# Scaling up the assessment of logging's impact on forest structure in Central Africa using field and UAV data

Chloé Dupuis, Gauthier Ligot, Jean-François Bastin, Philippe Lejeune, Jean-Louis Doucet, Vivien Rossi, Adeline Fayolle

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Chloé Dupuis, Gauthier Ligot, Jean-François Bastin, Philippe Lejeune, Jean-Louis Doucet, et al. Scaling up the assessment of logging's impact on forest structure in Central Africa using field and UAV data. Environmental Research Letters, 2025, 20 (1), pp.014018. 10.1088/1748-9326/ad99ea. hal-05183297

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LETTER • OPEN ACCESS

Scaling up the assessment of logging's impact on forest structure in Central Africa using field and UAV data

To cite this article: Chloé Dupuis et al 2025 *Environ. Res. Lett.* 20 014018

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LETTER

# Scaling up the assessment of logging's impact on forest structure in Central Africa using field and UAV data

Chloé Dupuis<fnref n="1" />,* [ID], Gauthier Ligot<fnref n="1" /> [ID], Jean-François Bastin<fnref n="1" /> [ID], Philippe Lejeune<fnref n="1" />, Jean-Louis Doucet<fnref n="1" />, Vivien Rossi<fnref n="2" /> [ID] and Adeline Fayolle<fnref n="1" />,<fnref n="2" /> [ID]

E-mail: chloedupuis719@protonmail.com

**Keywords:** selective logging, Central Africa, forest structure, canopy opening, logging intensity, forest disturbance, upscaling
Supplementary material for this article is available [online](#)

Abstract

A third of the forest area in Central Africa has been granted to logging companies. Logging is highly selective in the region, with an average of 0.7–4.0 trees harvested per ha, but its direct impact on forest structure and the spatial variation of this impact remain understudied. Here, we investigated the direct impact of logging on forest structure, we related this impact to logging intensity and canopy opening. We compiled unique datasets collecting field measurements and aerial observations in four FSC certified concessions. Our data includes pre- and post-logging inventory of forest plots covering 38 ha, records of over 6000 harvested trees, and drone RGB images covering over 6000 ha. In average, logging activities reduced forest above-ground biomass by 8.8%, stem density by 6.5%, basal-area by 8.5% and canopy cover by 4.4%. Strong relationships were found between the reduction in biomass, stem density, or basal area with logging intensity, canopy opening and the number and volume of harvested trees (relative root mean squared error (rRMSE) between 0.128 and 0.164). Additionally, we demonstrated that canopy opening can be a good indicator to monitor and upscale logging intensity (rRMSE between 0.0005 and 0.0022). This study is the first covering extensive inventory plots and uninhabited aerial vehicle images before and after logging in different locations in Central Africa, providing a valuable reference to evaluate the impact of logging on forest structure. It demonstrates how canopy opening can be used to estimate measurements usually collected in the field and provides to the remote sensing community a unique dataset that will help improving monitoring systems (Dupuis *et al* 2024 (available at: [https://hdl.handle.net/2268/323683](https://hdl.handle.net/2268/323683))). These findings also have significant implications to control and manage logging activities, especially for certification standards, forest administrations, and European regulations.

1. Introduction

In recent years (2015–2019), tropical forests degradation has increased by 38% while annual deforestation has decreased by 5% (Vancutsem et al 2021). Degradation, defined as ‘a disturbance in the tree cover canopy that is visible from space over a short time period, leading to a loss of biodiversity and/or carbon storage’ (Vancutsem et al 2021), is attributed to human activities such as agriculture, logging, fires, road construction, mining, and wood fuel collection

(Vancutsem et al 2021, Bayas et al 2022, Tyukavina et al 2018). In Central Africa, small-scale agriculture is the primary driver of forest loss measured on Landsat imagery (84%), followed by industrial logging activities (9.5%) (Tyukavina et al 2018). Industrial logging in the region covers one-third of the forest area (Eba’a Atyi et al 2022) and is highly selective, targeting a limited number of high-value tree species and harvesting a restricted number of trees (0.7–4.0 trees·ha⁻<fnref n="1" />) every 20–35 years (BAD 2018, Medjibe et al 2011). In Central Africa, logging companies must

<footnote n="1">¹ TERRA Teaching and Research Centre (Forest is Life), Gembloux Agro-Bio Tech, Université de Liège, Passage des Déportés n°2, Gembloux 5030, Belgium</footnote>

<footnote n="2">² CIRAD, Forêts et Sociétés, F-34398 Montpellier, France</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
produce Forest Management Plans, which address various environmental and social issues including Reduced Impact Logging (BAD 2018, Medjibe et al 2011). About 10% of production forests are certified by the Forest Stewardship Council ensuring that Forest Management Plans are adopted and implemented by these companies (Tritsch et al 2020, FSC 2022).

