# Managed forests support higher carbon density and sequestration in the Congo basin

Le Bienfaiteur Sagang, Ricardo Dalagnol, Stephanie Georges, Samuel Favrichon, Shuang Li, Fabien Wagner, Zhihua Liu, Dafeng Zhang, Alfred Ngomanda, Vincent Medjibe, et al.

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Le Bienfaiteur Sagang, Ricardo Dalagnol, Stephanie Georges, Samuel Favrichon, Shuang Li, et al. Managed forests support higher carbon density and sequestration in the Congo basin. 2025. hal-05304501

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# Managed forests support higher carbon density and sequestration in the Congo basin

**Le Bienfaiteur Sagang**

sagang.bienfaiteur@yahoo.fr

University of California https://orcid.org/0000-0001-8778-3121

**Ricardo Dalagnol**

Remote Sensing Division, National Institute for Space Research (INPE) https://orcid.org/0000-0002-7151-8697

**Stephanie Georges**

Ctrees

**Samuel Favrichon**

Gamma Remote Sensing AG

**Shuang Li**

Ctrees

**Fabien Wagner**

Ctrees

**Zhihua Liu**

Institute of Applied Ecology, Chinese Academy of Sciences

**Dafeng Zhang**

UCLA

**Alfred Ngomanda**

CENAREST

**Vincent Medjibe**

Independent

**Bonaventure Sonké**

University of Yaounde

**Nicolas Barbier**

IRD

**Elsa Ordway**

UCLA https://orcid.org/0000-0002-7720-1754

**Lee White**

Institut de Recherche en Écologie Tropicale https://orcid.org/0000-0002-4595-5981

**Sassan Saatchi**
## Article

### Keywords:

**Posted Date:** October 1st, 2025

**DOI:** https://doi.org/10.21203/rs.3.rs-7512165/v1

**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License.

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**Additional Declarations:** There is NO Competing Interest.
1 **Title: Managed forests support higher carbon density and sequestration in the Congo basin**

2 Le Bienfaiteur Sagang<sup>a,c</sup>, Ricardo Dalagnol<sup>c</sup>, Stephanie Georges<sup>c</sup>, Samuel Favrichon<sup>k</sup>, Shuang Li<sup>c</sup>,

3 Fabien Wagner<sup>c</sup>, Zhihua Liu<sup>c</sup>, Dafeng Zhang<sup>a</sup>, Alfred Ngomanda<sup>e</sup>, Vincent Medjibe<sup>g</sup>, Bonaventure

4 Sonké<sup>e</sup>, Nicolas Barbier<sup>d</sup>, Elsa M. Ordway<sup>a,h</sup>, Lee White<sup>i,j</sup>, Sassan Saatchi<sup>a,b</sup>

5 <sup>a</sup>Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095 USA

6 <sup>b</sup>Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA

7 <sup>c</sup>Ctrees, Pasadena, CA, USA

8 <sup>d</sup>AMAP, Univ Montpellier, IRD, CIRAD, CNRS, INRAE, Montpellier, France

9 <sup>e</sup> Department of Biology, Plant Systematics and Ecology Laboratory, Higher Teachers' Training College, University of Yaoundé I, P.O. Box 047, Yaoundé, Cameroon

10 <sup>f</sup>Centre National de la Recherche Scientifique et Technologique (CENAREST), Libreville, Gabon

11 <sup>g</sup>Libreville, Gabon

12 <sup>h</sup>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA

13 <sup>i</sup>School of Natural Sciences, University of Stirling, Stirling, UK

14 <sup>j</sup>Institut de recherche en écologie tropicale (IRET), Libreville, Gabon

15 <sup>k</sup>Gamma Remote Sensing AG, Worbstrasse 225, CH-3073 Gümligen, Switzerland

17 **Abstract**

18 Land-use drives forest loss and carbon emissions in the Congo basin, undermining its role in
19 climate change mitigation. Here, we assess how aboveground live carbon (AGC) stocks and fluxes
20 vary across land-use in the Congo basin rainforests by developing AGC density map for the year
21 2020 and integrating it with high-resolution forest cover loss data over 30 years (1990-2020) to
22 estimate carbon emissions and removals. Our findings reveal higher forest integrity under managed
23 disturbances, with logged forests having 7.53% (range: 4.86%–9.5%) less AGC compared to old
24 growth, unlike 50% differences observed with slash-and-burn and other unmanaged disturbances.

25 Unmanaged areas hold 50% of the AGC storage, implying that much of the basin remains highly
26 vulnerable to rapid carbon loss from unmanaged land-use. Overall, the basin remained a net AGC
27 sink of -37.5 ± 4.29 Tg C yr⁻¹, dominated my 98% removals from managed and intact forests
28 within logging concessions (21.3 ± 2.39 Tg C yr⁻¹) and protected areas (15.73 ± 2.24 Tg C yr⁻¹),
29 while unmanaged forests nearly remained carbon neutral. These findings underscore the critical
30 need to integrate sustainable management strategies for unmanaged forests into climate mitigation
31 frameworks such as REDD+ to enhance carbon retention in the Congo basin.
32 **Main**

33 The Congo basin rainforest, the second-largest expanse of tropical forest globally is home to an
34 estimated 7,000 endemic plant species (Sosef et al., 2017) as well as iconic animal biodiversity
35 (WWF, 2024) including chimpanzees, gorillas, forest elephants, and okapi, and sustains the
36 livelihoods of millions of people (FAO, 2010). It is also the main driver of the world's terrestrial
37 carbon sink and harbors the largest tropical peatlands (Dargie et al., 2017; Harris et al., 2021;
38 Hubau et al., 2020; Xu et al., 2021). Combined pressures from climate change, subsistence slash-
39 and-burn agriculture and increasing resource extraction resulting from human demand locally and
40 globally (Goldman et al., 2020; Hubau et al., 2020; Luckeneder et al., 2021; Shapiro et al., 2023;
41 Stoop et al., 2019; Tyukavina et al., 2018) threaten the integrity of this ecosystem with significant
42 consequences for the Congo basin's carbon cycle, climate regulation across the basin and wider
43 afield, and biodiversity (de Wasseige et al., 2015; Ellison et al., 2017; Hubau et al., 2020; Stévart
44 et al., 2019).

45 Long-term land-use management that safeguards forest resources and biodiversity while
46 promoting sustainable income revenues are essential to successfully integrate the Congo basin
47 rainforest into global change adaptation and mitigation efforts (Bele et al., 2015). Tropical forest
48 live carbon stock and fluxes vary widely across gradients of functional composition, environmental
49 drivers and disturbance regime (Muller-Landau et al., 2021). However, the region remains
50 understudied (Abernethy et al., 2016; White et al., 2021), and the impacts of main land-use
51 activities such as smallholder farming, logging, and associated canopy cover disturbances
52 (Masolele et al., 2024; Shapiro et al., 2023; Vancutsem et al., 2021), on forest structure and
53 dynamics remain uncertain or underestimated (Pearson et al., 2017). This hinders our ability to
54 assess how forest carbon stocks and fluxes vary across different management practices, predict
55 their response to global changes and ultimately inform decision-making to protect and govern the
56 Congo basin rainforest in ways that also support economic growth. Traditional approaches used to
57 characterize forest carbon allocation relied on sparse field inventory plot data that were often
58 geographically biased and failed to adequately capture the variability of carbon density across
59 gradients of forest functional traits and land-use intensity (Gourlet-Fleury et al., 2013a; Hubau et
60 al., 2020; Lewis et al., 2013, 2009; Suarez et al., 2019). The airborne and spaceborne Light
61 Detection and Ranging (LiDAR) technologies offers an opportunity to sample across functional
62 and structural gradients at climate mitigation policy-relevant scales (Bossy et al., 2025; Dubayah
et al., 2021; Ganivet and Bloomberg, 2019; Kumar et al., 2015), and interpolate forest measurements using wall-to-wall satellite products and machine learning (ML) techniques (Lang et al., 2019; Potapov et al., 2021; Sagang et al., 2020). Furthermore, the release of high-spatial resolution (4.77 m) data from Planet's constellation through Norway's International Climate and Forest Initiative (NICFI, 2021) from 2015-2024 offered unprecedented spatial and temporal details (monthly to bi-annual) enabling a fine-scale delineation of areas impacted by logging, slash-and-burn practices, and the expansion of minor unpaved roads across the tropics based on deep learning (DL-DEGRAD, Dalagnol et al., 2023). In addition, the democratization of nearly four decades of optical satellite image archives from Landsat (USGS, 2020), allows for consistent, long-term characterization of changes in tropical moist forest (TMF) cover and the tracking of TMF gains and losses (Hansen et al., 2013; Vancutsem et al., 2021) at moderate-spatial resolution (30 m), making it ideal for historical trend analysis (Heinrich et al., 2023, 2021).

