Description: This layer displays forest patch cover by ownership category for Baltimore, MD. The forest patch map is derived from 2017/18 high resolution tree canopy data (CBPO 2022).Publicly available parcel data from the municipality was used to assign ownership classes to each parcel containing forest patches. Ownership categories include: Federal, State, Municipal, Commercial/Industrial, Institutional, and Private Residential ownership. Within parcel datasets, building type and owner name fields were most frequently used to determine ownership category and Google Maps imagery was used to provide additional information in challenging cases. Local experts were consulted about errors or gaps in the parcel data and supplementary datasets were used (e.g., Integrated Property Information System (IPIS) public lands database in NYC). Institutional properties included private schools, universities, religious properties, medical centers, hospitals, community centers, veterans administrations, cemeteries, foreign-owned properties, museums, non-profits, psychiatric centers, and boy scouts properties. Condominiums, apartment complexes, and other large multi-unit residential structures were categorized as commercial ownership, while properties under the names of individual people were generally classified as private residential ownership, unless contradicted by imagery and other sources of information. Parcels with unknown ownership were generally assigned municipal ownership, except for property associated with state or interstate transportation corridors, which was classified as state ownership. Conservation easements were classified as state owned property. Forest patches often cross parcel boundaries, so one patch may have multiple owners and ownership classes.
Copyright Text: Chesapeake Bay Program Office (CBPO), 2022. One-meter Resolution Land Cover Dataset for the Chesapeake Bay Watershed, 2017/18. Developed by the
University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey. [Accessed July 1, 2024], [https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/lulc-data-project-2022/].
USDA Forest Service, Northern Research Station