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Old growth longleaf pine stand, scorched by fire (top); longleaf pine stand after timber removal (bottom) Before European settlement, longleaf pine forest dominated as much as 90,000,000 acres (360,000 km 2) stretching from Virginia south to Florida and west to East Texas. Its range was defined by the frequent widespread fires that were lit by ...
Government ArcGIS server addresses: Curated list of 3,500+ ArcGIS server addresses from federal to local. Updated once per week. Bad links fixed or flagged. NOAA Big Data Project: NOAA generates tens of terabytes of data a day from satellites, radars, ships, weather models, and other sources. While these data are publicly available, it is ...
Windows 8/10, Windows Phone 7/8/10, Microsoft Office (Access, Outlook, Excel – Power View, Power Maps, Power BI), Microsoft SQL Reporting Services, Microsoft Dynamic CRM, Microsoft SharePoint, Microsoft Cortana, Bing Weather, Microsoft Research WorldWide Telescope, AutoCad, ESRI ArcGIS
The longleaf pine ecosystem is a temperate coniferous forest ecosystem found within the Southern United States. Spanning pine savannas , sandhills and montane forests , it includes many rare plant and animal species, and is one of the most biodiverse in North America . [ 1 ]
Unlike the OGC Web Map Service (WMS), which portrays spatial data as static, server-rendered images (maps), the Web Coverage Service delivers underlying data values along with their detailed descriptions. This enables a rich syntax for queries against the data and returns information with its original semantics, rather than just pictures ...
Formerly the Triangle Multiple Listing Service, Doorify MLS aims to bring “transparency” to real estate market amid sweeping changes.
A Web Map Service (WMS) is a standard protocol developed by the Open Geospatial Consortium in 1999 for serving georeferenced map images over the Internet. [1] These images are typically produced by a map server from data provided by a GIS database.
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.
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