Identification of Pine Wilt Disease-Infested Stands Based on Single- and Multi-Temporal Medium-Resolution Satellite Data

Abstract

Pine wilt disease (PWD) is known for its high lethality and rapid transmission, earning it the name “cancer of the pine tree”. The prompt removal of infested pine trees is an effective measure for preventing and controlling pine wilt disease. Accurate and efficient monitoring technologies are crucial for the scientific prevention and control of this plant disease. Currently, numerous remote sensing monitoring studies have been conducted on pine wilt disease. However, there is limited research on the temporal identification of PWD-infested forest stands over large areas. To build classification models, this study utilized three machine learning algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). We aimed to investigate the effectiveness of single-temporal and multi-temporal Landsat and Sentinel-2 satellite images PWD-infested forest stands detection. The results indicated that, at a spatial resolution of 30 m, Landsat-9 and Sentinel-2 remote sensing images effectively identified PWD-infested forest stands, with classification accuracies of 77.87% and 78.91%, respectively. Higher spatial resolutions in Sentinel-2 remote sensing images were associated with improved identification capabilities. Furthermore, multi-temporal Landsat satellite data (with a classification accuracy of 85.95%) significantly enhanced the performance of the monitoring model compared to single-temporal Landsat satellite data (with a classification accuracy of 77.87%). The RGI difference was found to be the optimal vegetation index. In conclusion, by combining multi-temporal and single-time-phase Landsat remote sensing data, a monitoring model for PWD-infested forest stands was constructed. It achieved a classification accuracy of 88.26%. In this study, a higher accuracy in identifying pine wilt disease and a lower economic cost were achieved by Landsat and Sentinel images, offering valuable insights for the management of pine wilt disease.