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Salt Lake City, UT, United States

Clark J.,Remote Sensing Applications Center
Landslide Science and Practice: Global Environmental Change

Burned Area Emergency Response (BAER) teams are dispatched to fires in the United States to make an immediate post-fire assessment of the potential danger to various values due to wildfire. The U.S. Forest Service Remote Sensing Applications Center assists in the assessment by providing remote sensing and geospatial support to teams. The remote sensing support includes creation of a preliminary burn severity map called the Burned Area Reflectance Classification, or BARC. This data layer is field validated or adjusted to match ground conditions and then used in subsequent modeling to predict the hydrologic response to watersheds. © Springer-Verlag Berlin Heidelberg 2013. Source

Purpose: Kumarkhera in Narendra Nagar township of Lesser Himalaya (Tehri Garhwal district of Uttarakhand in India) is showing signs of an impending disaster. The potential mass wastage zone may take toll of human lives in the near future and cause damage to residential and commercial area and infrastructure, namely road, telephone and electric lines and water supply lines. This paper aims to document the strategy for managing this potential disaster. Design/methodology/approach: Study of satellite images and field investigations were carried out in order to probe the possibility of potential landslides in the near future and to find out the risk enhancing anthropogenic activities. Furthermore, the elements at risk were also assessed in order to evolve a disaster management strategy. Findings: It is suggested that a series of prevention and mitigation measures (both structural and non-structural) with the involvement of the local community are required to avoid an impending disaster in the study area. Originality/value: This paper highlights the need for reading the signs of landslides and identifying the elements at risk and also calls for timely initiatives, including structural and non-structural mitigation measures, so that the impact of the disaster can be minimized. © Emerald Group Publishing Limited. Source

Zhang X.,Earth Resources Technology Inc. | Zhang X.,National Oceanic and Atmospheric Administration | Kondragunta S.,National Oceanic and Atmospheric Administration | Quayle B.,Remote Sensing Applications Center
IEEE Transactions on Geoscience and Remote Sensing

Biomass burning releases a significant amount of trace gases and aerosols into the atmosphere and affects climate change, carbon cycle, and air quality. Accurate estimates of emissions depend strongly on the calculations of burned areas. Here, we present an algorithm that is used to derive burned areas by blending active fire observations from multiple satellites which are provided in the Hazard Mapping System (HMS). The HMS consolidates automated fire detections from Geostationary Operational Environmental Satellite (GOES) Imager, Advanced Very High Resolution Radiometer (AVHRR), and MODerate resolution Imaging Spectroradiometer (MODIS). Our goals are to derive burned areas in each GOES fire pixel across contiguous United States (CONUS) from 2004 to 2007 and to validate the estimates using Landsat Thematic Mapper/Enhanced Thematic Mapper plus (TM/ETM+) burn scars and National Fire Inventory data. The results show that annual fire events burn 0.4% (3.4 × 104km2) of total land across CONUS, which consists of 0.49% of total forests, 0.64% of savannas, 0.68% of shrublands, 0.40% of grasslands, and 0.30% of croplands. The large burned areas are dominantly distributed in the western CONUS, followed by the states in the southeast region and along the Mississippi Valley. Extensive validation shows that MODIS+AVHRR+GOES instruments greatly improve the determination of fire duration and fire detection rate compared to single instrument detections. The detection rate of small fire events (< 10km 2) from multiple instruments is 24% and 36% higher than that from MODIS and GOES, respectively. The error in the burned-area estimate is less than 30% in individual ecosystems, and it decreases exponentially with the increase of burn scar size. Overall, the accuracy of total burned area across CONUS is 98.9% when compared to TM/ETM+-based burn scars and 83% when compared to national inventory data. © 2011 IEEE. Source

Pandey U.,Indian Institute of Remote Sensing | Kushwaha S.P.S.,Indian Institute of Remote Sensing | Kachhwaha T.S.,Remote Sensing Applications Center | Kunwar P.,Remote Sensing Applications Center | Dadhwal V.K.,Indian Institute of Remote Sensing
Tropical Ecology

This study explores the potential of Envisat ASAR data for biomass estimation in Dudhwa National Park of Lakhimpur-Kheri district in Uttar Pradesh state of India. The DNP has sal forest, forest plantations, grasslands, croplands, settlements, wetlands and the water bodies. Various window sizes and filters were tried to minimize the speckles in ASAR data and a 7 x 7 pixel window and Gamma-MAP filter combination was found to be most suitable. Different combinations of un-filtered, filtered, and texture images were used for forest/land cover classification followed by mapping accuracy assessment. The mapping accuracy was lowest with raw data. Filtering and texture transformation increased the accuracy marginally. The correlation coefficient (R2) between the backscatter and biomass was noticed to be better for low biomass (R2=0.86) than high biomass (R2=0.01). In general, the HV polarization showed better relationship than HH polarization. © International Society for Tropical Ecology. Source

Sreenivas K.,Indian National Remote Sensing Centre | Dwivedi R.S.,Indian National Remote Sensing Centre | Singh A.N.,Remote Sensing Applications Center | Raviprakash S.,Remote Sensing Applications Center
Journal of the Indian Society of Remote Sensing

Waterlogging due to rising ground watertable, being a sub-surface phenomenon, is not amenable to detection by optical remote sensing. Microwave and thermal sensor data have, however, shown some promise in the detection of sub-surface waterlogging. The present study was taken up to evaluate the potential of near-IR, short-wave IR (SWIR) and thermal-IR data from Moderate Resolution Imaging Spectrometer (MODIS) aboard Terra-1 acquired during day-and-night time postmonsoon data for detection of sub-surface waterlogging. The approach involves retrieval of day-and-night land surface temperature (LST), generation of normalized difference of channel-2 and 6 (ND26); 2 and 7 (ND27); ground truth collection involving concurrent ground water table observations to satellite date of pass, thresholding of normalized differences (NDs) and correlating the NDs with depth of ground water table. Amongst various spectral indices, day and night-time LST difference (DLST) and night-time LST have been found to correlate well with the incidence of waterlogging (water table depth < 2m), followed by normalized difference of band-2 (841-876 nm) and band-7 (2105-2155 nm). The sensitivity of threshold limits for these indices was maximum for DLST followed by ND26 and ND27. Poor accuracy of detecting sub-surface waterlogging with thermal bands during day time is attributed to the noncorresponding of the time of Terra MODIS data acquisitions with thermal maxima of the terrain. Though the ND27 gave better accuracy to detect subsurface waterlogging, it is very sensitive to threshold limits. © 2010 Indian Society of Remote Sensing. Source

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