FIRE SEVERITY ASSESSMENT OF AN ALPINE FOREST FIRE WITH SENTINEL-2 IMAGERY

We propose a method for the automated detection of the uppermost forest lines with the aim of supporting their monitoring through a replicable mapping that can be adopted in different geographical contexts and at different scales of analysis according to the available datasets. We adopted a trend analysis of Landsat-based wetness and greenness index time series of the last 40 years, detecting an increase in forest cover along the forest line ecotone in both the Italian Alps and the Apennines.

Alpine treelines reflect the impacts of climate and land use changes on ecosystems. Using low-cost drones and deep learning, we developed a method to map tree spatial patterns at fine scales across diverse environments. Our results reveal accurate detection and delineation of trees and spatial trends like clustering and size-class interactions. This efficient, adaptable approach enhances forest monitoring, aiding global efforts to assess treeline dynamics and their responses to global change.