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

Lei Zhu, Wei Li, Philippe Ciais, Jiaying He, Alessandro Cescatti, Maurizio Santoro, Katsumasa Tanaka, Oliver Cartus, Zhe Zhao, Yidi Xu, et al.

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Lei Zhu, Wei Li, Philippe Ciais, Jiaying He, Alessandro Cescatti, et al. Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation. Nature Geoscience, 2023, 16 (3), pp.244-249. 10.1038/s41561-023-01137-y. hal-04041256

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

Received: 28 April 2022

Accepted: 27 January 2023

Published online: 2 March 2023

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Lei Zhu<sup>1</sup>, Wei Li<sup>1,2</sup>✉, Philippe Ciais<sup>1,3</sup>, Jiaying He<sup>1</sup>, Alessandro Cescatti<sup>4</sup>, Maurizio Santoro<sup>1,5</sup>, Katsumasa Tanaka<sup>1,3,6</sup>, Oliver Cartus<sup>5</sup>, Zhe Zhao<sup>1</sup>, Yidi Xu<sup>3</sup>, Minxuan Sun<sup>1</sup> & Jingmeng Wang<sup>1</sup>

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.

Forests make up most of the global land carbon sink<sup>1,2</sup> and impact the surface energy budget through biophysical processes that include evaporative cooling in the tropics and albedo-change-induced warming in the north<sup>3,4</sup>. Deforestation in the tropics results in carbon emissions<sup>5</sup> and causes biophysical warming<sup>6</sup>, but the effects of forest degradation, induced by natural and anthropogenic disturbances, are also now

receiving increasing attention<sup>7-11</sup>. The area of degraded tropical forests is greater than the area of deforestation in the Brazilian Amazon<sup>12</sup>. Further, although carbon losses from degradation (biogeochemical effects) are uncertain, they could exceed those from deforestation<sup>13,14</sup>. Forest degradation refers to forests that have a reduced canopy cover and a loss of biomass caused by fragmentation, fire, selective logging

<footnote n="1">¹Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China. ²Ministry of Education Ecological Field Station for East Asian Migratory Birds, Department of Earth System Science, Tsinghua University, Beijing, China. ³Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France. ⁴European Commission, Joint Research Centre, Ispra, Italy. ⁵Gamma Remote Sensing, Gümligen, Switzerland. ⁶Earth System Risk Analysis Section, Earth System Division, National Institute for Environmental Studies (NIES), Tsukuba, Japan. ✉e-mail: wli2019@tsinghua.edu.cn</footnote>
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**Fig. 1 | Distribution of the dominant forest degradation types and the area fractions in TMF in 2010.** The most abundant type of degraded forest with area fraction >5% is shown in each 0.25° × 0.25° grid cell. The colour gradient represents the area fraction of the dominant forest degradation type in each grid cell. Grid cells that have <5% of the dominant forest degradation type and >5% of

the interior forest are masked in light grey, indicating non-degraded forest.

**a–c.** Maps of tropical America (a), tropical Africa (b) and tropical Asia (c).

**d.** The area fraction of the four degraded forests and the interior forests over the tropics. Similar pie charts for each region are shown as insets in **a–c**.

and unusual weather events¹²,¹⁴⁻¹⁶. Unlike deforestation, forest degradation does not change the land cover type, which poses a challenge for satellite monitoring¹⁷,¹⁸. Because forest degradation lowers the biomass and modifies the structure of forests, ecological functions (for example, photosynthesis and transpiration) and surface biophysical properties (for example, albedo and roughness) of degraded forests are altered¹⁹,²⁰. Changes in these biophysical processes impact the surface energy balance and local temperature²¹.

The implementation of the Reducing Emissions from Deforestation and forest Degradation (REDD+) programme focuses more on deforestation, which is easier to monitor than degradation¹⁸. Forest degradation is not explicitly reported in national greenhouse gas (GHG) inventories for many counties, although it covers a larger area than deforestation and causes pervasive CO₂ emissions to the atmosphere²². Further, in the tropics, forest degradation is likely to increase local temperature through biophysical mechanisms such as reduced evaporative cooling²⁰. However, little attention has been paid to the biophysical effect of forest degradation in mitigation policies²³,²⁴. Ignoring this process may hinder the achievement of the goal of limiting global warming below 2 °C or 1.5 °C. Therefore, it is urgent to comprehensively quantify the biophysical and biogeochemical effects of forest degradation, especially in the tropics, where forests experience extensive loss and degradation.

