遥感时间序列蕴含自然因素和人类活动等不同驱动因子导致的森林组成、结构、功能变化及其差异信息，这为融合遥感时间序列中蕴含的时间维、空间维和光谱维（时空谱）信息开展森林扰动检测与归因提供了理论支撑，其能有效提升对森林演替过程、发展态势及其驱动和响应机制的理解能力。本文系统评述了融合遥感时间序列时空谱信息的森林扰动检测与归因研究进展。首先，从数据、特征、算法等多个角度阐述当前森林扰动检测进展：数据角度，介绍了面向稠密和稀疏时间序列等不同观测频次的变化检测方法；特征角度，归纳了森林扰动光谱响应特征和多光谱特征集成变化检测策略，总结了面向森林扰动检测的时序与空间特征融合方式；算法角度，介绍了并行和串行两种多算法集成策略，描述了面向森林低强度扰动的变化检测算法研究进展。森林扰动归因的本质是面向森林多类型扰动分类问题，本文在分别按照时间顺序和特征维度归纳作为森林扰动归因输入的归因特征的基础上，总结了基于遥感时间序列时空谱和地形特征融合的森林多类型扰动归因方法。最后，本文分析了森林扰动遥感监测目前存在的问题，并对未来研究方向进行了展望，以期为融合遥感时间序列时空谱信息的变化检测和归因研究提供借鉴。

Abstract: Remote sensing time series contain the changes and their difference information of forest composition, structure, function driven by natural factors and human activities. This provides theoretical support for forest disturbance detection and attribution by integrating spatio-temporal-spectral information from remote sensing time series data, which can effectively improve the understanding of forest succession processes, developmental trend and their driving and response mechanism. This paper systematically reviewed the research progress of forest disturbance detection and attribution by integrating spatio-temporal-spectral information from remote sensing time series. The prerequisite for forest disturbance attribution is the detection of forest disturbance events, and the accuracy of disturbance detection directly affects the accuracy of subsequent attribution. In this paper, current forest disturbance detection methods and techniques are highlighted from multiple perspectives, including data (time series observation frequency selection), features (spectral feature selection, fusion of spatial and temporal feature), and algorithms (multi-algorithm integration, forest low-intensity disturbance detection). From the data perspective, based on the frequency of available observations in different regions, the change detection methods for dense and sparse time series are introduced respectively. From the feature perspective, the spectral response characteristics of forest disturbances are summarized. The change detection strategy of multi-spectral feature integration is introduced to address the problems of change detection based on single spectral features. The fusion of temporal and spatial features for forest disturbance detection is summarized. From the algorithmic perspective, to address the issues such as differences in the results of different change detection algorithms and the fact that a single algorithm may not be the most efficient way to describe all conditions, two multi-algorithm integration strategies, parallel and serial, are presented. Based on the analysis of the reasons for the poor detection of low-intensity disturbances (e.g., selective logging, pests and diseases, drought, etc.), progress in research on change detection oriented to mid- and low-intensity disturbances in forests is described. The essence of forest disturbance attribution is a classification problem for multiple types of forest disturbances, which identifies disturbance types with the help of remote sensing features of forest disturbances caused by different driving factors as the input of classification algorithms. In this paper, we first summarized the attribution features as the input of forest disturbance attribution, that is, pre-, mid- and post-disturbance features in chronological order, and temporal, spatial, spectral and topographic features in feature dimensions. Then, according to the condition that whether disturbance detection before attribution of disturbances, methods for attributing multiple types of forest disturbance based on the spatio-temporal-spectral and topographic features of remote sensing time series are summarized and compared: the direct method and the two-stage method. At last, we analyzed the current problems in forest disturbance monitoring using remote sensing, and prospected the future research directions such as fusion of spatio-temporal-spectral features, simultaneous detection of forest multi-intensity disturbance and attribution of forest multi-type disturbance under limited sample conditions. We hope this article provides reference for detection and attribution of changes using an ensemble of spatio-temporal-spectral information from remote sensing time series.