20220616
15541600
1.0
North American Profile of ISO19115 2003
FALSE
Forest_2012_KC
1458076.829577
1644015.329747
465426.000012
598792.500011
1
002
Projected
GCS_North_American_1983_HARN
Linear Unit: Foot_US (0.304801)
NAD_1983_HARN_StatePlane_Kentucky_North_FIPS_1601_Feet
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/2.9.0'><WKT>PROJCS["NAD_1983_HARN_StatePlane_Kentucky_North_FIPS_1601_Feet",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1640416.666666667],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-84.25],PARAMETER["Standard_Parallel_1",37.96666666666667],PARAMETER["Standard_Parallel_2",38.96666666666667],PARAMETER["Latitude_Of_Origin",37.5],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",2891]]</WKT><XOrigin>-120428400</XOrigin><YOrigin>-95345400</YOrigin><XYScale>3048.0060960121914</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.0032808333333333335</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>2891</WKID><LatestWKID>2891</LatestWKID></ProjectedCoordinateSystem>
20230424
14384700
20230424
14384700
FGDC
625000
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
ArcGIS Metadata
1.0
2014-07-30T00:00:00
remote-sensing image
Forest Patch Class, 2012
First
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
Jarlath O'Neil-Dunne
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
Jarlath O'Neil-Dunne
<DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 11 0;"><SPAN>This dataset is a representation of forest patches derived from the tree canopy class in the 2012high-resolution land cover dataset. Three classes of forest patches were derived: 1) small, 2) medium, and 3) large. A customized algorithm using object-based image analysis techniques within the eCognition software package was to divide the tree canopy into the forest patch classes based on a combination of </SPAN><SPAN><SPAN>a combination of size, edge to perimeter ratio, length, and width. In general, small patches represent individual trees or small rows of trees. Medium patches represent clumps of trees, often in suburban or agricultural settings, that are fragmented and may or may not have a duff layer. Large forest patches consist of forested stands that in most cases will have a duff layer.</SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN>End users of this layer should note that this forest patch classification is one approach and the dataset should be evaluated so as to understand its capabilities and limitations before using for decision making purposes.</SPAN></P></DIV></DIV></DIV>
This dataset is a representation of forest patches based on 2012 high-resolution land cover.
University of Vermont Spatial Analysis Laboratory
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
Kentucky
Campbell County
Boone County
Kenton County
2012
Tree Canopy
Land cover
Watershed
Forest
Forest
Patch
Land Cover
-76.57146
-76.069677
39.389266
39.722959
ground condition
2012-08-01T00:00:00
The methods used were developed by the University of Vermont Spatial Analysis Laboratory in collaboration with the SavATree Consulting Group.
5000
Esri ArcGIS 12.9.4.32739
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>None.</SPAN></P></DIV></DIV></DIV>
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>None.</SPAN></P></DIV></DIV></DIV>
High-resolution land cover
The forest patch analysis was derived from the 2012 high-resolution land cover dataset for the region.
Forest patch algorithm
2014-08-14T00:00:00
Topology check for overlaps and gaps.
Complete for the study area.
Boundary check.
Forest patch class
Forest_2012_KC
UTC
Feature Class
0
Internal
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
Class
Class
String
20
0
0
Three classes of forest patches were derived: 1) small, 2) medium, and 3) large. A customized algorithm using object-based image analysis techniques within the eCognition software package was to divide the tree canopy into the forest patch classes based on a combination of a combination of size, edge to perimeter ratio, length, and width. In general, small patches represent individual trees or small rows of trees. Medium patches represent clumps of trees, often in suburban or agricultural settings, that are fragmented and may or may not have a duff layer. Large forest patches consist of forested stands that in most cases will have a duff layer.
Project
Forest patch class
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
File Geodatabase Feature Class
dataset
EPSG
6.3(9.0.0)
0
Simple
FALSE
0
TRUE
FALSE
20230424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