20191009
17475500
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FALSE
UTC_Land_Cover_2012_Single_Raster
002
12145000.000000
12259000.000000
3368000.000000
3509000.000000
1
file://\\vbas0225\data\Misc\UTC_Land_Cover_2012\UTC_Land_Cover_2012.gdb
Local Area Network
Projected
GCS_North_American_1983_HARN
Linear Unit: Foot_US (0.304801)
NAD_1983_HARN_StatePlane_Virginia_South_FIPS_4502_Feet
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20191009
17475500
20230425
10502700
150000000
5000
ItemDescription
Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.2.2.3552
Land_Cover_2012
1
-76.245916
-75.844488
36.938191
36.542912
This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for Virginia Beach, VA. As such, it represents a "top down" mapping perspective.
<div style='text-align:Left;'><p><span>This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for Virginia Beach, VA. As such, it represents a "top down" mapping perspective. This layer is a high-resolution land-cover dataset for Virginia Beach, Virginia. Nine land-cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads; (7) other paved surfaces; (8) forested wetlands; and (9) non-forested wetlands. The primary sources used to derive this land cover layer were 2012 LiDAR and 2013 3-band orthoimagery. Ancillary data sources included GIS data provided by Virginia Beach (e.g., county boundary, building footprints, water, parking lots, roads, utility poles, bridges, airfields) or developed by the UVM Spatial Analysis Laboratory (bare soils, utility lines, modified National Wetland Inventory wetlands polygons). This land cover dataset is considered current as of 2012. Object-based image analysis (OBIA) was used 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 used to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to thorough manual quality control. More than 50,000 corrections were made to the classification</span></p></div>
University of Vermont Spatial Analysis Laboratory
Tree Canopy
File Geodatabase Raster Dataset
dataset
EPSG
8.2.6
Pixel
282000
228000
0.500000
0.500000
4
1
Upper Left
TRUE
LZ77
1
pixel codes
FGDBR
TRUE
row and column
0.500000
0.500000
VAT_UTC_Land_Cover_2012_Single_Raster
Table
16
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Value
Value
Integer
4
0
0
Count
Count
Double
8
0
0
Red
Red
Double
8
0
0
Green
Green
Double
8
0
0
Blue
Blue
Double
8
0
0
Opacity
Opacity
Double
8
0
0
Desc_
Desc_
String
254
0
0
20230425