由于气候变化和人类活动等多种因素的共同作用，森林病虫灾害干扰的频率和规模不断增加，严重影响了森林生态系统的结构和服务。准确地识别区域性森林病虫灾害扰动，分析其爆发的时空特征对于森林生态系统的保护具有重要意义。本研究基于Landsat 8卫星年度时序数据，以辽宁省朝阳市为研究区域，全面分析了森林冠层时序光谱特征对火灾、砍伐和森林病虫灾害的可分离性，调整LandTrendr算法的控制参数提升了森林弱扰动信息提取的“敏感性”，采用随机森林算法提取了2013至2023年的森林病虫灾害扰动时空信息。结果表明：(1)中分辨率卫星影像的时序光谱特征能够有效地区分朝阳市森林病虫灾害与火灾、砍伐两类森林扰动，可以作为区域性森林病虫灾害识别依据。(2)时序卫星影像可以有效地提取森林扰动时空信息并用于森林病虫灾害识别，本研究的森林扰动识别和病虫灾害监测总体精度分别为0.893和0.891，Kappa系数分别为0.785和0.850。(3) 朝阳市森林扰动以病虫灾害为主，森林病虫灾害主要发生在西部的建平县和凌源市，发生面积占全市病虫灾害发生面积的67.97%；朝阳市森林病虫灾害在时间维度上存在“间歇性”爆发现象。研究结果可以为森林经营管理提供数据支持，为不同森林扰动的分类以及森林病虫灾害时空监测提供方法借鉴。

【Objective】Due to the combined effects of climate change and human activities, the frequency and scale of forest pest disturbances have been continuously increasing, significantly affecting the structure and services of forest ecosystems. Accurately identifying regional forest pest disturbances and analyzing their spatiotemporal characteristics of outbreaks are of great significance for the protection of forest ecosystems. 【Method】In this study, based on Landsat 8 satellite annual time series data, with Chaoyang City in Liaoning Province as the study area, we comprehensively analyzed the separability of forest canopy temporal spectral characteristics for fire, logging, and forest pest disturbances. Adjusting the control parameters of the LandTrendr algorithm improved the "sensitivity" of extracting weak forest disturbance information, and ultimately, the random forest algorithm was used to extract the spatiotemporal information of forest pest disturbances from 2013 to 2023. 【Result】The results showed that: (1) The temporal spectral characteristics of medium-resolution satellite images can effectively distinguish forest pest disturbances from fire and logging in Chaoyang City, providing a basis for identifying regional forest pest disturbances. (2) Temporal satellite images can effectively extract spatiotemporal information of forest disturbances and be used for forest pest disturbance identification. The overall accuracy of forest disturbance identification and pest disturbance monitoring in this study were 0.893 and 0.891, respectively, with Kappa coefficients of 0.785 and 0.850. (3) Forest disturbances in Chaoyang City are mainly due to pest infestations, primarily occurring in Jianping County and Lingyuan City in the west, accounting for 67.97% of the total pest disturbance area in the city. The forest pest disturbances in Chaoyang City exhibit an "intermittent" outbreak phenomenon in the temporal dimension. 【Conclusion】The study results can provide data support for forest management and offer methodological references for the classification of different forest disturbances and the spatiotemporal monitoring of forest pest disturbances.