In this work we generated a wall-to-wall map of the aboveground carbon (AGC) density across the Congo basin for circa 2020 at 100 m resolution by applying stratified ML models calibrated with extensive airborne LiDAR-derived AGC samples. This approach integrated canopy height metrics from the Global Ecosystem Dynamics Investigation (GEDI, Dubayah et al., 2021) alongside optical and radar satellite predictors. We analyzed AGC distribution across main forest types (central and coastal evergreen, semi-deciduous, peatlands and mangroves; Dinerstein et al., 2017; Réjou-Méchain et al., 2021), and land-use including managed forests (logging concessions with certified and uncertified management plans), protected unprotected areas. We evaluated the influence of managed disturbances on AGC by combining our AGC map with the DL-DEGRAD dataset and comparing the AGC differences between intact forests and those impacted by logging, slash-and-burn, and other forms of unmanaged disturbances. We integrated the 30-year TMF cover change dataset (1990-2020) with our AGC map to quantify AGC recovery following stand clearing and degradation across different land-uses and forest types, using a space-for-time approach that models AGC accumulation as function of the years since the last disturbance (Heinrich et al., 2023, 2021). We integrated the carbon gains and losses across old growth, recovering, and disturbed forests to assess the AGC budget of the Congo basin rainforests from 1990 to 2020.

## 91 Results

### 92 Distribution of the AGC density across the Congo basin rainforest
The AGC estimates derived from our Gradient Boosted Regression Trees (GBRT) models trained on ~825,000 ha of reference LiDAR-derived AGC data (Figure 1, see the Materials and Methods), achieved the best performances along AGC gradients compared to existing global and regional products ($R^2$ = 0.59; RMSE = 27.94 Mg C ha⁻¹ and bias = -4.46; Figure 1a). The average AGC of 134.06 SD ± 35.11 Mg C ha⁻¹ derived from our map is comparable to the estimate derived from nearly 100,000 ha of aggregated in situ forest management inventories in the region (134.3 ± 38.5 Mg C ha⁻¹; Ploton et al., 2020; Figure S4); although being about 6% lower than estimates from 673 1-ha plot data (145.5 ± 51.5 Mg C ha⁻¹; Figure S4) and LiDAR-derived AGC data (143.6 ± 49.6 Mg C ha⁻¹). We noticed a 7.52% overestimation (154.4 ± 38.7 Mg C ha⁻¹; bias:-2.1 to 30.5 Mg C ha⁻¹) with the 2020 biomass map from the Climate Change Initiative (CCI) (Santoro and Cartus, 2023), and 33.63% underestimation (95.3 ± 34 Mg C ha⁻¹; bias: -60.4 to -1.9 Mg C ha⁻¹) with GEDI L4B data (Dubayah et al., 2022).

<figure><img src="image_3.png" /><figcaption>Figure 1: Aboveground carbon (AGC, Mg C ha⁻¹) density and storage (Pg C) in the Congo basin (CB) rainforest for the year circa 2020. (a) Performance of Gradient Boosted Regression Trees (GBRT) models for each forest type using</figcaption></figure>
a 10 spatial fold cross validation (see Materials and Methods for more details). The scatter plots (black solid dots in background) are comparison between the reference airborne LiDAR-based AGC density and the prediction from model and other published maps across the CB. Filled colored points are average AGC predictions from this study (red), CCI 2020 (cyan), GEDI L4b (violet) and aggregated management inventory measurements (Ploton al., 2020; orange), within 10 percentiles intervals of the reference LiDAR-based AGC. (b) Spatial distribution of AGC density across the CB with the inserts of forest types (Réjou-Méchain et al., 2020, Dinerstein et al., 2017; bottom left) and land-use (bottom right). (c) Distribution of AGC density across forest types (grey filled curves) with their means (white dots), and the respective boxplot showing AGC density variation between land-use. (d) Estimated total live AGC storage (Pg C) within forest types with colors representing the proportion allocated within each land-use.

An ANOVA test shows that central evergreen forests had the highest AGC density on average (146.7 ± 22.2 Mg C ha⁻¹, Figure 1c) followed by coastal evergreen (137.47 ± 26.33 Mg C ha⁻¹) and semideciduous forests (131.13 ± 39.4 Mg C ha⁻¹), with mangroves having the lowest AGC density (50.33 ± 34 Mg C ha⁻¹). Managed forests within logging concessions were found to store 10% more AGC density on average (143.95 ± 24.9 Mg C ha⁻¹) compared to other land-use, except for central evergreen forests, where protected areas had the highest AGC density (150.4 ± 20.65 Mg C ha⁻¹). Mangrove forests found within unprotected areas had higher AGC density (56.52 ± 33.04 Mg C ha⁻¹) than protected mangroves (45.6 ± 33.9 Mg C ha⁻¹). We estimated at 26.14 ± 3.16 Pg C of total live AGC stock in the Congo basin rainforests in 2020 with 46.03% (12.04 ± 0.85 Pg C) managed either in logging concessions (7.32 ± 0.56 Pg C) or protected areas (4.71 ± 0.64 Pg C).

## Land-use drives aboveground carbon storage and fluxes

Managed disturbances associated with logging showed the lowest impacts on forest AGC (Figure 2), with differences between logged and intact forests ranging from 4.86% in coastal evergreen forests (Figure 2b) to 9.5% in semi-deciduous forests (Figure 2c) with an average of 7.53%. An exception was observed within peatlands where logged forests had 26.64% more AGC density (158.32 ± 26.34) than intact forests (125 ± 34.71). Forests affected by slash-and-burn and other disturbances showed the largest AGC difference compared to intact forests, reaching up to 50.05% in central evergreen forests (Figure 2a) except in coastal evergreen forests where slash-and-burn resulted in forests with the lowest AGC.
<figure><img src="image_4.png" /><figcaption>Figure 2: Variation in aboveground carbon density (AGC, Mg C ha⁻¹) between disturbed and intact forests for each forest type and land-use with their respective means and standard deviations (a-d). The spatial extent of forest disturbance (maps in the left panel) and the labeling of the drivers were modified from Dalagnol et al. (2023) over the Congo basin. These drivers were categorized into three types: logging activities (top left map), slash-and-burn disturbances, and other degradation, which includes forest roads and canopy openings outside logging concessions. The intact forest landscape map of 2020 (IFL, Potapov et al., 2017) was used to define the extent of intact forests with no evident signs of recent disturbances as detected from satellite images. To reduce the effect of human disturbances on forest structure, the initial 1 km buffer around roads and settlements was used as one of the criteria to define IFL was expanded to 4 km.</figcaption></figure>

