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Remote Sens., Volume 2, Issue 1 (January 2010), Pages 1-387

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Open AccessArticle Potential of MODIS EVI in Identifying Hurricane Disturbance to Coastal Vegetation in the Northern Gulf of Mexico
Remote Sens. 2010, 2(1), 1-18; doi:10.3390/rs2010001
Received: 3 November 2009 / Revised: 14 December 2009 / Accepted: 18 December 2009 / Published: 24 December 2009
Cited by 12 | PDF Full-text (2411 KB) | HTML Full-text | XML Full-text
Abstract
Frequent hurricane landfalls along the northern Gulf of Mexico, in addition to causing immediate damage to vegetation, also have long term effects on coastal ecosystem structure and function. This study investigated the utility of using time series enhanced vegetation index (EVI) imagery [...] Read more.
Frequent hurricane landfalls along the northern Gulf of Mexico, in addition to causing immediate damage to vegetation, also have long term effects on coastal ecosystem structure and function. This study investigated the utility of using time series enhanced vegetation index (EVI) imagery composited in MODIS product MOD13Q1 for assessing hurricane damage to vegetation and its recovery. Vegetation in four US coastal states disturbed by five hurricanes between 2002 and 2008 were explored by change imagery derived from pre- and post-hurricane EVI data. Interpretation of the EVI changes within months and between years distinguished a clear disturbance pattern caused by Hurricanes Katrina and Rita in 2005, and a recovering trend of the vegetation between 2005 and 2008, particularly within the 100 km coastal zone. However, for Hurricanes Gustav, Ike, and Lili, the disturbance pattern which varied by the change imagery were not noticeable in some images due to lighter vegetation damage. The EVI pre- and post-hurricane differences between two adjacent years and around one month after hurricane disturbance provided the most likely damage area and patterns. The study also revealed that as hurricanes damaged vegetation in some coastal areas, strong precipitation associated with these storms may benefit growth of vegetation in other areas. Overall, the study illustrated that the MODIS product could be employed to detect severe hurricane damage to vegetation, monitor vegetation recovery dynamics, and assess benefits of hurricanes to vegetation. Full article
Open AccessArticle Individual Tree Species Classification by Illuminated—Shaded Area Separation
Remote Sens. 2010, 2(1), 19-35; doi:10.3390/rs2010019
Received: 10 October 2009 / Revised: 11 December 2009 / Accepted: 16 December 2009 / Published: 28 December 2009
Cited by 18 | PDF Full-text (2144 KB) | HTML Full-text | XML Full-text
Abstract
A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the [...] Read more.
A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the tree crown are then used in species classification. Tree crown division is achieved by comparing the projected location of an aerial image pixel with its neighbours on a Canopy Height Model (CHM), which is calculated from a synchronized LIDAR point cloud. The sun position together with the mapping aircraft position are also utilised in illumination status detection. The new method was tested on a dataset of 295 trees and the classification results were compared with ones measured with two other feature extraction methods. The results of the developed method gave a clear improvement in overall tree species classification accuracy. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
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Open AccessArticle Application of Microwave Remote Sensing to Dynamic Testing of Stay-Cables
Remote Sens. 2010, 2(1), 36-51; doi:10.3390/rs2010036
Received: 20 October 2009 / Revised: 14 December 2009 / Accepted: 23 December 2009 / Published: 28 December 2009
Cited by 12 | PDF Full-text (1121 KB) | HTML Full-text | XML Full-text
Abstract
Recent advances in radar techniques and systems have favoured the development of microwave interferometers, suitable for the non-contact vibration monitoring of large structures. The paper addresses the application of microwave remote sensing to the measurement of the vibration response in the stay-cables [...] Read more.
