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Sarah McDonald
U.S. Geological Survey, Lower-Mississippi Gulf Water Science Center
Geographer
(800) 968-7229
1750 Forest Drive
Suire 130
Annapolis
MD
21403
US
smcdonald@chesapeakebay.net
20250623
ArcGIS Metadata
1.0
U.S. Geological Survey - ScienceBase
1-888-275-8747
Denver Federal Center
Building 810
Mail Stop 302
Denver
CO
80225
sciencebase@usgs.gov
None
Digital Data
https://doi.org/10.5066/P14BEBRC
https://doi.org/10.5066/P14BEBRC
Raster Dataset
ChesapeakeConservancy2022LULCSelectLocalities
2025-06-23
2024
Peter R. Claggett
Sarah M. McDonald
Jarlath O'Neil-Dunne
Sean MacFaden
Katie S. Walker
Steven Guinn
Labeeb Ahmed
Ernie Buford
Elliott Kurtz
Patrick McCabe
Jackie A. Pickford
Kelly Shulze
raster digital data
Claggett, P. R., McDonald, S. M., O’Neil-Dunne, J., MacFaden, S., Walker, K., Guinn, S., Ahmed, L., Buford, E., Kurtz, E., McCabe, P., Pickford, J. A., Royar, A., Schulze, K., 2025, Chesapeake Bay Land Use/Land Cover (LULC) Database 2024 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P14BEBRC.
The Chesapeake Bay Land Use/Land Cover (LULC) Database facilitates the characterization of the landscape over time for three discrete time periods at 1-meter spatial resolution. The database was developed by Chesapeake Conservancy (CC) in cooperation with the University of Vermont’s Spatial Analysis Laboratory and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter, 56-class Land Use/Land Cover (LULC) for all counties (205) within or adjacent to the Chesapeake Bay watershed for 2013/14, 2017/18, and 2021/22 depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. The data are built from 11-class Land Cover (LC), which is attributed to the LULC raster data Additionally, 56 LULC classes are generalized into 18 general LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 11 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 56 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 56x56 LULC classes and 11x11 LC classes. Sheets with Chesapeake Bay Water (CBW) in the name indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested in change transitions across the entire county, they will use LULC Matrix. Additionally, tabular summaries of 56 LULC, 18 LULC, and 11 LC representing the acres per class by county, state, and Chesapeake Bay portion of county and states and the net acres of change per class by county, state, and Chesapeake Bay portion of county and states. The database includes the following data: 1. 2013/2014 Land Use and Land Cover (LULC) 2. 2017/2018 Land Use and Land Cover (LULC) 3. 2021/2022 Land Use and Land Cover (LULC) 4. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) rasters and LULCC matrices 5. 2013/2014 to 2021/2022 Land Use and Land Cover Change (LULCC) rasters and LULCC matrices 6. 2017/2018 to 2021/2022 Land Use and Land Cover Change (LULCC) rasters and LULCC matrices 7. LULC State and County Summary Tables 8. LULC State and County Summary Tables (Chesapeake Bay Watershed Portion) 9. LULCC State and County Summary Tables 10. Land Use/Land Cover Change State and County Summary Tables (Chesapeake Bay Watershed Portion)
The database was produced to inform effects of land use and land cover change on the water quality in the Chesapeake Bay watershed by providing states, counties and local jurisdictions with detailed and consistent land use information to make data-driven and effective management decisions. The products were developed to be broadly used for informing both conservation and restoration efforts decisions such as habitat suitability assessments, riparian buffer implementation, agriculture and forest conservation, and tracking the conversion of forests and farmlands to other uses.
Funding provided by U.S. Environmental Protection Agency Chesapeake Bay Program. Cooperative participants: University of Vermont Spatial Analysis Laboratory, Chesapeake Conservancy and U.S. Geological Survey.
