Date of Award
Summer 8-2-2024
Document Type
Thesis
Degree Name
Master of Arts (MA)
Department
Geography
First Advisor
Andrew Reinmann
Second Advisor
Wenge Ni-Meister
Academic Program Adviser
Jochen Albrecht
Abstract
This project addresses the need for accessible, cost-effective tools for quantifying spatial and temporal changes in tree canopy cover in urban areas. Urban tree canopy provides a wide range of ecosystem services, including lowering air temperatures, reducing pollution, and mitigating stormwater runoff. Cities around the world have placed the expansion of their urban forests at the center of their sustainability goals. Consistent and timely data on urban tree canopy is essential for urban greening initiatives to succeed. Existing methods of accessing information about urban tree canopy are highly technical, costly, and labor-intensive, while the freely available source of tree canopy cover data from the National Land Cover Database (NLCD) is known to underestimate tree canopy cover in urban areas. I developed a machine learning approach to mapping urban tree canopy using data from publicly available satellite imagery from NASA and the European Space Agency (ESA) with the goal of filling in the temporal and spatial gaps in lidar-derived data and offering relatively accessible, up-to-date, and accurate information on urban tree canopy.
Recommended Citation
McGuinness, Rosemary, "Mapping Urban Tree Canopy Using Publicly Available Satellite Data" (2024). CUNY Academic Works.
https://academicworks.cuny.edu/hc_sas_etds/1244
Included in
Data Science Commons, Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Forest Management Commons, Natural Resources and Conservation Commons, Natural Resources Management and Policy Commons, Numerical Analysis and Scientific Computing Commons, Sustainability Commons