Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV hyperspectral images

Abstract

The on-year and off-year phenomenon is a distinctive phenological characteristic of Moso bamboo, reflecting variations in nutrient dynamics and endogenous hormonal rhythms during the transition from bamboo shoot to the culm. This phenomenon also influences pest resistance between the on-year and off-year cycles of Moso bamboo. Pantana phyllostachysae Chao is a leaf-feeding pest that affects Moso bamboo. However, monitoring P. phyllostachysae damage using remote sensing data is challenging because the off-year Moso bamboo has physiological characteristics similar to on-year Moso bamboo infested with P. phyllostachysae. This study utilizes the Recursive Feature Elimination (RFE) algorithm to investigate hyperspectral remote sensing characteristics of P. phyllostachysae in Moso bamboo forests. We analyzed the impact of on-year and off-year phenological characteristics on the accuracy of hazard extraction and developed detection models for P. phyllostachysae hazard levels in on-year and off-year Moso bamboo using Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and one-dimensional Convolutional Neural Network (1D-CNN). The results demonstrate that classical machine learning and deep learning models can effectively detect P. phyllostachysae damage, with the 1D-CNN algorithm achieving the best performance. Analyzing the impact of the phenological differences between on-year and off-year Moso bamboo on pest identification accuracy revealed that when four machine learning models accounted for these phenological characteristics, their accuracy in identifying pests was significantly higher than that of a model which did not take into account the bamboo phenology. This finding highlights that considering the phenological characteristics of on-year and off-year Moso bamboo can substantially improve the detection accuracy of UAV hyperspectral remote sensing in monitoring P. phyllostachysae damage. This provides more accurate technical support for the health management and resource protection of bamboo forests and offers a scientific basis for maximizing the ecological and economic benefits of bamboo forests.

Keywords

• Moso bamboo forests
• on-year and off-year phenological characteristics
• Pantana phyllostachysae Chao
• unmanned aerial vehicle (UAV) hyperspectral images
• machine learning