Simulation of Pantana phyllostachysae Chao Hazard Spread in Moso Bamboo (Phyllostachys pubescens) Forests Based on XGBoost-CA Model

Abstract

Pantana phyllostachysae Chao is a destructive leaf-eating pest that poses a significant threat to the health of bamboo forests and the bamboo industry. However, the spatial and temporal spread mechanisms of this pest are still unclear. To better understand and predict the spread of this pest, we used Sentinel-2 A/B images from the pest detection period of 2018–2021, to identify association factors from five dimensions, including forest stand, meteorology, topography, pest sources, and human environment factors. The association factor sets for the spread of P. phyllostachysae were established under both existence and nonexistence pest control scenarios. The extreme gradient boosting (XGBoost) model was employed to derive conversion rules for the respective spread models, enabling the determination of suitability probabilities for both healthy and damaged bamboo forests. These probabilities were then utilized in conjunction with cellular automata (CA) to simulate the spread of P. phyllostachysae under two scenarios. The results showed that the OA and Kappa reached more than 85% and 0.7% in both scenarios, respectively. Meanwhile, the division of pest control scenarios and the selection of XGBoost both help to improve the spreading simulation accuracy. Our models effectively coupled the research results of leaf hosts of different damage levels, simulated the spread of P. phyllostachysae, and identified the dynamic mechanisms of the pest’s spread. These findings provide decision support for interrupting the spread path of the pest and achieving precise control, thus safeguarding forest ecological security.