

NASA Astrophysics Data System (ADS)

Jia, Z.; Liu, S.; Ziwei, X.; Liang, S.

2012-12-01

The land surface evapotranspiration plays an important role in the surface energy balance and the water cycle. There have been significant technical and theoretical advances in our knowledge of evapotranspiration over the past two decades. Acquisition of the temporally and spatially continuous distribution of evapotranspiration using remote sensing technology has attracted the widespread attention of researchers and managers. However, remote sensing technology still has many uncertainties coming from model mechanism, model inputs, parameterization schemes, and scaling issue in the regional estimation. Achieving remotely sensed evapotranspiration (RS_ET) with confident certainty is required but difficult. As a result, it is indispensable to develop the validation methods to quantitatively assess the accuracy and error sources of the regional RS_ET estimations. This study proposes an innovative validation method based on multi-scale evapotranspiration acquired from field measurements, with the validation results including the accuracy assessment, error source analysis, and uncertainty analysis of the validation process. It is a potentially useful approach to evaluate the accuracy and analyze the spatio-temporal properties of RS_ET at both the basin and local scales, and is appropriate to validate RS_ET in diverse resolutions at different time-scales. An independent RS_ET validation using this method was presented over the Hai River Basin, China in 2002-2009 as a case study. Validation at the basin scale showed good agreements between the 1 km annual RS_ET and the validation data such as the water balanced evapotranspiration, MODIS evapotranspiration products, precipitation, and landuse types. Validation at the local scale also had good results for monthly, daily RS_ET at 30 m and 1 km resolutions, comparing to the multi-scale evapotranspiration measurements from the EC and LAS, respectively, with the footprint model over three typical landscapes. Although some validation experiments demonstrated that the models yield accurate estimates at flux measurement sites, the question remains whether they are performing well over the broader landscape. Moreover, a large number of RS_ET products have been released in recent years. Thus, we also pay attention to the cross-validation method of RS_ET derived from multi-source models. "The Multi-scale Observation Experiment on Evapotranspiration over Heterogeneous Land Surfaces: Flux Observation Matrix" campaign is carried out at the middle reaches of the Heihe River Basin, China in 2012. Flux measurements from an observation matrix composed of 22 EC and 4 LAS are acquired to investigate the cross-validation of multi-source models over different landscapes. In this case, six remote sensing models, including the empirical statistical model, the one-source and two-source models, the Penman-Monteith equation based model, the Priestley-Taylor equation based model, and the complementary relationship based model, are used to perform an intercomparison. All the results from the two cases of RS_ET validation showed that the proposed validation methods are reasonable and feasible.

Supervised classification of aerial imagery and multi-source data fusion for flood assessment

NASA Astrophysics Data System (ADS)

Sava, E.; Harding, L.; Cervone, G.

2015-12-01

Floods are among the most devastating natural hazards and the ability to produce an accurate and timely flood assessment before, during, and after an event is critical for their mitigation and response. Remote sensing technologies have become the de-facto approach for observing the Earth and its environment. However, satellite remote sensing data are not always available. For these reasons, it is crucial to develop new techniques in order to produce flood assessments during and after an event. Recent advancements in data fusion techniques of remote sensing with near real time heterogeneous datasets have allowed emergency responders to more efficiently extract increasingly precise and relevant knowledge from the available information. This research presents a fusion technique using satellite remote sensing imagery coupled with non-authoritative data such as Civil Air Patrol (CAP) and tweets. A new computational methodology is proposed based on machine learning algorithms to automatically identify water pixels in CAP imagery. Specifically, wavelet transformations are paired with multiple classifiers, run in parallel, to build models discriminating water and non-water regions. The learned classification models are first tested against a set of control cases, and then used to automatically classify each image separately. A measure of uncertainty is computed for each pixel in an image proportional to the number of models classifying the pixel as water. Geo-tagged tweets are continuously harvested and stored on a MongoDB and queried in real time. They are fused with CAP classified data, and with satellite remote sensing derived flood extent results to produce comprehensive flood assessment maps. The final maps are then compared with FEMA generated flood extents to assess their accuracy. The proposed methodology is applied on two test cases, relative to the 2013 floods in Boulder CO, and the 2015 floods in Texas.

A Framework for Sharing and Integrating Remote Sensing and GIS Models Based on Web Service

PubMed Central

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. PMID:24901016

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

Rokhmana, Catur Aries; Andaru, Ruli

2017-07-01

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

Monitoring landscape level processes using remote sensing of large plots

Treesearch

Raymond L. Czaplewski

1991-01-01

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

Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

USDA-ARS?s Scientific Manuscript database

Remote sensing technology can rapidly provide spatial information on crop growth status, which ideally could be used to invert radiative transfer models or ecophysiological models for estimating a variety of crop biophysical properties. However, the outcome of the model inversion procedure will be ...

Study of Diagenetic Features in Rudist Buildups of Cretaceous Edwards Formation Using Ground Based Hyperspectral Scanning and Terrestrial LiDAR

NASA Astrophysics Data System (ADS)

Krupnik, D.; Khan, S.; Okyay, U.; Hartzell, P. J.; Biber, K.

2015-12-01

Ground based remote sensing is a novel technique for development of digital outcrop models which can be instrumental in performing detailed qualitative and quantitative sedimentological analysis for the study of depositional environment, diagenetic processes, and hydrocarbon reservoir characterization. For this investigation, ground-based hyperspectral data collection is combined with terrestrial LiDAR to study outcrops of Late Albian rudist buildups of the Edwards formation in the Lake Georgetown Spillway in Williamson County, Texas. The Edwards formation consists of shallow water deposits of reef and associated inter-reef facies, including rudist bioherms and biostromes. It is a significant aquifer and was investigated as a hydrocarbon play in south central Texas. Hyperspectral data were used to map compositional variation in the outcrop by distinguishing spectral properties unique to each material. Lithological variation was mapped in detail to investigate the structure and composition of rudist buildups. Hyperspectral imagery was registered to a 3D model produced from the LiDAR point cloud with an accuracy of up to one pixel. Flat-topped toucasid-rich bioherm facies were distinguished from overlying toucasid-rich biostrome facies containing chert nodules, overlying sucrosic dolostones, and uppermost peloid wackestones and packstones of back-reef facies. Ground truth was established by petrographic study of samples from this area and has validated classification products of remote sensing data. Several types of porosity were observed and have been associated with increased dolomitization. This ongoing research involves integration of remotely sensed datasets to analyze geometrical and compositional properties of this carbonate formation at a finer scale than traditional methods have achieved and seeks to develop a workflow for quick and efficient ground based remote sensing-assisted outcrop studies.

Fully Engaging Students in the Remote Sensing Process through Field Experience

ERIC Educational Resources Information Center

Rundquist, Bradley C.; Vandeberg, Gregory S.

2013-01-01

Field data collection is often crucial to the success of investigations based upon remotely sensed data. Students of environmental remote sensing typically learn about the discipline through classroom lectures, a textbook, and computer laboratory sessions focused on the interpretation and processing of aircraft and satellite data. The importance…

Polarimetric Interferometry - Remote Sensing Applications

DTIC Science & Technology

2007-02-01

This lecture is mainly based on the work of S.R. Cloude and presents examples for remote sensing applications Polarimetric SAR Interferometry...PolInSAR). PolInSAR has its origins in remote sensing and was first developed for applications in 1997 using SIRC L-Band data [1,2]. In its original form it

Mapping the Risks of Malaria, Dengue and Influenza Using Satellite Data

NASA Astrophysics Data System (ADS)

Kiang, R. K.; Soebiyanto, R. P.

2012-07-01

It has long been recognized that environment and climate may affect the transmission of infectious diseases. The effects are most obvious for vector-borne infectious diseases, such as malaria and dengue, but less so for airborne and contact diseases, such as seasonal influenza. In this paper, we examined the meteorological and environmental parameters that influence the transmission of malaria, dengue and seasonal influenza. Remotely sensed parameters that provide such parameters were discussed. Both statistical and biologically inspired, processed based models can be used to model the transmission of these diseases utilizing the remotely sensed parameters as input. Examples were given for modelling malaria in Thailand, dengue in Indonesia, and seasonal influenza in Hong Kong.

Cybernetic group method of data handling (GMDH) statistical learning for hyperspectral remote sensing inverse problems in coastal ocean optics

NASA Astrophysics Data System (ADS)

Filippi, Anthony Matthew

For complex systems, sufficient a priori knowledge is often lacking about the mathematical or empirical relationship between cause and effect or between inputs and outputs of a given system. Automated machine learning may offer a useful solution in such cases. Coastal marine optical environments represent such a case, as the optical remote sensing inverse problem remains largely unsolved. A self-organizing, cybernetic mathematical modeling approach known as the group method of data handling (GMDH), a type of statistical learning network (SLN), was used to generate explicit spectral inversion models for optically shallow coastal waters. Optically shallow water light fields represent a particularly difficult challenge in oceanographic remote sensing. Several algorithm-input data treatment combinations were utilized in multiple experiments to automatically generate inverse solutions for various inherent optical property (IOP), bottom optical property (BOP), constituent concentration, and bottom depth estimations. The objective was to identify the optimal remote-sensing reflectance Rrs(lambda) inversion algorithm. The GMDH also has the potential of inductive discovery of physical hydro-optical laws. Simulated data were used to develop generalized, quasi-universal relationships. The Hydrolight numerical forward model, based on radiative transfer theory, was used to compute simulated above-water remote-sensing reflectance Rrs(lambda) psuedodata, matching the spectral channels and resolution of the experimental Naval Research Laboratory Ocean PHILLS (Portable Hyperspectral Imager for Low-Light Spectroscopy) sensor. The input-output pairs were for GMDH and artificial neural network (ANN) model development, the latter of which was used as a baseline, or control, algorithm. Both types of models were applied to in situ and aircraft data. Also, in situ spectroradiometer-derived Rrs(lambda) were used as input to an optimization-based inversion procedure. Target variables included bottom depth z b, chlorophyll a concentration [chl- a], spectral bottom irradiance reflectance Rb(lambda), and spectral total absorption a(lambda) and spectral total backscattering bb(lambda) coefficients. When applying the cybernetic and neural models to in situ HyperTSRB-derived Rrs, the difference in the means of the absolute error of the inversion estimates for zb was significant (alpha = 0.05). GMDH yielded significantly better zb than the ANN. The ANN model posted a mean absolute error (MAE) of 0.62214 m, compared with 0.55161 m for GMDH.

Vegetation Coverage and Impervious Surface Area Estimated Based on the Estarfm Model and Remote Sensing Monitoring

NASA Astrophysics Data System (ADS)

Hu, Rongming; Wang, Shu; Guo, Jiao; Guo, Liankun

2018-04-01

Impervious surface area and vegetation coverage are important biophysical indicators of urban surface features which can be derived from medium-resolution images. However, remote sensing data obtained by a single sensor are easily affected by many factors such as weather conditions, and the spatial and temporal resolution can not meet the needs for soil erosion estimation. Therefore, the integrated multi-source remote sensing data are needed to carry out high spatio-temporal resolution vegetation coverage estimation. Two spatial and temporal vegetation coverage data and impervious data were obtained from MODIS and Landsat 8 remote sensing images. Based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the vegetation coverage data of two scales were fused and the data of vegetation coverage fusion (ESTARFM FVC) and impervious layer with high spatiotemporal resolution (30 m, 8 day) were obtained. On this basis, the spatial variability of the seepage-free surface and the vegetation cover landscape in the study area was measured by means of statistics and spatial autocorrelation analysis. The results showed that: 1) ESTARFM FVC and impermeable surface have higher accuracy and can characterize the characteristics of the biophysical components covered by the earth's surface; 2) The average impervious surface proportion and the spatial configuration of each area are different, which are affected by natural conditions and urbanization. In the urban area of Xi'an, which has typical characteristics of spontaneous urbanization, landscapes are fragmented and have less spatial dependence.

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

NASA Technical Reports Server (NTRS)

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

1991-01-01

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

Accessing, Utilizing and Visualizing NASA Remote Sensing Data for Malaria Modeling and Surveillance

NASA Technical Reports Server (NTRS)

Kiang, Richard K.; Adimi, Farida; Kempler, Steven

2007-01-01

This poster presentation reviews the use of NASA remote sensing data that can be used to extract environmental information for modeling malaria transmission. The authors discuss the remote sensing data from Landsat, Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Earth Observing One (EO-1), Advanced Land Imager (ALI) and Seasonal to Interannual Earth Science Information Partner (SIESIP) dataset.

Physics teaching by infrared remote sensing of vegetation

NASA Astrophysics Data System (ADS)

Schüttler, Tobias; Maman, Shimrit; Girwidz, Raimund

2018-05-01

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

Remote Sensing in Geography in the New Millennium: Prospects, Challenges, and Opportunities

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Jensen, John R.; Morain, Stanley A.; Walsh, Stephen J.; Ridd, Merrill K.

