Filters: Tags: Landsat thematic mapper (X)14 results (52ms)
Monitoring the effects of Dracunculiasis remediation on agricultural productivity using satellite data
Remotely-sensed regional-scale evapotranspiration of a semi-arid Great Basin desert and its relationship to geomorphology, soils, and vegetation
Multi-temporal environmental analysis of oil field activities in south-central Oklahoma using Landsat thematic mapper, aerial photography and GIS
This set of sixteen Landsat Thematic Mapper (TM)and Operational Land Imager (OLI)(Path 014 and Rows 032 and 033) surface reflectance data sets were collected between 2000 and 2015. This data presents a time-series analysis that uses linear spectral unmixing of composite Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Soil Index data, to estimate the percentages of marsh vegetation, water, and exposed marsh substrate on the New Jersey intracoastal marshes. We used the composition of the marshes in terms of the percentage of marsh vegetation, water, and marsh substrate to produce Marsh Surface Condition Index (MSCI) maps consisting of three classes of marshes: severely...
These are two land cover datasets derived from Landsat Thematic Mapper and Operational Land Imager (spatial resolution 30-m)Path 014 and Rows 032 and 033 surface reflectance data collected on July 14, 2011 and July 19, 2013, before and after Hurricane Sandy made landfall near Brigantine, New Jersey on October 29, 2012. The two land cover data sets provide a means of evaluating the effect of Hurricane Sandy of data sets collected at times that represent or approach peak vegetation growth. The most accurate results of the land cover classification are based on twelve classes, some of which occur adjacent to the marshes but not on the New Jersey intracoastal marshes. Twelve classes were used in the supervised maximum...
Analysis of the effect of Hurricane Sandy on New Jersey Atlantic coastal marshes based on landsat thematic mapper and operational land imager data: 2000-2015
This USGS Data Release represents geospatial data sets that were created for the analysis of the effect of Hurricane Sandy on New Jersey Atlantic Coastal Marshes. The following listed image products were generated: 1) Fifteen marsh surface condition index (MSCI) data sets were calculated from yearly summer collections of ETM+ image data from 2000 to 2015. Three classes described the results of the MSCI mapping; classs1-severely impacted, class 2-moderately impacted, and class 3-intact marsh. 2) Marsh change data product using Landsat images of July 14, 2011 (before) and July 19, 2013 (after) Hurricane Sandy is based on the difference in the percentage of vegetation. It shows a pattern of an increasing loss of marsh...
Testing the Woodcock-Harward image segmentation algorithm in an area of southern California chaparral and woodland vegetation
Characterizing lineaments from satellite images and field studies in the central Ebro basin (NE Spain)
Mapping seabird nesting habitats in Franz Josef Land, Russian High Arctic, using digital Landsat Thematic Mapper imagery
The change detection data is the classified difference in the percentage of vegetation on the July 14, 2011 Landsat Thematic Mapper(TM) data set collected before Hurricane Sandy and the July 19, 2013 Landsat Operational Land Imager (OLI)data set collected after Hurricane Sandy. Hurricane Sandy made landfall near Brigandine, New Jersey on October 29, 2012. The actual difference in the percentage of vegetation is used in the calculation, not the three-class classification that is the basis of the Marsh Surface Condition Index data. The eleven classes consist of five classes (5-20%,>20%-40%,>40%-60%,>60%-80%,>80%)with decreases in the percentage of vegetation cover after Hurricane Sandy, Three classes (5-20%,>20%-40%,>40%-60%)with...
Principal vegetation types in a natural area close to the city of Rome as observed by ERS-1 SAR and Landsat TM
Wildlife habitat analysis for 'sambar' (cervus unicolor) in Kanha national park using remote sensing
Application of remote sensing and geographic information systems to the delineation and analysis of riparian buffer zones