Here, we use high-resolution satellite observations of tropical moist forest (TMF) cover¹⁵ (30 m), aboveground biomass²⁵ (AGB; 25 m) and burned area²⁶ (250 m) data to identify four types of forest degradation in the humid tropics (Methods). Specifically, we separate burned forest (with at least one burned area detection during the past five years), isolated forest patches (area <100 km²), edge forest²⁷ (defined from biomass and distance between edges and interior forests at the same location), and other degraded forest caused by logging and unusual weather events¹⁵ (for example, hurricane, drought and blowdown;

Methods). We quantify the temperature changes induced by forest degradation using the 30 m Landsat land surface temperature (LST) product²⁸ and the 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) LST product²⁹. The carbon deficit caused by forest degradation is analysed and converted to a tropical temperature warming effect²⁴. We define the carbon deficit as the difference in biomass stocks between degraded forests and adjacent interior 'intact' forests. Part of this deficit already exists in the absence of human-induced land-use perturbation and fire ignitions and thus does not cause a warming trend. Therefore, it is the increase of the deficit since 1990, due to the expansion of degraded forests, which is used to calculate both a biophysical warming trend and a warming trend from cumulative CO₂ losses (Methods and Extended Data Fig. 1). The biogeochemical climate warming signal is quantified by LST, rather than air temperature, because fine-scale air temperature data are not currently available from remote sensing.

## Distribution of degraded forests

In 2010, 24.1% of TMFs belonged to one of the four categories of degraded forest (Fig. 1d). More than one-third of moist forests are degraded in tropical Asia, while the fraction of degraded forests is 27.5% and 17.6% in tropical Africa and tropical America, respectively. The corresponding areas are 0.18, 1.09, 1.29 and 0.23 million km² for burned, isolated, edge and other degraded forests, respectively (Extended Data Table 1).

Edge forest, with lower biomass than nearby interior forest, is the most extensive degradation type, accounting for 11.1% of the total TMF area. Edge effects have a footprint (distance to interior) of 133 ± 113 m (mean ± standard deviation (s.d.)) in TMFs (Extended Data Fig. 2). Our estimate of edge distance is within the range of previous observations¹²,²⁷,³⁰⁻³³ (Supplementary Text 1.1). At the regional scale, edge forests are the dominant degradation type in tropical Africa and tropical
<figure><img src="image_4.png" /><figcaption>**Fig. 2 | ΔLST of forest degradation estimated from different satellite data.**
ΔLST is calculated as the LST of each land cover type minus the LST of paired interior forests. **a–c**, Kernel density distributions of ΔLST for Landsat daytime (**a**), MODIS daytime (**b**) and MODIS nighttime (**c**). Coloured dashed lines and</figcaption></figure>

numbers show the weighted mean values. **d**, Biophysical ΔLST averaged over all tropical land areas from different satellite data. Dots and error bars represent the mean values and one standard deviation of all 0.25° grid cells in the tropics (n = 43,740).

Asia (Fig. 1b,c). The area fraction of isolated forests in the pan-tropics is 9.4% (Fig. 1d). Isolated forests have the largest area fraction among the four degradation types in tropical America (Fig. 1a), but they are the second largest in tropical Africa and tropical Asia. Burned forests share the smallest proportion of the total tropical forest area (1.5%; Fig. 1d), being mostly distributed in tropical Africa (Fig. 1b). Other degraded forests, caused by logging and unusual weather events, account for 2.0% of the total TMF area (Fig. 1d), mainly distributed in tropical Asia (4.4%; Fig. 1c). The spatial distribution of each type of degraded forest and the interior forest is shown in Extended Data Fig. 3.

## Biophysical effects of forest degradation

To determine the impact of forest degradation on local temperature, we calculate the LST difference relative to paired interior forests (ΔLST) in each 0.25° × 0.25° grid cell, using high-resolution Landsat daytime LST data (30 m). We find that the local daytime temperature in burned, isolated, edge and other degraded forests is significantly higher than that in the interior forests by 1.12 ± 0.75, 0.90 ± 1.15, 0.76 ± 0.75 and 0.25 ± 0.47 °C (mean ± s.d.), respectively (Fig. 2a). The mean LST warming magnitude of all degraded forests is 0.78 ± 0.88 °C, equivalent to 18% of the warming effect of deforestation area (4.40 ± 2.67 °C; Fig. 2a). Out of the four forest degradation types, warming effect is the largest for isolated forests, and the smallest, but still significant (*P* < 0.01), for other degraded forests (Fig. 2a). When these local effects are averaged over all tropical land areas (with a 5.6% degraded forest fraction), the four types of degraded forest induce an overall daytime warming of 0.050 ± 0.023 °C (Fig. 2d). Isolated forests make the largest contribution (0.023 ± 0.019 °C), while other degraded forests caused by logging and unusual weather events contribute the least (0.0013 ± 0.0015 °C; Fig. 2d).

