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Special Issue "Remote Sensing of Natural Resources and the Environment"

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 April 2008)

Special Issue Editor

Guest Editor
Prof. Dr. Assefa M. Melesse (Website)

Department of Earth and Environment, AHC-5-390, Florida International University, 11200 SW 8th Street, Miami, FL 33199, USA
Fax: +1-305-348-3877.
Interests: watershed modelling; sediment dynamics, climate change, evapotranspiration and energy fluxes; system analysis; remote sensing hydrology

Special Issue Information

Dear Colleagues,

The design, performance and application of sensors for remote sensing of natural resources (vegetation, water, impervious surfaces, nutrients, and soil), water and energy fluxes, clouds, atmospheric pollutants, surface temperature and other land and aquatic resources is a very important front of remote sensing research to understands the physical, ecological, hydrological and environmental characteristics of surfaces and substances. The Special Issue of Remote Sensing Sensors will publish those full research and high rated manuscripts addressing the above issues and seek to understand the surface characteristics of land natural resources at various spatiotemporal scales. Sensor design to bridge the research gap of high spatial resolution with acceptable temporal scale, evaluation of performance of existing sensors and recommendations for improvement, identification of new windows of bands to discriminate noises from images, soil moisture sensors, wetland mapping, soil properties characterization, hydrological application, precipitation estimation and others applied to ecohydrological studies will be accepted.

Dr. Assefa M. Melesse
Guest Editor

Keywords

  • land surface characterization
  • land-cover
  • water and energy fluxes
  • soil moisture
  • albedo
  • emssivity
  • surface temperature
  • wetland delineation
  • latent heat flux
  • sensible heat flux
  • remote sensing of environment

Published Papers (51 papers)

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Open AccessArticle Remote Sensing Monitoring of Changes in Soil Salinity: A Case Study in Inner Mongolia, China
Sensors 2008, 8(11), 7035-7049; doi:10.3390/s8117035
Received: 18 September 2008 / Revised: 29 October 2008 / Accepted: 4 November 2008 / Published: 7 November 2008
Cited by 15 | PDF Full-text (953 KB) | HTML Full-text | XML Full-text
Abstract
This study used archived remote sensing images to depict the history of changes in soil salinity in the Hetao Irrigation District in Inner Mongolia, China, with the purpose of linking these changes with land and water management practices and to draw lessons [...] Read more.
This study used archived remote sensing images to depict the history of changes in soil salinity in the Hetao Irrigation District in Inner Mongolia, China, with the purpose of linking these changes with land and water management practices and to draw lessons for salinity control. Most data came from LANDSAT satellite images taken in 1973, 1977, 1988, 1991, 1996, 2001, and 2006. In these years salt-affected areas were detected using a normal supervised classification method. Corresponding cropped areas were detected from NVDI (Normalized Difference Vegetation Index) values using an unsupervised method. Field samples and agricultural statistics were used to estimate the accuracy of the classification. Historical data concerning irrigation/drainage and the groundwater table were used to analyze the relation between changes in soil salinity and land and water management practices. Results showed that: (1) the overall accuracy of remote sensing in detecting soil salinity was 90.2%, and in detecting cropped area, 98%; (2) the installation/innovation of the drainage system did help to control salinity; and (3) a low ratio of cropped land helped control salinity in the Hetao Irrigation District. These findings suggest that remote sensing is a useful tool to detect soil salinity and has potential in evaluating and improving land and water management practices. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
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Open AccessArticle Empirical Evidence for Impacts of Internal Migration on Vegetation Dynamics in China from 1982 to 2000
Sensors 2008, 8(8), 5069-5080; doi:10.3390/s8085069
Received: 28 July 2008 / Revised: 20 August 2008 / Accepted: 22 August 2008 / Published: 27 August 2008
Cited by 6 | PDF Full-text (214 KB) | HTML Full-text | XML Full-text
Abstract
Migration is one of the major socio-economic characteristics of China since the country adopted the policy of economic reform in late 1970s. Many studies have been dedicated to understand why and how people move, and the consequences of their welfare. The purpose [...] Read more.
Migration is one of the major socio-economic characteristics of China since the country adopted the policy of economic reform in late 1970s. Many studies have been dedicated to understand why and how people move, and the consequences of their welfare. The purpose of this study is to investigate the environmental impacts of the large scale movement of population in China. We analyzed the trend in the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) along with China migration data from the 1 percent national survey during 1982-1987, the 4th national census during 1985-1990 and the 5th national census during1995~2000. We found that the internal migration in China has a statistically significant negative impact on vegetation growth at the provincial scale from 1982 to 2000 even though the overall vegetation abundance increased in China. The impact from migration (R2=0.47, P=0.0001) on vegetation dynamics is the second strongest as among the factors considered, including changes in annual mean air temperature (R2=0.50, P=0.0001) and annual total precipitation (R2=0.30, P=0.0049) and gross domestic production (R2= 0.25, P=0.0102). The negative statistical relationship between the rate of increase in total migration and the change in vegetation abundance is stronger (R2=0.56, P=0.0000) after controlling for the effects of changes in temperature and precipitation. In-migration dominates the impacts of migration on vegetation dynamics. Therefore, it is important for policy makers in China to take the impacts of migration on vegetation growth into account while making policies aiming at sustainable humanenvironment relations. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis And 3D Single Tree Modelling in Forest
Sensors 2008, 8(6), 3938-3951; doi:10.3390/s8063938
Received: 29 January 2008 / Revised: 5 June 2008 / Accepted: 6 June 2008 / Published: 12 June 2008
Cited by 52 | PDF Full-text (577 KB) | HTML Full-text | XML Full-text
Abstract
A procedure for both vertical canopy structure analysis and 3D single tree modelling based on Lidar point cloud is presented in this paper. The whole area of research is segmented into small study cells by a raster net. For each cell, a [...] Read more.
A procedure for both vertical canopy structure analysis and 3D single tree modelling based on Lidar point cloud is presented in this paper. The whole area of research is segmented into small study cells by a raster net. For each cell, a normalized point cloud whose point heights represent the absolute heights of the ground objects is generated from the original Lidar raw point cloud. The main tree canopy layers and the height ranges of the layers are detected according to a statistical analysis of the height distribution probability of the normalized raw points. For the 3D modelling of individual trees, individual trees are detected and delineated not only from the top canopy layer but also from the sub canopy layer. The normalized points are resampled into a local voxel space. A series of horizontal 2D projection images at the different height levels are then generated respect to the voxel space. Tree crown regions are detected from the projection images. Individual trees are then extracted by means of a pre-order forest traversal process through all the tree crown regions at the different height levels. Finally, 3D tree crown models of the extracted individual trees are reconstructed. With further analyses on the 3D models of individual tree crowns, important parameters such as crown height range, crown volume and crown contours at the different height levels can be derived. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data
Sensors 2008, 8(6), 3767-3779; doi:10.3390/s8063767
Received: 31 January 2008 / Revised: 29 May 2008 / Accepted: 30 May 2008 / Published: 6 June 2008
Cited by 5 | PDF Full-text (156 KB) | HTML Full-text | XML Full-text
Abstract
Directional gap probability or gap fraction is a basic parameter in the optical remote sensing modeling. Although some approaches have been proposed to estimate this gap probability from remotely sensed measurements, few efforts have been made to investigate the scaling effects of [...] Read more.
Directional gap probability or gap fraction is a basic parameter in the optical remote sensing modeling. Although some approaches have been proposed to estimate this gap probability from remotely sensed measurements, few efforts have been made to investigate the scaling effects of this parameter. This paper analyzes the scaling effect through aggregating the high-resolution directional gap probability (pixel size of 20 meters) estimated from leaf area index (LAI) images of VALERI database by means of Beer's law and introduces an extension of clumping index, Ĉ, to compensate the scaling bias. The results show that the scaling effect depends on both the surface heterogeneity and the nonlinearity degree of the retrieved function. Analytical expressions for the scaling bias of gap probability and Ĉ are established in function of the variance of LAI and the mean value of LAI in a coarse pixel. With the VALERI dataset, the study in this paper shows that relative scaling bias of gap probability increases with decreasing spatial resolution for most of land cover types. Large relative biases are found for most of crops sites and a mixed forest site due to their relative large variance of LAI, while very small biases occur over grassland and shrubs sites. As for Ĉ, it varies slowly in the pure forest, grassland and shrubs sites, while more significantly in crops and mixed forest. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle A Spatial-Spectral Approach for Visualization of Vegetation Stress Resulting from Pipeline Leakage
Sensors 2008, 8(6), 3733-3743; doi:10.3390/s8063733
Received: 20 January 2008 / Revised: 25 May 2008 / Accepted: 26 May 2008 / Published: 4 June 2008
Cited by 14 | PDF Full-text (938 KB) | HTML Full-text | XML Full-text
Abstract
Hydrocarbon leakage into the environment has large economic and environmental impact. Traditional methods for investigating seepages and their resulting pollution, such as drilling, are destructive, time consuming and expensive. Remote sensing is an efficient tool that offers a non-destructive investigation method. Optical [...] Read more.
Hydrocarbon leakage into the environment has large economic and environmental impact. Traditional methods for investigating seepages and their resulting pollution, such as drilling, are destructive, time consuming and expensive. Remote sensing is an efficient tool that offers a non-destructive investigation method. Optical remote sensing has been extensively tested for exploration of onshore hydrocarbon reservoirs and detection of hydrocarbons at the Earth’s surface. In this research, we investigate indirect manifestations of pipeline leakage by way of visualizing vegetation anomalies in airborne hyperspectral imagery. Agricultural land-use causes a heterogeneous landcover; variation in red edge position between fields was much larger than infield red edge position variation that could be related to hydrocarbon pollution. A moving and growing kernel procedure was developed to normalzie red edge values relative to values of neighbouring pixels to enhance pollution related anomalies in the image. Comparison of the spatial distribution of anomalies with geochemical data obtained by drilling showed that 8 out of 10 polluted sites were predicted correctly while 2 out of 30 sites that were predicted clean were actually polluted. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Estimation of Actual Evapotranspiration by Remote Sensing: Application in Thessaly Plain, Greece
Sensors 2008, 8(6), 3586-3600; doi:10.3390/s8063586
Received: 3 December 2007 / Revised: 9 May 2008 / Accepted: 9 May 2008 / Published: 1 June 2008
Cited by 8 | PDF Full-text (188 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing can assist in improving the estimation of the geographical distribution of evapotranspiration, and consequently water demand in large cultivated areas for irrigation purposes and sustainable water resources management. In the direction of these objectives, the daily actual evapotranspiration was calculated [...] Read more.