1999-01-01

Remote sensing science contributes greatly to our understanding of the Earth's ecosystems and cultural landscapes. Almost all the natural and social sciences, including geography, rely heavily on remote sensing to provide quantitative, and indispensable spatial information. Many geographers have made significant contributions to remote sensing science since the 1970s, including the specification of advanced remote sensing systems, improvements in analog and digital image analysis, biophysical modeling, and terrain analysis. In fact, the Remote Sensing Specialty Group (RSSG) is one of the largest specialty groups within the AAG with over 500 members. Remote sensing in concert with a geographic information systems, offers much value to geography as both an incisive spatial-analytical tool and as a scholarly pursuit that adds to the body of geographic knowledge on the whole. The "power" of remote sensing as a research endeavor in geography lies in its capabilities for obtaining synoptic, near-real time data at many spatial and temporal scales, and in many regions of the electromagnetic spectrum - from microwave, to RADAR, to visible, and reflective and thermal infrared. In turn, these data present a vast compendium of information for assessing Earth attributes and characte6stics that are at the very core of geography. Here we revisit how remote sensing has become a fundamental and important tool for geographical research, and how with the advent of new and improved sensing systems to be launched in the near future, remote sensing will further advance geographical analysis in the approaching New Millennium.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Automated railroad reconstruction from remote sensing image based on texture filter

NASA Astrophysics Data System (ADS)

Xiao, Jie; Lu, Kaixia

2018-03-01

Techniques of remote sensing have been improved incredibly in recent years and very accurate results and high resolution images can be acquired. There exist possible ways to use such data to reconstruct railroads. In this paper, an automated railroad reconstruction method from remote sensing images based on Gabor filter was proposed. The method is divided in three steps. Firstly, the edge-oriented railroad characteristics (such as line features) in a remote sensing image are detected using Gabor filter. Secondly, two response images with the filtering orientations perpendicular to each other are fused to suppress the noise and acquire a long stripe smooth region of railroads. Thirdly, a set of smooth regions can be extracted by firstly computing global threshold for the previous result image using Otsu's method and then converting it to a binary image based on the previous threshold. This workflow is tested on a set of remote sensing images and was found to deliver very accurate results in a quickly and highly automated manner.

Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information

NASA Technical Reports Server (NTRS)

Zobler, L.; Lewis, R.

1988-01-01

The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.

A study of application of remote sensing to river forecasting. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

1975-01-01

A project is described whose goal was to define, implement and evaluate a pilot demonstration test to show the practicability of applying remotely sensed data to operational river forecasting in gaged or previously ungaged watersheds. A secondary objective was to provide NASA with documentation describing the computer programs that comprise the streamflow forecasting simulation model used. A computer-based simulation model was adapted to a streamflow forecasting application and implemented in an IBM System/360 Model 44 computer, operating in a dedicated mode, with operator interactive control through a Model 2250 keyboard/graphic CRT terminal. The test site whose hydrologic behavior was simulated is a small basin (365 square kilometers) designated Town Creek near Geraldine, Alabama.

Remote sensing the plasmasphere, plasmapause, plumes and other features using ground-based magnetometers

NASA Astrophysics Data System (ADS)

Menk, Frederick; Kale, Zoë; Sciffer, Murray; Robinson, Peter; Waters, Colin; Grew, Russell; Clilverd, Mark; Mann, Ian

2014-11-01

The plasmapause is a highly dynamic boundary between different magnetospheric particle populations and convection regimes. Some of the most important space weather processes involve wave-particle interactions in this region, but wave properties may also be used to remote sense the plasmasphere and plasmapause, contributing to plasmasphere models. This paper discusses the use of existing ground magnetometer arrays for such remote sensing. Using case studies we illustrate measurement of plasmapause location, shape and movement during storms; refilling of flux tubes within and outside the plasmasphere; storm-time increase in heavy ion concentration near the plasmapause; and detection and mapping of density irregularities near the plasmapause, including drainage plumes, biteouts and bulges. We also use a 2D MHD model of wave propagation through the magnetosphere, incorporating a realistic ionosphere boundary and Alfvén speed profile, to simulate ground array observations of power and cross-phase spectra, hence confirming the signatures of plumes and other density structures.

International Symposium on Remote Sensing of Environment, 17th, University of Michigan, Ann Arbor, MI, May 9-13, 1983, Proceedings. Volumes 1, 2 & 3

NASA Technical Reports Server (NTRS)

1984-01-01

The collection, processing, and analysis of remote-sensing data from ground-based, airborne, and spaceborne instruments for application to the monitoring and management of the earth and environment and resources are examined in reviews and reports, some in summary form. Subject areas covered include US policy and directions on remote sensing (RS); the future of terrestrial RS from space; RS of land, oceans, and atmosphere from a global perspective; RS in hydrological modeling; microprocessing technology; array processors; geobased information systems; artificial intelligence; the Shuttle imaging radar; and current results from Landsat-4. Among the specific topics discussed are RS application to hydrocarbon exploration, airborne gamma-radiation assessment of snow water equivalent, surface-vegetation-biomass modeling from AVHRR and Landsat data, Landsat imagery of Mediterranean pollution, fast two-dimensional filtering of thermal-scanner data, RS of severe convective storms, registration of rotated images by invariant moments, and the geometric accuracy of Landsat-4 Thematic-Mapper P-tapes.

Modeling Global Urbanization Supported by Nighttime Light Remote Sensing

NASA Astrophysics Data System (ADS)

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.

Reducing uncertainty for estimating forest carbon stocks and dynamics using integrated remote sensing, forest inventory and process-based modeling

NASA Astrophysics Data System (ADS)

Poulter, B.; Ciais, P.; Joetzjer, E.; Maignan, F.; Luyssaert, S.; Barichivich, J.

2015-12-01

Accurately estimating forest biomass and forest carbon dynamics requires new integrated remote sensing, forest inventory, and carbon cycle modeling approaches. Presently, there is an increasing and urgent need to reduce forest biomass uncertainty in order to meet the requirements of carbon mitigation treaties, such as Reducing Emissions from Deforestation and forest Degradation (REDD+). Here we describe a new parameterization and assimilation methodology used to estimate tropical forest biomass using the ORCHIDEE-CAN dynamic global vegetation model. ORCHIDEE-CAN simulates carbon uptake and allocation to individual trees using a mechanistic representation of photosynthesis, respiration and other first-order processes. The model is first parameterized using forest inventory data to constrain background mortality rates, i.e., self-thinning, and productivity. Satellite remote sensing data for forest structure, i.e., canopy height, is used to constrain simulated forest stand conditions using a look-up table approach to match canopy height distributions. The resulting forest biomass estimates are provided for spatial grids that match REDD+ project boundaries and aim to provide carbon estimates for the criteria described in the IPCC Good Practice Guidelines Tier 3 category. With the increasing availability of forest structure variables derived from high-resolution LIDAR, RADAR, and optical imagery, new methodologies and applications with process-based carbon cycle models are becoming more readily available to inform land management.

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

PubMed Central

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-01-01

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. PMID:29414893

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement.

PubMed

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-02-07

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L ₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

Research on Method of Interactive Segmentation Based on Remote Sensing Images

NASA Astrophysics Data System (ADS)

Yang, Y.; Li, H.; Han, Y.; Yu, F.

2017-09-01

In this paper, we aim to solve the object extraction problem in remote sensing images using interactive segmentation tools. Firstly, an overview of the interactive segmentation algorithm is proposed. Then, our detailed implementation of intelligent scissors and GrabCut for remote sensing images is described. Finally, several experiments on different typical features (water area, vegetation) in remote sensing images are performed respectively. Compared with the manual result, it indicates that our tools maintain good feature boundaries and show good performance.

The role of remotely sensed and other spatial data for predictive modeling: the Umatilla, Oregon example

USGS Publications Warehouse

Loveland, Thomas R.; Johnson, Gary E.

1981-01-01

The U. S. Geological Survey's Earth Resources Observations Systems Data Center, in cooperation with the U.S. Army Corps of Engineers, Portland District, developed and tested techniques that used remotely sensed and other spatial data in predictive models to evaluate irrigation agriculture in the Umatilla River Basin of north-central Oregon. Landsat data and 1:24,000-scale aerial photographs were initially used to map he expansion of irrigate from 1973 to 1979 and to identify crops under irrigation in 1979. The crop data were then used with historical water requirement figures and digital topographic and hydrographic data to estimate water and power use for the 1979 irrigation season. The final project task involved production of a composite map of land suitability for irrigation development based on land cover (from Landsat), land-ownership, soil irrigability, slope gradient, and potential energy costs. The methods and data used in the study demonstrated the flexibility of remotely sensed and other spatial data as input for predictive models. When combined, they provided useful answers to complex questions facing resource managers.

A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

USGS Publications Warehouse

McShane, Ryan R.; Driscoll, Katelyn P.; Sando, Roy

2017-09-27

Many approaches have been developed for measuring or estimating actual evapotranspiration (ETa), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ETa. Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ETa estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ETa and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ETa that are currently applied successfully in the United States. The METRIC model can produce maps of ETa at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ETa against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ETa. The SSEBop model has been used to produce maps of ETa over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ETa estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ETa estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth EngineTM has been used to implement METRIC with automated calibration for regional-scale estimates of ETa using Landsat data. The U.S. Geological Survey also is using Google Earth EngineTM to implement SSEBop for estimating ETa in the United States at a continental scale using Landsat data.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

NASA Astrophysics Data System (ADS)

Waldhoff, Guido; Lussem, Ulrike; Bareth, Georg

2017-09-01

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008-2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

Estimation CODMN in Guangzhou Section of Pearl River Based on GF-1 Images

NASA Astrophysics Data System (ADS)

Feng, Y. B.; He, Y. Q.; Fu, Q. H.; Liu, C. Q.; Pan, H. Z.; Yin, B.

2018-04-01

Due to the way that remote sensing works, it has natural advantage to detect optical constituents in waters. And many kinds of inversion models were constructed based on the three main optical constituents, namely chlorophyll-a (Chl-a), suspended particulate matter (SPM), colored dissolved organic matter (CDOM). Except Chl-a used as an indicator of eutrophication, however, the public generally cares less about other two parameters and is more familiar with Grade I V scheme for utilization and protection purposes. Notice the three main optical constituents are also organic-related to some extent. It offers a possible way to estimate CODMn via remote sensing. According to field measurement conducted along the Guangzhou section of Pearl River (GPR for short), the spatial variation of CODMn in GPR shows some kinds of geographical feature, so does the correlation between CODMn and water color constituents. It indicated the complicated contribution of CODMn in GPR or some other urban rivers. Based on the band setting of GF-1 satellite, two kinds of inversion model of CODMn in GPR were finally constructed. One directly achieved CODMn from regression models of which predictors were different band combinations in different channels of GPR. To make the study more practical, the other one first provided empirical models of the three optical constituents, and then estimated CODMn of GPR based on its relationship with optical constituents. After all, Chl-a, SPM and CDOM could be distinguished optically, and remote sensing models of these three constituents in other studies may also be available.

Apollo 16 landing site: Summary of earth based remote sensing data, part W

NASA Technical Reports Server (NTRS)

Zisk, S. H.; Masursky, H.; Milton, D. J.; Schaber, G. G.; Shorthill, R. W.; Thompson, T. W.

1972-01-01

Infrared and radar studies of the Apollo 16 landing site are summarized. Correlations and comparisons between earth based remote sensing data, IR observations, and other data are discussed in detail. Remote sensing studies were devoted to solving two problems: (1) determining the physical difference between Cayley and Descartes geologic units near the landing site; and (2) determining the nature of the bright unit of Descartes mountain material.

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

NASA Technical Reports Server (NTRS)

2002-01-01

Contents include the following: Monitoring the Ancient Countryside: Remote Sensing and GIS at the Chora of Chersonesos (Crimea, Ukraine). Integration of Remote Sensing and GIS for Management Decision Support in the Pendjari Biosphere Reserve (Republic of Benin). Monitoring of deforestation invasion in natural reserves of northern Madagascar based on space imagery. Cartography of Kahuzi-Biega National Park. Cartography and Land Use Change of World Heritage Areas and the Benefits of Remote Sensing and GIS for Conservation. Assessing and Monitoring Vegetation in Nabq Protected Area, South Sinai, Egypt, using combine approach of Satellite Imagery and Land Surveys. Evaluation of forage resources in semi-arid savannah environments with satellite imagery: contribution to the management of a protected area (Nakuru National Park) in Kenya. SOGHA, the Surveillance of Gorilla Habitat in World Heritage sites using Space Technologies. Application of Remote Sensing to monitor the Mont-Saint-Michel Bay (France). Application of Remote Sensing & GIS for the Conservation of Natural and Cultural Heritage Sites of the Southern Province of Sri Lanka. Social and Environmental monitoring of a UNESCO Biosphere Reserve: Case Study over the Vosges du Nord and Pfalzerwald Parks using Corona and Spot Imagery. Satellite Remote Sensing as tool to Monitor Indian Reservation in the Brazilian Amazonia. Remote Sensing and GIS Technology for Monitoring UNESCO World Heritage Sites - A Pilot Project. Urban Green Spaces: Modern Heritage. Monitoring of the technical condition of the St. Sophia Cathedral and related monastic buildings in Kiev with Space Applications, geo-positioning systems and GIS tools. The Murghab delta palaeochannel Reconstruction on the Basis of Remote Sensing from Space. Acquisition, Registration and Application of IKONOS Space Imagery for the cultural World Heritage site at Mew, Turkmenistan. Remote Sensing and VR applications for the reconstruction of archaeological landscapes. Archaeology through Space: Experience in Indian Subcontinent. The creation of a GIS Archaeological Site Location Catalogue in Yucatan: A Tool to preserve its Cultural Heritage. Mapping the Ancient Anasazi Roads of Southeast Utah. Remote Sensing and GIS Technology for Identification of Conservation and Heritage sites in Urban Planning. Mapping Angkor: For a new appraisal of the Angkor region. Angkor and radar imaging: seeing a vast pre-industrial low-density, dispersed urban complex. Technical and methodological aspects of archaeological CRM integrating high resolution satellite imagery. The contribution of satellite imagery to archaeological survey: an example from western Syria. The use of satellite images, digital elevation models and ground truth for the monitoring of land degradation in the "Cinque Terre" National park. Remote Sensing and GIS Applications for Protection and Conservation of World Heritage Site on the coast - Case Study of Tamil Nadu Coast, India. Multispectral high resolution satellite imagery in combination with "traditional" remote sensing and ground survey methods to the study of archaeological landscapes. The case study of Tuscany. Use of Remotely-Sensed Imagery in Cultural Landscape. Characterisation at Fort Hood, Texas. Heritage Learning and Data Collection: Biodiversity & Heritage Conservation through Collaborative Monitoring & Research. A collaborative project by UNESCO's WHC (World Heritage Center) & The GLOBE Program (Global Learning and Observations to Benefit the Environment). Practical Remote Sensing Activities in an Interdisciplinary Master-Level Space Course.

Application of airborne hyperspectral remote sensing for the retrieval of forest inventory parameters

NASA Astrophysics Data System (ADS)

Dmitriev, Yegor V.; Kozoderov, Vladimir V.; Sokolov, Anton A.

2016-04-01

Collecting and updating forest inventory data play an important part in the forest management. The data can be obtained directly by using exact enough but low efficient ground based methods as well as from the remote sensing measurements. We present applications of airborne hyperspectral remote sensing for the retrieval of such important inventory parameters as the forest species and age composition. The hyperspectral images of the test region were obtained from the airplane equipped by the produced in Russia light-weight airborne video-spectrometer of visible and near infrared spectral range and high resolution photo-camera on the same gyro-stabilized platform. The quality of the thematic processing depends on many factors such as the atmospheric conditions, characteristics of measuring instruments, corrections and preprocessing methods, etc. An important role plays the construction of the classifier together with methods of the reduction of the feature space. The performance of different spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation. For the reduction of the feature space we used the earlier proposed stable feature selection method. The results of the classification of hyperspectral airborne images by using the Multiclass Support Vector Machine method with Gaussian kernel and the parametric Bayesian classifier based on the Gaussian mixture model and their comparative analysis are demonstrated.

Photogrammetric Processing of Planetary Linear Pushbroom Images Based on Approximate Orthophotos

NASA Astrophysics Data System (ADS)

Geng, X.; Xu, Q.; Xing, S.; Hou, Y. F.; Lan, C. Z.; Zhang, J. J.

2018-04-01

It is still a great challenging task to efficiently produce planetary mapping products from orbital remote sensing images. There are many disadvantages in photogrammetric processing of planetary stereo images, such as lacking ground control information and informative features. Among which, image matching is the most difficult job in planetary photogrammetry. This paper designs a photogrammetric processing framework for planetary remote sensing images based on approximate orthophotos. Both tie points extraction for bundle adjustment and dense image matching for generating digital terrain model (DTM) are performed on approximate orthophotos. Since most of planetary remote sensing images are acquired by linear scanner cameras, we mainly deal with linear pushbroom images. In order to improve the computational efficiency of orthophotos generation and coordinates transformation, a fast back-projection algorithm of linear pushbroom images is introduced. Moreover, an iteratively refined DTM and orthophotos scheme was adopted in the DTM generation process, which is helpful to reduce search space of image matching and improve matching accuracy of conjugate points. With the advantages of approximate orthophotos, the matching results of planetary remote sensing images can be greatly improved. We tested the proposed approach with Mars Express (MEX) High Resolution Stereo Camera (HRSC) and Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images. The preliminary experimental results demonstrate the feasibility of the proposed approach.

a Coarse-To Model for Airplane Detection from Large Remote Sensing Images Using Saliency Modle and Deep Learning

NASA Astrophysics Data System (ADS)

Song, Z. N.; Sui, H. G.

2018-04-01

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

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

Poudyal, R.; Singh, M.; Gautam, R.; Gatebe, C. K.

2016-12-01

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

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

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

Can we infer plant facilitation from remote sensing? A test across global drylands

PubMed Central

Xu, Chi; Holmgren, Milena; Van Nes, Egbert H.; Maestre, Fernando T.; Soliveres, Santiago; Berdugo, Miguel; Kéfi, Sonia; Marquet, Pablo A.; Abades, Sebastian; Scheffer, Marten

2016-01-01

Facilitation is a major force shaping the structure and diversity of plant communities in terrestrial ecosystems. Detecting positive plant-plant interactions relies on the combination of field experimentation and the demonstration of spatial association between neighboring plants. This has often restricted the study of facilitation to particular sites, limiting the development of systematic assessments of facilitation over regional and global scales. Here we explore whether the frequency of plant spatial associations detected from high-resolution remotely-sensed images can be used to infer plant facilitation at the community level in drylands around the globe. We correlated the information from remotely-sensed images freely available through Google Earth™ with detailed field assessments, and used a simple individual-based model to generate patch-size distributions using different assumptions about the type and strength of plant-plant interactions. Most of the patterns found from the remotely-sensed images were more right-skewed than the patterns from the null model simulating a random distribution. This suggests that the plants in the studied drylands show stronger spatial clustering than expected by chance. We found that positive plant co-occurrence, as measured in the field, was significantly related to the skewness of vegetation patch-size distribution measured using Google Earth™ images. Our findings suggest that the relative frequency of facilitation may be inferred from spatial pattern signals measured from remotely-sensed images, since facilitation often determines positive co-occurrence among neighboring plants. They pave the road for a systematic global assessment of the role of facilitation in terrestrial ecosystems. PMID:26552256

Investigation related to multispectral imaging systems

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Erickson, J. D.

1974-01-01

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community.

Needs Assessment for the Use of NASA Remote Sensing Data for Regulatory Water Quality

NASA Technical Reports Server (NTRS)

Spiering, Bruce; Underwood, Lauren

2010-01-01

This slide presentation reviews the assessment of the needs that NASA can use for the remote sensing of water quality. The goal of this project is to provide information for decision-making activities (water quality standards) using remotely sensed/satellite based water quality data from MODIS and Landsat data.

Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification

USDA-ARS?s Scientific Manuscript database

Remote sensing systems based on consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. However, the performance of consumer-grade cameras for practical applications have not been well documented in related ...

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

Treesearch

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

2014-01-01

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more...

Natural Resource Information System. Remote Sensing Studies.

ERIC Educational Resources Information Center

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…

Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model

USDA-ARS?s Scientific Manuscript database

Spatial extrapolation of cropping systems models for regional crop growth and water use assessment and farm-level precision management has been limited by the vast model input requirements and the model sensitivity to parameter uncertainty. Remote sensing has been proposed as a viable source of spat...

Secure distribution for high resolution remote sensing images

NASA Astrophysics Data System (ADS)

Liu, Jin; Sun, Jing; Xu, Zheng Q.

2010-09-01

The use of remote sensing images collected by space platforms is becoming more and more widespread. The increasing value of space data and its use in critical scenarios call for adoption of proper security measures to protect these data against unauthorized access and fraudulent use. In this paper, based on the characteristics of remote sensing image data and application requirements on secure distribution, a secure distribution method is proposed, including users and regions classification, hierarchical control and keys generation, and multi-level encryption based on regions. The combination of the three parts can make that the same remote sensing images after multi-level encryption processing are distributed to different permission users through multicast, but different permission users can obtain different degree information after decryption through their own decryption keys. It well meets user access control and security needs in the process of high resolution remote sensing image distribution. The experimental results prove the effectiveness of the proposed method which is suitable for practical use in the secure transmission of remote sensing images including confidential information over internet.

A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

NASA Astrophysics Data System (ADS)

Liu, Likun

2018-01-01

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.

PubMed

Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi

2015-03-15

Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate statistical modeling techniques, demonstrated advantages for estimating the TP concentration in a large lake and had a strong potential for universal application for the TP concentration estimation in large lake waters worldwide. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Forest Disturbance Analysis with LANDSAT-8 Oli Data Related to a Parametric Wind Field: a Case Study for Typhoon Rammasun (201409)

NASA Astrophysics Data System (ADS)

Tan, C.; Fang, W.

2018-04-01

Forest disturbance induced by tropical cyclone often has significant and profound effects on the structure and function of forest ecosystem. Detection and analysis of post-disaster forest disturbance based on remote sensing technology has been widely applied. At present, it is necessary to conduct further quantitative analysis of the magnitude of forest disturbance with the intensity of typhoon. In this study, taking the case of super typhoon Rammasun (201409), we analysed the sensitivity of four common used remote sensing indices and explored the relationship between remote sensing index and corresponding wind speeds based on pre-and post- Landsat-8 OLI (Operational Land Imager) images and a parameterized wind field model. The results proved that NBR is the most sensitive index for the detection of forest disturbance induced by Typhoon Rammasun and the variation of NBR has a significant linear dependence relation with the simulated 3-second gust wind speed.

Into the environment of mosquito-borne disease: A spatial analysis of vector distribution using traditional and remotely sensed methods

NASA Astrophysics Data System (ADS)

Brown, Heidi E.

Spatially explicit information is increasingly available for infectious disease modeling. However, such information is reluctantly or inappropriately incorporated. My dissertation research uses spatially explicit data to assess relationships between landscape and mosquito species distribution and discusses challenges regarding accurate predictive risk modeling. The goal of my research is to use remotely sensed environmental information and spatial statistical methods to better understand mosquito-borne disease epidemiology for improvement of public health responses. In addition to reviewing the progress of spatial infectious disease modeling, I present four research projects. I begin by evaluating the biases in surveillance data and build up to predictive modeling of mosquito species presence. In the first study I explore how mosquito surveillance trap types influence estimations of mosquito populations. Then. I use county-based human surveillance data and landscape variables to identify risk factors for West Nile virus disease. The third study uses satellite-based vegetation indices to identify spatial variation among West Nile virus vectors in an urban area and relates the variability to virus transmission dynamics. Finally, I explore how information from three satellite sensors of differing spatial and spectral resolution can be used to identify and distinguish mosquito habitat across central Connecticut wetlands. Analyses presented here constitute improvements to the prediction of mosquito distribution and therefore identification of disease risk factors. Current methods for mosquito surveillance data collection are labor intensive and provide an extremely limited, incomplete picture of the species composition and abundance. Human surveillance data offers additional challenges with respect to reporting bias and resolution, but is nonetheless informative in identifying environmental risk factors and disease transmission dynamics. Remotely sensed imagery supports mosquito and human disease surveillance data by providing spatially explicit, line resolution information about environmental factors relevant to vector-borne disease processes. Together, surveillance and remotely sensed environmental data facilitate improved description and modeling of disease transmission. Remote sensing can be used to develop predictive maps of mosquito distribution in relation to disease risk. This has implications for increased accuracy of mosquito control efforts. The projects presented in this dissertation enhance current public health capacities by examining the applications of spatial modeling with respect to mosquito-borne disease.

The investigation of advanced remote sensing, radiative transfer and inversion techniques for the measurement of atmospheric constituents

NASA Technical Reports Server (NTRS)

Deepak, Adarsh; Wang, Pi-Huan

1985-01-01

The research program is documented for developing space and ground-based remote sensing techniques performed during the period from December 15, 1977 to March 15, 1985. The program involved the application of sophisticated radiative transfer codes and inversion methods to various advanced remote sensing concepts for determining atmospheric constituents, particularly aerosols. It covers detailed discussions of the solar aureole technique for monitoring columnar aerosol size distribution, and the multispectral limb scattered radiance and limb attenuated radiance (solar occultation) techniques, as well as the upwelling scattered solar radiance method for determining the aerosol and gaseous characteristics. In addition, analytical models of aerosol size distribution and simulation studies of the limb solar aureole radiance technique and the variability of ozone at high altitudes during satellite sunrise/sunset events are also described in detail.

[Study on extraction method of Panax notoginseng plots in Wenshan of Yunnan province based on decision tree model].

PubMed

Shi, Ting-Ting; Zhang, Xiao-Bo; Guo, Lan-Ping; Huang, Lu-Qi

2017-11-01

The herbs used as the material for traditional Chinese medicine are always planted in the mountainous area where the natural environment is suitable. As the mountain terrain is complex and the distribution of planting plots is scattered, the traditional survey method is difficult to obtain accurate planting area. It is of great significance to provide decision support for the conservation and utilization of traditional Chinese medicine resources by studying the method of extraction of Chinese herbal medicine planting area based on remote sensing and realizing the dynamic monitoring and reserve estimation of Chinese herbal medicines. In this paper, taking the Panax notoginseng plots in Wenshan prefecture of Yunnan province as an example, the China-made GF-1multispectral remote sensing images with a 16 m×16 m resolution were obtained. Then, the time series that can reflect the difference of spectrum of P. notoginseng shed and the background objects were selected to the maximum extent, and the decision tree model of extraction the of P. notoginseng plots was constructed according to the spectral characteristics of the surface features. The results showed that the remote sensing classification method based on the decision tree model could extract P. notoginseng plots in the study area effectively. The method can provide technical support for extraction of P. notoginseng plots at county level. Copyright© by the Chinese Pharmaceutical Association.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

A new multi-sensor integrated index for drought monitoring

NASA Astrophysics Data System (ADS)

Jiao, W.; Wang, L.; Tian, C.

2017-12-01

Drought is perceived as one of the most expensive and least understood natural disasters. The remote-sensing-based integrated drought indices, which integrate multiple variables, could reflect the drought conditions more comprehensively than single drought indices. However, most of current remote-sensing-based integrated drought indices focus on agricultural drought (i.e., deficit in soil moisture), their application in monitoring meteorological drought (i.e., deficit in precipitation) was limited. More importantly, most of the remote-sensing-based integrated drought indices did not take into consideration of the spatially non-stationary nature of the related variables, so such indices may lose essential local details when integrating multiple variables. In this regard, we proposed a new mathematical framework for generating integrated drought index for meteorological drought monitoring. The geographically weighted regression (GWR) model and principal component analysis were used to composite Moderate-resolution Imaging Spectroradiometer (MODIS) based temperature condition index (TCI), the Vegetation Index based on the Universal Pattern Decomposition method (VIUPD) based vegetation condition index (VCI), tropical rainfall measuring mission (TRMM) based Precipitation Condition Index (PCI) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) based soil moisture condition index (SMCI). We called the new remote-sensing-based integrated drought index geographical-location-based integrated drought index (GLIDI). We examined the utility of the GLIDI for drought monitoring in various climate divisions across the continental United States (CONUS). GLIDI showed high correlations with in-situ drought indices and outperformed most other existing drought indices. The results also indicate that the performance of GLIDI is not affected by environmental factors such as land cover, precipitation, temperature and soil conditions. As such, the GLIDI has considerable potential for drought monitoring across various environmental conditions.