Recent advances in radar techniques and systems have favoured the development of microwave interferometers, suitable for the non-contact vibration monitoring of large structures. The paper addresses the application of microwave remote sensing to the measurement of the vibration response in the stay-cables of cable-stayed bridges. The reliability and accuracy of the proposed technique were investigated by comparing the natural frequencies (and the cable tensions predicted from natural frequencies) identified from radar data and the corresponding quantities obtained using more conventional techniques. The investigation, carried out on the cables of two different cable-stayed bridges, clearly highlights: (a) the accuracy of the results provided by the microwave remote sensing; (b) the simplicity of use of the radar technique (especially when compared with conventional approaches) and its effectiveness to simultaneously measuring the dynamic response of all the stay-cables of an array. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessArticle Textural and Compositional Characterization of Wadi Feiran Deposits, Sinai Peninsula, Egypt, Using Radarsat-1, PALSAR, SRTM and ETM+ Data
Remote Sens. 2010, 2(1), 52-75; doi:10.3390/rs2010052
Received: 30 October 2009 / Revised: 2 December 2009 / Accepted: 23 December 2009 / Published: 28 December 2009
Cited by 10 | PDF Full-text (1882 KB) | HTML Full-text | XML Full-text
Abstract
The present work aims at identifying favorable locations for groundwater resources harvesting and extraction along the Wadi Feiran basin, SW Sinai Peninsula, Egypt, in an effort to facilitate new development projects in this area. Landsat ETM+, Radarsat-1 and PALSAR images of Wadi [...] Read more.
The present work aims at identifying favorable locations for groundwater resources harvesting and extraction along the Wadi Feiran basin, SW Sinai Peninsula, Egypt, in an effort to facilitate new development projects in this area. Landsat ETM+, Radarsat-1 and PALSAR images of Wadi Feiran basin were used in this work to perform multisource data fusion and texture analysis, in order to classify the wadi deposits based on grain size distribution and predominant rock composition as this information may lead to the location of new groundwater resources. An unsupervised classification was first performed on two sets of fused images (i.e., ETM+/Radarsat-1 and ETM+/PALSAR) resulting in five classes (hybrid classes) describing the main alluvial sediments in the wadi system. Some variations in the spatial distribution of individual classes were observed, due to the different spectral and spatial resolutions of Radarsat-1 (C-band, 12.5 m) and PALSAR (L-band, 6.25 m) data. Alluvial deposits are mixtures of parent rocks located further upstream often at a great distance. In order to classify the alluvial deposits in terms of individual rock types (endmembers), a spectral linear unmixing of the optical ETM+ image was performed. Subsequently, each class of the fused (hybrid) images was correlated with (1) individual rock type fractions (endmembers) obtained from spectrally unmixing the ETM+ image, (2) the geocoded and calibrated radar images (Radarsat-1 and PALSAR) and, (3) the slope map generated from the SRTM data. The goal was to determine predominant rock composition, mean backscatter and slope values for each of the five hybrid classes. Backscatter coefficient values extracted from both radar data (C- and L-band) were correlated and checked in the field, confirming that both wavelengths produced more or less similar textural classes that correspond to specific grain or fragment sizes of alluvial deposits. However, comparison of the spatial distribution of matching hybrid classes showed some variations due to the greater discrimination power of surface texture by Radarsat-1 C-band despite its lower spatial resolution. Furthermore, both hybrid classification results showed that regardless of elevation, areas that are covered by fine and moderate grains (fine sand to pebble) and are located along gentle terrains are favorable for groundwater recharge; while areas that are covered by very coarse grains (cobble to boulder) and are located along steep terrains are more likely to be affected by flash floods. Full article
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Open AccessArticle Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)
Remote Sens. 2010, 2(1), 76-114; doi:10.3390/rs2010076
Received: 14 October 2009 / Revised: 11 November 2009 / Accepted: 11 December 2009 / Published: 29 December 2009
Cited by 9 | PDF Full-text (1610 KB) | HTML Full-text | XML Full-text
Abstract
During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal [...] Read more.
