# An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa

Karimon Nesha, Martin Herold, Johannes Reiche, Robert N. Masolele, Kristell Anaïk Hergoualc'H, Erin Swails, Daniel Murdiyarso, Corneille Ewango

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Karimon Nesha, Martin Herold, Johannes Reiche, Robert N. Masolele, Kristell Anaïk Hergoualc'H, et al.. An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa. Environmental Research Letters, 2024, 19 (10), pp.104031. 10.1088/1748-9326/ad6679. hal-05182407

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# An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa

To cite this article: Karimon Nesha et al 2024 *Environ. Res. Lett.* **19** 104031

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LETTER

# An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa

Karimon Nesha<fnref n="1" />,* ①, Martin Herold<fnref n="1" />,<fnref n="2" /> ①, Johannes Reiche<fnref n="1" /> ①, Robert N Masolele<fnref n="1" /> ①, Kristell Hergoualc'h<fnref n="3" />,<fnref n="4" />, Erin Swails<fnref n="3" />, Daniel Murdiyarso<fnref n="5" />,<fnref n="6" /> and Corneille E N Ewango<fnref n="7" />,<fnref n="8" />

E-mail: karimon.nesha@wur.nl and karimonnesha@gmail.com

**Keywords:** peat forests, peat forest disturbances, RADD alert, direct drivers, visual interpretation, smallholder agriculture, Cuvette Centrale

Abstract

The largest tropical peatland complex in the Cuvette Centrale is marked by persistent knowledge
gaps. We assessed recent peat forest disturbances and their direct drivers from 2019 to 2021 in
Cuvette Centrale, spanning the Republic of Congo (ROC) and the Democratic Republic of Congo
(DRC). Utilizing peatland maps and Radar for Detecting Deforestation alert data, we analyzed
spatial and temporal patterns of disturbances. Further, we examined 2267 randomly sampled peat
forest disturbance events through visual interpretation of monthly Planet and Sentinel 2A data to
identify direct drivers. Our findings revealed that between 2019 and 2021, about 91% of
disturbances occurred in DRC, with hotspots concentrated in the northwest Sud-Ubangi district.
Disturbances predominantly followed a sharp seasonal pattern, recurring during the first half of
each year with temporal hotspots emerging between February and May, closely associated with
smallholder agriculture activities. Smallholder agriculture accounted for over 88% of disturbances
in Cuvette Centrale, representing a leading role both in ROC (~77%) and DRC (~89%). While
small-scale logging contributed 7% to the disturbances in the region, it constituted an important
driver (18%) in the ROC. Other drivers included floods, roads, and settlements. Approximately
77% of disturbances occurred outside managed forest concessions in Cuvette Centrale, with 40%
extending into protected areas. About 90% of disturbances were concentrated within 1 km of peat
forest edges and ~76% of the disturbances occurred within 5 km of road or river networks. The
insights underscore the crucial need for effective peat forest conservation strategies in Cuvette
Centrale and can inform national policies targeting peatland protection, aligning with
commitments in the Brazzaville Declaration and the Paris Agreement. Further, our findings on
direct driver assessment could serve as a reference dataset for machine learning models to automate
the visual interpretation and upscale the assessment across the entire region.

<footnote n="1">¹ Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands</footnote>

<footnote n="2">² Helmholtz GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg, Potsdam 14473, Germany</footnote>

<footnote n="3">³ Center for International Forestry Research (CIFOR) c/o Centro Internacional de la Papa (CIP), Av. La Molina 1895, La Molina, Apdo Postal 1558, 15024 Lima, Peru</footnote>

<footnote n="4">⁴ Centre de coopération International en Recherche Agronomique pour le Développement (CIRAD), UMR Eco&Sols, Montpellier, France</footnote>

<footnote n="5">⁵ Center for International Forestry Research—World Agroforestry, Jalan CIFOR, Situ Gede, Sindang Barang, Bogor 16115, Indonesia</footnote>

<footnote n="6">⁶ Department of Geophysics and Meteorology, IPB University, Kampus Darmaga, Bogor 16680, Indonesia</footnote>

<footnote n="7">⁷ Faculté de Gestion des Ressources Naturelles Renouvelables, Université de Kisangani, Kisangani, Congo (Democratic Republic)</footnote>

<footnote n="8">⁸ Faculté des Sciences, Université de Kisangani, Kisangani, Congo (Democratic Republic)</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
# 1. Introduction

Peatlands are distinctive wetlands characterized by the presence of a carbon-rich peat soil layer formed from dead and decaying plant material under water-logged and low oxygen conditions (Joosten and Clarke 2002, Charman 2009). Peatlands occupy only ~3% of the global land surface (Rydin et al 2013, Xu et al 2018), but store approximately one-third of the world's soil carbon (Page et al 2011, Yu 2012, Jackson et al 2017, Hugelius et al 2020), making them a vital component of the global carbon balance (Page et al 2011, Yu et al 2011, Scharlemann et al 2014). They are also crucial for maintaining global biodiversity providing habitats for various plants and animals including rare species (Posa et al 2011, Carroll et al 2015, Husson et al 2018). Further, these ecosystems offer important regulating, provisioning, and cultural services (Reed et al 2014, Bonn et al 2016, Gao et al 2016).

Peatlands, featuring mainly bogs and fens, (Charman 2009, Finlayson and Milton 2018), are extensively covered by rainforest in the tropics and denoted as peat swamp forests (Lähteenoja and Page 2011, Dargie et al 2017, Gumbricht et al 2017a). The tropics contribute to approximately one-quarter of the global peatlands extent, with South America accounting for 11.5%, Southeast Asia (SEA) 7.2%, and Africa 4.4% (Page et al 2011, Draper et al 2014, Gumbricht et al 2017b, Xu et al 2018, Melton et al 2022). Until recently, information on the peatlands extent in Africa was highly uncertain. Two recent studies have specifically mapped peatlands extent from 145 500 km² to 167 600 km², in the central Congo Basin in Africa, known as Cuvette Centrale (Dargie et al 2017, Crezee et al 2022). This marks the Cuvette Centrale as the largest tropical peatland complex, spanning the Republic of Congo (ROC) and the Democratic Republic of Congo (DRC) (Dargie et al 2017, Crezee et al 2022).

DRC and ROC are the second and third most significant countries for tropical peatlands following Indonesia (Xu et al 2018), storing 19.1–19.3 and 9.6–11.5 Pg C, respectively, and hosting together 29% of the total tropical peat carbon stock (Dargie et al 2017, Crezee et al 2022). Notably, belowground carbon stock of Cuvette Centrale is comparable to aboveground forest carbon stocks for the entire Congo Basin (Verhegghen et al 2012). The Cuvette Centrale peatlands are entirely overlaid by swamp forests, featuring mainly palm and hardwood dominated vegetation (Dargie et al 2017). The massive peat and vegetation carbon stocks underscore the importance of protecting and monitoring the peatlands in Cuvette Centrale. Recognizing this, DRC, ROC, and Indonesia jointly signed the Brazzaville Declaration in 2018 to protect and sustainably manage these ecosystems aligning with the Paris Agreement and UN-Reducing

Emissions from Deforestation and Degradation (UN-REDD). Similarly, international partnerships e.g. United Nations Environment Programme's Global Peatlands Initiative, Tropical Peatland Initiative, International Tropical Peatlands Center, and International Peatland Society share a common vision.

Peatland disturbance drivers, carbon emissions, and management options have been studied in SEA, particularly in Indonesia (Hooijer et al 2010, Koh et al 2011, Tarigan et al 2015, Uda et al 2017, Danylo et al 2021, Yuwati et al 2021), and in Peruvian Amazon (Roucoux et al 2017, Baker and Coronado 2019, van Lent et al 2019, Bourgeau-Chavez et al 2021, Hastie et al 2022, Hergoualc'h et al 2023, Marcus et al 2024). The Cuvette Centrale peatlands have a clear history of disturbances (Dargie et al 2019), yet these ecosystems have until now remained relatively understudied. Previous studies have offered only general indications of prevailing threats to the peatlands in Cuvette Centrale, including land-use and climate change (Dargie et al 2019). If left unchecked, these threats could potentially accelerate, leading to irreversible damage to these ecosystems, necessitating an immediate investigation into the drivers of peat disturbances to undertake necessary actions at (inter)national levels.

A combination of earth observation data sources can play a critical role in monitoring disturbances and related drivers in Cuvette Centrale peat forests. Recent remote sensing (RS) based peatland maps offer detailed spatial information on their distribution (Dargie et al 2017, Gumbricht et al 2017b, Xu et al 2018, Crezee et al 2022). Further, satellite-based alert systems offer spatial and temporal information on new disturbances (Diniz et al 2015, Hansen et al 2016, Watanabe et al 2018, Reiche et al 2021). Being affected by persistent cloud cover (Lindquist et al 2008) and small-scale disturbances (Tyukavina et al 2018, Reiche et al 2021, Laso Bayas et al 2022, Slagter et al 2023), disturbance dynamics in Cuvette Centrale forests can only be evaluated using cloud-free and high-resolution data in space and time. Radar for Detecting Deforestation (RADD) alert offers cloud-free disturbance data weekly at 10 m resolution (Reiche et al 2021). Further, Planet Labs offers high-frequency and high-resolution earth observation imagery, with analysis-ready mosaics for tropical forests accessible through Norway's International Climate & Forests Initiative (NICFI) (Planet Labs 2019). Other data sources (e.g. Sentinel-2) are also available for forest applications. Visual interpretation of the Planet and other imagery can be used for driver analysis of peat forest disturbances in Cuvette Centrale, as employed for other tropical forest disturbances (Koh et al 2011, Leblois et al 2017, Murillo-Sandoval et al 2018, Turubanova et al 2018, Austin et al 2019, De Sy et al 2019, Laso Bayas et al 2022, Slagter et al 2023).
In this paper, we systematically assessed recent peat forest disturbances and related direct drivers in Cuvette Centrale, spanning DRC and ROC from 2019 to 2021 using multiple earth observation data. We mainly employed RADD alert forest disturbances data (Reiche et al 2021), two recent peatland maps (Gumbricht et al 2017b, Crezee et al 2022), and NICFI Planet and Sentinel-2A imagery for the analysis. Our study pioneers the analysis of peat forest disturbances and associated direct drivers in Cuvette Centrale. Given the current limited knowledge on this subject, this research offers a crucial contribution to filling existing knowledge gaps which could support more effective strategies to meet national and international commitments on peatland conservation and restoration, including those outlined in the Paris Agreement and Brazzaville Declaration.

