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Human fingerprint on structural density of forests globally

Nature Sustainability (2023)Cite this article

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Abstract

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 https://lpdaac.usgs.gov/. The ecoregion data are available at https://ecoregions2017.appspot.com/. The WDPA data are available at www.protectedplanet.net. The IFLs data are available at http://www.intactforests.org/. The human footprint score data are available at http://www.ciesin.org/. The human modification index data are available at https://sedac.ciesin.columbia.edu/data/set/lulc-human-modification-terrestrial-systems/data-download. The travel time to the nearest city data are available at https://www.map.ox.ac.uk/accessibility_to_cities/. All the other environmental data including climate, fire, soil and topography dataset are available in the data catalogue of Google Earth Engine at https://developers.google.com/earth-engine/datasets/catalog.

Code availability

The codes that support the main findings in this study are available at the Zenodo repository: https://doi.org/10.5281/zenodo.7266231.

References

  1. Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).

    Article  Google Scholar 

  2. Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. https://doi.org/10.1126/sciadv.1600821 (2017).

  3. Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).

    Article  CAS  Google Scholar 

  4. Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).

    Article  CAS  Google Scholar 

  5. Grantham, H. S. et al. The emerging threat of extractives sector to intact forest landscapes. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2021.692338 (2021).

  6. IPBES: Summary for Policymakers. In The Global Assessment Report on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) (IPBES, 2019).

  7. Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01026-5 (2021).

    Article  Google Scholar 

  8. Maxwell, S. L. et al. Degradation and forgone removals increase the carbon impact of intact forest loss by 626%. Sci. Adv. 5, eaax2546 (2019).

    Article  CAS  Google Scholar 

  9. Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).

    Article  CAS  Google Scholar 

  10. Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).

    Article  Google Scholar 

  11. Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).

    Article  CAS  Google Scholar 

  12. Coad, L. et al. Measuring impact of protected area management interventions: current and future use of the global database of protected area management effectiveness. Phil. Trans. R. Soc. B 370, 20140281 (2015).

    Article  Google Scholar 

  13. Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).

    Article  CAS  Google Scholar 

  14. Ehbrecht, M. et al. Global patterns and climatic controls of forest structural complexity. Nat. Commun. 12, 519 (2021).

    Article  CAS  Google Scholar 

  15. Zhang, J., Nielsen, S. E., Mao, L., Chen, S. & Svenning, J. C. Regional and historical factors supplement current climate in shaping global forest canopy height. J. Ecol. 104, 469–478 (2016).

    Article  Google Scholar 

  16. Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl Acad. Sci. USA 118, e2023483118 (2021).

    Article  CAS  Google Scholar 

  17. Knight, C. A. et al. Land management explains major trends in forest structure and composition over the last millennium in California’s Klamath Mountains. Proc. Natl Acad. Sci. USA 119, e2116264119 (2022).

    Article  CAS  Google Scholar 

  18. Stephens, L. et al. Archaeological assessment reveals Earth’s early transformation through land use. Science 365, 897–902 (2019).

    Article  CAS  Google Scholar 

  19. Asner, G. P., Llactayo, W., Tupayachi, R. & Luna, E. R. Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring. Proc. Natl Acad. Sci. USA 110, 18454–18459 (2013).

    Article  CAS  Google Scholar 

  20. Hoang, N. T. & Kanemoto, K. Mapping the deforestation footprint of nations reveals growing threat to tropical forests. Nat. Ecol. Evol. 5, 845–853 (2021).

    Article  Google Scholar 

  21. Lim, C. L., Prescott, G. W., De Alban, J. D. T., Ziegler, A. D. & Webb, E. L. Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar. Conserv. Biol. 31, 1362–1372 (2017).

    Article  Google Scholar 

  22. Sandel, B. & Svenning, J. C. Human impacts drive a global topographic signature in tree cover. Nat Commun. https://doi.org/10.1038/ncomms3474 (2013).

  23. Potapov, P. et al. Mapping the world’s intact forest landscapes by remote sensing. Ecol. Soc. 13, 51 (2008).

    Article  Google Scholar 

  24. Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).

    Article  CAS  Google Scholar 

  25. Yang, H. et al. A global assessment of the impact of individual protected areas on preventing forest loss. Sci. Total Environ. 777, 145995 (2021).

    Article  CAS  Google Scholar 

  26. Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).

