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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...
Categories: Publication; Types: Citation; Tags: Remote Sensing
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Expectations of the Operational Land Imager (OLI) radiometric performance onboard Landsat-8 have been met or exceeded. The calibration activities that occurred prior to launch provided calibration parameters that enabled ground processing to produce imagery that met most requirements when data were transmitted to the ground. Since launch, calibration updates have improved the image quality even more, so that all requirements are met. These updates range from detector gain coefficients to reduce striping and banding to alignment parameters to improve the geometric accuracy. This paper concentrates on the on-orbit radiometric performance of the OLI, excepting the radiometric calibration performance. Topics discussed...
Categories: Publication; Types: Citation; Tags: Remote Sensing
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The geographic information system (GIS) format spatial data set of vegetation for Apostle Islands National Lakeshore (APIS) was created for the National Park Service (NPS) Vegetation Inventory Program (VIP). The APIS covers an area of approximately 28,972 ha (71,591 acres). The map classification scheme used to create the vegetation data set is designed to represent local vegetation types at the finest level possible using the National Vegetation Classification (NVC) Standard (Vr 2). Physiognomic information was also recorded, including height (woody vegetation), canopy density, and coverage patterns. The vegetation data set was developed by interpreting aerial photographs collected in 2004 and extensive field surveys....


map background search result map search result map An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data Apostle Islands National Lakeshore Vegetation Mapping Project - Spatial Vegetation Data Apostle Islands National Lakeshore Vegetation Mapping Project - Spatial Vegetation Data An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data