Our results reveal distinctive patterns of AGC recovery over 30 years following forest clearing and degradation (Figure 3). The average annual rates of AGC accumulation of recovering forests from clearing within the first 20 years after disturbance is 2.09 ± 0.15 Mg C ha⁻¹yr⁻¹ (Figure 3a) with no significant differences across forest types and land-uses, except in protected areas (Table S1), where the highest rates were found in semideciduous forests (2.63 ± 0.15 Mg C ha⁻¹yr⁻¹) and the lowest rates in peatlands (1.62 ± 0.14 Mg C ha⁻¹yr⁻¹). Young (≤ 20 years) degraded forests displayed mean AGC accumulation of 1.7 ± 0.19 Mg C ha⁻¹ yr⁻¹ (Figure 3b). Degradation within logging concessions maintained higher levels of AGC (above 50% of the mean AGC of intact forests, Figure 3a) within the first year after disturbance except for central evergreen forests and peatlands. AGC accumulation in older recovering forests (>30 years) was 2.6 times higher following forest clearing (0.81 ± 0.32 Mg C ha⁻¹ yr⁻¹) as compared to degraded areas (0.37 ± 0.28 Mg C ha⁻¹ yr⁻¹), with an overall average AGC accumulation rate of 0.59 ± 0.42 Mg C ha⁻¹ yr⁻¹.
<figure><img src="image_5.png" /><figcaption>Figure 3: AGC recovery following forest clearing and degradation across land-use and forest types. Solid lines are the rates of AGC accumulation (ΔAGC; Mg C ha⁻¹ year⁻¹; left y axis) with the associated 95% confidence interval and dotted lines are the estimated relative AGC density expressed as percentage of AGC of old growth forests found within intact forest landscapes (IFL, right y axis) with the associated 95% confidence interval.</figcaption></figure>

## Managed forests in the Congo basin act as a net AGC sink

We applied our AGC accumulation models across all recovering (< 30 years) and old growth forests (including undisturbed TMF cover in 2020 and those recovering beyond 30 years; Table S1) for each land-use and forest types in the Congo basin, resulting in a total AGC sequestration of -3,415.604 ± 273.2 Tg C from 1990 to 2020 (Figure 4a, negative values). About 83.78% (-2,861.86 ± 314.8 Tg C) of the total AGC removal occurred in undisturbed old growth forests, followed by degraded forests (-536.81 ± 64.56 Tg C; 15.71%) and secondary forests (-16.91 ± 2.38 Tg C; 0.65%). Integrating the areas of TMF cover loss with the associated AGC emissions from this study (Table S2) resulted in a total AGC emission in the Congo basin of 2,293 ± 183.44 Tg C (Figure 4a, positive values). About 79% of the total emissions occurred within unprotected areas (1815.91 ± 136.19 Tg C), and 21% within managed areas (282.25 ± 25.82 Tg C). By integrating total AGC losses and gains from 1990 to 2020 (Figure 4b), we estimated a net annual AGC sink of -37.5 ± 4.29 Tg C yr⁻¹ in the Congo basin rainforest. Managed land-use contributed 98.77% (-37.04 ± 4.81 Tg C yr⁻¹) to these net removals with logging concessions and protected
areas contributing 56.8% and 41.9% respectively. In contrast, unprotected areas nearly remained carbon neutral with -0.46 ± 0.11 Tg C yr⁻¹ of net AGC removals.

<figure><img src="image_6.png" /><figcaption>Figure 4: Aboveground carbon (AGC) removals and emissions over the Congo basin rainforest between 1990-2020. (a) Bar chart showing the AGC emission (positive values) and removals (negative values) for each forest type across land-use. Gains include AGC accumulation within degraded, secondary and old growth forests, while losses include AGC emissions from degradation and clearing of degraded and old growth forests. (b) Bar chart of the total living AGC budget (loss - gain; Pg C) within each forest type and management practice and the overall net total over the entire Congo basin (grey bar). CEE = central evergreen forests, COE = coastal evergreen forests, SEM = semideciduous forests, PEA = Peatlands.</figcaption></figure>

## Discussion

Our findings provide evidence that managed forests within logging concessions and protected areas sustain the highest aboveground carbon (AGC) density on average and act as the major carbon sink in the Congo basin rainforest. These results are supported by our new and more accurate AGC map at 100 m resolution over the area, which enables a systematic assessment of the forest AGC allocation across forest types and land-use in the region.

The better performances achieved by our AGC predictions in comparison to existing products result from the integration of satellite predictors into state-of-the-art geospatial techniques of AGC modelling consisting of: 1) applying locally trained machine learning (ML) algorithms within 20 km × 20 km grid cells to interpolate canopy height metrics from GEDI; and 2) leveraging the most extensive LiDAR-derived AGC samples available over the region (~ 825,000 ha; Table 3) to
calibrate stratified ML algorithms for each forest type and predict AGC. The localized ML approach integrates the spatial variability in the forest structure and has been demonstrated to provide better performances in predicting canopy structure at regional (Csillik et al., 2019; Sagang et al., 2024) and global (Potapov et al., 2021) scales. Upscaling AGC estimates from local to regional scales has been improved by integrating LiDAR-derived AGC samples (Réjou-Méchain et al., 2019; Sagang et al., 2020). The latter provides a more comprehensive sampling and therefore a better characterization of the forest structure across the AGC gradient compared to field plot data. The average AGC density estimated from our wall-to-wall AGC map (134.06 ± 35.11 Mg C ha⁻¹) are similar to the nearly 100,000 ha of aggregated *in situ* forest management inventories in the Congo basin (134.3 ± 38.5 Mg C ha⁻¹; Ploton et al., 2020) and overestimate by 8% 673 1-ha plot data (145.5 ± 51.5 Mg C ha⁻¹) and by 7% reference LiDAR-derived AGC estimates (143.6 ± 49.6 Mg C ha⁻¹). Global datasets either overestimated LiDAR-derived AGC by 7.52% (CCI, 154.4 ± 38.7 Mg C ha⁻¹) or underestimated it by 33.63% (GEDI L4B, 95.3 ± 34 Mg C ha⁻¹). This underscores the huge uncertainties when relying on field inventory data or global maps to assess the contribution of the Congo basin rainforest to the global climate change mitigation efforts like the United Nations Framework Convention on Climate Change (UNFCC), and highlights the need for locally calibrated, wall-to-wall products improved by intermediate LiDAR-derived AGC estimates.

Higher AGC values observed in evergreen forests (142.08 ± 34.43 Mg C ha⁻¹), are driven by high wood density species (Réjou-Méchain et al., 2021) and productive conditions (Muller-Landau et al., 2021), particularly in the lowland central evergreen forests (~400 m above sea level; Dinerstein et al., 2017), where the AGC within undisturbed forests reached up to 147.9 ± 27.8 Mg C ha⁻¹.

Historical heavy tree harvesting during slave trade and colonial occupation along the coast (16th–20th century; CNC, 2021; Collomb et al., 2000) has likely contributed to lower AGC (137.47 ± 26.33 Mg C ha⁻¹) of the evergreen forests found there. Managed forests within logging concessions support 10% more AGC (143.95 ± 24.9 Mg C ha⁻¹) compared to other land-use, which aligns with a pattern already documented in previous studies in the region (Medjibe, 2020; Poulsen et al., 2020; Sagang et al., 2024). Five key factors contribute to sustaining these forests with high AGC: 1) logging concessions generally created in areas with a higher abundance of large trees suitable for logging; 2) selective logging generally involving harvesting commercial-value tree species (generally 0.7-2 trees per hectare), resulting in minimal carbon loss from clearing (Medjibe et al.,
2013, 2011; Tritsch et al., 2020); 3) higher rates of carbon sequestration following tree harvesting (Gourlet-Fleury et al., 2013b; Medjibe, 2020); 4) stricter control of concession access through the closure of old logging roads (Kleinschroth et al., 2019) to prevent illegal activities such as shifting agriculture, hunting, and unauthorized timber or fuelwood harvesting; and 5) extended rotation cycles of at least 20 years, as implemented in Gabon (Pérez et al., 2005; République Gabonaise, 2001), which provide more time for forest recovery after disturbances. Our extensive data across the basin confirms the limited effect of logging disturbances on forest AGC (Dupuis et al., 2025; Medjibe et al., 2011; Umunay et al., 2019), with logged forests having on average 7.53% (range: 4.86% - 9.5%) less AGC density as compared to intact forests. An exception is however observed within central evergreen forests where the highest AGC values were found within protected areas (150.4 ± 20.65 Mg C ha⁻¹). In fact, central evergreen forests are located within the Democratic Republic of Congo (DRC) where forestry laws permit the establishment of various human activities within logging areas, promoting the expansion of shifting agriculture (Hugel and Mashini, 2012; Tyukavina et al., 2018), unregulated timber harvest for community needs (Lescuyer et al., 2014; Schure et al., 2011), human settlements (Engunda et al., 2018), and mining (Stoop et al., 2019). The rates of deforestation and forest degradation inside concessions in DRC have been shown to be two to three times higher than in other Congo basin countries (Ernst et al., 2013; Kleinschroth et al., 2019). The expansion of unmanaged disturbances dominated by small scale farming (Shapiro et al., 2023), result into forests with about 50% less AGC as compared to undisturbed old growth, with lower AGC recovery within central evergreen forests. Peatlands and mangroves displayed lower AGC within protected areas (unlike high AGC found in logging areas) as wetland protection in Central Africa typically focuses on permanently or temporarily flooded areas (Harrison et al., 2016; Lee et al., 2006; USFS, 2015), where edaphic conditions limit forest stands at lower AGC storage (Poulsen et al., 2020; Xu et al., 2017) and recovery as observed in peatlands.