    Article  CAS  Google Scholar 

  27. Clerici, N. et al. Deforestation in Colombian protected areas increased during post-conflict periods. Sci. Rep. 10, 4971 (2020).

    Article  CAS  Google Scholar 

  28. Heino, M. et al. Forest loss in protected areas and intact forest landscapes: a global analysis. PLoS ONE 10, e0138918 (2015).

    Article  Google Scholar 

  29. Leberger, R., Rosa, I. M. D., Guerra, C. A., Wolf, F. & Pereira, H. M. Global patterns of forest loss across IUCN categories of protected areas. Biol. Conserv. 241, 108299 (2020).

    Article  Google Scholar 

  30. Wade, C. M. et al. What is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018. Forests 11, 539 (2020).

    Article  Google Scholar 

  31. Transforming Our World: The 2030 Agenda for Sustainable Development (UN DESA, 2016).

  32. Burleson, E. Paris Agreement and consensus to address climate challenge. ASIL Insight 20, 8 (2016).

    Google Scholar 

  33. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  CAS  Google Scholar 

  34. Quegan, S. et al. The European Space Agency BIOMASS mission: measuring forest above-ground biomass from space. Remote Sens. Environ. 227, 44–60 (2019).

    Article  Google Scholar 

  35. Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2011JG001708 (2011).

  36. Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).

    Article  Google Scholar 

  37. Atkins, J. W., Fahey, R. T., Hardiman, B. S. & Gough, C. M. Forest canopy structural complexity and light absorption relationships at the subcontinental scale. J. Geophys. Res. Biogeosci. 123, 1387–1405 (2018).

    Article  Google Scholar 

  38. Scarth, P., Armston, J., Lucas, R. & Bunting, P. A structural classification of Australian vegetation using ICESat/GLAS, ALOS PALSAR, and Landsat sensor data. Remote Sens. 11, 147 (2019).

    Article  Google Scholar 

  39. Dubayah, R. et al. The global ecosystem dynamics investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).

    Article  Google Scholar 

  40. Lang, N. et al. Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sens. Environ. 268, 112760 (2022).

    Article  Google Scholar 

  41. Marselis, S. M., Keil, P., Chase, J. M. & Dubayah, R. The use of GEDI canopy structure for explaining variation in tree species richness in natural forests. Environ. Res. Lett. 17, 045003 (2022).

    Article  Google Scholar 

  42. MacArthur, R. H. & MacArthur, J. W. On bird species diversity. Ecology 42, 594–598 (1961).

    Article  Google Scholar 

  43. Walter, J. A., Stovall, A. E. L. & Atkins, J. W. Vegetation structural complexity and biodiversity in the Great Smoky Mountains. Ecosphere 12, e03390 (2021).

    Article  Google Scholar 

  44. Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).

    Article  CAS  Google Scholar 

  45. Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S. & Kiesecker, J. Managing the middle: a shift in conservation priorities based on the global human modification gradient. Glob. Change Biol. 25, 811–826 (2019).

    Article  Google Scholar 

  46. Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).

    Article  CAS  Google Scholar 

  47. Chazdon, R. L. et al. A policy‐driven knowledge agenda for global forest and landscape restoration. Conserv. Lett. 10, 125–132 (2017).

    Article  Google Scholar 

  48. Skidmore, A. K. et al. Priority list of biodiversity metrics to observe from space. Nat. Ecol. Evol. 5, 896–906 (2021).

    Article  Google Scholar 

  49. Schneider, F. D. et al. Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat. Commun. 8, 1441 (2017).

    Article  Google Scholar 

  50. Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).

    Article  CAS  Google Scholar 

  51. Ponta, N. et al. Drivers of transgression: what pushes people to enter protected areas. Biol. Conserv. 257, 109121 (2021).

    Article  Google Scholar 

  52. Pack, S. M. et al. Protected area downgrading, downsizing, and degazettement (PADDD) in the Amazon. Biol. Conserv. 197, 32–39 (2016).

    Article  Google Scholar 

  53. Tollefson, J. Illegal mining in the Amazon hits record high amid Indigenous protests. Nature 598, 15–16 (2021).

    Article  CAS  Google Scholar 

  54. Thies, C., Rosoman, G., Cotter, J. & Meaden, S. Intact Forest Landscapes. Why It Is Crucial to Protect Them from Industrial Exploitation Technical Note Bd 5 (Greenpeace, 2011).

