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

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.

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Publication Details

Journal
MethodsX
Published
2025-04-06
DOI
https://doi.org/10.1016/j.mex.2025.103303
Citations
2
Primary Topic
Forest ecology and management
Type
article
Field-Weighted Citation Impact
4.63

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article

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

Manuel Wolff, Benjamin Labohm, Dagmar Haase
2 citations
MethodsX
Forest ecology and management
4.63
article

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

Manuel Wolff, Benjamin Labohm, Dagmar Haase
article en
2 citations

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.

MethodsXVol. 14
Helmholtz Centre for Environmental Research (DE), Humboldt-Universität zu Berlin (DE)
Horizon 2020
Openalex Percentile: Top 7%
Forest ecology and management
4.63
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