Remote sensing can assist in improving the estimation of the geographical distribution of evapotranspiration, and consequently water demand in large cultivated areas for irrigation purposes and sustainable water resources management. In the direction of these objectives, the daily actual evapotranspiration was calculated in this study during the summer season of 2001 over the Thessaly plain in Greece, a wide irrigated area of great agricultural importance. Three different methods were adapted and applied: the remotesensing methods by Granger (2000) and Carlson and Buffum (1989) that use satellite data in conjunction with ground meteorological measurements and an adapted FAO (Food and Agriculture Organisation) Penman-Monteith method (Allen at al. 1998), which was selected to be the reference method. The satellite data were used in conjunction with ground data collected on the three closest meteorological stations. All three methods, exploit visible channels 1 and 2 and infrared channels 4 and 5 of NOAA-AVHRR (National Oceanic and Atmospheric Administration - Advanced Very High Resolution Radiometer) sensor images to calculate albedo and NDVI (Normalised Difference Vegetation Index), as well as surface temperatures. The FAO Penman-Monteith and the Granger method have used exclusively NOAA-15 satellite images to obtain mean surface temperatures. For the Carlson-Buffum method a combination of NOAA-14 and ΝΟΑΑ-15 satellite images was used, since the average rate of surface temperature rise during the morning was required. The resulting estimations show that both the Carlson-Buffum and Granger methods follow in general the variations of the reference FAO Penman-Monteith method. Both methods have potential for estimating the spatial distribution of evapotranspiration, whereby the degree of the relative agreement with the reference FAO Penman-Monteith method depends on the crop growth stage. In particular, the Carlson- Buffum method performed better during the first half of the crop development stage, while the Granger method performed better during the remaining of the development stage and the entire maturing stage. The parameter that influences the estimations significantly is the wind speed whose high values result in high underestimates of evapotranspiration. Thus, it should be studied further in future. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Remote Sensing and Wetland Ecology: a South African Case Study
Sensors 2008, 8(5), 3542-3556; doi:10.3390/s8053542
Received: 29 April 2008 / Accepted: 15 May 2008 / Published: 26 May 2008
Cited by 15 | PDF Full-text (370 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing offers a cost efficient means for identifying and monitoring wetlands over a large area and at different moments in time. In this study, we aim at providing ecologically relevant information on characteristics of temporary and permanent isolated open water wetlands, [...] Read more.
Remote sensing offers a cost efficient means for identifying and monitoring wetlands over a large area and at different moments in time. In this study, we aim at providing ecologically relevant information on characteristics of temporary and permanent isolated open water wetlands, obtained by standard techniques and relatively cheap imagery. The number, surface area, nearest distance, and dynamics of isolated temporary and permanent wetlands were determined for the Western Cape, South Africa. Open water bodies (wetlands) were mapped from seven Landsat images (acquired during 1987 – 2002) using supervised maximum likelihood classification. The number of wetlands fluctuated over time. Most wetlands were detected in the winter of 2000 and 2002, probably related to road constructions. Imagery acquired in summer contained fewer wetlands than in winter. Most wetlands identified from Landsat images were smaller than one hectare. The average distance to the nearest wetland was larger in summer. In comparison to temporary wetlands, fewer, but larger permanent wetlands were detected. In addition, classification of non-vegetated wetlands on an Envisat ASAR radar image (acquired in June 2005) was evaluated. The number of detected small wetlands was lower for radar imagery than optical imagery (acquired in June 2002), probably because of deterioration of the spatial information content due the extensive pre-processing requirements of the radar image. Both optical and radar classifications allow to assess wetland characteristics that potentially influence plant and animal metacommunity structure. Envisat imagery, however, was less suitable than Landsat imagery for the extraction of detailed ecological information, as only large wetlands can be detected. This study has indicated that ecologically relevant data can be generated for the larger wetlands through relatively cheap imagery and standard techniques, despite the relatively low resolution of Landsat and Envisat imagery. For the characterisation of very small wetlands, high spatial resolution optical or radar images are needed. This study exemplifies the benefits of integrating remote sensing and ecology and hence stimulates interdisciplinary research of isolated wetlands. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Assessing the Potentialities of FORMOSAT-2 Data for Water and Crop Monitoring at Small Regional Scale in South-Eastern France
Sensors 2008, 8(5), 3460-3481; doi:10.3390/s8053460
Received: 1 February 2008 / Accepted: 20 May 2008 / Published: 23 May 2008
Cited by 18 | PDF Full-text (946 KB) | HTML Full-text | XML Full-text
Abstract
Water monitoring at the scale of a small agricultural region is a key point to insure a good crop development particularly in South-Eastern France, where extreme climatic conditions result in long dry periods in spring and summer with very sparse precipitation events, [...] Read more.
Water monitoring at the scale of a small agricultural region is a key point to insure a good crop development particularly in South-Eastern France, where extreme climatic conditions result in long dry periods in spring and summer with very sparse precipitation events, corresponding to a crucial period of crop development. Remote sensing with the increasing imagery resolution is a useful tool to provide information on plant water status over various temporal and spatial scales. The current study focussed on assessing the potentialities of FORMOSAT-2 data, characterized by high spatial (8m pixel) and temporal resolutions (1-3 day/time revisit), to improve crop modeling and spatial estimation of the main land properties. Thirty cloud free images were acquired from March to October 2006 over a small region called Crau-Camargue in SE France, while numerous ground measurements were performed simultaneously over various crop types. We have compared two models simulating energy transfers between soil, vegetation and atmosphere: SEBAL and PBLs. Maps of evapotranspiration were analyzed according to the agricultural practices at field scale. These practices were well identified from FORMOSAT-2 images, which provided accurate input surface parameters to the SVAT models. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Non-Invasive Glucose Measurement by Use of Metabolic Heat Conformation Method
Sensors 2008, 8(5), 3335-3344; doi:10.3390/s8053335
Received: 23 April 2008 / Accepted: 20 May 2008 / Published: 21 May 2008
Cited by 5 | PDF Full-text (142 KB) | HTML Full-text | XML Full-text
Abstract
A non-invasive glucose measurement system based on the method of metabolic heat conformation (MHC) is presented in this paper. This system consists of three temperature sensors, two humidity sensors, an infrared sensor and an optical measurement device. The glucose level can be [...] Read more.
A non-invasive glucose measurement system based on the method of metabolic heat conformation (MHC) is presented in this paper. This system consists of three temperature sensors, two humidity sensors, an infrared sensor and an optical measurement device. The glucose level can be deduced from the quantity of heat dissipation, blood flow rate of local tissue and degree of blood oxygen saturation. The methodology of the data process and the measurement error are also analyzed. The system is applied in a primary clinical test. Compared with the results of a commercial automated chemistry analyzer, the correlation coefficient of the collected data from the system is 0.856. Result shows that the correlation coefficient improves when the factor of heat dissipated by evaporation of the skin is added in. A non-invasive method of measuring the blood flow rate of local tissue by heat transmission between skin and contacted conductor is also introduced. Theoretical derivation and numerical simulation are completed as well. The so-called normalized difference mean (NDM) is chosen to express the quantity of the blood flow rate. The correlation coefficient between the blood flow rates by this method and the results of a Doppler blood flow meter is equal to 0.914. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle ASPIS, A Flexible Multispectral System for Airborne Remote Sensing Environmental Applications
Sensors 2008, 8(5), 3240-3256; doi:10.3390/s8053240
Received: 30 March 2008 / Accepted: 14 May 2008 / Published: 16 May 2008
Cited by 5 | PDF Full-text (324 KB) | HTML Full-text | XML Full-text
Abstract
Airborne multispectral and hyperspectral remote sensing is a powerful tool for environmental monitoring applications. In this paper we describe a new system (ASPIS) composed by a 4-CCD spectral sensor, a thermal IR camera and a laser altimeter that is mounted on a [...] Read more.
Airborne multispectral and hyperspectral remote sensing is a powerful tool for environmental monitoring applications. In this paper we describe a new system (ASPIS) composed by a 4-CCD spectral sensor, a thermal IR camera and a laser altimeter that is mounted on a flexible Sky-Arrow airplane. A test application of the multispectral sensor to estimate durum wheat quality is also presented. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle PAU/GNSS-R: Implementation, Performance and First Results of a Real-Time Delay-Doppler Map Reflectometer Using Global Navigation Satellite System Signals
Sensors 2008, 8(5), 3005-3019; doi:10.3390/s8053005
Received: 16 November 2007 / Accepted: 24 April 2008 / Published: 6 May 2008
Cited by 9 | PDF Full-text (8026 KB) | HTML Full-text | XML Full-text
Abstract
Signals from Global Navigation Satellite Systems (GNSS) were originally conceived for position and speed determination, but they can be used as signals of opportunity as well. The reflection process over a given surface modifies the properties of the scattered signal, and therefore, [...] Read more.
Signals from Global Navigation Satellite Systems (GNSS) were originally conceived for position and speed determination, but they can be used as signals of opportunity as well. The reflection process over a given surface modifies the properties of the scattered signal, and therefore, by processing the reflected signal, relevant geophysical data regarding the surface under study (land, sea, ice…) can be retrieved. In essence, a GNSS-R receiver is a multi-channel GNSS receiver that computes the received power from a given satellite at a number of different delay and Doppler bins of the incoming signal. The first approaches to build such a receiver consisted of sampling and storing the scattered signal for later post-processing. However, a real-time approach to the problem is desirable to obtain immediately useful geophysical variables and reduce the amount of data. The use of FPGA technology makes this possible, while at the same time the system can be easily reconfigured. The signal tracking and processing constraints made necessary to fully design several new blocks. The uniqueness of the implemented system described in this work is the capability to compute in real-time Delay-Doppler maps (DDMs) either for four simultaneous satellites or just one, but with a larger number of bins. The first tests have been conducted from a cliff over the sea and demonstrate the successful performance of the instrument to compute DDMs in real-time from the measured reflected GNSS/R signals. The processing of these measurements shall yield quantitative relationships between the sea state (mainly driven by the surface wind and the swell) and the overall DDM shape. The ultimate goal is to use the DDM shape to correct the sea state influence on the L-band brightness temperature to improve the retrieval of the sea surface salinity (SSS). Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object- Oriented Approach
Sensors 2008, 8(5), 3020-3036; doi:10.3390/s8053020
Received: 31 January 2008 / Accepted: 29 April 2008 / Published: 6 May 2008
Cited by 25 | PDF Full-text (633 KB) | HTML Full-text | XML Full-text
Abstract
The objective of the current study was to analyze the seasonal effect on differentiating tree species in an urban environment using multi-temporal hyperspectral data, Light Detection And Ranging (LiDAR) data, and a tree species database collected from the field. Two Airborne Imaging [...] Read more.
The objective of the current study was to analyze the seasonal effect on differentiating tree species in an urban environment using multi-temporal hyperspectral data, Light Detection And Ranging (LiDAR) data, and a tree species database collected from the field. Two Airborne Imaging Spectrometer for Applications (AISA) hyperspectral images were collected, covering the Summer and Fall seasons. In order to make both datasets spatially and spectrally compatible, several preprocessing steps, including band reduction and a spatial degradation, were performed. An object-oriented classification was performed on both images using training data collected randomly from the tree species database. The seven dominant tree species (Gleditsia triacanthos, Acer saccharum, Tilia Americana, Quercus palustris, Pinus strobus and Picea glauca) were used in the classification. The results from this analysis did not show any major difference in overall accuracy between the two seasons. Overall accuracy was approximately 57% for the Summer dataset and 56% for the Fall dataset. However, the Fall dataset provided more consistent results for all tree species while the Summer dataset had a few higher individual class accuracies. Further, adding LiDAR into the classification improved the results by 19% for both fall and summer. This is mainly due to the removal of shadow effect and the addition of elevation data to separate low and high vegetation. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements
Sensors 2008, 8(4), 2833-2853; doi:10.3390/s8042833
Received: 31 January 2008 / Accepted: 14 April 2008 / Published: 23 April 2008
Cited by 33 | PDF Full-text (1254 KB) | HTML Full-text | XML Full-text
Abstract
This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between [...] Read more.
