ABOUT PROJECT

Natural disturbances—ranging from strong winds, wildfires, and droughts to insect outbreaks—have shaped forest structure for centuries; however, under rising temperatures and prolonged dry spells, their scale and pace have markedly accelerated. In Central European forests, the greatest damage is caused by the European spruce bark beetle (Ips typographus L.), which exploits the vulnerability of spruces weakened by water deficits. Since 2012, this specific insect has triggered an unprecedented wave of dieback in the Białowieża Forest, resulting in the death of nearly two million spruce trees between 2015 and 2019. This has created extensive canopy gaps, increased deadwood volume, and accelerated the transition toward broadleaved dominance, opening a new ecological chapter while raising critical questions about the future resilience and biodiversity of the Białowieża Forest stands.

The complexity of these transitions exceeds the capabilities of traditional field surveys, requiring full-coverage, multi-temporal remote sensing data to provide a comprehensive picture. Modern remote sensing techniques—particularly Airborne Laser Scanning (ALS) and multispectral imagery—provide three-dimensional information on tree height, canopy cover, and deadwood distribution, while simultaneously allowing for species discrimination based on their spectral and structural properties. Our ALS dataset, spanning four time periods (2015, 2019, 2022, and 2026), captures the phases before, during, and after the peak of the outbreak, offering a globally unique timeline to track both the full trajectory of the disturbance and the recovery of this unique forest ecosystem.

The project aims to explain the spatial and temporal processes of succession, structural reorganization, and biodiversity shifts in the Białowieża Forest stands following natural forest ecosystem disturbances. The analysis will cover both the peak phase of tree mortality and the early stages of regeneration. By combining four-interval ALS data and multispectral imagery with permanent monitoring plot data, we will investigate the rate and pathways of spruce replacement by broadleaved species across various habitats and conservation zones; measure the dynamics of structural parameters (height, canopy cover, gaps) and their relationships with selected biodiversity indicators; and evaluate the effectiveness of multi-temporal remote sensing data and machine learning algorithms in mapping these processes from the individual tree to the landscape scale.

The project will deliver a series of maps showing species composition, vertical structure, and biodiversity dynamics, while spatial-statistical models will identify the factors driving or hindering post-disturbance succession. The final results will be shared via an interactive web-GIS platform, enabling foresters, researchers, and conservationists to monitor regeneration, delineate passive protection zones, and forecast future outbreak risks. Driven by an interdisciplinary team combining expertise in remote sensing, geoinformatics, statistics, and forest ecology, the project will provide not only new scientific insights but also practical tools to support sustainable forest management under growing climate pressures, while also supporting the implementation of EU biodiversity strategies and climate reporting.

WHAT DATA WE USED