Because Landsat LST data are only available for daytime, we also analysed the MODIS LST data (1 km; Methods), which includes both daytime and nighttime LST. Consistent with the Landsat data, daytime LST from MODIS in the four degraded forest types is also higher than in the nearby interior forests (Fig. 2b). The daytime warming effect averaged over all tropical land areas is 0.018 ± 0.0026 °C, which is lower than the value derived from the Landsat data (Fig. 2d), mainly due to the

coarser resolution of the MODIS data (Supplementary Text 2.1). During the nighttime, LST in the degraded forests is slightly lower than in the interior forests, inducing a cooling effect of -0.007 ± 0.0015 °C over all tropical land areas (Fig. 2c,d). Combining daytime ΔLST from Landsat and nighttime ΔLST from MODIS, the net daily biophysical effect over the tropics remains net warming (0.022 ± 0.014 °C), which is a higher value than when using both daytime and nighttime ΔLST from MODIS (0.005 ± 0.014 °C; Fig. 2d).

The biophysical warming may be caused by the tree cover (TC) loss in degraded forests and also by mechanisms that do not involve TC loss but shift the surface energy budget (for example, more shallow roots limiting transpiration cooling). To analyse the effect of TC reduction on biophysical warming, we calculate the TC difference between interior forests and degraded forests (ΔTC) in each 0.25° × 0.25° grid cell using the Landsat 30 m data. We find that TC in degraded forests is 8.3 ± 8.6% (mean ± s.d.) lower than that in interior forests (89.5 ± 11.7%; Extended Data Fig. 4a) and there is a significant (*P* < 0.01) correlation between ΔTC and ΔLST for all degraded forest types (Extended Data Fig. 4b and Supplementary Text 3). Similar to the mechanisms of deforestation-induced biophysical impacts³,⁴, the reduced TC is expected to result in decreased latent heat from evapotranspiration and thus lead to a local warming effect. However, the signal of reduced evapotranspiration cannot be fully reflected by the current satellite-based evapotranspiration products³⁴,³⁵, due to the coarse resolution and the large uncertainties (Supplementary Text 4).

## Biogeochemical effects of forest degradation

We further estimate the aboveground carbon (AGC) deficit by comparing AGC in the degraded forests with that in the paired interior forests using the satellite-based AGC data (Methods). The AGC density is 21.8 ± 16.6 MgC ha⁻¹ lower in the degraded forests than in the interior forests, with the largest difference found for burned forests (27.4 ± 17.7 MgC ha⁻¹; Fig. 3a). The total cumulative carbon deficit in tropical degraded forests reaches 6.1 ± 2.0 PgC. Out of the four degradation types, edge forests contribute the largest AGC deficit (3.2 ± 1.6 PgC) and isolated forests rank the second (2.3 ± 1.1 PgC), in both the whole tropics (Fig. 3b) and each region (Fig. 3c). Forests degraded from
<figure><img src="image_5.png" /></figure>

**Fig. 3 | AGC deficit in degraded forests.** The AGC deficit is calculated as the AGC difference between the degraded forests and the interior forests. **a,** Kernel density distributions of AGC carbon density deficit for the four degraded forest types. Coloured dashed lines and numbers show the weighted mean values.

**b.** AGC deficit for the entire tropics. **c.** AGC deficit in the three tropical regions. The dots and error bars in **b** and **c** denote the mean values and one standard deviation (n = 43,740 in **b** and n = 19,151, 12,786 and 11,803 for tropical America, tropical Africa and tropical Asia in **c**, respectively).

logging and unusual weather events contribute the smallest amount to the AGC deficit (0.10 ± 0.05 PgC), with most of their contribution coming from tropical America (Fig. 3b,c). The carbon deficit from burned forests is mainly contributed by tropical Africa (Fig. 3c). Our estimates of the degraded forest carbon deficit agree well with previous estimates<sup>27,33,36–39</sup> (Supplementary Text 1.2).

We then estimate the biogeochemical warming effect of forest degradation on LST of the atmospheric CO₂ lost by degraded forests, using a transient climate response to cumulative carbon emissions metric (TCRE)⁴⁰ (Methods). This approach allows us to compare the biogeochemical LST warming effect from CO₂ losses with the biophysical LST changes due to changes in the surface energy budget. The AGC deficit is equivalent to an LST increase of 0.026 ± 0.013 °C over tropical land areas, which is of comparable magnitude to the biophysical warming (0.022 ± 0.014 °C), illustrating the importance of considering both biophysical and biogeochemical effects when evaluating the full climate impacts of forest degradation (Methods and Supplementary Text 5.1). However, part of this biogeochemical warming is 'natural', that is, it has always existed or is due to climate disturbances, while part of it derives from a net CO₂ loss from direct human-induced degradation such as logging and increased fragmentation. This partition is addressed in the following section.

## Degradation from recent anthropogenic disturbances

Here, we attempt to isolate the biophysical and biogeochemical warming signals from recently created isolated and edge forests caused by anthropogenic disturbances after 1990 from those formed naturally or by human disturbances that occurred before 1990 (Methods). Burned forests induced by wild and anthropogenic fires are not included in the following analysis due to a lack of reliable data and methods⁴¹. Other degraded forests caused by logging and unusual weather events are also not included because natural disturbances are difficult to separate from logging, and 50% of the area of this degradation category will recover in 6 months¹⁵.

