The ASTER sensor onboard NASA’s Earth observing satellite Terra is an optical remote sensor comprised of 14 spectral bands ranging from the visible to thermal infrared region. With its multispectral bands the sensor bears the potential to provide data for both a detailed land use classification of heterogeneously vegetated areas. This study evaluates the potential of ASTER data for land use cla...

Abstract. Land use and land cover (LULC) classification has long been an essential topic in Earth Observation research plays a key role the sustainable development of agriculture. This study evaluated accuracy LULC based on initial clustering step heterogeneous agricultural landscape using PlanetScope imagery while checking for variability among their Normalized Difference Vegetation Index (NDV...

The main objective of this project was to derive digital land use and land cover information from satellite remote sensing data for Connecticut, Long Island, and those watersheds contributing to Long Island Sound. In doing so, four methods of land use and land cover classification were evaluated on two test quadrangles to determine which method was best suited, in terms of time and costs balanc...

In the presented methodology, multitemporal Landsat images were used to develop enhanced information about complex assemblages of vegetation and patterns of seasonal land cover variability, thereby facilitating improved land use and land cover (LULC) classification of urbanized areas among sensitive environments along the Mississippi Gulf Coast. For Landsat-5 and Landsat-7 images acquired for l...

With the advancement of satellite technology, a considerable amount very high-resolution imagery has become available to be used for Land Cover and Use (LCLU) classification task aiming categorize remotely sensed images based on their semantic content. Recently, Deep Neural Networks (DNNs) have been widely different applications in field remote sensing they profound impacts; however, improvemen...