This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG).We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand largescale vegetation growth dynamics above the tree line in the European Alps. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Relative Radiometric Normalization and Atmospheric Correction of a SPOT 5 Time Series
Sensors 2008, 8(4), 2774-2791; doi:10.3390/s8042774
Received: 17 December 2007 / Accepted: 17 April 2008 / Published: 18 April 2008
Cited by 45 | PDF Full-text (553 KB) | HTML Full-text | XML Full-text
Abstract
Multi-temporal images acquired at high spatial and temporal resolution are an important tool for detecting change and analyzing trends, especially in agricultural applications. However, to insure a reliable use of this kind of data, a rigorous radiometric normalization step is required. Normalization [...] Read more.
Multi-temporal images acquired at high spatial and temporal resolution are an important tool for detecting change and analyzing trends, especially in agricultural applications. However, to insure a reliable use of this kind of data, a rigorous radiometric normalization step is required. Normalization can be addressed by performing an atmospheric correction of each image in the time series. The main problem is the difficulty of obtaining an atmospheric characterization at a given acquisition date. In this paper, we investigate whether relative radiometric normalization can substitute for atmospheric correction. We develop an automatic method for relative radiometric normalization based on calculating linear regressions between unnormalized and reference images. Regressions are obtained using the reflectances of automatically selected invariant targets. We compare this method with an atmospheric correction method that uses the 6S model. The performances of both methods are compared using 18 images from of a SPOT 5 time series acquired over Reunion Island. Results obtained for a set of manually selected invariant targets show excellent agreement between the two methods in all spectral bands: values of the coefficient of determination (r²) exceed 0.960, and bias magnitude values are less than 2.65. There is also a strong correlation between normalized NDVI values of sugarcane fields (r² = 0.959). Despite a relative error of 12.66% between values, very comparable NDVI patterns are observed. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Intercomparison of Evapotranspiration Over the Savannah Volta Basin in West Africa Using Remote Sensing Data
Sensors 2008, 8(4), 2736-2761; doi:10.3390/s8042736
Received: 7 January 2008 / Accepted: 19 March 2008 / Published: 17 April 2008
Cited by 8 | PDF Full-text (1912 KB) | HTML Full-text | XML Full-text
Abstract
This paper compares evapotranspiration estimates from two complementary satellite sensors – NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and ESA’s ENVISAT Advanced Along-Track Scanning Radiometer (AATSR) over the savannah area of the Volta basin in West Africa. This was achieved through solving for [...] Read more.
This paper compares evapotranspiration estimates from two complementary satellite sensors – NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and ESA’s ENVISAT Advanced Along-Track Scanning Radiometer (AATSR) over the savannah area of the Volta basin in West Africa. This was achieved through solving for evapotranspiration on the basis of the regional energy balance equation, which was computationally-driven by the Surface Energy Balance Algorithm for Land algorithm (SEBAL). The results showed that both sensors are potentially good sources of evapotranspiration estimates over large heterogeneous landscapes. The MODIS sensor measured daily evapotranspiration reasonably well with a strong spatial correlation (R2=0.71) with Landsat ETM+ but underperformed with deviations up to ~2.0 mm day-1, when compared with local eddy correlation observations and the Penman-Monteith method mainly because of scale mismatch. The AATSR sensor produced much poorer correlations (R2=0.13) with Landsat ETM+ and conventional ET methods also because of differences in atmospheric correction and sensor calibration over land. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Inter-Comparison of ASTER and MODIS Surface Reflectance and Vegetation Index Products for Synergistic Applications to Natural Resource Monitoring
Sensors 2008, 8(4), 2480-2499; doi:10.3390/s8042480
Received: 31 January 2008 / Accepted: 3 April 2008 / Published: 8 April 2008
Cited by 20 | PDF Full-text (1475 KB) | HTML Full-text | XML Full-text
Abstract
Synergistic applications of multi-resolution satellite data have been of a great interest among user communities for the development of an improved and more effective operational monitoring system of natural resources, including vegetation and soil. In this study, we conducted an inter-comparison of [...] Read more.
Synergistic applications of multi-resolution satellite data have been of a great interest among user communities for the development of an improved and more effective operational monitoring system of natural resources, including vegetation and soil. In this study, we conducted an inter-comparison of two remote sensing products, namely, visible/near-infrared surface reflectances and spectral vegetation indices (VIs), from the high resolution Advanced Thermal Emission and Reflection Radiometer (ASTER) (15 m) and lower resolution Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m – 500 m) sensors onboard the Terra platform. Our analysis was aimed at understanding the degree of radiometric compatibility between the two sensors’ products due to sensor spectral bandpasses and product generation algorithms. Multiple pairs of ASTER and MODIS standard surface reflectance products were obtained at randomly-selected, globally-distributed locations, from which two types of VIs were computed: the normalized difference vegetation index and the enhanced vegetation indices with and without a blue band. Our results showed that these surface reflectance products and the derived VIs compared well between the two sensors at a global scale, but subject to systematic differences, of which magnitudes varied among scene pairs. An independent assessment of the accuracy of ASTER and MODIS standard products, in which “in-house” surface reflectances were obtained using in situ Aeronet atmospheric data for comparison, suggested that the performance of the ASTER atmospheric correction algorithm may be variable, reducing overall quality of its standard reflectance product. Atmospheric aerosols, which were not corrected for in the ASTER algorithm, were found not to impact the quality of the derived reflectances. Further investigation is needed to identify the sources of inconsistent atmospheric correction results associated with the ASTER algorithm, including additional quality assessments of the ASTER and MODIS products with other atmospheric radiative transfer codes. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Airborne Laser Scanning Quantification of Disturbances from Hurricanes and Lightning Strikes to Mangrove Forests in Everglades National Park, USA
Sensors 2008, 8(4), 2262-2292; doi:10.3390/s8042262
Received: 5 February 2008 / Accepted: 25 March 2008 / Published: 1 April 2008
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Abstract
Airborne light detection and ranging (LIDAR) measurements derived before and after Hurricanes Katrina and Wilma (2005) were used to quantify the impact of hurricanes and lightning strikes on the mangrove forest at two sites in Everglades National Park (ENP). Analysis of LIDAR [...] Read more.
Airborne light detection and ranging (LIDAR) measurements derived before and after Hurricanes Katrina and Wilma (2005) were used to quantify the impact of hurricanes and lightning strikes on the mangrove forest at two sites in Everglades National Park (ENP). Analysis of LIDAR measurements covering 61 and 68 ha areas of mangrove forest at the Shark River and Broad River sites showed that the proportion of high tree canopy detected by the LIDAR after the 2005 hurricane season decreased significantly due to defoliation and breakage of branches and trunks, while the proportion of low canopy and the ground increased drastically. Tall mangrove forests distant from tidal creeks suffered more damage than lower mangrove forests adjacent to the tidal creeks. The hurricanes created numerous canopy gaps, and the number of gaps per square kilometer increased from about 400~500 to 4000 after Katrina and Wilma. The total area of gaps in the forest increased from about 1~2% of the total forest area to 12%. The relative contribution of hurricanes to mangrove forest disturbance in ENP is at least 2 times more than that from lightning strikes. However, hurricanes and lightning strikes disturb the mangrove forest in a related way. Most seedlings in lightning gaps survived the hurricane impact due to the protection of trees surrounding the gaps, and therefore provide an important resource for forest recovery after the hurricane. This research demonstrated that LIDAR is an effective remote sensing tool to quantify the effects of disturbances such as hurricanes and lightning strikes in the mangrove forest. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Assessment of the Spatial Co-registration of Multitemporal Imagery from Large Format Digital Cameras in the Context of Detailed Change Detection
Sensors 2008, 8(4), 2161-2173; doi:10.3390/s8042161
Received: 30 January 2008 / Accepted: 26 March 2008 / Published: 28 March 2008
Cited by 15 | PDF Full-text (361 KB) | HTML Full-text | XML Full-text
Abstract
Large format digital camera (LFDC) systems are becoming more broadly available and regularly collect image data over large areas. Spectral and radiometric attributes of imagery from LFDC systems make this type of image data appropriate for semi-automated change detection. However, achieving accurate [...] Read more.
Large format digital camera (LFDC) systems are becoming more broadly available and regularly collect image data over large areas. Spectral and radiometric attributes of imagery from LFDC systems make this type of image data appropriate for semi-automated change detection. However, achieving accurate spatial co-registration between multitemporal image sets is necessary for semi-automated change detection. This study investigates the accuracy of co-registration between multitemporal image sets acquired using the Leica Geosystems ADS40, Intergraph Z/I Imaging® DMC, and Vexcel UltraCam-D sensors in areas of gentle, moderate, and extreme terrain relief. Custom image sets were collected and orthorectified by imagery vendors, with guidance from the authors. Results indicate that imagery acquired by vendors operating LFDC systems may be coregistered with pixel or sub-pixel level accuracy, even for environments with high terrain relief. Specific image acquisition and processing procedures facilitating this level of coregistration are discussed. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data
Sensors 2008, 8(3), 2017-2042; doi:10.3390/s8032017
Received: 29 January 2008 / Accepted: 21 March 2008 / Published: 25 March 2008
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Abstract
This study examines how satellite based time-series vegetation greenness data and phenological measurements can be used to monitor and quantify vegetation recovery after wildfire disturbances and examine how pre-fire fuel reduction restoration treatments impact fire severity and impact vegetation recovery trajectories. Pairs [...] Read more.
This study examines how satellite based time-series vegetation greenness data and phenological measurements can be used to monitor and quantify vegetation recovery after wildfire disturbances and examine how pre-fire fuel reduction restoration treatments impact fire severity and impact vegetation recovery trajectories. Pairs of wildfire affected sites and a nearby unburned reference site were chosen to measure the post-disturbance recovery in relation to climate variation. All site pairs were chosen in forested uplands in Arizona and were restricted to the area of the Rodeo-Chediski fire that occurred in 2002. Fuel reduction treatments were performed in 1999 and 2001. The inter-annual and seasonal vegetation dynamics before, during, and after wildfire events can be monitored using a time series of biweekly composited MODIS NDVI (Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index) data. Time series analysis methods included difference metrics, smoothing filters, and fitting functions that were applied to extract seasonal and inter-annual change and phenological metrics from the NDVI time series data from 2000 to 2007. Pre- and post-fire Landsat data were used to compute the Normalized Burn Ratio (NBR) and examine burn severity at the selected sites. The phenological metrics (pheno-metrics) included the timing and greenness (i.e. NDVI) for the start, peak and end of the growing season as well as proxy measures for the rate of green-up and senescence and the annual vegetation productivity. Pre-fire fuel reduction treatments resulted in lower fire severity, which reduced annual productivity much less than untreated areas within the Rodeo-Chediski fire perimeter. The seasonal metrics were shown to be useful for estimating the rate of post-fire disturbance recovery and the timing of phenological greenness phases. The use of satellite time series NDVI data and derived pheno-metrics show potential for tracking vegetation cover dynamics and successional changes in response to drought, wildfire disturbances, and forest restoration treatments in fire-suppressed forests. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Role of Satellite Sensors in Groundwater Exploration
Sensors 2008, 8(3), 2006-2016; doi:10.3390/s8032006
Received: 1 November 2007 / Accepted: 7 February 2008 / Published: 24 March 2008
Cited by 8 | PDF Full-text (2415 KB) | HTML Full-text | XML Full-text
Abstract
Spatial as well as spectral resolution has a very important role to play in water resource management. It was a challenge to explore the groundwater and rainwater harvesting sites in the Aravalli Quartzite-Granite-Pegmatite Precambrian terrain of Delhi, India. Use of only panchromatic [...] Read more.
