| snippet:
|
This dataset was developed as part of an Urban Tree Canopy (UTC) assessment for Prince George's County, Maryland. It shows how tree canopy changed during the period 2014-2018, highlighting trees that were gained or lost during the 4-year period. It is intended for use in monitoring patterns of change in Prince George's County, Maryland tree canopy. |
| summary:
|
This dataset was developed as part of an Urban Tree Canopy (UTC) assessment for Prince George's County, Maryland. It shows how tree canopy changed during the period 2014-2018, highlighting trees that were gained or lost during the 4-year period. It is intended for use in monitoring patterns of change in Prince George's County, Maryland tree canopy. |
| accessInformation:
|
The University of Vermont Spatial Analysis Laboratory created this datasets in collaboration with Sanborn. |
| thumbnail:
|
|
| maxScale:
|
NaN |
| typeKeywords:
|
[] |
| description:
|
This layer is a high-resolution tree canopy change-detection layer for Prince George's County, Maryland. It contains three tree-canopy classes for the period 2014-2018: (1) No Change; (2) Gain; and (3) Loss. It was created by mapping the change from the source LiDAR and imagery for the two time periods. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed, felled in storms, or canopy to disease were assigned to the Loss class. New tree canopy, either the result of natural growth or new plantings was assigned to the Gain class . Change was mapped using object-based image analysis (OBIA) techniques and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs) for the two time periods. 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 a detailed manual review and correction. A minimum mapping unit of 40 square feet was enforced on isolate areas. Data was retained for areas smaller than 40 square feet where a border was shared with another area, making the combined area greater than the minimum mapping unit of 40 square feet. |
| licenseInfo:
|
The University of Vermont makes no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.Although every effort has been made to assure the accuracy of features and their attributes, the University of Vermont is not accountable for any errors or misuse of the data. The data should be used for general mapping purposes only. |
| catalogPath:
|
|
| title:
|
PGCOITGIS02.DBO.Tree_Canopy_2014_2018_Chg_Py |
| type:
|
|
| url:
|
|
| tags:
|
["Maryland","urban","Prince George's County","change detection","2014-2018","tree canopy","imagery","Base Maps","Earth Cover"] |
| culture:
|
en-US |
| portalUrl:
|
|
| name:
|
|
| guid:
|
|
| minScale:
|
NaN |
| spatialReference:
|
|