The isolated and edge forests generated from 1990 to 2010 (attributed to direct human-induced degradation) account for 38.9 and 42.3% of each type of degraded forest, respectively (Fig. 4a and Extended Data Fig. 5). The large proportion of recently created isolated and edge forests reflects a strong acceleration of forest fragmentation

induced by deforestation and direct human activities from 1990 to 2010. We find that human-induced degradation of forests since 1990 induces a daytime biophysical warming effect of 0.018 ± 0.008 °C over tropical land, accounting for 36% of the total daytime biophysical effects from all degraded forests (Fig. 4b). The corresponding carbon deficit (2.26 ± 0.82 PgC) and equivalent daily warming effect (0.010 ± 0.005 °C) of these forests are 37% of the total biogeochemical effects from all degraded forests (Fig. 4b,c). Recently created edge forests with a larger area contribute more to the biophysical warming effects and carbon deficits than isolated forests. Notably, the biophysical and biogeochemical effects of the recent forest degradation from 1990 to 2010 are of the same order of magnitude as the warming effect from deforestation (Extended Data Fig. 6 and Supplementary Text 6).

## Towards a better understanding of forest degradation

Our study considers four degraded forest types induced by fire, forest fragmentation, logging and unusual weather events. The derived biophysical and biogeochemical effects are based on an assumption that all these degraded forests come from interior forests, which is different from the warming and carbon emissions in reality. For the definition of degraded forests, we do not explicitly distinguish all the natural or anthropogenic causes except for the recently created isolated and edge forests after 1990 by human deforestation (Fig. 4). For example, some flooded edge forests naturally have lower biomass than the interior forests⁴². The definition of degraded forests could also be influenced by Indigenous-managed forests⁴³,⁴⁴. We thus conduct further analyses to quantify their impacts on our main results (Supplementary Text 7 and Supplementary Figs. 10–12).

In this study, we mainly focus on the daytime LST change under clear sky because the Landsat satellites overpass at around 9:45, and nighttime LST is not measured²⁸. To complement the Landsat LST, we also analysed the daytime and nighttime temperature changes using the MODIS product, although these data have larger uncertainties due to their coarser resolution (Supplementary Text 2.1). Nevertheless, we have demonstrated that the daytime biophysical effects of forest degradation are robust regardless of the chosen satellite temperature dataset (Fig. 2). In addition, the data coverage of the Landsat LST product could lead to uncertainties (Supplementary Text 4.1, and Supplementary Figs. 2 and 13).
<figure><img src="image_6.png" /><figcaption>**Fig. 4** | *Isolated and edge forests formed before and after 1990.* *a*, Total area. *b*, Biophysical daytime LST averaged over the entire tropical land area. *c*, Total AGC deficit in the tropics. Dots and error bars in *b* and *c* denote the mean values and one standard deviation (n = 33,837 and 41,457 for isolated forest formed before and after 1990, respectively; n = 21,720 and 21,894 for edge forest formed before and after 1990, respectively).</figcaption></figure>

We use LST rather than air temperature in our analyses due to the lack of fine-scale air temperature data. The magnitude of the air temperature change due to the biophysical effects of forest degradation is expected to be smaller than that of LST change, but the difference between changes in air temperature and LST due to the biogeochemical effects of forest degradation is expected to be small<sup>6,45</sup> (see details in Supplementary Text 8).

For the isolation of the temperature warming caused by degraded forests from direct human activities, we only consider changes since 1990 because of the availability of high-resolution deforestation data after that year. Our estimates of forest degradation area induced by direct anthropogenic disturbances should therefore be underestimated because we do not include degraded forests generated by human activities before 1990. Although we ignore anthropogenic forest degradation from selective logging and fire, the area of burned forests and other degraded forests caused by logging and unusual weather events is relatively small (<15% of the total degraded forest area; Extended Data Table 1).

The AGB map we used<sup>25</sup> may underestimate AGB in the high-AGB-density area (>250 Mg ha<sup>-1</sup>) and overestimate the biomass in low-density areas<sup>25</sup>. A previous study has proved that the underestimation of biomass in high-density areas has little impact on the estimates of forest edge distance in Africa<sup>27</sup>. However, there could be an inevitable underestimation of carbon density in the interior forests of the tropical dense forest area with AGB density >250 Mg ha<sup>-1</sup>. Thus, we have probably underestimated the carbon deficit and the corresponding biogeochemical warming effect. We further validated our results using an improved biomass data version from the European Space Agency's Climate Change Initiative (CCI)<sup>46</sup>, which has a resolution of 100 m rather than 25 m and obtained consistent results (Supplementary Fig. 14).

This study proves the urgent need for high-resolution observational data for temperature, forest disturbance and biomass with full temporal and spatial coverage to reduce uncertainties in the biophysical and biogeochemical effects of forest degradation. Furthermore, long-term data would also help to accurately track changes in forest degradation rather than relying on the space-for-time method as here.