Specifically, we answered the following research questions:

1. What are the spatial and temporal patterns of peat forest disturbances?

2. How do accessibility and protection status affect the distribution of disturbances?

3. What are the direct drivers of peat forest disturbances and how do they vary in space and time?

## 2. Data and methods

### 2.1. Study area, data and processing

#### 2.1.1. Peatland extent and forest baseline map

For peatland extent in Cuvette Centrale (figure 2(a)), we relied on two peat maps (figure 2(b)): the Gumbricht map with 231 m spatial resolution (Gumbricht et al 2017b) and Crezee map with 50 m resolution (Crezee et al 2022). The Gumbricht map was the most recent and robust peatland dataset available for the entire tropical region, while the Crezee map was the most recent data specific to peatlands in Cuvette Centrale. The source RS data of the Gumbricht map were obtained from MODIS product for 2011 (Gumbricht et al 2017b), while the source RS data of the Crezee map were obtained from Landsat and ALOS PALSAR as composites of multiple years between 2000 and 2010 (Crezee et al 2022). Both peatland maps have been reported with different degrees of uncertainties (Gumbricht et al 2017b, Crezee et al 2022). We integrated these maps to include the maximum available extent of peatlands, which we termed as combined peat map. Additionally, we created agreement and non-agreement peat maps, with the former featuring matching peatland pixels from both Gumbricht and Crezee maps, and the latter including pixels present in either Gumbricht or Crezee map but not in both. We used a 10 m spatial resolution forest baseline mask in Africa for the year 2018 (Turubanova et al 2018) with the peat maps

to generate a combined (figure 2(b)), agreement and non-agreement peat forest maps (figure 2(b)). We harmonized the resolutions of Gumbricht and Crezee peat maps to 10 m for these computations and subsequent processing, by employing the nearest neighbor resampling method in Google Earth Engine (GEE) (details in appendix A). An overview of the datasets and methodological framework employed in this study is shown in figure 1.

#### 2.1.2. Peat forest disturbance data

For delineating forest disturbances, we utilized the RADD alert system, which is a Sentinel-1 based forest disturbances dataset in primary humid tropical forests in near real-time from 2019 onwards (Reiche et al 2021). In this system, forest disturbance refers to the complete (associated with stand-replacement disturbance) or partial (mainly associated with boundary pixels and selective logging) removal of tree cover within a 10 m Sentinel-1 pixel. The alert data contained two bands: Alert confidence and Date when the disturbance was first detected. The alert band had low (forest disturbance probabilities above 85%), and high confidence (forest disturbance probability surpassing 97.5%) alert values. We only employed the high confidence alert values in our analysis. We masked out peat forest disturbances from the RADD alert forest disturbances data using the combined peat forest map in Cuvette Centrale (figure 2(b)). Disturbances smaller than 0.2 ha until July 2020 and smaller than 0.1 ha thereafter were not mapped in RADD alert products due to the minimum mapping units (MMUs) applied (Reiche et al 2021). Further, RADD alerts include a number of false alerts in swamp forests (Reiche et al 2021). Consequently, our analysis may underrepresent the frequency and extent of disturbances, leading to conservative estimations reported in our study. Notably, the intersection of RADD alert data with the peatlands and forest base layer to determine the extent of peat forest disturbances resulted in some artifact disturbance areas being smaller than the MMU of 0.1 ha (appendix L).

#### 2.1.3. Protection status and accessibility data

We compiled data on protection status, encompassing officially designated protected areas and managed areas such as forest concessions. Data on national protected areas was obtained from the forest atlas (MECNDD & MEDD-DIAF 2023) and world protected areas (e.g. strict natural reserve, Ramsar site) from the world database on protected areas (UNEP-WCMC & IUCN 2023). Forest concession boundaries were sourced from WRI (2021). For accessibility, we utilized road network data from Kleinschroth et al (2019), and river networks from the global land cover facility's inland water dataset (Feng et al 2016).
<figure><img src="image_4.png" /><figcaption>Figure 1. Methodological framework outlining key datasets and processes contributing to the analysis of peat forest disturbances and their direct drivers in Cuvette Centrale from 2019 to 2021.</figcaption></figure>

## 2.2. Spatio-temporal analysis of peat forest disturbances

We assessed the spatial distribution of peat forest disturbances by calculating the relative intensity of disturbances per unit area using a 1 km × 1 km grid cell system. The relative intensity was quantified as the ratio of peat forest disturbance pixels (10 m) to peat forest pixels (10 m) in each grid cell. We categorized disturbances into low, medium, and high intensities. Grid cells exhibiting intensities below 2% were classified as low-intensity disturbances, cells ranging from 2% to 20% were categorized as medium-intensity disturbances, and those exceeding 20% were identified as high-intensity disturbances or spatial hotspots (appendix B).

Further, we analyzed the temporal pattern of peat forest disturbances on a monthly basis, aggregated per year. We examined intra-annual and interannual patterns of disturbances over the study period. We investigated whether certain months exhibited a notable increase in disturbances compared to the average

monthly occurrence to identify the temporal hot-spots, defined as instances where disturbances exceed 10% per month, aggregated annually. The temporal analysis was performed at the pixel level (10 m), utilizing the date values from the high confidence alert dataset.

## 2.3. Distribution of disturbances by protection status and accessibility

We analyzed disturbances distribution within and outside forest concessions, and national and world protected areas (figures 2(c) and (d), appendix A). Further, we evaluated the proximity of the disturbances to peat forest edges, by transforming the combined Gumbricht-Crezee peat map into a distance map using the Haversine equation (equation (1)) (van Brummelen 2017). In the distance map, edge pixels denoted a zero value, with values increasing for pixels towards the forest interior. We analyzed disturbances along road and river networks, measuring their distance in km (appendix C)

$$d = 2\text{rasin}\left(\sqrt{\sin^2(\varphi_2 + \varphi_1/2) + \cos\varphi_1 \cdot \cos\varphi_2 \cdot \sin^2(\lambda_2 - \lambda_1/2)}\right) \quad (1)$$
<figure><img src="image_5.png" /></figure>
where *d* stands for distance between pixel centers, *r* is the radius of the Earth, $\phi_1$, $\phi_2$ are latitudes of pixel 1 and pixel 2, $\lambda_1$, $\lambda_2$ are the longitude of pixel 1 and pixel 2.

## 2.4. Direct driver analysis of peat forest disturbances

### 2.4.1. Defining direct drivers

We considered exclusively the direct drivers of forest disturbances as defined in the framework of proximate causes (Geist and Lambin 2002). We identified five direct drivers namely smallholder agriculture, small-scale logging, floods, roads, and settlements (table 1). We excluded large-scale agriculture as we did not observe any such activities. This aligns with previous studies citing smallholder agriculture and logging as predominant drivers in the region (Tyukavina et al 2018, Dargie et al 2019). We

adopted the globally most used criterion for defining smallholder agriculture with land size, including croplands operating on $\leqslant 2$ ha (GRAIN 2014, Khalil et al 2017). We included flood as heavy rains frequently cause flooding along the Congo and Ubangi Rivers impacting large forest areas in the Congo Basin (OCHA 2019, 2020, Reliefweb 2020, 2021, 2022, Gou et al 2022). The study area also manifests infrastructure development e.g. roads and settlements (Kleinschroth et al 2016, Kleinschroth and Healey 2017). We assigned an ‘unidentified’ class for drivers that defied classification.

### 2.4.2. Sampling peat forest disturbance events for driver analysis

We employed random sampling on the peat forest disturbance pixels aggregated as events. The distinct spatially connected disturbance pixels were grouped into

<table><caption>**Table 1.** Direct drivers of peat forest disturbances in the Cuvette Centrale—class description and criteria that are visible on the Planet and/or Sentinel-2A imagery.</caption><thead><tr><td>Driver class</td><td>Description</td></tr></thead><tbody><tr><td>Smallholder agriculture</td><td><ul><li>Vegetation regrowth in the sample events within one year</li><li>At least one complete/partial harvesting at the sample events within two years of vegetation regrowth</li><li>In events with no visible harvesting vegetation texture smooth and comparable with adjoining crop fields i.e. similar vegetation patterns followed in the adjoining crop fields</li><li>Large events containing multiple croplands (appendix G)</li><li>Usually located adjacent to crop fields over large areas</li><li>No adjoining crop fields (i.e. an individual/isolated event), new disturbances expanding in its vicinity within a year</li><li>Small events usually expanding from an existing crop field (appendix H)</li><li>Usually located close to settlements/roads/river networks</li><li>Usually non-mechanized clearing of croplands (appendix H)</li></ul></td></tr><tr><td>Small-scale logging</td><td><ul><li>No clearing of vegetation within a year after post-disturbance regrowth and tree canopy clearly visible in 1–2 years</li><li>No vegetation regrowth and the patch stays bare for more than a year</li><li>Usually small to medium events</li></ul></td></tr><tr><td>Flood (appendix J)</td><td><ul><li>The event occurred in the second half of the year 2019</li><li>Located along the rivers</li><li>No adjoining crop fields</li><li>No cropping pattern in the sample events</li></ul></td></tr><tr><td>Roads</td><td><ul><li>Linear canopy opening visible for more than three months after opening</li><li>Usually part of existing road networks</li><li>Usually connected with disturbances/settlements</li></ul></td></tr><tr><td>Settlements</td><td><ul><li>Houses and house roofs clearly visible</li><li>Appears very bright on the image</li><li>Usually, several houses collocated</li></ul></td></tr><tr><td>Unidentified</td><td><ul><li>Events not presenting the criteria of the abovementioned classes</li><li>Significant spatial mismatch of the RADD alert events due to shifting in Planet images</li><li>Events not recognizable due to unclear/cloudy Planet and/or Sentinel-2 images</li></ul></td></tr></tbody></table>
one single object that we defined as a peat forest dis-
turbance event. The connectivity of pixels was spe-
cified based on the eight-neighbor connectivity (Liu
and Mason 2016, ITC 2018). We randomly selected
around 4% of events from the total (61779), leading
to 2267 events (figure 2(e) and appendix D). Events
delineation and event-based random sampling were
implemented in the GEE. Following sampling, we
classified the sampled events into small (<0.5 ha),
medium (0.5–2 ha), and large (>2 ha) events based
on their area sizes. We further grouped the events
based on their distribution across countries, agree-
ment vs. non-agreement peat forests, and hardwood
vs. palm dominated peat forests (figure 2(d)). Data
on hardwood and palm dominated peat forests were
sourced from the Crezee et al (2022) study.