Spatial as well as spectral resolution has a very important role to play in water resource management. It was a challenge to explore the groundwater and rainwater harvesting sites in the Aravalli Quartzite-Granite-Pegmatite Precambrian terrain of Delhi, India. Use of only panchromatic sensor data of IRS-1D satellite with 5.8-meter spatial resolution has the potential to infer lineaments and faults in this hard rock area. It is essential to identify the location of interconnected lineaments below buried pediment plains in the hard rock area for targeting sub-surface water resources. Linear Image Self Scanning sensor data of the same satellite with 23.5-meter resolution when merged with the panchromatic data has produced very good results in delineation of interconnected lineaments over buried pediment plains as vegetation anomaly. These specific locations of vegetation anomaly were detected as dark red patches in various hard rock areas of Delhi. Field investigation was carried out on these patches by resistivity and magnetic survey in parts of Jawaharlal Nehru University (JNU), Indira Gandhi national Open University, Research and Referral Hospital and Humayuns Tomb areas. Drilling was carried out in four locations of JNU that proved to be the most potential site with ground water discharge ranging from 20,000 to 30,000 liters per hour with 2 to 4 meters draw down. Further the impact of urbanization on groundwater recharging in the terrain was studied by generating Normalized difference Vegetation Index (NDVI) map which was possible to generate by using the LISS-III sensor of IRS-1D satellite. Selection of suitable sensors has definitely a cutting edge on natural resource exploration and management including groundwater. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Sensor Performance Requirements for the Retrieval of Atmospheric Aerosols by Airborne Optical Remote Sensing
Sensors 2008, 8(3), 1901-1914; doi:10.3390/s8031901
Received: 30 January 2008 / Accepted: 17 March 2008 / Published: 18 March 2008
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Abstract
This study explores performance requirements for the retrieval of the atmospheric aerosol optical depth (AOD) by airborne optical remote sensing instruments. Independent of any retrieval techniques, the calculated AOD retrieval requirements are compared with the expected performance parameters of the upcoming hyperspectral [...] Read more.
This study explores performance requirements for the retrieval of the atmospheric aerosol optical depth (AOD) by airborne optical remote sensing instruments. Independent of any retrieval techniques, the calculated AOD retrieval requirements are compared with the expected performance parameters of the upcoming hyperspectral sensor APEX at the reference wavelength of 550nm. The AOD accuracy requirements are defined to be capable of resolving transmittance differences of 0.01 to 0.04 according to the demands of atmospheric corrections for remote sensing applications. For the purposes of this analysis, the signal at the sensor level is simulated by radiation transfer equations. The resulting radiances are translated into the AOD retrieval sensitivity (Δτλaer ) and compared to the available measuring sensitivity of the sensor (NE ΔLλsensor). This is done for multiple signal-to-noise ratios (SNR) and surface reflectance values. It is shown that an SNR of 100 is adequate for AOD retrieval at 550nm under typical remote sensing conditions and a surface reflectance of 10% or less. Such dark surfaces require the lowest SNR values and therefore offer the best sensitivity for measuring AOD. Brighter surfaces with up to 30% reflectance require an SNR of around 300. It is shown that AOD retrieval for targets above 50% surface reflectance is more problematic with the current sensor performance as it may require an SNR larger than 1000. In general, feasibility is proven for the analyzed cases under simulated conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Assessment and Analysis of QuikSCAT Vector Wind Products for the Gulf of Mexico: A Long-Term and Hurricane Analysis
Sensors 2008, 8(3), 1927-1949; doi:10.3390/s8031927
Received: 9 November 2007 / Accepted: 15 March 2008 / Published: 18 March 2008
Cited by 19 | PDF Full-text (3054 KB) | HTML Full-text | XML Full-text
Abstract
The northern Gulf of Mexico is a region that has been frequently impacted in recent years by natural disasters such as hurricanes. The use of remote sensing data such as winds from NASA’s QuikSCAT satellite sensor would be useful for emergency preparedness [...] Read more.
The northern Gulf of Mexico is a region that has been frequently impacted in recent years by natural disasters such as hurricanes. The use of remote sensing data such as winds from NASA’s QuikSCAT satellite sensor would be useful for emergency preparedness during such events. In this study, the performance of QuikSCAT products, including JPL’s latest Level 2B (L2B) 12.5 km swath winds, were evaluated with respect to buoy-measured winds in the Gulf of Mexico for the period January 2005 to February 2007. Regression analyses indicated better accuracy of QuikSCAT’s L2B DIRTH, 12.5 km than the Level 3 (L3), 25 km wind product. QuikSCAT wind data were compared directly with buoy data keeping a maximum time interval of 20 min and spatial interval of 0.1° (≈10 km). R2 values for moderate wind speeds were 0.88 and 0.93 for L2B, and 0.75 and 0.89 for L3 for speed and direction, respectively. QuikSCAT wind comparisons for buoys located offshore were better than those located near the coast. Hurricanes that took place during 2002-06 were studied individually to obtain regressions of QuikSCAT versus buoys for those events. Results show QuikSCAT’s L2B DIRTH wind product compared well with buoys during hurricanes up to the limit of buoy measurements. Comparisons with the National Hurricane Center (NHC) best track analyses indicated QuikSCAT winds to be lower than those obtained by NHC, possibly due to rain contamination, while buoy measurements appeared to be constrained at high wind speeds. This study has confirmed good agreement of the new QuikSCAT L2B product with buoy measurements and further suggests its potential use during extreme weather conditions in the Gulf of Mexico. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Estimation of the Total Atmospheric Water Vapor Content and Land Surface Temperature Based on AATSR Thermal Data
Sensors 2008, 8(3), 1832-1845; doi:10.3390/s8031832
Received: 7 November 2007 / Accepted: 8 February 2008 / Published: 16 March 2008
Cited by 2 | PDF Full-text (354 KB) | HTML Full-text | XML Full-text
Abstract
The total atmospheric water vapor content (TAWV) and land surfacetemperature (LST) play important roles in meteorology, hydrology, ecology and some otherdisciplines. In this paper, the ENVISAT/AATSR (The Advanced Along-Track ScanningRadiometer) thermal data are used to estimate the TAWV and LST over the [...] Read more.
The total atmospheric water vapor content (TAWV) and land surfacetemperature (LST) play important roles in meteorology, hydrology, ecology and some otherdisciplines. In this paper, the ENVISAT/AATSR (The Advanced Along-Track ScanningRadiometer) thermal data are used to estimate the TAWV and LST over the Loess Plateauin China by using a practical split window algorithm. The distribution of the TAWV isaccord with that of the MODIS TAWV products, which indicates that the estimation of thetotal atmospheric water vapor content is reliable. Validations of the LST by comparingwith the ground measurements indicate that the maximum absolute derivation, themaximum relative error and the average relative error is 4.0K, 11.8% and 5.0%respectively, which shows that the retrievals are believable; this algorithm can provide anew way to estimate the LST from AATSR data. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Assessing Steady-state Fluorescence and PRI from Hyperspectral Proximal Sensing as Early Indicators of Plant Stress: The Case of Ozone Exposure
Sensors 2008, 8(3), 1740-1754; doi:10.3390/s8031740
Received: 31 January 2008 / Accepted: 11 March 2008 / Published: 13 March 2008
Cited by 42 | PDF Full-text (246 KB) | HTML Full-text | XML Full-text
Abstract
High spectral resolution spectrometers were used to detect optical signals ofongoing plant stress in potted white clover canopies subjected to ozone fumigation. Thecase of ozone stress is used in this manuscript as a paradigm of oxidative stress. Steadystatefluorescence (Fs) and the Photochemical [...] Read more.
High spectral resolution spectrometers were used to detect optical signals ofongoing plant stress in potted white clover canopies subjected to ozone fumigation. Thecase of ozone stress is used in this manuscript as a paradigm of oxidative stress. Steadystatefluorescence (Fs) and the Photochemical Reflectance Index (PRI) were investigatedas advanced hyperspectral remote sensing techniques able to sense variations in the excessenergy dissipation pathways occurring when photosynthesis declines in plants exposed to astress agent. Fs and PRI were monitored in control and ozone fumigated canopies during a21-day experiment together with the traditional Normalized Difference Vegetation Index(NDVI) and physiological measurements commonly employed by physiologists to describestress development (i.e. net CO2 assimilation, active fluorimetry, chlorophyll concentrationand visible injuries). It is shown that remote detection of an ongoing stress through Fs andPRI can be achieved in an early phase, characterized by the decline of photosynthesis. Onthe contrary, NDVI was able to detect the stress only when damage occurred. These resultsopen up new possibilities for assessment of plant stress by means of hyperspectral remotesensing. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Topographic Effects on the Surface Emissivity of a Mountainous Area Observed by a Spaceborne Microwave Radiometer
Sensors 2008, 8(3), 1459-1474; doi:10.3390/s8031459
Received: 23 January 2008 / Accepted: 27 February 2008 / Published: 3 March 2008
Cited by 12 | PDF Full-text (297 KB) | HTML Full-text | XML Full-text
Abstract
A simulation study to understand the influence of topography on the surfaceemissivity observed by a satellite microwave radiometer is carried out. We analyze theeffects due to changes in observation angle, including the rotation of the polarization plane.A mountainous area in the Alps [...] Read more.
A simulation study to understand the influence of topography on the surfaceemissivity observed by a satellite microwave radiometer is carried out. We analyze theeffects due to changes in observation angle, including the rotation of the polarization plane.A mountainous area in the Alps (Northern Italy) is considered and the information on therelief extracted from a digital elevation model is exploited. The numerical simulation refersto a radiometric image, acquired by a conically-scanning radiometer similar to AMSR-E,i.e., flying at 705 km of altitude with an observation angle of 55°. To single out the impacton surface emissivity, scattering of the radiation due to the atmosphere or neighboringelevated surfaces is not considered. C and X bands, for which atmospheric effects arenegligible, and Ka band are analyzed. The results indicate that the changes in the localobservation angle tend to lower the apparent emissivity of a radiometric pixel with respectto the corresponding flat surface characteristics. The effect of the rotation of thepolarization plane enlarges (vertical polarization), or attenuates (horizontal polarization)this decrease. By doing some simplifying assumptions for the radiometer antenna, theconclusion is that the microwave emissivity at vertical polarization is underestimated,whilst the opposite occurs for horizontal polarization, except for Ka band, for which bothunder- and overprediction may occur. A quantification of the differences with respect to aflat soil and an approximate evaluation of their impact on soil moisture retrieval areyielded. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle The Successive Projection Algorithm (SPA), an Algorithm with a Spatial Constraint for the Automatic Search of Endmembers in Hyperspectral Data
Sensors 2008, 8(2), 1321-1342; doi:10.3390/s8021321
Received: 28 December 2007 / Accepted: 19 February 2008 / Published: 22 February 2008
Cited by 24 | PDF Full-text (2793 KB) | HTML Full-text | XML Full-text
Abstract
Spectral mixing is a problem inherent to remote sensing data and results in fewimage pixel spectra representing "pure" targets. Linear spectral mixture analysis isdesigned to address this problem and it assumes that the pixel-to-pixel variability in ascene results from varying proportions of [...] Read more.