Carbon emissions from forest degradation deserve more attention in climate mitigation and need to be included in national GHG inventories. A previous study found that carbon loss from edge effects

accounts for up to one-third of the total carbon loss from deforestation in Amazonia<sup>32</sup>. If considering all types of forest degradation, carbon emissions from forest degradation could be more than two times larger than deforestation in the Brazilian Amazon<sup>14</sup>. Our study estimates a carbon deficit of 2.26 PgC from the recently created edge and isolated forests during 1990–2010. This amount is about 7.7% of the global total land-use change emissions during 1990–2010<sup>2</sup>. Yet, it is largely underrepresented in the current estimation of land-use change emissions. Carbon emissions from forest fragmentation are not reported in the national GHG inventories (Supplementary Table 1). In addition, explicit representation of forest degradation is missing in current land surface models, the part of Earth system models used for future projections.

More than 140 countries committed to 'halt and reverse forest loss and land degradation by 2030' at the 26th United Nations Climate Change Conference of the Parties (COP26). Fulfilling these pledges may prove to be very challenging because the growing human population will probably demand extra land for settlements and agriculture<sup>9,47</sup>. Our study warns that further expansion of tropical degraded forests will come with considerable additional climate warming. In fact, we demonstrate that in tropical regions, the biophysical warming effect of forest degradation is comparable to the biogeochemical effect and to the effects of deforestation. This combination of warming effects has been largely overlooked by previous studies and by climate mitigation strategies and policies. The biophysical effects are likely to become more important in the future<sup>48</sup> and, therefore, need to be taken into account when assessing the climate impacts of forest disturbance. Our results reinforce the necessity of forest protection programmes such as REDD+ and the implementation of deforestation pledges made at COP26 to achieve the climate mitigation target of the Paris Agreement.

## Online content

Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41561-023-01137-y.

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# Methods

## Forest degradation classification in 2010

Three high-resolution datasets for the tropics (23.5° S to 23.5° N) were used to identify forest degradation areas: (1) the 30 m TMF cover based on Landsat¹⁵; (2) the 25 m GlobBiomass AGB map derived from satellite synthetic aperture radar observations²⁵; and (3) the 250 m FireCCI burned area data based on MODIS²⁶. All these datasets were first projected to WGS84 geographical coordinates and resampled to 0.00025° (-30 m). Because the GlobBiomass biomass map is available only for the epoch circa 2010, our study focuses on forest degradation in 2010. We grouped ‘undisturbed TMF’, ‘degraded TMF’ and ‘forest regrowth’ pixels from TMF data as our forest area. We also used ‘deforested land’ from TMF data during 1990–2010.

The four forest degradation types (burned forests, isolated forests, edge forests and other degraded forests) were then classified as follows (Extended Data Fig. 1).

**Burned forests.** The FireCCI burned area from 2005 to 2009 was used (Supplementary Text 2.4). We define burned forests as forest pixels that have experienced at least one fire event during 2005–2009.

**Isolated forests.** We grouped forest pixels into patches using a region-growing approach. Specifically, we searched the nearest four pixels of each forest pixel. If the nearest four pixels contained forest pixels, they would all be grouped as part of the same patch, otherwise, the pixel would form a single-pixel patch. Finally, we calculated the area of each forest patch and classified those patches with an area smaller than 100 km² as isolated forests¹². Grouping the forest patches was performed using the SciPy package in Python 3.8.

**Edge forests.** The detection of edge forests followed the method of ref. ²⁷. First, we calculated the distance of forest pixels to the nearest non-forest pixel. Then we fitted distance-biomass curves (equation (1)) for moist forests in each 0.25° × 0.25° grid cell containing 1,000 × 1,000 pixels:

$$y_{AGC} = a - b \times e^{-cx} \quad (1)$$

where $y_{AGC}$ represents the biomass density, a is the fitted maximum biomass density in the interior forests, b and c are fitting parameters, e is Euler’s number and x is the distance to the non-forest edge. We define the edge distance to be the distance x at which $y_{AGC}$ equals 90% of a, and forest pixels that have a smaller distance than the edge distance were classified as edge forests. Model fitting was performed using the SciPy package in Python 3.8. As a sensitivity test, we also tried using a fixed value for the edge distance to define the forest edge zone (Supplementary Text 1.1 and Supplementary Fig. 15).

**Other degraded forests.** The forest degradation layer from the TMF dataset in 2010 includes selective logging, fires, and unusual weather events¹⁵ (for example, hurricane, drought, and blowdown). We thus overlapped this data layer with the burned forest layer, and pixels in the TMF forest degradation layer that were not burned forests were considered to be other degraded forests caused by selective logging and unusual weather events.

Forest pixels could be classified as more than one degradation type. To ensure that there was no such double-counting in our forest degradation map, for such pixels, the degradation type was assigned with the following priority: burned forests, isolated forests, edge forests and other degraded forests. Forest pixels that were not classified as degraded forests were treated as interior forests.

