{
 "currentVersion": 10.81,
 "serviceDescription": "2003 - Urban Forest Canopy was created from LIDAR data to determine the volume of Forest Canopy in the City of Seattle. The work was done in 2003 by a consultant under contract by Seattle Parks Horticulture (Urban Forestry)\n\n2016 - 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.",
 "mapName": "Layers",
 "description": "",
 "copyrightText": "City of Seattle",
 "supportsDynamicLayers": true,
 "layers": [
  {
   "id": 0,
   "name": "Tree Canopy - 2016",
   "parentLayerId": -1,
   "defaultVisibility": false,
   "subLayerIds": null,
   "minScale": 50000,
   "maxScale": 0,
   "type": "Feature Layer",
   "geometryType": "esriGeometryPolygon"
  },
  {
   "id": 1,
   "name": "Tree Canopy - 2003",
   "parentLayerId": -1,
   "defaultVisibility": false,
   "subLayerIds": null,
   "minScale": 0,
   "maxScale": 0,
   "type": "Feature Layer",
   "geometryType": "esriGeometryPolygon"
  }
 ],
 "tables": [],
 "spatialReference": {
  "wkid": 2926,
  "latestWkid": 2926
 },
 "singleFusedMapCache": false,
 "initialExtent": {
  "xmin": 1265640.4492478939,
  "ymin": 233910.1416528333,
  "xmax": 1266614.407581227,
  "ymax": 234997.38123616626,
  "spatialReference": {
   "wkid": 2926,
   "latestWkid": 2926
  }
 },
 "fullExtent": {
  "xmin": 1245784.2701586485,
  "ymin": 183326.00004778802,
  "xmax": 1294500.0000103712,
  "ymax": 272240.00001083314,
  "spatialReference": {
   "wkid": 2926,
   "latestWkid": 2926
  }
 },
 "minScale": 0,
 "maxScale": 0,
 "units": "esriFeet",
 "supportedImageFormatTypes": "PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP",
 "documentInfo": {
  "Title": "Tree Canopy",
  "Author": "City of Seattle",
  "Comments": "2003 - Urban Forest Canopy was created from LIDAR data to determine the volume of Forest Canopy in the City of Seattle. The work was done in 2003 by a consultant under contract by Seattle Parks Horticulture (Urban Forestry)\n\n2016 - 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.",
  "Subject": "Urban Tree Canopy in 2003 and 2016.",
  "Category": "",
  "AntialiasingMode": "None",
  "TextAntialiasingMode": "Force",
  "Keywords": "tree,tree canopy,canopy,trees,city of seattle,gis,forest,urban forest"
 },
 "capabilities": "Map,Query,Data",
 "supportedQueryFormats": "JSON, geoJSON",
 "exportTilesAllowed": false,
 "referenceScale": 0,
 "supportsDatumTransformation": true,
 "maxRecordCount": 1000,
 "maxImageHeight": 4096,
 "maxImageWidth": 4096,
 "supportedExtensions": ""
}