Spectral mixing is a problem inherent to remote sensing data and results in fewimage pixel spectra representing "pure" targets. Linear spectral mixture analysis isdesigned to address this problem and it assumes that the pixel-to-pixel variability in ascene results from varying proportions of spectral endmembers. In this paper we present adifferent endmember-search algorithm called the Successive Projection Algorithm (SPA).SPA builds on convex geometry and orthogonal projection common to other endmembersearch algorithms by including a constraint on the spatial adjacency of endmembercandidate pixels. Consequently it can reduce the susceptibility to outlier pixels andgenerates realistic endmembers.This is demonstrated using two case studies (AVIRISCuprite cube and Probe-1 imagery for Baffin Island) where image endmembers can bevalidated with ground truth data. The SPA algorithm extracts endmembers fromhyperspectral data without having to reduce the data dimensionality. It uses the spectralangle (alike IEA) and the spatial adjacency of pixels in the image to constrain the selectionof candidate pixels representing an endmember. We designed SPA based on theobservation that many targets have spatial continuity (e.g. bedrock lithologies) in imageryand thus a spatial constraint would be beneficial in the endmember search. An additionalproduct of the SPA is data describing the change of the simplex volume ratio between successive iterations during the endmember extraction. It illustrates the influence of a newendmember on the data structure, and provides information on the convergence of thealgorithm. It can provide a general guideline to constrain the total number of endmembersin a search. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Laboratory Evaluation of Acoustic Backscatter and LISST Methods for Measurements of Suspended Sediments
Sensors 2008, 8(2), 979-993; doi:10.3390/s8020979
Received: 30 December 2007 / Accepted: 7 February 2008 / Published: 19 February 2008
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Abstract
The limitation of traditional sampling method to provide detailed spatial andtemporal profiles of suspended sediment concentration has led to an interest in alternativedevices and methods based on scattering of underwater sound and light . In the presentwork, acoustic backscatter and LISST (the [...] Read more.
The limitation of traditional sampling method to provide detailed spatial andtemporal profiles of suspended sediment concentration has led to an interest in alternativedevices and methods based on scattering of underwater sound and light . In the presentwork, acoustic backscatter and LISST (the Laser In Situ Scattering Transmissometry)devices, and methodologies were given. Besides a laboratory study was conducted tocompare pumping methods for different sediment radiuses at the same concentration. Theglass spheres (ballotini) of three different radiuses of 115, 137 and 163 μm were used toobtain suspension in the sediment tower at laboratory. A quite good agreement wasobtained between these methods and pumping results with the range at 60.6-94.2% forsediment concentration and 91.3-100% for radius measurements. These results and theother studies show that these methods have potential for research tools for sedimentstudies. In addition further studies are needed to determine the ability of these methods forsediment measurement under different water and sediment material conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing based Estimates of Impervious Surface Cover
Sensors 2008, 8(2), 910-932; doi:10.3390/s8020910
Received: 15 January 2008 / Accepted: 7 February 2008 / Published: 15 February 2008
Cited by 39 | PDF Full-text (5602 KB) | HTML Full-text | XML Full-text
Abstract
The amount and intensity of runoff on catchment scale are strongly determinedby the presence of impervious land-cover types, which are the predominant cover types inurbanized areas. This paper examines the impact of different methods for estimatingimpervious surface cover on the prediction of [...] Read more.
The amount and intensity of runoff on catchment scale are strongly determinedby the presence of impervious land-cover types, which are the predominant cover types inurbanized areas. This paper examines the impact of different methods for estimatingimpervious surface cover on the prediction of peak discharges, as determined by a fullydistributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe Rivercatchment in the southeastern part of Brussels. The study shows that detailed informationon the spatial distribution of impervious surfaces, as obtained from remotely sensed data,produces substantially different estimates of peak discharges than traditional approachesbased on expert judgment of average imperviousness for different types of urban land use.The study also demonstrates that sub-pixel estimation of imperviousness may be a usefulalternative for more expensive high-resolution mapping for rainfall-runoff modelling atcatchment scale. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data
Sensors 2008, 8(2), 933-951; doi:10.3390/s8020933
Received: 3 January 2008 / Accepted: 31 January 2008 / Published: 14 February 2008
Cited by 46 | PDF Full-text (898 KB) | HTML Full-text | XML Full-text
Abstract
On the basis of the radiative transfer theory, this paper addressed the estimate ofLand Surface Temperature (LST) from the Chinese first operational geostationarymeteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1,10.3-11.3 μ m and IR2, 11.5-12.5 μ m ), using the [...] Read more.
On the basis of the radiative transfer theory, this paper addressed the estimate ofLand Surface Temperature (LST) from the Chinese first operational geostationarymeteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1,10.3-11.3 μ m and IR2, 11.5-12.5 μ m ), using the Generalized Split-Window (GSW)algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithmcorresponding to a series of overlapping ranging of the mean emissivity, the atmosphericWater Vapor Content (WVC), and the LST were derived using a statistical regressionmethod from the numerical values simulated with an accurate atmospheric radiativetransfer model MODTRAN 4 over a wide range of atmospheric and surface conditions.The simulation analysis showed that the LST could be estimated by the GSW algorithmwith the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with theViewing Zenith Angle (VZA) less than 30° or for the sub-rangs with VZA less than 60°and the atmospheric WVC less than 3.5 g/cm2 provided that the Land Surface Emissivities(LSEs) are known. In order to determine the range for the optimum coefficients of theGSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according tothe land surface classification or using the method proposed by Jiang et al. (2006); and theWVC could be obtained from MODIS total precipitable water product MOD05, or beretrieved using Li et al.’ method (2003). The sensitivity and error analyses in term of theuncertainty of the LSE and WVC as well as the instrumental noise were performed. Inaddition, in order to compare the different formulations of the split-window algorithms,several recently proposed split-window algorithms were used to estimate the LST with thesame simulated FY-2C data. The result of the intercomparsion showed that most of thealgorithms give comparable results Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS
Sensors 2008, 8(1), 529-560; doi:10.3390/s8010529
Received: 29 November 2007 / Accepted: 22 January 2008 / Published: 24 January 2008
Cited by 41 | PDF Full-text (3477 KB) | HTML Full-text | XML Full-text
Abstract
Forest inventory data often provide the required base data to enable the largearea mapping of biomass over a range of scales. However, spatially explicit estimates ofabove-ground biomass (AGB) over large areas may be limited by the spatial extent of theforest inventory relative [...] Read more.
Forest inventory data often provide the required base data to enable the largearea mapping of biomass over a range of scales. However, spatially explicit estimates ofabove-ground biomass (AGB) over large areas may be limited by the spatial extent of theforest inventory relative to the area of interest (i.e., inventories not spatially exhaustive), orby the omission of inventory attributes required for biomass estimation. These spatial andattributional gaps in the forest inventory may result in an underestimation of large areaAGB. The continuous nature and synoptic coverage of remotely sensed data have led totheir increased application for AGB estimation over large areas, although the use of thesedata remains challenging in complex forest environments. In this paper, we present anapproach to generating spatially explicit estimates of large area AGB by integrating AGBestimates from multiple data sources; 1. using a lookup table of conversion factors appliedto a non-spatially exhaustive forest inventory dataset (R2 = 0.64; RMSE = 16.95 t/ha), 2.applying a lookup table to unique combinations of land cover and vegetation densityoutputs derived from remotely sensed data (R2 = 0.52; RMSE = 19.97 t/ha), and 3. hybridmapping by augmenting forest inventory AGB estimates with remotely sensed AGB estimates where there are spatial or attributional gaps in the forest inventory data. Over our714,852 ha study area in central Saskatchewan, Canada, the AGB estimate generated fromthe forest inventory was approximately 40 Mega tonnes (Mt); however, the inventoryestimate represents only 51% of the total study area. The AGB estimate generated from theremotely sensed outputs that overlap those made from the forest inventory based approachdiffer by only 2 %; however in total, the remotely sensed estimate is 30 % greater (58 Mt)than the estimate generated from the forest inventory when the entire study area isaccounted for. Finally, using the hybrid approach, whereby the remotely sensed inputswere used to fill spatial gaps in the forest inventory, the total AGB for the study area wasestimated at 62 Mt. In the example presented, data integration facilitates comprehensiveand spatially explicit estimation of AGB for the entire study area. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data
Sensors 2008, 8(1), 314-326; doi:10.3390/s8010314
Received: 15 November 2007 / Accepted: 9 January 2008 / Published: 21 January 2008
PDF Full-text (315 KB) | HTML Full-text | XML Full-text
Abstract
Agricultural producers require knowledge of soil water at plant rooting depths,while many remote sensing studies have focused on surface soil water or mechanisticmodels that are not easily parameterized. We developed site-specific empirical models topredict spring soil water content for two Montana ranches. [...] Read more.
Agricultural producers require knowledge of soil water at plant rooting depths,while many remote sensing studies have focused on surface soil water or mechanisticmodels that are not easily parameterized. We developed site-specific empirical models topredict spring soil water content for two Montana ranches. Calibration data sample sizeswere based on the estimated variability of soil water and the desired level of precision forthe soil water estimates. Models used Landsat imagery, a digital elevation model, and asoil survey as predictor variables. Our objectives were to see whether soil water could bepredicted accurately with easily obtainable calibration data and predictor variables and toconsider the relative influence of the three sources of predictor variables. Independentvalidation showed that multiple regression models predicted soil water with average error(RMSD) within 0.04 mass water content. This was similar to the accuracy expected basedon a statistical power test based on our sample size (n = 41 and n = 50). Improvedprediction precision could be achieved with additional calibration samples, and rangemanagers can readily balance the desired level of precision with the amount of effort tocollect calibration data. Spring soil water prediction effectively utilized a combination ofland surface imagery, terrain data, and subsurface soil characterization data. Rancherscould use accurate spring soil water content predictions to set stocking rates. Suchmanagement can help ensure that water, soil, and vegetation resources are usedconservatively in irrigated and non-irrigated rangeland systems. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Changes in Spectral Properties, Chlorophyll Content and Internal Mesophyll Structure of Senescing Populus balsamifera and Populus tremuloides Leaves
Sensors 2008, 8(1), 51-69; doi:10.3390/s8010051
Received: 19 November 2007 / Accepted: 19 December 2007 / Published: 9 January 2008
Cited by 18 | PDF Full-text (615 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we compare leaf traits and spectral reflectance for sunlit andshaded leaves of Populus tremuloides and Populus balsamifera during autumnsenescence using information derived from an Analytical Spectral Devise (ASD) FullRange spectrometer. The modified simple ratio (mSR705) and modified [...] Read more.
