Spatiotemporal Variability of Land Surface Temperature in Chicago, Illinois: A Census-Based Multivariable Analysis of Land Cover Change Using Remote Sensing and Machine Learning

Date of Award

4-2024

Degree Name

Master of Science

Department

Geography

First Advisor

Laiyin Zhu, Ph.D.

Second Advisor

Kathleen M. Baker, Ph.D.

Third Advisor

Adam Mathews, Ph.D.

Keywords

GIS, land cover change, land surface temperature, machine learning, remote sensing, urban heat

Access Setting

Masters Thesis-Abstract Only

Restricted to Campus until

4-1-2034

Abstract

Urban areas face escalating challenges due to global warming and urbanization, necessitating a deeper understanding of their environmental dynamics. This study investigates the complex interactions between land use/land cover (LULC) dynamics, land surface temperature (LST) variations, and carbon storage in the urban environment, with a focus on Chicago as a case study. Through the integration of geographic information system (GIS), remote sensing (RS), and machine learning techniques, the study aims to analyze historical climate data, explore the spatial and temporal variability of LST, and predict LST patterns based on multivariable parameters over a 22-year period (2000-2022). Key objectives include analyzing long-term temperature trends, assessing the spatial and temporal variability of LST based on census tracts and land cover dynamics, and constructing predictive models to explore the relationships between various environmental parameters and LST. This study's significance lies in its potential to inform targeted interventions and urban planning strategies aimed at mitigating the adverse effects of urbanization on local microclimates and ecosystem services. By predicting LST patterns and identifying key drivers of LST variability using machine learning, this research enables stakeholders to anticipate and mitigate heat-related concerns, evaluate the efficacy of urban heat mitigation measures, and advance our understanding of the complex interactions shaping urban environments.

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