Chao is a leaf-eating pest that poses a significant threat to bamboo forest health. Current research mainly focuses on statically identifying damage using remote sensing images. However, the mechanism behind the damage's traceability remains unclear, making it difficult to pinpoint early infestation sources accurately. Additionally, our understanding of the pest's spreading laws is limited. This study leverages Sentinel-2A/B images from February to November 2021 to investigate infestation traceability through the dynamic age algorithm and indicator analysis method. The results shed light on the distribution of early pest sources over the study period. By analyzing both the overall pest infestation “cluster” and its center of gravity, we dissect infestation characteristics and paths monthly throughout the study period. Our findings reveal three zones with strong spreading momentum, three with slow spreading momentum, and two transitional zones during the February-November period, aligning with occurrence patterns. However, the direction of spreading varies, likely due to a combination of meteorological, topographical, vegetative biochemical, and human activity factors. This study introduces innovative approaches for identifying early pest source points and understand their spreading laws, contributing to more effective pest prevention and control in forest ecosystems.

中文翻译：

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利用Sentinel-2A/B图像揭示毛竹林早期害虫源点及毛竹传播规律


Chao 是一种食叶害虫，对竹林健康构成重大威胁。目前的研究主要集中在利用遥感图像静态识别损伤。然而，损害可追溯性背后的机制仍不清楚，因此很难准确查明早期感染源。此外，我们对害虫传播规律的了解是有限的。本研究利用2021年2月至11月的Sentinel-2A/B图像，通过动态年龄算法和指标分析方法研究侵染溯源性。结果揭示了研究期间早期害虫来源的分布。通过分析总体害虫侵扰“集群”及其重心，我们在整个研究期间每月剖析虫害特征和路径。我们的研究结果显示，2 月至 11 月期间存在三个传播势头强劲的区域、三个传播势头较慢的区域和两个过渡区域，这与发生模式相符。然而，传播方向各不相同，可能是由于气象、地形、植物生化和人类活动因素的综合影响。这项研究介绍了识别早期害虫源点并了解其传播规律的创新方法，有助于更有效地预防和控制森林生态系统的害虫。