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Short Citation:
Office for Coastal Management, 2022: C-CAP Land Cover, Connecticut, 2016, https://www.fisheries.noaa.gov/inport/item/61289.

Item Identification

Title: C-CAP Land Cover, Connecticut, 2016
Status: Completed
Publication Date: 2020-08-03
Abstract:

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. The C-CAP program will use addtional image sources such as leaf off or tide controlled low tide imagery if it is available. 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.

Purpose:

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).

Supplemental Information:

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 Deciduous Forest,

10 Evergreen Forest,

11 Mixed 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.

Keywords

Theme Keywords

Thesaurus Keyword
Global Change Master Directory (GCMD) Science Keywords
EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER
Global Change Master Directory (GCMD) Science Keywords
EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER > LAND USE/LAND COVER CLASSIFICATION
UNCONTROLLED
Global Change Master Directory (GCMD) Instrument Keywords Earth Remote Sensing Instruments > Passive Remote Sensing > Photon/Optical Detectors > Cameras > CAMERAS
Global Change Master Directory (GCMD) Platform Keywords Aircraft > AIRCRAFT
ISO 19115 Topic Category imageryBaseMapsEarthCover
None Land Cover
None Land Cover Analysis
None Remotely Sensed Imagery/Photos

Temporal Keywords

Thesaurus Keyword
UNCONTROLLED
None 2016

Spatial Keywords

Thesaurus Keyword
UNCONTROLLED
Global Change Master Directory (GCMD) Location Keywords Ocean > North America > United States of America > Connecticut
None Coastal Zone
None Connecticut
None CT

Physical Location

Organization: Office for Coastal Management
City: Charleston
State/Province: SC

Data Set Information

Data Set Scope Code: Data Set
Maintenance Note:

5 years

Data Presentation Form: Image (digital)
Distribution Liability:

Users must assume responsibility to determine the usability of these data.

Support Roles

Data Steward

CC ID: 940265
Date Effective From: 2020-08-03
Date Effective To:
Contact (Organization): NOAA Office for Coastal Management (NOAA/OCM)
Address: 2234 South Hobson Ave
Charleston, SC 29405-2413
Email Address: coastal.info@noaa.gov
Phone: (843) 740-1202
URL: https://coast.noaa.gov

Distributor

CC ID: 940267
Date Effective From: 2020-08-03
Date Effective To:
Contact (Organization): NOAA Office for Coastal Management (NOAA/OCM)
Address: 2234 South Hobson Ave
Charleston, SC 29405-2413
Email Address: coastal.info@noaa.gov
Phone: (843) 740-1202
URL: https://coast.noaa.gov

Metadata Contact

CC ID: 940268
Date Effective From: 2020-08-03
Date Effective To:
Contact (Organization): NOAA Office for Coastal Management (NOAA/OCM)
Address: 2234 South Hobson Ave
Charleston, SC 29405-2413
Email Address: coastal.info@noaa.gov
Phone: (843) 740-1202
URL: https://coast.noaa.gov

Point of Contact

CC ID: 940266
Date Effective From: 2020-08-03
Date Effective To:
Contact (Organization): NOAA Office for Coastal Management (NOAA/OCM)
Address: 2234 South Hobson Ave
Charleston, SC 29405-2413
Email Address: coastal.info@noaa.gov
Phone: (843) 740-1202
URL: https://coast.noaa.gov

Extents

Currentness Reference: Acquisition date of the NAIP imagery

Extent Group 1

Extent Group 1 / Geographic Area 1

CC ID: 940278
W° Bound: -73.73
E° Bound: -71.76694444444
N° Bound: 42.11083333333
S° Bound: 40.995

Extent Group 1 / Time Frame 1

CC ID: 940277
Time Frame Type: Discrete
Start: 2016-08-15

Spatial Information

Spatial Representation

Representations Used

Grid: No

Access Information

Security Class: Unclassified
Data Access Constraints:

None

Data Use Constraints:

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.

