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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.