文献类型：期刊文献

英文题名：Monthly Prediction of Pine Stress Probability Caused by Pine Shoot Beetle Infestation Using Sentinel-2 Satellite Data

作者：Jia, Wen[1,2] Meng, Shili[1,2,3] Qin, Xianlin[1,2] Pang, Yong[1,2] Wu, Honggan[1,2] Jin, Jia[4] Zhang, Yunteng[5]

第一作者：Jia, Wen;荚文

通信作者：Meng, SL[1];Meng, SL[2];Meng, SL[3]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Natl Forestry & Grassland Sci Data Ctr, Beijing 100091, Peoples R China;[4]Beijing Normal Univ, Sch Artificial Intelligence, Beijing 100875, Peoples R China;[5]Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China

年份：2024

卷号：16

期号：23

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20245117532115);Scopus(收录号：2-s2.0-85211815429);WOS:【SCI-EXPANDED(收录号:WOS:001377663700001)】；

基金：This research was funded by the National Key R&D Program of China (Grant No. 2022YFD1400400), and by the National Natural Science Foundation of China (Grant No. 42101403).

语种：英文

外文关键词：forest health; plant diseases and pests; outbreak prediction; pine shoot beetle; Sentinel-2; forest disturbance; random forest; CCDC

摘要：Due to the significant threat to forest health posed by beetle infestations on pine trees, timely and accurate predictions are crucial for effective forest management. This study developed a pine tree stress probability prediction workflow based on monthly cloud-free Sentinel-2 composite images to address this challenge. First, representative pine tree stress samples were selected by combining long-term forest disturbance data using the Continuous Change Detection and Classification (CCDC) algorithm with high-resolution remote sensing imagery. Monthly cloud-free Sentinel-2 images were then composited using the Multifactor Weighting (MFW) method. Finally, a Random Forest (RF) algorithm was employed to build the pine tree stress probability model and analyze the importance of spectral, topographic, and meteorological features. The model achieved prediction precisions of 0.876, 0.900, and 0.883, and overall accuracies of 89.5%, 91.6%, and 90.2% for January, February, and March 2023, respectively. The results indicate that spectral features, such as band reflectance and vegetation indices, ranked among the top five in importance (i.e., SWIR2, SWIR1, Red band, NDVI, and NBR). They more effectively reflected changes in canopy pigments and leaf moisture content under stress compared with topographic and meteorological features. Additionally, combining long-term stress disturbance data with high-resolution imagery to select training samples improved their spatial and temporal representativeness, enhancing the model's predictive capability. This approach provides valuable insights for improving forest health monitoring and uncovers opportunities to predict future beetle outbreaks and take preventive measures.