Skip to main content

Thank you for visiting 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.

  • Article
  • Published:

Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation


Tropical forests have undergone extensive deforestation and degradation during the past few decades, but the area and the carbon loss due to degradation could be larger than the losses from deforestation. Degraded forests also induce biophysical feedback on climate, as they sustain less cooling from evapotranspiration. Here we estimate the biophysical and biogeochemical temperature changes caused by tropical moist forest degradation using high-resolution remote sensing data from 2010. Degraded forests, including burned, isolated, edge and other degraded forests, account for 24.1% of the total tropical moist forest area. The land surface temperature of degraded tropical moist forests is higher than that of nearby intact forests, leading to a warming effect of 0.022 ± 0.014 °C over the tropics. The cumulative carbon deficit of degraded forests reaches 6.1 ± 2.0 PgC, equivalent to a biogeochemical warming effect of 0.026 ± 0.013 °C. Forest degradation caused by anthropogenic disturbances from 1990 to 2010 induces a daytime warming effect of 0.018 ± 0.008 °C and a carbon deficit of 2.3 ± 0.8 PgC. These values are of the same order of magnitude as those due to deforestation. Our results emphasize the importance of accounting for the combined biophysical and biogeochemical effects in mitigation pledges related to reducing forest degradation and the restoration of tropical forest.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Distribution of the dominant forest degradation types and the area fractions in TMF in 2010.
Fig. 2: ΔLST of forest degradation estimated from different satellite data.
Fig. 3: AGC deficit in degraded forests.
Fig. 4: Isolated and edge forests formed before and after 1990.

Similar content being viewed by others

Data availability

The 30 m TMF15 dataset can be downloaded from The 25 m biomass data from GlobBiomass can be downloaded from (Africa), (South America, north), (South America, south) and (South Asia). A search in Zenodo for ‘Globbiomass’ will return data for other regions. The 100 m biomass data from CCIBiomass46 can be downloaded from The 250 m FireCCI burned area data26 can be downloaded from The 30 m tree cover map in 201052 can be downloaded from The 30 m Landsat LST dataset28 can be downloaded from The 30 m ASTER DEM V3 dataset can be downloaded from The 1 km MODIS LST dataset29 can be downloaded from CMIP5 data can be downloaded from The source data for Figs.14 and Extended Data Figs. 26 are available via Zenodo at

Code availability

The Python script used to analyse the data is available at


  1. Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).

    Article  Google Scholar 

  2. Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).

    Article  Google Scholar 

  3. Peng, S.-S. et al. Afforestation in China cools local land surface temperature. Proc. Natl Acad. Sci. USA 111, 2915–2919 (2014).

    Article  Google Scholar 

  4. Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 6603 (2015).

    Article  Google Scholar 

  5. Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850-2015. Glob. Biogeochem. Cycles 31, 456–472 (2017).

    Article  Google Scholar 

  6. Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).

    Article  Google Scholar 

  7. Longo, M. et al. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Glob. Biogeochem. Cycles 30, 1639–1660 (2016).

    Article  Google Scholar 

  8. Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).

    Article  Google Scholar 

  9. Smith, I. A., Hutyra, L. R., Reinmann, A. B., Marrs, J. K. & Thompson, J. R. Piecing together the fragments: elucidating edge effects on forest carbon dynamics. Front. Ecol. Environ. 16, 213–221 (2018).

    Article  Google Scholar 

  10. Franklin, C. M. A., Harper, K. A. & Clarke, M. J. Trends in studies of edge influence on vegetation at human-created and natural forest edges across time and space. Can. J. For. Res. 51, 274–282 (2020).

    Article  Google Scholar 

  11. Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).

    Article  Google Scholar 

  14. Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Schoene, D., Killmann, W., Lüpke, H. V. & LoycheWilkie, M. Forests and Climate Change Working Paper 5: Definitional Issues Related to Reducing Emissions from Deforestation in Developing Countries (FAO, 2007).

  17. Goetz, S. J. et al. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environ. Res. Lett. 10, 123001 (2015).

    Article  Google Scholar 

  18. Pearson, T. R. H., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12, 3 (2017).

    Article  Google Scholar 

  19. Cadenasso, M. L., Traynor, M. M. & Pickett, S. T. Functional location of forest edges: gradients of multiple physical factors. Can. J. For. Res. 27, 774–782 (1997).

    Article  Google Scholar 

  20. Schmidt, M., Jochheim, H., Kersebaum, K.-C., Lischeid, G. & Nendel, C. Gradients of microclimate, carbon and nitrogen in transition zones of fragmented landscapes – a review. Agric. For. Meteorol. 232, 659–671 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Silva Junior, C. H. L. et al. Amazonian forest degradation must be incorporated into the COP26 agenda. Nat. Geosci. 14, 634–635 (2021).

    Article  Google Scholar 

  23. Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl Acad. Sci. USA 104, 6550–6555 (2007).

    Article  Google Scholar 

  24. Windisch, M. G., Davin, E. L. & Seneviratne, S. I. Prioritizing forestation based on biogeochemical and local biogeophysical impacts. Nat. Clim. Change 11, 867–871 (2021).

    Article  Google Scholar 

  25. Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data 13, 3927–3950 (2021).

    Article  Google Scholar 

  26. Chuvieco, E. et al. Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth Syst. Sci. Data 10, 2015–2031 (2018).

    Article  Google Scholar 

  27. Zhao, Z. et al. Fire enhances forest degradation within forest edge zones in Africa. Nat. Geosci. (2021).

  28. Cook, M., Schott, J. R., Mandel, J. & Raqueno, N. Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive. Remote Sens. (2014).

  29. Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ. 140, 36–45 (2014).

