20191030
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landcover_2018_virginiabeach_one_foot.tif
file://\\vbas0225\data\Misc\UTC_Land_Cover_2018\landcover_2018_virginiabeach_one_foot.tif
Local Area Network
12144561.000000
12259394.000000
3368059.000000
3527771.000000
1
002
Projected
GCS_NAD83_2011
Linear Unit: Foot_US (0.304801)
NSRS_2011_Virginia_State_Planes_South_Zone_US_Foot
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LZW
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continuous
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Rename O:\Data_Projects\States\VA\VirginiaBeach\Data\LandCover\landcover_2018_virginiabeach_v5.tif O:\Data_Projects\States\VA\VirginiaBeach\Data\LandCover\landcover_2018_virginiabeach.tif RasterDataset
20191216
16460300
20191216
16460300
50000
5000
Jarlath O'Neil-Dunne
University of Vermont Spatial Analysis Laboratory
Director
802-656-3324
802-656-8683
81 Carrigan Drive
Aiken Center Room 205
Burlington
VT
05405
US
joneildu@uvm.edu
Land_Cover_2018
University of Vermont Spatial Analysis Laboratory
remote-sensing image
<div style='text-align:Left;'><div><p><span>High resolution land cover dataset for Virginia Beach, Virginia. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2018 LiDAR data and 2018 NAIP imagery. Ancillary data sources included GIS data provided by Virginia Beach, Virginia Beach, Virginia or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3500 and all observable errors were corrected.</span></p></div></div>
This dataset was developed as part of the Tree Canopy Change Assessment Virginia Beach 2012-2018. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area.
University of Vermont Spatial Analysis Laboratory in collaboration with Tree Canopy Change Assessment Virginia Beach 2012-2018.
Jarlath O'Neil-Dunne
University of Vermont Spatial Analysis Laboratory
Director
802-656-3324
802-656-8683
Aiken Room 205
81 Carrigan Drive
Burlington
VT
05405
US
joneildu@uvm.edu
Virginia Beach
Virginia Beach
Virginia
2018
tree canopy
Urban
UTC
Land cover
tree canopy
Urban
UTC
Virginia Beach
Land cover
Virginia Beach
2018
Virginia
Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.4.0.5524
0
0
0
0
ground condition
This assessment was completed by the University of Vermont's Spatial Analysis Laboratory with funding the Virginia Beach Parks and Recreation. The methods used were developed by the University of Vermont Spatial Analysis Laboratory with funding from the Virginia Beach Parks and Recreation. Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establishing tree canopy goals.
1
-76.247405
-75.841333
36.989762
36.543044
Valid raster dataset.
Complete coverage for the study area using the most recent boundary data.
No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control (QC). Accuracy assessments from previous UTC land cover datasets indicate that the use OBIA techniques in combination with a detailed QC yield an overall accuracy of 95% or better.
Object based image analysis (OBIA) using a rule-based expert system
Manual corrections to land cover classification
Vector
Planimetric data
Planimetric data
ground condition
12000
National Agricultural Imagery Program 4-band data
NAIP
ground condition
LiDAR
LiDAR
ground condition
2
0
1.000000
0
1.000000
1
0
12144561.000000 3368059.000000
12144561.000000 3527771.000000
12259394.000000 3527771.000000
12259394.000000 3368059.000000
12201977.500000 3447915.000000
landcover_2018_virginiabeach.tif
UTC
Table
7
OID
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
OID
OID
4
0
0
Count
# pixels per land cover class
Internal
Count
Double
19
0
0
Value
Land cover code
UTC
2
Grass/Shrub
6
Road/Railroad
5
Building
4
Water
7
Other Paved Surfaces
3
Bare Soil
1
Tree Canopy
Value
Integer
9
9
0
Desc_
Desc_
String
50
0
0
Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads/railroads, and (7) other paved surfaces.
Raster Dataset
dataset
Band_1
7.000000
1.000000
4
20191216