During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispetral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats). Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessArticle Study of Soil Scattering Coefficients in Combination with Diesel for a Slightly Rough Surface in the Cj Band
Remote Sens. 2010, 2(1), 115-123; doi:10.3390/rs2010115
Received: 13 October 2009 / Revised: 8 December 2009 / Accepted: 17 December 2009 / Published: 29 December 2009
Cited by 2 | PDF Full-text (328 KB) | HTML Full-text | XML Full-textRetraction
Abstract
The value of the back-scattering coefficient of soil is dependent on its dielectric constant. An attempt has been made to estimate the scattering coefficient for a slightly rough surface for soil in combination with diesel, using the Perturbation Model. A database of [...] Read more.
The value of the back-scattering coefficient of soil is dependent on its dielectric constant. An attempt has been made to estimate the scattering coefficient for a slightly rough surface for soil in combination with diesel, using the Perturbation Model. A database of the estimated Cj band (5.3 GHz) scattering coefficients for soil in combination with diesel for both horizontal and vertical polarization and different look angles has been generated. The results show that as the diesel contamination increases, the scattering coefficient decreases in both horizontal and vertical polarization. For active microwave remote sensing the scattering coefficient data for soil in combination with diesel for different weight percentage content is useful for image analysis and its applications. By using this database it is possible to design an active microwave sensor for remote sensing detection of oil, which would be useful in the field of environmental science. The backscattering coefficient for three different look angles (45, 50 and 55) has been calculated, which is desirable for space borne remote sensing sensors. Full article
Open AccessArticle Application of Remote-sensing Data and Decision-Tree Analysis to Mapping Salt-Affected Soils over Large Areas
Remote Sens. 2010, 2(1), 151-165; doi:10.3390/rs2010151
Received: 22 October 2009 / Revised: 20 December 2009 / Accepted: 21 December 2009 / Published: 30 December 2009
Cited by 25 | PDF Full-text (1195 KB) | HTML Full-text | XML Full-text
Abstract
Expert assessments for crop and range productivity of very-large arid and semiarid areas worldwide are ever more in demand and these studies require greater sensitivity in delineating the different grades or levels of soil salinity. In conjunction with field study in arid southeastern Oregon, we assess the merit of adding decision-tree analysis (DTA) to a commonly used remote-sensing method. Randomly sampled surface soil horizons were analyzed for saturation percentage, field capacity, pH and electrical conductivity (EC). IFSAR data were acquired for terrain analysis and surficial geological mapping, followed by derivation of layers for analysis. Significant correlation was found between EC values and surface elevation, bands 1, 2, 3 and 4 of the Landsat TM image, and brightness and wetness indices. Maximum-likelihood supervised classification of the Landsat images yields two salinity classes: non-saline soils (EC < 4 dSm–1), prediction accuracy of 97%, and saline soils (EC < 4 dSm–1), prediction accuracy 60%. Addition of DTA results in successful prediction of five classes of soil salinity and an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data only compared to that predicted by additionally using DTA. DTA is a promising approach for mapping soil salinity in more productive and accurate ways compared to only using remote-sensing analysis. Full article
Open AccessArticle Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture
Remote Sens. 2010, 2(1), 166-190; doi:10.3390/rs2010166
Received: 10 October 2009 / Revised: 9 December 2009 / Accepted: 23 December 2009 / Published: 31 December 2009
Cited by 7 | PDF Full-text (538 KB) | HTML Full-text | XML Full-text
Abstract
Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and [...] Read more.
Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method. A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in solving the problem of predicting “out-of-range” soil moisture values. However, the applicability of this method is highly dependent on the accuracy of the mean and standard deviation values within the sub-region, potentially limiting its routine application. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessArticle Normality Analysis for RFI Detection in Microwave Radiometry
Remote Sens. 2010, 2(1), 191-210; doi:10.3390/rs2010191
Received: 26 October 2009 / Revised: 26 November 2009 / Accepted: 23 December 2009 / Published: 31 December 2009
Cited by 18 | PDF Full-text (760 KB) | HTML Full-text | XML Full-text
Abstract
Radio-frequency interference (RFI) present in microwave radiometry measurements leads to erroneous radiometric results. Sources of RFI include spurious signals and harmonics from lower frequency bands, spread-spectrum signals overlapping the “protected” band of operation, or out-of-band emissions not properly rejected by the pre-detection [...] Read more.