### 2.4.3. Visual interpretation of the direct drivers

We labeled the direct drivers through visual interpretation of post-disturbance land use on an event-by-event basis using the high-resolution 4.77 m Planet and 10 m Sentinel-2A satellite imagery (figure 3). For assigning a specific driver, we applied a set of distinct criteria for each driver class, discernible on the images following disturbances (table 1, appendices E and F). Previous studies have successfully implemented visual interpretation techniques for driver identification (De Sy et al 2019, Slagter et al 2023). We centered our analysis at the event level, however, we zoomed out to the neighboring areas to understand contextual land use patterns to support our driver assessment. We accessed the Planet dataset for tropical Africa through NICFI using the GEE platform and Planet QGIS Plugin in QGIS. We accessed Level 2A Surface Reflectance Sentinel Imagery from GEE archives. The Sentinel 2A images are atmospherically corrected using sen2cor (ESA 2013). We masked cloud and shadow following s2cloudless approach in GEE (2023).

## 3. Results

### 3.1. Spatial and temporal analysis of peat forest disturbances

From 2019 to 2021, disturbances encompassed an
area of 30 294 ha in the peat forests of Cuvette
Centrale. Most areas encountered medium-intensity
disturbances, ranging from 2% to 20% disturbances
spanning over 28% of the grid cells and covering
an area of 21 049 ha (figure 6(a) and appendices B
and D). Although low-intensity disturbances (<2%
disturbances) spanned across 70% of the grid cells,
they only covered an area of 6414 ha. Spatial hot-
spots were relatively less common, covering an area
of 2831 ha across just 2% of the grid cells. Among
these, certain grid cells exhibited a 100% loss of forest
cover.

The great majority of the disturbances, accounting for around 91% of the total 30 294 ha were concentrated in DRC, with a substantial portion of spatial hotspots occurring in the northwest region, particularly in the Sud-Ubangi district. Many high-intensity disturbances were also observed along the Congo River. While there exists a disparity in the extent of forest disturbances between DRC and ROC, our analysis did not reveal substantial differences in the intensity of these disturbances between the two countries.

The temporal distribution of peat forest disturbances displayed a consistent interannual pattern, predominantly occurring during the first half of each year (figure 4(a) and appendix I(a)). Disturbances in the first half of the year accounted for 62% in 2019, 69% in 2020, and 78% in 2021. This seasonal trend began from January onwards, with temporal hotspots emerging between February and May, and it gradually declined thereafter. Although a minor peak occurred in the latter part of each year, this pattern was not consistent over the period. There was

<figure><img src="image_6.png" /><figcaption>**Figure 3.** Example Planet images relating to direct drivers of peat forest disturbances in the Cuvette Centrale. Disturbances as detected in the RADD alerts from 2019–2021 and identified direct drivers following disturbances visualized on the 4.8 m spatial resolution Planet and 10 m resolution Sentinel-2 imagery. For each disturbance event, we examined 24 monthly images in the subsequent two years from both Planet and Sentinel-2. For example, if an event occurred later in 2021, we analyzed the subsequent monthly images from both Planet and Sentinel-2 over 2022 and 2023. While not all images were cloud-free, available time series of clear monthly images over two years were sufficient to reliably identify a driver to a disturbance event.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Temporal distribution of disturbances in Cuvette Centrale. The distribution of disturbances (in % on the y-axis) is depicted month-wise aggregated by year from 2019 to 2021 (x-axis). Lines for November and December 2021 are dashed in black to indicate that high-confidence data for these months was not fully available. (a) Temporal distribution of all disturbances. (b) Temporal distribution of all drivers. (b.1) Agriculture, (b.2) logging, (b.3) other events, respectively. Other events include roads, settlements, and unidentified. The monthly percentage of disturbances (y-axis) was calculated considering the total sum of all drivers per year, segregated by months on the x-axis. The scales are much lower in figures (b.2) to (b.3), with logging and other factors contributing only a small fraction of disturbances compared to agriculture in figure (b.1).</figcaption></figure>

no intra-annual trend. The year 2019 was exceptional, featuring notably extensive disturbances (20%) during November and December.

## 3.2. Distribution of disturbances by protection status and accessibility

Our findings revealed that more than three-quarters of the disturbances (~77%) occurred outside the managed forest concession areas (figure 2(d)), extending greatly to the designated national and world protected areas within the peat forests in Cuvette Centrale. Around 40% of the disturbances occurred within these protected areas, with ~35% occurring specifically inside the world protected areas.

Disturbances were primarily concentrated along the peat forest edges, with the core region remaining relatively undisturbed (figure 5). Approximately 90% of disturbances took place within a 1 km distance from the edges and 99% of them within a 3 km distance. Further, around 76% of disturbances occurred within 5 km distance from the river or road networks, and around 94% within 10 km. More disturbances were observed along the road networks compared with river networks at equivalent distances. For instance, disturbances within a 5–10 km radius of road networks were approximately 1.3 times higher than those along the rivers.

## 3.3. Direct drivers of peat forest disturbances

Between 2019 and 2021, we identified five direct drivers of peat forest disturbances in Cuvette Centrale: smallholder agriculture, small-scale logging, floods, roads, and settlements (figures 6(b)–(d)). Smallholder agriculture was the most prevalent, accounting for over 88% of the 2267 sampled events, followed by logging at ~7% (table 2). A small percentage of events (<1%) remained undefined. We reclassified roads, settlements, and undefined events into others.

### 3.3.1. Spatial distribution of direct drivers

Over 90% of the drivers were concentrated in DRC. In both countries, smallholder agriculture remained the leading driver of peat disturbances, accounting for roughly 89% of disturbance areas in DRC and 77% in ROC (table 2). Logging events constituted 18% of disturbance areas in ROC, which is nearly three times higher than those in DRC. The majority of flood events (98%) were located along the Congo River, where all small settlements were situated near rivers and water channels in DRC. Most of the road events (~75%) were found in the ROC.

Most disturbances (74%) took place in the hardwood dominated peat forests (appendix K). The main driver in both palm (85%) and hardwood (89%) dominated forests was smallholder agriculture. Flood events (96%) occurred primarily in palm dominated
<figure><img src="image_8.png" /><figcaption>Figure 5. Distribution of all disturbances along peat forest edges, road and river networks in Cuvette Centrale from 2019 to 2021.</figcaption></figure>

<table><caption>Table 2. Distribution of drivers of peat forest disturbances in the Cuvette Centrale by countries, DRC, and ROC from 2019 to 2021.</caption><thead><tr><th rowspan="2">Drivers</th><th colspan="4">ROC</th><th colspan="4">DRC</th></tr><tr><th>Number of events</th><th>Events area (ha)</th><th>Area (%) relative to events in ROC</th><th>Area (%) relative to events in Cuvette Centrale</th><th>Number of events</th><th>Events area (ha)</th><th>Area (%) relative to events in DRC</th><th>Area (%) relative to events in Cuvette Centrale</th></tr></thead><tbody><tr><td>Agriculture</td><td>142</td><td>80.7</td><td>77.0</td><td>6.7</td><td>1791</td><td>979.6</td><td>89.1</td><td>81.4</td></tr><tr><td>Logging</td><td>49</td><td>18.5</td><td>17.6</td><td>1.5</td><td>177</td><td>68.8</td><td>6.3</td><td>5.7</td></tr><tr><td>Flood</td><td>5</td><td>0.7</td><td>0.7</td><td>0.1</td><td>69</td><td>36.5</td><td>3.3</td><td>3.0</td></tr><tr><td>Roads</td><td>3</td><td>1.4</td><td>1.4</td><td>0.1</td><td>1</td><td>0.6</td><td>0.1</td><td>0.1</td></tr><tr><td>Settlements</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>12</td><td>4.3</td><td>0.4</td><td>0.4</td></tr><tr><td>Unidentified</td><td>3</td><td>3.5</td><td>3.3</td><td>0.3</td><td>16</td><td>9.4</td><td>0.9</td><td>0.8</td></tr><tr><td>Total</td><td></td><td>104.7</td><td>100.0</td><td>8.7</td><td></td><td>1099.2</td><td>100.0</td><td>91.3</td></tr></tbody></table>

forests. We observed more than three-quarters (76%) of the disturbances in the non-agreement peat forests (appendix L), with smallholder agriculture being the primary driver, accounting for around 88% of disturbances in both agreement and non-agreement peat forests.

Smallholder agriculture consistently emerged as the predominant driver across events of all sizes (appendix M). While small events occurred more frequently, constituting approximately two-thirds of all events, medium events (0.5–2 hectares) had a more substantial impact, covering 53% of the total disturbance area. With 80% of the total disturbance area was covered by small to medium events (appendix M) and large events typically encompassed multiple croplands (appendix G), this implies that disturbances were driven by smallholder activities.

