Abstract Understanding forest dynamics at the landscape scale is critical given the demands of sustainable forest management and climate change mitigation. This study proposes an approach for holistically characterising and analysing forest dynamics across large areas and long-time periods using information derived from Landsat time-series. To achieve this, we first developed a two-phase classification process to predictively map (1) disturbance and recovery levels and (2) disturbance causal agents for multiple detected disturbance events. The model explanatory data included a range of trajectory-based metrics derived from Landsat time-series, while model training and validation data were derived from a human-interpreted reference dataset. While previous studies have often described forest dynamics using some specific spectral change metrics, we demonstrated an ensemble approach to map disturbance and recovery trends (by treating them as a single metric) and to characterise not only abruptly occurring change events (e.g., clear-fell logging and wildfire) but also events of low severity (e.g., prescribed burning and selective logging). In addition, we adopted a space-time data cube concept to simultaneously report both newly detected disturbance events (detected disturbances) as well as events that have previously occurred but are ongoing (ongoing disturbances). This ongoing element of forest dynamics is often under-reported. The method was tested over 3.7 million ha of public land sclerophyll forests, where multiple disturbance events have occurred over the last 30 years (1987–2016). Our models of disturbance and recovery levels obtained overall accuracies of 78.6% and 72.3% for primary and secondary disturbance events, respectively. The overall accuracies for the models of disturbance causal agents were 80.7% and 73.0%, respectively. The data cube reported an average annual disturbance rate of 4.2% per year. This was dominated by newly detected disturbance (2.7% per year) as distinct from ongoing disturbance that was, however, considerable (1.5% per year). Our approach presented herein can improve the understanding of forest dynamics over long time periods and large areas and has potential for supporting land managers.

中文翻译：

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使用 Landsat 时间序列对森林动态进行时空分析

摘要 鉴于可持续森林管理和减缓气候变化的需求，了解景观尺度的森林动态至关重要。本研究提出了一种使用来自 Landsat 时间序列的信息全面表征和分析大面积和长期森林动态的方法。为实现这一目标，我们首先开发了一个两阶段分类过程，以预测性地绘制 (1) 干扰和恢复水平以及 (2) 多个检测到的干扰事件的干扰原因。模型解释数据包括一系列源自 Landsat 时间序列的基于轨迹的指标，而模型训练和验证数据源自人工解释的参考数据集。虽然以前的研究经常使用一些特定的光谱变化指标来描述森林动态，我们展示了一种集成方法来绘制干扰和恢复趋势（将它们视为单一指标），不仅表征突然发生的变化事件（例如，伐木和野火），而且表征低严重性的事件（例如，规定的燃烧和选择性记录）。此外，我们采用了时空数据立方体概念来同时报告新检测到的干扰事件（检测到的干扰）以及之前发生但正在进行的事件（正在进行的干扰）。森林动态的这一持续因素经常被低估。该方法在超过 370 万公顷的公共土地硬叶林中进行了测试，在过去 30 年（1987-2016 年）发生了多次干扰事件。我们的干扰和恢复水平模型获得了 78.6% 和 72 的总体准确率。初级和次级干扰事件分别为 3%。干扰因素模型的总体准确率分别为 80.7% 和 73.0%。数据立方体报告的平均年扰动率为每年 4.2%。这主要是新检测到的干扰（每年 2.7%），这与持续的干扰（每年 1.5%）不同。我们在此提出的方法可以提高对长期和大面积森林动态的理解，并具有支持土地管理者的潜力。每年 7%）与持续的干扰不同，然而，持续的干扰是相当大的（每年 1.5%）。我们在此提出的方法可以提高对长期和大面积森林动态的理解，并具有支持土地管理者的潜力。每年 7%）与持续的干扰不同，然而，持续的干扰是相当大的（每年 1.5%）。我们在此提出的方法可以提高对长期和大面积森林动态的理解，并具有支持土地管理者的潜力。