Early detection of pine shoot beetle infestation in Yunnan pine forests through UAV-based multispectral and thermal data integration

The pine shoot beetle (PSB, Tomicus spp.) is a destructive stem-boring pest threatening Yunnan pine ( Pinus yunnanensis ), a dominant conifer species in southwestern China. Early-stage infestation is particularly difficult to detect because only 11%−20% of shoots exhibit visible discoloration during the initial attack phase, limiting the effectiveness of conventional field surveys and single-source remote sensing approaches. Reliable early detection is therefore essential for timely intervention and outbreak prevention. This study assessed the potential of integrating unmanned aerial vehicle (UAV)-based multispectral and thermal infrared (TIR) imagery to improve early PSB detection at the individual-tree level. A total of 150 trees representing four damage levels (30 healthy, 50 lightly damaged, 40 moderately damaged, and 30 severely damaged trees) were identified through field-based visual interpretation. Twenty spectral and thermal metrics were extracted from UAV imagery, and commonly used machine learning approaches were applied under three feature scenarios: multispectral only, TIR only, and fused multispectral–TIR features, with 10-fold cross-validation. Most spectral and thermal variables differed significantly among infestation stages ( p < 0.05). The integration of multispectral and thermal features improved classification performance compared with single-source data. The optimal model (Random Forest) yielded an overall accuracy (OA) of 0.82 and a Kappa coefficient of 0.75. For lightly infested trees, Recall and Precision were 0.72 and 0.74, respectively. Vegetation indices (e.g., GRVI, NDVI, and RVI) were identified as primary predictors, while thermal variability metrics provided complementary information associated with early physiological stress. By enabling detection prior to widespread canopy damage, the proposed multisensor UAV framework supports timely forest health monitoring and provides a tool for integrated pest management under changing environmental conditions.