Most up to date layer for 'Updated_122821_Tempe_CanopyCover_CensusBlock_2019'This dataset contains tree canopy cover layers as derived and calculated via a land cover classification for the City of Tempe and Guadalupe using 2019 NAIP Imagery. The land cover classification utilized a Support Vector Machine Classifier and was calculated for various areas including city boundary, census tracts, census blocks, character areas, etc.This dataset also contains the point locations and attributes of trees maintained by the City of Tempe. The point dataset was obtained by WCA from WCA in Oct 2021. The attributes of interest to this study included unique TreeID, Exact DBH, DBH Range, Height Range, Botanical Name, Common Name, Latitude, and Longitude. Updates to the tree layer were made by joining the results from the Oct 2021 i-Tree report. An i-Tree Eco Analysis was run in Oct 2021 using i-Tree Eco v6.0.22 and the results were joined based on unique tree ID to the Tempe’s tree inventory. Attributes added were: Structural Value ($), Carbon Storage (lb), Carbon Storage ($), Gross Carbon Sequestration (lb/yr), Gross Carbon Sequestration ($/yr), Avoided Runoff (cubicFT/yr), Avoided Runoff ($/yr), Pollution Removal (oz/yr), Pollution Removal ($/yr), Total Annual Benefits ($/yr), Height (ft), Canopy Cover (sqft), Tree Condition, Leaf Area (sqft), Leaf Biomass (lb), Leaf Area Index Basal Area (sqft), Cond, i-Tree_ID_BotName, i-Tree_ID_ComName and i-Tree_ID Genus. The exact definitions, meanings, calculations, etc. for the i-Tree Values can be found on i-Tree’s website via the i-Tree Eco User Manual. For certain layers the individual i-Tree values were aggregated by census tract, census block, zip code, etc. These results can be seen in the polygon layers with the following attribute values: CanopyCoverPer_Final, COUNT_Tree_ID, SUM_Replacement_Value_, SUM_Carbon_Storage_lb_, SUM_Carbon_Storage_, SUM_Gross_Carbon_Sequestration_lb_, SUM_Gross_Carbon_Sequestration_y, SUM_Avoided_Runoff_ftÂ_yr_, SUM_Avoided_Runoff_yr_, SUM_Pollution_Removal_oz_yr_, SUM_Pollution_Removal_yr_, and SUM_Total_Annual_Benefits_yr_This dataset also contains the Tree Equity Score from American Forests. The Tree Equity Score is a product of American Forests and is a metric that helps cities assess how well they are delivering equitable tree canopy cover to all residents. The score combines measures of tree canopy cover need and priority for trees in urban neighborhoods. It is derived from tree canopy cover, climate, demographic and socioeconomic data. For more information please visit American Forests Tree Equity ScoreData dictionaries / resource manuals from i-Tree and American Forestshttps://www.itreetools.org/support/resources-overview/i-tree-manuals-workbookshttps://www.treeequityscore.org/methodology/Projected Coordinate System: NAD 1983 StatePlane Arizona Central FIPS 0202 (Intl Feet)

Land cover describes the surface of the earth. Land cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to earth surface is required. Land cover classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (80 - 100 cm) imagery.OutputClassified raster with the same classes as in the Chesapeake Bay Landcover dataset (2013/2014). By default, the output raster contains 9 classes. A simpler classification with 7 classes can be performed by setting the the 'detailed_classes' model argument to false.Note: The output classified raster will not contain 'Aberdeen Proving Ground' class. Find class descriptions here.Applicable geographiesThis model is applicable in the United States and is expected to produce best results in the Chesapeake Bay Region.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 86.5% for classification into 9 land cover classes and 87.86% for 7 classes. The table below summarizes the precision, recall and F1-score of the model on the validation dataset, for classification into 9 land cover classes:ClassPrecisionRecallF1 ScoreWater0.936140.930460.93329Wetlands0.816590.759050.78677Tree Canopy0.904770.931430.91791Shrubland0.516250.186430.27394Low Vegetation0.859770.866760.86325Barren0.671650.509220.57927Structures0.80510.848870.82641Impervious Surfaces0.735320.685560.70957Impervious Roads0.762810.812380.78682The table below summarizes the precision, recall and F1-score of the model on the validation dataset, for classification into 7 land cover classes: ClassPrecisionRecallF1 ScoreWater0.950.940.95Tree Canopy and Shrubs0.910.920.92Low Vegetation0.850.850.85Barren0.470.480.47Developed, Medium Intensity0.790.690.74Impervious Surfaces0.840.840.84Impervious Roads0.820.830.82Training dataThis model has been trained on the Chesapeake Bay high-resolution 7 class 2013/2014 NAIP Landcover dataset (produced by Chesapeake Conservancy with their partners University of Vermont Spatial Analysis Lab (UVM SAL), and Worldview Solutions, Inc. (WSI)) and other high resolution imagery. Find more information about the dataset here.Sample resultsHere are a few results from the model.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

