Overall Agreement for Collection 1.0 of Annual NLCD
Altered Content -
All Annual NLCD products are made with Deep Learning AI.
Detailed Description
Overall agreement (OAa) and standard error (SEa) (%) between Annual NLCD CU 1.0 at Anderson Level 1 land cover classes and primary (most likely) or alternate (but could be) land cover labels for CONUS, 1985-2023. For example, the 2023 land cover product was found to have an accuracy of 82.75% at Anderson Level 1 classes with a low standard error of 0.42, indicating that the accuracy measurement is reliable.
Sources/Usage
Public Domain.
Related
Reference and Validation
The Annual NLCD Reference & Validation (R&V) is designed to support the development of consistent land cover and land cover change products and data. The R&V process involves collecting reference data to validate Anderson Level II land cover classes and change products across the United States. The R&V team gathers, organizes, and refines an independent reference datasets to generate validation...
Annual National Land Cover Database
Land cover is foundational information for land managers, scientists, and the general public. Many types of scientific studies have applied annual land cover information including projecting future land use, documenting forest fragmentation or conservation practices, modeling wildfire hazards, tracking water quality and quantity, and understanding change in wildlife habitat and ecosystem...
Related
Reference and Validation
The Annual NLCD Reference & Validation (R&V) is designed to support the development of consistent land cover and land cover change products and data. The R&V process involves collecting reference data to validate Anderson Level II land cover classes and change products across the United States. The R&V team gathers, organizes, and refines an independent reference datasets to generate validation...
Annual National Land Cover Database
Land cover is foundational information for land managers, scientists, and the general public. Many types of scientific studies have applied annual land cover information including projecting future land use, documenting forest fragmentation or conservation practices, modeling wildfire hazards, tracking water quality and quantity, and understanding change in wildlife habitat and ecosystem...