The launch of NASA's Global Ecosystem Dynamics Investigation (GEDI) mission in 2018 opens new opportunities to quantitatively describe forest ecosystems across large scales. While GEDI's height‐related metrics have already been extensively evaluated, the utility of GEDI for assessing the full spectrum of structural variability—particularly in topographically complex terrain—remains incompletely understood. Here, we quantified GEDI's potential to estimate forest structure in mountain landscapes at the plot and landscape level, with a focus on variables of high relevance in ecological applications. We compared five GEDI metrics including relative height percentiles, plant area index, cover and understory cover to airborne laser scanning (ALS) data in two contrasting mountain landscapes in the European Alps. At the plot level, we investigated the impact of leaf phenology and topography on GEDI's accuracy. At the landscape‐scale, we evaluated the ability of GEDIs sample‐based approach to characterize complex mountain landscapes by comparing it to wall‐to‐wall ALS estimates and evaluated the capacity of GEDI to quantify important indicators of ecosystem functions and services (i.e., avalanche protection, habitat provision, carbon storage). Our results revealed only weak to moderate agreement between GEDI and ALS at the plot level (R2 from 0.03 to 0.61), with GEDI uncertainties increasing with slope. At the landscape‐level, however, the agreement between GEDI and ALS was generally high, with R2 values ranging between 0.51 and 0.79. Both GEDI and ALS agreed in identifying areas of high avalanche protection, habitat provision, and carbon storage, highlighting the potential of GEDI for landscape‐scale analyses in the context of ecosystem dynamics and management.

Forest ecosystems are shaped by both abiotic and biotic disturbances. Unlike sudden disturbance agents, such as wind, avalanches and fire, bark beetle infestation progresses gradually. By the time infestation is observable by the human eye, trees are already in the final stages of infestation—the red- and grey-attack. In the relevant phase—the green-attack—biochemical and biophysical processes take place, which, however, are not or hardly visible. In this study, we applied a time series analysis based on semantically enriched Sentinel-2 data and spectral vegetation indices (SVIs) to detect early traces of bark beetle infestation in the Berchtesgaden National Park, Germany. Our approach used a stratified and hierarchical hybrid remote sensing image understanding system for pre-selecting candidate pixels, followed by the use of SVIs to confirm or refute the initial selection, heading towards a 'convergence of evidence approach’. Our results revealed that the near-infrared (NIR) and short-wave-infrared (SWIR) parts of the electromagnetic spectrum provided the best separability between pixels classified as healthy and early infested. Referring to vegetation indices, we found that those related to water stress have proven to be most sensitive. Compared to a SVI-only model that did not incorporate the concept of candidate pixels, our approach achieved distinctively higher producer’s accuracy (76% vs. 63%) and user’s accuracy (61% vs. 42%). The temporal accuracy of our method depends on the availability of satellite data and varies up to 3 weeks before or after the first ground-based detection in the field. Nonetheless, our method offers valuable early detection capabilities that can aid in implementing timely interventions to address bark beetle infestations in the early stage.

<p>Natural disturbances and post-disturbance recovery are principal drivers of forest ecosystem dynamics and both are sensitive to climate change. 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. Recovery is considered an essential measure of forest resilience to climate change, especially with regard to ecosystem service provision (e.g., protection from avalanches, water purification). Disturbances remove the top tree canopy, exposing the forest floor composed of different land cover types, such as bare soil, grassland and shrubby vegetation, which will gradually transition to treed vegetation over succession. The assessment of forest recovery by means of medium resolution optical remote sensing data (i.e., ~20 m spatial grain) poses some challenges in analyzing those spatially and temporally heterogenous recovery trajectories. To tackle this problem, we employed a temporally generalized regression-based spectral unmixing approach to dense time series of Landsat and Sentinel-2 data with the aim of characterizing the post-disturbance recovery trajectories across a large study site covering the eastern Alps (~125,000 km²). For training the spectral unmixing approach, we developed a multi-year spectral library for three endmembers: treed vegetation, non-treed vegetation and bare soil. Selection of pure endmembers was based on the LUCAS database, a pan-European disturbance map and Google Earth imageries. Applying the generalized regression-based spectral unmixing approach to a dense time series of Landsat and Sentinel-2 images results in annual fraction maps for the three endmembers, which can be used to characterize recovery trajectories after major disturbance events. Each pixel’s post-disturbance trajectory can thereby be described in a three-dimensional space composed of variable fractions of treed vegetation, noon-treed vegetation and bare soil. To facilitate interpretation of recovery trajectories, we focus on specific disturbance events covering the storms Kyrill (2007), Uschi (2003), and Vaia (2018). This allows for identifying (dis-)similarities between recovery trajectories of the same disturbance event and thus to investigate the full breath of potential recovery patterns after natural disturbances.</p>