Tree Canopy 2016

Tree canopy for Seattle, WA, mapped to 2016 ground conditions. Tree canopy was mapped from leaf-off LiDAR collected in the spring of 2016 and leaf-on high-resolution imagery collected in the summer of 2015 to complete this tree canopy cover assessment. Tree canopy cover mapping was carried out using a semi-automated approach that coupled automated feature extraction with manual editing. Automated feature extraction was done using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) 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. Manual corrections carried out on a scale of 1:2,500, followed by a final review for completeness and consistency at a scale of 1:10,000.

Data and Resources

Field Value
accessLevel public
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
catalog_@id https://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://data-seattlecitygis.opendata.arcgis.com/datasets/SeattleCityGIS::tree-canopy-2016
issued 2018-05-10
landingPage https://data-seattlecitygis.opendata.arcgis.com/datasets/SeattleCityGIS::tree-canopy-2016
license http://opendatacommons.org/licenses/pddl/summary
metadata_type geospatial
modified 2018-12-20
old-spatial -122.4297,47.4934,-122.2424,47.7359
publisher City of Seattle GIS Program
resource-type Dataset
source_datajson_identifier true
source_hash 5057df652cc70bdb2e66b0956ba6e907d2be4494
source_schema_version 1.1
spatial {"type": "Polygon", "coordinates": [[[-122.4297, 47.4934], [-122.4297, 47.7359], [-122.2424, 47.7359], [-122.2424, 47.4934], [-122.4297, 47.4934]]]}
theme {geospatial}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • city-of-seattle
  • ckan
  • environment
  • geo
  • geoss
  • gis
  • national
  • north-america
  • tree
  • tree-canopy
  • trees
  • united-states
  • urban-forest
isopen False
license_id other-license-specified
license_title other-license-specified
maintainer site.admin_SeattleCityGIS
maintainer_email mapgis.mapgis@seattle.gov
metadata_created 2025-11-22T13:40:28.179805
metadata_modified 2025-11-22T13:40:28.179810
notes Tree canopy for Seattle, WA, mapped to 2016 ground conditions. Tree canopy was mapped from leaf-off LiDAR collected in the spring of 2016 and leaf-on high-resolution imagery collected in the summer of 2015 to complete this tree canopy cover assessment. Tree canopy cover mapping was carried out using a semi-automated approach that coupled automated feature extraction with manual editing. Automated feature extraction was done using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) 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. Manual corrections carried out on a scale of 1:2,500, followed by a final review for completeness and consistency at a scale of 1:10,000.
num_resources 6
num_tags 15
title Tree Canopy 2016