### 3.3.2. *Temporal distribution of direct drivers*

We observed a distinct temporal pattern, with the majority of the drivers occurring prominently in the

first half of each year (figure 4(b) and appendix I(b)). This trend was primarily linked to smallholder agriculture (figure 4(b.1) and appendix I(b.1)). Although less prominent, logging events were also concentrated during the first half of the year (figure 4(b.2) and appendix I(b.2)). Flood events were concentrated specifically from October to December in 2019 (appendix I). No clear pattern of seasonality was evident for other events, suggesting that they occurred randomly throughout the year (figure 4(b.3) and appendix I(b.3)).

## 4. Discussion

### 4.1. What are the patterns of peat forest disturbances?

Our findings reveal a total of 30 294 ha of peat forest disturbances in Cuvette Centrale from 2019 to 2021, representing less than 1% loss of peat forest area over this period. This reaffirms assertions by previous studies stating relatively undisturbed peat forests
<figure><img src="image_9.png" /></figure>

in Cuvette Centrale (Dargie *et al* 2017, 2019, Miles *et al* 2017, Vancutsem *et al* 2021). Nevertheless, the cumulative area impact of disturbances may substantially expand over a longer period if left unaddressed. Examples from SEA and the Amazon underscore this concern: SEA lost nearly 60% of its peat forests from 11.9 Mha in 1990 to 4.6 Mha in 2015 (Miettinen *et al* 2016). In the Peruvian Amazon, 11% of the 4.2 Mha palm swamp peat forests underwent degradation, and 2% experienced deforestation from 1990 to 2018 (Marcus *et al* 2024).

Our analysis shows that 91% of the peat forest disturbances in Cuvette Centrale are concentrated within DRC. This is consistent with previous findings that DRC alone accounts for two-thirds of all forest loss in the entire Congo Basin (Tyukavina et al 2018). This underscores the predominance of forest loss in DRC, encompassing both peat and mineral soil. The occurrence of 91% disturbances in DRC is concerning

given the share of peatlands between DRC (55%) and ROC (45%) does not differ greatly. Previous studies link the predominant forest loss in DRC with population growth, poverty, inadequate governance, and the dependence of the large population on forests for livelihoods (Tyukavina et al 2018, Cerutti et al 2023). These factors may also contribute to peat forest loss in DRC, necessitating further research for confirmation. The relatively low share of peat disturbances in ROC could be linked to the sparse population and infrequent human activities in its part of Cuvette Centrale (Dargie et al 2019).

A great majority of the total disturbances consistently occurred in the first half of each year from 2019 to 2021. This trend is primarily attributed to smallholder agriculture, responsible for 88% of the total disturbances, peaking in the first half of each year. This period in the Congo Basin consists of a dry season (January–February) and a short wet season
(March to May) (Creese et al 2019). Previous studies reported decreased precipitation from March to May and increased precipitation in the dry seasons—(December–February) and (June–August) in recent decades (Dyer et al 2017, Creese et al 2019, Jiang et al 2019, Wang et al 2021). The increasing drier conditions in the first half of the year could be linked to our observation of predominant disturbances during that period, suggesting a consistent interannual pattern. This aligns with earlier studies in the broader Congo Basin region (Sonwa et al 2020, Gou et al 2022, Slagter et al 2023). Nevertheless, a three-year study period is insufficient for discerning long term interannual patterns. Future research should use extended datasets to assess the persistence of these trends.

## 4.2. How do protection status and accessibility affect distribution of disturbances?

Our findings reveal that 99% of disturbances occurred within 3 km from the peat forest edges, possibly due to more accessibility for agricultural drainage. Further ground research is needed to investigate these aspects. The combination of 76% of disturbances in non-agreement forest areas and 90% within 1 km of the peat forest edges suggests a considerable overlap between them. Although core peat forests remain relatively intact, they could face considerable disturbances in coming years from agricultural drainage (Dargie et al 2017, 2019), lack of enforcement of protection regulations—supported by our findings, Lilleskov et al (2019) and Dargie et al (2017), high population pressure particularly in DRC (Tyukavina et al 2018, Cerutti et al 2023) and increased accessibility through roads and rivers as observed in our study. Although DRC has maintained a >20-year logging moratorium covering 18% of peatlands in its Congo Basin portion, the possibility of lifting it remains (Dargie et al 2019, Rainforest Foundation 2022). Approximately 80% of peatlands in Cuvette Centrale overlap with concessions for industrial agriculture, logging, or oil blocks (Greenpeace 2019). Recently, oil blocks have been granted for exploration in ROC and DRC, some spanning across peatlands (Feukeng 2021). Unless concessions are entirely halted and robust national protection measures are implemented, the Cuvette Centrale region may confront a fate similar to that of peatlands in Indonesia.

Approximately 76% of disturbances occurred within a 5 km distance from river or road networks and over 94% within 10 km. This aligns with prior research highlighting the role of accessibility in facilitating forest disturbances in the African rainforest (Ernst et al 2013, Barber et al 2014, Ordway et al 2017, Kleinschroth et al 2019). The greater share of disturbances within hardwood dominated peat forests compared to palm dominated, relative to their respective areas, indicates that hardwood dominated forests could host more valuable timber species, leading to

increased local or external resource demands. Other factors such as ease of access or protection status could also contribute to the more frequent disturbances in the hardwood dominated forests. Future research should explore these underlying factors contributing to peat forest disturbances. Further, over three-quarters of the peat forest loss outside designated forest concessions suggests widespread illicit forest clearing in Cuvette Centrale. Moreover, a higher proportion of disturbances in world protected areas compared to national ones underscores the need for effective protection enforcement strategies targeting world protected areas. Contrary to the assumption that protection status guarantees peat-land preservation, our findings, revealing 40% of disturbances within protected areas, underscore the necessity for stringent enforcement of (inter)national policies aligning with commitments in the Brazzaville Declaration and the Paris Agreement. The United Nations Environment Programme through the Global Peatlands Initiative, UN-REDD, International Tropical Peatlands Center, Tropical Peatland Initiative and International Peatland Society could also play a pivotal role in addressing peat forest disturbances.

## 4.3. What are the main drivers of peat forest disturbances and how do they vary?

We offer the first assessment of the direct drivers of peat forest disturbances in the Cuvette Centrale from 2019 to 2021. Smallholder agriculture emerges as the predominant driver, responsible for 88% of peat forest loss. This aligns with broader trends in the Congo Basin, where smallholder agriculture contributed 84% of total forest loss from 2000 to 2014 (Tyukavina et al 2018), consistent findings reported in several other studies (Geist and Lambin 2002, Tegegne et al 2016, Curtis et al 2018, Shapiro et al 2023). Drivers of peat loss in Cuvette Centrale differ from those observed in SEA. Industrial plantations have exhibited the most rapid expansion in SEA, covering 27% of peatlands while smallholder areas cover 22% (Miettinen et al 2011, 2016). Small-scale logging is the second most important driver in Cuvette Centrale, contributing 7% to peat forest disturbances. This mirrors prior research indicating logging shares 10% of the total forest loss across the entire Congo Basin (Tyukavina et al 2018).

At the country level, smallholder agriculture is the primary driver both in ROC and DRC, more prominent in DRC, accounting for around 90% of disturbances. This aligns with previous research attributing over 90% of total forest loss to smallholder agriculture in DRC (Tyukavina et al 2018). In ROC, logging also plays a considerable role, sharing 18% of peat disturbances, consistent with small-scale logging representing 20% of all forest loss in the country (Lawson 2014). National policies should address both agriculture and logging to mitigate peat forest loss
in ROC, with specific interventions targeting small-
holder agriculture in DRC. We observed that agri-
cultural events cluster over areas, while logging dis-
perses widely, mainly co-occurring with agriculture.
This finding aligns with previous studies in tropical
regions including the Congo Basin (Ferrer Velasco
et al 2020, Shapiro et al 2023). Logging could serve
as the initial stage of tropical forest degradation,
eventually culminating in widespread forest clear-
ing (Ahrends et al 2010). Further research with a
longer time series is essential to validate this pattern
in Cuvette Centrale peat forests. Although not dir-
ectly comparable to broader Congo Basin forests, our
findings reveal similar trends of small-scale clearing
for agriculture and logging in Cuvette Centrale peat
forests.

We observed potential higher disturbances in late 2020 and 2021, suggesting a connection to flood events. However, we did not include them as a flood driver due to limitations in identifying them using the RADD product and instead categorized them as unidentified. The impact of flood events on forest cover—whether complete or partial removal—remains unclear. While the area affected by flooding is relatively small compared to other disturbances, their regular occurrence and long-term consequences may be considerable. Future studies should assess the extent and long-term impact of flooding on forest cover.

Our findings could serve as a reference dataset for automating visual interpretation with machine learning, enabling the generation of a time series record of peat forest disturbance drivers in Cuvette Centrale. Recent studies have successfully implemented such automation (Curtis et al 2018, Masolele et al 2024). Our findings can further facilitate the upscaling of analysis using machine learning across the entire Cuvette Centrale region. We recommend conducting separate driver assessments in diverse peat forests, including South America and SEA, to create reference datasets. Combining findings from these distinct regions could facilitate upscaling the analysis to a pan-tropical level, as machine learning models perform better with region-specific input (Masolele et al 2021).

Our study lacks ground data for validating iden-
tified drivers and specific insights into the crop types.
Reports suggest rice crops are grown in Cuvette
Centrale (Cordon 2020). Future research should
combine ground data with RS observations to identify
specific crops, assess variations among field sizes,
and validate the RS based findings. Smallholder
agriculture may coexist with fuelwood extraction
and charcoal harvesting, as most households, espe-
cially in DRC, rely on these for cooking (Bilonda
2020). However, identifying such small-scale activit-
ies through satellite imagery poses limitations. Future
research should address these challenges in the region.