Includes metrics for tree canopy, impervious surface, grass/low-lying vegetation, bare soil, and open water for each census tract within the study area. With the size of the study area measured at approximately 536.43 square miles, a cost-effective and accurate strategy for assessing the urban forest is the use of remotely sensed and semi-automated classification methods to inventory the current canopy cover and to analyze data for future planting goals.The purpose of this project was to conduct a top down canopy assessment approach. Utilizing the most current 2018 National Agricultural Imagery Program (NAIP) 60cm imagery and advance remote sensing technology, land cover features were identified by using an object-based image analysis (OBIA) methodology to process and analyze high resolution imagery. This technique allows a more accurate and cost-effective automated feature extraction of land cover classes. The final GIS land cover layer allows the communities within the Oklahoma City Metropolitan Area to conduct additional spatial analyses necessary to identify and map the existing land cover layer for future.

Statewide landcover, ecosystem, and percentage canopy cover classifications for Texas were mapped at a spatial resolution of 10-meters. Classifications were run for 16 zones across the state corresponding to available cloud-free multitemporal Sentinel-2 satellite imagery for each zone. For each zone, RandomForest classifications were run using data stacks comprised of spectral bands from three dates (winter, early growing season, late growing season/leaf-off) of imagery, as well as multiple vegetation indices (NDVI, EVI2, MSAVI2). Over 50,000 training points were selected from ground trips and high-resolution aerial image surveys to run the entire pixel-based classification. The overlapping zones were merged using a feathering algorithm to produce a single statewide land-cover classification map. The landcover mapping results were further refined using multiple spatial masks (e.g., urban, water, crop) along with logical rulesets and ancillary data. To map ecological systems, the land-cover classification was then intersected with an enduring features dataset derived primarily from soil map-unit polygons (gSSURGO) and other geophysical variables. Additionally, we produced a statewide percentage canopy cover map at a 10-meter spatial resolution using multiple techniques. For the western 2/3 of the Texas, a nested machine learning approach was used (Sunde et al., 2020), and for the eastern 1/3 of the state, a combination of LiDAR derived training data and machine learning was used.

This dataset includes four items:

1. "TX_10m_landcover_2021.zip" - Statewide landcover classification for Texas (10-meter spatial resolution)
2. "TX_10m_ecoclass_map_2021.zip" - Statewide ecological systems classification for Texas (10-meter spatial resolution)
3. "TX_10m_canopy_cover_2021.zip" - Statewide percentage canopy cover map for Texas (10-meter spatial resolution)
4. "TX_lu_ecoclass_key.xlsx" - Table containing keys for the mapped landcover, canopy, and ecological systems classes

(To facilitate display of the datasets within ESRI software, .lyr files are included in the respective archive folders)

This work was funded by the Texas A&M Forest Service.

These data were compiled for assessing how geomorphic changes measured as topographic differences from repeat surveys represent measured and modelled estimates of aeolian sediment transport and dune mobility. Objective(s) of our study were to investigate whether topographic changes can serve as a proxy for aeolian transport and sediment mobility in dunefield environments. This was accomplished by relating topographic changes to modeled and observed estimates of sediment transport and dune mobility over months to decades within a partially vegetated dunefield starved of upwind sediment supplies. We specifically tested if topographic changes measured as net and total volume changes and topographic surface roughness differences provide evidence for intra-annual differences and decadal changes in sediment mobility for dune sand that is either currently bare, vegetated, or biocrust-covered. Lastly, these data were used as a framework for interpreting how aeolian transport and sediment mobility has changed for current land cover types over the preceding four decades. These data represent monthly topographic surveys and in-field sediment transport data collected between February 13, 2020 and December 16, 2020, piloted aerial imagery collected in 1984, 2002, 2009, 2013, and 2021, unoccupied aerial vehicle (UAV) imagery collected in March 2021, classification of land cover, and tabular summaries of topographic changes derived from these datasets. These data were collected between 1984 and 2021 within a small aeolian dunefield near the confluence of the Paria and Colorado Rivers, upstream of Grand Canyon National Park, Arizona. These data were collected by the U.S. Geological Survey. These data can be used to 1) to evaluate how dune surfaces with bare sand, sand with vegetated cover, and sand with biological soil crust cover (biocrust) change on a monthly time scale with differences in wind strength and 2) assess how the dunefield surface changed with vegetation loss and expansion over almost 4 decades. Additionally, these data could be used to assess detailed changes in landscape cover over monthly and decadal time scales.

