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Forest disturbance dynamics in Bavaria: A novel approach for identifying fragmentation processes based on canopy cover loss and morphological image processing

Year: 2026
Type: article
Abstract: Unprecedented canopy loss has become a feature of central European temperate forests in recent years and is expected to continue. Bavaria, the largest and most forested state in Germany lost 146,320 ha from September 2017 – July 2025. To investigate the dynamics of forest disturbance, we classified interior, exterior, and convergent fragmentation processes (perimeter loss, shrinkage, perforation, and bisection) over an 8-year study period by intersecting the Forest Canopy Cover Loss (FCCL) dataset with a spatial classification using morphological image processing of 10 m forest mask rasters. We delineated 762,602 individual clusters of forest canopy loss, ranging in size from 0.01 to 1228 ha. The largest clusters were identified as bisection processes and were initiated early in the study period and contained multiple sub-clusters, suggesting expansion and merging of disturbance events. Fragmentation process labeling was sensitive to the edge depth, however the most robust processes were perforation, which covered 43% of total cluster area and perimeter loss (1.8%). Although nearly 34% of forest core areas transitioned into edge zones due to perforations, perimeters were the most relatively affected zone, whereby >7% of canopy was lost. While the number of small forests (‘Islets’) increased by 167.9%, core forest area declined along with a 12.6% decrease in the number of patches. This pattern suggests perimeter loss and bisection of core forests. Because fragmentation processes precipitate localized canopy restructuring and landscape reconfiguration, which have distinct ecological outcomes, this approach can be further applied both for spatially-explicit modeling of forest disturbance and as an indicator for monitoring habitats and biodiversity.
Language: English
Open Access status: gold
Estimated APC paid: $3,650
OpenAlex ID: W7213362351
FWCI:
Cites: 77
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Related to: 10