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 through time. High resolution C-CAP products are developed utilizing high resolution aerial imagery, along with lidar and various available ancillary information, and can be used to track changes in the landscape through time. This is a full-scheme C-CAP land cover product, which includes up to 20 land cover classes, including impervious surfaces, agricultural areas, tree/shrub canopy, water, and wetlands. These high-resolution C-CAP land cover products enable local mapping and analyses for several applications, including water quality and stormwater management, land use planning and community development, risk assessment, natural resource management, and other community-level activities. This dataset represents land cover for the timeframe of 2021 for the Northeast Region of Ohio.

Process Steps:

• 2026-04-28 00:00:00 - This dataset was created as part of NOAA's Coastal Change Analysis Program (C-CAP), which is housed within the National Ocean Service, Office for Coastal Management (OCM). Contractor work was performed through NOAA’s Coastal Geospatial Services Contract (CGSC) vehicle. Initial Classification Initial 1m spatial resolution feature extraction for Impervious, Water, and Canopy (tree and scrub/shrub) mapping was conducted by Ecopia AI (https://www.ecopiatech.com/) using a proprietary feature recognition technology. This involves taking each image and classifying the pixels based on spectral and spatial properties, based on training examples and analyst refinement. Refined Classification The refinement of initial mapping layers, including the additional of up to 20 standard C-CAP categories of land cover feature types was conducted by NV5 (https://www.nv5.com/). Refinement specifics were broken down into a series of stages, and the specifics of each are discussed below. Impervious Surface Refinement: Commission and omission errors associated with the Impervious class were addressed through manual interpretation and clean up using OpenStreetMap road, building, and railroad layers as reference. Once the review and edits were complete, the refined Impervious class was incorporated back into the land cover data set. Water Refinement: Commission and omission errors associated with the Water class were addressed through a combination of modeling, expert rulesets, and manual interpretation. Once the review and edits were complete, the refined Water class was incorporated back into the land cover data set. Canopy Surface Refinement: Commission and omission errors associated with the Canopy class were addressed through manual interpretation and clean up. Once the review and edits were complete, the refined Canopy class was incorporated back into the land cover data set. Agriculture and Open Space Developed: Cultivated land, Pasture/Hay, and Open Space Developed features were derived from the grassland class of the initial land cover product using a combination of deep learning and image segmentation approaches. Predictions from a Convolutional Neural Network were summarized by image objects generated for grassland regions withing the AOI. Commission and omission errors associated with these categories were addressed through manual review and editing. Once the review and edits were complete, the new classes were incorporated into the land cover data set. Wetlands: Wetlands were derived through a modeling process which used ancillary data such as Soils (SSURGO), the National Wetlands Inventory (NWI), and topographic derivatives (derived from USGS OPR data). 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. Grass and Bare: Grass and Bare classifications were extracted from regions within the AOI not covered by the Ecopia AI Impervious, Water, and Canopy layer. This was accomplished through a combination of image segmentation, expert rulesets, and manual editing. Once the review and edits were complete, Bare and Grass features were incorporated into the land cover data set. Attributes for this product are as follows: 0 Background 1 Unclassified 2 Developed Impervious 3 4 Developed Impervious under Tree Canopy 5 Developed, Open Space 6 Cultivated Crops 7 Pasture/Hay 8 Grassland/Herbaceous 9 10 11 Upland Tree (Forest) 12 Scrub/Shrub 13 Palustrine Forested Wetland 14 Palustrine Scrub/Shrub Wetland 15 Palustrine Emergent Wetland 16 Estuarine Forested Wetland 17 Estuarine Scrub/Shrub Wetland 18 Estuarine Emergent Wetland 19 Unconsolidated Shore 20 Bare Land 21 Open Water 22 Palustrine Aquatic Bed 23 Estuarine Aquatic Bed