Abstract Forest disturbances greatly affect the ecological functioning of natural forests. Timely information regarding extent, timing and magnitude of forest disturbance events is crucial for effective disturbance management strategies. Yet, we still lack accurate, near-real-time and high-performance remote sensing tools for monitoring abrupt and subtle forest disturbances. This study presents a new approach called ‘Stochastic Continuous Change Detection (S-CCD)’ using a dense Landsat data time series. S-CCD improves upon the ‘COntinuous monitoring of Land Disturbance (COLD)’ approach by incorporating a mathematical tool called the ‘state space model’, which treats trends and seasonality as stochastic processes, allowing for modeling temporal dynamics of satellite observations in a recursive way. The quantitative accuracy assessment is evaluated based on 3782 Landsat-based disturbance reference plots (30 m) from a probability sampling distributed throughout the Conterminous United States. Validation results show that the overall accuracy (best F1 score) of S-CCD is 0.793 with 20% omission error and 21% commission error, slightly higher than that of COLD (0.789). Two disturbance sites respectively associated with wildfire and insect disturbances are used for qualitative map-based analysis. Both quantitative and qualitative analyses suggest that S-CCD achieves fewer omission errors than COLD for detecting those disturbances with subtle/gradual spectral change. In addition, S-CCD facilitates a better real-time monitoring, benefited by its complete recursive manner and a shorter lag for confirming disturbance than COLD (126 days vs. 166 days for alerting 50% disturbance events), and reached up to ~4.4 times speedup for computation. This research addresses the need for near-real-time monitoring and large-scale mapping of forest health and offers a new approach for operationally performing change detection tasks from dense Landsat-based time series.

中文翻译：

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使用 Landsat 时间序列监测森林干扰的近实时方法：随机连续变化检测

摘要 森林干扰严重影响天然林的生态功能。有关森林干扰事件的范围、时间和幅度的及时信息对于有效的干扰管理策略至关重要。然而，我们仍然缺乏用于监测突然和微妙的森林干扰的准确、近实时和高性能的遥感工具。这项研究提出了一种称为“随机连续变化检测 (S-CCD)”的新方法，使用密集的 Landsat 数据时间序列。S-CCD 通过结合称为“状态空间模型”的数学工具改进了“陆地干扰连续监测 (COLD)”方法，该工具将趋势和季节性视为随机过程，允许以递归方式对卫星观测的时间动态进行建模办法。定量准确性评估是基于 3782 个基于 Landsat 的干扰参考图 (30 m) 评估的，这些图来自分布在美国本土的概率抽样。验证结果表明，S-CCD 的整体准确率（最佳 F1 分数）为 0.793，遗漏误差为 20%，委托误差为 21%，略高于 COLD（0.789）。分别与野火和昆虫干扰相关的两个干扰地点用于基于地图的定性分析。定量和定性分析都表明，S-CCD 在检测那些具有细微/逐渐光谱变化的干扰方面比 COLD 实现更少的遗漏错误。此外，S-CCD 有利于更好的实时监测，这得益于其完全递归的方式和比 COLD 更短的确认干扰延迟（126 天对 126 天）。166 天用于警告 50% 的干扰事件），并达到了约 4.4 倍的计算加速。这项研究满足了对森林健康的近实时监测和大规模制图的需求，并提供了一种新方法，用于从密集的 Landsat 时间序列中执行变化检测任务。