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Built Environment
Dataset
Database of all active Tree Service Providers registered to do business in Seattle and have met the registration requirements for the Seattle Department of Construction & Inspections (SDCI) registry for tree work on private property, for the Seattle Department of Transportation’s (SDOT) registry for tree work in the public right-of-way, or both. All commercial tree work on private property, including removal, reportable work, or consulting services must be conducted by a SDCI registered tree service provider. All major pruning or tree removal work within the public right-of-way must be conducted by a SDOT registered tree service provider.
Tags registry service tree tree service tree service provider and 1 more
API Docs
Updated
October 15 2024
Views
275

External Link
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.

Displays data from CARTO.TreeCanopy_2016. Will not display when zoomed out beyond 1:50,000.

Static data layer, no updates.
Tags canopy cover gis tree urban forestry
Updated
September 16 2024
Views
64

External Link
Symbolized data from DPR.UFCanopy. Does not display when zoomed out beyond 1:56,000.

Static data, no updates.
Tags canopy cover gis tree urban forestry
Updated
September 16 2024
Views
60

External Link

This fruit tree inventory are the SPR fruit trees that are part of the Urban Food Systems (UFS) program. The UFS program manages fruit tree Orchards and UFS gardens that are used for public harvesting and donations.

These UPS Fruit Trees are not maintained by the SPR Tree Crew. The inventory of these trees may or may not be in the SPR Tree crew Inventory. To get a comprehensive list of SPR owned trees this inventory and the tree crew tree inventory may need to be combined.

Tags fruit spr tree ufs urban food systems
Updated
October 11 2024
Views
55

External Link
This dataset represents tree crowns derived from LiDAR data. Tree crowns are defined as circles that fitted to the approximated radius of a tree's branches and leaves. The tree crowns were derived using LiDAR data. The operation was constrained to those areas of tree canopy, using the tree canopy dataset developed separately for this project, which employed automated techniques coupled with manual editing to extract tree canopy from imagery and LiDAR. Mapping of tree crowns was performed using an automated feature extraction technique that incorporated segmentation and morphology routines. The automated routine first created objects from the tree canopy using an inverse watershed segmentation algorithm applied to the LiDAR nDSM (normalized digital surface model) datasets. These objects were then refined using the spatial properties of the objects. Centroids were computed by finding the geometric center of the tree object. Attributes include the tree height and radius. The height was calculated using the 98th quantile of the LiDAR nDSM height to reduce outlier values. The radius was then calculated from the tree centroid using the formula. This radius was used to derive the tree crowns.

For more information, please see the 2021 Tree Canopy Assessment.
Tags 2021 biota canopy environment tree and 3 more
Updated
September 16 2024
Views
71

External Link
Seattle Parks and Recreation's Tree Inventory of individual trees on SPR property.

This Hosted Feature Layer View is for public facing web maps and applications.  The HFL and view get updated weekly from the source on Monday morning through mid-day.
Tags dpr inventory maintenance management tree and 5 more
Updated
October 9 2024
Views
93

External Link
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 Laboratory

This dataset consists of hexagons 50-acres in area, or several city blocks. The dataset covers the following tree canopy categories:
  • Existing tree canopy percent
  • Possible tree canopy - vegetation percent
  • Relative percent change
  • Absolute percent change
  • Average maximum afternoon temperature (F)
  • Tree canopy percentage & average afternoon temperature (F)
For more information, please see the 2021 Tree Canopy Assessment.
Tags 2021 biota canopy environment tree and 7 more
Updated
September 16 2024
Views
97

External Link
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 Laboratory

This dataset consists of hexagons 50-acres in area, or several city blocks. The dataset covers the following tree canopy categories:
  • Existing tree canopy percent
  • Possible tree canopy - vegetation percent
  • Relative percent change
  • Absolute percent change
  • Average maximum afternoon temperature (F)
  • Tree canopy percentage & average afternoon temperature (F)
For more information, please see the 2021 Tree Canopy Assessment.
Tags 2021 biota canopy environment tree and 7 more
Updated
September 16 2024
Views
68

External Link
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 Laboratory

This dataset consists of hexagons 50-acres in area, or several city blocks. The dataset covers the following tree canopy categories:
  • Existing tree canopy percent
  • Possible tree canopy - vegetation percent
  • Relative percent change
  • Absolute percent change
  • Average maximum afternoon temperature (F)
  • Tree canopy percentage & average afternoon temperature (F)
For more information, please see the 2021 Tree Canopy Assessment.
Tags 2021 biota canopy environment tree and 7 more
Updated
September 16 2024
Views
68

External Link
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 Laboratory

This dataset consists of hexagons 50-acres in area, or several city blocks. The dataset covers the following tree canopy categories:
  • Existing tree canopy percent
  • Possible tree canopy - vegetation percent
  • Relative percent change
  • Absolute percent change
  • Average maximum afternoon temperature (F)
  • Tree canopy percentage & average afternoon temperature (F)
For more information, please see the 2021 Tree Canopy Assessment.
Tags 2021 biota canopy environment tree and 7 more
Updated
September 16 2024
Views
65

