Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The expansion of tree plantations across tropical biomes

Abstract

Across the tropics, recent agricultural shifts have led to a rapid expansion of tree plantations, often into intact forests and grasslands. However, this expansion is poorly characterized. Here, we report tropical tree plantation expansion between 2000 and 2012, based on classifying nearly 7 million unique patches of observed tree cover gain using optical and radar satellite imagery. The resulting map was a subsample of all tree cover gain but we coupled it with an extensive random accuracy assessment (n = 4,269 points) to provide unbiased estimates of expansion. Most predicted gain patches (69.2%) consisted of small patches of natural regrowth (31.6 ± 11.9 Mha). However, expansion of tree plantations also dominated increases in tree cover across the tropics (32.2 ± 9.4 Mha) with 92% of predicted plantation expansion occurring in biodiversity hotspots and 14% in arid biomes. We estimate that tree plantations expanded into 9.2% of accessible protected areas across the humid tropics, most frequently in southeast Asia, west Africa and Brazil. Given international tree planting commitments, it is critical to understand how future tree plantation expansion will affect remaining natural ecosystems.

Your institute does not have access to this article

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Pantropical distribution of natural regrowth and tree plantations.
Fig. 2: Estimated expansion of tree plantations and natural regrowth into terrestrial regions and biomes.
Fig. 3: Expansion of plantations into tropical protected areas.

Data availability

All data needed to replicate our results are available in the article, online or the supplementary information. Manually generated training data are available from the corresponding author, M.E.F., upon reasonable request. Predicted map outputs can be downloaded from the Global Forest Watch data repository: https://data.globalforestwatch.org/content/pantropical-tree-plantation-expansion-2000-2012/about

Code availability

All Python code needed to replicate our input data from Google Earth Engine are available on github at https://github.com/dohyung-kim/plantation. All R code for data analysis are available from the corresponding author, M.E.F., upon reasonable request, with the main R scripts available on github.

References

  1. Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).

    CAS  Article  Google Scholar 

  2. Gibbs, H. K. et al. Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proc. Natl Acad. Sci. USA 107, 16732–16737 (2010).

    CAS  Article  Google Scholar 

  3. Payn, T. et al. Changes in planted forests and future global implications. Ecol. Manag. 352, 57–67 (2015).

    Article  Google Scholar 

  4. Pendrill, F., Persson, U. M., Godar, J. & Kastner, T. Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition. Environ. Res. Lett. 14, 055003 (2019).

    Article  Google Scholar 

  5. Hurni, K. & Fox, J. The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014. J. Land Use Sci. 13, 198–219 (2018).

    Article  Google Scholar 

  6. Vijay, V. et al. The impacts of oil palm on recent deforestation and biodiversity loss. PLoS ONE 11, e0159668 (2016).

  7. Heilmayr, R., Echeverría, C. & Lambin, E. F. Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity. Nat. Sustain. 3, 701–709 (2020).

    Article  Google Scholar 

  8. le Maire, G., Dupuy, S., Nouvellon, Y., Loos, R. A. & Hakamada, R. Mapping short-rotation plantations at regional scale using MODIS time series: case of eucalypt plantations in Brazil. Remote Sens. Environ. 152, 136–149 (2014).

    Article  Google Scholar 

  9. Wang, M. M. H., Carrasco, L. R. & Edwards, D. P. Reconciling rubber expansion with biodiversity conservation. Curr. Biol. 30, 3825–3832 (2020).

    CAS  Article  Google Scholar 

  10. Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).

    CAS  Article  Google Scholar 

  11. Dave, R. et al. Second Bonn Challenge Progress Report: Application of the Barometer in 2018 (IUCN, 2019).

  12. Sloan, S., Meyfroidt, P., Rudel, T. K., Bongers, F. & Chazdon, R. The forest transformation: planted tree cover and regional dynamics of tree gains and losses. Glob. Environ. Change 59, 101988 (2019).

    Article  Google Scholar 

  13. Petersen, R. et al. Mapping Tree Plantations with Multispectral Imagery: Preliminary Results for Seven Tropical Countries (WRI, 2016).

