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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.

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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.

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


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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.

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The authors declare no competing interests.

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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.

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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.

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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).

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