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data.

PubMed

Vanegas, Fernando; Bratanov, Dmitry; Powell, Kevin; Weiss, John; Gonzalez, Felipe

2018-01-17

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used-the sensors, the UAV, and the flight operations-the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analising and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

Remote sensing provides a unique opportunity to inform and constrain a hydrological model and to increase its value as a decision-support tool. In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations. We used a rainfall-runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) and forced it using precipitation from two satellite-based rainfall estimates, FEWS-RFE (Famine Early Warning System rainfall estimate) and the Tropical Rainfall Measuring Mission (TRMM) 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function with contributions from (1) historical in situ discharge observations from the period 1953-1984 at six locations in the basin, (2) radar altimetry measurements of river stages by Envisat and Jason-2 at 12 locations in the basin and (3) GRACE (Gravity Recovery and Climate Experiment) total water storage change (TWSC). Additionally, we extracted CryoSat-2 observations throughout the basin using a Sentinel-1 SAR (synthetic aperture radar) imagery water mask and used the observations for validation of the model. The use of new satellite missions, including Sentinel-1 and CryoSat-2, increased the spatial characterization of river stage. Throughout the basin, we achieved good agreement between observed and simulated discharge and the river stage, with an RMSD between simulated and observed water amplitudes at virtual stations of 0.74 m for the TRMM-forced model and 0.87 m for the FEWS-RFE-forced model. The hydrological model also captures overall total water storage change patterns, although the amplitude of storage change is generally underestimated. By combining hydrological modeling with multi-mission remote sensing from 10 different satellite missions, we obtain new information on an otherwise unstudied basin. The proposed model is the best current baseline characterization of hydrological conditions in the Ogooué in light of the available observations.

Study of Burn Scar Extraction Automatically Based on Level Set Method using Remote Sensing Data

PubMed Central

Liu, Yang; Dai, Qin; Liu, JianBo; Liu, ShiBin; Yang, Jin

2014-01-01

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model. PMID:24503563

An Interactive Web-Based Analysis Framework for Remote Sensing Cloud Computing

NASA Astrophysics Data System (ADS)

Wang, X. Z.; Zhang, H. M.; Zhao, J. H.; Lin, Q. H.; Zhou, Y. C.; Li, J. H.

2015-07-01

Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users' private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook. Users can write complex data processing code on the web directly, so they can design their own data processing algorithm.

Estimates of Leaf Relative Water Content from Optical Polarization Measurements

NASA Astrophysics Data System (ADS)

Dahlgren, R. P.; Vanderbilt, V. C.; Daughtry, C. S. T.

2017-12-01

Remotely sensing the water status of plant canopies remains a long term goal of remote sensing research. Existing approaches to remotely sensing canopy water status, such as the Crop Water Stress Index (CWSI) and the Equivalent Water Thickness (EWT), have limitations. The CWSI, based upon remotely sensing canopy radiant temperature in the thermal infrared spectral region, does not work well in humid regions, requires estimates of the vapor pressure deficit near the canopy during the remote sensing over-flight and, once stomata close, provides little information regarding the canopy water status. The EWT is based upon the physics of water-light interaction in the 900-2000nm spectral region, not plant physiology. Our goal, development of a remote sensing technique for estimating plant water status based upon measurements in the VIS/NIR spectral region, would potentially provide remote sensing access to plant dehydration physiology - to the cellular photochemistry and structural changes associated with water deficits in leaves. In this research, we used optical, crossed polarization filters to measure the VIS/NIR light reflected from the leaf interior, R, as well as the leaf transmittance, T, for 78 corn (Zea mays) and soybean (Glycine max) leaves having relative water contents (RWC) between 0.60 and 0.98. Our results show that as RWC decreases R increases while T decreases. Our results tie R and T changes in the VIS/NIR to leaf physiological changes - linking the light scattered out of the drying leaf interior to its relative water content and to changes in leaf cellular structure and pigments. Our results suggest remotely sensing the physiological water status of a single leaf - and perhaps of a plant canopy - might be possible in the future.

Simulating Canopy-Level Solar Induced Fluorescence with CLM-SIF 4.5 at a Sub-Alpine Conifer Forest in the Colorado Rockies

NASA Astrophysics Data System (ADS)

Raczka, B. M.; Bowling, D. R.; Lin, J. C.; Lee, J. E.; Yang, X.; Duarte, H.; Zuromski, L.

2017-12-01

Forests of the Western United States are prone to drought, temperature extremes, forest fires and insect infestation. These disturbance render carbon stocks and land-atmosphere carbon exchanges highly variable and vulnerable to change. Regional estimates of carbon exchange from terrestrial ecosystem models are challenged, in part, by a lack of net ecosystem exchange observations (e.g. flux towers) due to the complex mountainous terrain. Alternatively, carbon estimates based on light use efficiency models that depend upon remotely-sensed greenness indices are challenged due to a weak relationship with GPP during the winter season. Recent advances in the retrieval of remotely sensed solar induced fluorescence (SIF) have demonstrated a strong seasonal relationship between GPP and SIF for deciduous, grass and, to a lesser extent, conifer species. This provides an important opportunity to use remotely-sensed SIF to calibrate terrestrial ecosystem models providing a more accurate regional representation of biomass and carbon exchange across mountainous terrain. Here we incorporate both leaf-level fluorescence and leaf-to-canopy radiative transfer represented by the SCOPE model into CLM 4.5 (CLM-SIF). We simulate canopy level fluorescence at a sub-alpine forest site (Niwot Ridge, Colorado) and test whether these simulations reproduce remotely-sensed SIF from a satellite (GOME2). We found that the average peak SIF during the growing season (yrs 2007-2013) was similar between the model and satellite observations (within 15%); however, simulated SIF during the winter season was significantly greater than the satellite observations (5x higher). This implies that the fluorescence yield is overestimated by the model during the winter season. It is important that the modeled representation of seasonal fluorescence yield is improved to provide an accurate seasonal representation of SIF across the Western United States.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

Current remote sensing methods can identify aerosol types within an atmospheric column, presenting an opportunity to incrementally bridge the gap between remote sensing and models. Here a new algorithm was designed for Creating Aerosol Types from CHemistry (CATCH). CATCH-derived aerosol types—dusty mix, maritime, urban, smoke, and fresh smoke—are based on first-generation airborne High Spectral Resolution Lidar (HSRL-1) retrievals during the Ship-Aircraft Bio-Optical Research (SABOR) campaign, July/August 2014. CATCH is designed to derive aerosol types from model output of chemical composition. CATCH-derived aerosol types are determined by multivariate clustering of model-calculated variables that have been trained using retrievals of aerosol types from HSRL-1. CATCH-derived aerosol types (with the exception of smoke) compare well with HSRL-1 retrievals during SABOR with an average difference in aerosol optical depth (AOD) <0.03. Data analysis shows that episodic free tropospheric transport of smoke is underpredicted by the Goddard Earth Observing System- with Chemistry (GEOS-Chem) model. Spatial distributions of CATCH-derived aerosol types for the North American model domain during July/August 2014 show that aerosol type-specific AOD values occurred over representative locations: urban over areas with large population, maritime over oceans, smoke, and fresh smoke over typical biomass burning regions. This study demonstrates that model-generated information on aerosol chemical composition can be translated into aerosol types analogous to those retrieved from remote sensing methods. In the future, spaceborne HSRL-1 and CATCH can be used to gain insight into chemical composition of aerosol types, reducing uncertainties in estimates of aerosol radiative forcing.

Natural resources evaluation by the use of remote sensing and GIS technology for agricultural development

NASA Astrophysics Data System (ADS)

Pham Viet, C.; Nguyen Phuong, M.

1993-11-01

A multilevel Geographic Information System based on Remote Sensing and GIS-Technology is established to assess erosion susceptibility and select suitable land for soil conservation and regional planning, management. A cross analysis between the thematic maps and field data is done to examine the relationship between natural condition and land suitability for agriculture. The Land resources evaluation models are affective for understanding the cultivation possibility and can be used as a regional project to be applied in various Vietnam regions for agricultural development.

Interfacing remote sensing and geographic information systems for global environmental change research

NASA Technical Reports Server (NTRS)

Lee, Jae K.; Randolph, J. C.; Lulla, Kamlesh P.; Helfert, Michael R.

1993-01-01

Because changes in the Earth's environment have become major global issues, continuous, longterm scientific information is required to assess global problems such as deforestation, desertification, greenhouse effects and climate variations. Global change studies require understanding of interactions of complex processes regulating the Earth system. Space-based Earth observation is an essential element in global change research for documenting changes in Earth environment. It provides synoptic data for conceptual predictive modeling of future environmental change. This paper provides a brief overview of remote sensing technology from the perspective of global change research.

Impact of remote sensing upon the planning, management, and development of water resources

NASA Technical Reports Server (NTRS)

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

1975-01-01

Principal water resources users were surveyed to determine the impact of remote data streams on hydrologic computer models. Analysis of responses demonstrated that: most water resources effort suitable to remote sensing inputs is conducted through federal agencies or through federally stimulated research; and, most hydrologic models suitable to remote sensing data are federally developed. Computer usage by major water resources users was analyzed to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era.

Spectral estimates of net radiation and soil heat flux

USGS Publications Warehouse

Daughtry, C.S.T.; Kustas, William P.; Moran, M.S.; Pinter, P. J.; Jackson, R. D.; Brown, P.W.; Nichols, W.D.; Gay, L.W.

1990-01-01

Conventional methods of measuring surface energy balance are point measurements and represent only a small area. Remote sensing offers a potential means of measuring outgoing fluxes over large areas at the spatial resolution of the sensor. The objective of this study was to estimate net radiation (Rn) and soil heat flux (G) using remotely sensed multispectral data acquired from an aircraft over large agricultural fields. Ground-based instruments measured Rn and G at nine locations along the flight lines. Incoming fluxes were also measured by ground-based instruments. Outgoing fluxes were estimated using remotely sensed data. Remote Rn, estimated as the algebraic sum of incoming and outgoing fluxes, slightly underestimated Rn measured by the ground-based net radiometers. The mean absolute errors for remote Rn minus measured Rn were less than 7%. Remote G, estimated as a function of a spectral vegetation index and remote Rn, slightly overestimated measured G; however, the mean absolute error for remote G was 13%. Some of the differences between measured and remote values of Rn and G are associated with differences in instrument designs and measurement techniques. The root mean square error for available energy (Rn - G) was 12%. Thus, methods using both ground-based and remotely sensed data can provide reliable estimates of the available energy which can be partitioned into sensible and latent heat under nonadvective conditions. ?? 1990.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73-0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error - observations standard deviation ratio (RSR) values <0.7 (0.39-0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32-0.52, PBIAS: ±32.73%, and RSR: 0.63-0.82). Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59-0.61, PBIAS: ±13.70%, and RSR: 0.63-0.64) while the evapotranspiration estimations were improved. Results from this assessment shows that incorporation of remotely sensed and spatially distributed data can improve the hydrological model performance if it is coupled with a right calibration technique.

A study of the potential of remote sensors in urban transportation planning

NASA Technical Reports Server (NTRS)

Rietschier, D.; Modlin, D. G., Jr.

1973-01-01

The potential uses of remotely sensed data as applied to the transportation planning process are presented. By utilizing the remote sensing technology developed by the National Aeronautics and Space Administration in the various space programs, it is hoped that both the expense and errors inherent in the conventional data collection techniques can be avoided. Additional bonuses derived from the use of remotely sensed data are those of the permanent record nature of the data and the traffic engineering data simultaneously made available. The major mathematical modeling phases and the role remotely sensed data might play in replacing conventionally collected data are discussed. Typical surveys undertaken in the overall planning process determine the nature and extent of travel desires, land uses, transportation facilities and socio-economic characteristics. Except for the socio-economic data, data collected in the other surveys mentioned can be taken from photographs in sufficient detail to be useful in the modeling procedures.

Variational method based on Retinex with double-norm hybrid constraints for uneven illumination correction

NASA Astrophysics Data System (ADS)

Li, Shuo; Wang, Hui; Wang, Liyong; Yu, Xiangzhou; Yang, Le

2018-01-01

The uneven illumination phenomenon reduces the quality of remote sensing image and causes interference in the subsequent processing and applications. A variational method based on Retinex with double-norm hybrid constraints for uneven illumination correction is proposed. The L1 norm and the L2 norm are adopted to constrain the textures and details of reflectance image and the smoothness of the illumination image, respectively. The problem of separating the illumination image from the reflectance image is transformed into the optimal solution of the variational model. In order to accelerate the solution, the split Bregman method is used to decompose the variational model into three subproblems, which are calculated by alternate iteration. Two groups of experiments are implemented on two synthetic images and three real remote sensing images. Compared with the variational Retinex method with single-norm constraint and the Mask method, the proposed method performs better in both visual evaluation and quantitative measurements. The proposed method can effectively eliminate the uneven illumination while maintaining the textures and details of the remote sensing image. Moreover, the proposed method using split Bregman method is more than 10 times faster than the method with the steepest descent method.

Remote sensing for industrial applications in the energy business: digital territorial data integration for planning of overhead power transmission lines (OHTLs)

NASA Astrophysics Data System (ADS)

Terrazzino, Alfonso; Volponi, Silvia; Borgogno Mondino, Enrico

2001-12-01

An investigation has been carried out, concerning remote sensing techniques, in order to assess their potential application to the energy system business: the most interesting results concern a new approach, based on digital data from remote sensing, to infrastructures with a large territorial distribution: in particular OverHead Transmission Lines, for the high voltage transmission and distribution of electricity on large distances. Remote sensing could in principle be applied to all the phases of the system lifetime, from planning to design, to construction, management, monitoring and maintenance. In this article, a remote sensing based approach is presented, targeted to the line planning: optimization of OHTLs path and layout, according to different parameters (technical, environmental and industrial). Planning new OHTLs is of particular interest in emerging markets, where typically the cartography is missing or available only on low accuracy scale (1:50.000 and lower), often not updated. Multi- spectral images can be used to generate thematic maps of the region of interest for the planning (soil coverage). Digital Elevation Models (DEMs), allow the planners to easily access the morphologic information of the surface. Other auxiliary information from local laws, environmental instances, international (IEC) standards can be integrated in order to perform an accurate optimized path choice and preliminary spotting of the OHTLs. This operation is carried out by an ABB proprietary optimization algorithm: the output is a preliminary path that bests fits the optimization parameters of the line in a life cycle approach.

