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Remote Sens., Volume 2, Issue 10 (October 2010), Pages 2313-2441

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Editorial

Jump to: Research, Other

Open AccessEditorial Ecological Status and Change by Remote Sensing
Remote Sens. 2010, 2(10), 2424-2425; doi:10.3390/rs2102424
Received: 15 October 2010 / Published: 19 October 2010
Cited by 2 | PDF Full-text (19 KB) | HTML Full-text | XML Full-text
Abstract
Evaluating ecological patterns and processes is crucial for the conservation of ecosystems [1]. In this view, remote sensing is a powerful tool for monitoring their status and change. This involves several tasks like biodiversity estimate, landscape ecology, and species distribution modeling, to name
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Evaluating ecological patterns and processes is crucial for the conservation of ecosystems [1]. In this view, remote sensing is a powerful tool for monitoring their status and change. This involves several tasks like biodiversity estimate, landscape ecology, and species distribution modeling, to name a few [2]. Due to the difficulties associated with field-based data collection [3], the use of remote sensing for estimating ecological status and change is promising since it provides a synoptic view of an area with a high temporal resolution [4]. Of course in some cases remote sensing should be viewed as a help to plan a field survey rather than a replacement of it. Further, its improper use may lead to pitfalls and misleading results. [...] Full article
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

Research

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Open AccessArticle Interferometric vs Spectral IASI Radiances: Effective Data-Reduction Approaches for the Satellite Sounding of Atmospheric Thermodynamical Parameters
Remote Sens. 2010, 2(10), 2323-2346; doi:10.3390/rs2102323
Received: 13 August 2010 / Revised: 29 August 2010 / Accepted: 25 September 2010 / Published: 30 September 2010
Cited by 11 | PDF Full-text (477 KB) | HTML Full-text | XML Full-text
Abstract
Abstract: Two data-reduction approaches for the Infrared Atmospheric Sounder Interferometer satellite instrument are discussed and compared. The approaches are intended for the purpose of devising and implementing fast near real time retrievals of atmospheric thermodynamical parameters. One approach is based on the usual
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Abstract: Two data-reduction approaches for the Infrared Atmospheric Sounder Interferometer satellite instrument are discussed and compared. The approaches are intended for the purpose of devising and implementing fast near real time retrievals of atmospheric thermodynamical parameters. One approach is based on the usual selection of sparse channels or portions of the spectrum. This approach may preserve the spectral resolution, but at the expense of the spectral coverage. The second approach considers a suitable truncation of the interferogram (the Fourier transform of the spectrum) at points below the nominal maximum optical path difference. This second approach is consistent with the Shannon-Whittaker sampling theorem, preserves the full spectral coverage, but at the expense of the spectral resolution. While the first data-reduction acts within the spectraldomain, the second can be performed within the interferogram domain and without any specific need to go back to the spectral domain for the purpose of retrieval. To assess the impact of these two different data-reduction strategies on retrieval of atmospheric parameters, we have used a statistical retrieval algorithm for skin temperature, temperature, water vapour and ozone profiles. The use of this retrieval algorithm is mostly intended for illustrative purposes and the user could choose a different inverse strategy. In fact, the interferogram-based data-reduction strategy is generic and independent of any inverse algorithm. It will be also shown that this strategy yields subset of interferometric radiances, which are less sensitive to potential interfering effects such as those possibly introduced by the day-night cycle (e.g., the solar component, and spectroscopic effect induced by sun energy) and unknown trace gases variability. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessArticle Dust and Smoke Detection for Multi-Channel Imagers
Remote Sens. 2010, 2(10), 2347-2368; doi:10.3390/rs2102347
Received: 27 August 2010 / Revised: 28 September 2010 / Accepted: 30 September 2010 / Published: 11 October 2010
Cited by 28 | PDF Full-text (2321 KB) | HTML Full-text | XML Full-text
Abstract
A detection algorithm of dust and smoke for application to satellite multi-channel imagers is introduced in this paper. The algorithm is simple and solely based on spectral and spatial threshold tests along with some uniformity texture. Detailed examinations of the threshold tests are
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A detection algorithm of dust and smoke for application to satellite multi-channel imagers is introduced in this paper. The algorithm is simple and solely based on spectral and spatial threshold tests along with some uniformity texture. Detailed examinations of the threshold tests are performed along with explanations of the physical basis. The detection is performed efficiently at the pixel level and output is in the form of an index (or flag): 0 (no dust/smoke) and 1 (dust/smoke). The detection algorithm is implemented sequentially and designed to run on segments of data instead of pixel by pixel for efficient processing. MODIS observations are used to test the operation and performance of the algorithm. The algorithm can capture heavy dust and smoke plumes very well over both land and ocean and therefore is used as a global detection algorithm. The method can be applied to any multi-channel imagers with channels at (or close to) 0.47, 0.64, 0.86, 1.38, 2.26, 3.9, 11.0, 12.0 μm (such as current EOS/MODIS and future JPSS/VIIRS and GOES-R/ABI) for the detection of dust and smoke. It can be used to operationally monitor the outbreak and dispersion of dust storms and smoke plumes that are potentially hazardous to our environment and impact climate. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Figures

