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NC1Map_Treecover (ImageServer)

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Service Description: This dataset was created by the NCDACS. Tree canopy cover data was extracting from the North Carolina 2016 NAIP image tiles for the entire state. The data is currently in a raster MrSID format and corresponds to the Stateplane coordinate system. Sections: The NAIP tiles were processed in batch processes defined by the date of acquisition documented in the attribute table of the NAIP imagery. The attribute "SrcImgDate" was used to derive 35 working project sections across NAIP tiles. For example, the naming convention used "08-v2a_2016-0526-northcentrl.zip". This zip file contains the TIF tiles of the tree canopy data for Section 8 that includes imagery acquired on or around May 26th, 2016 in the North Central portion of the state of NC. "v2a" represents a versioning (Version 2a) based on small modifications that were made during raster processing which required all the imagery in that specific section to be reprocessed. The “versioning” is not necessarily important unless future modifications in the process. Applications: A decision rule supervised classification process was specifically designed around the tonal differences inherent in NAIP imagery. It used with spectral and textural information derived for each NAIP Tile. A total of 3,564 tiles and 16TBs of data were processed in a 10-week timeframe. The classification resulted in a 2-class classification schema. Class 1 is Canopy and Class 2 Non-Canopy. Class 2 is set to transparent by default. Texture processing was applied to reduce mixed pixel values between tree canopy, healthy grass and agriculture land areas. These features have similar vegetation spectral response and would otherwise result in a significant number of mis-classified pixels. In many areas however, agriculture and grass land areas containing higher texture values still resulted in mixed canopy pixels. The approached is still an improved process compared to only using spectral information in the NAIP imagery. To meet the short 2-month timeframe of the project, automated and semi-automated processing methods were used in a series of batch processing models. The process derived indices and ratio layers that were then used in the decision rule process. Thematic raster post processing was then used to clean-up the data. The result is a 2-bit thematic raster TIF file for each NAIP tile with the extension "_canopy.tif". Manual editing or clean-up was not used at any time during the project to correct mixed pixels since it was out of the budgetary scope of the project. Sections: The NAIP Tiles were processes in batch processes defined by the date of acquisition documented in the attribute table of the NAIP imagery. The attribute "SrcImgDate" was used to derive 35 working project sections across NAIP tiles. For example, the naming convention used "08-v2a_2016-0526-northcentrl.zip". This zip file contains the TIF tiles of the tree canopy data for Section 8 that includes imagery acquired on or around May 26th, 2016 in the North Central portion of the state of NC. "v2a" represents a versioning (Version 2a) based on small modifications that were made during raster processing which required all the imagery in that specific section to be reprocessed. The “versioning” is not necessarily important unless future modifications in the process. Applications: This section is very important, especially for general users of remote sensing and GIS data. For large area applications, such as tree canopy per square mile analysis for a watershed or county, the data is pretty good as it stands. For more local tree canopy efforts, such as Urban Tree Canopy assessments, it is recommended that the data be evaluated for the application and, if necessary, further edited either manually or using other geospatial post-processing tools. The scope of the project in terms of time and cost did not allow for significant manual editing for local applications on a municipal level or city. For these types of local applications of the tree canopy data the existing thematic raster data can be further modified to meet end-user requirements. If proven useful, the project processes can be repeated for each NAIP Acquisition for North Carolina and/or other states. For small area project it might be useful to compare this data to tree canopy data derived from NCEM’s QL2 LIDAR project where available. Imperfections: The three main imperfections of the data include: 1.) agricultural crop field pixels being classified as tree canopy, 2.) Tonal difference inherent in the NAIP processing resulting in missed canopy pixels, mainly in visible tree canopy areas with “smooth” texture, and 3.) Conifer tree stands being missed in the classification due to lower vegetation values outside of the decision rule threshold that would have otherwise included more non-canopy vegetation pixels being classified as tree canopy. These 3 issues can be resolved by the end-user using manual editing or localized classification approaches to modify the current data product.

