RANDOM FOREST FOR CLASSIFYING AND MONITORING 50 YEARS OF VEGETATION DYNAMICS IN THREE DESERT CITIES OF THE UAE

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1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)69-76
Number of pages8
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume43
Issue numberB3-2022
DOIs
Publication statusPublished - May 30 2022
Event2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III - Nice, France
Duration: Jun 6 2022Jun 11 2022

Keywords

  • Change Analysis
  • Drylands
  • LULC Change Drivers
  • Landsat
  • Machine Learning
  • Mapping
  • Remote Sensing
  • Time-Series

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development

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