RANDOM FOREST FOR CLASSIFYING AND MONITORING 50 YEARS OF VEGETATION DYNAMICS IN THREE DESERT CITIES OF THE UAE
Keywords: Remote Sensing, Landsat, Time-Series, LULC Change Drivers, Mapping, Change Analysis, Machine Learning, Drylands
Abstract. The United Arab Emirates (UAE), a dryland country, has since its independence, emphasized on giant greening projects. Monitoring the trend of greening progress in the UAE has gained importance for environmental management and carbon footprint monitoring. Hence, this study created and analysed a time-series (TS) vegetation map to track and analyse vegetation dynamics over an extended period of fifty years. Study area included three selected desert cities of the UAE, Abu Dhabi (AD) capital city, Dubai city and Al Ain city. Random Forest algorithm was applied on Landsat multi-temporal images from 1972 until 2021 for classifying and monitoring the vegetation dynamics and change trajectories. Four vegetation subclasses (coastal/wetland vegetation, urban vegetation, farms/crop fields, and natural/artificial forests), were assessed then grouped and mapped as one vegetation class. With the adopted approach, we achieved overall classification accuracy ranging from 86% to 94%, with kappa coefficients ranging from 0.7200 to 0.8800. Current study showed that the vegetation cover extent in the UAE was at a constant growth for the past five decades from only 1,231.1 ha in 1972 to 23,176.46 ha in 2021, 19 times folds. Furthermore, it showed that desert cities tend to increase their vegetation cover while continuing their steady urban growth. The other drivers found include demographic increase and governmental policies (granting farms to locals and environmental protection laws). Finally, the approach implemented in this research can effectively and reliably be used in other urban centres for future monitoring and management of the vegetation cover status in the country.