Radio-frequency interference (RFI) present in microwave radiometry measurements leads to erroneous radiometric results. Sources of RFI include spurious signals and harmonics from lower frequency bands, spread-spectrum signals overlapping the “protected” band of operation, or out-of-band emissions not properly rejected by the pre-detection filters due to its finite rejection. The presence of RFI in the radiometric signal modifies the detected power and therefore the estimated antenna temperature from which the geophysical parameters will be retrieved. In recent years, techniques to detect the presence of RFI in radiometric measurements have been developed. They include time- and/or frequency domain analyses, or time and/or frequency domain statistical analysis of the received signal which, in the absence of RFI, must be a zero-mean Gaussian process. Statistical analyses performed to date include the calculation of the Kurtosis, and the Shapiro-Wilk normality test of the received signal. Nevertheless, statistical analysis of the received signal could be more extensive, as reported in the Statistics literature. The objective of this work is the study of the performance of a number of normality tests encountered in the Statistics literature when applied to the detection of the presence of RFI in the radiometric signal, which is Gaussian by nature. A description of the normality tests and the RFI detection results for different kinds of RFI are presented in view of determining an omnibus test that can deal with the blind spots of the currently used methods. Full article
Open AccessArticle A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches
Remote Sens. 2010, 2(1), 211-261; doi:10.3390/rs2010211
Received: 6 November 2009 / Revised: 26 November 2009 / Accepted: 2 January 2010 / Published: 4 January 2010
Cited by 26 | PDF Full-text (4513 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an exhaustive review of global croplands and their water use, for the end of last millennium, mapped using remote sensing and non-remote sensing approaches by world’s leading researchers on the subject. A comparison at country scale of global cropland [...] Read more.
This paper presents an exhaustive review of global croplands and their water use, for the end of last millennium, mapped using remote sensing and non-remote sensing approaches by world’s leading researchers on the subject. A comparison at country scale of global cropland area estimated by these studies had a high R2-value of 0.89–0.94. The global cropland area estimates amongst different studies are quite close and range between 1.47–1.53 billion hectares. However, significant uncertainties exist in determining irrigated areas which, globally, consume nearly 80% of all human water use. The estimates show that the total water use by global croplands varies between 6,685 to 7,500 km3 yr−1 and of this around 4,586 km3 yr−1 is by rainfed croplands (green water use) and the rest by irrigated croplands (blue water use). Irrigated areas use about 2,099 km3 yr−1 (1,180 km3 yr−1 of blue water and the rest from rain that falls over irrigated croplands). However, 1.6 to 2.5 times the blue water required by irrigated croplands is actually withdrawn from reservoirs or pumping of ground water, suggesting an irrigation efficiency of only between 40–62 percent. The weaknesses, trends, and future directions to precisely estimate the global croplands are examined. Finally, the paper links global croplands and their water use to a paradigm for ensuring future food security. Full article
(This article belongs to the Special Issue Global Croplands)
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Open AccessArticle Detection of Multidecadal Changes in UVB and Total Ozone Concentrations over the Continental US with NASA TOMS Data and USDA Ground-Based Measurements
Remote Sens. 2010, 2(1), 262-277; doi:10.3390/rs2010262
Received: 10 November 2009 / Revised: 17 December 2009 / Accepted: 30 December 2009 / Published: 5 January 2010
Cited by 3 | PDF Full-text (1078 KB) | HTML Full-text | XML Full-text
Abstract
Thinning of the atmospheric ozone layer leads to elevated levels of Ultraviolet-B (UVB) at the Earth's surface, resulting in an increase of health risks to living organisms due to DNA damage. This paper examines the multidecadal changes of total column ozone from [...] Read more.