## LST from Landsat

The Landsat LST product for 2010²⁸ was used to calculate LST for each forest type. We downloaded 11,000+ Landsat LST images of the tropics

with cloud cover less than 50% in Tier 1 from the Landsat 5 Thematic Mapper. We extracted LST with an error <3 K to exclude the area that was contaminated by clouds or other factors. Because the Landsat 5 satellite overpass time is around 9:45, we only focused on the daytime LST from Landsat. The coverage of the Landsat LST data in each season is shown in Supplementary Fig. 13.

We used a space-for-time method to estimate the LST changes induced by forest degradation. Specifically, for each Landsat image, we calculated the LST difference (ΔLST) between degraded forests and interior forests in each 0.25° × 0.25° grid cell:

$$\Delta LST_i = LST_i - LST_{interior\ forest} \quad (2)$$

where ΔLST_i is the LST difference for a certain degraded forest type i, LST_i represents the mean LST of a certain degraded forest type and LST_interior\ forest represents the mean LST of the interior forests. The positive (negative) sign of ΔLST_i indicates a warming (cooling) effect of forest degradation. ΔLST was calculated only when more than 100 pixels were available in both the degraded forest type in question and the interior forests. Similarly, for comparison, we also calculated ΔLST of deforested land to represent the effect of deforestation. Here, we assume that the background climate is similar for all land covers in each 0.25° × 0.25° grid cell, and ΔLST is only caused by differences between the various land covers. The 0.25° × 0.25° moving window is widely used in previous studies and can ensure the same environmental conditions and enough valid pixels within the window at the same time⁴,⁴⁹.

In addition, to avoid the biases caused by different elevations, we used the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 with a spatial resolution of 1 arcsec (-30 m) to exclude pixels with elevation differences >100 m from the median elevation in each 0.25° × 0.25° grid cell.

## LST from MODIS

We further calculated ΔLST using the 1 km MODIS 8-day Terra LST data²⁹ (from MOD11A2). The MODIS data contain both daytime and nighttime LST because the overpass times of Terra are around 10:30 and 22:30 each day. We selected 8-day Terra LST data closest to the middle of a month to represent the monthly temperature and extracted LST with an error <1 K.

Considering the difference in the resolutions of the degradation map (30 m) and the MODIS data (1 km), we applied a regression method to solve these mixed signals in the MODIS LST data for each 0.25° × 0.25° grid cell (25 × 25 1-km pixels).

In a 1 km × 1 km pixel, there could be seven different land covers including deforested land, burned forests, isolated forests, edge forests, other degraded forests, interior forests and others. We first calculated the fraction of these seven different land covers in each 1 km pixel and assumed that the LST value of each pixel was determined by the fraction of different land covers. Then we applied a multiple linear regression (MLR) to separate the temperature effect of each land cover. To avoid multicollinearity, we removed the dimension of the interior forest fraction from the independent variables, because this information had already been included in the other dimensions. The final MLR was written as:

$$y = \sum_{i=1}^{6} \beta_{i}x_{i} + \beta_{0} + \epsilon \quad (3)$$

where y is the vector of LST values in each pixel (pixel numbers ≤625), $x_i$ is the vector of fractions of land cover i in each pixel, $\beta_i$ is the regression coefficient of land cover i, and $\beta_0$ and $\epsilon$ are the intercept and the error term, respectively. Because we have removed the dimension of the interior forest fraction from MLR, the regression coefficient $\beta_i$ is exactly the ΔLST between the LST of the land cover i and the LST of the interior
forest. Similar to the Landsat data processing, we used the ASTER DEM data resampled to 0.01° to avoid biases induced by elevation.

We further applied additional constraints on the dependent and independent variables to ensure there was sufficient information for the regression results to be robust. Within a 0.25° grid cell, there should be at least 125 observations for the dependent variable (that is, the LST) and 50 observations for the independent variable. To overcome the problem that the land cover fraction was not uniformly distributed between 0 and 1, we adopted the index of uniform distribution ($I_U$) following a previous study$^{49}$. Specifically, the fraction of land cover $i$ in a 0.25° grid cell was stored in the vector $f_i$ of length $n$. We created a vector $U_i$ that was uniformly distributed between 0 and 1 with the same length of $n$. $I_U$ was derived from:

$$I_{Ui} = \frac{\sqrt{\sum_{j=0}^{n} (\min(u_{ij} - f_i))^2}}{\sqrt{\sum_{j=0}^{n} u_{ij}^2}} \quad (4)$$

where $I_{Ui}$ is the index of uniform distribution of land cover $i$ and $U_{ij}$ is the $j$th point in the vector $U_i$. If the vector $f_i$ is uniformly distributed as the vector $U_i$, $I_{Ui}$ tends to be zero. On the contrary, if all the points in vector $f_i$ are located at the origin (that is, the fraction of land cover $i$ all equals zero), $I_{Ui}$ is 1. Following ref. $^{49}$, we did not calculate ΔLST if $I_{Ui}$ was larger than 0.5 for land cover $i$ or interior forests due to there being insufficient information for the regression. Last, we masked out the outliers of ΔLST (outside the range of $3\sigma$ from the mean) to exclude some unrealistic estimates.

