Date of Award

5-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

College/School

College of Science and Mathematics

Department/Program

Earth and Environmental Studies

Thesis Sponsor/Dissertation Chair/Project Chair

Danlin Yu

Committee Member

Clement Alo

Committee Member

Yang Deng

Abstract

This study examines urban heat patterns in Newark, New Jersey, using remote sensing and spatial analytics to support targeted mitigation strategies. Land Surface Temperature (LST) was derived from a Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) scene acquired on July 28, 2025, representing a summer snapshot of surface thermal conditions. While urban heat island effects are typically considered long-term climatic phenomena, this study uses a single-date observation to capture spatial variability in temperature across the city. Land cover variables, including tree canopy and impervious surface fractions, were obtained from the National Land Cover Database (NLCD), and demographic variables, including total population and population density, were sourced from the 2020 U.S. Census. All variables were aggregated to the census tract level. Descriptive statistics, spatial autocorrelation analysis, and regression modeling were applied to examine spatial patterns and relationships. Global Moran’s I and Local Indicators of Spatial Association (LISA) were used to identify clustering of temperature values. Ordinary Least Squares (OLS) regression served as a baseline model, followed by spatial lag (SAR) and spatial error (SEM) models to account for spatial dependence. Results indicate that OLS residuals exhibited strong spatial autocorrelation (Moran’s I = 0.646, p < 0.001), confirming the need for spatial modeling. The spatial error model provided the best fit (AIC = 789.97), indicating the presence of unobserved spatial processes. Impervious surface was identified as a statistically significant predictor of elevated LST, while tree canopy and demographic variables were not statistically significant at the tract scale. Based on these findings, the study identifies distinct spatial clusters of census tracts and proposes a mitigation-oriented framework that classifies areas into vegetation-priority, materials-priority, and mixed-strategy zones. Although the analysis is based on a single-date observation and does not capture long-term climatic trends, it provides a practical and transferable approach for identifying urban heat hotspots and guiding targeted interventions. The persistence of strong spatial dependence suggests that additional environmental and urban form variables may further improve model performance. Overall, the findings highlight the dominant role of land surface characteristics in shaping urban heat patterns and underscore the importance of spatially explicit approaches in urban climate planning and environmental equity.

File Format

PDF

Download

Share

COinS