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Service Description: Davey Resource Group utilized an object-based image analysis (OBIA) semi-automated feature extraction method to process and analyze current high-resolution color infrared (CIR) aerial imagery and remotely-sensed data to identify tree canopy cover and land cover classifications. The use of imagery analysis is cost-effective and provides a highly accurate approach to assessing your community's existing tree canopy coverage. This supports responsible tree management, facilitates community forestry goal-setting, and improves urban resource planning for healthier and more sustainable urban environments. Advanced image analysis methods were used to classify, or separate, the land cover layers from the overall imagery. The semi-automated extraction process was completed using Feature Analyst, an extension of ArcGIS®. Feature Analyst uses an object-oriented approach to cluster together objects with similar spectral (i.e., color) and spatial/contextual (e.g., texture, size, shape, pattern, and spatial association) characteristics. The land cover results of the extraction process was post-processed and clipped to each project boundary prior to the manual editing process in order to create smaller, manageable, and more efficient file sizes. Secondary source data, high-resolution aerial imagery provided by each UTC city, and custom ArcGIS® tools were used to aid in the final manual editing, quality checking, and quality assurance processes (QA/QC). The manual QA/QC process was implemented to identify, define, and correct any misclassifications or omission errors in the final land cover layer.
Map Name: 2022 Las Vegas Urban Tree Canopy Assessment
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Service Item Id: b119ded9d9664e3c844e99a3665b67a0
Copyright Text: Nevada Division of Forestry, Davey Resource Group.
Spatial Reference:
102100
(3857)
Single Fused Map Cache: false
Initial Extent:
XMin: -1.2851405685636094E7
YMin: 4294396.936670813
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Spatial Reference: 102100
(3857)
Full Extent:
XMin: -1.28439155959E7
YMin: 4294442.261500001
XMax: -1.27948064669E7
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Spatial Reference: 102100
(3857)
Units: esriMeters
Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP
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Title: Map
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Keywords: data
AntialiasingMode: Fast
TextAntialiasingMode: Force
Supports Dynamic Layers: true
Resampling: false
MaxRecordCount: 2000
MaxImageHeight: 4096
MaxImageWidth: 4096
Supported Query Formats: JSON, geoJSON, PBF
Supports Query Data Elements: true
Min Scale: 0
Max Scale: 0
Supports Datum Transformation: true
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