Thinning of the atmospheric ozone layer leads to elevated levels of Ultraviolet-B (UVB) at the Earth's surface, resulting in an increase of health risks to living organisms due to DNA damage. This paper examines the multidecadal changes of total column ozone from 1979 to 2005 with the aid of ground-based UVB stations using the ultraviolet multifilter rotating shadow-band radiometer (UV-MFRSR). For the purpose of demonstration, four USDA ground stations, WA01, CO01, MD01, and AZ01, were selected for detailed comparisons against the satellite data. The major finding of this study is that over the course of the time series, on a monthly scale, the UV index (UVI) has increased at the four selected USDA stations while total ozone has decreased in the continental USA over the past three decades and spatial distributions of UVI and total ozone have shown substantial variations from coastal zones to the Midwest Regions of the USA, yet the tendency toward recovery of ozone layer in the continental USA cannot be fully confirmed. This leads to a conclusion that the UVI changes might have been influenced by other factors in addition to the total ozone in the atmospheric environment across at least 76% of the continental USA. Full article
Open AccessArticle Spatial Enhancement of MODIS-based Images of Leaf Area Index: Application to the Boreal Forest Region of Northern Alberta, Canada
Remote Sens. 2010, 2(1), 278-289; doi:10.3390/rs2010278
Received: 24 November 2009 / Revised: 4 January 2010 / Accepted: 5 January 2010 / Published: 8 January 2010
Cited by 12 | PDF Full-text (893 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area index (LAI) is one of the most commonly used ecological variables in describing forests. Since 2000, 1-km resolution Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composites of LAI have been operationally available from the National Aeronautics and Space Administration (NASA), USA, at no cost to the user. In this paper, we present a simple protocol to enhance the spatial resolution of NASA-produced LAI composites to 250-m resolution. This is done by fusing MODIS-based estimates of enhanced vegetation index (EVI), consisting of 16-day 250-m resolution composites (also from NASA), with estimates of LAI. We apply the protocol to derive 250-m resolution maps of LAI for the boreal forest region of northern Alberta, Canada. Data fusion was possible in this study because of the inherent linear correlation that exists between EVI and LAI for the April to October growing period of 2005–2008, producing r2-values of 0.85–0.95 and p-values < 0.0001. Comparison of MODIS-based LAI with field-based measurements using the Tracing Radiation and Architecture of Canopies (TRAC) sensor and LAI-2000 Plant Canopy Analyzer showed reasonable agreement across values; statistical comparison of LAI data points produced an r2-value of 0.71 and a p-value < 0.0001. Seventy one percent of MODIS-based LAI were within ±20% of field estimates. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
Open AccessArticle Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring
Remote Sens. 2010, 2(1), 290-305; doi:10.3390/rs2010290
Received: 8 November 2009 / Revised: 1 December 2009 / Accepted: 6 January 2010 / Published: 11 January 2010
Cited by 89 | PDF Full-text (1725 KB) | HTML Full-text | XML Full-text
Abstract
Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-megapixel camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting [...] Read more.
Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-megapixel camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the camera-UAV system may provide important information for site-specific agriculture. Full article
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Open AccessArticle Accessibility, Demography and Protection: Drivers of Forest Stability and Change at Multiple Scales in the Cauvery Basin, India
Remote Sens. 2010, 2(1), 306-332; doi:10.3390/rs2010306
Received: 25 November 2009 / Revised: 7 January 2010 / Accepted: 8 January 2010 / Published: 12 January 2010
Cited by 8 | PDF Full-text (758 KB) | HTML Full-text | XML Full-text
Abstract
The Cauvery basin of Karnataka State encompasses a range of land cover types, from dense forest areas and plantations in the Western Ghats hills, to fertile agricultural lands in the river valley. Recent demographic changes, rapid economic development and urbanization have led [...] Read more.
