## 1 Introduction  
Forests are the habitat of numerous natural species and serve multiple economic functions for human society [1, 2, 3]. Conditions conducive to stable forest growth include the absence of disturbances which includes meteorological disasters, human activities, etc. [4, 5, 6]. High-frequency disturbances will likely produce more severe impact on forests. However, high-frequency disturbances are closely related to long-term and repetitive human activities and are difficult to avoid. These may change the composition, structure, and ecosystem function of the forest, leading to nutrient and water cycle changes, soil erosion, lower carbon sequestration, and other negative outcomes [1, 3, 7]. Furthermore, they may affect other biological communities, as well as humans [8, 9]. Hence, investigating the forest response to high-frequency disturbances is important [10, 11]. High-frequency disturbances result in different types of changes that eventually cause complex alterations in the forest ecosystem through a series of spatiotemporal interactions. The decomposition of changes in the forest ecosystem and information regarding the time, place, and nature of these changes are crucial for revealing the regularities and causes of environmental change. Remote sensing is particularly useful for acquiring spatiotemporal information on forest ecosystem changes [12, 13, 14, 15]. Traditional analysis methods are mainly divided into bi-temporal change detection [16, 17], object-oriented methods [18, 19, 20, 21, 22], and temporal trajectory analysis [23, 24]. The advantage of these methods is that they can easily and intuitively obtain the information of change or no change and land conversion types. However, different changes in forest ecosystems have different spatiotemporal scales. Most of the current research addressing changes in the forest ecosystem only use temporal or spatial information, or adopt a two-stage independent mode, i.e., using one before the other [25; 26; 27; 28]. These approaches notably no longer meet the needs of forest ecosystem change monitoring. Therefore, the use of spatiotemporal information to capture forest ecosystem changes remains a challenge and has attracted the attention of numerous researchers. As a novel analytical method, the space-time cube has been applied in numerous fields. [PERSON] et al. [29] used the space-time cube to analyze the changes in spatial and temporal characteristics of traffic accidents over time among the elderly population of Seoul. [PERSON] et al. [30] reported the spatial and temporal patterns of the 2019 novel coronavirus disease outbreaks; they found the spatial and temporal distribution type and case trends based on the space-time cube. However, in these two methods, the size of each cube is a fixed value set by researchers, and the values are independent of the actual ground objects. This means that the space-time cube analysis method cannot represent the ecological change process. [PERSON] et al. [31] proposed the \"spatiotemporal cube (ST-cube)\" to improve application of land-cover classification and change detection with satellite image time series. ST-cubes are the units for representing actual ground objects and have a one-to-one correspondence with actual ground objects. In the high-frequency disturbance area, although disturbance occurs frequently, the effect of disturbance does not necessarily change the appearance of the forest [32; 33]. It may be reflected in the appearance only when it accumulates to a certain amount, which makes it difficult for ST- cube model to detect the real temporal and spatial changes of forest function in the high frequency disturbance area. Thus, this study improved the ST-cube model. To capture the spatiotemporal dynamics of the high-frequency disturbance forest ecosystem, we combined the ST-cube and vegetation index to develop the improved spatiotemporal cube (IST-cube) model.