基于无人机遥感和机器学习的松材线虫病监测研究

Pine wilt disease is a devastating pine disease，which seriously threatens forest ecological security. Timely and reliable acquisition of the extent and severity of pine wilt disease is very important for forest management and disease prevention and control. However，pine wilt disease spreads rapidly and is difficult to control，and the traditional manual survey methods can hardly meet the demand. Unmanned Aerial Vehicle（UAV）remote sensing can quickly and accurately obtain the extent and severity of forest diseases，and provide reliable information support for forest pest control and management. In this study，UAV was used to acquire high-resolution Red-Green-Blue（RGB）visible light images. Firstly，object-oriented multi-scale segmentation algorithm was used to extract the crown of a single tree，and Vegetation Index（VIs）and texture（GLCM）features were calculated. Then the feature selection algorithm was used to optimize the feature set，and Random Forest（RF）classification and Support Vector Machine（SVM）classification algorithm were used to construct the pine wilt disease classification model based on different feature sets. Through the ablation experiment，the optimal classification model was selected and the object-oriented method was used to monitor the disease degree and spatial distribution of pine wilt disease. The results show that the vegetation index and texture features of pine canopy with different disease degrees are different on the object-oriented single tree crown scale，and the accuracy of classification results using vegetation index was better than texture characteristics（VIs RF：OA=76.52%，Marco-F1=0.77；SVM：OA=79.68%，Marco-F1=0.79）. Compared with a single feature set，the combination of vegetation index and texture features can significantly improve the classification accuracy（VIs&GLCM RF：OA =79.47%，Marco-F1=0.80；SVM：OA=85.45%，Marco-F1=0.85），indicating that multi-feature combination can effectively improve the pine wilt disease classification. The SVM model outperforms the RF model for classification，both for single feature set modeling and combined feature set modeling. This study provides timely and reliable information to support a comprehensive grasp of the extent and severity of pine wilt disease，and helps to promote the construction of a major forestry pest control system and maintain ecological security.