56274
C-CAP Land Cover, Erie County, Ohio, 2015
Data Set
Published / External
47841
C-CAP
Project
Completed
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.
C-CAP is dedicated to the development, distribution, and application of land cover and change data for the coastal regions of the U.S. This effort is being conducted in close coordination with state coastal management agencies, the interagency Multi-Resolution Land Characteristics (MRLC) consortium, and the National Land Cover Database (NLCD).
Attributes for this product are as follows:
0 Background,
1 Unclassified (Cloud, Shadow, etc),
2 Impervious,
3
4
5 Developed Open Space,
6 Cultivated Land,
7 Pasture/Hay,
8 Grassland,
9
10
11 Upland 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,
24 Tundra,
25 Snow/Ice,
Recommended Citation. NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database. Data collected 1995-present. Charleston, SC: National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management. Data accessed at coast.noaa.gov/landcover.
Theme
Land Cover
Theme
Land Cover Analysis
Theme
Remotely Sensed Imagery/Photos
Theme
ISO 19115 Topic Category
imageryBaseMapsEarthCover
Theme
Global Change Master Directory (GCMD) Platform Keywords
Aircraft > AIRCRAFT
Theme
Global Change Master Directory (GCMD) Instrument Keywords
Earth Remote Sensing Instruments > Passive Remote Sensing > Photon/Optical Detectors > Cameras > CAMERAS
Theme
Global Change Master Directory (GCMD) Science Keywords
Earth Science > Land Surface > Land Use/Land Cover
Theme
Global Change Master Directory (GCMD) Science Keywords
Earth Science > Land Surface > Land Use/Land Cover > Land Use/Land Cover Classification
Spatial
Coastal Zone
Spatial
Erie County
Spatial
OH
Spatial
Ohio
Office for Coastal Management
Charleston
SC
Data Set
5 years
Published
2019-05-28
Image (digital)
Users must assume responsibility to determine the usability of these data.
Data Steward
2018-11-20
Person
Coastal Management, NOAA Office for
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
Distributor
2018-11-20
Person
Coastal Management, NOAA Office for
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
Metadata Contact
2018-11-20
Person
Coastal Management, NOAA Office for
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
Point of Contact
2018-11-20
Person
Coastal Management, NOAA Office for
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
Acquisition date of the NAIP imagery
-82.902
-82.338
41.485
41.28
Discrete
2015
No
Unclassified
None
Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, NOAA, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. NOAA makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.
https://coast.noaa.gov/htdata/raster1/landcover/HIRES_ErieOH_2015_8755
Data Files
https://coast.noaa.gov/ccapftp
Online Resource
https://coast.noaa.gov/dataviewer/#/landcover/search/where:ID=8755
Data Access Viewer
Online Resource
https://coast.noaa.gov/digitalcoast/data/ccaphighres
Online Resource
Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 3; ESRI ArcCatalog 9.2.2.1350
According to the accuracy assessment performed by NOAA Office for Coastal Management staff, the overall accuracy of this product is 92.3%. There were 270 total sample units distributed across the county. The accuracy of each class is as follows (Producer's, User's accuracies):
Impervious (98.8%, 94.1%)
Open Space Developed (80.8%, 89.1%)
Cultivated (93.5%, 99.3%)
Pasture/Hay (98.6%, 86.9%)
Grassland (92.3%, 60.4%)
Upland Forest (73.5%, 83.5%)
Shrub/Scrub (87.5%, 83.4%)
Palustrine Forested Wetland (84.9%, 100%)
Palustrine Shrub/Scrub Wetland (63.5%, 79.4)
Palustrine Emergent Wetland (89.2%, 81.7%)
Unconsolidated Shore (N/A, N/A)
Bare Land (100%, 100%)
Water (100%, 99.0%)
Palustrine Aquatic Bed (N/A, N/A)
Overall accuracy (92.3%)
Sample units were located using a stratified random distribution across the area with a minimum target number of 20. A few of the rare classes did not meet this threshold number. Unconsolidated Shore and Palustrine Aquatic Bed each received only one sample unit, not allowing for accuracy assessment calculations. Sample unit locations were initially established as a single coordinate. These locations were then used in eCognition software to generate image objects of homogenous land cover (based on the final map). The correct land cover call for the image objects were then determined through analyst interpretation of the imagery and ancillary data used to create the map. All sample units were interpreted by two analysts, plus a third who made the final determination when there was not agreement on the call. The accuracy assessment statistics were determined using area-weighted calculations (size of individual objects were factored into the calculations) as described in Congalton and Green (2019).
Congalton, Russell G., and Kass Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press, 2019.
Data does not exist for all classes.
There are no pixels representing class 9 (Deciduous Forest), 10 (Evergreen Forest), 24 (Tundra), 25 (Perennial Ice/Snow), 26 (Dwarf Scrub - Alaska specific class), 27 (Sedge/Herbaceous), and 28 (Moss - Alaska specific). Developed classes have been altered to exclude the percentage breakdown of impervious surfaces as the breakdown is not appropriate for high resolution mapping (Developed High Intensity (2), Developed Medium Intensity (3), and Developed Low Intensity (4) are reduced to Impervious (Class 2)).
Tests for logical consistency indicate that all row and column positions in the selected latitude/longitude window contain data. Conversion and integration with vector files indicates that all positions are consistent with earth coordinates covering the same area. Attribute files are logically consistent.
1
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 by EarthDefine 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.
Land Cover Refinement:
Quantum Spatial took the initial automated map and made refinements to the existing land cover categories and incorporated new categories including Palustrine Forest, Palustrine Shrub, Palustrine Emergent, Palustrine Aquatic Bed, and Unconsolidated Shore. This work was performed through a combination of modeling in the object-based and pixel-based environment. USGS Color Orthoimagery (2015) served as the primary imagery data source. Ancillary data was collected from a variety of sources including the NOAA Digital Coast, National Wetland Inventory, Soil Survey Geographic Database, Open Street Maps, and the Ohio Geographically Referenced Information Program. This refined map was delivered to NOAA for final refinement.
NOAA started with the refined map from Quantum Spatial and added Cultivated and Pastured land cover classes, as well as refining all land cover classes.
Agriculture:
Cultivated land and Pasture/Hay features were incorporated into the grassland category of the refined land cover product through a modeling process which relied on spectral data, parcel data from the state, 30m land cover from NOAA C-CAP, the National Land Cover Database, and the National Agriculture Statistics Service. Manual edits were made to the final classes.
Impervious Surface and Open Space Developed:
Ancillary data including road networks and parcel data were used in combination with spectral and spectral derivative data to refine these classes. Much of this refinement was performed on very small objects using parcel boundaries and vegetation indices to remove overestimated Impervious Surface (around buildings, along road edges, and in shadows). Open Space Developed was incorporated into areas as needed along road edges and homes, in industrial areas, and parks through a similar approach used to refine the impervious surface class. Additional cleanup was performed to remove speckle and slivers.
Morphology and speckle:
A final series of models were run on the land cover to clean-up illogical speckle (e.g. small grass feature in the middle of cultivated field) and refine the morphology of natural land cover classes.
2019-05-10T00:00:00
Erik Hund
2019-05-13T13:14:36
Erik Hund
2019-05-28T08:25:20
2019-05-28
Office for Coastal Management
OCM
2234 South Hobson Avenue
Charleston
SC
29405-2413
https://www.coast.noaa.gov/
1002
Public
No
2020-05-28