Data Management Plan (Deprecated)

The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats. The timeframe for this metadata is summer 2016. These maps are developed utilizing high resolution National Agriculture Imagery Program (NAIP) imagery, and can be used to track changes in the landscape through time. This trend information gives important feedback to managers on the success or failure of management policies and programs and aid in developing a scientific understanding of the Earth system and its response to natural and human-induced changes. This understanding allows for the prediction of impacts due to these changes and the assessment of their cumulative effects, helping coastal resource managers make more informed regional decisions. NOAA C-CAP is a contributing member to the Multi-Resolution Land Characteristics consortium and C-CAP products are included as the coastal expression of land cover within the National Land Cover Database.

Process Steps:

• 2018-11-01 00:00:00 - This dataset was created by NOAA's Ocean Service, Office for Coastal Management (OCM). Random Forest Classification: The initial 1m spatial resolution 6 class high resolution land cover product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. The resulting objects are the primary units for analysis. Additionally, these objects introduce additional spectral, shape, textural and contextual information into the mapping process and are utilized as independent variables in a supervised classification. Each object is labeled using a Random Forest Classifier which is ensemble version of a Decision Tree. Training data for the initial 6 classes (Herbaceous, Bare, Impervious, Water, Forest and Shrub) were generated through photo interpretation. The resulting Random Forest model was applied to the input data sets to create the initial automated map. Water Refinement: Commission and omission errors associated with the Water class were addressed through manual interpretation and clean up in ERDAS Imagine. Once the review and edits were complete, the refined Water class was incorporated back into the land cover data set. Unconsolidated Shore: Unconsolidated substrate features were extracted primarily through unsupervised classification and manual editing in ERDAS Imagine. This process relied on the USGS Color Orthoimagery (2013/2014) as a primary data source since it was collected at a lower tidal stage relative to other available imagery. Once these features were inserted into the land cover additional object based clean up algorithms were applied. Agriculture: Cultivated land and Pasture/Hay features were incorporated into the grassland category of the initial land cover product through a modeling process which relied on multiple dates of imagery as well as the 2005 statewide land use data set. Manual edits were made based on feedback from local experts. Wetlands: Wetlands were derived through a modeling process which used ancillary data such as Soils (SSURGO), the National Wetlands Inventory (NWI) and topographic derivatives. Forest, shrub and grassland objects within the initial land cover that exhibited hydric characteristics based on the input ancillary layers were designated to their appropriate wetland category. The process relied mainly on the NWI to determine palustrine and estuarine distinctions. Open Space Developed: Managed grasses and other low lying vegetation associated with development were derived using information in the 2005 statewide land use data set. Grassland features in the land cover data that intersected selected land use polygons were designated as Open Space Developed (OSD). To capture OSD features that have appeared since the 2005 land use data was developed, polygons with land use codes that were likely candidates for change were analyzed for the presence of impervious surface based on the 2015 land cover. If the polygon was occupied by a certain percent of impervious surface, the associated grass pixels were changed to Open Space Development. Additional clean up was performed to remove speckle and slivers.