This data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013). The data were derived with a rules-based expert system which facilitated integration of leaf-on LiDAR and imagery data into a single classification workflow, exploiting the spectral, height, and spatial information contained in the datasets. Additional states will be added as data processing is completed.

High resolution land cover dataset for the Delaware River Basin, an area comprised of parts of six counties in the state of New York and four counties in Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at six square meters. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 - 2011 NAIP imagery. LiDAR coverage was complete for the Pennsylvaia portion of the AOI, however, LiDAR was unavailable for large portions of the New York portion. Where LiDAR was not available, imagery was the primary data source. Ancillary data sources included GIS data (eg. such as hydrology, breakline and buildings) provided by the counties of Lackawana, Monroe, Pike and Wayne, PA, as well as the New York State GIS Clearinghouse. Some of these vector datasets were edited by the UVM Spatial Analysis lab through manual interpretation. Other datasets, such as bare soil, were created by the UVM Spatial Anyslsis Lab in order to assist in landcover creation. This land cover dataset is considered current for Pennsylvania portion of the study area as of summer 2010. The dataset is current as of summer 2011 for the New York counties of Chenango, Delaware, Orange and Sullivan. Broome County, NY, is considered current as of summer 2010. Ulster County, NY, employed data from both summer 2010 and summer 2011, therefore currentness varies throughout the county. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. 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 insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control.

This data is hosted at, and may be downloaded or accessed from PASDA, the Pennsylvania Spatial Data Access Geospatial Data Clearinghouse http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=3169

This document provides information about the findings of the Urban Forest Inventory and Assessment Pilot Project by Greg McPherson of UC Davis and partners to analyze San Jose's tree canopy.

This dataset contains estimates of tree canopy cover presence at high resolution (0.5m) across the state of Vermont for 2016 in Cloud-Optimized GeoTIFF (*.tif) format. Tree canopy was derived from 2016 high-resolution remotely sensed data as part of the Vermont High-Resolution Land Cover mapping project. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. 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. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected. Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establish tree canopy goals.

Includes metrics for tree canopy, impervious surface, grass/low-lying vegetation, bare soil, and open water for each block group within the study area.With the size of the study area measured at approximately 536.43 square miles, a cost-effective and accurate strategy for assessing the urban forest is the use of remotely sensed and semi-automated classification methods to inventory the current canopy cover and to analyze data for future planting goals.The purpose of this project was to conduct a top down canopy assessment approach. Utilizing the most current 2018 National Agricultural Imagery Program (NAIP) 60cm imagery and advance remote sensing technology, land cover features were identified by using an object-based image analysis (OBIA) methodology to process and analyze high resolution imagery. This technique allows a more accurate and cost-effective automated feature extraction of land cover classes. The final GIS land cover layer allows the communities within the Oklahoma City Metropolitan Area to conduct additional spatial analyses necessary to identify and map the existing land cover layer for future.

This layer displays change in US land cover between 2001 and 2011. Pixels that changed during this period display the land cover value that they changed to. Pixels with no change are transparent.The National Land Cover Database 2011 (NLCD 2011) is the most recent national data product created by the United States Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. NLCD 2011 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2011. As with two previous NLCD land cover products NLCD 2011 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2011 is based primarily on a decision-tree classification of circa 2011 Landsat satellite data.The 2001/2011 land cover change layer is one of five primary data products produced as part of the NLCD 2011: 1) NLCD 2011 Land Cover 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class 3) NLCD 2011 Percent Developed Imperviousness 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels 5) NLCD 2011 Tree Canopy Cover.Land cover class categories include forest, planted/cultivated lands, wetland, grassland, water, developed areas and barren land. Land cover information is critical for local, state, and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics, and developing land management policies

These data represent U.S. State boundaries. Land cover metrics have been calculated for the census geographies within the 2023 TIGER Census Urban Areas. Land cover acreages and percentages were calculated using NAIP imagery collected on the years indicated in the fields NAIP_y1 and NAIP_y2. See individual field descriptions for information on which land classes are included in each classification. Data coverage is limited to TIGER 2023 Census Urban Areas.

