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Human fingerprint on structural density of forests globally


Climate change and human activities strongly influence forests, but uncertainties persist about the pervasiveness of these stressors and how they will shape future forest structure. Disentangling the relative influences of climate and human activities on global forest structure is essential for understanding and predicting the role of forests in biosphere carbon cycling and biodiversity conservation as well as for climate mitigation strategies. Using a synthetic forest canopy structure index, we map forest structural density at a near-global scale using a satellite dataset. We find distinct latitudinal patterns of multidimensional forest structure and that forests in protected areas (PAs) and so-called intact forest landscapes (IFLs) have an overall higher structural density than other forests. Human factors are the second-most important driver of forest structure after climate (temperature and rainfall), both globally and regionally, with negative associations to structural density. Human factors are the dominant driver of regional-scale variation in structural density in 35.1% of forests globally and even of forest structure in 31.4% and 22.4% of forests in PAs and IFLs, respectively. As anthropogenic forest degradation clearly affects many areas that are formally protected or perceived to be intact, it is vital to counteract human impacts more effectively in the planning and sustainable management of PAs and IFLs.

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Fig. 1: The GEDI satellite facilitates better understanding of human influences on global forest structure.
Fig. 2: Global patterns in forest structural density at 5.5 km × 5.5 km equal-area grid cells.
Fig. 3: PAs and IFLs exhibited higher forest structural density both generally and within each major forest biome type.
Fig. 4: Human factors are the second-most important driver of forest structural density.
Fig. 5: Global forest structural density is negatively linked to human impacts even within PAs and IFLs.

Data availability

All data needed to evaluate the conclusions in the paper are open access and are present in the paper and/or the Supplementary Information. GEDI data are freely available at The ecoregion data are available at The WDPA data are available at The IFLs data are available at The human footprint score data are available at The human modification index data are available at The travel time to the nearest city data are available at All the other environmental data including climate, fire, soil and topography dataset are available in the data catalogue of Google Earth Engine at

Code availability

The codes that support the main findings in this study are available at the Zenodo repository:


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This study was financed by the Youth Innovation Promotion Association Chinese Academy of Sciences (grant 2018084, to W.L.), H2020 Marie Skłodowska-Curie Actions (grant 893060, to W.L. and J.-C.S.), VILLUM Investigator project ‘Biodiversity Dynamics in a Changing World’ funded by VILLUM FONDEN (grant 16549, to J.-C.S.), National Natural Science Foundation of China (grant 42171369, to W.L.; grant 41730107, to Z.N.), the Director Fund of the International Research Center of Big Data for Sustainable Development Goals (grant CBAS2022DF012, to W.L.), the National Key Research and Development Project of China (grant 2021YFE0117900, to L.W.) and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant XDA19030000, to F.C.). We further consider this study a contribution to Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by Danish National Research Foundation (grant DNRF173, to J.-C.S.). We appreciate NASA for providing the valuable GEDI and MODIS data for our analyses. We also appreciate the Freepik company for providing the graphic resources for Fig. 1a.

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Authors and Affiliations



W.L. and J.-C.S. designed the research. W.L. performed the research with help from W.-Y.G., Z.N., L.W, Y.Q., F.C. and J.-C.S. M.P. assisted with the contribution framing and political implications of the study. W.L. wrote the first draft of the manuscript with contributions from J.C.S., and all authors contributed to subsequent versions of the paper.

Corresponding author

Correspondence to Wang Li.

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Extended data

Extended Data Fig. 1 Global forest zones within the observation coverage of GEDI satellite.

(a) Forest area defined by global tree cover product updated from ref. 33. (b) Protected areas belonging to IUCN protected areas categories I to VI and categorized as ‘designated’, ‘inscribed’ or ‘established’. (c) Intact forest landscape data from ref. 23.

Extended Data Fig. 2 Global patterns in individual forest canopy structure metrics.

(a) Canopy height (RH100). (b) Plant area index (PAI). (c) Canopy cover (CC). (d) Foliage height diversity (FHD). (e) Kernel normalized difference vegetation index (kNDVI). (a)-(d) are derived from the GEDI LiDAR data, (e) from MODIS multispectral data.

Extended Data Fig. 3 Latitude patterns in individual forest canopy structure metrics.

These individual metrics include canopy height (RH100), plant area index (PAI), canopy cover (CC), foliage height diversity (FHD), and kernel normalized difference vegetation index (kNDVI). The color ramp represents human pressure index (HPI) values. The dashed lines represent the smoothed conditional means fitted by loess regression. The shaded areas around the lines represent the 95% confidence interval.

Extended Data Fig. 4 Spatial distribution for local coefficient of determination (R2).

R2 is fitted by geographically weighted regression to identify local dominant influencing factor on forest structural density represented by canopy structure index (CSI).

Extended Data Fig. 5 Representative examples of spatial patterns in forest structural density depicted by the canopy structure index (CSI) in forests under different human impacts.

Map in the first row shows the spatial CSI pattern for near-global forests. Maps in the second row show the spatial patterns in CSI and locations of example forest parcels with different levels of canopy structural density and human impacts centered by an area of 110-km × 110-km (1: Entire block of intact forests severely fragmented by selective logging for agriculture in northwestern Paraguay; 2: Intensively managed forests in southwestern China; 3: Degrading intact forests fragmented by logging and road expansion in northern Brazil (the left part of the seamless forests are still intact and protected while the right part is severely fragmented); 4: Protected intact forests in central Congo basin). The statistic numbers in the second row represent the CSI values (mean ± standard deviation) for the forest grid cells within the central 110-km × 110-km areas. The satellite images in the third row were obtained from the Landsat-8 satellite around the year 2020 and show the forested areas within the rectangles in the second row.

Extended Data Fig. 6 Canopy structure index (CSI) of near-global forest is positively correlated with forest landscape integrity index (FLII).

The FLII represents the degree of anthropogenic modification for the start of 2019. r represents the Pearson’s correlation coefficient.

Extended Data Fig. 7 Forest structural density is negatively linked to 1-Time2City in protected areas (PAs) and intact forest landscapes (IFLs).

Time2City represents the magnitude of land-based travel time to the nearest densely-populated city. The x-axis represents the normalized Time2City subtracted from 1. The scatter plots in each column represent the grid cells for forests in PAs (left) and IFLs (right). The gray dots represent the forest grid cells. The color ramp of the contours represents the density level (≥ 0.1) of dots. The regression fitted lines are overlaid in red. r represents the Pearson’s correlation coefficient.

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Li, W., Guo, WY., Pasgaard, M. et al. Human fingerprint on structural density of forests globally. Nat Sustain (2023).

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