5. Conclusions

Our study provides a pioneering analysis of peat
forest disturbances and their direct drivers in the
Cuvette Centrale. The findings indicate that 91% of
disturbances, totaling 30 294 ha, were concentrated
in the DRC, necessitating increased emphasis on
(inter)national policy interventions to address peat
forest loss in the country. Most peat forest areas in
Cuvette Centrale experienced medium-intensity dis-
turbances (2%–20%), covering 21 049 ha, while spa-
tial hotspots covered just 2831 ha, indicating the
need to prioritize interventions on medium-intensity
disturbances. Disturbances predominantly showed a
seasonal recurring pattern during the first half of each
year from 2019 to 2021 with temporal hotspots emer-
ging between February and May, closely linked to
smallholder agriculture activities. Smallholder agri-
culture accounted for over 88% of disturbances in
Cuvette Centrale, representing a leading role in both
the ROC (~77%) and the DRC (~89%). In the ROC,
logging events constituted an important share (18%).
National policy actions should address both agricul-
ture and logging to mitigate peat forest loss in ROC,
with specific interventions targeting smallholder agri-
culture in the DRC.

Approximately 76% of disturbances were con-
centrated within 5 km of river or road networks,
with more disturbances observed along roads, under-
scoring their importance in providing greater access-
ibility to peat forests. Nearly 90% of disturbances
occurred within 1 km of peat edges, with 99% within
a 3 km distance, indicating that disturbances predom-
inantly occurred at the peripheries, leaving core peat
regions relatively undisturbed. Around 77% of dis-
turbances occurred outside managed forest conces-
sions in Cuvette Centrale, with a large proportion
(40%) extending into protected areas. If these issues
are not addressed, the disturbances could intensify
in the coming years, leading to a substantial expan-
sion of their cumulative area impact over time.
Governments must proactively implement measures
to meet their peatland protection commitments out-
lined in the Brazzaville Declaration and the Paris
Agreement. International collaboration is also essen-
tial, necessitating proactive measures from related
platforms and partners.

Our findings on direct driver assessment could
serve as a reference dataset for machine learning
models, facilitating the automation of visual inter-
pretation to generate a time series record of drivers
and enabling the upscaling of assessments across
the entire Cuvette Centrale region. We recommend
conducting separate direct driver assessments in
diverse peat forests in South America and SEA to
create reference datasets. Combining findings from
these distinct regions could enable the upscaling of
the analysis to a pan-tropical level. Future research
should incorporate ground data with RS obser-
vations to identify specific crops and validate RS
based findings for greater accuracy. Additionally, it
is imperative to understand the dynamics of dir-
ect drivers of peat disturbances in relation to their
underlying causes for a more profound understand-
ing, laying the groundwork for informed policy
formulation.

Data availability statement

The data that support the findings of this study are
available upon reasonable request from the authors.

Acknowledgments

This research is part of CIFOR's Global Comparative
Study on REDD+ (www.cifor.org/gcs). The funding
partners that have supported this research include the
Norwegian International Climate and Forest

Initiative (NICFI) (Agreement INS 2078-
19/0010), the European Commission Horizon Europe
project 'Open-Earth- Monitor' (Grant Number
101059548), the US Government's SilvaCarbon pro-
gram,the Open Domain Science project (Project
Number: OCENW.M.21.203) of the Nederlandse
Organisatie voor Wetenschappelijk Onderzoek
(NWO), and the USAID supported SWAMP pro-
ject (Grant MTO- 069018). Planet imagery was
made available by the NICFI program and the Planet
Education & Research program.

Disclaimer

The usage of designations and the presentation of
material in the maps do not indicate any opinion
from the authors regarding the legal or constitu-
tional status of any country, territory, or sea area,
nor do they provide any stance on the delimitation of
frontiers.
## Appendix A

Harmonization of peatland maps with RADD alert dataset, and harmonizing protection status and forest concessions datasets with the peat forest disturbance data.

Nearest neighbor resampling method was chosen to harmonize the peatland datasets to 10 m resolution to match with RADD alert data as both peatland images contain categorical data. Nearest neighbor resampling preserves these original class values without introducing interpolated values that could misrepresent the data.

The protected area and concession boundary datasets were obtained as vector data, represented by polygons. To harmonize these datasets with the peat forest disturbances data, we employed a method of overlaying the polygon boundaries onto the disturbance events. The disturbance events were also converted into vector data, represented as point vectors. By intersecting the disturbance events with the polygons of the protected areas and concession boundaries, we were able to effectively integrate these datasets.

## Appendix B

This appendix explains the rationale for the breakpoints used to group disturbance intensity into low (<2%), medium (2%–20%), and high (>20%) intensity categories. These categories are based on the natural breaks in the distribution of disturbance intensities as observed in the histogram, with a high frequency of low-intensity disturbances and a gradual decline towards high-intensity disturbances. More specifically,

Low-intensity disturbances (<2%): The histogram shows a high frequency of disturbances at very low intensities, clustered around the leftmost part of the distribution. Setting the breakpoint at 2% captures the bulk of these low-intensity disturbances, which represent the most common disturbances in the dataset.

Medium-intensity disturbances (2%–20%): The medium-intensity range captures the middle part of the distribution where the frequency of disturbances starts to decline more gradually. This range includes a substantial number of events that are considerably higher than the low-intensity disturbances but still not extreme, ensuring a meaningful distinction between low and high-intensity groups.

High-intensity disturbances (>20%): The histogram shows that disturbances above 20% are relatively rare, indicated by the sparse right tail of the distribution. Setting the high-intensity threshold at 20% effectively isolates the most severe disturbances, which, while less frequent, represent substantial changes.

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

## Appendix C

This appendix shows the distance map where peat edge pixels represent zero value and values continually increase towards the forest interior. It also shows disturbance events within a 5 km distance of road and river
networks. It further details the methods employed for the harmonization of data used to produce the distance map.

**Data harmonization:**

* We converted the disturbance events into vector data, represented as point vectors.

* To extract the pixel values from the distance map for the disturbance events, we used the 'Extract Values to Points' tool in ArcGIS Pro.

* We measured distances in km along river and road networks by creating buffer zones as polygon vectors.

* We intersected the disturbance events with the polygon boundaries of these buffer zones to determine the distances of disturbance events from the rivers and roads.

<figure><img src="image_11.png" /></figure>
## Appendix D

Histograms showing the distribution of all disturbances (figure (a)) and sampled disturbances (figure (b)).

Histograms are based on disturbance areas (in ha) in the peat forests in Cuvette Centrale from 2019 to 2021. The area of the sampled events was 1204 ha and all disturbances 30 293.81 ha. The histograms bin the disturbances with a bin width of 0.1 ha. The x-axis represents the area of disturbance events in ha, while the y-axis represents the frequency of disturbance events within each bin. The blue dashed line indicates the mean area of the disturbances in both figures. The mean for all disturbances and sampled disturbances were 0.48 ha and 0.52 ha respectively.

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

## Appendix E

Guidelines for direct driver assessments using the driver class description in table 1.

We considered the description items in table 1 holistically, meaning not all criteria need to be met for classification. The presence of multiple indicators strengthens the confidence in the assignment, but the following key indicators are required for classification into each driver class:

### Smallholder Agriculture:

* Evidence of vegetation regrowth

* At least one complete or partial harvesting within two years, or patterns similar to adjoining crop fields for events showing no harvesting.

### Small-scale logging:

* Either visibility of tree canopy or bare patches in events with no vegetation regrowth in 1–2 years.

### Floods:

* The event timing in the second half of 2019, location along rivers and absence of adjoining crop fields.

### Roads:

* Linear canopy openings visible for more than three months.

### Settlements:

* The visibility of houses and their bright appearance on images.
**Unidentified events:**

* Events not matching the criteria for the above classes, spatial mismatches or unclear imagery.

The key indicators must be present to initiate classification. Additional context and evidence such as proximity to settlements or existing crop fields (see table 1 for all additional criteria) were used to confirm the classification.

**Appendix F**

Measures to mitigate unintended interpreter bias.

We implemented several measures to mitigate any unintended interpreter bias. Specifically, when uncertainty arose concerning drivers to certain disturbance events, Karimon consulted with the supervision team and coauthors. Decisions were made together regarding these ambiguous cases. Additionally, to assess the reliability of the interpretations, a subset of sample events was independently reviewed by coauthors following the class description in table 1 and without prior knowledge of Karimon's labeling. The consistency between two assessments confirmed the validity of our interpretations. Furthermore, we sought input from local experts and engaged in collaborative efforts to validate our findings. We have presented the initial findings in two online country-level workshops, where we solicited feedback and validation from local experts. Additionally, we collaborated with a local expert who served as a co-author on the paper. His extensive field experience in the peat forests of the DRC further validates the key findings of our driver assessment.

**Appendix G**

Large events encompassing multiple adjacent croplands.

Large events containing multiple croplands were distinguishable by separate crop fields, cropping periods and patterns within the events. An example from Planet images showing large events with multiple croplands is shown in the following Figure.

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

**Appendix H**

Small events usually expanding from an existing crop field (right column Figures), and non-mechanized clearing of croplands (left column Figures).

Small events are typically adjacent to existing crop fields, illustrating the expansion of agricultural land into nearby peat forest areas. We identified non-mechanized clearing of croplands by noting the irregular shapes of crop fields and the absence of machinery in their vicinity during the observed period.
<figure><img src="image_14.png" /></figure>

# Appendix I

Temporal distribution of disturbances (area ha) in Cuvette Centrale from 2019 to 2021.