Our estimates of annual AGC recovery for young (≤ 20 years) recovering forests from clearing an degradation (2.09 ± 0.15 Mg C ha⁻¹ yr⁻¹ and 1.7 ± 0.19 Mg C ha⁻¹ yr⁻¹) are similar to estimates from Heinrich et al. (2023) over Central Africa (2.51 ± 2.4 Mg C ha⁻¹ yr⁻¹ and 1.98 ± 0.4 Mg C ha⁻¹ yr⁻¹) using a space for time approach. Our estimate of AGC accumulation within old growth forests in 2020 (>30 years; 0.59 ± 0.42 Mg C ha⁻¹ yr⁻¹) falls within the lower range of carbon accumulation measured within field plots in 2015 (0.53-0.79 Mg C ha⁻¹ yr⁻¹; Hubau et al., 2020), indicating a
decrease in carbon storage of old growth forests. As of 2020, the total live AGC storage of the Congo basin rainforest was estimated at 26.14 ± 3.16 Pg C. Integrating belowground live carbon by applying to the AGC conversion factors of 0.235 and 0.39 respectively for *terra firme* forests (Mokany et al., 2006) and mangroves (Kauffman and Donato, 2012) brings the total live carbon (aboveground and belowground) to 32.28 ± 3.24 Pg C. This represents about 21% of the tropical moist forests total carbon stock (154 ± 1 Pg C; Xu et al., 2021) and about seven times the annual global emissions from anthropogenic disturbances (4.6 ± 2.1 Pg C yr⁻¹; Xu et al., 2021). About 50% (12.04 ± 0.85 Pg C) of the total AGC stored in the Congo basin is highly vulnerable to anthropogenic disturbances within unmanaged areas with the other half either protected (20%; 4.71 ± 0.64 Pg C) or managed within logging concessions (30%; 7.32 ± 0.56 Pg C). We demonstrated that forest management contributes the most (98.77%) in the net carbon sink of the Congo basin (-37.5 ± 4.29 Tg C yr⁻¹). Maintaining the region as a net carbon sink will be particularly difficult as it is expected to experience fivefold population growth by the end of this century with expanded land-use pressure on the remaining old growth forests dominated by large- and small-scale agricultural development (CIFOR, 2022; Engunda et al., 2018; Goldman et al., 2020; Luckeneder et al., 2021; Ordway et al., 2017; Shapiro et al., 2023; Tyukavina et al., 2018). Our findings highlight the need for strategic planning for global change mitigation efforts centered on the Congo basin rainforest to effectively reduce Greenhouse Gas (GHG) emissions, enhance carbon sequestration and maximize the associated environmental and socioeconomic benefits (IPCC, 2022; Mackey et al., 2020; Roe et al., 2021; Rossi et al., 2017; Schumacher, 2023; Seddon, 2022). Where national circumstances allow, prioritizing the protection of undisturbed old growth forests from deforestation and degradation is crucial, as they are key contributors (78%) to the annual AGC sequestration (95.39 ± 7.62 Tg C yr⁻¹). The reality is that the Congo basin rainforest falls within countries facing major development challenges that heavily depend on the economic benefits derived from these forests (Tchatchou et al., 2015; World Bank, 2022). Potential forest management models in the region are exemplified by the Republic of Congo and Gabon, each country having about 23Mha of forest cover in 2020 (Figure S5), with the largest proportions under management either within concessions (respectively 52.76% and 56.26%) and protected areas (35.91% and 16.58%). These countries displayed net annual AGC sinks (Republic of Congo: 0.33 Tg C yr⁻¹; Gabon: 0.38 Tg C yr⁻¹; Table S3) which were more than 1.3 times higher than the net sink recorded in DRC (0.26 Tg C yr⁻¹), the largest forested country in the region (115 Mha of forest
cover) with only 27.62% under management. Between 1990 and 2020, DRC witnessed the largest reduction of its undisturbed old growth forests (8.98%, 10.355 Mha) primary driven by forest cover loss occurring within unmanaged areas (8.61 Mha). Managed forests in Gabon and the Republic of Congo are sanctuaries of biodiversity (Zwerts et al., 2024), with logging concessions in Gabon being home to about 65% (61,822) of the national estimate of forest elephants (95,110, Laguardia et al., 2021). Logging concessions in Gabon are established on state-owned lands and operate under legally binding government-approved management plans that align with standards comparable to those governing national forests in the United States, considered as Type 6 protected areas by the International Union for Conservation of Nature (IUCN). The Gabonese government has also enforced a ban on unprocessed timber exports since 2010 that led to a fourfold increase in forest-related jobs, boosting by 322% the contribution of the forest sector to the country's GDP in 2022 (Watramez et al., 2023). However, the financial incentives from expanding logging concessions in the Congo basin does not heed important factors (Couturier, 2019). Logging concessions can lead to additional large-scale extractive industries, as seen in Brazil and Indonesia, driven by increased foreign investment. Also, abandoned logging roads often become hotspots for illegal activities such as poaching that greatly affect endangered species and threaten biodiversity worldwide (IPBES, 2019). Additional economic opportunities can come from implementing nature-based solutions to address global changes (Nyika and Dinka, 2022) in compensation to financial payments through mechanisms such as Reducing Emissions from Deforestation and Forest Degradation (REDD+). Nevertheless, payments for REDD+ in the Congo basin have yet to gain traction as the voluntary carbon market seems to be only slowly moving amidst criticisms regarding results and transparency as well as changes of methodologies (VERRA, 2023). To date, Gabon is the only country in the region with REDD+ results registered on the UNFCCC Lima Hub (CNC, 2021). Yet of the 187 million tons of validated CO2 emission reductions from land-use, payments have been made for only 3.4 million tons. This a clear demonstration that forest management in the Congo basin has a great potential to simultaneously mitigate climate change, safeguard forest resources and biodiversity while enabling sustainable rural development through income generation.