Sarah McDonald
U.S. Geological Survey, SOUTHEAST REGION
Geographer
(800) 968-7229
1750 Forest Drive
Suite 130
Annapolis
MD
21401
US
smcdonald@chesapeakebay.net
Chesapeake
Maryland
Delaware
District of Columbia
Pennsylvania
New York
Virginia
West Virginia
Common geographic areas
imageryBaseMapsEarthCover
ISO 19115 Topic Category
land use and land cover
land use change
maps and atlases
land change
USGS Thesaurus
imageryBaseMapsEarthCover
land use and land cover
land use change
maps and atlases
land change
Chesapeake
Maryland
Delaware
District of Columbia
Pennsylvania
New York
Virginia
West Virginia
The organizations responsible for generating and funding this dataset make no representations of any kind including, but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data. The data is provided on the condition that neither the USGS nor the U.S. Government, or any of the collaborating organizations shall be held liable for any damages resulting from the authorized or unauthorized use of the data. Although every effort has been made to ensure the accuracy of information, errors may be reflected in data supplied. Possible errors or limitations of the data have been documented but the list is not exhaustive. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors, original map scale, collection methodology, currency of data, and other conditions. Credit should always be given to the data source when this data is transferred, altered, or used for analysis.
Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty.
None. Please see 'Distribution Info' for details.
true
-81.1084
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44.7208
36.2129
ground condition
2013
2022
Esri ArcGIS 13.5.3.57366
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38.284403
36.497855
The User Guide describes the accuracies of the land cover and land cover change data. Evaluating accuracy of land use and its change classes is exceptionally challenging considering the spatial and categorical resolution of the data. However, various steps have been taken to ensure accuracy including consideration of the best available ancillary datasets corresponding to specific time intervals to inform mapping classifications and extensive visual inspections of the data by multiple team members to address issues such as large area or systematic misclassifications. Additionally, automated workflows were developed to track and capture certain erroneous classifications in the workflows.
The original data was processed at a county-scale and then a state mosaic was created for the data release. The values, classes and legends in the state-wide mosaics are consistent with those provided for the data for individual counties. Please refer to the overview and methods document for more details.
Dataset is considered complete for the information presented, as described in the abstract. Users are advised to read the entire metadata record carefully for additional details.
No formal positional accuracy tests were conducted
No formal positional accuracy tests were conducted
These are brief descriptions of the processing steps (listed in order of operations): (1) land cover (LC)
(2) land cover change (LCC)
(3) land use/land cover (LULC) 2021/2022
(4) land use/land cover (LULC) Change
(5) land use/land cover and change rasters
(6) LULC Class Transition Matrices
(7) Summary tables
2024
Land Cover: Land-cover change mapping was performed in eCognition, state-of-the-art software for performing object-based image analysis. This expert-system technique segments input datasets into meaningful landscape objects and classifies them according to both their individual characteristics (e.g., vegetation content from spectral imagery, vegetation height from LiDAR) and their relationships to adjacent features. The 2013/2014 and 2017/2018 land cover data from previous releases was updated using best available LiDAR, regional building footprints, local planimetric data representing roads and buildings, and NAIP Imagery. In the final LULC products, the Emergent Wetlands land cover class is treated as Low Vegetation, resulting in 11 land cover classes.
2024
Land cover change (LCC): To represent change across the analysis period, the original 12-class classification scheme was expanded to include all types of change likely to occur in the Chesapeake Bay Watershed. Change types with a low probability of occurrence or classes that could not be mapped effectively with the available data were excluded. However, change types that might be added during manual review and editing were included in the classification, including conversion of Low Vegetation to Shrublands. These additional classes were not systematically mapped across the entire study area; rather, they were incorporated when observed to ensure logical consistency with adjacent types of change or manual edits. In the final LULC products, the Emergent Wetlands land cover class is treated as Low Vegetation, resulting in 11 land cover classes.
2024
Land Use/Land Cover 2021/2022: This 56-class land use/land cover data was created by translating land cover to land use using a suite of geospatial models. The models were written in Python using open-source tools and technologies such as rasterio, geopandas, pandas, and gdal.
