Filters: Tags: Land Cover Analysis (X)
8 results (8ms)
Filters
Date Range
Extensions Types
Contacts
Categories Tag Types
|
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...
Types: Citation;
Tags: Atlantic County,
Barnegat Bay,
Cape May County,
Coastal Zone,
Environmental restoration,
This USGS Data Release represents geospatial data sets which were created to produce an Unvegetated to Vegetated Ratio (UVVR) for coastal wetlands of the conterminous United States (2014-2018). The following listed image products were generated 1) Annual spatial datasets (rasters) from 2014 to 2018 each containing 4 bands (Band 1: Unvegetated land fraction; Band 2: Vegetated land fraction; Band 3: Water fraction; Band 4: UVVR clipped into 3 coastal regions (Atlantic (ATL) Gulf of Mexico (GOM) and Pacific (PAC). 2) Calibration/Validation Datasets - datasets which were used in the calibration and validation of the above datasets 3) Mean of masked, multiyear composite - Mean vegetated fraction in coastal wetlands in...
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...
Categories: Data;
Types: Citation,
Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Atlantic County,
Barnegat Bay,
Cape May County,
Coastal Zone,
Environmental restoration,
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...
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...
Categories: Data;
Types: Citation,
Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Atlantic County,
Barnegat Bay,
Cape May County,
Coastal Zone,
Environmental restoration,
The Coastal Change Analysis Program (C-CAP) produces a nationally standardized database of land cover and land change information for the coastal regions of the U.S. C-CAP products provide inventories of coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring these habitats by updating the land cover maps every five years. C-CAP products are developed using multiple dates of remotely sensed imagery and consist of raster-based land cover maps for each date of analysis, as well as a file that highlights what changes have occurred between these dates and where the changes were located.
Tags: land cover analysis
This data is the 2010 era and the 1996-2010-era change classification of U.S.Gulf of Mexico region. This data set utilized full or partial Landsat scenes which were analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine land cover.
Categories: Data;
Tags: Academics & scientific researchers,
Alabama,
Alabama,
Alabama/Mississippi Region,
Change Detection Analysis,
The 2010 LCRE classified land cover data set, with an emphasis on estuarine and tidal freshwater vegetation types, was derived using a high resolution image segmentation and object based classification process. The primary data sources include the following: a) 2009 4 band, 1 meter resolution airborne imagery acquired by the USDA National Agriculture Inventory Program (NAIP); b) archived 30m LandSAT TM5 imagery from various dates ranging from 2007-2009; c) 2009-2010 LiDAR elevation data aquired by the US Army Corps of Engineers. The minimum mapping unit, defined as the smallest specified area for mapping an individual landform, is 0.25 acres.
Categories: Data;
Types: ArcGIS REST Map Service,
ArcGIS Service Definition,
Downloadable,
Map Service;
Tags: Coastal Zone,
Land Cover,
Land Cover Analysis,
Lower Columbia River Estuary,
Remotely Sensed Imagery/Photos,
|
|