    Article  Google Scholar 

  30. Broadbent, E. N. et al. Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biol. Conserv. 141, 1745–1757 (2008).

    Article  Google Scholar 

  31. Chaplin-Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 10158 (2015).

    Article  Google Scholar 

  32. Silva Junior, C. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaaz8360 (2020).

    Article  Google Scholar 

  33. Laurance, W. F. et al. Biomass collapse in Amazonian forest fragments. Science 278, 1117–1118 (1997).

    Article  Google Scholar 

  34. Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).

    Article  Google Scholar 

  35. Zheng, C., Jia, L. & Hu, G. Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite Earth observations. J. Hydrol. 613, 128444 (2022).

    Article  Google Scholar 

  36. Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 14855 (2017).

    Article  Google Scholar 

  37. Laurance, W. F. et al. The fate of Amazonian forest fragments: a 32-year investigation. Biol. Conserv. 144, 56–67 (2011).

    Article  Google Scholar 

  38. de Paula, M. D., Costa, C. P. A. & Tabarelli, M. Carbon storage in a fragmented landscape of Atlantic forest: the role played by edge-affected habitats and emergent trees. Trop. Conserv. Sci. 4, 349–358 (2011).

    Article  Google Scholar 

  39. van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).

    Article  Google Scholar 

  40. Gillett, N. P., Arora, V. K., Matthews, D. & Allen, M. R. Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim. 26, 6844–6858 (2013).

    Article  Google Scholar 

  41. Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).

    Article  Google Scholar 

  42. Kozlowski, T. T. Responses of woody plants to flooding and salinity. Tree Physiol. 17, 490–490 (1997).

    Article  Google Scholar 

  43. Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).

    Article  Google Scholar 

  44. Sze, J. S., Carrasco, L. R., Childs, D. & Edwards, D. P. Reduced deforestation and degradation in Indigenous lands pan-tropically. Nat. Sustain. 5, 123–130 (2022).

    Article  Google Scholar 

  45. Masson-Delmotte, V. et al. IPCC: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds) (Cambridge Univ. Press, 2021).

  46. Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest Above-Ground Biomass for the Years 2010, 2017 and 2018, v3 (NERC EDS Centre for Environmental Data Analysis, 2021);

  47. Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).

    Article  Google Scholar 

  48. Alkama, R. et al. Vegetation-based climate mitigation in a warmer and greener world. Nat. Commun. 13, 606 (2022).

    Article  Google Scholar 

  49. Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. 9, 679 (2018).

    Article  Google Scholar 

  50. Matthews, H. D., Gillett, N. P., Stott, P. A. & Zickfeld, K. The proportionality of global warming to cumulative carbon emissions. Nature 459, 829–832 (2009).

    Article  Google Scholar 

  51. Li, W. et al. Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences 14, 5053–5067 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references


This study was supported by the National Key R&D Program of China (grant number 2019YFA0606604, to W.L.), the National Natural Science Foundation of China (grant number 42175169, to W.L.) and the Tsinghua University Initiative Scientific Research Program (grant number 2022308004, to W.L.). P.C. acknowledges support from the ANR CLAND Convergence Institute 16-CONV-0003 (to P.C.). The authors thank M. G. Windisch for providing the TCRE data and S. T. Garnett for providing the Indigenous lands map.

Author information

Authors and Affiliations



W.L. designed the research. L.Z. performed analysis. M.S. and O.C. processed the biomass map data. L.Z. and W.L. drafted the paper. All authors contributed to the interpretation of the results and to the text.

Corresponding author

Correspondence to Wei Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Yue Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

Additional information

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

Extended data

Extended Data Fig. 1 Flow chart of input data (white), derived data (blue), and results (red) described in the Methods.

TMF, tropical moist forest; CCI, climate change initiative; DEM, digital elevation model; LST, land surface temperature; TCRE, transient climate response to cumulative carbon emissions.

Extended Data Fig. 2 The distribution of edge distance.

a, The spatial distribution of edge distance for TMFs. b, The kernel density distribution of edge distance for different regions. The dashed lines indicate the area-weighted means.

Extended Data Fig. 3 Spatial distribution of degraded forests (a−d) and interior forests (e).

The numbers show the total area.

Extended Data Fig. 4 Kernel density distribution of TC fraction change (ΔTC, a) and the correlation between ΔTC and ΔLST over the tropics (b).

ΔTC is calculated as the TC fraction of interior forests minus the TC fraction of the paired land cover type. ΔLST is calculated as the LST of each land cover type minus the LST of paired interior forests using the Landsat daytime LST data. Coloured dashed lines and numbers in a show the area-weighted mean values.

Extended Data Fig. 5 Spatial distribution of recently created degraded forest (a, c) and other isolated, edge (b, d) forest.

The numbers are the corresponding area for each forest degradation.

Extended Data Fig. 6 Comparison of carbon deficit from isolated and edge forests formed after 2000 with the deforestation carbon emissions from the national reports (UNFCCC) and satellite observations (GFC) during 2000–2010 for nine countries.

Error bars denote the standard deviation. N = 4609 (2458), 13862 (9985), 4820 (3004), 2402 (1444), 838 (427), 2930 (1240), 1175 (606), 391 (404) and 211 (234) for carbon deficit from forest degradation (deforestation) in the nine countries (from left to right in the figure), respectively. UNFCCC, United Nations Framework Convention on Climate Change; GFC, Global Forest Change; TMF, Tropical Moist Forest; DRC, Democratic Republic of the Congo; PNG, Papua New Guinea.

Extended Data Table 1 Area of each degraded forest and the interior forest in different regions

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Texts 1–8, Supplementary Figs. 1–18 and Supplementary Table 1.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, L., Li, W., Ciais, P. et al. Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation. Nat. Geosci. 16, 244–249 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


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