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.
翻译:提议大规模植树造林是减少碳排放的低成本自然解决办法,但因种植园选址不善而受到阻碍,特别是在发展中国家。为了协助选址,我们开发了电子规划站助理(ePSA)建议系统,其依据是将物理/传统林业科学知识与机器学习相结合的算法组合。 ePSA协助林区官员查明林区内的空白补丁,并根据森林生长潜力对每个补丁进行排序。 实验、用户研究和部署结果表明,推荐者系统在塑造植树造林的长期成功方面有用,是印度北部和其他地区减少碳的自然气候解决办法。