(1) Union tax parcel data with vector image segments, derived from National Agriculture Imagery Program (NAIP) imagery using eCognition software, to create the modeling segmentation called "psegs". Each psegs polygon represents a single land cover class.
(2) Various raster ancillary datasets are summarized by psegs such as county land use, NASS Cropland Data Layer and USGS National Land Cover Database.
(3) psegs are processed through a series of rule-based geospatial models that use tabulated raster (attribute) and ancillary vector data to classify land use. Examples of vector data includes extractive, landfill, solar fields, and transmission lines.
(4) Three separate models produce overlays that are used to classify certain land cover classes such as emergent wetlands, tree canopy and water. These layers are treated separately because their segmentation differs from the psegs. The overlays provide spatial context to create land use classes such as tree canopy: forest, tree canopy over turf grass; wetlands: riverine and terrene wetlands; water: estuarine, lakes and reservoirs.
(5) Review and update the time series of LULC to ensure illogical transitions are not present. This includes updates to the 2021/22 land use. (6) Finally, the psegs and overlays are rasterized and reconciled using a rule-based approach to final land use/land cover data.
2024
Land Use/Land Cover Change (1) LULC change is produced by back-casting 2021/2022 land use where LULC change occurred. The driver of LULC change is the LC Change. LULC change is not limited to LC change but can be located using land cover change and tax parcels. (2) Build a vector database of LC change and other raster (e.g. CDL and NLCD) information used to classify the previous land use. (3) To classify areas of previous LULC where LC change occurred, a geospatial workflow is applied using 2021/2022 LULC for context, along with other ancillary information such as the National Land Cover Database (NLCD) and Cropland Data Layer (CDL). Tax parcels and LC change are used to identify areas of LULC change that are not a change in land cover e.g. Cropland Herbaceous to Turf Grass in a newly developed parcel. (4) Three separate models produce overlays that are used to classify certain land cover classes such as emergent wetlands, tree canopy and water. These layers are treated separately because their segmentation differs from the psegs. The overlays provide spatial context to create land use classes such as tree canopy: forest, tree canopy over turf grass; wetlands: riverine and terrene wetlands; water: estuarine, lakes and reservoirs. (5) Review and update the time series of LULC to ensure illogical transitions are not present. This includes updates to the 2021/22 land use. (6) Finally, the change vectors and overlays are rasterized and reconciled using a rule-based approach to final land use/land cover change data.
2024
Land Use/Land Cover 2013/2014 and 2017/2018
LULC for these earlier time periods is derived from LULC change and 2021/2022 LULC by 'burning' LULC change values on to the static 2021/2022 LULC.
2024
LULC Class Transition Matrices: Data on LULC change represent transitions of LULC between two time periods: an early date (e.g., Time 1, 2013 or 2014) and a late date (e.g., Time 2, 2017 or 2018). A concise way of illustrating such changes is to construct a cross-tabulation, aka “pivot table”, between the two datasets. The result is a tabular LULC change matrix that shows all observed changes in the LULC change raster. The early date values (acres of land use X) are represented in rows and the late date values represented in columns. The values along the diagonal are absent because they would represent no change and are not included in the LULC change raster data.
2024
Summary Tables: To allow for easy comparison of Land Use/Land Cover and Land Use/Land Cover trends at the county, state, and Chesapeake Bay portion of county and states, summary tables in the form of Excel Workbooks are provided. These data are produced by aggregating the information stored in the Raster Attribute Tables of the static LULC and LULC Change rasters. The Chesapeake Bay portion of the static LULC was produced by executing ArcGIS Pro’s Tabulate Area tool.
2024
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1632075.000000 1858066.000000
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1778296.000000 1684095.000000
1705185.500000 1771080.500000
Raster Attribute Table for LULC Rasters
Raster
Producer Defined
Table
55
OID
OID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Value
Raster Value
Producer Defined
0
NoData where cells are outside the extent of the mapped jurisdiction.
