Labohm Benjamin, Wolff Manuel, Haase Dagmar
Department of Geography, Lab for Landscape Ecology, Humboldt Universität zu Berlin, Unter den Linden 6, 10117 Berlin, Germany.
Department of Urban and Environmental Sociology, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 8 15, 04318 Leipzig, Germany.
MethodsX. 2025 Apr 3;14:103303. doi: 10.1016/j.mex.2025.103303. eCollection 2025 Jun.
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.
本研究通过开发一个方法框架来生成并提供森林动态的空间明确且互补的指标，以满足建立一个具有凝聚力的泛欧森林监测系统的需求。利用哥白尼高分辨率层树木覆盖密度数据，我们实施了两个关键指标——森林范围和状况，这对于欧洲全面的森林监测至关重要。我们的多步骤数据处理方法增强了数据的互操作性和可用性，减少了偏差。通过整合2012年至2018年期间森林面积和树冠密度的变化，我们的方法提供了对森林动态的细致洞察。这些指标提供了有力的监测，支持在气候变化影响和其他压力源下对森林恢复力的评估。本文提供了一个关于欧洲森林动态的现成数据集，利用先进技术和大数据可用性来支持可持续森林管理和对2030年议程目标的评估。
• 开发森林范围和状况的空间明确指标。
• 使用标准化处理框架整合哥白尼高分辨率层树木覆盖密度数据。
• 应用多步骤数据处理以确保数据质量和可靠性。
