Tree Canopy Metrics Table for (2013 Tree Canopy Data)

Tree Canopy (TC) Assessment metrics for Boston, MA. These datasets consist of TC metrics summarized using various geographies. The metrics presented in these tables are based on 2019 high resolution land cover and 2019 leaf-on LiDAR. The TC Assessment is a top-down approach to analyzing the forest. Its purpose is to integrate high resolution land cover data with other GIS datasets to produce a set of detailed metrics on the forest that allow decision makers to know how much tree canopy currently exists (termed Existing TC). Existing TC is determined by extracting all features classified as tree canopy from a high resolution land cover dataset. Possible TC is queried out from this overlay and consists of all land that was not existing canopy, not water, not a building, and not a road. Possible TC is further divided into two subcategories: Possible-impervious and Possible-vegetation. Possible-impervious consists of all impervious land that, through modification, could support tree canopy. Examples of such features are parking lots, driveways (through overhanging coverage) and playgrounds. Possible-vegetation consists of all land that is low-lying vegetation, primarily grass or shrubs, which could conceivably be converted to support tree canopy. Examples of such features include residential lawns and playing fields. TC metrics do not serve to address the issues of where it is socially desirable or financially feasible to plant trees. Rather, the TC metrics serve as the basis for beginning to form answers to these questions. TC metrics are presented in the attribute table as both absolute area (in map units) and relative area (percentage of land area) per parcel. For example, an Existing TC Area (TC_E_A) value of 13,677 and an Existing TC Percentage (TC_E_P) of 21.8 indicate that for the area in question the area of Existing TC is 13,677 (in map units) and 21.8% of that feature is tree canopy.

TC_E_A =Tree canopy existing area. The area of tree canopy present when viewed from above using aerial or satellite imagery, excluding water.

TC_PV_A= Possible vegetation area. Grass or shrub area that is theoretically available for the establishment of tree canopy.

TC_Land_A = Land area. Land area excluding water bodies.

TC_Pi_A= Possible impervious area. Asphalt or concrete surfaces or bare soil, excluding roads and buildings, that are theoretically available for the establishment of tree canopy.

TC_P_A= Possible area. Area theoretically available for establishment of tree canopy.

TC_E_P = Existing percent. The amount of tree canopy present when viewed from above using aerial or satellite imagery, excluding water as a percentage.

TC_Pv_P = Possible vegetation percent. Grass/shrub area that is theoretically available for the establishment of tree canopy as a percentage of land area.

TC_P_P= Possible tree canopy percent. Area theoretically suitable for tree canopy as a percentage of land area.

TC_Pi_P= Possible impervious canopy percent. Asphalt or concrete surfaces or bare soil, excluding roads and buildings, that are theoretically available for the establishment of tree canopy as a percentage of land area.

Common_Nam: Describes the common name for the type of tree. {String} Genus: A principal taxonomic category that ranks above species and below family. {String} Species: Describes the species of the tree. {String} Health: Describes the health of the tree. {String} Native_Sta: Describes if the tree is native or invasive. {String} Latin Name: Describes the name of the tree in Latin. {String} DBH: Stands for diameter at breast height. {Double}

Oswego County was formed from part of Onondaga County and part of Oneida County in 1816.The County seat in 1816 was within the village of east Oswego in the town of Scriba. In 1848 the county seat became the newly incorporated city of Oswego and has remained as such to present day.Present-day Oswego County consists of 2 cities, 24 towns, and 9 villages. The oldest incorporated town is Mexico (1792) with the youngest town being Minetto (1915). The oldest incorporated village is Pulaski (1832) with the youngest being Central Square (1889).

Learn more about the project and how to use the canopy assessment data by visiting the StoryMap!

Data Dictionary

Landcover Metrics Tax Parcels Table for Tree Canopy (2013)

Learn more about the project and how to use the canopy assessment data by visiting the StoryMap!

Data Dictionary

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:

• Existing tree canopy percent
• Possible tree canopy - vegetation percent
• Relative percent change
• Absolute percent change

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

The percentage of possible tree canopy data by tax parcels. (2010 Tree Canopy Data)

Davey Resource Group utilized LiDAR and aerial imagery to identify tree canopy cover and land cover classifications. Advanced image analysis methods were used to classify, or separate, the land cover layers from the overall imagery. The semi-automated extraction process was completed using Feature Analyst, an extension of ArcGIS®. Feature Analyst uses an object-oriented approach to cluster together objects with similar spectral (i.e., color) and spatial/contextual (e.g., texture, size, shape, pattern, and spatial association) characteristics.

Contact: Department of Environmental Services

Data Accessibility: Publicly Available

Update Frequency: Never

Last Revision Date: 11/22/2024

Creation Date: 11/22/2024

Feature Dataset Name: Tree_Canopy

Layer Name: Tree_Canopy_2023_poly

The ZIP file consist of GIS files with information about the excavations, findings and other metadata about the archaeological survey.

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.

Discount retailers, such as Dollar General, Family Dollar, Dollar Tree, and 99 Cent Only Stores, fall under the category of variety stores. These establishments cater to budget-conscious consumers by offering a diverse range of products at affordable prices. Each store operates on the premise of providing customers with value for their money through low-cost items that span various categories, including household goods, groceries, personal care products, and more. Dollar General, Family Dollar, and Dollar Tree are prominent players in this market, with a focus on delivering convenience and savings. 99 Cent Only Stores, as the name suggests, specializes in offering most of its products at the enticing price point of 99 cents. These variety stores have become popular choices for shoppers seeking cost-effective alternatives without compromising on the essentials. Their strategic pricing models and expansive product selections contribute to their widespread appeal, making them go-to destinations for individuals looking to stretch their budgets without sacrificing quality.

The ZIP file consist of GIS files with information about the excavations, findings and other metadata about the archaeological survey.