Producer defined
10
Tidal Waters
Producer defined
11
Lakes and Reservoirs
Producer defined
12
Riverine Ponds
Producer defined
13
Terrene Ponds
Producer defined
14
Streams and Rivers
Producer defined
15
Bare Shore
Producer defined
20
Roads
Producer defined
21
Structures
Producer defined
22
Other Impervious
Producer defined
23
Tree Canopy Over Roads
Producer defined
24
Tree Canopy Over Structures
Producer defined
25
Tree Canopy Over Other Impervious
Producer defined
26
Tree Canopy Over Turf Grass
Producer defined
27
Turf Grass
Producer defined
28
Bare Developed
Producer defined
30
Extractive Barren
Producer defined
31
Extractive Impervious
Producer defined
32
Solar Field Panel Arrays
Producer defined
33
Solar Field Barren
Producer defined
34
Solar Field Herbaceous
Producer defined
35
Solar Field Shrubland
Producer defined
36
Suspended Succession Barren
Producer defined
37
Suspended Succession Herbaceous
Producer defined
38
Suspended Succession Shrubland
Producer defined
40
Forest
Producer defined
41
Forested Other
Producer defined
42
Natural Succession Barren
Producer defined
43
Natural Succession Herbaceous
Producer defined
44
Natural Succession Shrubland
Producer defined
45
Harvested Forest Barren
Producer defined
46
Harvested Forest Herbaceous
Producer defined
50
Riverine Wetlands Barren
Producer defined
51
Riverine Wetlands Herbaceous
Producer defined
52
Riverine Wetlands Shrubland
Producer defined
53
Riverine Wetlands Forested Other
Producer defined
54
Riverine Wetlands Forest
Producer defined
55
Riverine Wetlands Harvested Forest
Producer defined
60
Terrene Wetlands Barren
Producer defined
61
Terrene Wetlands Herbaceous
Producer defined
62
Terrene Wetlands Shrubland
Producer defined
63
Terrene Wetlands Forested Other
Producer defined
64
Terrene Wetlands Forest
Producer defined
65
Terrene Wetlands Harvested Forest
Producer defined
70
Tidal Wetlands Barren
Producer defined
71
Tidal Wetlands Herbaceous
Producer defined
72
Tidal Wetlands Shrubland
Producer defined
73
Tidal Wetlands Forested Other
Producer defined
74
Tidal Wetlands Forest
Producer defined
75
Tidal Wetlands Harvested Forest
Producer defined
80
Cropland Barren
Producer defined
81
Cropland Herbaceous
Producer defined
82
Orchards and Vineyards Barren
Producer defined
83
Orchards and Vineyards Herbaceous
Producer defined
84
Orchards and Vineyards Shrubland
Producer defined
85
Pasture and Hay Barren
Producer defined
86
Pasture and Hay Herbaceous
Producer defined
Value
Integer
10
10
0
Count
Count
Double
19
0
0
lulc
lulc
String
254
0
0
lu
lu
String
254
0
0
lc
lc
String
254
0
0
general
general
String
254
0
0
macro
macro
String
254
0
0
year
year
String
254
0
0
edition
edition
String
254
0
0
Red
Red
Integer
10
10
0
Green
Green
Integer
10
10
0
Blue
Blue
Integer
10
10
0
GenLU_Def
GenLU_Def
String
254
0
0
GenLU_Def2
GenLU_Def2
String
254
0
0
GenLU_Def3
GenLU_Def3
String
254
0
0
DefSource
DefSource
String
254
0
0
Description of LULC raster values
Claggett, P. R., McDonald, S. M., O’Neil-Dunne, J., MacFaden, S., Walker, K., Guinn, S., Ahmed, L., Buford, E., Kurtz, E., McCabe, P., Pickford, J. A., Royar, A., Schulze, K., 2025, Chesapeake Bay Land Use/Land Cover (LULC) Database 2024 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P14BEBRC.
dataset
Esri
8.1.2
Band_1
86.000000
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