Award Date
May 2024
Degree Type
Thesis
Degree Name
Master of Science in Engineering (MSE)
Department
Civil and Environmental Engineering and Construction
First Committee Member
Sajjad Ahmad
Second Committee Member
Haroon Stephen
Third Committee Member
Marie-Odile Fortier
Fourth Committee Member
Ashok Singh
Number of Pages
171
Abstract
Floods are one of the most frequent and most devastating natural disasters, which cause widespread destruction and pose significant risks to human life, infrastructure, and the environment. Advancement in remote sensing technologies and methodologies have demonstrated their efficacy in disaster-related applications, such as the detection, monitoring, and analysis of floods. This study explores the utilization of Synthetic Aperture Radar (SAR) and optical imagery for flood extent mapping and studying the extent of flood over various land cover and land use classes in Pakistan's Sindh province, utilizing the cloud computing power of Google Earth Engine. The change detection method identified extensive flooding in Sindh province, covering an area of 25,229 km2 in August and 19,181 km2 in the first 19 days of September 2022. The Land Use/Land Cover dataset was developed for the pre-flood period. The study highlighted the effectiveness of Random Forest classification in distinguishing Land Use/Land Cover (LULC) types more accurately than K-means clustering. Additionally, the analysis provided insights into the spatial distribution of flood extent and vulnerability of land use/land cover classes such as urban areas, agricultural areas, and sparse natural vegetation as significant areas remained inundated in the province.
Keywords
change detection; flood extent mapping; Google Earth Engine; Land use/Land cover; LULC; Sentinel-1 Flooding
Disciplines
Remote Sensing | Water Resource Management
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Shrestha, Alina, "Evaluation of the Extent of Floods in 2022 in Different Land Cover and Land Use Classes in Sindh Province, Pakistan Using Sentinel-1 and Sentinel-2 Imageries" (2024). UNLV Theses, Dissertations, Professional Papers, and Capstones. 5081.
https://digitalscholarship.unlv.edu/thesesdissertations/5081
Rights
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