Planting trees at the right places: Recommending suitable sites for growing trees using algorithm fusion
This work addresses site selection for tree planting to aid forest range officers in developing countries, but it is incremental as it combines existing methods.
The paper tackled the problem of poor site selection for large-scale tree planting by developing the ePSA recommendation system, which identifies blank patches in forests and ranks them based on tree growth potential, with experiments and deployment showing utility in northern India and beyond.
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.