Name: NC1Map_Treecover

Description: This dataset was created by the NCDACS. Tree canopy cover data was extracting from the North Carolina 2016 NAIP image tiles for the entire state. The data is currently in a raster MrSID format and corresponds to the Stateplane coordinate system. Sections: The NAIP tiles were processed in batch processes defined by the date of acquisition documented in the attribute table of the NAIP imagery. The attribute "SrcImgDate" was used to derive 35 working project sections across NAIP tiles. For example, the naming convention used "08-v2a_2016-0526-northcentrl.zip". This zip file contains the TIF tiles of the tree canopy data for Section 8 that includes imagery acquired on or around May 26th, 2016 in the North Central portion of the state of NC. "v2a" represents a versioning (Version 2a) based on small modifications that were made during raster processing which required all the imagery in that specific section to be reprocessed. The “versioning” is not necessarily important unless future modifications in the process. Applications: A decision rule supervised classification process was specifically designed around the tonal differences inherent in NAIP imagery. It used with spectral and textural information derived for each NAIP Tile. A total of 3,564 tiles and 16TBs of data were processed in a 10-week timeframe. The classification resulted in a 2-class classification schema. Class 1 is Canopy and Class 2 Non-Canopy. Class 2 is set to transparent by default. Texture processing was applied to reduce mixed pixel values between tree canopy, healthy grass and agriculture land areas. These features have similar vegetation spectral response and would otherwise result in a significant number of mis-classified pixels. In many areas however, agriculture and grass land areas containing higher texture values still resulted in mixed canopy pixels. The approached is still an improved process compared to only using spectral information in the NAIP imagery. To meet the short 2-month timeframe of the project, automated and semi-automated processing methods were used in a series of batch processing models. The process derived indices and ratio layers that were then used in the decision rule process. Thematic raster post processing was then used to clean-up the data. The result is a 2-bit thematic raster TIF file for each NAIP tile with the extension "_canopy.tif". Manual editing or clean-up was not used at any time during the project to correct mixed pixels since it was out of the budgetary scope of the project. Sections: The NAIP Tiles were processes in batch processes defined by the date of acquisition documented in the attribute table of the NAIP imagery. The attribute "SrcImgDate" was used to derive 35 working project sections across NAIP tiles. For example, the naming convention used "08-v2a_2016-0526-northcentrl.zip". This zip file contains the TIF tiles of the tree canopy data for Section 8 that includes imagery acquired on or around May 26th, 2016 in the North Central portion of the state of NC. "v2a" represents a versioning (Version 2a) based on small modifications that were made during raster processing which required all the imagery in that specific section to be reprocessed. The “versioning” is not necessarily important unless future modifications in the process. Applications: This section is very important, especially for general users of remote sensing and GIS data. For large area applications, such as tree canopy per square mile analysis for a watershed or county, the data is pretty good as it stands. For more local tree canopy efforts, such as Urban Tree Canopy assessments, it is recommended that the data be evaluated for the application and, if necessary, further edited either manually or using other geospatial post-processing tools. The scope of the project in terms of time and cost did not allow for significant manual editing for local applications on a municipal level or city. For these types of local applications of the tree canopy data the existing thematic raster data can be further modified to meet end-user requirements. If proven useful, the project processes can be repeated for each NAIP Acquisition for North Carolina and/or other states. For small area project it might be useful to compare this data to tree canopy data derived from NCEM’s QL2 LIDAR project where available. Imperfections: The three main imperfections of the data include: 1.) agricultural crop field pixels being classified as tree canopy, 2.) Tonal difference inherent in the NAIP processing resulting in missed canopy pixels, mainly in visible tree canopy areas with “smooth” texture, and 3.) Conifer tree stands being missed in the classification due to lower vegetation values outside of the decision rule threshold that would have otherwise included more non-canopy vegetation pixels being classified as tree canopy. These 3 issues can be resolved by the end-user using manual editing or localized classification approaches to modify the current data product.

Single Fused Map Cache: false

Extent: Initial Extent: Full Extent: Pixel Size X: 3.279005758322838

Pixel Size Y: 3.279005758322838

Band Count: 4

Pixel Type: U8

RasterFunction Infos: {"name":"None","description":"A No-Op Function.","help":""}, {"name":"treecover2","description":"A raster function template.","help":""}, {"name":"treecover2","description":"A raster function template.","help":""}, {"name":"None","description":"A No-Op Function.","help":""}

Mensuration Capabilities:

Has Histograms: true

Has Colormap: false

Has Multi Dimensions : false

Rendering Rule:

Min Scale: 0

Max Scale: 0

Copyright Text: NC Department of Agriculture and Consumer Services; NC Center for Geographic Information and Analysis; NC OneMap

Service Data Type: esriImageServiceDataTypeGeneric

Min Values: 0, 0, 25.1, 0

Max Values: 255, 255, 229.9, 255

Mean Values: 95.39826732673268, 110.62623762376238, 55.04693069306931, 133.34084158415843

Standard Deviation Values: 91.63825209382493, 104.52196516988175, 39.27684253450393, 125.33589992155274

Object ID Field: OBJECTID

Fields: Default Mosaic Method: Northwest

Allowed Mosaic Methods: NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None

SortField:

SortValue: null

Mosaic Operator: First

Default Compression Quality: 75

Default Resampling Method: Bilinear

Max Record Count: 1000

Max Image Height: 4100

Max Image Width: 15000

Max Download Image Count: 20

Max Mosaic Image Count: 20

Allow Raster Function: true

Allow Compute TiePoints: false

Supports Statistics: true

Supports Advanced Queries: true

Use StandardizedQueries: true

Raster Type Infos: Has Raster Attribute Table: false

Edit Fields Info: null

Ownership Based AccessControl For Rasters: null

Child Resources:   Info   Histograms   Key Properties   Legend   MultiDimensionalInfo   rasterFunctionInfos

Supported Operations:   Export Image   Query   Identify   Compute Histograms   Compute Statistics Histograms   Get Samples   Compute Class Statistics   Query Boundary