Abstract
Transportation networks threaten global forests, but prior assessments have been regional or limited to single metrics (e.g., forest cover). Here, we present a global analysis of multidimensional road effects on forests, using high-resolution remote sensing data and a Grid-wise Environmental Matching for Background Reference (GEM-BR) strategy. We detect 18.6% lower forest cover, 2.7 m shorter canopy height, 52.2 gC m-2 yr-1 reduced net primary productivity, and 23.0 patches per km2 higher fragmentation within 1 km of roads compared to reference areas. Impacts extend up to 5 km with a clear distance decay effect, totaling 4.26 million km2 of forest loss—equivalent to 10.7% of the 2020 global forest extent. The Global South (tropics accounting for 54.8%) faces severe, worsening degradation (2000–2020), while the Global North shows milder impacts, with partial recovery. Critically, 89% of grid cells exhibit conflicting long-term trends across metrics, highlighting the inadequacy of cover-only assessments. We further find that road-linked degradation is tightly coupled with local human activity, and that global protected areas have insufficient capacity to curb ongoing degradation. Differences in impacts among regions suggest that road-linked forest degradation is tied to governance choices—urging integrated transport-forest planning to balance development and conservation.
Data availability
All the processed source data generated in this study (supporting bar, column, line charts, and statistical analyses) are available as a Source Data file. The raw public datasets used in this study are available in their respective official or recommended public repositories under the following persistent access links/DOIs: OpenStreetMap (OSM) vector road data in Geofabrik (https://download.geofabrik.de/); Global Roads Inventory Project 4 (GRIP4) dataset in the GLOBIO information portal (https://www.globio.info/download-grip-dataset); GLAD Global Land Cover and Land Use Change (GLCLUC2020) datasets in the University of Maryland’s GLAD Laboratory (https://glad.umd.edu/dataset/GLCLUC2020/); MOD17A3HGF V061 product in NASA’s EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) (https://lpdaac.usgs.gov/products/mod17a3hgfv061/)77; SoilGrids Version 2 in the International Soil Reference and Information Center (ISRIC) (https://files.isric.org/soilgrids/latest/); CHELSA Version 2.1 in the CHELSA Climate Portal (https://www.chelsa-climate.org/)90; ASTER Global Digital Elevation Model (GDEM) Version 3 in NASA Earthdata Search (https://search.earthdata.nasa.gov/); 2019 Human Footprint Index (ml-HFI) data in the Mountain Scholar repository (https://hdl.handle.net/10217/216207)91; 2020 nighttime light datasets in Figshare (https://doi.org/10.6084/m9.figshare.9828827.v2)92; World Database on Protected Areas (WDPA) in Protected Planet (https://www.protectedplanet.net/en); Köppen-Geiger climate zone map in the GLOH2O portal (https://www.gloh2o.org/koppen/); and Version 2 global map of plantation establishment years (1982–2020) in Figshare (https://doi.org/10.6084/m9.figshare.19070084.v2)93. Large spatial distribution raster datasets are not provided in the Supplementary Information or Source Data file due to their substantial file sizes, but are openly accessible via the original public repositories listed above. All DOIs for datasets have been included in the Reference list. No access restrictions apply to any of the minimum dataset necessary for interpreting, verifying, or extending the research—all data are freely available without undue qualifications. Source data are provided in this paper.
Code availability
The Python code used to calculate the Comprehensive Environmental Index (CEI) and quantify road impacts in road/RRI zones, along with example data to support reproducibility of the analyses in this study, is publicly available via GitHub (https://github.com/DechengZHOU/RoadImpactsForests.git). The exact version (v1.0.0) used in the manuscript has been assigned a permanent, citable DOI and is included in the reference list94. The code is provided for non-commercial, academic research purposes only, with permission for use, modification, and redistribution provided appropriate attribution to the original authors and this study is included. Unauthorized re-publication or commercial exploitation of the code is prohibited.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 42571127; D.Z.), the National Key R&D Program of China (Grant No. 2021YFB2600100; L.H.), and the Hainan Talent Convergence Initiative (Grant No. HNYT20250005; S.L.). J.X. was supported by bridge support and the Iola Hubbard Climate Change Endowment from the University of New Hampshire.
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D.Z., J.X., and S.Z. conceived the study and designed the research framework. D.Z. and L.Z. curated, processed, and analyzed the datasets. D.Z. drafted the initial manuscript. S.Z. supervised the entire research process. D.Z., J.X., S.L., L.H., J.F., and S.Z. contributed to data interpretation, manuscript drafting, and critical revision of intellectual content. All authors approved the final version for submission.
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Zhou, D., Xiao, J., Liu, S. et al. Global impacts of transportation infrastructure on forest degradation and loss. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69150-4
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DOI: https://doi.org/10.1038/s41467-026-69150-4