The Cauvery basin of Karnataka State encompasses a range of land cover types, from dense forest areas and plantations in the Western Ghats hills, to fertile agricultural lands in the river valley. Recent demographic changes, rapid economic development and urbanization have led to the conversion of vast stretches of forested land into plantations and permanent agriculture. We examine the human drivers of forest cover change between 2001 and 2006, using MODIS 250 m data at multiple spatial scales of nested administrative units i.e., districts and taluks. Population density does not emerge as a major driver of forest distribution or deforestation. Protected areas and landscape accessibility play a major role in driving the distribution of stable forest cover at different spatial scales. The availability of forested land for further clearing emerges as a major factor impacting the distribution of deforestation, with new deforestation taking place in regions with challenging topography. This research highlights the importance of using a regional approach to study land cover change, and indicates that the drivers of forest change may be very different in long settled landscapes, for which little is known in comparison to frontier forests. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
Open AccessArticle Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data
Remote Sens. 2010, 2(1), 333-351; doi:10.3390/rs1020333
Received: 1 December 2009 / Revised: 8 January 2010 / Accepted: 11 January 2010 / Published: 18 January 2010
Cited by 64 | PDF Full-text (1757 KB)
Abstract
Continuous monitoring of extreme environments, such as the European Alps, is hampered by the sparse and/or irregular distribution of meteorological stations, the difficulties in performing ground surveys and the complexity of interpolating existing station data. Remotely sensed Land Surface Temperature (LST) is [...] Read more.
Continuous monitoring of extreme environments, such as the European Alps, is hampered by the sparse and/or irregular distribution of meteorological stations, the difficulties in performing ground surveys and the complexity of interpolating existing station data. Remotely sensed Land Surface Temperature (LST) is therefore of major interest for a variety of environmental and ecological applications. But while MODIS LST data from the Terra and Aqua satellites are aimed at closing the gap between data demand and availability, clouds and other atmospheric disturbances often obscure parts or even the entirety of these satellite images. A novel algorithm is presented in this paper, which is able to reconstruct incomplete MODIS LST maps. All nine years of the available daily LST data (2000–2008) have been processed, allowing the original LST map resolution of 1,000 m to be improved to 200 m, which means the resulting LST maps can be applied at a regional level. Extracted time series and aggregated data are shown as examples and are compared to meteorological station time series as an indication of the quality obtained. Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
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Open AccessArticle Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations
Remote Sens. 2010, 2(1), 352-374; doi:10.3390/rs2010352
Received: 25 November 2009 / Revised: 23 December 2009 / Accepted: 11 January 2010 / Published: 20 January 2010
Cited by 2 | PDF Full-text (306 KB) | HTML Full-text | XML Full-text
Abstract
The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), launched on November 2009, is an unprecedented initiative to globally monitor surface soil moisture using a novel 2-D L-band interferometric radiometer concept. Airborne campaigns and ground-based field experiments [...] Read more.
The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), launched on November 2009, is an unprecedented initiative to globally monitor surface soil moisture using a novel 2-D L-band interferometric radiometer concept. Airborne campaigns and ground-based field experiments have proven that radiometers operating at L-band are highly sensitive to soil moisture, due to the large contrast between the dielectric constant of soil minerals and water. Still, soil moisture inversion from passive microwave observations is complex, since the microwave emission from soils depends strongly on its moisture content but also on other surface characteristics such as soil type, soil roughness, surface temperature and vegetation cover, and their contributions must be carefully de-coupled in the retrieval process. In the present study, different soil moisture retrieval configurations are examined, depending on whether prior information is used in the inversion process or not. Retrievals are formulated in terms of vertical (Tvv) and horizontal (Thh) polarizations separately and using the first Stokes parameter (TI ), over six main surface conditions combining dry, moist and wet soils with bare and vegetation-covered surfaces. A sensitivity analysis illustrates the influence that the geophysical variables dominating the Earth’s emission at L-band have on the precision of the retrievals, for each configuration. It shows that, if adequate constraints on the ancillary data are added, the algorithm should converge to more accurate estimations. SMOS-like brightness temperatures are also generated by the SMOS End-to-end Performance Simulator (SEPS) to assess the retrieval errors produced by the different cost function configurations. Better soil moisture retrievals are obtained when the inversion is constrained with prior information, in line with the sensitivity study, and more robust estimates are obtained using TI than using Tvv and Thh. This paper analyzes key issues to devise an optimal soil moisture inversion algorithm for SMOS and results can be readily transferred to the upcoming SMOS data to produce the much needed global maps of the Earth’s surface soil moisture. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessArticle Combining Satellite Remote Sensing Data with the FAO-56 Dual Approach for Water Use Mapping In Irrigated Wheat Fields of a Semi-Arid Region
Remote Sens. 2010, 2(1), 375-387; doi:10.3390/rs2010375
Received: 7 December 2009 / Revised: 7 January 2010 / Accepted: 18 January 2010 / Published: 20 January 2010
Cited by 26 | PDF Full-text (451 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this study was to combine the FAO-56 dual approach and remotely-sensed data for mapping water use (ETc) in irrigated wheat crops of a semi-arid region. The method is based on the relationships established between Normalized Difference Vegetation Index (NDVI) [...] Read more.