## Estimation of tropical temperature changes

Tropical LST change induced by the biophysical impacts of forest degradation (ΔLST$_{BPH}$) was estimated using an area-weighted mean method$^{6}$:

$$\Delta \text{LST}_{\text{BPH}} = \frac{\sum_{i=1}^{n} \text{Area}_i \times \Delta \text{LST}_i}{\text{Area}_{\text{tropics}}} \quad (5)$$

where $\text{Area}_i$ and ΔLST$_i$ are the area and ΔLST of a certain degraded forest type in the grid cell $i$, respectively, and $\text{Area}_{\text{tropics}}$ is the total tropical land area. In 0.25° × 0.25° grid cells with detected degraded forests but without available ΔLST$_i$, due to the lack of LST data from Landsat or MODIS, we used the averaged value from the nearest ten grid cells as ΔLST$_i$.

## Estimation of AGC deficit from forest degradation

We first converted AGB (Mg) to AGC (MgC) by scaling by a factor of 0.5 (ref. $^{5}$). We assumed the AGC density in the interior forests to be the AGC density for forests without degradation in the 0.25° × 0.25° grid cell. We thus defined carbon density deficit and carbon deficit as:

$$\text{Carbon density deficit} = \rho_{\text{IF}} - \rho_{\text{DF}} \quad (6)$$

$$\text{Carbon deficit} = (\rho_{\text{IF}} - \rho_{\text{DF}}) \times \text{area}_{\text{DF}} \quad (7)$$

where $\rho_{\text{IF}}$ is the AGC density of the interior forests, $\rho_{\text{DF}}$ is the AGC density of the degraded forests and $\text{area}_{\text{DF}}$ is the area of degraded forests. If there was no interior forest in a 0.25° × 0.25° grid cell, we used the averaged value from the nearest ten grid cells as $\rho_{\text{IF}}$ for this grid cell.

## Estimation of biogeochemical ΔLST from the carbon deficit

We used the transient climate response to cumulative carbon emissions metric$^{40}$ to convert the AGC deficit from forest degradation to ΔLST by assuming that all these AGC deficits were released into the atmosphere. Previous studies have proved the linear relationship between air temperature and cumulative carbon emissions$^{40,50}$, and we further confirmed that LST also linearly increases with the cumulative emissions (Supplementary Fig. 16 and Supplementary Text 5.2). The gridded LST change response to the doubling of airborne CO$_2$ (Δ°C)

was derived from an ensemble of 21 Earth system models from Coupled Model Intercomparison Project Phase 5$^{24}$ (CMIP5). The tropical land TCRE (TCRE$_{tropic}$) was estimated as:

$$\text{TCRE}_{\text{tropic}} = \frac{\sum_{i}^{n} \Delta \text{C}_i \times A_i}{\sum_{i}^{n} A_i \times C_{\text{emission}}} \quad (8)$$

where $i$ is the $i$th grid cell on the tropical land, $A_i$ is the area of the $i$th grid cell and $C_{\text{emission}}$ is the carbon emission from the doubling of airborne CO$_2$ following ref. $^{24}$. TCRE$_{tropic}$ was estimated to be 1.45 ± 0.51 K per 1,000 PgC.

We assume that the carbon emissions from the doubling of airborne CO$_2$ experiment are evenly distributed on the global land surface$^{24}$, so the carbon deficit in the tropics should be scaled to the global emissions to make a fair comparison between biogeochemical effects and ΔLST$_{BPH}$ for tropical forest degradation. The biogeochemical impact of forest degradation on LST (ΔLST$_{BGC}$) was thus calculated as:

$$\Delta \text{LST}_{\text{BGC}} = \text{TCRE}_{\text{tropic}} \times \text{CD} \times \frac{A_{\text{GL}}}{A_{\text{TL}}} \quad (9)$$

where CD is the carbon deficit from forest degradation, $A_{\text{TL}}$ is the total tropical land area and $A_{\text{GL}}$ is the total global land area. We also provided an additional estimate based on the assumption that the carbon emission is evenly distributed over the Earth's surface (including the ocean; Supplementary Text 5.1).

## Separation of recently created degraded forests since 1990

For edge forests, we first searched for the nearest non-forest pixel (30 m) of each edge forest pixel (30 m). If this nearest non-forest pixel was created after 1990, the edge forest pixel was classified as a recently created edge forest pixel. For isolated forests, we first separated the forests in 1990 into isolated forests and other forests using the above-mentioned method. The isolated forest pixels in 2010 that were not part of the isolated forests in 1990 were treated as recently created isolated forests.