Open AccessArticle Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology
Remote Sens. 2010, 2(10), 2369-2387; doi:10.3390/rs2102369
Received: 25 August 2010 / Revised: 26 September 2010 / Accepted: 9 October 2010 / Published: 15 October 2010
Cited by 65 | PDF Full-text (4378 KB) | HTML Full-text | XML Full-text
Abstract
We evaluated the use of the Green-Red Vegetation Index (GRVI) as a phenological indicator based on multiyear stand-level observations of spectral reflectance and phenology at several representative ecosystems in Japan. The results showed the relationships between GRVI values and the seasonal change of
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We evaluated the use of the Green-Red Vegetation Index (GRVI) as a phenological indicator based on multiyear stand-level observations of spectral reflectance and phenology at several representative ecosystems in Japan. The results showed the relationships between GRVI values and the seasonal change of vegetation and ground surface with high temporal resolution. We found that GRVI has the following advantages as a phenological indicator: (1) “GRVI = 0” can be a site-independent single threshold fordetection of the early phase of leaf green-up and the middle phase of autumn coloring, and (2) GRVI can show a distinct response to subtle disturbance and the difference of ecosystem types. Full article
Open AccessArticle Mapping Irrigated Lands at 250-m Scale by Merging MODIS Data and National Agricultural Statistics
Remote Sens. 2010, 2(10), 2388-2412; doi:10.3390/rs2102388
Received: 26 August 2010 / Revised: 14 October 2010 / Accepted: 15 October 2010 / Published: 19 October 2010
Cited by 34 | PDF Full-text (639 KB) | HTML Full-text | XML Full-text
Abstract
Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that
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Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics. Full article
Open AccessArticle Evaluating Airborne Multispectral Digital Video to Differentiate Giant Salvinia from Other Features in Northeast Texas
Remote Sens. 2010, 2(10), 2413-2423; doi:10.3390/rs2102413
Received: 20 August 2010 / Revised: 24 September 2010 / Accepted: 13 October 2010 / Published: 19 October 2010
Cited by 4 | PDF Full-text (235 KB) | HTML Full-text | XML Full-text
Abstract
Giant Salvinia (Salvinia molesta) is one of the world’s most invasive aquatic weeds. We evaluated the accuracy of airborne multispectral digital video imagery for separating giant salvinia from other aquatic and terrestrial features at a study site located in northeast, Texas.
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Giant Salvinia (Salvinia molesta) is one of the world’s most invasive aquatic weeds. We evaluated the accuracy of airborne multispectral digital video imagery for separating giant salvinia from other aquatic and terrestrial features at a study site located in northeast, Texas. The five-band multispectral digital video imagery was subjected to an unsupervised computer analysis to derive a thematic map of the infested area. User’s and producer’s accuracies of the giant salvinia class were 74.6% and 87.2%, respectively. Aerial multispectral digital videography has potential as a remote sensing tool for differentiating giant salvinia from other terrestrial and aquatic features. Full article
Open AccessArticle Inferring Snow Water Equivalent for a Snow-Covered Ground Reflector Using GPS Multipath Signals
Remote Sens. 2010, 2(10), 2426-2441; doi:10.3390/rs2102426
Received: 9 September 2010 / Revised: 13 October 2010 / Accepted: 18 October 2010 / Published: 20 October 2010
Cited by 18 | PDF Full-text (590 KB) | HTML Full-text | XML Full-text
Abstract
A nonlinear least squares fitting algorithm is used to estimate both snow depth and snow density for a snow-layer above a flat ground reflector. The product of these two quantities, snow depth and density, provides an estimate of the snow water equivalent. The
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A nonlinear least squares fitting algorithm is used to estimate both snow depth and snow density for a snow-layer above a flat ground reflector. The product of these two quantities, snow depth and density, provides an estimate of the snow water equivalent. The input to this algorithm is a simple ray model that includes a speculary reflected signal along with a direct signal. These signals are transmitted from the global positioning system satellites at 1.57542 GHz with right-hand circularly polarization. The elevation angles of interest at the GPS receiving antenna are between 5° and 30°. The results from this nonlinear algorithm show potential for inferring snow water equivalent using GPS multipath signals. Full article
(This article belongs to the Special Issue Global Positioning Systems (GPS) and Applications)

Other

Jump to: Editorial, Research

Open AccessLetter A Comparison of AMSR-E/Aqua Snow Products with in situ Observations and MODIS Snow Cover Products in the Mackenzie River Basin, Canada
Remote Sens. 2010, 2(10), 2313-2322; doi:10.3390/rs2102313
Received: 16 August 2010 / Revised: 1 September 2010 / Accepted: 25 September 2010 / Published: 28 September 2010
Cited by 7 | PDF Full-text (249 KB) | HTML Full-text | XML Full-text
Abstract
Since 2002, global snow water equivalent (SWE) estimates have been generated using Advanced Microwave Scanning Radiometer (AMSR-E)/Aqua data. Accurate estimates of SWE are important to improve monitoring and managing of water resources in specific regions. SWE and snow map product accuracy are functions
[...] Read more.
Since 2002, global snow water equivalent (SWE) estimates have been generated using Advanced Microwave Scanning Radiometer (AMSR-E)/Aqua data. Accurate estimates of SWE are important to improve monitoring and managing of water resources in specific regions. SWE and snow map product accuracy are functions of topography and of land cover type because landscape characteristics have a strong influence on redistribution and physical properties of snow cover, and influence the microwave properties of the surface. Here we evaluate the AMSR-E SWE and derived snow map products in the Mackenzie River Basin (MRB), Canada, which is characterized by complex topography and varying land cover types from tundra to boreal forest. We compare in situ snow depth observations and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover maps from January 2003 to December 2007 with passive microwave remotely sensed SWE from AMSR-E and derived snow cover maps. In the MRB the mean absolute error ranges from 12 mm in the early winter season to 50 mm in the late winter season and overestimations of snow cover maps based on a 1 mm threshold of AMSR-E SWE varies from 4% to 8%. The optimal threshold for AMSR-E SWE to classify the pixels as snow ranges from 6 mm to 9 mm. The overall accuracy of new snow cover maps from AMSR-E varies from 91% to 94% in different sub-basins in the MRB. Full article

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