The distribution of disturbances (in ha on the y-axis) is depicted month-wise aggregated by each year from 2019 to 2021 (x-axis). Lines for November and December 2021 are dashed in black to indicate that high-confidence data for these months was not fully available. (a) Temporal distribution of all disturbances (all pixels). (b) Temporal distribution of all drivers (sample-based). All drivers represent 4% samples of the total disturbances. (b.1) Temporal distribution of agriculture, (b.2) Logging, (b.3) Other events, respectively. Other events include roads, settlements, and unidentified. The monthly disturbances (ha) on the y-axis in these figures were calculated in relation to the total sum of all drivers per year, segregated by months on the x-axis. The scales are much lower in figures (b.2)–(b.3), with logging, and other factors contributing only a small fraction of disturbances compared to agriculture in figure (b.1).
<figure><img src="image_15.png" /></figure>

## Appendix J

Distribution of flood events, mainly along the Congo River in the Cuvette Centrale in the year 2019.

We observed that the proportion of forest disturbances increased along the Congo River since October 2019. This is connected to heavy rains from October 2019 onwards that caused a major flood event in the region (OCHA 2019, Gou et al 2022). This explains the comparatively higher proportion of disturbances in 2019 compared to other years. We incorporated flood events exclusively for the year 2019 due to the well-documented major flooding event that year (OCHA 2019, Gou et al 2022), which we confidently identified using the RADD alert product. While flash floods were reported in 2020 and 2021, we excluded them due to limitations in identifying them using the RADD product.

<figure><img src="image_16.png" /></figure>

## Appendix K

Distribution of drivers of peat forest disturbances in the Cuvette Centrale by peat forest type (hardwood, palm dominated) from 2019 to 2021.

The peat forests were categorized into palm dominated, hardwood dominated, and other types. For the 'other' category, vegetation type information was unavailable. Our calculations of the total disturbance area across these three peat forest types revealed that the ratio between palm dominated and hardwood dominated
peat swamp forests align closely with the approximately 1:3 ratio observed in the sample disturbance areas, validating the representativeness of our sampling approach.

<table><thead><tr><th rowspan="2">Drivers</th><th colspan="4">Palm dominated peat forests</th><th colspan="4">Hardwood dominated peat forests</th></tr><tr><th>Number of events</th><th>Events area (ha)</th><th>Area (%) relative to events in palm forests</th><th>Area (%) relative to events in both forests</th><th>Number of events</th><th>Events area (ha)</th><th>Area (%) relative to events in hardwood forests</th><th>Area (%) relative to events in both forests</th></tr></thead><tbody><tr><td>Agriculture</td><td>442</td><td>204.8</td><td>85.4</td><td>22.2</td><td>1085</td><td>609.1</td><td>89.1</td><td>65.9</td></tr><tr><td>Logging</td><td>32</td><td>8.9</td><td>3.7</td><td>1.0</td><td>162</td><td>64.8</td><td>9.5</td><td>7.0</td></tr><tr><td>Flood</td><td>42</td><td>22.0</td><td>9.2</td><td>2.4</td><td>5</td><td>2.3</td><td>0.3</td><td>0.3</td></tr><tr><td>Roads</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>4</td><td>2.0</td><td>0.3</td><td>0.2</td></tr><tr><td>Settlements</td><td>5</td><td>1.4</td><td>0.6</td><td>0.2</td><td>2</td><td>0.6</td><td>0.1</td><td>0.1</td></tr><tr><td>Unidentified</td><td>7</td><td>2.8</td><td>1.2</td><td>0.3</td><td>8</td><td>5.2</td><td>0.8</td><td>0.6</td></tr><tr><td></td><td>528</td><td>239.8</td><td>100.0</td><td>26.0</td><td>1266</td><td>684.0</td><td>100.0</td><td>74.0</td></tr></tbody></table>

## Appendix L

Distribution of direct drivers in agreement vs non-agreement peat forests in Cuvette Centrale from 2019 to 2021.

More than two-thirds of the areas were cleared for smallholder agriculture in the non-agreement peat forests, which is around 3.2 times higher than those in the agreement forests. Logging related disturbances were roughly 5 times higher in non-agreement forests.

<table><thead><tr><th rowspan="2">Drivers</th><th colspan="4">Events in agreement peat forests</th><th colspan="4">Events in non-agreement peat forests</th></tr><tr><th>Number of events</th><th>Events area (ha)</th><th>Area (%) relative to events in agreement forests</th><th>Area (%) relative to events in both forests</th><th>Number of events</th><th>Events area (ha)</th><th>Area (%) relative to events in non-agreement forests</th><th>Area (%) relative to events in both forests</th></tr></thead><tbody><tr><td>Agriculture</td><td>428</td><td>252.1</td><td>88.4</td><td>20.9</td><td>1505</td><td>808.2</td><td>88.0</td><td>67.1</td></tr><tr><td>Logging</td><td>51</td><td>14.5</td><td>5.1</td><td>1.2</td><td>175</td><td>72.8</td><td>7.9</td><td>6.0</td></tr><tr><td>Flood</td><td>34</td><td>16.0</td><td>5.6</td><td>1.3</td><td>39</td><td>21.3</td><td>2.3</td><td>1.8</td></tr><tr><td>Roads</td><td>3</td><td>0.9</td><td>0.3</td><td>0.1</td><td>1</td><td>1.1</td><td>0.1</td><td>0.1</td></tr><tr><td>Settlements</td><td>1</td><td>0.1<sup>a</sup></td><td>0.0</td><td>0.0</td><td>11</td><td>4.3</td><td>0.5</td><td>0.4</td></tr><tr><td>Unidentified</td><td>5</td><td>1.8</td><td>0.6</td><td>0.2</td><td>14</td><td>11.1</td><td>1.2</td><td>0.9</td></tr><tr><td>Total</td><td>522</td><td>285.2</td><td>100.0</td><td>23.7</td><td>1745</td><td>918.7</td><td>100.0</td><td>76.3</td></tr></tbody></table>

It is important to note that the RADD alerts are generated for humid tropical forests without further distinguishing between forest types. In the Cuvette Centrale region, where peat forests are interspersed with other forest types, intersecting the RADD alerts with the peat forest layer and for our study period (2019–2021) produced disturbance areas smaller than 0.1 ha. Additionally, we have segregated the peat forest disturbances data based on hardwood and palm dominated peat forests, as well as agreement and non-agreement peat forests. This segregation has also contributed to the creation of smaller areas.

## Appendix M

Distribution of direct drivers by area size of the events in peat forests of Cuvette Centrale from 2019 to 2021: small (<0.5 ha), medium (0.5–2 ha), and large events (>2 ha).

Notably, agriculture events exhibited an upward trend relating to event sizes: area proportion continually increased from 84% in small events to 95% in large events (appendix M). Logging events showed a contrasting pattern, as area proportion decreased from 12% in small events to a mere 4% in large events. Floods, roads and settlements were relatively small events with large events being non-existent.

<footnote><sup>a</sup> Events smaller than the MMU of 0.1 ha in the RADD alert forest disturbances data have been rounded up to 0.1 ha. We intersected the peat forest disturbance data in space and time, resulting in events having areas smaller than the MMU. This can be observed in the table, where one settlement event in agreement peat forests was notably smaller (0.04 ha).</footnote>
<table><thead><tr><th rowspan="2">Drivers</th><th colspan="3">Small events (&lt; 0.5 ha)</th><th colspan="3">Medium events (0.5-2 ha)</th><th colspan="3">Large events (&gt;2 ha)</th></tr><tr><th>Number of events</th><th>Events area (ha)</th><th>Area (%) relative to small events</th><th>Area (%) relative to all events</th><th>Number of events</th><th>Events area (ha)</th><th>Area (%) relative to medium events</th><th>Area (%) relative to large events</th><th>Area (%) relative to all events</th></tr></thead><tbody><tr><td>Agriculture</td><td>1269</td><td>274.2</td><td>84.1</td><td>22.8</td><td>605</td><td>562.4</td><td>87.5</td><td>46.7</td><td>59</td><td>223.6</td><td>95.1</td><td>18.6</td></tr><tr><td>Logging</td><td>174</td><td>37.8</td><td>11.6</td><td>3.1</td><td>48</td><td>40.5</td><td>6.3</td><td>3.4</td><td>4</td><td>8.9</td><td>3.8</td><td>0.7</td></tr><tr><td>Flood</td><td>43</td><td>10.0</td><td>3.1</td><td>0.8</td><td>30</td><td>27.2</td><td>4.2</td><td>2.3</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>Roads</td><td>3</td><td>0.6</td><td>0.2</td><td>0.1</td><td>1</td><td>1.4</td><td>0.2</td><td>0.1</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>Settlements</td><td>8</td><td>1.8</td><td>0.6</td><td>0.2</td><td>4</td><td>2.5</td><td>0.4</td><td>0.2</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>Unidentified</td><td>7</td><td>1.6</td><td>0.5</td><td>0.1</td><td>11</td><td>8.7</td><td>1.4</td><td>0.7</td><td>1</td><td>2.6</td><td>1.1</td><td>0.2</td></tr><tr><td>Total</td><td>1504</td><td>326.1</td><td>100.0</td><td>27.1</td><td>699</td><td>642.7</td><td>100.0</td><td>53.4</td><td>64</td><td>235.1</td><td>100.0</td><td>19.5</td></tr></tbody></table>
ORCID iDs

Karimon Nesha https://orcid.org/0000-0002-4990-4356

Martin Herold https://orcid.org/0000-0003-0246-6886

Johannes Reiche https://orcid.org/0000-0002-4327-4349

Robert N Masolele https://orcid.org/0000-0001-6108-3282

References

Ahrends A, Burgess N D, Milledge S A H, Bulling M T, Fisher B, Smart J C R, Clarke G P, Mhoro B E and Lewis S L 2010 Predictable waves of sequential forest degradation and biodiversity loss spreading from an African city *Proc. Natl Acad. Sci.* **107** 14556–61

Austin K G, Schwantes A, Gu Y and Kasibhatla P S 2019 What causes deforestation in Indonesia? What causes deforestation in Indonesia? *Environ. Res. Lett.* **14** 024007

