FR_URBAN_FORESTRY/FR_Urban_Tree_Canopy_Raster

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Service Description: Tree canopy data for all urban areas in the State of Wisconsin, as identified in the 2010 Census, as well as adjacent lands. Pixels have a 1 square meter resolution. The classification was derived from imagery collected by the U.S. Department of Agriculture’s National Agriculture Imagery Program in 2022 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. Users should be aware that, 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, this 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.

Name: FR_URBAN_FORESTRY/FR_Urban_Tree_Canopy_Raster_2022

Description: Tree canopy data for all urban areas in the State of Wisconsin, as identified in the 2010 Census, as well as adjacent lands. Pixels have a 1 square meter resolution. The classification was derived from imagery collected by the U.S. Department of Agriculture’s National Agriculture Imagery Program in 2022 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. Users should be aware that, 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, this 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.

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Supported Operations: Export Image Identify Measure Compute Histograms Compute Statistics Histograms Get Samples Compute Class Statistics Query Boundary Compute Pixel Location Compute Angles Validate Project

FR_URBAN_FORESTRY/FR_Urban_Tree_Canopy_Raster_2022 (ImageServer)