FR_URBAN_FORESTRY/FR_Urban_Tree_Density_500m

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Service Description: Tree canopy data averaged across 500-meter squares for all urban areas in the State of Wisconsin, as identified in the 2010 Census, as well as adjacent lands. Each pixel has a ¼-km (500 meter) resolution and represents the estimated tree canopy cover within that area. The classification was produced from tree canopy data derived from imagery collected by the U.S. Department of Agriculture’s National Agriculture Imagery Program in 2020 using a supervised machine learning classifier. The tree model was created specifically to only identify trees. A statewide training dataset composed of tree, turf, building, road, and water data was utilized to build that model. Quality analysis of the results showed that there were no systematic biases based on geography or community size. Because of the wide geographic spread of this classification and the need to mosaic together photographs from different times of the year, times of the day, and from different angles, there are many sources of error inherent in the underlying source imagery. These issues include significant discrepancies in shadow angle, direction and intensity, and wide variations in phenology or climate based on the underlying geography or habitat. The use of the “indeterminable” value reflects this uncertainty for many pixels. It is not recommended to use these and similar data for change detection; that is – the comparison from one time period to another. There is too much uncertainty within a given year’s imagery, and that uncertainty is exacerbated when comparing multiple years. Furthermore, the underlying classification was trained on urban elements – trees, grass, and impervious surfaces in highly developed spaces; the fields and wetlands that are sometimes on urban peripheries were not part of this study’s focus and thus there may be discrepancies in those types of spaces between the model and reality. Use caution before conducting any additional analyses. What this layer can show, however, is how tree canopy is generally distributed across a city or region.

Map Name: FR_Urban_Tree_Density_500m_2020

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Urban Tree Density 500m 2020 (0)

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Spatial Reference: 3071 (3071)

Single Fused Map Cache: false

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Units: esriMeters

Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP

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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: 1000000

Max Scale: 0

Supports Datum Transformation: true

Child Resources: Info Dynamic Layer

Supported Operations: Export Map Identify QueryLegends QueryDomains Find Return Updates

FR_URBAN_FORESTRY/FR_Urban_Tree_Density_500m_2020 (MapServer)