Attribution of forest disturbance types using satellite remote sensing is practicable and several methods have been developed to automate the procedure. However, limited by commonly used data and the methodology, achieving accurate and rapid attribution of forest disturbance types over broad spatial extents remains challenging. In this study, we developed a method for attributing forest disturbance types using Dynamic World class probability data (i.e., probabilities for Dynamic World land use land cover types). Specifically, we first obtained a high-quality probability time series by pre-processing the class probability data. Then, we segmented the entire time series into several subseries and classified them according to the hypothetical trajectories. Finally, we completed the attribution of forest disturbance types using the variables derived from the probability time series and the results of the subseries classification. We used the developed method to investigate the forest disturbance types in Myanmar from 2017 to 2023 and validated its effectiveness by conducting unbiased accuracy assessment. The overall accuracy of the type for the acquired map was approximately 93.3%, and the overall accuracy of the year was approximately 96.7%, proving that the method is feasible. This method is based on the Google Earth Engine, which allows users to attribute forest disturbance types in different areas rapidly by simple parameter adjustments. Even if available classes do not satisfy users’ needs, the method can facilitate more detailed attribution of disturbance types.

中文翻译：

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基于动态世界类概率数据的森林干扰类型归因——以缅甸为例


使用卫星遥感对森林干扰类型进行归因是可行的，并且已经开发了几种方法来自动化该过程。然而，受常用数据和方法的限制，在广泛的空间范围内实现森林干扰类型的准确和快速归因仍然具有挑战性。在这项研究中，我们开发了一种使用 Dynamic World 类概率数据（即 Dynamic World 土地利用土地覆盖类型的概率）来归因森林干扰类型的方法。具体来说，我们首先通过对类概率数据进行预处理，得到了一个高质量的概率时间序列。然后，我们将整个时间序列分割成几个子序列，并根据假设的轨迹对它们进行分类。最后，我们使用从概率时间序列和子序列分类结果得出的变量完成了森林干扰类型的归因。我们使用开发的方法调查了 2017 年至 2023 年缅甸的森林干扰类型，并通过进行无偏精度评估验证了其有效性。所获取地图的类型总体准确率约为 93.3%，当年总体准确率约为 96.7%，证明该方法是可行的。该方法基于 Google Earth Engine，允许用户通过简单的参数调整快速归因不同区域的森林干扰类型。即使可用的类别不能满足用户的需求，该方法也可以促进对干扰类型的更详细归因。