The goal of the Cooperative Land Cover (CLC) map project is to collaboratively update and maintain a detailed land-cover map and dataset for use by the Florida Fish and Wildlife Conservation Commission and its conservation partners. It maintains a single, comprehensive land-cover dataset for the state and lends itself to species modeling and spatial analysis by providing a common base dataset. The Cooperative Land Cover map provides an up-to-date data set of Florida’s natural and semi-natural communities and land uses. Currently, approximately 2 million features are delineated, representing 231 Land Use, Land Cover classes. FWC staff lead the ongoing effort to maintain, update and enhance the CLC, working collaboratively with the Florida Natural Areas Inventory.
The need for data layers that reflect the true spatial extent and/or configuration for priority habitats and refinement of some other habitats was identified as a “Priority Data Gap” in the original State Wildlife Action Plan (SWAP). The CLC and its classification system facilitate the creation of these data layers to address this data gap and produce a habitat classification system with well-defined habitat classes that are unique to the state of Florida but can also be incorporated with schemas in neighboring states, as well as regionally. A common habitat classification allows for consistency across multiple users/uses and projects and allows for easier integration of species-specific research projects initiated by the implementation of the SWAP.
The CLC provides the foundational data layer for FWC created species probabilistic and habitat affinity models. Additionally, the Florida conservation blueprint, developed collaboratively with partners through the Peninsular Florida Landscape Conservation Cooperative, is based upon selected priority land cover types in the CLC. The Florida conservation blueprint is used to provide input into the Southeast blueprint, a regional effort to identify areas with conservation value throughout the Southeast United States.
An example of two approaches used for unsupervised classification of a pine plantation.
An example of improved classification of scrub mangrove habitat. Note the much more detailed figure in the bottom right corner.