Work

Integration of high-resolution data for a complementary assessment of forest dynamics in Europe

Year: 2025

Type: article

Abstract: This study addresses the need for a cohesive pan-European forest monitoring system by developing a methodological framework to generate and provide spatially explicit and complementary indicators of forest dynamics. Utilizing Copernicus High-Resolution Layer Tree Cover Density data, we operationalize two key indicators-forest extent and condition-essential for robust forest monitoring across Europe. Our multi-step data processing methodology enhances data interoperability and usability, mitigating biases. By integrating both, changes in forest area and canopy density between 2012 and 2018, our approach provides nuanced insights into forest dynamics. These indicators offer robust monitoring supporting the assessment of forest resilience amidst climate change impacts and other stressors. This paper contributes a ready-to-use dataset on European forest dynamics, leveraging advanced technologies and big data availability to support sustainable forest management and the evaluation of Agenda 2030 goals. • Development of spatially explicit indicators for forest extent and condition. • Integration of Copernicus HRL TCD data using a standardized processing framework. • Application of multi-step data processing to ensure data quality and reliability.

Source: MethodsX

DOI: 10.1016/j.mex.2025.103303

Authors: Benjamin Labohm, Manuel Wolff, Dagmar Haase

Institutions: Humboldt-Universität zu Berlin, Helmholtz Centre for Environmental Research

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $1,260