Dissertations, Theses, and Capstone Projects

Date of Degree

2-2024

Document Type

Dissertation

Degree Name

Ph.D.

Program

Earth & Environmental Sciences

Advisor

Hamidreza Norouzi

Committee Members

Kathleen Weather

Marzieh Azarderakhsh

Reza Khanbilvardi

Reginald Blake

Subject Categories

Life Sciences | Social and Behavioral Sciences

Abstract

Surface temperature changes can be effectively monitored by employing remote sensing thermal infrared (TIR) data, offering advantages such as improved spatial and temporal coverage. Notably, this approach enables observation without disturbing the studied object and overcomes accessibility challenges in the study regions. The study utilizes remotely sensed data from Landsat 8, Geostationary Operational Environmental Satellites-R series (GOES-R), and the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands. These satellite TIR data are instrumental in model development and understanding patterns and processes in environmental systems. In my dissertation, I employed remote sensing tools, specifically Landsat 8, GOES-R, and MODIS Aqua temperature data, for three primary purposes: developing a downscaling method to identify surface urban heat islands in New York City, developing a model for predicting ice in and ice out dates in north temperate lakes, and analyzing temperature dynamics in 431 global lakes. The first remote sensing application involves creating a land surface temperature (LST) database with 5-minute temporal and 30-meter spatial resolution through spatial downscaling of GOES-R series LST product over New York City. The method effectively reproduces v temporal and spatial variability with a spatial root mean square error (RMSE) of approximately 2 K compared to independent Landsat-8 observations. The second remote sensing application involves developing a model using MODIS Aqua temperature observations to approximate ice-in and ice-out dates for lakes in Maine from 2002/2003 to 2017/2018. The model shows good agreement with observed dates, with correlation coefficients of 0.71 for ice-in and 0.67 for ice-out. Additionally, analysis of MODIS Aqua temperature observations on a global scale reveals an average warming trend of 0.223°C per decade between 2002 and 2023. Seasonal and monthly variations in lake surface water temperature (LSWT) and LST show different warming patterns across climate zones. Furthermore, the study investigates the correlation between lake temperature changes, latitudinal changes, and lake geomorphometric characteristics. Most studied lakes are shrinking (67.51%), with changes falling within a narrow range. Smaller lakes exhibit greater variability in LSWT compared to larger lakes in relation to lake surface area. This study contributes crucial insights for predicting the future of lakes, an aspect often overlooked by climate models, marking an essential first step before identifying the drivers behind these changes.

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