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A new data layer provides global coastal segments at 1 km or shorter resolution produced from a partitioning of a 30 m Landsat-derived shoreline vector that was segmented into 4 million 1 km or shorter segments. Each segment was attributed with values from 10 variables that represent the ecological settings in which the coastline occurs, including properties of the adjacent water, adjacent land, and coastline itself. The 4 million segments were classified into 81,000 coastal segment units (CSUs) as unique combinations of variable classes.
The World Terrestrial Ecosystems datalayer is a global raster dataset at a 250 m spatial resolution where 431 ecosystem types are identified and mapped. Each ecosystem type is a unique combination of vegetation/land cover, climate region, and landform. The data is available as attached file "USGSEsriTNCWorldTerrestrialEcosystems2020.mpkx" at https://doi.org/10.5066/P9DO61LP. The data are distributed in Esri map package format (USGSEsriTNCWorldTerrestrialEcosystems2020.mpkx) and can be used by any software that can process this format, sometimes requiring minor format adjustments. NOTE: This global dataset replaces the Terrestrial Ecosystems of the Conterminous United States, NGDAID4, https://data.usgs.gov/datacatalog/data/USGS:94fd91d2-5197-4950-b14a-2381c0f66ff1
The existence, sources, distribution, circulation, and physicochemical nature of macro-scale oceanic water bodies have long been a focus of oceanographic inquiry. Building on that work, this paper describes an objectively derived and globally comprehensive set of 37 distinct volumetric region units, called ecological marine units (EMUs). They are constructed on a regularly spaced ocean point-mesh grid, from sea surface to seafloor, and attributed with data from the 2013 World Ocean Atlas version 2. The point attribute data are the means of the decadal averages from a 57-year climatology of six physical and chemical environment parameters (temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate)....
A new, high-resolution (250 m) map of global mountains derived from terrain characteristics. This new 250-m resource documents a larger global mountain extent than previous characterizations, although it excludes plateaus, hilly forelands, and other landforms that are often considered part of mountain areas. This resource was developed by the U.S. Geological Survey (USGS), in partnership with Esri, the Center for Development and Environment of the University of Bern (CDE), the Global Mountain Biodiversity Assessment (GMBA), and the Mountain Research Initiative (MRI).
A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, and the resulting polygons were mapped into four size classes of islands: Continental Mainlands, Big Islands (greater than 1 km2), Small Islands (less than or equal to 1 km2 and greater than or equal to 0.0036 km2), and Very Small Islands (less than 0.0036 km2).
Categories: Data;
Types: ArcGIS Map Package,
Downloadable;
Tags: Geography,
Remote Sensing,
USGS Science Data Catalog (SDC),
global,
islands
In response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterized in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest...
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