  14. Erb, K.-H. et al. Land management: data availability and process understanding for global change studies. Glob. Change Biol. 23, 512–533 (2017).

    Article  Google Scholar 

  15. Souza, C. M. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat Archive and Earth Engine. Remote Sens. 12, 2735 (2020).

    Article  Google Scholar 

  16. Miettinen, J. et al. Extent of industrial plantations on Southeast Asian peatlands in 2010 with analysis of historical expansion and future projections. GCB Bioenergy 4, 908–918 (2012).

    Article  Google Scholar 

  17. Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).

  18. Puyravaud, J.-P., Davidar, P. & Laurance, W. F. Cryptic destruction of India’s native forests. Conserv. Lett. 3, 390–394 (2010).

    Article  Google Scholar 

  19. Fagan, M. E. et al. Mapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing data. Remote Sens. Environ. 216, 415–426 (2018).

    Article  Google Scholar 

  20. Tropek, R. et al. Comment on “High-resolution global maps of 21st-century forest cover change”. Science 344, 981 (2014).

    CAS  Article  Google Scholar 

  21. Global Forest Resources Assessment 2020 (FAO, 2020).

  22. FAOSTAT Agricultural Statistics Database (FAO, 2019); http://faostat.fao.org/site/291/default.aspx

  23. Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).

    CAS  Article  Google Scholar 

  24. Hurni, K., Schneider, A., Heinimann, A., Nong, D. H. & Fox, J. Mapping the expansion of boom crops in mainland Southeast Asia using dense time stacks of Landsat data. Remote Sens. 9, 320 (2017).

    Article  Google Scholar 

  25. Miettinen, J., Shi, C. & Liew, S. C. 2015 Land cover map of Southeast Asia at 250 m spatial resolution. Remote Sens. Lett. 7, 701–710 (2016).

    Article  Google Scholar 

  26. Torbick, N., Ledoux, L., Salas, W. & M. Zhao, M. Regional mapping of plantation extent using multisensor imagery. Remote Sens. 8, 236 (2016).

  27. Azizan, F. A., Kiloes, A. M., Astuti, I. S. & Abdul Aziz, A. Application of optical remote sensing in rubber plantations: a systematic review. Remote Sens. 13, 429 (2021).

    Article  Google Scholar 

  28. Bégué, A. et al. Remote sensing and cropping practices: a review. Remote Sens. 10, 99 (2018).

    Article  Google Scholar 

  29. Bey, A. & Meyfroidt, P. Improved land monitoring to assess large-scale tree plantation expansion and trajectories in Northern Mozambique. Environ. Res. Commun. 3, 115009 (2021).

  30. Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecol. Lett. 21, 989–1000 (2018).

    Article  Google Scholar 

  31. Féret, J.-B. & Asner, G. P. Spectroscopic classification of tropical forest species using radiative transfer modeling. Remote Sens. Environ. 115, 2415–2422 (2011).

    Article  Google Scholar 

  32. Poortinga, A. et al. Mapping plantations in Myanmar by fusing Landsat-8, Sentinel-2 and Sentinel-1 data along with systematic error quantification. Remote Sens. 11, 831 (2019).

    Article  Google Scholar 

  33. Gutiérrez-Vélez, V. H. et al. High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon. Environ. Res. Lett. 6, 044029 (2011).

    Article  Google Scholar 

  34. Descals, A. et al. High-resolution global map of smallholder and industrial closed-canopy oil palm plantations. Earth Syst. Sci. Data. 13, 1211–1231 (2021).

    Article  Google Scholar 

  35. Ordway, E. M., Naylor, R. L., Nkongho, R. N. & Lambin, E. F. Oil palm expansion and deforestation in Southwest Cameroon associated with proliferation of informal mills. Nat. Commun. 10, 114 (2019).

    CAS  Article  Google Scholar 

  36. Heilmayr, R., Echeverría, C., Fuentes, R. & Lambin, E. F. A plantation-dominated forest transition in Chile. Appl. Geogr. 75, 71–82 (2016).

