20251223
16313400
1.0
FALSE
PLC_Impervious_PairwiseInter
002
-8436794.963439
-8348919.943700
4643354.266429
4842649.266429
1
Projected
GCS_WGS_1984
Linear Unit: Meter (1.000000)
WGS_1984_Web_Mercator_Auxiliary_Sphere
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20251212
11352300
20251223
10251800
150000000
5000
FGDC
dataset
Paige Brochu
University of Vermont Spatial Analysis Laboratory
Director
81 Carrigan Dr.
Burlington
Vermont
05405
Paige.Brochu@uvm.edu
US
802-656-3324
Paige Brochu
FirstMap
State of Delaware
801 Silver Lake Blvd.
Dover
DE
19904
FirstMap@delaware.gov
302-739-9500
20251223
Enterprise Geodatabase Feature Class
Impervious Surfaces, 2022, Delaware
2024-06-28T00:00:00
Paige Brochu
University of Vermont Spatial Analysis Laboratory
Director
81 Carrigan Dr.
Burlington
Vermont
05405
Paige.Brochu@uvm.edu
US
802-656-3324
This map shows impervious surfaces in Delaware, as of 2022. This is a polygon layer created from the original raster created by University of Vermont.
The University of Vermont Spatial Analysis Laboratory developed this map in cooperation with the Chesapeake Conservancy and the U.S. Geological Survey.
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This map was derived from a 12-class land-cover product developed for the Chesapeake Bay Watershed. It was extracted from a map showing land-cover conversion in the Chesapeake Bay Watershed during the period 2017/2018-2021/2022. The change-detection map was modeled with an existing 2017/2018 (Time Period 2, or T2) land-cover map containing the following classes: Water, Emergent Wetlands, Tree Canopy, Scrub\Shrub, Low Vegetation, Barren, Impervious Structures, Other Impervious, Impervious Roads, Tree Canopy Over Impervious Structures, Tree Canopy Over Other Impervious, and Tree Canopy Over Impervious Roads. Using object-based image analysis mapping techniques, the T2 map was first refined, where necessary, using all remote-sensing imagery and GIS datasets available for 2021\2022, including LiDAR, multispectral imagery, and thematic layers (e.g., roads, building footprints). The refined T2 map was then compared to imagery and GIS data corresponding to the period 2021/2022 (Time Period 3, or T3), identifying features that had changed during the analysis interval. Each change combination was assigned to a unique category that indicated both the "from" and "to" classes, enabling monitoring of specific types of change. Features that had not changed during the analysis interval were also incorporated into the change map, making it possible to reconstitute full land cover for either T2 or T3. Draft output was manually reviewed and edited to eliminate obvious errors of omission and commission, focusing on the most important types of landscape change: 1) tree-canopy loss; and 2) impervious-surfaces gain. The final map contained 79 change classes plus the original 12 classes for features that had not changed. The specific analysis periods for the watershed's 7 states\districts were: Delaware, 2018-2021; District of Columbia, 2017-2021; Maryland, 2018-2021; New York, 2017-2022; Pennsylvania, 2017-2022; Virginia, 2018-2021; and West Virginia, 2018-2022. The 12-class extract of the change-detection map was created by reassigning all change classes to their T3 status.</SPAN></P><P><SPAN>For specific use in land-use\land-cover (LULC) analyses for Delaware, the T3 map produced for the Chesapeake Bay Watershed was reclassifed into two classes: Impervious (1); and Pervious (2). It was reviewed and edited against 2022 leaf-off imagery to update it to 2022 conditions. </SPAN></P><P><SPAN>This map was based on a draft version of Chesapeake Bay Watershed land cover. A publicly-available version of the final Chesapeake Bay map will be available in autumn 2024, through the Chesapeake Conservancy (https://www.chesapeakeconservancy.org/).</SPAN></P></DIV></DIV></DIV>
impervious surfaces
2022
Delaware
impervious surfaces
Delaware
2022
Esri ArcGIS 13.3.4.52636
1
-75.789019
-74.999624
39.839521
38.451140
Derived from 2022 aerial imagery.
