RVI was the most successful, explaining 40% of the variance at two sites. VIs that could explain the largest variability (R2 > 0.3) between injured and uninjured trees were: inverse ratio index (IRVI), green–red vegetation index (GRVI), normalized difference vegetation index (NDVI), normalized ratio index (NRVI), and ratio vegetation index (RVI). At the site where injuries were induced only four months before the UAV survey, the identification of injured trees was not possible. The identification of injured trees based on VIs was possible at the sites where rockfall injuries were induced at least one year before the UAV survey, and they could still be identifiable six years after the initial injuries. The same model was also used for VI differentiations among the recorded injury groups and size of the injuries. A total of 14 VIs were considered, including individual multispectral bands (green, red, red edge, and near-infrared) by using regression models to differentiate between the injured and uninjured groups for a single year and for three consecutive years. Multiband images were used to extract different vegetation indices (VIs) at the tree crown level and were further studied to see which VIs can identify the injured trees and how successfully. At one site, surveys were performed three years in a row. At the second site, they were induced one year after the initial injuries, and at the third site, they were induced six years after the first injuries. At one site, rockfall injuries were induced in the same year as the survey. A survey with a multispectral camera was performed on three rockfall sites with versatile tree species (Fagus sylvatica L., Larix decidua Mill., Pinus sylvestris L., Picea abies (L.) Karsten, and Abies alba Mill.) and with different characterizations of rockfalls and rockfall-induced injuries. In this paper, we present an identification of rockfall-injured trees based on multiband images obtained by an unmanned aerial vehicle (UAV). Such restructuring could pose a challenge for forest management, as open-canopy forests have lower capacities for providing important ecosystem services. Within 35 years after disturbance, 72% of forests recovered to a closed-canopy state, except in submediterranean forests, where recovery is slow and long-lasting transitions to open-canopy conditions are more likely.Īs climate warming increases disturbances and causes thermophilization of vegetation, transitions to open-canopy conditions could become more likely in the future. Disturbances caused a transition to open-canopy conditions in approximately 50% of cases. In the absence of disturbance, open-canopy forests occurred at high elevations, forest edges, and warm, dry sites. We found two alternative states of forest structure that emerged consistently across all forest types of the Alps: short, open-canopy forests (24%) and tall, closed-canopy forests (76%). We combined GEDI-derived structural metrics with Landsat-based disturbance maps and related structure to topography, climate, landscape configuration, and past disturbances. We used spaceborne lidar (GEDI) to characterize forest structure across the European Alps. We investigate factors determining the occurrence of structural states and the role of disturbance and recovery in transitions between states. Here, we characterize large-scale patterns in the horizontal and vertical structure of mountain forests and test for the presence of alternative structural states. Drivers such as changing disturbance regimes are increasingly altering forest structure, but large-scale characterizations of forest structure and disturbance-mediated structural dynamics remain rare. Structure is a central dimension of forest ecosystems that is closely linked to their capacity to provide ecosystem services.
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