| Pine Wilt Disease (PWD), known as the cancer of pine, has a high infection rate and high mortality rate, posing a serious threat to China's forest resources and causing significant economic, social and ecological losses to China. Timely detection and cleaning of infected wood is an effective means to curb the spread of PWD, and accurate monitoring of infected wood is a prerequisite for prevention and control of PWD. However, there is a lack of technical methods to identify infected wood of PWD in a large area at this stage. This paper aims to explore the recognition ability of Sentinel-2 and Landsat-8 on infected wood of PWD and to establish PWD monitoring models using four machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT) and Extreme Gradient Boosting (XGBoost). The results showed that the monitoring models based on Sentinel-2 image had higher recognition accuracy than Landsat-8 image. In addition, the models with highest recognition accuracy were based on 10 m resolution image, and these models built by RF, DT, SVM and XGBoost reached accuracy at 79.3%, 76.2 %, 78.7% and 78.9%, respectively. The accuracy, kappa coefficient and ROC values of the RF, SVM and XGBoost were close and all significantly better than DT in the three different images. Green band, red band, short-wave NIR band and long-wave NIR band in the spectral features and NBRI, NGRDI, TVI, NDVI and PSSR in the vegetation indices had the highest contribution values to PWD surveillance models. Mean Decrease in Impurity (MDI) was the most effective method in filtering the feature parameters, and the number of features was reduced from 50 to 35. PWD surveillance models established in this paper provide technical support for scientific prevention and control of PWD. |
About Journal