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

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,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:,,,For more information, please see the 2021 Tree Canopy Assessment.,

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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.,

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.