Assessing the Suitability of Available Global Forest Maps as Reference Tools for EUDR-Compliant Deforestation Monitoring
Year: 2025
Type: article
Abstract: Deforestation monitoring is critical to support compliance with regulatory frameworks such as the EU Deforestation Regulation (EUDR), which requires that products containing or derived from beef, cacao, coffee, oil palm, rubber, soy, and timber are deforestation-free after December 31, 2020. Earth Observation (EO) offers a means to assess deforestation, yet map-based verification remains technically limited and uncertain. This study addresses the lack of a systematic assessment of global Forest/Non-Forest (FNF) and Land Use/Land Cover (LULC) datasets by identifying and evaluating 21 publicly available global forest reference maps for their alignment with EUDR criteria. Based on EUDR-related parameters (temporal proximity, spatial resolution, and forest definition) and accuracy metrics, eight datasets are evaluated as suitable. However, most fail to fully meet all EUDR requirements, particularly forest definitions, with only two datasets satisfying all indicators. Reported accuracy metrics reveal a general overestimation of forest areas, while canopy height-based maps tend to underestimate tree cover, potentially excluding forested regions. Regional comparisons show more consistent estimates in South America, while Europe and North America display greater variability. These findings support informed decision-making by companies and policymakers for selecting suitable datasets, while also highlighting reveals underlying conflicts and challenges associated with the use of global forest maps for regulatory compliance.
Source: Remote Sensing
DOI: 10.3390/rs17173012
Authors: Juliana Freitas Beyer, Margret Köthke, Melvin Lippe
Institution: Johann Heinrich von Thünen-Institut
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $3,258
Award: G877980647
OpenAlex ID: W4413824696