## 319 Materials and Methods

## 320 Forest types and land-uses
The forest composition map from (Réjou-Méchain et al., 2021) available over the Congo basin at 10 km resolution was combined with the ecoregion boundaries (Dinerstein et al., 2017) to define three main forest types: Coastal evergreen (COE), Semideciduous (SEM) and Central evergreen (CEE) as described in Table 2. The COE class included all the Atlantic forests, which showed little taxonomic affinity from management inventories with the CEE class. We combined deciduous and semideciduous forests into one SEM class as they show functional convergence from the management inventory data (Réjou-Méchain et al., 2021). The extent of terrestrial flooded forests, here referred to as peatland (PEA), was determined from the 2020 global land cover map from the European Space Agency and Copernicus Climate Change (ESA, 2017) available at 300 m resolution. Finally, we used the global mangrove extent in 2020 (Bunting et al., 2022) available at 25 m resolution to delineate mangroves (MAN). We derived our 2020 forest extent baseline from the 30 m Tropical Moist Forest (TMF) dataset (Vancutsem et al., 2021), resampled to 100 m resolution by including only pixels containing at least nine 30 m forested pixels. Our baseline of forest cover at 100 m was used to resample all the different forest type maps using the nearest neighbor algorithm to fill gaps, resulting in a 100 m forest type map with five classes: CEE, COE, SEM, PEA, MAN. We categorized forest land-use based on the type of management the forest was assigned for in 2020, including logging concessions, protected areas, and unprotected forests. We differentiated logging concessions with an officially validated sustainable forest management plan (Certified concessions) from those that either do not have an official management plan or have uncertain status (Uncertified concessions) from the map provided by Réjou-Méchain et al. (2021). Protected areas were extracted from the World Database of Protected Areas (WDPA) available online through ProtectedPlanet.net. We excluded marine, hunting and game-oriented areas. Unprotected forests were remaining forest areas that fell outside all the abovementioned management categories.

<table><caption>Table 2: Criteria for determining forest types and the respective forest cover (Mha) in 2020 based on TMF dataset.</caption><thead><tr><th>Réjou-Méchain et al., 2021</th><th>Dinerstein et al., 2017</th><th>This study</th><th>Area (Mha)</th></tr></thead><tbody><tr><td>1- Atlantic highland evergreen</td><td>Congolian coastal forests;</td><td rowspan="3">Coastal evergreen (COE)</td><td rowspan="3">26.609</td></tr><tr><td>2- Atlantic coastal evergreen</td><td>Cross-Sanaga-Bioko coastal forests</td></tr><tr><td>3- Atlantic inland evergreen</td><td></td></tr><tr><td>4- Margin semideciduous</td><td rowspan="6">Northern Congolian Forest-Savanna; Northwest Congolian lowland forests; Northeast Congolian lowland forests; Southern Congolian forest-savanna</td><td rowspan="6">Semi deciduous (SEM)</td><td rowspan="6">113.166</td></tr><tr><td>5- Evergreen semideciduous on sandstone</td></tr><tr><td>6- Semideciduous</td></tr><tr><td>8- Mixed evergreen</td></tr><tr><td>9- Degraded semideciduous</td></tr><tr><td>10-Semideciduous evergreen transition</td></tr></tbody></table>
<table><tr><td>7- Central evergreen</td><td>Central Congolian lowland forests</td><td>Central evergreen (CEE)</td><td>37.118</td></tr><tr><td>-</td><td>Bunting et al. (2022)</td><td>Mangroves (MAN)</td><td>0.741</td></tr><tr><td>-</td><td>ESA CCI (2017)</td><td>Peatlands (PEA)</td><td>17.49</td></tr></table>

### Forest disturbance data

We used pantropical forest degradation dataset derived from deep learning (DL-DEGRAD; Dalagnol et al., 2023) to delineate the extent of areas impacted by logging practices, forest fire from slash-and-burn, and other types of degradation, including unpaved road openings. The DL-DEGRAD applies a U-Net model to detect combined tree loss and textural variations over very high spatial (4.77 m) and temporal (bi-annual to monthly) resolution Planet NICFI images from 2016 to 2020 (NICFI, 2021). The multi-year/annual DL-DEGRAD dataset over the Congo basin was consolidated into a single degradation raster, with adjacent pixels of the same driver merged into distinct polygons. To refine these polygons, we integrated the tree cover loss dataset from Global Forest Change (GFC, Hansen et al., 2013) and the TMF product (Vancutsem et al., 2021) to filter out the DL-DEGRAD polygons where no disturbance was detected by both former dataset between 2016 and 2020. We defined three categories of disturbances: 1) logging, corresponding to all logging detection happening within concessions which either follow certified management practices with reduced impact logging or uncertified practices with more damage to the forest (Medjibe et al., 2011), 2) slash-and-burn, as all forest fire detection following forest clearing for small-scale agriculture, and 3) other disturbances, including forest roads and unmanaged canopy openings outside logging concessions. The intact forest landscape map (IFL, Potapov et al., 2017) was used to define the extent of stable forests with no evident signs of recent disturbances as detected from satellite images. We limited the edge effect from the IFL map by expanding the initial 1 km buffer around roads and settlements used in the original IFL map to 4 km, since approximately 80% of degradation and deforestation in the Congo basin occur within 4 km of road or settlement (Shapiro et al., 2023).

### Mapping forest structure

#### Canopy height

Satellite data from the Harmonized Landsat Sentinel-2 (HLS; Claverie et al., 2018) and ALOS PALSAR (Shimada et al., 2014) available in 2020, were used as variables to interpolate canopy relative height (RH) measurements from GEDI (Dubayah et al., 2021) into wall-to-wall canopy
height maps. Optical inputs included the near-infrared (NIR), shortwave infrared (SWIR 1 & SWIR 2), and the Normalized Difference Vegetation Index (NDVI), while the horizontal (HH) and vertical (HV) polarizations were considered as predictors from radar. We retained the GEDI metrics RH50, RH75, and RH98 corresponding to the 50th, 75th, and 98th percentile of energy return height relative to the ground to capture the vertical structure of the different strata of the forest. The 25 m GEDI data available over the area between 2019 and 2020 were aggregated to a 100 m resolution using spatial averaging. We then set aside 20% of the 100 m GEDI pixels randomly selected for validation and the remaining 80% were subsequently incorporated into localized Gradient Boosted Regression Trees (GBRT; Friedman, 2002) models. GBRT models have been proven successful in predicting forest structural metrics with good performance (Colin et al., 2017; Xu et al., 2021). These models (GBRT-RH) were trained and applied within 20 × 20 km tiles with 70% overlap, ensuring that a minimum of 500 GEDI pixels were available to train the model within the current tile and randomly sampling pixels from adjacent tiles if the condition was not met (see Sagang et al., 2024 for more details). This resulted in three canopy height predictions (RH50, RH75, and RH98) for each tile for circa 2020 at 1-ha resolution. A wall-to-wall RH prediction over the Congo basin was obtained by mosaicking estimates from each tile considering the median value of overlapping pixels to minimize discontinuity effects between tiles (Csillik et al., 2019).

## Aboveground live biomass

We gathered extensive airborne LiDAR-derived aboveground biomass (ALS-AGB; Figure S2 & S3, Table 3) data available across the main forest types within the CB at 100 m (Armston et al., 2016; Rodda et al., 2024; Sagang et al., 2024; Xu et al., 2017) to train GBRT-AGB models specific to each forest type. We excluded ALS-AGB pixels for which a canopy cover loss was recorded between the time of acquisition and 2020, resulting in ~ 825,000 pixels available for training at 1-ha (see Figure S3a for the size of training sample within each forest type).

<table><caption>Table 3: LiDAR-based reference aboveground carbon maps over the Congo basin used in this study</caption><thead><tr><th>Dataset</th><th>Country</th><th>Acquisition Year</th><th>Area (ha)</th><th>AGC (Mg C ha<sup>-1</sup>)</th><th>Relative RMSE (%)</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>Xu et al., 2017</td><td>Democratic Republic of Congo</td><td>2016</td><td>582,000</td><td>115.32 ± 62.51</td><td>20.3</td><td>0.82</td></tr><tr><td>Rodda et al., 2024</td><td>Cameroon, Gabon</td><td>2012-2022</td><td>44,750</td><td>77.78 ± 58.29</td><td>15.2</td><td>0.89</td></tr><tr><td>Armston et al., 2016</td><td>Gabon</td><td>2016</td><td>320,018</td><td>102.14 ± 39.43</td><td>25.72</td><td>0.86</td></tr></tbody></table>
The GBRT models to predict AGB (GBRT-AGB) included the HLS and radar predictors in addition to the predicted RH maps, and the Digital Elevation Model (DEM) from Copernicus. The performance of each GBRT-AGB model was assessed using a spatial cross validation approach. The dataset was split into 10 spatial folds created by clustering the coordinates of the pixels using the k-means algorithm (Figure S3b). The model was trained 10 times and for each iteration, one cluster was set aside to validate the model while the remaining folders were used for training. Training data over PEA and MAN were split into 5 folds considering their smaller extent to keep a sufficient size of data for validation (≥ 500 pixels). We compared our results with 1) the AGB product from the European Space Agency (ESA) through the Climate Change Initiative (CCI) version 4 (Santoro and Cartus, 2023); 2) the AGB product from GEDI L4B (Dubayah et al., 2022); 3) the aggregated AGB map from forest management inventory data in the Congo basin (CoFor-AGB, Ploton et al. (2020) and 4) the AGB measurements from 673 1-ha plots from the Central African plot network and the national forest inventory (NFI) in Gabon. We used a factor of 0.456 to convert AGB to carbon density (AGC) and an ANOVA test was applied to compare the mean AGC density across management and forest types. The total live AGC stock was obtained by multiplying the AGC density with the total forest area in 2020 as determined by TMF forest cover.

