DPD council districts shore clip - Relative % Change

<p style='margin-top:0px; margin-bottom:1.5rem; font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>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.</p><p style='margin-top:0px; margin-bottom:1.5rem; font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>University of Vermont Spatial Analysis Laboratory</p><p style='margin-top:0px; margin-bottom:1.5rem; font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>This dataset consists of City of Seattle Council District areas as they existed in the first comparison year (2016) which cover the following tree canopy categories:</p><p style='margin-top:0px; margin-bottom:0px; font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'></p><ul><li>Existing tree canopy percent</li><li>Possible tree canopy - vegetation percent</li><li>Relative percent change</li><li>Absolute percent change</li></ul><p style='margin-top:0px; margin-bottom:0px; font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'></p><p style='margin-top:0px; margin-bottom:1.5rem; font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>For more information, please see the <a href='https://seattle.gov/documents/Departments/OSE/Urban%20Forestry/2021%20Tree%20Canopy%20Assessment%20Report_FINAL_230227.pdf' style='color:rgb(0, 97, 155); text-decoration-line:none; font-family:inherit;' target='_blank' rel='nofollow ugc noopener noreferrer'>2021 Tree Canopy Assessment</a>.</p>

Tags: 2016, 2021, biota, council-districts, environment, flora, land-cover, seattle, seattle-gis-open-data, seattle-office-of-sustainability-environment, sustainability, tree-canopy
Organization: City of Seattle