Planting trees at the right places: Recommending suitable sites for growing trees using algorithm fusion

TL;DR

This paper introduces ePSA, a recommendation system that fuses physics-based forestry science with machine learning to identify optimal planting sites, improving large-scale tree planting efforts for carbon mitigation.

Contribution

The paper presents a novel algorithm fusion approach combining forestry science and machine learning for site recommendation in tree planting.

Findings

ePSA effectively identifies suitable planting patches.

User studies show improved decision-making for forest officers.

Deployment results demonstrate increased success in tree growth and carbon mitigation.

Abstract

Large-scale planting of trees has been proposed as a low-cost natural solution for carbon mitigation, but is hampered by poor selection of plantation sites, especially in developing countries. To aid in site selection, we develop the ePSA (e-Plantation Site Assistant) recommendation system based on algorithm fusion that combines physics-based/traditional forestry science knowledge with machine learning. ePSA assists forest range officers by identifying blank patches inside forest areas and ranking each such patch based on their tree growth potential. Experiments, user studies, and deployment results characterize the utility of the recommender system in shaping the long-term success of tree plantations as a nature climate solution for carbon mitigation in northern India and beyond.