## Quantifying carbon emissions and recovery

We used the TMF product to track the trajectory of forest carbon recovery from when the forest was at its lowest AGC following stand clearing and degradation. This TMF dataset is a Landsat based monitoring of canopy gain and loss since 1984, at a spatial scale of 30 m. The TMF dataset can be used to estimate the time since the last disturbance event which is considered a good proxy of the age of the recovering forest. We combined the TMF-derived forest age map with our predicted 2020 AGC map to determine the AGC with increasing age using a space for time approach as described by (Heinrich et al., 2023). We grouped spatially contiguous recovering pixels of the same year of disturbance into clusters and filtered out clusters with less than 9 pixels (0.81 ha) to align with the 100 m spatial scale of the AGC map. Furthermore, we removed plantation areas from TMF recovering forests using the pantropical oil palm cover available for the year 2019 (Descals et al., 2021) and the 2020 land cover map for ESA (ESA, 2017). This resulted in about 80 thousand clusters from which we extracted individual values of median AGC. We used the Chapman-Richard model for growth (Richards, 1959) to model the AGC
accumulation with increasing age of regrowing forest for each of the land-use within each ecoregion:

$$Y_t = A(1 - e^{-kt})^c \pm \varepsilon; A, k \text{ and } c > 0 \quad (1)$$

Where $Y_t$ refers to the AGC at year ($t$); $A$ is the AGC asymptote or the AGC of the old growth forest; $k$ is a growth-rate coefficient of $Y$ as a function of age; $c$ is a coefficient that determines the shape of the growth curve; and $\varepsilon$ is an error term.

We quantified the median AGC within different ages of recovering forests. We considered the 25th quantile of AGC for 1-year old recovering forest from clearing to ensure that the fit begins near 0 at that age. This correction was not necessary for degraded forests as degradation still maintains some level of AGC after disturbance (Heinrich et al., 2023). The 95th quantile of AGC found within the IFL map of 2020 (Potapov et al., 2017) was considered as AGC estimates for old growth forests and used as the asymptote "A" at which the AGC could return to after a given number of years. The equation implemented using the 'nls' function in R statistical software (R Core Team, 2018). The difference in estimated carbon between consecutive years was considered as the yearly carbon accumulation ($\Delta$ AGC, Mg C ha⁻¹ year⁻¹).

## Quantifying forest carbon gains and losses

To quantify the carbon gains of the region, we multiplied our modelled rates of annual AGC accumulation with the respective areas of forests recovering from clearing and degradation for each forest type and land use. We used the mean rate of recovering forests beyond 30 years (0.59 ± 0.42 Mg C ha⁻¹ yr⁻¹; Table S1) as the removal factor for old growth forests which include undisturbed TMF cover and old growth forests (> 30 years). This estimated carbon removal rate of old growth forests falls within the range measured from field plots in the region (0.53 - 0.79 Mg C ha⁻¹ yr⁻¹; Hubau et al., 2020). We added all AGC gains from the different pools to obtain the total AGC gain (Tg C). Total AGC losses were derived by combining emissions from forest clearing and degradation. For clearing, we used the TMF dataset to identify the year of forest pixel loss and multiplied the total cleared area between 1990 and 2020 by the median AGC of old growth forests (found within the IFL region), assuming complete carbon loss. For degradation, we estimated losses as the difference between AGC in old growth forests and the modelled AGC during the first year following disturbance (Heinrich et al., 2023) for each forest type and land use. These differences served as emission factors for degraded forests and were multiplied by the degraded forest area in 2020 to quantify total AGC losses from degradation. The net carbon budget
for each land-use and forest type was then determined by calculating the difference between total carbon gains and losses from 1990 to 2020.

## Uncertainty analysis

We independently computed uncertainties associated with RH and AGC predictions (GBRT-RH and GBRT-AGC). For GBRT-RH, we used the 20% validation dataset for each of the three RH metrics (RH50, RH75, and RH98) to calculate the average root mean squared error (RMSE) of the RH predictions within successive bins of 10% percentiles and fitted a function to describe how the absolute and relative errors propagate along a gradient of RH predictions (Figure S1). LiDAR-derived AGC datasets were available with pixel-level uncertainties at 1-ha. We followed the same procedure with RH uncertainties and fitted a function to propagate the errors along gradients of LiDAR-AGC and AGC predictions from GBRT models. We considered the predicted RH metrics without any uncertainty, therefore, the total uncertainty at 1-ha is obtained by computing the squared root of the sum of the errors associated with LiDAR-AGC and GBRT models (Eq. 2).

$$ \bar{\sigma}(AGC) = \sqrt{\bar{\sigma}(AGC_{LiDAR})^2 + \bar{\sigma}(AGC_{GBRT})^2} \quad (2) $$

where $\bar{\sigma}(AGC)$ is the combined uncertainty for AGC at 1-ha, $\bar{\sigma}(AGC_{LiDAR})$ is the uncertainty of LiDAR-derived AGC, and $\bar{\sigma}(AGC_{GBRT})$ is the uncertainty of our AGC estimates from GBRT models.

The uncertainty associated with the AGC accumulation models was quantified using a Monte Carlo simulation approach. We computed the median value of 50 clusters randomly selected from each chronosequence defined based on the time since the last disturbance. We repeated the procedure 100 times, and for each iteration, we used the Chapman-Richard model for growth (Richards, 1959) to model the AGC accumulation with increasing age of regrowing forest for each of the land-use within each ecoregion. We quantified the rate of carbon accumulation for each iteration and determined a 95% confidence interval.

## Data availability

While most of the datasets used in this study are publicly accessible through online repositories, certain datasets require third-party authorization and can be provided upon reasonable request.

All the satellite data are freely available from the Google Earth Engine repositories at https://developers.google.com/earth-engine/datasets/catalog. The Relative Height data from GEDI at footprint-level were taken from the GEDI02_A height and elevation product, available at LPDAAC: https://doi.org/10.5067/GEDI/GEDI02_A.002. The Biomass dataset from GEDI's was taken from the GEDI04_A product also available at LPDAAC:
https://doi.org/10.3334/ORNLDAAC/2056. The floristic maps from Réjou-Méchain et al. (2021) and Dinerstein et al. (2017) are available at https://doi.org/10.18167/DVN1/UCNCA7 and https://hub.arcgis.com/datasets/esri::resolve-ecoregions-and-biomes/about. The mangrove dataset is available at https://zenodo.org/records/6894273. The Intact Forest Landscape dataset for 2020 can be downloaded at https://intactforests.org/data.ifl.html. Forest concessions in 2020 were downloaded at https://data.globalforestwatch.org/documents/gfw::managed-forest-concessions-downloadable/about. The extent of protected Areas in 2020 was downloaded at www.protectedplanet.net. The tree cover change data from TMF and GFC can be accessed at https://forobs.jrc.ec.europa.eu/TMF/data.php gee and https://earthenginepartners appspot.com/science-2013-global-forest/download_v1.7.html. The DL-DEGRAD dataset (Dalagnol et al. 2023) is available upon request to the corresponding author. The ESA biomass and vegetation data are available at https://climate.esa.int/en/data/#/dashboard. The AGB map for management inventories is available at https://doi.org/10.6084/m9 figshare.11865450. The LIDAR-derived AGB data are available at https://doi.org/10.23708/H2MHXF. (Rodda et al., 2024), https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1775 (Armston et al., 2016) and upon request to the corresponding author for Xu et al. (2017). The AGB data from 1-ha field plots are available from the Central African plot network; the Centre National de la Recherche Scientifique et Technologique (CENAREST) of Gabon and the supplementary materials from Xu et al., 2017 (92 plots over DRC).