Optical remote sensing and correlation of office equipment functional state and stress levels via power quality disturbances inefficiencies

NASA Astrophysics Data System (ADS)

Sternberg, Oren; Bednarski, Valerie R.; Perez, Israel; Wheeland, Sara; Rockway, John D.

2016-09-01

Non-invasive optical techniques pertaining to the remote sensing of power quality disturbances (PQD) are part of an emerging technology field typically dominated by radio frequency (RF) and invasive-based techniques. Algorithms and methods to analyze and address PQD such as probabilistic neural networks and fully informed particle swarms have been explored in industry and academia. Such methods are tuned to work with RF equipment and electronics in existing power grids. As both commercial and defense assets are heavily power-dependent, understanding electrical transients and failure events using non-invasive detection techniques is crucial. In this paper we correlate power quality empirical models to the observed optical response. We also empirically demonstrate a first-order approach to map household, office and commercial equipment PQD to user functions and stress levels. We employ a physics-based image and signal processing approach, which demonstrates measured non-invasive (remote sensing) techniques to detect and map the base frequency associated with the power source to the various PQD on a calibrated source.

Improved Lower Mekong River Basin Hydrological Decision Making Using NASA Satellite-based Earth Observation Systems

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Mohammed, I. N.; Srinivasan, R.; Lakshmi, V.

2017-12-01

Better understanding of the hydrological cycle of the Lower Mekong River Basin (LMRB) and addressing the value-added information of using remote sensing data on the spatial variability of soil moisture over the Mekong Basin is the objective of this work. In this work, we present the development and assessment of the LMRB (drainage area of 495,000 km2) Soil and Water Assessment Tool (SWAT). The coupled model framework presented is part of SERVIR, a joint capacity building venture between NASA and the U.S. Agency for International Development, providing state-of-the-art, satellite-based earth monitoring, imaging and mapping data, geospatial information, predictive models, and science applications to improve environmental decision-making among multiple developing nations. The developed LMRB SWAT model enables the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil texture, and land cover and land use data to drive SWAT model simulations over the Lower Mekong River Basin. The LMRB SWAT model driven by remote sensing climate data was calibrated and verified with observed runoff data at the watershed outlet as well as at multiple sites along the main river course. Another LMRB SWAT model set driven by in-situ climate observations was also calibrated and verified to streamflow data. Simulated soil moisture estimates from the two models were then examined and compared to a downscaled Soil Moisture Active Passive Sensor (SMAP) 36 km radiometer products. Results from this work present a framework for improving SWAT performance by utilizing a downscaled SMAP soil moisture products used for model calibration and validation. Index Terms: 1622: Earth system modeling; 1631: Land/atmosphere interactions; 1800: Hydrology; 1836 Hydrological cycles and budgets; 1840 Hydrometeorology; 1855: Remote sensing; 1866: Soil moisture; 6334: Regional Planning

An assessment of aerosol optical properties from remote-sensing observations and regional chemistry-climate coupled models over Europe

NASA Astrophysics Data System (ADS)

Palacios-Peña, Laura; Baró, Rocío; Baklanov, Alexander; Balzarini, Alessandra; Brunner, Dominik; Forkel, Renate; Hirtl, Marcus; Honzak, Luka; María López-Romero, José; Montávez, Juan Pedro; Pérez, Juan Luis; Pirovano, Guido; San José, Roberto; Schröder, Wolfram; Werhahn, Johannes; Wolke, Ralf; Žabkar, Rahela; Jiménez-Guerrero, Pedro

2018-04-01

Atmospheric aerosols modify the radiative budget of the Earth due to their optical, microphysical and chemical properties, and are considered one of the most uncertain climate forcing agents. In order to characterise the uncertainties associated with satellite and modelling approaches to represent aerosol optical properties, mainly aerosol optical depth (AOD) and Ångström exponent (AE), their representation by different remote-sensing sensors and regional online coupled chemistry-climate models over Europe are evaluated. This work also characterises whether the inclusion of aerosol-radiation (ARI) or/and aerosol-cloud interactions (ACI) help improve the skills of modelling outputs.Two case studies were selected within the EuMetChem COST Action ES1004 framework when important aerosol episodes in 2010 all over Europe took place: a Russian wildfire episode and a Saharan desert dust outbreak that covered most of the Mediterranean Sea. The model data came from different regional air-quality-climate simulations performed by working group 2 of EuMetChem, which differed according to whether ARI or ACI was included or not. The remote-sensing data came from three different sensors: MODIS, OMI and SeaWIFS. The evaluation used classical statistical metrics to first compare satellite data versus the ground-based instrument network (AERONET) and then to evaluate model versus the observational data (both satellite and ground-based data).Regarding the uncertainty in the satellite representation of AOD, MODIS presented the best agreement with the AERONET observations compared to other satellite AOD observations. The differences found between remote-sensing sensors highlighted the uncertainty in the observations, which have to be taken into account when evaluating models. When modelling results were considered, a common trend for underestimating high AOD levels was observed. For the AE, models tended to underestimate its variability, except when considering a sectional approach in the aerosol representation. The modelling results showed better skills when ARI+ACI interactions were included; hence this improvement in the representation of AOD (above 30 % in the model error) and AE (between 20 and 75 %) is important to provide a better description of aerosol-radiation-cloud interactions in regional climate models.

An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies.

PubMed

Yang, Xiaohuan; Huang, Yaohuan; Dong, Pinliang; Jiang, Dong; Liu, Honghui

2009-01-01

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Using computational modeling of river flow with remotely sensed data to infer channel bathymetry

USGS Publications Warehouse

Nelson, Jonathan M.; McDonald, Richard R.; Kinzel, Paul J.; Shimizu, Y.

2012-01-01

As part of an ongoing investigation into the use of computational river flow and morphodynamic models for the purpose of correcting and extending remotely sensed river datasets, a simple method for inferring channel bathymetry is developed and discussed. The method is based on an inversion of the equations expressing conservation of mass and momentum to develop equations that can be solved for depth given known values of vertically-averaged velocity and water-surface elevation. The ultimate goal of this work is to combine imperfect remotely sensed data on river planform, water-surface elevation and water-surface velocity in order to estimate depth and other physical parameters of river channels. In this paper, the technique is examined using synthetic data sets that are developed directly from the application of forward two-and three-dimensional flow models. These data sets are constrained to satisfy conservation of mass and momentum, unlike typical remotely sensed field data sets. This provides a better understanding of the process and also allows assessment of how simple inaccuracies in remotely sensed estimates might propagate into depth estimates. The technique is applied to three simple cases: First, depth is extracted from a synthetic dataset of vertically averaged velocity and water-surface elevation; second, depth is extracted from the same data set but with a normally-distributed random error added to the water-surface elevation; third, depth is extracted from a synthetic data set for the same river reach using computed water-surface velocities (in place of depth-integrated values) and water-surface elevations. In each case, the extracted depths are compared to the actual measured depths used to construct the synthetic data sets (with two- and three-dimensional flow models). Errors in water-surface elevation and velocity that are very small degrade depth estimates and cannot be recovered. Errors in depth estimates associated with assuming water-surface velocities equal to depth-integrated velocities are substantial, but can be reduced with simple corrections.

On validating remote sensing simulations using coincident real data

NASA Astrophysics Data System (ADS)

Wang, Mingming; Yao, Wei; Brown, Scott; Goodenough, Adam; van Aardt, Jan

2016-05-01

The remote sensing community often requires data simulation, either via spectral/spatial downsampling or through virtual, physics-based models, to assess systems and algorithms. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is one such first-principles, physics-based model for simulating imagery for a range of modalities. Complex simulation of vegetation environments subsequently has become possible, as scene rendering technology and software advanced. This in turn has created questions related to the validity of such complex models, with potential multiple scattering, bidirectional distribution function (BRDF), etc. phenomena that could impact results in the case of complex vegetation scenes. We selected three sites, located in the Pacific Southwest domain (Fresno, CA) of the National Ecological Observatory Network (NEON). These sites represent oak savanna, hardwood forests, and conifer-manzanita-mixed forests. We constructed corresponding virtual scenes, using airborne LiDAR and imaging spectroscopy data from NEON, ground-based LiDAR data, and field-collected spectra to characterize the scenes. Imaging spectroscopy data for these virtual sites then were generated using the DIRSIG simulation environment. This simulated imagery was compared to real AVIRIS imagery (15m spatial resolution; 12 pixels/scene) and NEON Airborne Observation Platform (AOP) data (1m spatial resolution; 180 pixels/scene). These tests were performed using a distribution-comparison approach for select spectral statistics, e.g., established the spectra's shape, for each simulated versus real distribution pair. The initial comparison results of the spectral distributions indicated that the shapes of spectra between the virtual and real sites were closely matched.

eFarm: A Tool for Better Observing Agricultural Land Systems

PubMed Central

Yu, Qiangyi; Shi, Yun; Tang, Huajun; Yang, Peng; Xie, Ankun; Liu, Bin; Wu, Wenbin

2017-01-01

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies. PMID:28245554

UAV-based remote sensing of the Heumoes landslide, Austria Vorarlberg

NASA Astrophysics Data System (ADS)

Niethammer, U.; Joswig, M.

2009-04-01

The Heumoes landslide, is located in the eastern Vorarlberg Alps, Austria, 10 km southeast of Dornbirn. The extension of the landslide is about 2000 m in west to east direction and about 500 m at its widest extent in north to south direction. It occurs between an elevation of 940 m in the east and 1360 m in the west, slope angles of more than 60 % can be observed as well as almost flat areas. Its total volume is estimated to be 9.400.000 cubic meters and its average velocities amount to some centimeter per year. Surface signatures or 'photolineations' of creeping landslides, e.g. fractures and rupture lines in sediments and street pavings, and vegetation contrasts by changes of water table in shallow vegetation in principle can be resolved by remote sensing. The necessary ground cell resolution of few centimeters, however, generally can't be achieved by routine areal or satellite imagery. The fast technological progress of unmanned areal vehicles (UAV) and the reduced payload by miniaturized optical cameras now allow for UAV remote sensing applications that are below the high financial limits of military intelligence. Even with 'low-cost' equipment, the necessary centimeter-scale ground cell resolution can be achieved by adapting the flight altitude to some ten to one hundred meters. Operated by scientists experienced in remote-control flight models, UAV remote sensing can now be performed routinely, and campaign-wise after any significant event of, e.g., heavy rainfall, or partial mudflow. We have investigated a concept of UAV-borne remote sensing based on motorized gliders, and four-propeller helicopters or 'quad-rotors'. Several missions were flown over the Heumoes landslide. Between 2006 and 2008 three series UAV-borne photographs of the Heumoes landslide were taken and could be combined to orto-mosaics of the slope area within few centimeters ground cell resolution. We will present the concept of our low cost quad-rotor UAV system and first results of the image-processing based evaluation of the acquired images to characterize spatial and temporal details of landslide behaviour. We will also sketch first schemes of joint interpretation or 'data fusion' of UAV-based remote sensing with the results from geophysical mapping of underground distribution of soil moisture and fracture processes (Walter & Joswig, EGU 2009).

Assessing the Interdisciplinary Use of Socioeconomic and Remote Sensing Data in the Earth Sciences

NASA Astrophysics Data System (ADS)

Chen, R. S.; Downs, R. R.; Schumacher, J.

2013-12-01

Remotely sensed data are widely used in Earth science research and applications not just to improve understanding of natural systems but also to elucidate interactions between natural and human systems and to model and predict human impacts on the environment, whether planned or unplanned. It is therefore often necessary for both remote sensing and socioeconomic data to be used together in both Earth science and social science research, for example in modeling past, present, and future land cover change, in assessing societal vulnerability to geophysical and climatological hazards, in measuring the human health impacts of air and water pollution, or in developing improved approaches to managing water, ecological, and other resources. The NASA Socioeconomic Data and Applications Center (SEDAC) was established as part of the Earth Observing System Data and Information System (EOSDIS) to facilitate access to and use of socioeconomic data in conjunction with remote sensing data in both research and applications. SEDAC provides access both to socioeconomic data that have been transformed into forms more readily usable by Earth scientists and other users, and to integrated datasets that incorporate both socioeconomic and remote sensing data. SEDAC data have been cited in at least 2,000 scientific papers covering a wide range of scientific disciplines and problem areas. In many cases, SEDAC data are cited in these papers along with other remote sensing datasets available from NASA or other sources. However, such citations do not necessarily indicate significant, integrated use of SEDAC and remote sensing data. To assess the level and type of integrated data use, we analyze a selection of recent SEDAC data citations in Earth science journals to characterize the ways in which SEDAC data have been used in the underlying research project and the paper itself. Papers were selected based on the presence of a SEDAC data citation and one or more keywords related to a remote sensing instrument or dataset. We assess if and how the SEDAC and remote sensing data are used together, e.g., in an empirical analysis, model, and/or visualization. We also ascertain the multidisciplinary backgrounds of the author or authors, as well as the Web of Science category and impact factor associated with the journal, to help characterize the user community and the overall scientific impact of the data use. Another issue is whether or not authors are formally citing SEDAC data and remote sensing in reference sections as opposed to referring to data informally, e.g., in figure captions. A key challenge in promoting the cross-disciplinary use of scientific data is the identification of ways in which scientists and other users not only access data from other disciplines but also use these data in their research. Objective assessment of scientific outputs such as the peer-reviewed scientific literature provides important insight into how individual scientists and scientific teams are taking advantage of the ongoing explosion in the variety and quantity of digital data from multiple disciplines to address pressing research problems and applications.