In this paper we compare leaf traits and spectral reflectance for sunlit andshaded leaves of Populus tremuloides and Populus balsamifera during autumnsenescence using information derived from an Analytical Spectral Devise (ASD) FullRange spectrometer. The modified simple ratio (mSR705) and modified normalizeddifference index (mND705) were effective in describing changes in chlorophyll contentover this period. Highly significant (P less than 0.01) correlation coefficients were found betweenthe chlorophyll indices (mSR705, mND705)) and chlorophyll a, b, total chlorophyll andchlorophyll a/b. Changes in mesophyll structure were better described by the plantsenescence reflectance index (PSRI) than by near-infrared wavebands. Overall, P.balsamifera exhibited lower total chlorophyll and earlier senescence than P. tremuloides.Leaves of P. balsamifera were also thicker, had a higher proportion of intercellular spacein the spongy mesophyll, and higher reflectance at 800 nm. Further research, using largersample sizes over a broader range of sites will extend our understanding of the spectraland temporal dynamics of senescence in P. tremuloides and P. balsamifera and will beparticularly useful if species differences are detectable at the crown level using remotelysensed imagery. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Evaluation of Grassland Dynamics in the Northern-Tibet Plateau of China Using Remote Sensing and Climate Data
Sensors 2007, 7(12), 3312-3328; doi:10.3390/s7123312
Received: 13 October 2007 / Accepted: 14 December 2007 / Published: 17 December 2007
Cited by 21 | PDF Full-text (591 KB) | HTML Full-text | XML Full-text
Abstract
The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is verysensitive to weather and climate conditions of the region. In this study, we investigate thespatial and temporal variations of the grassland ecosystem in the NTP using theNOAA/AVHRR ten-day maximum NDVI composite [...] Read more.
The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is verysensitive to weather and climate conditions of the region. In this study, we investigate thespatial and temporal variations of the grassland ecosystem in the NTP using theNOAA/AVHRR ten-day maximum NDVI composite data of 1981-2001. The relationshipsamong Vegetation Peak-Normalized Difference Vegetation Index (VP-NDVI) and climatevariables were quantified for six counties within the NTP. The notable and unevenalterations of the grassland in response to variation of climate and human impact in theNTP were revealed. Over the last two decades of the 20th century, the maximum greennessof the grassland has exhibited high increase, slight increase, no-change, slight decrease andhigh decrease, each occupies 0.27%, 8.71%, 77.27%, 13.06% and 0.69% of the total area ofthe NTP, respectively. A remarkable increase (decrease) in VP-NDVI occurred in thecentral-eastern (eastern) NTP whereas little change was observed in the western andnorthwestern NTP. A strong negative relationship between VP-NDVI and ET0 was foundin sub-frigid, semi-arid and frigid- arid regions of the NTP (i.e., Nakchu, Shantsa, Palgonand Amdo counties), suggesting that the ET0 is one limiting factor affecting grasslanddegradation. In the temperate-humid, sub-frigid and sub-humid regions of the NTP (Chaliand Sokshan counties), a significant inverse correlation between VP-NDVI and populationindicates that human activities have adversely affected the grassland condition as waspreviously reported in the literature. Results from this research suggest that the alterationand degradation of the grassland in the lower altitude of the NTP over the last two decades of the 20th century are likely caused by variations of climate and anthropogenic activities. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products
Sensors 2007, 7(12), 3043-3057; doi:10.3390/s7123043
Received: 25 October 2007 / Accepted: 2 December 2007 / Published: 4 December 2007
Cited by 19 | PDF Full-text (833 KB) | HTML Full-text | XML Full-text
Abstract
Human-induced global warming has significantly increased the importance ofsatellite monitoring of land surface temperature (LST) on a global scale. The MODerate-resolution Imaging Spectroradiometer (MODIS) provides a 1-km resolution LST productwith almost daily coverage of the Earth, invaluable to both local and global [...] Read more.
Human-induced global warming has significantly increased the importance ofsatellite monitoring of land surface temperature (LST) on a global scale. The MODerate-resolution Imaging Spectroradiometer (MODIS) provides a 1-km resolution LST productwith almost daily coverage of the Earth, invaluable to both local and global change studies.The Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) provides aLST product with a high spatial resolution of 90-m and a 16-day recurrent cycle,simultaneously acquired at the same height and nadir view as MODIS. ASTER andMODIS are complementary in resolution, offering a unique opportunity for scale-relatedstudies. ASTER and MODIS LST have been widely used but the errors in LST were mostlydisregarded. Correction of ASTER-to-MODIS LST discrepancies is essential for studiesreliant upon the joint use of these sensors. In this study, we compared three correctionapproaches: the Wan et al.’s approach, the refined Wan et al.’s approach, and thegeneralized split window (GSW) algorithm based approach. The Wan et al.’s approachcorrects the MODIS 1-km LST using MODIS 5-km LST. The refined approach modifiesthe Wan et al.’s approach through incorporating ASTER emissivity and MODIS 5-km data.The GSW algorithm approach does not use MODIS 5-km but only ASTER emissivity data. We examined the case over a semi-arid terrain area for the part of the Loess Plateau of China. All the approaches reduced the ASTER-to-MODIS LST discrepancy effectively. With terrain correction, the original ASTER-to-MODIS LST difference reduced from 2.7±1.28 K to -0.1±1.87 K for the Wan et al.’s approach, 0.2±1.57 K for the refined approach, and 0.1±1.33 K for the GSW algorithm based approach. Among all the approaches, the GSW algorithm based approach performed best in terms of mean, standard deviation, root mean square root, and correlation coefficient. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest
Sensors 2007, 7(11), 2636-2651; doi:10.3390/s7112636
Received: 2 August 2007 / Accepted: 30 October 2007 / Published: 5 November 2007
Cited by 63 | PDF Full-text (757 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation indices play an important role in monitoring variations in vegetation.The Enhanced Vegetation Index (EVI) proposed by the MODIS Land Discipline Groupand the Normalized Difference Vegetation Index (NDVI) are both global-based vegetationindices aimed at providing consistent spatial and temporal information regarding globalvegetation. [...] Read more.
Vegetation indices play an important role in monitoring variations in vegetation.The Enhanced Vegetation Index (EVI) proposed by the MODIS Land Discipline Groupand the Normalized Difference Vegetation Index (NDVI) are both global-based vegetationindices aimed at providing consistent spatial and temporal information regarding globalvegetation. However, many environmental factors such as atmospheric conditions and soilbackground may produce errors in these indices. The topographic effect is another veryimportant factor, especially when the indices are used in areas of rough terrain. In thispaper, we theoretically analyzed differences in the topographic effect on the EVI and theNDVI based on a non-Lambertian model and two airborne-based images acquired from amountainous area covered by high-density Japanese cypress plantation were used as a casestudy. The results indicate that the soil adjustment factor “L” in the EVI makes it moresensitive to topographic conditions than is the NDVI. Based on these results, we stronglyrecommend that the topographic effect should be removed in the reflectance data beforethe EVI was calculated—as well as from other vegetation indices that similarly include a term without a band ratio format (e.g., the PVI and SAVI)—when these indices are used in the area of rough terrain, where the topographic effect on the vegetation indices having only a band ratio format (e.g., the NDVI) can usually be ignored. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Sub-pixel Area Calculation Methods for Estimating Irrigated Areas
Sensors 2007, 7(11), 2519-2538; doi:10.3390/s7112519
Received: 1 October 2007 / Accepted: 21 October 2007 / Published: 31 October 2007
Cited by 25 | PDF Full-text (3010 KB) | HTML Full-text | XML Full-text
Abstract
The goal of this paper was to develop and demonstrate practical methods forcomputing sub-pixel areas (SPAs) from coarse-resolution satellite sensor data. Themethods were tested and verified using: (a) global irrigated area map (GIAM) at 10-kmresolution based, primarily, on AVHRR data, and (b) [...] Read more.
The goal of this paper was to develop and demonstrate practical methods forcomputing sub-pixel areas (SPAs) from coarse-resolution satellite sensor data. Themethods were tested and verified using: (a) global irrigated area map (GIAM) at 10-kmresolution based, primarily, on AVHRR data, and (b) irrigated area map for India at 500-mbased, primarily, on MODIS data. The sub-pixel irrigated areas (SPIAs) from coarse-resolution satellite sensor data were estimated by multiplying the full pixel irrigated areas(FPIAs) with irrigated area fractions (IAFs). Three methods were presented for IAFcomputation: (a) Google Earth Estimate (IAF-GEE); (b) High resolution imagery (IAF-HRI); and (c) Sub-pixel de-composition technique (IAF-SPDT). The IAF-GEE involvedthe use of “zoom-in-views” of sub-meter to 4-meter very high resolution imagery (VHRI)from Google Earth and helped determine total area available for irrigation (TAAI) or netirrigated areas that does not consider intensity or seasonality of irrigation. The IAF-HRI isa well known method that uses finer-resolution data to determine SPAs of the coarser-resolution imagery. The IAF-SPDT is a unique and innovative method wherein SPAs aredetermined based on the precise location of every pixel of a class in 2-dimensionalbrightness-greenness-wetness (BGW) feature-space plot of red band versus near-infraredband spectral reflectivity. The SPIAs computed using IAF-SPDT for the GIAM was within2 % of the SPIA computed using well known IAF-HRI. Further the fractions from the 2 methods were significantly correlated. The IAF-HRI and IAF-SPDT help to determine annualized or gross irrigated areas (AIA) that does consider intensity or seasonality (e.g., sum of areas from season 1, season 2, and continuous year-round crops). The national census based irrigated areas for the top 40 irrigated nations (which covers about 90% of global irrigation) was significantly better related (and had lesser uncertainties and errors) when compared to SPIAs than FPIAs derived using 10-km and 500-m data. The SPIAs were closer to actual areas whereas FPIAs grossly over-estimate areas. The research clearly demonstrated the value and the importance of sub-pixel areas as opposed to full pixel areas and presented 3 innovative methods for computing the same. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Operational Mapping of Soil Moisture Using Synthetic Aperture Radar Data: Application to the Touch Basin (France)
Sensors 2007, 7(10), 2458-2483; doi:10.3390/s7102458
Received: 31 August 2007 / Accepted: 21 October 2007 / Published: 22 October 2007
Cited by 34 | PDF Full-text (801 KB) | HTML Full-text | XML Full-text
Abstract
Soil moisture is a key parameter in different environmental applications, suchas hydrology and natural risk assessment. In this paper, surface soil moisture mappingwas carried out over a basin in France using satellite synthetic aperture radar (SAR)images acquired in 2006 and 2007 by [...] Read more.
Soil moisture is a key parameter in different environmental applications, suchas hydrology and natural risk assessment. In this paper, surface soil moisture mappingwas carried out over a basin in France using satellite synthetic aperture radar (SAR)images acquired in 2006 and 2007 by C-band (5.3 GHz) sensors. The comparisonbetween soil moisture estimated from SAR data and in situ measurements shows goodagreement, with a mapping accuracy better than 3%. This result shows that themonitoring of soil moisture from SAR images is possible in operational phase. Moreover,moistures simulated by the operational Météo-France ISBA soil-vegetation-atmospheretransfer model in the SIM-Safran-ISBA-Modcou chain were compared to radar moistureestimates to validate its pertinence. The difference between ISBA simulations and radarestimates fluctuates between 0.4 and 10% (RMSE). The comparison between ISBA andgravimetric measurements of the 12 March 2007 shows a RMSE of about 6%. Generally,these results are very encouraging. Results show also that the soil moisture estimatedfrom SAR images is not correlated with the textural units defined in the European Soil Geographical Database (SGDBE) at 1:1000000 scale. However, dependence was observed between texture maps and ISBA moisture. This dependence is induced by the use of the texture map as an input parameter in the ISBA model. Even if this parameter is very important for soil moisture estimations, radar results shown that the textural map scale at 1:1000000 is not appropriate to differentiate moistures zones. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle A Wetness Index Using Terrain-Corrected Surface Temperature and Normalized Difference Vegetation Index Derived from Standard MODIS Products: An Evaluation of Its Use in a Humid Forest-Dominated Region of Eastern Canada
Sensors 2007, 7(10), 2028-2048; doi:10.3390/s7102028
Received: 6 September 2007 / Accepted: 26 September 2007 / Published: 1 October 2007
Cited by 25 | PDF Full-text (1607 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we develop a method to estimate land-surface water content in amostly forest-dominated (humid) and topographically-varied region of eastern Canada. Theapproach is centered on a temperature-vegetation wetness index (TVWI) that uses standard 8-day MODIS-based image composites of land surface temperature [...] Read more.
