Optimizing mangrove afforestation site selection in gulf cooperation council nations using remote sensing and machine
Abhilash Dutta Roy1, Midhun Mohan2, Ian Hendy3
1Ecoresolve, San Francisco, CA, United States; Mediterranean Forestry and Natural Resources Management, School of Agriculture, University of Lisbon, Lisbon, Portugal; Department of Agricultural and Forest Sciences and Engineering, School of Agrifood and Forestry Engineering and Veterinary Medicine, University of Lleida, Lleida, Spain.
The Science of the Total Environment
|June 6, 2025
Summary
Identifying optimal sites for mangrove afforestation, reforestation, and revegetation (ARR) in the Arabian Peninsula is crucial for coastal ecosystem restoration and blue carbon goals. Our study pinpoints key environmental factors and uses machine learning to guide successful mangrove restoration efforts.
Area of Science:
- Coastal Ecology and Restoration
- Blue Carbon Ecosystems
- Remote Sensing Applications
Background:
- Mangrove forests in the Gulf Cooperation Council (GCC) are vital blue carbon sinks but face threats from anthropogenic activities like urban development and pollution.
- Previous mangrove afforestation, reforestation, and revegetation (ARR) efforts have yielded inconsistent results, necessitating improved site selection strategies.
- Remote sensing offers a powerful tool for assessing environmental factors crucial for mangrove survival and growth across large geographical areas.
Purpose of the Study:
- To identify high-potential sites for mangrove ARR in GCC countries using remote sensing data.
- To determine the key environmental variables influencing the success of mangrove ARR.
- To develop a predictive framework for optimal mangrove site selection to enhance restoration outcomes.
Main Methods:
- Correlation analyses were performed to identify eight critical factors for mangrove ARR: elevation, soil pH, precipitation, land surface temperature (LST), soil salinity, soil texture, and proximity to urban areas.
- Four machine learning classification algorithms (Random Forest, XGBoost, Support Vector Machines, Naive Bayes) were compared for mangrove suitability prediction.
- The Random Forest model demonstrated superior performance, achieving high accuracy metrics (F1-score=0.96, ROC-AUC=0.99).
Main Results:
- Median LST (32-37°C), soil texture (clayey), and median precipitation (>10 mm/month) were identified as the most influential variables for mangrove establishment.
- Favorable conditions include lower elevations, greater distances from urban centers, slightly acidic to neutral soil pH, and moderate-to-high soil salinity.
- The study identified significant opportunities for mangrove afforestation across the GCC region.
Conclusions:
- A robust framework for identifying optimal mangrove ARR sites in the Arabian Peninsula has been established.
- Successful implementation of this framework can significantly improve ARR success rates, supporting biodiversity conservation and blue carbon sequestration goals.
- This research provides critical insights for policymakers and conservationists aiming to restore and expand mangrove ecosystems in the GCC.
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