Recovery from forest disturbances is a pivotal metric of forest resilience. Forests globally are facing unprecedented levels of both natural and anthropogenic disturbances, yet our understanding of their recovery from these disturbances remains incomplete. Remote sensing is an effective tool for understanding post-disturbance recovery, but existing approaches largely rely on spectral recovery indicators that are difficult to interpret and require long time series after disturbance, which limits their applicability to recent disturbance pulses. We here introduce a novel, ecologically informed set of recovery indicators based on fractional cover maps derived from spectral unmixing analysis of Landsat and Sentinel-2 time series. We estimated annual pre- and post-disturbance tree cover and bare ground fractions over the eastern Alps (∼130,000 km) for the period from 1990 to 2021. From these fraction time series, we derived recovery intervals defined as the time it takes to reach a pre-defined tree cover threshold after disturbance, referred to as canopy recovery. We found mean recovery intervals between 5.5 and 13.4 years, depending on recovery threshold and disturbance severity. Comparing our results to traditional remote sensing-based approaches of mapping forest recovery, we found that spectral unmixing-based recovery indicators give considerably more realistic recovery intervals than approaches based on spectral indices because they effectively distinguish tree regeneration from other post-disturbance vegetation (e.g., shrubs, grasses). Finally, we were able to accurately predict the long-term forest recovery success based on the information available only three years after disturbance, which underlines the high importance of a short window of reorganization post-disturbance, and highlights the utility of remote sensing to inform post-disturbance forest management (e.g., in identifying areas in need of tree planting). Our study thus provides an important step ahead in the remote sensing-based monitoring of forest recovery and resilience, which is urgently needed in a time of rapid forest change.

中文翻译：

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基于分解的森林恢复指标，用于预测长期恢复成功


森林干扰的恢复是森林恢复能力的一个关键指标。全球森林正面临前所未有的自然和人为干扰，但我们对森林从这些干扰中恢复的了解仍然不完整。遥感是了解扰动后恢复的有效工具，但现有方法很大程度上依赖于难以解释的频谱恢复指标，并且需要扰动后较长的时间序列，这限制了它们对近期扰动脉冲的适用性。我们在这里介绍一套新颖的、生态学上知情的恢复指标，该指标基于从 Landsat 和 Sentinel-2 时间序列的光谱分解分析中得出的分数覆盖图。我们估算了 1990 年至 2021 年间东阿尔卑斯山（约 130,000 公里）每年扰动前和扰动后的树木覆盖率和裸露地面比例。从这些比例时间序列中，我们得出了恢复间隔，定义为达到目标所需的时间干扰后预定义的树木覆盖阈值，称为树冠恢复。我们发现平均恢复间隔在 5.5 到 13.4 年之间，具体取决于恢复阈值和干扰严重程度。将我们的结果与传统的基于遥感的森林恢复绘图方法进行比较，我们发现基于光谱分解的恢复指标比基于光谱指数的方法提供了更现实的恢复间隔，因为它们有效地区分了树木再生与其他干扰后植被（例如，树木再生）。 、灌木、草）。 最后，我们能够根据干扰后仅三年的可用信息准确预测长期森林恢复的成功，这强调了干扰后短窗口重组的高度重要性，并强调了遥感在提供信息方面的实用性干扰后森林管理（例如，确定需要植树的区域）。因此，我们的研究为基于遥感的森林恢复和复原力监测迈出了重要一步，这在森林快速变化的时期是迫切需要的。