URLs

URL 1

CC ID: 940269
URL: https://coast.noaa.gov/dataviewer/#/landcover/search/where:ID=8690
Name: Data Access Viewer
URL Type:
Online Resource

URL 2

CC ID: 940270
URL: https://coast.noaa.gov/htdata/raster1/landcover/bulkdownload/hires
URL Type:
Online Resource

URL 3

CC ID: 940271
URL: https://coast.noaa.gov/digitalcoast/data/ccaphighres
URL Type:
Online Resource

Technical Environment

Description:

Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 3; ESRI ArcCatalog 9.2.2.1350

Data Quality

Accuracy:

A total of 755 accuracy assessment sample locations were identified in the 2016 land cover product. These locations were placed via a stratified random sample, based upon the final land cover map. Truth calls were based upon photo-interpretation of these locations compared to high resolution imagery from the same time period. Two separate analysts interpreted each point, with a third making a call where those analysts were not in agreement. A standard error matrix was created and the overall map accuracy was 85.3% based on the primary truth designation, 92.6% if fuzzy designations were included. Fuzzy designations are alternate calls for the truth category where the truth may not have been completely obvious, or where point locations are located so they could possibly be more than one category.

Individual land cover class accuracies are below, with Class Number Class Name (user's accuracy, producer's accuracy). 0 Background (N/A, N/A), 1 Unclassified (N/A, N/A), 2 Impervious Developed (96.8, 90.9), 5 Developed Open Space (85.6, 88.3), 6 Cultivated Land (95.2, 85.1), 7 Pasture/Hay (82.5, 94.0), 8 Grassland (72.5, 63.0), 11 Mixed Forest (81.8, 92.8), 12 Scrub/Shrub (84.0, 82.4), 13 Palustrine Forested Wetland (88.2, 90.0), 14 Palustrine Scrub/Shrub Wetland (65.9, 80.6), 15 Palustrine Emergent Wetland (77.1, 65.9), 17 Estuarine Scrub/Shrub Wetland (33.3, 100), 18 Estuarine Emergent Wetland (100, 90.0), 19 Unconsolidated Shore (85.7, 92.3), 20 Bare Land (89.5, 75.6), 21 Open Water (97.8, 89.9), 22 Palustrine Aquatic Bed (44.4, 66.7), 23 Estuarine Aquatic Bed (N/A, N/A)

Completeness Report:

Data does not exist for all classes.

There are no pixels representing class 9 (Deciduous Forest), 10 (Evergreen Forest), 23 (Estuarine Aquatic Bed), 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)).

Conceptual Consistency:

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.

Lineage

Process Steps

Process Step 1

CC ID: 940275
Description:

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.

Impervious Surface refinement:

To create an impervious surface class with more spatial detail, 2012 impervious surface data (http://cteco.uconn.edu/projects/ms4/impervious2012.htm) from the UConn Center for Land Use Education and Research (CLEAR) was incorporated into the land cover map. Updates from the random forest classification were isolated and retained through a series of spectral and contextual threshold models.

Forest refinement:

Forest features from the initial classification were refined using 2016 lidar data (http://cteco.uconn.edu/data/flight2016/index.htm) collected through The Capitol Region Council of Governments. Point clouds were processed into normalized digital surface models to clean up the boundaries between forest, grass and shrub features.

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.

Process Date/Time: 2020-08-01 00:00:00

Process Step 2

CC ID: 987750
Description:

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 tide contolled imagery collected by National Geodetic Survey (NGS). Once these features were inserted into the land cover additional object based algorithms were applied to clean up the results.

Agriculture:

Cultivated land and Pasture/Hay features were mapped from the grassland and bare categories using a Convolutional Neural Network (CNN). The CNN was training using existing high resolution C-CAP data and NAIP imagery available in the region. Predictions made by the CNN were post-processed and inserted into the land cover which was finalized through internal QA and manual editing.

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 from the grassland category of the draft 2016 land cover through intersections with ancillary vector-based land use data as well as analysis of impervious surface coverage within those land use polygons. Herbaceous land cover areas that intersected select features from the OpenStreetMap landuse and points of interest layers were converted to Open Space Developed. Additionally, parcel features from CoreLogic parcel data were used along with relative area and adjacency thresholds of the impervious surface class to further map the Open Space Developed class. Manual edits were performed to fine tune the classifications.

Process Date/Time: 2020-08-01 00:00:00

Catalog Details

Catalog Item ID: 61289
GUID: gov.noaa.nmfs.inport:61289
Metadata Record Created By: Erik Hund
Metadata Record Created: 2020-07-29 09:18+0000
Metadata Record Last Modified By: SysAdmin InPortAdmin
Metadata Record Last Modified: 2021-11-30 16:28+0000
Metadata Record Published: 2020-08-17
Owner Org: OCM
Metadata Publication Status: Published Externally
Do Not Publish?: N
Metadata Review Frequency: 1 Year