Fifty years of land use and land cover mapping in the United Arab Emirates: a machine learning approach using Landsat satellite data - Sultan, Mubbashra; Issa, Salem; Dahy, Basam; Saleous, Nazmi; Sami, Mabrouk (Frontiers in Earth Science, 2024)
This study analyses the spatiotemporal distribution of land use and land cover (LULC) in the United Arab Emirates (UAE) over the past 50 years (1972–2021) using 72 multi-temporal Landsat satellite images. Three machine learning (ML) classifiers, Classification and Regression Tree (CART), Support Vector Machine (SVM) and Random Forest (RF), were tested, with RF finally chosen for its higher performance. Spectral, spatial, topographic, and object aspect attributes were extracted and used as input for the RF algorithm to enhance the classification accuracy. A dataset comprising 46,146 polygons representing four LULC classes was created, with 80% allocated for training and 20% for testing, ensuring robust model validation. The algorithm was trained to develop a machine learning model that classified the data into four LULC classes namely: built areas, vegetation, water, and desert and mountainous regions, producing eight thematic maps for the years 1972, 1986, 1992, 1997, 2002, 2013, 2017, and 2021. The results reveal the dominance of desert and mountainous regions, with their coverage gradually declining from over 97% in 1972 to nearly 91% in 2021. In contrast, built areas grew from less than 1% to nearly 6%, while vegetation cover increased from 0.71% to 2.85%. Water bodies have exhibited periodic fluctuations between 0.4% and 0.35%. These changes are attributed to extensive urbanization, agricultural expansion, forest plantation programs, land reclamation, and megaprojects. Accuracy assessment of the classified maps showed high overall accuracy, ranging from 85.11% to 98.4%. The study provides a unique long-term analysis of the UAE over 50 years, capturing key developments from the 1970s oil boom through subsequent megaprojects at the onset of the new millennium, leading to reduced reliance on oil. These findings underscore the role of machine learning and geospatial technologies in monitoring LULC distribution in challenging environments, and the results serve as a vital tool for policymakers to manage land resources, urban planning, and environmental conservation.