Committee Chair

Hossain, A.K.M. Azad

Committee Member

Hunt, Nyssa; Qin, Hong

Department

Dept. of Biological and Environmental Sciences

College

College of Arts and Sciences

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

Chattanooga, Tennessee is among many cities experiencing rapid urbanization and subsequent losses to urban forest area. Using remote sensing and digital image processing, this research 1) applied supervised hybrid classification across Landsat imagery that quantified the extent of urban forest loss across Chattanooga between 1984 and 2021, 2) modeled the carbon sequestered in the biomass of Chattanooga’s urban trees using field data and vegetation indices, and finally 3) developed the first city-wide high-resolution land cover map across Chattanooga using SkySat imagery and object-based classification. Results found that Chattanooga has lost up to 43% of its urban tree canopy and gained up to 134% of urban land area. Additionally, a methodology for modeling sequestered carbon across urban forests was identified. Finally, using high-resolution imagery and the object-based workflow as described here, it is capable of producing accurate maps of urban tree canopy distribution with overall accuracy quantified in excess of 93%.

Acknowledgments

I would like to thank the European Space Agency (ESA), the National Aeronautics and Space Administration (NASA), and the United States Geological Survey (USGS) for providing this research with pre-processed multispectral imagery free of charge, the Lyndhurst foundation for funding the acquisition of high resolution SkySat imagery acquired by Planet Labs, the City of Chattanooga, Tennessee, and Green|Spaces for also providing financial support and for seeing the value of this research, Department of Biology, Geology, and Environmental Science at the University of Tennessee at Chattanooga (UTC) for providing the research opportunities to make this work possible, UTC’s Interdisciplinary Geospatial Technology (IGT) Lab staff for always offering critical thinking assistance, advice, and support, and the Multidisciplinary Research Center (SimCenter) for creating the distributed processing environments that made this research possible. A special thanks is due to: • Dr. Azad Hossain, for accepting me as a research student under his supervision, sparking my passion for Earth systems monitoring, allowing me the flexibility to choose my own specific research interests, always keeping me grounded and tasked, and for all the wisdom you have imparted. • Nyssa Hunt for being an amazing friend, editor, technical support guru, my graduate school mentor, and literal life coach throughout this research. Your confidence, optimism, and seamlessly never ending patience was much appreciated. • Dr. Hong Qin for the valuable input and insights that contributed to the success of this research and served to improve the quality of the obtained results. • Charlie Mix for all your advice, for the instrumental role you played in helping to turn an idea into a physical project, and for the instrumental communications that allowed my access to UTC distributed processing environment for SkySat imagery processing. • Peter Stewart, for never turning down an opportunity to help answer questions and assist during critical thinking, and for connecting me with Dr. Eric Wiseman, whose comprehensive reports were valuable sources during the field sampling conducted in this research. • Dr. Shannon McCarragher, for the passion, patience, and belief in me, which started this long, strange trip. I cannot thank you enough. • McKenzie Whitten, for sticking around through the good and the bad, never losing faith in me, and most of all for keeping my mind from straying too far away from my passions. • And finally, for all of my friends and family for the endless support you provided throughout.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

5-2023

Subject

Urban forestry--Tennessee--Chattanooga; Trees in cities--Valuation--Tennessee--Chattanooga; Carbon sequestration--Tennessee--Chattanooga; Geographic information systems

Keyword

Remote Sensing; Urban Forestry, Carbon Sequestration, Spatiotemporal Analysis, High Resolution Imagery, Object-Based Classification

Document Type

Masters theses

DCMI Type

Text

Extent

xix, 190 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by-nc-nd/4.0/

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