| Abstrakt: |
Chinese pine has been extensively planted in the Loess Plateau, but it faces significant threats from Gansu zokor. Traditional methods for monitoring rodent damage rely on manual surveys to assess damage rates but are time-consuming and often underestimate the actual degree of damage, particularly in mildly affected pines. This study proposes a remote sensing monitoring method that integrates hyperspectral analysis with physiological and biochemical parameter models to enhance the accuracy of rodent damage detection. Using ASD Field Spec 4, we analyzed spectral data from 125 Chinese pine needles, measuring chlorophyll (CHC), carotenoid (CAC), and water content (WAC). Through correlation analysis, we identified sensitive vegetation indices (VIs) and red-edge parameters (REPs) linked to different levels of damage. We report several key results. The 680 nm spectral band is instrumental in monitoring damage, with significant decreases in CHC, CAC, and WAC corresponding to increased damage severity. We identified six VIs and five REPs, which were later predicted using stepwise regression (SR), support vector machine (SVM), and random forest (RF) models. Among all models, the vegetation index-based RF model exhibited the best predictive performance, achieving coefficient of determination (R2) values of 0.988, 0.949, and 0.999 for CHC, CAC, and WAC, with root mean square errors (RMSEs) of 0.115 mg/g, 0.042 mg/g, and 0.007 mg/g, and mean relative errors (MREs) of 8.413%, 9.169%, and 1.678%. This study demonstrates the potential of hyperspectral remote sensing technology for monitoring rodent infestations in Chinese pines, providing a reliable basis for large-scale assessments and effective management strategies for pest control. [ABSTRACT FROM AUTHOR] |
