Date of Defense
29-4-2025 11:00 AM
Location
H1 - 1116
Document Type
Thesis Defense
Degree Name
Master of Science in Remote Sensing and Geographic Information Systems
College
CHSS
Department
Geography and Urban Sustainability
First Advisor
Dr. Nazmi Saleous
Keywords
LULCC, UAE Desert, Quality of Life, SVM
Abstract
Urbanization is happening at a rate twice the increase in population on a worldwide scale. This has great environmental and social impacts, along with large impacts on regional climate and one of the reasons for this is the ever-continuous change of land use and land cover. The availability of high-resolution satellite imagery in addition to the advancements in geospatial technology allow mapping of LULC changes to be done accurately, efficiently and covering wide areas.
This thesis studies the urban Land Use and Land Cover Change (LULCC) that happened in Abu Dhabi city for the last 3 decades by utilizing geospatial technologies integrated with machine learning. The goal of this research is to utilize Support Vector Machine (SVM) to model the urban LULCC that occurred in Abu Dhabi metropolitan area between 1990 and 2022. Landsat imagery of the years 1990, 2000, 2014 and 2022 were retrieved to analyze the changes.
The analysis split the Land Use into two main classes: urban and non-urban to better understand the change in urban land that occurred. Post-Classification Comparison (PCC) was chosen to map the change that occurred between the consecutive years as well as for the entire study period. The area of urban land was extracted from each of the maps and presented to clearly quantify the amount of change that occurred between each period.
The results from this research aim to demonstrate the ability of supervised machine learning in modelling the urban growth in Abu Dhabi. It also aims to visualize the change that occurred over the study period and present a temporal model which can both offer valuable insight to urban planners and policymakers for the future.
MAPPING AND ANALYZING URBAN GROWTH IN THE ABU DHABI METROPOLITAN USING GEOSPATIAL TECHNOLOGIES INTEGRATED WITH MACHINE LEARNING
H1 - 1116
Urbanization is happening at a rate twice the increase in population on a worldwide scale. This has great environmental and social impacts, along with large impacts on regional climate and one of the reasons for this is the ever-continuous change of land use and land cover. The availability of high-resolution satellite imagery in addition to the advancements in geospatial technology allow mapping of LULC changes to be done accurately, efficiently and covering wide areas.
This thesis studies the urban Land Use and Land Cover Change (LULCC) that happened in Abu Dhabi city for the last 3 decades by utilizing geospatial technologies integrated with machine learning. The goal of this research is to utilize Support Vector Machine (SVM) to model the urban LULCC that occurred in Abu Dhabi metropolitan area between 1990 and 2022. Landsat imagery of the years 1990, 2000, 2014 and 2022 were retrieved to analyze the changes.
The analysis split the Land Use into two main classes: urban and non-urban to better understand the change in urban land that occurred. Post-Classification Comparison (PCC) was chosen to map the change that occurred between the consecutive years as well as for the entire study period. The area of urban land was extracted from each of the maps and presented to clearly quantify the amount of change that occurred between each period.
The results from this research aim to demonstrate the ability of supervised machine learning in modelling the urban growth in Abu Dhabi. It also aims to visualize the change that occurred over the study period and present a temporal model which can both offer valuable insight to urban planners and policymakers for the future.