The aim of this study was to combine the FAO-56 dual approach and remotely-sensed data for mapping water use (ETc) in irrigated wheat crops of a semi-arid region. The method is based on the relationships established between Normalized Difference Vegetation Index (NDVI) and crop biophysical variables such as basal crop coefficient, cover fraction and soil evaporation. A time series of high spatial resolution SPOT and Landsat images acquired during the 2002/2003 agricultural season has been used to generate the profiles of NDVI in each pixel that have been related to crop biophysical parameters which were used in conjunction with FAO-56 dual source approach. The obtained results showed that the spatial distribution of seasonal ETc varied between 200 and 450 mm depending to sowing date and the development of the vegetation. The validation of spatial results showed that the ETc estimated by FAO-56 corresponded well with actual ET measured by eddy covariance system over test sites of wheat, especially when soil evaporation and plant water stress are not encountered. Full article

Review

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Open AccessReview Satellite Remote Sensing in Seismology. A Review
Remote Sens. 2010, 2(1), 124-150; doi:10.3390/rs2010124
Received: 15 October 2009 / Revised: 18 December 2009 / Accepted: 23 December 2009 / Published: 30 December 2009
Cited by 22 | PDF Full-text (10396 KB) | HTML Full-text | XML Full-text
Abstract
A wide range of satellite methods is applied now in seismology. The first applications of satellite data for earthquake exploration were initiated in the ‘70s, when active faults were mapped on satellite images. It was a pure and simple extrapolation of airphoto [...] Read more.
A wide range of satellite methods is applied now in seismology. The first applications of satellite data for earthquake exploration were initiated in the ‘70s, when active faults were mapped on satellite images. It was a pure and simple extrapolation of airphoto geological interpretation methods into space. The modern embodiment of this method is alignment analysis. Time series of alignments on the Earth's surface are investigated before and after the earthquake. A further application of satellite data in seismology is related with geophysical methods. Electromagnetic methods have about the same long history of application for seismology. Stable statistical estimations of ionosphere-lithosphere relation were obtained based on satellite ionozonds. The most successful current project "DEMETER" shows impressive results. Satellite thermal infra-red data were applied for earthquake research in the next step. Numerous results have confirmed previous observations of thermal anomalies on the Earth's surface prior to earthquakes. A modern trend is the application of the outgoing long-wave radiation for earthquake research. In ‘80s a new technology—satellite radar interferometry—opened a new page. Spectacular pictures of co-seismic deformations were presented. Current researches are moving in the direction of pre-earthquake deformation detection. GPS technology is also widely used in seismology both for ionosphere sounding and for ground movement detection. Satellite gravimetry has demonstrated its first very impressive results on the example of the catastrophic Indonesian earthquake in 2004. Relatively new applications of remote sensing for seismology as atmospheric sounding, gas observations, and cloud analysis are considered as possible candidates for applications. Full article
(This article belongs to the Special Issue Remote Sensing in Seismology)

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