文献类型：期刊文献

英文题名：Monitoring Dendrolimus punctatus Walker Infestations Using Sentinel-2: A Monthly Time-Series Approach

作者：Meng, Fangxin[1,2] Qin, Xianlin[1,2] Shao, Yakui[1,2] Hu, Xinyu[1,2] Jiang, Feng[1,2] Huang, Shuisheng[1,2] Yu, Linfeng[1,2]

第一作者：Meng, Fangxin

通信作者：Qin, XL[1];Qin, XL[2]

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

年份：2026

卷号：18

期号：2

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20260519995877);Scopus(收录号：2-s2.0-105028761669);WOS:【SCI-EXPANDED(收录号:WOS:001671693500001)】；

基金：This research was funded by the National Key R&D Program of China (Grant No. 2022YFD1400400).

语种：英文

外文关键词：pest monitoring; Sentinel-2; random forest; time-series analysis

摘要：Highlights What are the main findings? A Sentinel-2-based monthly monitoring framework integrating a Weighted Composite Index (WCI), time-series features, and Random Forest models successfully classified infestation severity with accuracies exceeding 86.9% (Kappa: 0.825-0.858) across 2019-2024. Dendrolimus punctatus WalkerMulti-year monitoring revealed recurring outbreak events during the study period (2019, 2021, and 2023). Infestation dynamics generally progressed from scattered mild damage to more concentrated and severe distributions, indicating structured spatiotemporal patterns rather than strictly periodic cycles. What are the implications of the main findings? The WCI approach synthesizing IRECI, EVI, and NDVI with temporal dynamics provides an operational and transferable methodology for precision forest pest monitoring using freely available satellite data, substantially reducing dependence on costly field surveys. Understanding biennial cyclical patterns and spatial progression of infestation enables monitoring modeling and strategic planning for proactive forest protection and sustainable ecosystem management.Highlights What are the main findings? A Sentinel-2-based monthly monitoring framework integrating a Weighted Composite Index (WCI), time-series features, and Random Forest models successfully classified infestation severity with accuracies exceeding 86.9% (Kappa: 0.825-0.858) across 2019-2024. Dendrolimus punctatus WalkerMulti-year monitoring revealed recurring outbreak events during the study period (2019, 2021, and 2023). Infestation dynamics generally progressed from scattered mild damage to more concentrated and severe distributions, indicating structured spatiotemporal patterns rather than strictly periodic cycles. What are the implications of the main findings? The WCI approach synthesizing IRECI, EVI, and NDVI with temporal dynamics provides an operational and transferable methodology for precision forest pest monitoring using freely available satellite data, substantially reducing dependence on costly field surveys. Understanding biennial cyclical patterns and spatial progression of infestation enables monitoring modeling and strategic planning for proactive forest protection and sustainable ecosystem management.Abstract Infestations of () pose significant threats to forest ecosystem health, necessitating accurate and efficient monitoring for sustainable forest management. A monthly monitoring framework integrating spectral bands, vegetation indices, time-series features, meteorological variables, and topographic characteristics was developed. First, cloud-free Sentinel-2 composites were generated via median synthesis, and training samples were selected by integrating GF-1/2 data. Subsequently, a Weighted Composite Index (WCI) was constructed through logistic regression to quantitatively classify infestation severity levels. Meanwhile, time-series features extracted from vegetation indices were incorporated to characterize temporal damage dynamics. Finally, Random Forest (RF) models were then trained for monthly monitoring, achieving overall accuracies exceeding 86.9% with Kappa coefficients ranging from 0.825 to 0.858. The Inverted Red Edge Chlorophyll Index (IRECI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) exhibited the highest sensitivity to damage and thus received the greatest weights in the WCI. Time-series features ranked second in importance after vegetation indices, substantially enhancing model performance. Monitoring results from 2019 to 2024 revealed that infestation in Qianshan City exhibited an occurrence pattern progressing from mild to severe and from scattered to aggregated distributions, with major outbreak periods in 2019, 2021, and 2023 reflecting characteristic cyclical dynamics. This study advances existing quantitative monitoring methodologies for and provides technical support and a scientific foundation for precision pest monitoring and forest health management.