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,Data Updated: Quarterly,Metadata,,A conservation easement restricts the use or development of real property for the following reasons: -Retaining or protecting natural or open-space values of the property;-Assuring its availability for agricultural, forestall, recreational, or open-space use;-Protecting natural resources;-Maintaining or enhancing air or water quality; or-Preserving the historical, architectural or archaeological aspects of the property.A landowner may convey such an easement to a public body or a qualified private conservation organization. In Virginia, public bodies may accept these types of easements under the Open Space Land Act, Va. Code § 10.1-1700 et seq. Qualifying private land trusts operate under the Conservation Easement Act, Va. Code § 10.1-1009 et seq.While this data will be updated as needed, it may not contain recently recorded easements. There are over 75,000 acres of land with permanent conservation easements within the County. For more information on conservation easements, please see www.loudoun.gov/conservationeasements,

An Assessment of Existing and Potential Tree Canopy in Loudoun County, Virginia.,

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Depicts the boundaries for the Collaborative Forest Landscape Restoration (CFLR) and High Priority Restoration (HRP) projects. Metadata

,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.,

This archive publishes and preserves short and long-term research data collected from studies funded by:,,,Each archived data set (i.e., 'data publication') contains at least one data set, complete metadata for the data set(s), and any other documentation the researcher deemed important to understanding the data set(s). The data catalog entries present the metadata and a link to the data. In some cases the data link is to a different archive.,