Assessing the Accuracy of Passive Microwave Estimates of Snow Water Equivalent in Data-Scarce Regions for Use in Water Resource Applications: A Case Study in the Upper Helmand Watershed, Afghanistan

DTIC Science & Technology

2011-03-01

to remotely sensed SCA and SWE. The first analysis, a comparison to SCA imagery, tests the models ability to correctly estimate the snow extent...remotely sensed data (Con- galton and Green 2009). The producer’s accuracies consistently show the model underestimating the snow extent at the end...and K. Green. 2009. Assessing the accuracy of remotely sensed data: principals and practices, Second edition. CRC Press, Taylor & Francis Group

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

NASA Technical Reports Server (NTRS)

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.

Voxel-Based LIDAR Analysis and Applications

NASA Astrophysics Data System (ADS)

Hagstrom, Shea T.

One of the greatest recent changes in the field of remote sensing is the addition of high-quality Light Detection and Ranging (LIDAR) instruments. In particular, the past few decades have been greatly beneficial to these systems because of increases in data collection speed and accuracy, as well as a reduction in the costs of components. These improvements allow modern airborne instruments to resolve sub-meter details, making them ideal for a wide variety of applications. Because LIDAR uses active illumination to capture 3D information, its output is fundamentally different from other modalities. Despite this difference, LIDAR datasets are often processed using methods appropriate for 2D images and that do not take advantage of its primary virtue of 3-dimensional data. It is this problem we explore by using volumetric voxel modeling. Voxel-based analysis has been used in many applications, especially medical imaging, but rarely in traditional remote sensing. In part this is because the memory requirements are substantial when handling large areas, but with modern computing and storage this is no longer a significant impediment. Our reason for using voxels to model scenes from LIDAR data is that there are several advantages over standard triangle-based models, including better handling of overlapping surfaces and complex shapes. We show how incorporating system position information from early in the LIDAR point cloud generation process allows radiometrically-correct transmission and other novel voxel properties to be recovered. This voxelization technique is validated on simulated data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, a first-principles based ray-tracer developed at the Rochester Institute of Technology. Voxel-based modeling of LIDAR can be useful on its own, but we believe its primary advantage is when applied to problems where simpler surface-based 3D models conflict with the requirement of realistic geometry. To show the voxel model's advantage, we apply it to several outstanding problems in remote sensing: LIDAR quality metrics, line-of-sight mapping, and multi-model fusion. Each of these applications is derived, validated, and examined in detail, and our results compared with other state-of-the-art methods. In most cases the voxel-based methods demonstrate superior results and are able to derive information not available to existing methods. Realizing these improvements requires only a shift away from traditional 3D model generation, and our results give a small indicator of what is possible. Many examples of possible areas for future improvement and expansion of algorithms beyond the scope of our work are also noted.

Remote sensing - A new view for public health

NASA Technical Reports Server (NTRS)

Morrison, D. R.; Barnes, C. M.; Fuller, C. E.

1973-01-01

It is shown that the technology of remote sensing can be of great importance to the field of public health. This possibility is based on the deepened understanding of the biologies and ecologies of the vector/organism/host interelationships of arthropod-, soil-, and water-borne diseases to result from the information that remote sensing can provide.

Using a remote sensing-based, percent tree cover map to enhance forest inventory estimation

Treesearch

Ronald E. McRoberts; Greg C. Liknes; Grant M. Domke

2014-01-01

For most national forest inventories, the variables of primary interest to users are forest area and growing stock volume. The precision of estimates of parameters related to these variables can be increased using remotely sensed auxiliary variables, often in combination with stratified estimators. However, acquisition and processing of large amounts of remotely sensed...

Utility of an automated thermal-based approach for monitoring evapotranspiration

USDA-ARS?s Scientific Manuscript database

A very simple remote sensing-based model for water use monitoring is presented. The model acronym DATTUTDUT, (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature) is a Dutch word which loosely translates as “It’s unbelievable that it works”. DATTUTDUT is fully automated and o...

A remote-sensing driven tool for estimating crop stress and yields

USDA-ARS?s Scientific Manuscript database

Biophysical crop simulation models are normally forced with precipitation data recorded with either gages or ground-based radar. However, ground based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would...

Evaluation of Multispectral Based Radiative Transfer Model Inversion to Estimate Leaf Area Index in Wheat

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is a critical variable for predicting the growth and productivity of crops. Remote sensing estimates of LAI have relied upon empirical relationships between spectral vegetation indices and ground measurements that are costly to obtain. Radiative transfer model inversion based o...

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase two, volume 4 : web-based bridge information database--visualization analytics and distributed sensing.

DOT National Transportation Integrated Search

2012-03-01

This report introduces the design and implementation of a Web-based bridge information visual analytics system. This : project integrates Internet, multiple databases, remote sensing, and other visualization technologies. The result : combines a GIS ...

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

Li, Jiang

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

SMERGE: A multi-decadal root-zone soil moisture product for CONUS

NASA Astrophysics Data System (ADS)

Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

2017-12-01

Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

PIXELS: Using field-based learning to investigate students' concepts of pixels and sense of scale

NASA Astrophysics Data System (ADS)

Pope, A.; Tinigin, L.; Petcovic, H. L.; Ormand, C. J.; LaDue, N.

2015-12-01

Empirical work over the past decade supports the notion that a high level of spatial thinking skill is critical to success in the geosciences. Spatial thinking incorporates a host of sub-skills such as mentally rotating an object, imagining the inside of a 3D object based on outside patterns, unfolding a landscape, and disembedding critical patterns from background noise. In this study, we focus on sense of scale, which refers to how an individual quantified space, and is thought to develop through kinesthetic experiences. Remote sensing data are increasingly being used for wide-reaching and high impact research. A sense of scale is critical to many areas of the geosciences, including understanding and interpreting remotely sensed imagery. In this exploratory study, students (N=17) attending the Juneau Icefield Research Program participated in a 3-hour exercise designed to study how a field-based activity might impact their sense of scale and their conceptions of pixels in remotely sensed imagery. Prior to the activity, students had an introductory remote sensing lecture and completed the Sense of Scale inventory. Students walked and/or skied the perimeter of several pixel types, including a 1 m square (representing a WorldView sensor's pixel), a 30 m square (a Landsat pixel) and a 500 m square (a MODIS pixel). The group took reflectance measurements using a field radiometer as they physically traced out the pixel. The exercise was repeated in two different areas, one with homogenous reflectance, and another with heterogeneous reflectance. After the exercise, students again completed the Sense of Scale instrument and a demographic survey. This presentation will share the effects and efficacy of the field-based intervention to teach remote sensing concepts and to investigate potential relationships between students' concepts of pixels and sense of scale.

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

PubMed

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.

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

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

EPA Science Inventory

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...

Application research on land use remote sensing dynamic monitoring: A case study of Anning district, Lanzhou

NASA Astrophysics Data System (ADS)

Zhu, Yunqiang; Zhu, Huazhong; Lu, Heli; Ni, Jianguang; Zhu, Shaoxia

2005-10-01

Remote sensing dynamic monitoring of land use can detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper discusses the technological procedure of remote sensing dynamic monitoring of land use including the process of remote sensing images, the extraction of annual change information of land use, field survey, indoor post processing and accuracy assessment. Especially, we emphasize on comparative research on the choice of remote sensing rectifying models, image fusion algorithms and accuracy assessment methods. Taking Anning district in Lanzhou as an example, we extract the land use change information of the district during 2002-2003, access monitoring accuracy and analyze the reason of land use change.

Quantifying ecosystem carbon losses and gains following development in New England: A combined field, modeling, and remote sensing approach

NASA Astrophysics Data System (ADS)

Raciti, S. M.; Hutyra, L.; Briber, B. M.; Dunn, A. L.; Friedl, M. A.; Woodcock, C.; Zhu, Z.; Olofsson, P.

2013-12-01

If current trends continue, the world's urban population may double and urban land area may quadruple over the next 50 years. Despite the rapid expansion of urban areas, the trajectories of carbon losses and gains following development remain poorly quantified. We are using a combination of field measurements, modeling, and remote sensing to advance our ability to measure and monitor trajectories of ecosystem carbon over space and time. To characterize how carbon stocks change across urban-to-rural gradients, we previously established field plots to survey live and dead tree biomass, tree canopy, soil and foliar carbon and nitrogen concentrations, and a range of landscape characteristics (Raciti et al. 2012). In 2013, we extended our field sampling to focus specifically on places that experienced land use and land cover change over the past 35 years. This chronosequence approach was informed by Landsat time series (1982-present) and property records (before 1982). The Landsat time series approach differs from traditional remote-sensing-based land use change detection methods because it leverages the entire Landsat archive of imagery using a Fourier fitting approach (Zhu et al. 2012). The result is a temporally and spatially continuous map of land use and land cover change across the study region. We used these field and remote sensing data to inform a carbon bookkeeping model that estimates changes in past and potential future carbon stocks over time. Here we present preliminary results of this work for eastern Massachusetts.

[Small unmanned aerial vehicles for low-altitude remote sensing and its application progress in ecology.

PubMed

Sun, Zhong Yu; Chen, Yan Qiao; Yang, Long; Tang, Guang Liang; Yuan, Shao Xiong; Lin, Zhi Wen

2017-02-01

Low-altitude unmanned aerial vehicles (UAV) remote sensing system overcomes the deficiencies of space and aerial remote sensing system in resolution, revisit period, cloud cover and cost, which provides a novel method for ecological research on mesoscale. This study introduced the composition of UAV remote sensing system, reviewed its applications in species, population, community and ecosystem ecology research. Challenges and opportunities of UAV ecology were identified to direct future research. The promising research area of UAV ecology includes the establishment of species morphology and spectral characteristic data base, species automatic identification, the revelation of relationship between spectral index and plant physiological processes, three-dimension monitoring of ecosystem, and the integration of remote sensing data from multi resources and multi scales. With the development of UAV platform, data transformation and sensors, UAV remote sensing technology will have wide application in ecology research.

Impact of 3D Canopy Structure on Remote Sensing Vegetation Index and Solar Induced Chlorophyll Fluorescence

NASA Astrophysics Data System (ADS)

Zeng, Y.; Berry, J. A.; Jing, L.; Qinhuo, L.

2017-12-01

Terrestrial ecosystem plays a critical role in removing CO2 from atmosphere by photosynthesis. Remote sensing provides a possible way to monitor the Gross Primary Production (GPP) at the global scale. Vegetation Indices (VI), e.g., NDVI and NIRv, and Solar Induced Fluorescence (SIF) have been widely used as a proxy for GPP, while the impact of 3D canopy structure on VI and SIF has not be comprehensively studied yet. In this research, firstly, a unified radiative transfer model for visible/near-infrared reflectance and solar induced chlorophyll fluorescence has been developed based on recollision probability and directional escape probability. Then, the impact of view angles, solar angles, weather conditions, leaf area index, and multi-layer leaf angle distribution (LAD) on VI and SIF has been studied. Results suggest that canopy structure plays a critical role in distorting pixel-scale remote sensing signal from leaf-scale scattering. In thin canopy, LAD affects both of the remote sensing estimated GPP and real GPP, while in dense canopy, SIF variations are mainly due to canopy structure, instead of just due to physiology. At the microscale, leaf angle reflects the plant strategy to light on the photosynthesis efficiency, and at the macroscale, a priori knowledge of leaf angle distribution for specific species can improve the global GPP estimation by remote sensing.

Application of Multi-Source Remote Sensing Image in Yunnan Province Grassland Resources Investigation

NASA Astrophysics Data System (ADS)

Li, J.; Wen, G.; Li, D.

2018-04-01

Trough mastering background information of Yunnan province grassland resources utilization and ecological conditions to improves grassland elaborating management capacity, it carried out grassland resource investigation work by Yunnan province agriculture department in 2017. The traditional grassland resource investigation method is ground based investigation, which is time-consuming and inefficient, especially not suitable for large scale and hard-to-reach areas. While remote sensing is low cost, wide range and efficient, which can reflect grassland resources present situation objectively. It has become indispensable grassland monitoring technology and data sources and it has got more and more recognition and application in grassland resources monitoring research. This paper researches application of multi-source remote sensing image in Yunnan province grassland resources investigation. First of all, it extracts grassland resources thematic information and conducts field investigation through BJ-2 high space resolution image segmentation. Secondly, it classifies grassland types and evaluates grassland degradation degree through high resolution characteristics of Landsat 8 image. Thirdly, it obtained grass yield model and quality classification through high resolution and wide scanning width characteristics of MODIS images and sample investigate data. Finally, it performs grassland field qualitative analysis through UAV remote sensing image. According to project area implementation, it proves that multi-source remote sensing data can be applied to the grassland resources investigation in Yunnan province and it is indispensable method.

Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

2017-04-01

Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models developed for Gorontalo showed the highest drought accuracy and the lowest regression error. West Java showed higher drought accuracy compared to West Sumatra, while West Sumatra showed lower regression error compared to West Java. The lower error in West Sumatra may be because of the smaller sample size used for training and evaluation for the region. Regional differences of forecast skill are determined by the effect of ENSO and the following forecast skill of the long-range climate forecast models. While shown somewhat high in West Sumatra, relative importance of remote sensing variables was mostly low in most cases. High importance of the variables based on long-range climate forecast models indicates that the forecast skill of the machine learning models are mostly determined by the forecast skill of the climate models.