In this paper we develop a method to estimate land-surface water content in amostly forest-dominated (humid) and topographically-varied region of eastern Canada. Theapproach is centered on a temperature-vegetation wetness index (TVWI) that uses standard 8-day MODIS-based image composites of land surface temperature (TS) and surface reflectanceas primary input. In an attempt to improve estimates of TVWI in high elevation areas, terrain-induced variations in TS are removed by applying grid, digital elevation model-basedcalculations of vertical atmospheric pressure to calculations of surface potential temperature(θS). Here, θS corrects TS to the temperature value to what it would be at mean sea level (i.e.,~101.3 kPa) in a neutral atmosphere. The vegetation component of the TVWI uses 8-daycomposites of surface reflectance in the calculation of normalized difference vegetation index(NDVI) values. TVWI and corresponding wet and dry edges are based on an interpretation ofscatterplots generated by plotting θS as a function of NDVI. A comparison of spatially-averaged field measurements of volumetric soil water content (VSWC) and TVWI for the 2003-2005 period revealed that variation with time to both was similar in magnitudes. Growing season, point mean measurements of VSWC and TVWI were 31.0% and 28.8% for 2003, 28.6% and 29.4% for 2004, and 40.0% and 38.4% for 2005, respectively. An evaluation of the long-term spatial distribution of land-surface wetness generated with the new θS-NDVI function and a process-based model of soil water content showed a strong relationship (i.e., r2 = 95.7%). Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle The Airborne Visible / Infrared Imaging Spectrometer AVIS: Design, Characterization and Calibration
Sensors 2007, 7(9), 1934-1953; doi:10.3390/s7091934
Received: 31 July 2007 / Accepted: 13 September 2007 / Published: 14 September 2007
Cited by 18 | PDF Full-text (5925 KB) | HTML Full-text | XML Full-text
Abstract
The Airborne Visible / Infrared imaging Spectrometer AVIS is a hyperspectralimager designed for environmental monitoring purposes. The sensor, which wasconstructed entirely from commercially available components, has been successfullydeployed during several experiments between 1999 and 2007. We describe the instrumentdesign and present the [...] Read more.
The Airborne Visible / Infrared imaging Spectrometer AVIS is a hyperspectralimager designed for environmental monitoring purposes. The sensor, which wasconstructed entirely from commercially available components, has been successfullydeployed during several experiments between 1999 and 2007. We describe the instrumentdesign and present the results of laboratory characterization and calibration of the system’ssecond generation, AVIS-2, which is currently being operated. The processing of the datais described and examples of remote sensing reflectance data are presented. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle A New Method to Retrieve the Data Requirements of the Remote Sensing Community – Exemplarily Demonstrated for Hyperspectral User NEEDS
Sensors 2007, 7(8), 1545-1558; doi:10.3390/s7081545
Received: 18 May 2007 / Accepted: 11 July 2007 / Published: 17 August 2007
Cited by 4 | PDF Full-text (390 KB) | HTML Full-text | XML Full-text
Abstract
User-driven requirements for remote sensing data are difficult to define,especially details on geometric, spectral and radiometric parameters. Even more difficult isa decent assessment of the required degrees of processing and corresponding data quality. Itis therefore a real challenge to appropriately assess data [...] Read more.
User-driven requirements for remote sensing data are difficult to define,especially details on geometric, spectral and radiometric parameters. Even more difficult isa decent assessment of the required degrees of processing and corresponding data quality. Itis therefore a real challenge to appropriately assess data costs and services to be provided.In 2006, the HYRESSA project was initiated within the framework 6 programme of theEuropean Commission to analyze the user needs of the hyperspectral remote sensingcommunity. Special focus was given to finding an answer to the key question, “What arethe individual user requirements for hyperspectral imagery and its related data products?”.A Value-Benefit Analysis (VBA) was performed to retrieve user needs and address openitems accordingly. The VBA is an established tool for systematic problem solving bysupporting the possibility of comparing competing projects or solutions. It enablesevaluation on the basis of a multidimensional objective model and can be augmented withexpert’s preferences. After undergoing a VBA, the scaling method (e.g., Law ofComparative Judgment) was applied for achieving the desired ranking judgments. Theresult, which is the relative value of projects with respect to a well-defined main objective,can therefore be produced analytically using a VBA. A multidimensional objective modeladhering to VBA methodology was established. Thereafter, end users and experts wererequested to fill out a Questionnaire of User Needs (QUN) at the highest level of detail -the value indicator level. The end user was additionally requested to report personalpreferences for his particular research field. In the end, results from the experts’ evaluationand results from a sensor data survey can be compared in order to understand user needsand the drawbacks of existing data products. The investigation – focusing on the needs of the hyperspectral user community in Europe – showed that a VBA is a suitable method for analyzing the needs of hyperspectral data users and supporting the sensor/data specification-building process. The VBA has the advantage of being easy to handle, resulting in a comprehensive evaluation. The primary disadvantage is the large effort in realizing such an analysis because the level of detail is extremely high. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields
Sensors 2007, 7(6), 979-1000; doi:10.3390/s7060979
Received: 14 May 2007 / Accepted: 12 June 2007 / Published: 15 June 2007
Cited by 74 | PDF Full-text (2223 KB) | HTML Full-text | XML Full-text
Abstract
Accurate crop performance monitoring and production estimation are critical fortimely assessment of the food balance of several countries in the world. Since 2001, theFamine Early Warning Systems Network (FEWS NET) has been monitoring cropperformance and relative production using satellite-derived data and simulation [...] Read more.
Accurate crop performance monitoring and production estimation are critical fortimely assessment of the food balance of several countries in the world. Since 2001, theFamine Early Warning Systems Network (FEWS NET) has been monitoring cropperformance and relative production using satellite-derived data and simulation models inAfrica, Central America, and Afghanistan where ground-based monitoring is limitedbecause of a scarcity of weather stations. The commonly used crop monitoring models arebased on a crop water-balance algorithm with inputs from satellite-derived rainfallestimates. These models are useful to monitor rainfed agriculture, but they are ineffectivefor irrigated areas. This study focused on Afghanistan, where over 80 percent ofagricultural production comes from irrigated lands. We developed and implemented aSimplified Surface Energy Balance (SSEB) model to monitor and assess the performanceof irrigated agriculture in Afghanistan using a combination of 1-km thermal data and 250-m Normalized Difference Vegetation Index (NDVI) data, both from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We estimated seasonal actual evapotranspiration (ETa) over a period of six years (2000-2005) for two major irrigated river basins in Afghanistan, the Kabul and the Helmand, by analyzing up to 19 cloud-free thermal and NDVI images from each year. These seasonal ETa estimates were used as relative indicators of year-to-year production magnitude differences. The temporal water- use pattern of the two irrigated basins was indicative of the cropping patterns specific to each region. Our results were comparable to field reports and to estimates based on watershed-wide crop water-balance model results. For example, both methods found that the 2003 seasonal ETa was the highest of all six years. The method also captured water management scenarios where a unique year-to-year variability was identified in addition to water-use differences between upstream and downstream basins. A major advantage of the energy-balance approach is that it can be used to quantify spatial extent of irrigated fields and their water-use dynamics without reference to source of water as opposed to a water- balance model which requires knowledge of both the magnitude and temporal distribution of rainfall and irrigation applied to fields. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Nonlinear Bayesian Algorithms for Gas Plume Detection and Estimation from Hyper-spectral Thermal Image Data
Sensors 2007, 7(6), 905-920; doi:10.3390/s7060905
Received: 12 April 2007 / Accepted: 6 June 2007 / Published: 7 June 2007
Cited by 14 | PDF Full-text (444 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a nonlinear Bayesian regression algorithm for detecting and estimating gas plume content from hyper-spectral data. Remote sensing data, by its very nature, is collected under less controlled conditions than laboratory data. As a result, the physics-based model that is [...] Read more.
This paper presents a nonlinear Bayesian regression algorithm for detecting and estimating gas plume content from hyper-spectral data. Remote sensing data, by its very nature, is collected under less controlled conditions than laboratory data. As a result, the physics-based model that is used to describe the relationship between the observed remote- sensing spectra, and the terrestrial (or atmospheric) parameters that are estimated is typically littered with many unknown ”nuisance” parameters. Bayesian methods are well-suited for this context as they automatically incorporate the uncertainties associated with all nuisance parameters into the error estimates of the parameters of interest. The nonlinear Bayesian re- gression methodology is illustrated on simulated data from a three-layer model for longwave infrared (LWIR) measurements from a passive instrument. The generated LWIR scenes con- tain plumes of varying intensities, and this allows estimation uncertainty and probability of detection to be quantified. The results show that this approach should permit more accurate estimation as well as a more reasonable description of estimate uncertainty. Specifically, the methodology produces a standard error that is more realistic than that produced by matched filter estimation. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Comparison of Three Operative Models for Estimating the Surface Water Deficit Using ASTER Reflective and Thermal Data
Sensors 2007, 7(6), 860-883; doi:10.3390/s7060860
Received: 5 April 2007 / Accepted: 4 June 2007 / Published: 6 June 2007
Cited by 10 | PDF Full-text (2633 KB) | HTML Full-text | XML Full-text
Abstract
Three operative models with minimum input data requirements for estimatingthe partition of available surface energy into sensible and latent heat flux using ASTERdata have been evaluated in a semiarid area in SE Spain. The non-evaporative fraction(NEF) is proposed as an indicator of [...] Read more.
Three operative models with minimum input data requirements for estimatingthe partition of available surface energy into sensible and latent heat flux using ASTERdata have been evaluated in a semiarid area in SE Spain. The non-evaporative fraction(NEF) is proposed as an indicator of the surface water deficit. The best results wereachieved with NEF estimated using the “Simplified relationship” for unstable conditions(NEFSeguin) and with the S-SEBI (Simplified Surface Energy Balance Index) modelcorrected for atmospheric conditions (NEFS-SEBIt,) which both produced equivalent results.However, results with a third model, NEFCarlson, that estimates the exchange coefficient forsensible heat transfer from NDVI, were unrealistic for sites with scarce vegetation cover.These results are very promising for an operative monitoring of the surface water deficit,as validation with field data shows reasonable errors, within those reported in the literature(RMSE were 0.18 and 0.11 for the NEF, and 29.12 Wm-2 and 25.97 Wm-2 for sensible heatflux, with the Seguin and S-SEBIt models, respectively). Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessArticle Can Satellite-derived Chlorophyll Imagery Be Used to Trace Surface Dynamics in Coastal Zone? A Case Study in the Northwestern Mediterranean Sea
Sensors 2007, 7(6), 884-904; doi:10.3390/s7060884
Received: 3 May 2007 / Accepted: 4 June 2007 / Published: 6 June 2007
Cited by 9 | PDF Full-text (2759 KB) | HTML Full-text | XML Full-text
Abstract
A comparison of chlorophyll data from SeaWiFS imagery and modeling results from a 3D hydrodynamical model was performed over the northwestern Mediterranean for the entire year of 2001. The study aims at investigating the information content brought by satellite-derived chlorophyll concentration ([Chl]) [...] Read more.