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  38. Bond, W. J., Stevens, N., Midgley, G. F. & Lehmann, C. E. R. The trouble with trees: afforestation plans for Africa. Trends Ecol. Evol. 34, 963–965 (2019).

    Article  Google Scholar 

  39. Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).

    Article  Google Scholar 

  40. Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).

    Article  Google Scholar 

  41. Fagan, M. E. A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps. Glob. Change Biol. 26, 4679–4690 (2020).

  42. Bastin, J. F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).

    CAS  Article  Google Scholar 

  43. Fagan, M. E., Reid, J. L., Holland, M. B., Drew, J. G. & Zahawi, R. A. How feasible are global forest restoration commitments? Conserv. Lett. 13, e12700 (2020).

    Article  Google Scholar 

  44. Malkamäki, A. et al. A systematic review of the socio-economic impacts of large-scale tree plantations, worldwide. Glob. Environ. Change 53, 90–103 (2018).

    Article  Google Scholar 

  45. Schwartz, N. B., Aide, T. M., Graesser, J., Grau, H. R. & Uriarte, M. Reversals of reforestation across Latin America limit climate mitigation potential of tropical forests. Front. For. Glob. Change 3, 85 (2020).

    Article  Google Scholar 

  46. Noojipady, P. et al. Managing fire risk during drought: the influence of certification and El Niño on fire-driven forest conversion for oil palm in Southeast Asia. Earth Syst. Dynam. 8, 749–771 (2017).

  47. Bullock, E. L., Woodcock, C. E., Souza, C. Jr. & Olofsson, P. Satellite-based estimates reveal widespread forest degradation in the Amazon. Glob. Change Biol. 26, 2956–2969 (2020).

    Article  Google Scholar 

  48. Sloan, S. & Sayer, J. A. Forest Ecology and Management Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. Ecol. Manag. 352, 134–145 (2015).

    Article  Google Scholar 

  49. Heinrich, V. H. A. et al. Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change. Nat. Commun. 12, 1785 (2021).

    CAS  Article  Google Scholar 

  50. 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 

  51. Bernal, B., Murray, L. T. & Pearson, T. R. H. Global carbon dioxide removal rates from forest landscape restoration activities. Carbon Balance Manag. 13, 22 (2018).

    CAS  Article  Google Scholar 

  52. Li, W., Goodchild, M. F. & Church, R. An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems. Int. J. Geogr. Inf. Sci. 27, 1227–1250 (2013).

    Article  Google Scholar 

  53. Asner, G. P. Cloud cover in Landsat observations of the Brazilian Amazon. Int. J. Remote Sens. 22, 3855–3862 (2001).

    Article  Google Scholar 

  54. Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).

    Article  CAS  Google Scholar 

  55. Gutiérrez-Vélez, V. H. & DeFries, R. Annual multi-resolution detection of land cover conversion to oil palm in the Peruvian Amazon. Remote Sens. Environ. 129, 154–167 (2013).

    Article  Google Scholar 

  56. Reiche, J. et al. Combining satellite data for better tropical forest monitoring. Nat. Clim. Change 6, 120–122 (2016).

    Article  Google Scholar 

  57. Erinjery, J. J., Singh, M. & Kent, R. Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery. Remote Sens. Environ. 216, 345–354 (2018).

    Article  Google Scholar 

  58. Shimada, M. et al. New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens. Environ. 155, 13–31 (2014).

    Article  Google Scholar 

  59. Torres, R. et al. GMES Sentinel-1 mission. Remote Sens. Environ. 120, 9–24 (2012).

    Article  Google Scholar 

  60. Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).

    Article  Google Scholar 

  61. World Database on Protected Areas User Manual 1.4 (UNEP-WCMC, 2016).

  62. AutoML: Automatic Machine Learning (H2O.ai, 2020); https://h2o-release.s3.amazonaws.com/h2o/rel-yau/5/docs-website/h2o-docs/automl.html

  63. Healey, S. P. et al. Mapping forest change using stacked generalization: an ensemble approach. Remote Sens. Environ. 204, 717–728 (2018).