Modelisation de l'architecture des forets pour ameliorer la teledetection des attributs forestiers

NASA Astrophysics Data System (ADS)

Cote, Jean-Francois

The quality of indirect measurements of canopy structure, from in situ and satellite remote sensing, is based on knowledge of vegetation canopy architecture. Technological advances in ground-based, airborne or satellite remote sensing can now significantly improve the effectiveness of measurement programs on forest resources. The structure of vegetation canopy describes the position, orientation, size and shape of elements of the canopy. The complexity of the canopy in forest environments greatly limits our ability to characterize forest structural attributes. Architectural models have been developed to help the interpretation of canopy structural measurements by remote sensing. Recently, the terrestrial LiDAR systems, or TLiDAR (Terrestrial Light Detection and Ranging), are used to gather information on the structure of individual trees or forest stands. The TLiDAR allows the extraction of 3D structural information under the canopy at the centimetre scale. The methodology proposed in my Ph.D. thesis is a strategy to overcome the weakness in the structural sampling of vegetation cover. The main objective of the Ph.D. is to develop an architectural model of vegetation canopy, called L-Architect (LiDAR data to vegetation Architecture), and to focus on the ability to document forest sites and to get information on canopy structure from remote sensing tools. Specifically, L-Architect reconstructs the architecture of individual conifer trees from TLiDAR data. Quantitative evaluation of L-Architect consisted to investigate (i) the structural consistency of the reconstructed trees and (ii) the radiative coherence by the inclusion of reconstructed trees in a 3D radiative transfer model. Then, a methodology was developed to quasi-automatically reconstruct the structure of individual trees from an optimization algorithm using TLiDAR data and allometric relationships. L-Architect thus provides an explicit link between the range measurements of TLiDAR and structural attributes of individual trees. L-Architect has finally been applied to model the architecture of forest canopy for better characterization of vertical and horizontal structure with airborne LiDAR data. This project provides a mean to answer requests of detailed canopy architectural data, difficult to obtain, to reproduce a variety of forest covers. Because of the importance of architectural models, L-Architect provides a significant contribution for improving the capacity of parameters' inversion in vegetation cover for optical and lidar remote sensing. Mots-cles: modelisation architecturale, lidar terrestre, couvert forestier, parametres structuraux, teledetection.

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

USGS Publications Warehouse

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

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.

Remote Sensing Image Fusion Method Based on Nonsubsampled Shearlet Transform and Sparse Representation

NASA Astrophysics Data System (ADS)

Moonon, Altan-Ulzii; Hu, Jianwen; Li, Shutao

2015-12-01

The remote sensing image fusion is an important preprocessing technique in remote sensing image processing. In this paper, a remote sensing image fusion method based on the nonsubsampled shearlet transform (NSST) with sparse representation (SR) is proposed. Firstly, the low resolution multispectral (MS) image is upsampled and color space is transformed from Red-Green-Blue (RGB) to Intensity-Hue-Saturation (IHS). Then, the high resolution panchromatic (PAN) image and intensity component of MS image are decomposed by NSST to high and low frequency coefficients. The low frequency coefficients of PAN and the intensity component are fused by the SR with the learned dictionary. The high frequency coefficients of intensity component and PAN image are fused by local energy based fusion rule. Finally, the fused result is obtained by performing inverse NSST and inverse IHS transform. The experimental results on IKONOS and QuickBird satellites demonstrate that the proposed method provides better spectral quality and superior spatial information in the fused image than other remote sensing image fusion methods both in visual effect and object evaluation.

Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

NASA Astrophysics Data System (ADS)

Shew, A. M.; Ghosh, A.

2017-10-01

Remote sensing in the optical domain is widely used in agricultural monitoring; however, such initiatives pose a challenge for developing countries due to a lack of high quality in situ information. Our proposed methodology could help developing countries bridge this gap by demonstrating the potential to quantify patterns of dry season rice production in Bangladesh. To analyze approximately 90,000 km2 of cultivated land in Bangladesh at 30 m spatial resolution, we used two decades of remote sensing data from the Landsat archive and Google Earth Engine (GEE), a cloud-based geospatial data analysis platform built on Google infrastructure and capable of processing petabyte-scale remote sensing data. We reconstructed the seasonal patterns of vegetation indices (VIs) for each pixel using a harmonic time series (HTS) model, which minimizes the effects of missing observations and noise. Next, we combined the seasonality information of VIs with our knowledge of rice cultivation systems in Bangladesh to delineate rice areas in the dry season, which are predominantly hybrid and High Yielding Varieties (HYV). Based on historical Landsat imagery, the harmonic time series of vegetation indices (HTS-VIs) model estimated 4.605 million ha, 3.519 million ha, and 4.021 million ha of rice production for Bangladesh in 2005, 2010, and 2015 respectively. Fine spatial scale information on HYV rice over the last 20 years will greatly improve our understanding of double-cropped rice systems, current status of production, and potential for HYV rice adoption in Bangladesh during the dry season.

Change detection from remotely sensed images: From pixel-based to object-based approaches

NASA Astrophysics Data System (ADS)

Hussain, Masroor; Chen, Dongmei; Cheng, Angela; Wei, Hui; Stanley, David

2013-06-01

The appetite for up-to-date information about earth's surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

Use of Satellite Remote Sensing to Improve Coastal Hypoxia Prediction

EPA Science Inventory

We describe the use of Giovanni satellite remote sensing products in the development and testing of a new modeling system that represents the processes leading to hypoxia (defined as water O2 concentration < 63 mmol m-3) on the Louisiana continental shelf (LCS). The modeling ...

A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

PubMed Central

Brolly, Matthew; Woodhouse, Iain H.; Niklas, Karl J.; Hammond, Sean T.

2012-01-01

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100. PMID:22457800

A macroecological analysis of SERA derived forest heights and implications for forest volume remote sensing.

PubMed

Brolly, Matthew; Woodhouse, Iain H; Niklas, Karl J; Hammond, Sean T

2012-01-01

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H₁₀₀, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H₁₀₀ and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 10²-10⁶ plants/hectare and heights 6-49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H₁₀₀.

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

Remote Sensing Information Sciences Research Group, year four

NASA Technical Reports Server (NTRS)

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.

A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

Treesearch

Ryan R. McShane; Katelyn P. Driscoll; Roy Sando

2017-01-01

Many approaches have been developed for measuring or estimating actual evapotranspiration (ETa), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ETa. Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large...

Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model

USGS Publications Warehouse

Byrd, Kristin B.; Windham-Myers, Lisamarie; Leeuw, Thomas; Downing, Bryan D.; Morris, James T.; Ferner, Matthew C.

2016-01-01

Reducing uncertainty in data inputs at relevant spatial scales can improve tidal marsh forecasting models, and their usefulness in coastal climate change adaptation decisions. The Marsh Equilibrium Model (MEM), a one-dimensional mechanistic elevation model, incorporates feedbacks of organic and inorganic inputs to project elevations under sea-level rise scenarios. We tested the feasibility of deriving two key MEM inputs—average annual suspended sediment concentration (SSC) and aboveground peak biomass—from remote sensing data in order to apply MEM across a broader geographic region. We analyzed the precision and representativeness (spatial distribution) of these remote sensing inputs to improve understanding of our study region, a brackish tidal marsh in San Francisco Bay, and to test the applicable spatial extent for coastal modeling. We compared biomass and SSC models derived from Landsat 8, DigitalGlobe WorldView-2, and hyperspectral airborne imagery. Landsat 8-derived inputs were evaluated in a MEM sensitivity analysis. Biomass models were comparable although peak biomass from Landsat 8 best matched field-measured values. The Portable Remote Imaging Spectrometer SSC model was most accurate, although a Landsat 8 time series provided annual average SSC estimates. Landsat 8-measured peak biomass values were randomly distributed, and annual average SSC (30 mg/L) was well represented in the main channels (IQR: 29–32 mg/L), illustrating the suitability of these inputs across the model domain. Trend response surface analysis identified significant diversion between field and remote sensing-based model runs at 60 yr due to model sensitivity at the marsh edge (80–140 cm NAVD88), although at 100 yr, elevation forecasts differed less than 10 cm across 97% of the marsh surface (150–200 cm NAVD88). Results demonstrate the utility of Landsat 8 for landscape-scale tidal marsh elevation projections due to its comparable performance with the other sensors, temporal frequency, and cost. Integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially. Improving information for coastal modeling will support planning for ecosystem services, including habitat, carbon storage, and flood protection.

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

USGS Publications Warehouse

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

2010-01-01

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

Investigating impacts of natural and human-induced environmental changes on hydrological processes and flood hazards using a GIS-based hydrological/hydraulic model and remote sensing data

NASA Astrophysics Data System (ADS)

Wang, Lei

Natural and human-induced environmental changes have been altering the earth's surface and hydrological processes, and thus directly contribute to the severity of flood hazards. To understand these changes and their impacts, this research developed a GIS-based hydrological and hydraulic modeling system, which incorporates state-of-the-art remote sensing data to simulate flood under various scenarios. The conceptual framework and technical issues of incorporating multi-scale remote sensing data have been addressed. This research develops an object-oriented hydrological modeling framework. Compared with traditional lumped or cell-based distributed hydrological modeling frameworks, the object-oriented framework allows basic spatial hydrologic units to have various size and irregular shape. This framework is capable of assimilating various GIS and remotely-sensed data with different spatial resolutions. It ensures the computational efficiency, while preserving sufficient spatial details of input data and model outputs. Sensitivity analysis and comparison of high resolution LIDAR DEM with traditional USGS 30m resolution DEM suggests that the use of LIDAR DEMs can greatly reduce uncertainty in calibration of flow parameters in the hydrologic model and hence increase the reliability of modeling results. In addition, subtle topographic features and hydrologic objects like surface depressions and detention basins can be extracted from the high resolution LiDAR DEMs. An innovative algorithm has been developed to efficiently delineate surface depressions and detention basins from LiDAR DEMs. Using a time series of Landsat images, a retrospective analysis of surface imperviousness has been conducted to assess the hydrologic impact of urbanization. The analysis reveals that with rapid urbanization the impervious surface has been increased from 10.1% to 38.4% for the case study area during 1974--2002. As a result, the peak flow for a 100-year flood event has increased by 20% and the floodplain extent has expanded by about 21.6%. The quantitative analysis suggests that the large regional detentions basins have effectively offset the adverse effect of increased impervious surface during the urbanization process. Based on the simulation and scenario analyses of land subsidence and potential climate changes, some planning measures and policy implications have been derived for guiding smart urban growth and sustainable resource development and management to minimize flood hazards.

Accurate estimation of motion blur parameters in noisy remote sensing image

NASA Astrophysics Data System (ADS)

Shi, Xueyan; Wang, Lin; Shao, Xiaopeng; Wang, Huilin; Tao, Zhong

2015-05-01

The relative motion between remote sensing satellite sensor and objects is one of the most common reasons for remote sensing image degradation. It seriously weakens image data interpretation and information extraction. In practice, point spread function (PSF) should be estimated firstly for image restoration. Identifying motion blur direction and length accurately is very crucial for PSF and restoring image with precision. In general, the regular light-and-dark stripes in the spectrum can be employed to obtain the parameters by using Radon transform. However, serious noise existing in actual remote sensing images often causes the stripes unobvious. The parameters would be difficult to calculate and the error of the result relatively big. In this paper, an improved motion blur parameter identification method to noisy remote sensing image is proposed to solve this problem. The spectrum characteristic of noisy remote sensing image is analyzed firstly. An interactive image segmentation method based on graph theory called GrabCut is adopted to effectively extract the edge of the light center in the spectrum. Motion blur direction is estimated by applying Radon transform on the segmentation result. In order to reduce random error, a method based on whole column statistics is used during calculating blur length. Finally, Lucy-Richardson algorithm is applied to restore the remote sensing images of the moon after estimating blur parameters. The experimental results verify the effectiveness and robustness of our algorithm.

Sensitivity of geographic information system outputs to errors in remotely sensed data

NASA Technical Reports Server (NTRS)

Ramapriyan, H. K.; Boyd, R. K.; Gunther, F. J.; Lu, Y. C.

1981-01-01

The sensitivity of the outputs of a geographic information system (GIS) to errors in inputs derived from remotely sensed data (RSD) is investigated using a suitability model with per-cell decisions and a gridded geographic data base whose cells are larger than the RSD pixels. The process of preparing RSD as input to a GIS is analyzed, and the errors associated with classification and registration are examined. In the case of the model considered, it is found that the errors caused during classification and registration are partially compensated by the aggregation of pixels. The compensation is quantified by means of an analytical model, a Monte Carlo simulation, and experiments with Landsat data. The results show that error reductions of the order of 50% occur because of aggregation when 25 pixels of RSD are used per cell in the geographic data base.

Imputing forest structure attributes from stand inventory and remotely sensed data in western Oregon, USA

Treesearch

Andrew T. Hudak; A. Tod Haren; Nicholas L. Crookston; Robert J. Liebermann; Janet L. Ohmann

2014-01-01

Imputation is commonly used to assign reference stand observations to target stands based on covariate relationships to remotely sensed data to assign inventory attributes across the entire landscape. However, most remotely sensed data are collected at higher resolution than the stand inventory data often used by operational foresters. Our primary goal was to compare...

The Sophia-Antipolis Conference: General presentation and basic documents. [remote sensing for agriculture, forestry, water resources, and environment management in France

NASA Technical Reports Server (NTRS)

1980-01-01

The procedures and techniques used in NASA's aerospace technology transfer program are reviewed for consideration in establishing priorities and bases for joint action by technicians and users of remotely sensed data in France. Particular emphasis is given to remote sensing in agriculture, forestry, water resources, environment management, and urban research.

Post-classification approaches to estimating change in forest area using remotely sense auxiliary data.

Treesearch

Ronald E. McRoberts

2014-01-01

Multiple remote sensing-based approaches to estimating gross afforestation, gross deforestation, and net deforestation are possible. However, many of these approaches have severe data requirements in the form of long time series of remotely sensed data and/or large numbers of observations of land cover change to train classifiers and assess the accuracy of...

From ships to robots: The social relations of sensing the world ocean.

PubMed

Lehman, Jessica

2018-02-01

The dominant practices of physical oceanography have recently shifted from being based on ship-based ocean sampling and sensing to being based on remote and robotic sensing using satellites, drifting floats and remotely operated and autonomous underwater vehicles. What are the implications of this change for the social relations of oceanographic science? This paper contributes to efforts to address this question, pursuing a situated view of ocean sensing technologies so as to contextualize and analyze new representations of the sea, and interactions between individual scientists, technologies and the ocean. By taking a broad view on oceanography through a 50-year shift from ship-based to remote and robotic sensing, I show the ways in which new technologies may provide an opportunity to fight what Oreskes has called 'ideologies of scientific heroism'. In particular, new sensing relations may emphasize the contributions of women and scientists from less well-funded institutions, as well as the ways in which oceanographic knowledge is always partial and dependent on interactions between nonhuman animals, technologies, and different humans. Thus, I argue that remote and robotic sensing technologies do not simply create more abstracted relations between scientists and the sea, but also may provide opportunities for more equitable scientific practice and refigured sensing relations.

Use of remote sensing in agriculture

NASA Technical Reports Server (NTRS)

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

1975-01-01

The remote sensing studies of (a) cultivated peanut areas in Southeastern Virginia; (b) studies at the Virginia Truck and Ornamentals Research Station near Painter, Virginia, the Eastern Virginia Research Station near Warsaw, Virginia, the Tidewater Research and Continuing Education Center near Suffolk, Virginia, and the Southern Piedmont Research and Continuing Education Center Blackstone, Virginia; and (c) land use classification studies at Virginia Beach, Virginia are presented. The practical feasibility of using false color infrared imagery to detect and determine the areal extent of peanut disease infestation of Cylindrocladium black rot and Sclerotinia blight is demonstrated. These diseases pose a severe hazard to this major agricultural food commodity. The value of remote sensing technology in terrain analyses and land use classification of diverse land areas is also investigated. Continued refinement of spectral signatures of major agronomic crops and documentation of pertinent environmental variables have provided a data base for the generation of an agricultural-environmental prediction model.

The role of NASA's Water Resources applications area in improving access to water quality-related information and water resources management

NASA Astrophysics Data System (ADS)

Lee, C. M.

2016-02-01

The NASA Applied Sciences Program plays a unique role in facilitating access to remote sensing-based water information derived from US federal assets towards the goal of improving science and evidence-based decision-making in water resources management. The Water Resources Application Area within NASA Applied Sciences works specifically to develop and improve water data products to support improved management of water resources, with partners who are faced with real-world constraints and conditions including cost and regulatory standards. This poster will highlight the efforts and collaborations enabled by this program that have resulted in integration of remote sensing-based information for water quality modeling and monitoring within an operational context.

The role of NASA's Water Resources applications area in improving access to water quality-related information and water resources management

NASA Astrophysics Data System (ADS)

Lee, C. M.

2016-12-01

The NASA Applied Sciences Program plays a unique role in facilitating access to remote sensing-based water information derived from US federal assets towards the goal of improving science and evidence-based decision-making in water resources management. The Water Resources Application Area within NASA Applied Sciences works specifically to develop and improve water data products to support improved management of water resources, with partners who are faced with real-world constraints and conditions including cost and regulatory standards. This poster will highlight the efforts and collaborations enabled by this program that have resulted in integration of remote sensing-based information for water quality modeling and monitoring within an operational context.

Evaluating ESA CCI soil moisture in East Africa.

PubMed

McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R; Wang, Shugong; Peters-Lidard, Christa D; Verdin, James P

2016-06-01

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

Radar Altimetry for Hydrological Modeling and Monitoring in the Zambezi River Basin

NASA Astrophysics Data System (ADS)

Michailovsky, C. I.; Berry, P. A.; Smith, R. G.; Bauer-Gottwein, P.

2011-12-01

Hydrological model forecasts are subject to large uncertainties stemming from uncertain input data, model structure, parameterization and lack of sufficient calibration/validation data. For real-time or near-real-time applications data assimilation techniques such as the Ensemble Kalman Filter (EnKF) can be used to reduce forecast uncertainty by updating model states as new data becomes available. The use of remote sensing data is attractive for such applications as it provides wide geographical coverage and continuous time-series without the typically long delays that exist in obtaining in-situ data. River discharge is one of the main hydrological variables of interest, and while it cannot currently be directly measured remotely, water levels in rivers can be obtained from satellite based radar altimetry and converted to discharge through rating curves. This study aims to give a realistic assessment of the improvements that can be derived from the use of satellite radar altimetry measurements from the Envisat mission for discharge monitoring and modeling on the basin scale for the Zambezi River. The altimetry data used is the Radar AlTimetry (RAT) product developed at the Earth and Planetary Remote Sensing Laboratory at the De Montfort University. The first step in analyzing the data is the determination of potential altimetry targets which are the locations at which the Envisat orbit and the river network cross in order to select data points corresponding to surface water. The quality of the water level time-series is then analyzed for all targets and the exploitable targets identified. Rating curves are derived from in-situ or remotely-sensed data depending on data-availability at the various locations and discharge time-series are established. A Monte Carlo analysis is carried out to assess the uncertainties on the computed discharge. It was found that having a single cross-section and associated discharge measurement at one point in time significantly reduces discharge uncertainty. To assess improvements in model predictions, a model of the Zambezi River basin based on remote sensing data is set up with the Soil and Water Assessment Tool and calibrated with available in-situ data. The discharge data from altimetry is then used in an EnKF framework to update discharge in the model as it runs. The method showed improvements in prediction uncertainties for short lead times.

Remote sensing of multimodal transportation systems : research brief.

DOT National Transportation Integrated Search

2016-09-01

Remote Sensing of Multimodal Transportation Systems : Rapid condition monitoring and performance evaluations of the vast and vulnerable transportation infrastructure has been elusive. The framework and models developed in this research will enable th...

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

NASA Astrophysics Data System (ADS)

Yu, Le; Zhang, Dengrong; Holden, Eun-Jung

2008-07-01

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.

Water environmental management with the aid of remote sensing and GIS technology

NASA Astrophysics Data System (ADS)

Chen, Xiaoling; Yuan, Zhongzhi; Li, Yok-Sheung; Song, Hong; Hou, Yingzi; Xu, Zhanhua; Liu, Honghua; Wai, Onyx W.

2005-01-01

Water environment is associated with many disciplinary fields including sciences and management which makes it difficult to study. Timely observation, data getting and analysis on water environment are very important for decision makers who play an important role to maintain the sustainable development. This study focused on developing a plateform of water environment management based on remote sensing and GIS technology, and its main target is to provide with necessary information on water environment through spatial analysis and visual display in a suitable way. The work especially focused on three points, and the first one is related to technical issues of spatial data organization and communication with a combination of GIS and statistical software. A data-related model was proposed to solve the data communication between the mentioned systems. The second one is spatio-temporal analysis based on remote sensing and GIS. Water quality parameters of suspended sediment concentration and BOD5 were specially analyzed in this case, and the results suggested an obvious influence of land source pollution quantitatively in a spatial domain. The third one is 3D visualization of surface feature based on RS and GIS technology. The Pearl River estuary and HongKong's coastal waters in the South China Sea were taken as a case in this study. The software ARCGIS was taken as a basic platform to develop a water environmental management system. The sampling data of water quality in 76 monitoring stations of coastal water bodies and remote sensed images were selected in this study.

Machine processing of remotely sensed data - quantifying global process: Models, sensor systems, and analytical methods; Proceedings of the Eleventh International Symposium, Purdue University, West Lafayette, IN, June 25-27, 1985

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mengel, S.K.; Morrison, D.B.

1985-01-01

Consideration is given to global biogeochemical issues, image processing, remote sensing of tropical environments, global processes, geology, landcover hydrology, and ecosystems modeling. Topics discussed include multisensor remote sensing strategies, geographic information systems, radars, and agricultural remote sensing. Papers are presented on fast feature extraction; a computational approach for adjusting TM imagery terrain distortions; the segmentation of a textured image by a maximum likelihood classifier; analysis of MSS Landsat data; sun angle and background effects on spectral response of simulated forest canopies; an integrated approach for vegetation/landcover mapping with digital Landsat images; geological and geomorphological studies using an image processing technique;more » and wavelength intensity indices in relation to tree conditions and leaf-nutrient content.« less

Satellite Remote Sensing of Aerosol Forcing

NASA Technical Reports Server (NTRS)

Remer, Lorraine; Kaufman, Yoram; Ramaprasad, Jaya; Procopio, Aline; Levin, Zev

1999-01-01

Aerosol and cloud impacts on the earth's climate become a recent hot topic in climate studies. Having near future earth observing satellites, EOS-AM1 (Earth Observing System-AM1), ENVISAT (Environmental Satellites) and ADEOS-2 (Advanced Earth Observation Satellite-2), it will be a good timing to discuss how to obtain and use the microphysical parameters of aerosols and clouds for studying their climate impacts. Center for Climate System Research (CCSR) of the University of Tokyo invites you to 'Symposium on synergy between satellite-remote sensing and climate modeling in aerosol and cloud issues.' Here, we like to discuss the current and future issues in the remote sensing of aerosol and cloud microphysical parameters and their climate modeling studies. This workshop is also one of workshop series on aerosol remote sensing held in 1996, Washington D. C., and Meribel, France in 1999. It should be reminded that NASDA/ADEOS-1 & -2 (National Space Development Agency of Japan/Advanced Earth Observation Satellite-1 & -2) Workshop will be held in the following week (Dec. 6-10, 1999), so that this opportunity will be a perfect period for you to attend two meetings for satellite remote sensing in Japan. A weekend in Kyoto, the old capital of Japan, will add a nice memory to your visiting Japan. *Issues in the symposium: 1) most recent topics in aerosol and cloud remot sensing, and 2) utility of satellite products on climate modeling of cloud-aerosol effects.

Model for the Interpretation of Hyperspectral Remote-Sensing Reflectance

NASA Technical Reports Server (NTRS)

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.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Use of multispectral satellite remote sensing to assess mixing of suspended sediment downstream of large river confluences

NASA Astrophysics Data System (ADS)

Umar, M.; Rhoads, Bruce L.; Greenberg, Jonathan A.

2018-01-01

Although past work has noted that contrasts in turbidity often are detectable on remotely sensed images of rivers downstream from confluences, no systematic methodology has been developed for assessing mixing over distance of confluent flows with differing surficial suspended sediment concentrations (SSSC). In contrast to field measurements of mixing below confluences, satellite remote-sensing can provide detailed information on spatial distributions of SSSC over long distances. This paper presents a methodology that uses remote-sensing data to estimate spatial patterns of SSSC downstream of confluences along large rivers and to determine changes in the amount of mixing over distance from confluences. The method develops a calibrated Random Forest (RF) model by relating training SSSC data from river gaging stations to derived spectral indices for the pixels corresponding to gaging-station locations. The calibrated model is then used to predict SSSC values for every river pixel in a remotely sensed image, which provides the basis for mapping of spatial variability in SSSCs along the river. The pixel data are used to estimate average surficial values of SSSC at cross sections spaced uniformly along the river. Based on the cross-section data, a mixing metric is computed for each cross section. The spatial pattern of change in this metric over distance can be used to define rates and length scales of surficial mixing of suspended sediment downstream of a confluence. This type of information is useful for exploring the potential influence of various controlling factors on mixing downstream of confluences, for evaluating how mixing in a river system varies over time and space, and for determining how these variations influence water quality and ecological conditions along the river.

A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

NASA Astrophysics Data System (ADS)

Asal Kzar, Ahmed; Mat Jafri, M. Z.; Hwee San, Lim; Al-Zuky, Ali A.; Mutter, Kussay N.; Hassan Al-Saleh, Anwar

2016-06-01

There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.

Airborne and satellite remote sensors for precision agriculture

USDA-ARS?s Scientific Manuscript database

Remote sensing provides an important source of information to characterize soil and crop variability for both within-season and after-season management despite the availability of numerous ground-based soil and crop sensors. Remote sensing applications in precision agriculture have been steadily inc...

Polarimetric Remote Sensing of Atmospheric Particulate Pollutants

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Remote sensing and the Mississippi high accuracy reference network

NASA Technical Reports Server (NTRS)

Mick, Mark; Alexander, Timothy M.; Woolley, Stan

1994-01-01

Since 1986, NASA's Commercial Remote Sensing Program (CRSP) at Stennis Space Center has supported commercial remote sensing partnerships with industry. CRSP's mission is to maximize U.S. market exploitation of remote sensing and related space-based technologies and to develop advanced technical solutions for spatial information requirements. Observation, geolocation, and communications technologies are converging and their integration is critical to realize the economic potential for spatial informational needs. Global positioning system (GPS) technology enables a virtual revolution in geopositionally accurate remote sensing of the earth. A majority of states are creating GPS-based reference networks, or high accuracy reference networks (HARN). A HARN can be defined for a variety of local applications and tied to aerial or satellite observations to provide an important contribution to geographic information systems (GIS). This paper details CRSP's experience in the design and implementation of a HARN in Mississippi and the design and support of future applications of integrated earth observations, geolocation, and communications technology.

Hydrological research in Ethiopia

NASA Astrophysics Data System (ADS)

Gebremichael, M.

2012-12-01

Almost all major development problems in Ethiopia are water-related: food insecurity, low economic development, recurrent droughts, disastrous floods, poor health conditions, and low energy condition. In order to develop and manage existing water resources in a sustainable manner, knowledge is required about water availability, water quality, water demand in various sectors, and the impacts of water resource projects on health and the environment. The lack of ground-based data has been a major challenge for generating this knowledge. Current advances in remote sensing and computer simulation technology could provide alternative source of datasets. In this talk, I will present the challenges and opportunities in using remote sensing datasets and hydrological models in regions such as Africa where ground-based datasets are scarce.