Baker T and Coronado E H 2019 The challenges for achieving conservation and sustainable development within the wetlands of the Pastaza-Marañón basin, Peru *Peru: Deforestation in Times of Climate Change* ed A Chirif (International Work Group for Indigenous Affairs-IWGIA)

Barber C P, Cochrane M A, Souza C M and Laurance W F 2014 Roads, deforestation, and the mitigating effect of protected areas in the Amazon *Biol. Conserv.* **177** 203–9

Bilonda M K 2020 Burning of Biomass in the Democratic Republic of Congo *Biomass Burning in Sub-Saharan Africa* (Springer) pp 57–70

Bonn A, Allott T, Evans M, Joosten H and Stoneman R 2016 Peatland restoration and ecosystem services: science, policy and practice *Peatland Restoration and Ecosystem Services: Science, Policy and Practice* pp 1–493

Bourgeau-Chavez L L *et al* 2021 Advances in Amazonian peatland discrimination with multi-temporal PALSAR refines estimates of peatland distribution, C stocks and deforestation *Front. Earth Sci.* **9** 1019

Cannon J 2021 Holding agriculture and logging at bay in the Congo peatlands (available at: https://news.mongabay.com/2021/12/holding-agriculture-and-logging-at-bay-in-the-congo-peatlands/)

Carroll M J, Heinemeyer A, Pearce-Higgins J W, Dennis P, West C, Holden J, Wallage Z E and Thomas C D 2015 Hydrologically driven ecosystem processes determine the distribution and persistence of ecosystem-specialist predators under climate change *Nat. Commun.* **6** 7851

Cerutti P O, Uehara T K and Wallace J 2023 Deforestation in Africa (available at: www.chathamhouse.org/2023/05/deforestation-africa)

Charman D J 2009 Peat and peatlands *Encyclopedia of Inland Waters* (Elsevier) pp 541–8

Cordon S 2020 Seeking sustainable livelihoods for peatland farmers (available at: https://forestsnews.cifor.org/66827/seeking-sustainable-livelihoods-for-peatland-farmers?fnl=en)

Creese A, Washington R and Jones R 2019 Climate change in the Congo Basin: processes related to wetting in the December–February dry season *Clim. Dyn.* **53** 3583–602

Crezee B *et al* 2022 Mapping peat thickness and carbon stocks of the central Congo Basin using field data *Nat. Geosci.* **15** 1–6

Curtis P G, Slay C M, Harris N L, Tyukavina A and Hansen M C 2018 Classifying drivers of global forest loss *Science* **361** 1108–11

Danylo O, Pirker J, Lemoine G, Ceccherini G, See L, McCallum I, Hadi, Kraxner F, Achard F and Fritz S 2021 A map of the extent and year of detection of oil palm plantations in Indonesia, Malaysia and Thailand *Sci. Data* **8** 96

Dargie G C, Lawson I T, Rayden T J, Miles L, Mitchard E T A, Page S E, Bocko Y E, Ifo S A and Lewis S L 2019 Congo Basin peatlands: threats and conservation priorities *Mitig. Adapt. Strateg. Glob. Change* **24** 669–86

Dargie G C, Lewis S L, Lawson I T, Mitchard E T A, Page S E, Bocko Y E and Ifo S A 2017 Age, extent and carbon storage of the central Congo Basin peatland complex *Nature* **542** 86–90

De Sy V, Herold M, Achard F, Avitabile V, Baccini A, Carter S, Clevers J G P W, Lindquist E, Pereira M and Verchot L 2019 Tropical deforestation drivers and associated carbon emission factors derived from remote sensing data *Environ. Res. Lett.* **14** 094022

Diniz C G *et al* 2015 DETER-B: the new Amazon near real-time deforestation detection system *IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.* **8** 3619–28

Draper F C, Roucoux K H, Lawson I T, Mitchard E T A, Honorio Coronado E N, Lähteenjoa O, Montenegro L T, Sandoval E V, Zaráte R and Baker T R 2014 The distribution and amount of carbon in the largest peatland complex in Amazonia *Environ. Res. Lett.* **9** 124017

Dyer E L E, Jones D B A, Nusbaumer J, Li H, Collins O, Vettoretti G and Noone D 2017 Congo Basin precipitation: assessing seasonality, regional interactions, and sources of moisture *J. Geophys. Res. Atmos.* **122** 6882–98

Ernst C, Mayaux P, Verhegghen A, Bodart C, Christophe M and Defourny P 2013 National forest cover change in Congo Basin: deforestation, reforestation, degradation and regeneration for the years 1990, 2000 and 2005 *Glob. Change Biol.* **19** 1173–87

ESA 2013 SENTINEL-2 user handbook (available at: https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook)

Feng M, Sexton J O, Channan S and Townshend J R 2016 A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm *Int. J. Digit. Earth* **9** 113–33

Ferrer Velasco R, Köthke M, Lippe M, Günter S and Koukoulas S 2020 Scale and context dependency of deforestation drivers: insights from spatial econometrics in the tropics *PLoS One* **15** e0226830

Feukeng L 2021 The Democratic Republic of Congo plans to sell its forests to oil companies (available at: www.greenpeace.org/africa/fr/communiques-de-presse/13057/la-republique-democratique-du-congo-compte-vendre-ses-forets-aux-compagnies-petrolieres/)

Finlayson C M and Milton G R 2018 Peatlands *The Wetland Book* (Springer) pp 227–44

Gao J, Holden J and Kirkby M 2016 The impact of land-cover change on flood peaks in peatland basins *Water Resour. Res.* **52** 3477–92

GEE 2023 Sentinel-2 cloud masking with s2cloudless (available at: https://developers.google.com/earth-engine/tutorials/community/sentinel-2-s2cloudless)

Geist H J and Lambin E F 2002 Proximate causes and underlying driving forces of tropical deforestation *BioScience* **52** 143–50

Gou Y, Balling J, De Sy V, Herold M, De Keersmaecker W, Slagter B, Mullissa A, Shang X and Reiche J 2022 Intra-annual relationship between precipitation and forest disturbance in the African rainforest *Environ. Res. Lett.* **17** 044044

GRAIN 2014 Hungry for land: small farmers feed the world with less than a quarter of all farmland

Greenpeace 2019 Position paper of civil society organizations on Congo Basin peatlands (available at: www.greenpeace.org/static/planet4-africa-stateless/2019/12/b2e9da84-peatlands-position-p-engd.pdf)

Gumbricht T, Román-Cuesta R M, Verchot L V, Herold M, Wittmann F, Householder E, Herold N and Murdiyarso D 2017b Tropical and subtropical wetlands distribution version 2 (V3 edn) (Center for International Forestry Research (CIFOR)) (https://doi.org/10.17528/CIFOR/DATA.00058)
Gumbricht T, Roman-Cuesta R M, Verchot L, Herold M, Wittmann F, Householder E, Herold N and Murdiyarso D 2017a An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor *Glob. Change Biol.* 23 3581–99

Hansen M C, Krylov A, Tyukavina A, Potapov P V, Turubanova S, Zutta B, Ifo S, Margono B, Stolle F and Moore R 2016 Humid tropical forest disturbance alerts using Landsat data *Environ. Res. Lett.* 11 034008

Hastie A *et al* 2022 Risks to carbon storage from land-use change revealed by peat thickness maps of Peru *Nat. Geosci.* 15 369–74

Hergoualc'h K, van Lent J, Dezzeo N, Verchot L V, van Groenigen J W, López Gonzales M and Grandez-Rios J 2023 Major carbon losses from degradation of Maurititia flexuosa peat swamp forests in western Amazonia *Biogeochemistry* 167 327–45

Hooijer A, Page S, Canadell J G, Silvius M, Kwadijk J, Wösten H and Jauhiainen J 2010 Current and future CO₂ emissions from drained peatlands in Southeast Asia *Biogeosciences* 7 1505–14

Hugelius G *et al* 2020 Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw *Proc. Natl Acad. Sci.* 117 20438–46

Husson S *et al* 2018 Biodiversity of the Sebangau tropical peat swamp forest *Indonesian Borneo* 22 1–50

ITC 2018 Spatial data analysis: neighborhood and connectivity operations *ILWIS User's Guide* vol 3, 3rd edn (available at: https://filetransfer.itc.nl/pub/ilwis/pdf/usrch09.pdf)

Jackson R B, Lajtha K, Crow S E, Hugelius G, Kramer M G and Piñeiro G 2017 The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls *Annu. Rev. Ecol. Evol. Syst.* 48 419–45

Jiang Y, Zhou L, Tucker C J, Raghavendra A, Hua W, Liu Y Y and Joiner J 2019 Widespread increase of boreal summer dry season length over the Congo rainforest *Nat. Clim. Change* 9 617–22

Joosten H and Clarke D 2002 Wise use of mires and peatlands

Khalil C A, Conforti P, Ergin I and Gennari P 2017 Defining small scale food producers to monitor target 2.3 of the 2030 agenda for sustainable development (*ESS/17-12; Working Paper Series*)

Kleinschroth F and Healey J R 2017 Impacts of logging roads on tropical forests *Biotropica* 49 620–35

Kleinschroth F, Healey J R, Sist P, Mortier F and Gourlet-Fleury S 2016 How persistent are the impacts of logging roads on Central African forest vegetation? *J. Appl. Ecol.* 53 1127–37

Kleinschroth F, Laporte N, Laurance W F, Goetz S J and Ghazoul J 2019 Road expansion and persistence in forests of the Congo Basin *Nat. Sustain.* 2 628–34

Koh L P, Miettinen J, Liew S C and Ghazoul J 2011 Remotely sensed evidence of tropical peatland conversion to oil palm *Proc. Natl Acad. Sci. USA* 108 5127–32

Lähteenoja O and Page S 2011 High diversity of tropical peatland ecosystem types in the Pastaza-Marañón basin, Peruvian Amazonia *J. Geophys. Res.* 116 G02025