A comparison of chlorophyll data from SeaWiFS imagery and modeling results from a 3D hydrodynamical model was performed over the northwestern Mediterranean for the entire year of 2001. The study aims at investigating the information content brought by satellite-derived chlorophyll concentration ([Chl]) maps concerning surface dynamics in coastal zone. The study is mainly focused on the Gulf of Lions (GoL) and its outer region, which are mainly influenced by the Rhône River, local winds and the Northern Current (NC) flowing from the East along the continental slope. The physical hydrodynamical model was continuously run and 40 SeaWiFS images, presenting a significant coverage of the studied area, were selected. The comparison between [Chl] and sea surface salinity (SSS) fields on a pixel basis showed no definite correlation trends. Three reasons are given in discussion for that result. However, the comparison emphasized areas close to the coasts which were under the influence of different inputs not considered in the model and also of upwellings. A qualitative analysis of the data performed out of these regions exhibited significant similarities between [Chl] and SSS features. The signature of the Rhône ROFI (Region of Fresh Water Influence) and, in some cases, of the NC, was evidenced on [Chl] maps. We found that the intensity of this signature is seasonally modulated, e.g., it is low in open sea during the summer, oligotrophic, season. In addition, the signature of the Rhône ROFI in the western part of the GoL can be only partial due to local chlorophyll deficits. We conclude that, for the regional case studied, chlorophyll imagery can be used as a tracer of surface dynamics through surface salinity but with limitations, especially near the coasts. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)

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Open AccessReview Hydrologic Remote Sensing and Land Surface Data Assimilation
Sensors 2008, 8(5), 2986-3004; doi:10.3390/s8052986
Received: 5 February 2008 / Accepted: 22 April 2008 / Published: 6 May 2008
Cited by 71 | PDF Full-text (192 KB) | HTML Full-text | XML Full-text
Abstract
Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control [...] Read more.
Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface–atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessReview Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape
Sensors 2008, 8(4), 2136-2160; doi:10.3390/s8042136
Received: 30 January 2008 / Accepted: 25 March 2008 / Published: 28 March 2008
Cited by 167 | PDF Full-text (291 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation indices (VIs) are among the oldest tools in remote sensing studies. Although many variations exist, most of them ratio the reflection of light in the red and NIR sections of the spectrum to separate the landscape into water, soil, and vegetation. [...] Read more.
Vegetation indices (VIs) are among the oldest tools in remote sensing studies. Although many variations exist, most of them ratio the reflection of light in the red and NIR sections of the spectrum to separate the landscape into water, soil, and vegetation. Theoretical analyses and field studies have shown that VIs are near-linearly related to photosynthetically active radiation absorbed by a plant canopy, and therefore to light-dependent physiological processes, such as photosynthesis, occurring in the upper canopy. Practical studies have used time-series VIs to measure primary production and evapotranspiration, but these are limited in accuracy to that of the data used in ground truthing or calibrating the models used. VIs are also used to estimate a wide variety of other canopy attributes that are used in Soil-Vegetation-Atmosphere Transfer (SVAT), Surface Energy Balance (SEB), and Global Climate Models (GCM). These attributes include fractional vegetation cover, leaf area index, roughness lengths for turbulent transfer, emissivity and albedo. However, VIs often exhibit only moderate, non-linear relationships to these canopy attributes, compromising the accuracy of the models. We use case studies to illustrate the use and misuse of VIs, and argue for using VIs most simply as a measurement of canopy light absorption rather than as a surrogate for detailed features of canopy architecture. Used this way, VIs are compatible with "Big Leaf" SVAT and GCMs that assume that canopy carbon and moisture fluxes have the same relative response to the environment as any single leaf, simplifying the task of modeling complex landscapes. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessReview Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation
Sensors 2008, 8(1), 70-117; doi:10.3390/s8010070
Received: 1 October 2007 / Accepted: 7 January 2008 / Published: 9 January 2008
Cited by 97 | PDF Full-text (645 KB) | HTML Full-text | XML Full-text
Abstract
The proper assessment of evapotranspiration and soil moisture content arefundamental in food security research, land management, pollution detection, nutrient flows,(wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc.This paper takes an extensive, though not exhaustive sample of international [...] Read more.
The proper assessment of evapotranspiration and soil moisture content arefundamental in food security research, land management, pollution detection, nutrient flows,(wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc.This paper takes an extensive, though not exhaustive sample of international scientificliterature to discuss different approaches to estimate land surface and ecosystem relatedevapotranspiration and soil moisture content. This review presents:(i) a summary of the generally accepted cohesion theory of plant water uptake andtransport including a shortlist of meteorological and plant factors influencing planttranspiration;(ii) a summary on evapotranspiration assessment at different scales of observation (sapflow,porometer, lysimeter, field and catchment water balance, Bowen ratio,scintillometer, eddy correlation, Penman-Monteith and related approaches);(iii) a summary on data assimilation schemes conceived to estimate evapotranspirationusing optical and thermal remote sensing; and(iv) for soil moisture content, a summary on soil moisture retrieval techniques atdifferent spatial and temporal scales is presented.Concluding remarks on the best available approaches to assess evapotranspiration and soilmoisture content with and emphasis on remote sensing data assimilation, are provided. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessReview Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling
Sensors 2007, 7(12), 3209-3241; doi:10.3390/s7123209
Received: 8 November 2007 / Accepted: 26 November 2007 / Published: 11 November 2007
Cited by 35 | PDF Full-text (1584 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessReview Wetland Restoration Response Analysis using MODIS and Groundwater Data
Sensors 2007, 7(9), 1916-1933; doi:10.3390/s7091916
Received: 31 August 2007 / Accepted: 7 September 2007 / Published: 14 September 2007
Cited by 13 | PDF Full-text (2692 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation cover and groundwater level changes over the period of restorationare the two most important indicators of the level of success in wetland ecohydrologicalrestoration. As a result of the regular presence of water and dense vegetation, the highestevapotranspiration (latent heat) rates usually [...] Read more.
Vegetation cover and groundwater level changes over the period of restorationare the two most important indicators of the level of success in wetland ecohydrologicalrestoration. As a result of the regular presence of water and dense vegetation, the highestevapotranspiration (latent heat) rates usually occur within wetlands. Vegetation cover andevapotranspiration of large areas of restoration like that of Kissimmee River basin, SouthFlorida will be best estimated using remote sensing technique than point measurements.Kissimmee River basin has been the area of ecological restoration for some years. Thecurrent ecohydrological restoration activities were evaluated through fractional vegetationcover (FVC) changes and latent heat flux using Moderate Resolution ImagingSpectroradiometer (MODIS) data. Groundwater level data were also analyzed for selectedeight groundwater monitoring wells in the basin. Results have shown that the averagefractional vegetation cover and latent heat along 10 km buffer of Kissimmee River betweenLake Kissimmee and Lake Okeechobee was higher in 2004 than in 2000. It is evident thatover the 5-year period of time, vegetated and areas covered with wetlands have increasedsignificantly especially along the restoration corridor. Analysis of groundwater level data(2000-2004) from eight monitoring wells showed that, the average monthly level ofgroundwater was increased by 20 cm and 34 cm between 2000 and 2004, and 2000 and2003, respectively. This change was more evident for wells along the river. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessReview A Compact Laboratory Spectro-Goniometer (CLabSpeG) to Assess the BRDF of Materials. Presentation, Calibration and Implementation on Fagus sylvatica L. Leaves
Sensors 2007, 7(9), 1846-1870; doi:10.3390/s7091846
Received: 2 August 2007 / Accepted: 6 September 2007 / Published: 7 September 2007
Cited by 15 | PDF Full-text (706 KB) | HTML Full-text | XML Full-text
Abstract
The design and calibration of a new hyperspectral Compact Laboratory Spectro-Goniometer (CLabSpeG) is presented. CLabSpeG effectively measures the bidirectionalreflectance Factor (BRF) of a sample, using a halogen light source and an AnalyticalSpectral Devices (ASD) spectroradiometer. The apparatus collects 4356 reflectance datareadings covering [...] Read more.
The design and calibration of a new hyperspectral Compact Laboratory Spectro-Goniometer (CLabSpeG) is presented. CLabSpeG effectively measures the bidirectionalreflectance Factor (BRF) of a sample, using a halogen light source and an AnalyticalSpectral Devices (ASD) spectroradiometer. The apparatus collects 4356 reflectance datareadings covering the spectrum from 350 nm to 2500 nm by independent positioning of thesensor, sample holder, and light source. It has an azimuth and zenith resolution of 30 and15 degrees, respectively. CLabSpeG is used to collect BRF data and extract BidirectionalReflectance Distribution Function (BRDF) data of non-isotropic vegetation elements suchas bark, soil, and leaves. Accurate calibration has ensured robust geometric accuracy of theapparatus, correction for the conicality of the light source, while sufficient radiometricstability and repeatability between measurements are obtained. The bidirectionalreflectance data collection is automated and remotely controlled and takes approximatelytwo and half hours for a BRF measurement cycle over a full hemisphere with 125 cmradius and 2.4 minutes for a single BRF acquisition. A specific protocol for vegetative leafcollection and measurement was established in order to investigate the possibility to extractBRDF values from Fagus sylvatica L. leaves under laboratory conditions. Drying leafeffects induce a reflectance change during the BRF measurements due to the laboratorySensors 2007, 7 1847 illumination source. Therefore, the full hemisphere could not be covered with one leaf. Instead 12 BRF measurements per leaf were acquired covering all azimuth positions for a single light source zenith position. Data are collected in radiance format and reflectance is calculated by dividing the leaf cycle measurement with a radiance cycle of a Spectralon reference panel, multiplied by a Spectralon reflectance correction factor and a factor to correct for the conical effect of the light source. BRF results of measured leaves are presented. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Open AccessReview An Overview of the "Triangle Method" for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery
Sensors 2007, 7(8), 1612-1629; doi:10.3390/s7081612
Received: 7 August 2007 / Accepted: 21 August 2007 / Published: 24 August 2007
Cited by 183 | PDF Full-text (446 KB) | HTML Full-text | XML Full-text
Abstract
An overview of the ‘triangle’ method for estimating soil surface wetness and evapotranspiration fraction from satellite imagery is presented here. The method is insensitive to initial atmospheric and surface conditions, net radiation and atmospheric correction, yet can yield accuracies comparable to other [...] Read more.
An overview of the ‘triangle’ method for estimating soil surface wetness and evapotranspiration fraction from satellite imagery is presented here. The method is insensitive to initial atmospheric and surface conditions, net radiation and atmospheric correction, yet can yield accuracies comparable to other methods. We describe the method first from the standpoint of the how the triangle is observed as obtained from aircraft and satellite image data and then show how the triangle can be created from a land surface model. By superimposing the model triangle over the observed one, pixel values from the image are determined for all points within the triangle. We further show how the stretched (or ‘universal’) triangle can be used to interpret pixel configurations within the triangle, showing how the temporal trajectories of points uniquely describe patterns of land use change. Finally, we conclude the paper with a brief assessment of the method’s limitations. Full article
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)

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