Data Management Plan

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 late summer 2013. 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:

• 2019-04-29 00:00:00 - This dataset was created by NOAA's Ocean Service, Office for Coastal Management (OCM). Initial Classification: 1m spatial resolution land cover data developed by the University of Vermont Spatial Analysis Laboratory for the Chesapeake Bay Conservancy was the starting point for this dataset. This product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework applied to NAIP imagery and Lidar data. 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. The original dataset can be downloaded here: https://chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/ Agriculture: Cultivated land and Pasture/Hay features were incorporated into the grassland category of the Chesapeake Bay land cover product through a modeling process which relied on agricultural information found in the 2007 statewide land use data set developed by the Sanborn Map Company for the state of Delaware. The original dataset can be downloaded here: https://opendata.arcgis.com/datasets/7f0f0b3bf2654c46b9359f747398fb39_0.zip Once Cultivated and Pasture/Hay classes were added. Manual review and edits were performed at scale of 1:3000. Open Space Developed: Managed grasses and other low lying vegetation associated with development were derived using information found in the 2007 statewide land use data set mentioned in the Agriculture section. Grassland features in the land cover data that intersected selected land use polygons were designated as Open Space Developed (OSD). Once the initial OSD classification was performed, manual review for errors in addition to edits were performed at scale of 1:3000. 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. Manual review and edits were performed at scale of 1:3000. Coastal waters and Unconsolidated Shore: Unconsolidated substrate and estuarine water features were extracted primarily through unsupervised classification and manual editing in ERDAS Imagine. This process relied on the MLLW imagery acquired by NOAA NGS during 2011 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. Manual review and edits were performed at scale of 1:3000. Evergreen and Deciduous Forest: The availability of a 2012 leaf-off imagery data set made it possible to subdivide the Upland Forest into Evergreen and Deciduous. Normalized difference vegetation indices (NDVI) were used in conjunction with the rest of the spectral data to separate these two forest categories in a raster-based environment. These rasters were then brought into eCognition to incorporate into segments prior to manual review at edits at the scale of 1:3000.