## Code availability

Satellite and spaceborne data processing was implemented in GEE cloud computing platform and the code can be made available upon reasonable request. The rest of the processing was implemented in R statistical software (version 4.3.1) and the code to generate the results is available on Zenodo (link).

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875

876 Acknowledgements

877 This study was funded by the U.S. Forest Service under the Grant No. 447711-SR-21406-05. We also acknowledge the contributions of field data from Gabon by the Centre National de la Recherche Scientifique et Technologique du Gabon (CENAREST) and the Central African Plot Network.

881 Supplementary information

<figure><img src="image_7.png" /><figcaption>Figure S1: Validation of GEDI relative height (RH50; RH75 and RH98) predictions over the Congo basin. (a) Scatter plot depicting the relation between RH predictions and 20% validation subset of GEDI samples, averaged across successive 10% percentile bins, with 95% confidence intervals represented by colored polygons. (b and c) Root mean squared error (RMSE) of each RH prediction, expressed in absolute (m) or relative (%) terms, with the function used to propagate model uncertainty.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure S2: Validation of your AGC map with 418 plots from the National Field Inventory (NFI) data from Gabon and 163 plots from the Central African plot network.</figcaption></figure>

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<figure><img src="image_9.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>c</figcaption></figure>

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

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

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

<figure><img src="image_14.png" /><figcaption>Figure S3: Comparison in the spatial distribution of above ground carbon (Mg C ha⁻¹) from different products over the Congo basin rain forest all aggregated at 10 km resolution for visual illustration. (a) LiDAR-derived AGC samples over the different forest types in background and the number of 1-ha samples for each forest type (~ 825,000). (b) Spatial pattern of the 10 folds used for spatial cross validation. (c) This study. (d) ESA CCI 2020. (e) GEDI L4d. (f) management inventories in the Congo basin (CoFor-AGB; Ploton et al., 2020).</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Figure S4: Distributions of the aboveground carbon density (AGC, Mg C ha⁻¹) with the respective means from the different maps and additional 673 1-ha plots data including 92 plots from Xu et al., 2017 over DRC, 163 plots from</figcaption></figure>
900 the Central African plot network (https://central-african-plot-network.netlify.app/) and 418 plots from the National
901 Field Inventory (NFI) data from Gabon.

<table><caption>Table S1: Aboveground carbon accumulation rates (Mg C ha⁻¹y⁻¹) within recovering forests from clearing and degradation across land-uses and chronosequence, with 10 years intervals following the last disturbance.</caption><thead><tr><th rowspan="2">Age (years)</th><th colspan="4">Concessions</th><th colspan="4">Protected</th><th colspan="4">Unprotected</th></tr><tr><th>CEE</th><th>COE</th><th>SEMI</th><th>PEA</th><th>CEE</th><th>COE</th><th>SEMI</th><th>PEA</th><th>CEE</th><th>COE</th><th>SEMI</th><th>PEA</th></tr></thead><tbody><tr><th colspan="13" style="text-align: center;">Forest clearing</th></tr><tr><th>≤10</th><td>2.71<br>±0.14</td><td>2.98<br>±0.23</td><td>3.26<br>±0.19</td><td>2.24<br>±0.18</td><td>3.20<br>±0.16</td><td>2.22<br>±0.20</td><td>3.50<br>±0.19</td><td>1.13<br>±0.13</td><td>2.87<br>±0.15</td><td>3.28<br>±0.15</td><td>3.12<br>±0.15</td><td>2.32<br>±0.15</td></tr><tr><th>10-20</th><td>1.44<br>±0.11</td><td>1.39<br>±0.18</td><td>1.63<br>±0.13</td><td>1.44<br>±0.12</td><td>1.46<br>±0.11</td><td>1.34<br>±0.25</td><td>1.77<br>±0.12</td><td>0.88<br>±0.12</td><td>1.27<br>±0.11</td><td>1.52<br>±0.12</td><td>1.52<br>±0.10</td><td>1.27<br>±0.12</td></tr><tr><th>20-30</th><td>1.1<br>±0.15</td><td>1.01<br>±0.25</td><td>1.23<br>±0.16</td><td>1.18<br>±0.14</td><td>1.11<br>±0.14</td><td>1.07<br>±0.42</td><td>1.32<br>±0.13</td><td>0.79<br>±0.11</td><td>0.71<br>±0.19</td><td>0.91<br>±0.13</td><td>1.13<br>±0.11</td><td>0.98<br>±0.14</td></tr><tr><th>&gt;30</th><td>0.82<br>±0.28</td><td>0.72<br>±0.41</td><td>0.89<br>±0.29</td><td>0.92<br>±0.31</td><td>0.74<br>±0.24</td><td>0.84<br>±0.73</td><td>0.94<br>±0.24</td><td>0.69<br>±0.3</td><td>0.71<br>±0.19</td><td>0.63<br>±0.22</td><td>0.81<br>±0.2</td><td>0.74<br>±0.26</td></tr></tbody></table>

<table><caption>Table S2: Aboveground carbon accumulation rates (ΔAGC, Mg C ha⁻¹y⁻¹) and density (AGC, Mg C ha⁻¹) for recovering forests across land-uses and chronosequence, with 10-year intervals following the last disturbance.</caption><thead><tr><th colspan="13" style="text-align: center;">Forest degradation</th></tr><tr><th>≤10</th><td>2.57<br>±0.26</td><td>2.66<br>±0.16</td><td>2.32<br>±0.23</td><td>2.61<br>±0.26</td><td>2.31<br>±0.25</td><td>2.98<br>±0.27</td><td>2.33<br>±0.21</td><td>2.33<br>±0.28</td><td>2.14<br>±0.23</td><td>3.04<br>±0.14</td><td>2.41<br>±0.22</td><td>2.33<br>±0.23</td></tr><tr><th>10-20</th><td>0.97<br>±0.17</td><td>0.86<br>±0.12</td><td>0.74<br>±0.16</td><td>0.97<br>±0.19</td><td>0.76<br>±0.17</td><td>1.21<br>±0.19</td><td>0.75<br>±0.14</td><td>0.85<br>±0.21</td><td>0.71<br>±0.15</td><td>1.09<br>±0.10</td><td>0.82<br>±0.14</td><td>0.85<br>±0.15</td></tr><tr><th>20-30</th><td>0.65<br>±0.22</td><td>0.55<br>±0.15</td><td>0.47<br>±0.19</td><td>0.65<br>±0.26</td><td>0.49<br>±0.21</td><td>0.84<br>±0.25</td><td>0.48<br>±0.17</td><td>0.57<br>±0.26</td><td>0.46<br>±0.18</td><td>0.72<br>±0.12</td><td>0.53<br>±0.17</td><td>0.56<br>±0.19</td></tr><tr><th>&gt;30</th><td>0.42<br>±0.33</td><td>0.34<br>±0.2</td><td>0.29<br>±0.26</td><td>0.42<br>±0.38</td><td>0.30<br>±0.3</td><td>0.56<br>±0.38</td><td>0.29<br>±0.23</td><td>0.36<br>±0.38</td><td>0.28<br>±0.25</td><td>0.46<br>±0.18</td><td>0.33<br>±0.24</td><td>0.36<br>±0.28</td></tr></thead></table>

