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Updated: Jul 30, 2025

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Assessment of long-term mangrove distribution using optimised machine learning algorithms and landscape pattern

Ahmed Ali Bindajam1, Javed Mallick2, Swapan Talukdar3

  • 1Department of Architecture and Planning, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia.

Environmental Science and Pollution Research International
|May 17, 2023
PubMed
Summary

Mangrove mapping in Saudi Arabia

Keywords:
Connectivity modellingLULCLandscape structureMangrove habitatOptimised machine learning algorithmsRed SeaSaudi Arabia

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Area of Science:

  • Remote Sensing and Geospatial Analysis
  • Ecology and Conservation Biology
  • Machine Learning Applications

Background:

  • Mangrove ecosystems offer vital services like carbon storage and coastal protection.
  • Accurate mapping of Red Sea mangroves is limited by data scarcity and expertise gaps.
  • This study addresses the need for precise mangrove status assessment in data-limited regions.

Purpose of the Study:

  • To develop and apply advanced machine learning algorithms for high-resolution mangrove mapping.
  • To assess changes in mangrove distribution, connectivity, and landscape structure in Al Wajh Bank.
  • To provide accurate data for mangrove conservation and restoration efforts in the Red Sea.

Main Methods:

  • Utilized high-resolution multispectral imagery and image fusion techniques.
  • Applied and compared machine learning models: Artificial Neural Networks, Random Forests, and Support Vector Machines.
  • Employed landscape fragmentation and Getis-Ord statistics for spatial analysis.

Main Results:

  • Random Forests model demonstrated superior performance for land use/land cover classification.
  • Mangrove cover in Al Wajh Bank significantly increased from 11.94 km² in 2014 to approximately 27.6-34.99 km² in 2022.
  • Analysis revealed increased mangrove connectivity and shifts in landscape structure, indicating ecosystem expansion.

Conclusions:

  • The study successfully produced accurate, high-resolution mangrove maps for the Red Sea region.
  • The findings highlight a substantial increase in mangrove area and improved connectivity, supporting biodiversity.
  • This research provides crucial data to inform mangrove protection, conservation, and restoration initiatives.