## Estimation of uncertainties

For biophysical effects, we used the standard deviation of all 0.25° grid cells in the tropics as the uncertainty for ΔLST over tropical land areas. The original uncertainty of the biomass data is provided by the biomass product as the standard deviation in each pixel$^{25}$. We thus calculated the mean relative error for the whole 0.25° grid cell. To estimate the error for the entire tropical land area, we directly summed the grid-cell-level errors by assuming full spatial error correlation among the grid cells and thus obtaining a maximum estimate of the error$^{51}$. The uncertainty in TCRE was estimated by calculating the standard deviation of the output of the 21 CMIP5 models. We used the error propagation formula to give the uncertainty for the entire degraded forest area ($\sigma$) from the uncertainty of each degradation type ($\sigma_i$):

$$\sigma = \sqrt{\sum_{i=1}^{n} \sigma_i^2} \quad (10)$$

The error propagation formula of the uncertainty of ΔLST$_{BGC}$ (that is, $\sigma_{\Delta LST_{BGC}}$) was estimated as:

$$\sigma_{\Delta \text{LST}_{\text{BGC}}} = \text{TCRE} \times \text{CD} \times \frac{A_{\text{GL}}}{A_{\text{TL}}} \times \sqrt{\left(\frac{\sigma_{\text{TCRE}}}{\text{TCRE}}\right)^2 + \left(\frac{\sigma_{\text{CD}}}{\text{CD}}\right)^2} \quad (11)$$

where $\sigma_{\text{TCRE}}$ is the uncertainty in TCRE and $\sigma_{\text{CD}}$ is the uncertainty in CD.

## Data availability

The 30 m TMF$^{15}$ dataset can be downloaded from https://forobs.jrc.ec.europa.eu/TMF/download/. The 25 m biomass data from
GlobBiomass can be downloaded from https://doi.org/10.5281/zenodo.4725667 (Africa), https://doi.org/10.5281/zenodo.7544238 (South America, north), https://doi.org/10.5281/zenodo.7544946 (South America, south) and https://doi.org/10.5281/zenodo.7545054 (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 https://doi.org/10.5285/5f331c418e9f4935b8eb1b836f8a91b8. The 250 m FireCCI burned area data²⁶ can be downloaded from https://geogra.uah.es/fireCCI/firecci51.php. The 30 m tree cover map in 2010⁵² can be downloaded from https://glad.umd.edu/dataset/global-2010-tree-cover-30-m. The 30 m Landsat LST dataset²⁸ can be downloaded from https://earthexplorer.usgs.gov/. The 30 m ASTER DEM V3 dataset can be downloaded from https://search.earthdata.nasa.gov/search. The 1 km MODIS LST dataset²⁹ can be downloaded from https://ladsweb.modaps.eosdis.nasa.gov/. CMIP5 data can be downloaded from https://esgf-node.llnl.gov/search/cmip5/. The source data for Figs. 1–4 and Extended Data Figs. 2–6 are available via Zenodo at https://doi.org/10.5281/zenodo.7544889.

## Code availability

The Python script used to analyse the data is available at https://github.com/zhu3210/Tropical-forest-degradation.

## References

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

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).

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).

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

## Acknowledgements

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 contributions

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.

## Competing interests

The authors declare no competing interests.

## Additional information

Extended data is available for this paper at
https://doi.org/10.1038/s41561-023-01137-y.

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41561-023-01137-y.

**Correspondence and requests for materials** should be addressed to Wei Li.

**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.

**Reprints and permissions information** is available at
www.nature.com/reprints.
<figure><img src="image_7.png" /><figcaption>**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.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**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.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Extended Data Fig. 3 | Spatial distribution of degraded forests (a–d) and interior forests (e). The numbers show the total area.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**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</figcaption></figure>

<figure><img src="image_11.png" /></figure>

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.
<figure><img src="image_12.png" /><figcaption>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.</figcaption></figure>
<figure><img src="image_13.png" /></figure>

**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.
<table><caption>Extended Data Table 1 | Area of each degraded forest and the interior forest in different regions</caption><thead><tr><th></th><th>Tropical America<br>(Mkm²)</th><th>Tropical Africa<br>(Mkm²)</th><th>Tropical Asia<br>(Mkm²)</th><th>Tropics<br>(Mkm²)</th></tr></thead><tbody><tr><td>Burned forest</td><td>0.05</td><td>0.11</td><td>0.02</td><td>0.18</td></tr><tr><td>Isolated forest</td><td>0.51</td><td>0.22</td><td>0.36</td><td>1.09</td></tr><tr><td>Edge forest</td><td>0.49</td><td>0.35</td><td>0.46</td><td>1.29</td></tr><tr><td>Other degraded forest</td><td>0.09</td><td>0.03</td><td>0.11</td><td>0.23</td></tr><tr><td>Total degraded forest</td><td>1.13</td><td>0.71</td><td>0.95</td><td>2.79</td></tr><tr><td>Interior forest</td><td>5.29</td><td>1.88</td><td>1.64</td><td>8.81</td></tr></tbody></table>