Filters: Tags: remote sensing (X)1,944 results (110ms)
Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model
Snow covered area (SCA) observations from the Landsat Enhanced Thematic Mapper (ETM+) were used in combination with a distributed snowmelt model to estimate snow water equivalent (SWE) in the headwaters of the Rio Grande basin (3,419 km2) - a spatial scale that is an order of magnitude greater than previous reconstruction model applications. In this reconstruction approach, modeled snowmelt over each pixel is integrated over the time of ETM+ observed snow cover to estimate SWE. Considerable differences in the magnitude of SWE were simulated during the study. Basin-wide mean SWE was 2�6 times greater in April 2001 versus 2002. Despite these climatological differences, the model adequately recovered SWE at intensive...
Nonclassical cold front observed during COPS-91: frontal structure and the process of severe storm initiation
Spectral reflectance of melting snow in a high arctic watershed on Svalbard: some implications for optical satellite remote sensing studies
Integration of remote sensing and spatial information technologies for mapping black mangrove on the Texas Gulf Coast
Relationships between soil properties and vegetation at the Northern Experimental Forest, Howland, Maine
The Satellite View of Hawaii map layer is a 200-meter-resolution simulated-natural-color image of Hawaii. Vegetation is generally green, with forests in darker green and grasslands or pasture in lighter green. Areas of volcanic rock and soil are represented by shades of brown. The image was produced by combining Landsat Thematic Mapper(TM) imagery from the Landsat 4 and Landsat 5 satellites.
An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data
The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary...