| snippet:
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This dataset was developed as part of an Urban Tree Canopy (UTC) assessment for Delaware County, Pennsylvania. It shows how tree canopy changed during the period 2010-2022, highlighting trees that were gained or lost during the 5-year period. It is intended for use in monitoring patterns of change in Delaware County, Pennsylvania tree canopy. |
| summary:
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This dataset was developed as part of an Urban Tree Canopy (UTC) assessment for Delaware County, Pennsylvania. It shows how tree canopy changed during the period 2010-2022, highlighting trees that were gained or lost during the 5-year period. It is intended for use in monitoring patterns of change in Delaware County, Pennsylvania tree canopy. |
| extent:
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[[-75.6090350865336,39.7941961727611],[-75.2004120680835,40.0741805745378]] |
| accessInformation:
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University of Vermont Spatial Analysis Laboratory in collaboration with Delaware County PA, SavATree. |
| thumbnail:
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thumbnail/thumbnail.png |
| maxScale:
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1.7976931348623157E308 |
| typeKeywords:
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["ArcGIS","ArcGIS Server","Data","Map Service","Service"] |
| description:
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This layer is a high-resolution tree canopy change-detection layer for Delaware County, Pennsylvania. Tree canopy change was mapped by using remotely sensed data from two time periods (2010 and 2022). 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. |
| licenseInfo:
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| catalogPath:
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|
| title:
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Delaware County Tree Canopy Change 2010_2022 |
| type:
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Map Service |
| url:
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|
| tags:
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["imageryBaseMapsEarthCover","tree canopy","change detection","urban"] |
| culture:
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en-US |
| name:
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Delaware_County_Tree_Canopy_Change_2010_2022 |
| guid:
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42DCFBEB-4FB7-4359-8A4A-3A2B74FB52E2 |
| minScale:
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0 |
| spatialReference:
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WGS_1984_Web_Mercator_Auxiliary_Sphere |