  55. Chazdon, R. L. Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science 320, 1458–1460 (2008).

    Article  CAS  Google Scholar 

  56. Lindenmayer, D. B. et al. New policies for old trees: averting a global crisis in a keystone ecological structure. Conserv. Lett. 7, 61–69 (2014).

    Article  Google Scholar 

  57. Dave, R. et al. Second Bonn Challenge Progress Report: Application of the Barometer in 2018 (IUCN, 2018).

  58. Tang, H. & Armston, J. Algorithm Theoretical Basis Document (ATBD) for GEDI L2B Footprint Canopy Cover and Vertical Profile Metrics (Goddard Space Flight Center, 2019).

  59. Adam, M., Urbazaev, M., Dubois, C. & Schmullius, C. Accuracy assessment of GEDI terrain elevation and canopy height estimates in European temperate forests: influence of environmental and acquisition parameters. Remote Sens. 12, 3948 (2020).

    Article  Google Scholar 

  60. Dorado-Roda, I. et al. Assessing the accuracy of GEDI data for canopy height and aboveground biomass estimates in Mediterranean forests. Remote Sens. 13, 2279 (2021).

    Article  Google Scholar 

  61. Duncanson, L. et al. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sens. Environ. 270, 112845 (2022).

    Article  Google Scholar 

  62. Hofton, M., Blair, J. B., Story, S. & Yi, D. Algorithm Theoretical Basis Document (ATBD) (NASA, 2020).

  63. Dubayah, R. et al. GEDI L3 Gridded Land Surface Metrics v.2 (ORNL DAAC, 2021).

  64. Roy, D. P., Kashongwe, H. B. & Armston, J. The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring. Sci. Remote Sens. 4, 100024 (2021).

    Article  Google Scholar 

  65. Potapov, P., Hansen, M. C., Stehman, S. V., Loveland, T. R. & Pittman, K. Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss. Remote Sens. Environ. 112, 3708–3719 (2008).

    Article  Google Scholar 

  66. Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).

    Article  Google Scholar 

  67. Silva, C. A. et al. rGEDI: NASA’s global ecosystem ynamics investigation (GEDI) data visualization and processing. R package version 0.1.2. (2020).

  68. The R Project for Statistical Computing (The R Foundation, 2014); https://www.R-project.org/

  69. Fischer, B., Smith, M., Pau, G., Morgan, M. & van Twisk, D. rhdf5: R interface to HDF5. R package version 2.40.0 (2022).

  70. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).

    Article  Google Scholar 

  71. Giglio, L., Loboda, T., Roy, D. P., Quayle, B. & Justice, C. O. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sens. Environ. 113, 408–420 (2009).

    Article  Google Scholar 

  72. Hengl, T. & Wheeler, I. Soil organic carbon content in x 5 g/kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Zenodo https://doi.org/10.5281/zenodo.1475458 (2018).

  73. Farr, T. The shuttle radar topography mission. Rev. Geophys. https://doi.org/10.1029/2005RG000183 (2007).

  74. James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).

  75. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  76. Bivand, R. et al. Package ‘spdep’: spatial dependence: weighting schemes, statistics version 1.2-7 (The Comprehensive R Archive Network, 2015).

  77. Bivand, R., Yu, D., Nakaya, T., Garcia-Lopez, M.-A. & Bivand, M. R. Package ‘spgwr’: geographically eighted regression. R package version 0.6-35 (2020).

  78. Fotheringham, A. S., Brunsdon, C. & Charlton, M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (Wiley, 2003).

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Acknowledgements

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

  1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Wang Li, Zheng Niu & Li Wang

  2. Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Biology, Aarhus University, Aarhus, Denmark

    Wang Li, Wen-Yong Guo, Maya Pasgaard & Jens-Christian Svenning

  3. Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark

    Wang Li, Wen-Yong Guo, Maya Pasgaard & Jens-Christian Svenning

  4. Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Research Center for Global Change and Complex Ecosystems, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China

    Wen-Yong Guo

  5. Center for Sustainable Landscapes under Global Change (SustainScapes), Aarhus University, Aarhus, Denmark

    Maya Pasgaard

  6. International Research Center of Big Data for Sustainable Development Goals (CBAS), Beijing, China

    Fang Chen & Yuchu Qin

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Contributions

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.

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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). https://doi.org/10.1038/s41893-022-01020-5

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