    Article  Google Scholar 

  64. Lagomasino, D. et al. Measuring mangrove carbon loss and gain in deltas. Environ. Res. Lett. 14, 25002 (2019).

    Article  Google Scholar 

  65. Bunting, P. et al. The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sens. 10, 1669 (2018).

    Article  Google Scholar 

  66. Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).

    Article  Google Scholar 

  67. Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).

    Article  Google Scholar 

  68. Stehman, S. V. Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. Int. J. Remote Sens. 35, 4923–4939 (2014).

    Article  Google Scholar 

  69. Olofsson, P. et al. Mitigating the effects of omission errors on area and area change estimates. Remote Sens. Environ. 236, 111492 (2020).

    Article  Google Scholar 

  70. Database of Global Administrative Areas (GADM) v.3.6 (GADM, 2018); https://gadm.org/download_country_v3.html

  71. Hijmans, R. J., Williams, E., Vennes, C. M. & Hijmans, M. R. J. Package ‘geosphere’ version 1.5-10. Spherical trigonometry (2017).

  72. Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).

    Article  Google Scholar 

  73. Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. in Biodiversity Hotspots: Distribution and Protection of Conservation Priority Areas (eds Zachos, F. E. & Habel, J. C.) 3–22 (Springer, 2011).

  74. Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).

Download references

Acknowledgements

We thank R. L. Chazdon, R. Crouzeilles, H. L. Beyer, B. C. Tice, S. Stehman and D. Lagomasino for their contributions to this project’s development. This research was supported by the National Aeronautics and Space Administration under grant no. 80NSSC21K0297.

Author information

Authors and Affiliations

Authors

Contributions

M.E.F. and D.H.K. were responsible for conceptualization and formal analysis. M.E.F. undertook visualization, project administration and supervision. M.E.F., D.H.K. and A.T. developed the methodology. M.E.F., L.F., J.D., H.C., W.S., J. Slaughter, J. Schaferbien and A.T. conducted validation. N.L.H., E.G. and E.M.O. provided resources (datasets). M.E.F., D.H.K., N.L.H., A.T., E.G. and E.M.O. were responsible for writing.

Corresponding author

Correspondence to Matthew E. Fagan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Sustainability thanks Sean Sloan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Results, Figs. 1–13 and Tables 1–15.

Reporting Summary

Supplementary Data 1

The testing data used to assess model predictive accuracy is enclosed (“NatureSust_Fagan_test_fin_111520_allC_locsXY_selected2.csv”). The format is a comma-separated text data table (n = 2,000); because some large polygons were sampled more than once, there are only 1,881 unique rows. See the notes column for column name explanations; the X and Y columns describe the patch polygon centroids. The full polygons were used to assess accuracy. The vector polygon boundaries are available from the corresponding author upon request.

Supplementary Data 2

The independent map accuracy assessment data is enclosed (“strRandSampRef_allJoinALL_v5fBCGsel_NatureSustSuppData.csv”). The format is a comma-separated text data table (n = 4,269), with each row representing a stratified random point location. See the description column for column name explanations.

Supplementary Data 3

The land cover class conversion table used to reclassify the TMF map product to match our reference data is enclosed (“Reclass_moist_forest_analysis3_NatSustainSuppData.csv”). The format is a comma-separated text data table, with each row representing a TMF land cover transition class subtype. See the notes column for column name explanations.

Supplementary Data 4

The comparative accuracy assessment data used to assess the GFC and TMF map products across the humid biome is enclosed (“pointsAccA_selectedHansenTMF_fin_NatSustainSuppData.csv”). The format is a comma-separated text data table (n = 2,691), with each row representing a random point location. See the notes column for column name explanations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fagan, M.E., Kim, DH., Settle, W. et al. The expansion of tree plantations across tropical biomes. Nat Sustain (2022). https://doi.org/10.1038/s41893-022-00904-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41893-022-00904-w

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing