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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geoinformatics
Volume 12 - 2024 | doi: 10.3389/feart.2024.1510510

Fifty Years of Land Use and Land Cover Mapping in the United Arab Emirates: A Machine Learning Approach Using Landsat Satellite Data

Provisionally accepted
Mubbashra Sultan Mubbashra Sultan 1Salem Issa Salem Issa 1Basam Dahy Basam Dahy 2Nazmi Saleous Nazmi Saleous 3*Mabrouk Sami Mabrouk Sami 1
  • 1 College of Science, United Arab Emirates University, AlAin, Abu Dhabi, United Arab Emirates
  • 2 New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
  • 3 United Arab Emirates University, Al-Ain, United Arab Emirates

The final, formatted version of the article will be published soon.

    This study analyses the spatiotemporal distribution of land use and land cover (LULC) in the United Arab Emirates (UAE) over the past fifty 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

    Keywords: remote sensing1, multispectral2, Classification3, random forest4, accuracy assessment5, arid environment6, multitemporal7, urban expansion8

    Received: 13 Oct 2024; Accepted: 25 Nov 2024.

    Copyright: © 2024 Sultan, Issa, Dahy, Saleous and Sami. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Nazmi Saleous, United Arab Emirates University, Al-Ain, United Arab Emirates

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