Cooperative Land Cover Version 3.1

by Jennylyn Redner


landcover map
Click on map to view larger image

The newest version of the Cooperative Land Cover Dataset (CLC) is now available on FWC’s data library and public GIS data site as a 10.0 file geodatabase feature class and 10m raster.

The Florida Cooperative Land Cover Map is a partnership between the Florida Fish and Wildlife Conservation Commission (FWC) and Florida Natural Areas Inventory (FNAI) to develop an ecologically-based statewide land cover data set. The CLC was developed using existing federal, state, and local data sources and expert review of aerial photography and ground conditions. The CLC uses the Florida Land Cover Classification System (Kawula, 2009), which was developed to address the need for a single classification system that incorporates the level of detail and flexibility needed by the FWC and its conservation partners. The classification schema creates a system that uses well-defined land cover classes that are unique to the state of Florida, but can also be incorporated with systems in neighboring states, as well as regionally. This classification system is limited to terrestrial, wetland, and inland aquatic (i.e. non-marine) classes and does not attempt to develop a classification system for marine habitats.

The classification system is hierarchically organized such that it can be applied at multiple scales. STATE classes represent the broadest level of differentiation used in the CLC, and are most appropriate for regional or state level scales, or for representing a generalized land cover class. SITE classes represent increasing levels of land cover details, and are most appropriately used to represent site or local level scales.

The Cooperative Land Cover Map is continuously revised, with new versions being released every 6 – 12 months. FWC now takes lead on updating and maintaining the dataset. FNAI will continue to provide guidance for the classification of natural communities, and site specific data sources based on their mapping and revision efforts.

Feedback from users is welcome, and users are encouraged to contribute datasets, send questions and report errors to

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