2022-03-04T00:00:00
2022-04-01T00:00:00
FirstMap
State of Delaware
801 Silver Lake Blvd.
Dover
DE
19904
FirstMap@delaware.gov
302-739-9500
FirstMap
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The State of Delaware makes no warranty or representation, expressed or implied, with respect to the quality, content, accuracy, completeness, currency, or non-infringement of proprietary rights, of any of the GIS or other data or information, or any other materials and items, that are displayed or made available for download from this site. All such data, information, items and materials (collectively, the “FirstMap Data”) are provided "as is" and users are fully and solely responsible for any consequences of use. FirstMap Data may have been created from a variety of sources, including sources beyond the control of the State of Delaware, and are subject to change without notice. To the extent you use, apply, add to, modify or implement this information in your own information system or other setting, or otherwise for your own purposes, you do so at your own risk. In no event shall the State of Delaware or its agencies, officers, employees, agents, or representatives be liable for any damages of any kind or nature whatsoever including, but not limited to, direct, indirect, special, punitive, incidental, exemplary or consequential damages arising from your downloading, modifying, sharing, distributing, or using of FirstMap Data even if notified of the possibility of such damages. Further, the State of Delaware does not accept liability for any damages or misrepresentation caused by inaccuracies in the FirstMap Data or as a result of changes to the FirstMap Data, nor is there responsibility assumed to maintain the FirstMap Data in any manner or form.</SPAN></P></DIV></DIV></DIV>
This map was based on a land cover change-detection map for the Chesapeake Bay Watershed, previously developed for the period 2013\2014-2017\2018 (Time Period 1 through Time Period 2, or T1-T2). To update this map for the next analysis period, 2017\2018-2021\2022 (Time Period 2 through Timer Period 3, or T2-T3), the T2 map was first refined as necessary using LiDAR, multispectral imagery, and thematic GIS data, where available, that corresponded to the same 2017\2018 time period. Landscape changes that had occurred since T2 were then identified using LiDAR, multispectral imagery, and thematic GIS data, where available, that corresponded to 2021\2022 (Time Period 3, or T3). Each combination of land-cover change was coded into the output map, making it possible to reconstitute land cover for either T2 or T3. All modeling was performed in eCognition, state-of-the-art mapping software that uses object-based image analysis to segment and classify source data into meaningful landscape features. To create the T3 extract, all T2-T3 change classes were converted to their T3 status using eCognition, reducing the map to the standard 12-category classification.
For specific use in land-use\land-cover (LULC) analyses for Delaware, the original 12-class map was reclassified into two classes: Impervious (1); Pervious (2). It was so reviewed and edited against 2022 leaf-off orthimagery to update it to 2022 conditions.
12-class Land Cover Map for the Chesapeake Bay Watershed, 2017\2018 (Time Period 2, or T2), 1-meter Resolution. Developed by the the University of Vermont and the Chesapeake Conservancy. This map contains the following classes: 1) Water; 2) Emergent Wetlands; 3) Tree Canopy; 4) Scrub\Shrub; 5) Low Vegetation; 6) Barren; 7) Impervious Structures; 8) Other Impervious; 9) Impervious Roads; 10) Tree Canopy Over Impervious Structures; 11) Tree Canopy Over Other Impervious; and 12) Tree Canopy Over Impervious Roads. This 12-class map was derived from a T1-T2 change-detection map previously developed for the Chesapeake Bay for the period 2013\2014-2017\2018.
LiDAR, 2017/2018 (Time Period 2, or T2), Resolution Dependent on Location. Where available, original LiDAR point clouds were converted into derivatives that facilitate landscape analysis, including a digital elevation model (DEM) and a digital surface model (DSM). The DEM was subtracted from the DSM to produce a normalized digital surface model (nDSM) that indicates the height of aboveground features such as buildings and trees. NOTE: T2 LiDAR was not available for every county in the Chesapeake Bay Watershed. In such cases, modeling relied more on other input datasets and subsequent manual review.
LiDAR, 2018/2021 (Time Period 3, or T3), Resolution Dependent on Location. Where available, original LiDAR point clouds were converted into derivatives that facilitate landscape analysis, including a digital elevation model (DEM) and a digital surface model (DSM). The DEM was subtracted from the DSM to produce a normalized digital surface model (nDSM) that indicates the height of aboveground features such as buildings and trees. NOTE: T3 LiDAR was not available for every county in the Chesapeake Bay Watershed. In such cases, modeling relied more on other input datasets and subsequent manual review.
National Agricultural Imagery Program (NAIP) 4-band Imagery, Leaf-on, 2017/2018 (Time Period 2, or T2), Resolution Dependent on Location.
National Agricultural Imagery Program (NAIP) 4-band Imagery, Leaf-on, 2021/2022 (Time Period 3, or T3), Resolution Dependent on Location.
Thematic GIS Datasets, Various Dates. Thematic datasets representing building footprints, roads, road centerlines, sidewalks, driveways, parking lots, alleys, county boundaries, and other datasets were used to augment change detection. NOTE: Because datasets varied widely in availability, quality, and chronology, they were not used in all Chesapeake Bay counties.
Delaware Statewide 4-band Orthoimagery, Leaf-off, 2022 leaf-off, 0.152-m resolution.
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. First, the existing T2 land-cover map for the Chesapeake Bay Watershed was refined, where necessary, using available LiDAR, multispectral imagery, and thematic GIS datasets. Refinement of the T2 map maximized its value in subsequent change detection. The T2 map was then compared to T3 data inputs, focusing on the most important areas of landscape change: 1) tree-canopy loss; and 2) impervious-surfaces gain. The final map contained 79 change classes plus the original 12 classes for features that had not changed. Change combinations unlikely to occur in the study area during the analysis period were ignored (e.g., Impervious Structures to Scrub\Shrub). The resulting T2-T3 change-detection map was then exported from its original raster format into vector polygons for subsequent QA\QC.
