Unmixing-based forest recovery indicators for predicting long-term recovery success

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 km2) 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.