A high-resolution (1-meter) land cover classification raster dataset was completed for three different geographic areas in Minnesota: Duluth, Rochester, and the seven-county Twin Cities Metropolitan area. This classification was created using high-resolution multispectral National Agriculture Imagery Program (NAIP) leaf-on imagery (2015), spring leaf-off imagery (2011- 2014), Multispectral derived indices, LiDAR data, LiDAR derived products, and other thematic ancillary data including the updated National Wetlands Inventory, LiDAR building footprints, airport, OpenStreetMap roads and railroads centerlines. These data sets were integrated using an Object-Based Image Analysis (OBIA) approach to classify 12 land cover classes: Deciduous Tree Canopy, Coniferous Tree Canopy, Buildings, Bare Soil, other Paved surface, Extraction, Row Crop, Grass/Shrub, Lakes, Rivers, Emergent Wetland, Forest and Shrub Wetland.

We mapped the 12 classes by using an OBIA approach through the creation of customized rule sets for each area. We used the Cognition Network Language (CNL) within the software eCognition Developer to develop the customized rule sets. The eCognition Server was used to execute a batch and parallel processing which greatly reduced the amount of time to produce the classification. The classification results were evaluated for each area using independent stratified randomly generated points. Accuracy assessment estimators included overall accuracies, producers accuracy, users accuracy, and kappa coefficient. The combination of spectral data and LiDAR through an OBIA method helped to improve the overall accuracy results providing more aesthetically pleasing maps of land cover classes with highly accurate results.

The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats. The timeframe for this data is 2015, 2016, or 2017 (depending on the exact date of imagery used). These maps are developed through the automated classification of high resolution National Agriculture Imagery Program (NAIP) imagery, available Lidar digital elevation data, and assorted ancillary information. While produced as part of the Coastal Change Analysis Program (C-CAP), these products should not be compared directly to past dates of 30-meter C-CAP to identify change, as there will be vast differences caused by the different methods and the classes mapped. These data should be considered to be BETA-level or draft products. They are based on 1-meter land cover mapping that were entirely automated and the relationship of those data to existing wetlands data. As such, there may be issues that result from the different vintages of these products, as well as the errors included in each. While not perfect, the data should provide an example of what level of detail would be possible through such higher-resolution mapping. These data are not jurisdictional or intended for use in litigation. NOAA does not assume liability for any damages or misrepresentations caused by inaccuracies in the data, or as a result of the data used on a particular system. NOAA makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

6 inch resolution raster image of New York City, classified by landcover type. High resolution land cover data set for New York City. This is the 6 inch version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. 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 insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

This data set was created by the North Carolina Department of Agriculture and Consumer Services (NCDA&CS). This Forest (Tree) Land Cover data was derived from the North Carolina, 4 band, 2016, USDA National Agriculture Imagery Program (NAIP) imagery.It includes the entire state of NC, except Ft. Bragg. It is one (1) meter pixel resolution which makes hiding errors difficult. Some errors (incorrect classification) exists but we estimate the data is better than 90% accurate. When viewing this data, NCDA&CS highly recommends using aerials from 2016 for a base map. The original NAIP (raster) data was in TIF format (DOQQ tiles) and was natively in UTM projection.A decision rule supervised classification process was specifically designed around the tonal differences inherent in NAIP imagery. It used with spectral and textural (to separation grasslands from trees) information derived for each 4 band NAIP tile (quarter quad). A total of 3,564 tiles or 16 TBs of data were processed. The classification resulted in a 2-class classification schema. Class 1 is Forest/Trees and Class 2 Non-forest/trees. Class 2 is set to white/transparent by default. Texture processing was applied to reduce mixed pixel values between tree canopy, healthy grass and agriculture land areas. These features have similar vegetation spectral response and would otherwise result in a significant number of misclassified pixels. In many areas however, agriculture and grass land areas containing higher texture values still resulted in mixed canopy pixels. We assume this introduces around a 5% error or misclassification rate.

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 insure 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 in collaboration with City of Seattle.This dataset consists of City of Seattle Topo Basins areas which cover the following tree canopy categories: Existing tree canopy percent Possible tree canopy - vegetation percent Relative percent change Absolute percent changeFor more information, please see the 2021 Tree Canopy Assessment.

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 in collaboration with City of Seattle.This dataset consists of City of Seattle SDOT Urban Forestry Management Units which cover the following tree canopy categories: Existing tree canopy percent Possible tree canopy - vegetation percent Relative percent change Absolute percent changeFor more information, please see the 2021 Tree Canopy Assessment.