<table><thead><tr><th rowspan="2">Age (years)</th><th colspan="2">Certified concessions</th><th colspan="2">Uncertified concessions</th><th colspan="2">Protected</th><th colspan="2">Unprotected</th></tr><tr><th>ΔAGC</th><th>AGC</th><th>ΔAGC</th><th>AGC</th><th>ΔAGC</th><th>AGC</th><th>ΔAGC</th><th>AGC</th></tr></thead><tbody><tr><th colspan="9" style="text-align: center;">Forest clearing</th></tr><tr><th>≤10</th><td>2.95 ±0.2</td><td>32.56 ±11.9</td><td>2.64 ±0.17</td><td>25.99 ±10.5</td><td>2.51 ±0.17</td><td>24.76 ±13.9</td><td>2.9 ±0.16</td><td>34.76 ±10.8</td></tr><tr><th>10-20</th><td>1.49 ±0.11</td><td>53.2 ±10.4</td><td>1.46 ±0.14</td><td>45.37 ±8.8</td><td>1.36 ±0.15</td><td>42.43 ±16.3</td><td>1.39 ±0.11</td><td>54.90 ±8.5</td></tr><tr><th>20-30</th><td>1.12 ±0.17</td><td>66.34 ±8.8</td><td>1.14 ±0.18</td><td>58.49 ±8.9</td><td>1.06 ±0.2</td><td>54.34 ±9.9</td><td>1.03 ±0.13</td><td>67.33 ±7.08</td></tr><tr><th>&gt;30</th><td>0.82 ±0.33</td><td>84.78 ±16.6</td><td>0.85 ±0.33</td><td>77.53 ±16.4</td><td>0.81 ±0.38</td><td>71.81 ±18.9</td><td>0.74 ±0.23</td><td>84.44 ±12.1</td></tr><tr><th colspan="9" style="text-align: center;">Forest degradation</th></tr><tr><th>≤10</th><td>2.5 ±0.24</td><td>77.14 ±19.7</td><td>2.58 ±0.22</td><td>66.95 ±19.8</td><td>2.49 ±0.26</td><td>60.78 ±12.7</td><td>2.48 ±0.21</td><td>63.1 ±9.1</td></tr><tr><th>10-20</th><td>0.84 ±0.16</td><td>91.43 ±17.7</td><td>0.92 ±0.16</td><td>82.21 ±17.5</td><td>0.89 ±0.18</td><td>75.33 ±8.4</td><td>0.87 ±0.13</td><td>77.84 ±7.1</td></tr><tr><th>20-30</th><td>0.55 ±0.21</td><td>98.42 ±10.6</td><td>0.61 ±0.21</td><td>89.92 ±10.4</td><td>0.59 ±0.23</td><td>82.72 ±11.4</td><td>0.57 ±0.17</td><td>85.21 ±8.6</td></tr><tr><th>&gt;30</th><td>0.34 ±0.29</td><td>106.62 ±14.8</td><td>0.39 ±0.29</td><td>99.35 ±15.1</td><td>0.38 ±0.33</td><td>91.77 ±16.4</td><td>0.36 ±0.24</td><td>94.09 ±12.3</td></tr></tbody></table>
<figure><img src="image_16.png" /><figcaption>Figure S5: Comparison of TMF cover change (1990-2020) across management types for the Congo Basin countries.</figcaption></figure>

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<table><caption>Table S3: Aboveground carbon density (Mg C ha⁻¹), stocks (Tg C) and changes (Tg C ha⁻¹yr⁻¹) across management types for the Congo basin countries from 1990 to 2020. The net AGC changes, emissions and sinks (Pg C yr⁻¹) are reported as the long-term average fluxes.</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Management types</th><th rowspan="2">AGC</th><th rowspan="2">AGC stock</th><th colspan="3">Annual rate between [1990-2020]</th></tr><tr><th>AGC Emissions</th><th>AGC Removals</th><th>Net AGC change</th></tr></thead><tbody><tr><td rowspan="4">Cameroon</td><td>Certified concessions</td><td>148.05 ±18.1</td><td>980.166</td><td>0.324</td><td>-3.981</td><td>-3.652</td></tr><tr><td>Uncertified concessions</td><td>145.87 ±18.3</td><td>159.63</td><td>0.064</td><td>-0.605</td><td>-0.542</td></tr><tr><td>Protected</td><td>134.32 ±36.4</td><td>376.747</td><td>0.168</td><td>-1.851</td><td>-1.682</td></tr><tr><td>Unprotected</td><td>118.62 ±30.6</td><td>1323.339</td><td>6.204</td><td>-6.253</td><td>-0.048</td></tr><tr><td rowspan="4">Democratic Republic of Congo</td><td>Certified concessions</td><td>144.64 ±29.3</td><td>1355.626</td><td>4.348</td><td>-6.487</td><td>-2.139</td></tr><tr><td>Uncertified concessions</td><td>137.01 ±30.2</td><td>569.811</td><td>1.485</td><td>-3.142</td><td>-1.656</td></tr><tr><td>Protected</td><td>136.55 ±35.3</td><td>2276.852</td><td>4.557</td><td>-11.421</td><td>-6.863</td></tr><tr><td>Unprotected</td><td>132.92 ±38.6</td><td>9429.195</td><td>49.653</td><td>-46.821</td><td>2.833</td></tr><tr><td rowspan="4">Equatorial Guinea</td><td>Certified concessions</td><td>136.81 ±8.4</td><td>0.643</td><td>0.002</td><td>-0.002</td><td>-0.002</td></tr><tr><td>Uncertified concessions</td><td>130.78 ±20.6</td><td>91.844</td><td>0.064</td><td>-0.266</td><td>-0.202</td></tr><tr><td>Protected</td><td>126.62 ±29.6</td><td>58.787</td><td>0.026</td><td>-0.332</td><td>-0.305</td></tr><tr><td>Unprotected</td><td>129.66 ±20.4</td><td>179.927</td><td>0.276</td><td>-0.887</td><td>-0.612</td></tr><tr><td rowspan="4">Gabon</td><td>Certified concessions</td><td>151.5 ±16.6</td><td>1075.488</td><td>0.198</td><td>-4.069</td><td>-3.871</td></tr><tr><td>Uncertified concessions</td><td>148.42 ±17.9</td><td>923.928</td><td>0.191</td><td>-2.797</td><td>-2.606</td></tr><tr><td>Protected</td><td>137.9 ±32.4</td><td>541.76</td><td>0.254</td><td>-2.705</td><td>-2.451</td></tr><tr><td>Unprotected</td><td>137.6 ±29.9</td><td>884.962</td><td>0.737</td><td>-3.365</td><td>-2.628</td></tr><tr><td rowspan="2">Republic of Central Africa</td><td>Certified concessions</td><td>144.61 ±33.1</td><td>285.06</td><td>0.757</td><td>-1.342</td><td>-0.585</td></tr><tr><td>Uncertified concessions</td><td>147.63 ±28.1</td><td>137.211</td><td>0.251</td><td>-0.595</td><td>-0.343</td></tr></tbody></table>
<table><tr><td></td><td>Protected</td><td>125.14 ±28.9</td><td>152.243</td><td>0.548</td><td>-0.876</td><td>-0.328</td></tr><tr><td></td><td>Unprotected</td><td>84.09 ±24.11</td><td>330.291</td><td>2.49</td><td>-2.297</td><td>0.197</td></tr><tr><td rowspan="3">Republic of Congo</td><td>Certified concessions</td><td>144.42 ±24.3</td><td>1154.4</td><td>0.906</td><td>-4.812</td><td>-3.906</td></tr><tr><td>Uncertified concessions</td><td>133.3 ±28.9</td><td>579.809</td><td>0.8164</td><td>-2.608</td><td>-1.791</td></tr><tr><td>Protected</td><td>125.2 ±30.6</td><td>1134.948</td><td>0.857</td><td>-4.964</td><td>4.107</td></tr><tr><td></td><td>Unprotected</td><td>113.1 ±34.8</td><td>579.809</td><td>1.016</td><td>-1.365</td><td>-0.202</td></tr></table>

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