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Service Description: The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
Name: Vegetation/USFS_EDW_TCC_Science_SE_Hawaii
Description: The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/
Single Fused Map Cache: false
Extent:
XMin: -1.80250533272E7
YMin: 1829149.0954000019
XMax: -1.70501433272E7
YMax: 2859439.095400002
Spatial Reference: 102100
(3857)
LatestVCSWkid(0)
Initial Extent:
XMin: -1.80250533272E7
YMin: 1829149.0954000019
XMax: -1.70501433272E7
YMax: 2859439.095400002
Spatial Reference: 102100
(3857)
LatestVCSWkid(0)
Full Extent:
XMin: -1.80250533272E7
YMin: 1829149.0954000019
XMax: -1.70501433272E7
YMax: 2859439.095400002
Spatial Reference: 102100
(3857)
LatestVCSWkid(0)
Time Info:
Start Time Field: beginyear
End Time Field: endyear
Time Extent:
[2008/01/01 00:00:00 UTC, 2021/01/01 00:00:00 UTC]
Time Reference:
N/A
Pixel Size X: 30.0
Pixel Size Y: 30.0
Band Count: 1
Pixel Type: U16
RasterFunction Infos: {"rasterFunctionInfos": [
{
"help": "",
"name": "ScienceSE_RFT2",
"description": "ScienceSE RFT exported from Pro. Let's see if this works."
},
{
"help": "",
"name": "None",
"description": "Make a Raster or Raster Dataset into a Function Raster Dataset."
}
]}
Mensuration Capabilities: Basic
Inspection Capabilities:
Has Histograms: true
Has Colormap: false
Has Multi Dimensions : false
Rendering Rule:
Min Scale: 0
Max Scale: 0
Copyright Text: Funding for this project was provided by the U.S. Forest Service (USFS). RedCastle Resources produced the dataset under contract to the USFS Geospatial Technology and Applications Center.
Service Data Type: esriImageServiceDataTypeGeneric
Min Values: 0
Max Values: 65534
Mean Values: 2694.8590396314339
Standard Deviation Values: 8513.4147001249021
Object ID Field: objectid
Fields:
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category
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, [2: Overview]
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st_perimeter(shape)
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Default Mosaic Method: Northwest
Allowed Mosaic Methods: NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None
SortField:
SortValue: N/A
Mosaic Operator: First
Default Compression Quality: 75
Default Resampling Method: Nearest
Max Record Count: 1000
Max Image Height: 100000
Max Image Width: 100000
Max Download Image Count: 20
Max Mosaic Image Count: 100
Allow Raster Function: true
Allow Copy: true
Allow Analysis: true
Allow Compute TiePoints: false
Supports Statistics: true
Supports Advanced Queries: true
Use StandardizedQueries: true
Raster Type Infos:
Name: Raster Dataset
Description: Supports all ArcGIS Raster Datasets
Help:
Has Raster Attribute Table: false
Edit Fields Info: N/A
Ownership Based AccessControl For Rasters: N/A
Child Resources:
Info
Histograms
Statistics
Key Properties
Legend
Raster Function Infos
Supported Operations:
Export Image
Query
Identify
Measure
Compute Histograms
Compute Statistics Histograms
Get Samples
Compute Class Statistics
Query GPS Info
Find Images
Image to Map
Map to Image
Measure from Image
Image to Map Multiray
Query Boundary
Compute Pixel Location
Compute Angles
Validate
Project