2024-01-31T00:00:00
The draft T2-T3 land cover map was reviewed manually in ArcGIS Pro using the input NAIP imagery and LiDAR as reference datasets. At an approximate review scale of 1:6,000, errors of omission and commission were fixed using polygon editing and labeling tools. The edited map was then converted back into raster format and submitted to project cooperators for external review.
2024-02-29T00:00:00
After receiving comments from project cooperators, a second round of manual review and editing was conducted on the draft vector map, scanning for errors missed by the previous review and also addressing any issues identified by the external review. However, errors implying sub-meter accuracy were beyond the scope of this project and thus were not addressed at any point during processing or post-processing. The twice-reviewed and edited map was then converted once more in raster format for final processing.
2024-04-01T00:00:00
Using eCognition, a T3 extract from the T2-T3 change-detection map was created by converting all change classes to their T3 status, reducing the map to the standard 12-category classification.
2024-06-01T00:00:00
Using eCognition, a limited set of additional error-checking routines were applied to the final T2-T3 map, focusing on errors too small to be reliably identified during manual review. These routines included false new buildings and impervious features surrounded by tree canopy or water.
2024-06-01T00:00:00
Also for use in LULC analyses for Delaware, the original raster was re-projected from Albers to Delaware State Plane Meters. This conversion may result in the warping of some features.
2024-06-18T00:00:00
For specific use in land-use\land-cover analyses (LULC) for Delaware, the 2021 land-cover map developed for the Chesapeake Bay Watershed was reclassified in ArcPro GIS, simplifying the original 12 classes to 2 classes: Impervious (1); and Pervious (2). It was also reviewed and edited against 2022 leaf-off orthoimagery to update it to 2022 conditions.
2024-06-28T00:00:00
Converted raster to polygon using RasterToPolygon NO_SIMPLIFY VALUE MULTIPLE_OUTER_PART #. Created feature with over 1 million records.
2024-09-16T00:00:00
external
bfd6b84d3251a8f2ff48b1cfd3302a9f1c66797a
FirstMap
State of Delaware
801 Silver Lake Blvd.
Dover
DE
19904
302-739-9500
True
FirstMap
Used PairwiseIntersect with the impervious polygon layer and the USGS quad index to reduce the number of features to help make more manageable. Settings - "All attributes" # "Same as input"
2025-12-11T00:00:00
external
bfd6b84d3251a8f2ff48b1cfd3302a9f1c66797a
FirstMap
State of Delaware
801 Silver Lake Blvd.
Dover
DE
19904
302-739-9500
True
FirstMap
69683
1.000000
154120
1.000000
2
1
0
168045.358417 50101.361522
168045.358417 204221.361522
237728.358417 204221.361522
237728.358417 50101.361522
202886.858417 127161.361522
111
EPSG
6.18.3(9.3.1.2)
bit
Layer_1
2.000000
1.000000
8
Land Use Land Cover Raster
False
False
False
False
False
PLC_Impervious_PairwiseInter
Feature Class
111
Table
esri
OBJECTID
OBJECTID
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
FID_PLC_IMPERVIOUS_2022_POLY2
FID_PLC_Impervious_2022_Poly2
Integer
4
10
0
ID
Id
Integer
4
10
0
GRIDCODE
gridcode
Integer
4
10
0
IMPERV_AREA
Imperv_Area
Double
8
38
8
FID_BND_USGS_QUAD_INDEX
FID_BND_USGS_Quad_Index
Integer
4
10
0
PERIMETER
PERIMETER
Double
8
38
8
USGSINDEX_
USGSINDEX_
Double
8
38
8
QUADID
QUADID
Integer
4
10
0
CENTLAT
CENTLAT
Double
8
38
8
CENTLONG
CENTLONG
Double
8
38
8
NAME
NAME
String
35
0
0
STATE1
STATE1
String
2
0
0
STATE2
STATE2
String
2
0
0
STATE3
STATE3
String
2
0
0
STATE4
STATE4
String
2
0
0
LATLONG
LATLONG
String
9
0
0
QUADNO
QUADNO
String
6
0
0
K100
K100
String
35
0
0
K250
K250
String
35
0
0
TIF24K
TIF24K
String
15
0
0
TIF100K
TIF100K
String
15
0
0
TIF250K
TIF250K
String
15
0
0
DISCNAME
DISCNAME
String
15
0
0
QUADABV
QUADABV
String
6
0
0
SHAPE
Shape
Geometry
8
0
0
Feature geometry.
Esri
Coordinates defining the features.
SHAPE.AREA
SHAPE.AREA
Double
0
0
0
SHAPE.LEN
SHAPE.LEN
Double
0
0
0
Value
Numeric identifier for each land-cover class
University of Vermont Spatial Analysis Laboratory
1
Impervious
UVM
2
Pervious
UVM
Type
1
Details about the fields used to derive data
Impervious Data
UVM
Simple
FALSE
111
TRUE
FALSE