Laso Bayas J C *et al* 2022 Drivers of tropical forest loss between 2008 and 2019 *Sci. Data* 9 1–8

Lawson S 2014 Illegal logging in the Republic of Congo (available at: www.greenpeace.org/static/planet4-eastasia-stateless/2019/11/8bf87cff-8bf87cff-lawson_republic_of_congo_pp_2014.pdf)

Leblois A, Damette O and Wolfersberger J 2017 What has driven deforestation in developing countries since the 2000s? Evidence from new remote-sensing data *World Dev.* 92 82–102

Lilleskov E *et al* 2019 Is Indonesian peatland loss a cautionary tale for Peru? A two-country comparison of the magnitude and causes of tropical peatland degradation *Mitig. Adapt. Strateg. Glob. Change* 24 591–623

Lindquist E J, Hansen M C, Roy D P and Justice C O 2008 The suitability of decadal image data sets for mapping tropical forest cover change in the Democratic Republic of Congo: implications for the global land survey *Int. J. Remote Sens.* 29 7269–75

Liu J G and Mason P J 2016 *Image Processing and GIS for Remote Sensing* (Wiley) (https://doi.org/10.1002/9781118724194)

Marcus M S, Hergoualc'h K, Honorio Coronado E N and Gutiérrez-Vélez V H 2024 Spatial distribution of degradation and deforestation of palm swamp peatlands and associated carbon emissions in the Peruvian Amazon *J. Environ. Manage.* 351 119665

Masolele R N, De Sy V, Herold M, Marcos D, Verbesselt J, Gieseke F, Mullissa A G and Martius C 2021 Spatial and temporal deep learning methods for deriving land-use following deforestation: a pan-tropical case study using Landsat time series *Remote Sens. Environ.* 264 112600

Masolele R N, Marcos D, De Sy V, Abu I-O, Verbesselt J, Reiche J and Herold M 2024 Mapping the diversity of land uses following deforestation across Africa *Sci. Rep.* 14 1681

MECNDD & MEDD-DIAF 2023 Protected areas (available at: https://cod-data.forest-atlas.org/search?groups=0f551fb5585d45c6adcc61797fa62524)

Melton J R, Chan E, Millard K, Fortier M, Winton R S, Martín-López J M, Cadillo-Quiroz H, Kidd D and Verchot L V 2022 A map of global peatland extent created using machine learning (Peat-ML) *Geosci. Model Dev.* 15 4709–38

Miettinen J, Shi C and Liew S C 2011 Deforestation rates in insular Southeast Asia between 2000 and 2010 *Glob. Change Biol.* 17 2261–70

Miettinen J, Shi C and Liew S C 2016 Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990 *Glob. Ecol. Conserv.* 6 67–78

Miles L, Ravilious C, Garcia-Rangel S, de Lamo X, Dargie G and Lewis S 2017 Carbon, biodiversity and land-use in the Central Congo Basin Peatlands (available at: www.unep.org/resources/publication/carbon-biodiversity-and-land-use-central-congo-basin-peatlands)

Murillo-Sandoval P, Hilker T, Krawchuk M and Van Den Hoek J 2018 Detecting and attributing drivers of forest disturbance in the colombian andes using landsat time-series *Forests* 9 269

OCHA 2019 Republic of Congo: floods flash update n°1 (Accessed 10 December 2019)

OCHA 2020 Republic of Congo: floods flash update n°3 (available at: www.unocha.org/publications/report/congo/republic-congo-floods-flash-update-n-3-23-january-2020) (Accessed 23 January 2020)

Ordway E M, Asner G P and Lambin E F 2017 Deforestation risk due to commodity crop expansion in sub-Saharan Africa *Environ. Res. Lett.* 12 044015

Page S E, Rieley J O and Banks C J 2011 Global and regional importance of the tropical peatland carbon pool *Glob. Change Biol.* 17 798–818

Pandey P, Kington J, Kanwar A, Simmon R and Abraham L 2023 Planet basemaps for NICFI data program addendum to Basemaps product specification (available at: https://assets.planet.com/docs/Planet_Combined_Imagery_Product_Specs_letter scr)

Planet Labs 2019 Planet Basemaps product specification (available at: https://assets.planet.com/products/basemap/planet-basemaps-product-specifications.pdf)

Posa M R C, Wijedasa L S and Corlett R T 2011 Biodiversity and conservation of tropical peat swamp forests *BioScience* 61 49–57

Rainforest Foundation 2022 DRC national logging moratorium must be extended indefinitely following damning
audit of the industry logging (available at: www.rainforestfoundationuk.org/drc-national-logging-moratorium-must-be-extended-indefinitely-following-damning-audit-of-the-industry-logging/)

Reed M S, Bonn A, Evans C, Glenk K and Hansjürgens B 2014
Assessing and valuing peatland ecosystem services for
sustainable management *Ecosyst. Serv.* 9 1–4

Reiche J et al 2021 Forest disturbance alerts for the Congo Basin using Sentinel-1 *Environ. Res. Lett.* 16 024005

Reliefweb 2020 DR Congo: floods—Oct 2020 (available at: https://reliefweb.int/disaster/fl-2020-000206-cod)

Reliefweb 2021 Republic of Congo: floods—Nov 2021 (available at: https://reliefweb.int/disaster/ff-2021-000198-cog)

Reliefweb 2022 DR Congo: floods and landslides—Dec 2022 (available at: https://reliefweb.int/disaster/fl-2022-000376-cod)

Roucoux K H et al 2017 Threats to intact tropical peatlands and opportunities for their conservation *Conserv. Biol.* 31 1283–92

Rydin H, Jeglum J K and Bennett K D 2013 The biology of peatlands *Biology of Habitats* 2nd edn (Oxford University Press)

Scharlemann J P W, Tanner E V J, Hiederer R and Kapos V 2014
Global soil carbon: understanding and managing the largest
terrestrial carbon pool *Carbon Manage.* 5 81–91

Shapiro A et al 2023 Small scale agriculture continues to drive deforestation and degradation in fragmented forests in the Congo Basin (2015–2020) *Land Use Policy* 134 106922

Slagter B, Reiche J, Marcos D, Mullissa A, Lossou E, Peña-Claros M and Herold M 2023 Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel-1 and-2 data *Remote Sens. Environ.* 295 113655

Sonwa D J, Oumarou Farikou M, Martial G and Félix F L 2020
Living under a fluctuating climate and a drying Congo Basin
*Sustainability* 12 2936

Tarigan S D, Sunarti and Widyaliza S 2015 Expansion of oil palm plantations and forest cover changes in Bungo and Merangin districts, Jambi Province, Indonesia *Proc. Environ. Sci.* 24 199–205

Tegegne Y T, Lindner M, Fobissie K and Kanninen M 2016
Evolution of drivers of deforestation and forest degradation
in the Congo Basin forests: exploring possible policy options
to address forest loss *Land Use Policy* 51 312–24

Turubanova S, Potapov P V, Tyukavina A and Hansen M C 2018
Ongoing primary forest loss in Brazil, Democratic Republic
of the Congo, and Indonesia *Environ. Res. Lett.* 13 074028

Tyukavina A, Hansen M C, Potapov P, Parker D, Okpa C,
Stehman S V, Kommareddy I and Turubanova S 2018 Congo

Basin forest loss dominated by increasing smallholder
clearing *Sci. Adv.* 4 2993

Uda S K, Hein L and Sumarga E 2017 Towards sustainable
management of Indonesian tropical peatlands *Wetlands
Ecol. Manage.* 25 683–701

UNEP-WCMC & IUCN 2023 Protected planet: the world database on protected areas (WDPA) and world database on other effective area-based conservation measures (WD-OECM) (available at: www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA)

van Brummelen G 2017 *Heavenly Mathematics: The Forgotten Art of Spherical Trigonometry* (Princeton University Press)

van Lent J, Hergoualc'h K, Verchot L, Oenema O and van Groenigen J W 2019 Greenhouse gas emissions along a peat swamp forest degradation gradient in the Peruvian Amazon: soil moisture and palm roots effects *Mitig. Adapt. Strateg. Glob. Change* 24 625–43

Vancutsem C, Achard F, Pekel J-F, Vieilledent G, Carboni S,
Simonetti D, Gallego J, Aragão L E O C and Nasi R 2021
Long-term (1990–2019) monitoring of forest cover changes
in the humid tropics *Sci. Adv.*
7 eabe1603

Verhegghen A, Mayaux P, De Wasseige C and
Defourny P 2012 Mapping Congo Basin vegetation types
from 300 m and 1 km multi-sensor time series for
carbon stocks and forest areas estimation *Biogeosciences*
9 5061–79

Wang X-Y, Zhu J, Xin M, Song C, Li Y, Zhou Y and Li X 2021
Weakened seasonality of the African rainforest precipitation
in boreal winter and spring driven by tropical SST
variabilities *Geosci. Lett.* 8 22

Watanabe M, Koyama C N, Hayashi M, Nagatani I and
Shimada M 2018 Early-stage deforestation detection in the
tropics with L-band SAR *IEEE J. Sel. Top. Appl. Earth Obs.
Remote Sens.* 11 2127–33

WRI 2021 Managed forest concessions (available at: https://globil.panda.org/datasets/panda::managed-forest-concessions-wri/explore?location=-4.524753%2C29.326054%2C5.57)

Xu J, Morris P J, Liu J and Holden J 2018 PEATMAP: refining
estimates of global peatland distribution based on a
meta-analysis *CATENA* 160 134–40

Yu Z C 2012 Northern peatland carbon stocks and dynamics: a review *Biogeosciences* 9 4071–85

Yu Z, Beilman D W, Frolking S, MacDonald G M, Roulet N T,
Camill P and Charman D J 2011 Peatlands and their role in
the global carbon cycle *Eos Trans. Am. Geophys. Union*
92 97–98

Yuwati T W et al 2021 Restoration of degraded tropical peatland
in Indonesia: a review *Land* 10 1170