Monitoring of Larch Caterpillar (Dendrolimus superans) Infestation Dynamics Using Time-series Sentinel Images in Changbai Mountains National Nature Reserve, Northeast China

Recently, the outbreak and spread of larch caterpillar (Dendrolimus superans) pests have emerged as significant contributors to forest degradation in the Changbai Mountains, China. Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems. This study proposes a pest monitoring approach based on Sentinel imagery. Through time-series analysis, we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1, Sentinel-2, and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve (CMNNR), Northeast China. Our findings indicated that bands green (B3), near-infrared (B8), short wave infrared (B11 and B12) from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests. Specifically, the Normalized Difference Vegetation Index (NDVI) at the end of the growing season emerged as the most valuable feature for pest extraction. Incorporating Synthetic Aperture Radar (SAR) features along with optical data marginally enhances model performance. Furthermore, our approach unveiled the outbreak of larch caterpillar pests, achieving classification map with overall accuracy exceeding 85% and Kappa coefficient surpassing 0.8 for five study years. The pest outbreak began in 2019 and progressively intensified over time. In September 2019, the affected area spanned 114.23 km². The infested area exhibited a declining trend from 2020 to 2023. This study introduces a novel method for the high-precision identification of larch caterpillar pests, offering technical advancements and theoretical underpinnings to support forest management strategies.