Benchmarking remote sensing-based forest recovery indicators for predicting long-term recovery success

Natural disturbances and post-disturbance recovery are principal drivers of forest ecosystem dynamics. While disturbances and their causes and consequences have received considerable attention from the scientific community in recent years, there is – however – a substantial lack of knowledge on post-disturbance recovery, despite its importance for forest resilience, carbon storage and developing effective conservation and management strategies. This is particularly pertinent in mountain landscapes, such as the Alps, where steep topography and frequent climate extremes could hamper natural tree regeneration but closed canopy forests are needed for protecting infrastructure from natural hazards. In our study, we aim to close this knowledge gap by the means of Earth Observation. Specifically, we mapped land cover fractions (treed vegetation, non-treed vegetation and bare soil) annually at 30 m spatial grain and over the period 1990-2021 across the Alps. To do so, we employed a temporally generalized regression-based spectral unmixing approach to dense time series of Landsat and Sentinel-2 data, including more than 73,000 individual scenes. From this dataset, we characterized post-disturbance recovery intervals, that is the time it takes to reach a similar canopy closure than pre-disturbance, across 1.76*10⁶ disturbance patches, including both natural and human disturbances. Results show that disturbed sites close their canopy on average after 10.6 years, with 60% of the disturbances reaching closed canopy after 10 years. We then compared recovery intervals derived from spectral unmixing to existing recovery indicators based on simple vegetation indices (NDVI, NBR), showing that those recovery indicators underestimate post-disturbance canopy closure time by a factor of 1.5 – 2. Finally, we tested whether post-disturbance bare soil fractions and disturbance characteristics (i.e., pre-disturbance tree cover and relative severity) can be used to predict long-term recovery success.  Results show that long-term recovery success (defined as canopy closure at 10 years post-disturbance) could be predicted with > 80% accuracy. From our results we conclude that (i) recovery indicators based on spectral indices are not well suited to characterize post-disturbance recovery in complex landscapes such as mountain forests and (ii) that disturbance characteristics and post-disturbance bare soil fractions are largely sufficient to predict whether a pixel will recover in the future or not. Our approach thus overcomes a major limitation of past remote sensing-based recovery assessments, which required long time series (>10 years) to assess recovery and thus were limited in understanding changes in post-disturbance forest recovery over time.