A methodology to support species selection decisions for planting trees in urban spaces using explainable AI

Abstract In urban areas with poor plant growth environments, selecting trees for planting requires the consideration of the urban environment as well as the local conditions surrounding the proposed planting site. To derive the key factors for the optimal selection of urban trees for planting, this study aimed to employ the extreme gradient boosting machine learning method and Shapley additive explanations to create an eXplainable AI model. This approach enabled the analysis of trees planted within a similar period but exhibiting different vitalities using the normalized difference vegetation index and facilitated the identification of relationships between trees and a range of urban environments and local conditions. The explanatory power of the model was 0.454–0.605 based on the R 2 of different tree species. The spatial distance that should be considered when planting appropriate urban trees varied from 10 to 40 m. Notably, the priority of the influencing factors varied among tree species. Considering various factors, a map suggesting the best trees for planting within the experimental area was generated. Utilizing the results of these analyses, the trees selected are expected to grow well in areas with the given conditions when planted in urban spaces, ultimately providing a scientific basis for urban tree planting and management policies.