External Link
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 Laboratory

This dataset consists of hexagons 50-acres in area, or several city blocks. The dataset covers the following tree canopy categories:
  • Existing tree canopy percent
  • Possible tree canopy - vegetation percent
  • Relative percent change
  • Absolute percent change
  • Average maximum afternoon temperature (F)
  • Tree canopy percentage & average afternoon temperature (F)
For more information, please see the 2021 Tree Canopy Assessment.
Tags 2021 biota canopy environment tree and 7 more
Updated
April 12 2024
Views
61

External Link
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 Laboratory

This dataset consists of hexagons 50-acres in area, or several city blocks. The dataset covers the following tree canopy categories:
  • Existing tree canopy percent
  • Possible tree canopy - vegetation percent
  • Relative percent change
  • Absolute percent change
  • Average maximum afternoon temperature (F)
  • Tree canopy percentage & average afternoon temperature (F)
For more information, please see the 2021 Tree Canopy Assessment.
Tags 2021 biota canopy environment tree and 7 more
Updated
September 16 2024
Views
60

External Link
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 Laboratory

This dataset consists of hexagons 50-acres in area, or several city blocks. The dataset covers the following tree canopy categories:
  • Existing tree canopy percent
  • Possible tree canopy - vegetation percent
  • Relative percent change
  • Absolute percent change
  • Average maximum afternoon temperature (F)
  • Tree canopy percentage & average afternoon temperature (F)
For more information, please see the 2021 Tree Canopy Assessment.
Tags 2021 biota canopy environment tree and 7 more
Updated
September 16 2024
Views
50

External Link
This layer displays Citywide Green Stormwater Infrastructure
Best Management Practices (City-wide GSI BMP) and Citywide Green Stormwater
Infrastructure projects (City-wide GSI Project).

Citywide GSI BMP is sourced from CARTO.GSI_BMP_PT_PV. 
Labels are based on the attribute BMP.  BMP codes listed in the BEST
MANAGED PRACTICE (GB_BMP_TEXT) field are defined as follows:



Code Value




Text Value






BIO




Bioretention, infiltrating,
installed by others






BIN




Bioretention, infiltrating, installed by SPU






BND




Bioretention, non-infiltrating, with
detention






BSU




Biofiltration swale with underdrain






BSW




Biofiltration swale without
underdrain






BUC




Bioretention swale, non-infiltrating, with underdrain in
CSO basin






BUN




Bioretention Swale, non-infiltrating,
with underdrain in non-CSO basin






CRG




Cistern to rain garden





Tags best management practice biofiltration bioretention bmp tree and 19 more
Updated
October 14 2024
Views
211

External Link
The RainWise program helps eligible property owners manage stormwater by installing rain gardens and/or cisterns on private property. The program is jointly funded by King County and SPU as part of the effort to manage 700 million gallons of storm runoff using green infrastructure by 2025. Property owners must live in selected Combined Sewer Overflow basins to be eligible.
Tags best management practice biofiltration bioretention bmp tree and 19 more
Updated
October 14 2024
Views
94

External Link
This layer displays Citywide Green Stormwater Infrastructure
Best Management Practices (City-wide GSI BMP) and Citywide Green Stormwater
Infrastructure projects (City-wide GSI Project).

Citywide GSI BMP is sourced from CARTO.GSI_BMP_PT_PV. 
Labels are based on the attribute BMP.  BMP codes listed in the BEST
MANAGED PRACTICE (GB_BMP_TEXT) field are defined as follows:



Code Value




Text Value






BIO




Bioretention, infiltrating,
installed by others






BIN




Bioretention, infiltrating, installed by SPU






BND




Bioretention, non-infiltrating, with
detention






BSU




Biofiltration swale with underdrain






BSW




Biofiltration swale without
underdrain






BUC




Bioretention swale, non-infiltrating, with underdrain in
CSO basin






BUN




Bioretention Swale, non-infiltrating,
with underdrain in non-CSO basin






CRG




Cistern to rain garden





Tags best management practice biofiltration bioretention bmp tree and 19 more
Updated
October 14 2024
Views
98

External Link
This layer displays Citywide Green Stormwater Infrastructure
Best Management Practices (City-wide GSI BMP) and Citywide Green Stormwater
Infrastructure projects (City-wide GSI Project).

Citywide GSI BMP is sourced from CARTO.GSI_BMP_PT_PV. 
Labels are based on the attribute BMP.  BMP codes listed in the BEST
MANAGED PRACTICE (GB_BMP_TEXT) field are defined as follows:



Code Value




Text Value






BIO




Bioretention, infiltrating,
installed by others






BIN




Bioretention, infiltrating, installed by SPU






BND




Bioretention, non-infiltrating, with
detention






BSU




Biofiltration swale with underdrain






BSW




Biofiltration swale without
underdrain






BUC




Bioretention swale, non-infiltrating, with underdrain in
CSO basin






BUN




Bioretention Swale, non-infiltrating,
with underdrain in non-CSO basin






CRG




Cistern to rain garden





Tags best management practice biofiltration bioretention bmp tree and 19 more
Updated
October 14 2024
Views
86