Existing Tree Canopy %

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThe dataset covers the following tree canopy categories:Environmental Justice Priority AreasCensus tracts composite / quintileExisting tree canopy percentage & environmental justice priority levelExisting tree canopyPossible tree canopyRelative percentage changeFor more information, please see theĀ 2021 Tree Canopy Assessment.

Data and Resources

Field Value
accessLevel public
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
catalog_@id https://cos-data.seattle.gov/data.json
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
catalog_describedBy https://project-open-data.cio.gov/v1.1/schema/catalog.json
identifier https://www.arcgis.com/home/item.html?id=74599018ec234e4ea8a68d45df93244d&sublayer=37
issued 2023-06-28
landingPage https://data-seattlecitygis.opendata.arcgis.com/datasets/SeattleCityGIS::existing-tree-canopy--1
metadata_type geospatial
modified 2023-06-29
old-spatial -122.4308,47.4933,-122.2420,47.7357
publisher City of Seattle ArcGIS Online
resource-type Dataset
source_datajson_identifier true
source_hash 28cdb5eba1b971c50915bee809b31c1c918f9f343e5a51025306d594034c13d1
source_schema_version 1.1
spatial {"type": "Polygon", "coordinates": [[[-122.4308, 47.4933], [-122.4308, 47.7357], [-122.2420, 47.7357], [-122.2420, 47.4933], [-122.4308, 47.4933]]]}
theme {geospatial}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • 2016
  • 2021
  • AmeriGEO
  • AmeriGEOSS
  • CKAN
  • GEO
  • GEOSS
  • National
  • North America
  • United States
  • biota
  • block-groups
  • environment
  • land-cover
  • seattle
  • seattle-gis-open-data
  • seattle-office-of-sustainability-environment
  • tree-canopy
isopen False
license_id notspecified
license_title License not specified
maintainer SeattleData
maintainer_email mapgis.mapgis@seattle.gov
metadata_created 2025-09-25T01:08:05.821938
metadata_modified 2025-09-25T01:08:05.821945
notes <div style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.</div><div style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'><br /></div><div style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>University of Vermont Spatial Analysis Laboratory</div><div style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'><br /></div><div style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>The dataset covers the following tree canopy categories:</div><div style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'><ul><li>Environmental Justice Priority Areas</li><li>Census tracts composite / quintile</li><li>Existing tree canopy percentage &amp; environmental justice priority level</li><li>Existing tree canopy</li><li>Possible tree canopy</li><li>Relative percentage change</li></ul><div style='font-family:inherit;'>For more information, please see theĀ <a href='https://seattle.gov/documents/Departments/OSE/Urban%20Forestry/2021%20Tree%20Canopy%20Assessment%20Report_FINAL_230227.pdf' style='color:rgb(0, 97, 155); text-decoration-line:none; font-family:inherit;' target='_blank' rel='nofollow ugc noopener noreferrer'>2021 Tree Canopy Assessment</a>.</div></div>
num_resources 6
num_tags 18
title Existing Tree Canopy %