Date of Defense
30-4-2025 6:30 PM
Location
H1, 1116
Document Type
Dissertation Defense
College
College of Humanities and Social Sciences
Department
Geography and Urban Sustainability
First Advisor
Dr. Nazmi Saleous
Keywords
Risk Map, Flash Flood, Geographic Information Systems (GIS), Photogrammetry, LiDAR, Digital Terrain Model (DTM), Base Map, OrthoPhotos, Valleys, Streams, Low-lying Lands.
Abstract
Al Ain City has undergone significant land use transformations from 1992 to 2022, marked by a population increase of over 50% and extensive development activities. This period has seen the construction of new residential areas, infrastructure, and commercial establishments, as well as enhancements to green spaces. An analysis of the Digital Terrain Model (DTM) revealed an 83% alteration in the terrain due to urbanization and shifting sand dunes, with most elevation changes under 5 meters. The primary aim of this dissertation is to develop accurate flash flood risk maps for Al Ain using high-resolution remote sensing data. This research highlights the dramatic changes in the city’s terrain over the past three decades and emphasizes the need for maintaining an up-to-date DTM. Airborne LiDAR technology is proposed as the most effective method for generating precise DTMs, while high-resolution photogrammetry (e.g., 10cm resolution) is identified as a secondary option. The dissertation critiques the use of low-resolution open-source Digital Elevation Models (DEMs) like DEM-SRTM, which are inadequate for detailed urban flood risk assessments. To achieve these objectives, the methodology involved a literature review, acquisition of high-resolution remote sensing data, and the application of advanced processing techniques to create accurate DTMs. Historical terrain data from 1992 and 2022 were analyzed using change detection methods and GIS analysis. Flash flood risk maps were developed based on processed DTMs, identifying varying risk levels across the landscape. Preliminary findings indicate substantial land changes in Al Ain, with 257 km² transformed into built environments and green spaces. The study reveals that the total stream length increased from 221.451 km in 1992 to 2904.1 km by 2022, demonstrating significant environmental shifts. The project also emphasized the importance of accurate DTMs in flood risk management, prompting Al Ain Municipality to commit to regular updates of high-resolution geospatial data. In 2022, a project was initiated to enhance the DTM for Al Ain, involving high-resolution imagery and dense LiDAR point clouds. This collaborative effort aims to create accurate digital terrain and surface models, ultimately aiding in the development of reliable flash flood risk maps. The Municipality has agreed to allocate a budget for biennial updates, ensuring that geospatial resources support ongoing analysis and risk assessments. These findings underscore the necessity for continuous monitoring and assessment of Al Ain’s evolving landscape to enhance flood risk management strategies.
Included in
DEVELOPMENT OF AL AIN FLASH FLOOD RISK MAPS UTILIZING HIGH-RESOLUTION AND ACCURATE REMOTE SENSING DATA
H1, 1116
Al Ain City has undergone significant land use transformations from 1992 to 2022, marked by a population increase of over 50% and extensive development activities. This period has seen the construction of new residential areas, infrastructure, and commercial establishments, as well as enhancements to green spaces. An analysis of the Digital Terrain Model (DTM) revealed an 83% alteration in the terrain due to urbanization and shifting sand dunes, with most elevation changes under 5 meters. The primary aim of this dissertation is to develop accurate flash flood risk maps for Al Ain using high-resolution remote sensing data. This research highlights the dramatic changes in the city’s terrain over the past three decades and emphasizes the need for maintaining an up-to-date DTM. Airborne LiDAR technology is proposed as the most effective method for generating precise DTMs, while high-resolution photogrammetry (e.g., 10cm resolution) is identified as a secondary option. The dissertation critiques the use of low-resolution open-source Digital Elevation Models (DEMs) like DEM-SRTM, which are inadequate for detailed urban flood risk assessments. To achieve these objectives, the methodology involved a literature review, acquisition of high-resolution remote sensing data, and the application of advanced processing techniques to create accurate DTMs. Historical terrain data from 1992 and 2022 were analyzed using change detection methods and GIS analysis. Flash flood risk maps were developed based on processed DTMs, identifying varying risk levels across the landscape. Preliminary findings indicate substantial land changes in Al Ain, with 257 km² transformed into built environments and green spaces. The study reveals that the total stream length increased from 221.451 km in 1992 to 2904.1 km by 2022, demonstrating significant environmental shifts. The project also emphasized the importance of accurate DTMs in flood risk management, prompting Al Ain Municipality to commit to regular updates of high-resolution geospatial data. In 2022, a project was initiated to enhance the DTM for Al Ain, involving high-resolution imagery and dense LiDAR point clouds. This collaborative effort aims to create accurate digital terrain and surface models, ultimately aiding in the development of reliable flash flood risk maps. The Municipality has agreed to allocate a budget for biennial updates, ensuring that geospatial resources support ongoing analysis and risk assessments. These findings underscore the necessity for continuous monitoring and assessment of Al Ain’s evolving landscape to enhance flood risk management strategies.