Logging activities impact forest structure, leading to a direct loss of aboveground biomass (AGB) ranging from 7.1% to 13.4%, as measured on inventory plots, and a canopy opening of 4%–11%, observed using uninhabited aerial vehicle (UAV) and satellite imagery (Ngueguim et al 2009, Medjibe, et al 2013, Dupuis et al 2023). The extent of ground damage varies depending on the type of logging operations: felling gaps range in size from 218 to 578 m² (Doucet et al 2009, Medjibe et al 2011), while log yards are approximately 1200 m² (Durrieu de Madron et al 2000). Additionally, skid trails have a mean width of 4.1 m (Medjibe et al 2011), narrower than secondary (width = 24.8 m) and primary roads (width = 39.3 m) (Hirsh et al 2013). Earlier studies have demonstrated that the AGB rapidly recovers after logging, as illustrated in the M'Baïki experiment in the Central African Republic (Gourlet-Fleury et al 2013). It has also been shown that canopy opening for logging roads construction does not persist (Kleinschroth et al 2019). This rapid recovery is attributed to the growth stimulation of remaining trees and the recruitment of new trees, often including fast-growing pioneer species (Sist and Nguyen-Thé 2002, Peña-Claros et al 2008, Gourlet-Fleury et al 2013). Logging intensity has a direct impact on the amount of damages caused to the forest (Durrieu de Madron et al 2000) and its capacity to recover (Gourlet-Fleury et al 2013, Rutishauser et al 2015, Maurent et al 2023).

Logging activities are currently monitored on various scales. On a local level, research inventory plots ranging from 1 to 10 ha provide accurate data, e.g. to study forest recovery after logging (Gourlet-Fleury et al 2013). However, these plots may not fully represent the entire surface impacted by logging activities. On a broader scale, logging companies gather field data, recording the location of harvested trees along with their diameter and species to plan their activities (BAD 2018). Remote sensing tools on a larger scale have revolutionized the study of tropical forests that are difficult to access by enabling faster and more comprehensive data collection (Sanchez-Azofeifa et al 2017). While satellite-based systems allow systematic monitoring, they cannot detect small disturbances (Dupuis et al 2023). UAV RGB data has been identified as a potential bridge between satellite and field-collected data but remains poorly explored (Bourgoin et al 2020, Dupuis et al 2020). Canopy

opening is a frequently measured indicator using remote sensing tools and has great potential for integrating remote sensing and field data for large-scale monitoring but it has not been thoroughly explored yet (Dupuis et al 2020).

This study aims to assess the direct impact of selective logging on forest structure, and investigate how logging damages are affected by logging intensity. Field and UAV measurements will be integrated to spatially extent this information in Central Africa. The following questions are addressed. (1) What is the impact of logging on forest structure, specifically on AGB (in Mg·ha⁻¹), basal area (BA, in m²·ha⁻¹), stem density (N, ha⁻¹) and canopy cover (proportion of canopy opening), using inventory plots? (2) How logging intensity (harvested volume and number of trees) and canopy opening (proportion) can be used to estimate the impact of logging on forest structure? (3) How can canopy opening be used to predict logging intensity using a dataset combining UAV data with harvested trees on a larger scale?

## 2. Material and methods

### 2.1. Study sites

Field inventory campaigns were conducted in four FSC-certified logging companies, incorporating the installation of forest plots and UAV flights both before and after logging, where industrial and mechanized operations are employed (figures 1(a) and S1). The sites encompassed various types of tropical moist forests (Fayolle et al 2014, Réjou-Méchain et al 2021). Sites A and B corresponded to dense, mature semi-deciduous forests dominated by *Celtis* spp. These forests feature a stratified canopy, with deciduous trees allowing more light penetration during the dry season, promoting moderate understorey growth (Fayolle et al 2014, Loubota et al 2018). Site C, by contrast, is characterized by a highly open canopy, with widely spaced trees and a dense herbaceous understorey dominated by *Marantaceae* species, a typical feature of post-disturbance environments that promote herbaceous layer development (Gillet 2013). Finally, site D is dominated by *Aucoumea klaineana* (Okoumé) (Van Hoef et al 2019), the most important timber species in Gabon. The site underwent its first logging cycle prior to our study, which focuses on the second harvesting rotation.

### 2.2. Inventory plots and harvested trees

Forest inventory plots were installed before any logging activities in sites A (1 × 4 ha), B (2 × 9 ha) and D (4 × 4 ha) (figures 1 and 2(a)), following the protocol for the installation of permanent sampling plots proposed by Picard and Gourlet-Fleury (2008). Each plot was geolocated using a Garmin GPS for sites B and D, and a GPS with RTK/PPK corrections for site A. Prior
<figure><img src="image_6.png" /></figure>

<table><caption>**Figure 1.** Location of the study sites across Central Africa and information about the field and UAV data collected in each site. The map shows protected areas (in green), non-certified (in red) and sustainable forest management (SFM) in certified logging concessions (in yellow). Country limits are shown in black and main rivers are in blue.</caption><thead><tr><th colspan="2">Site</th><th colspan="2">Site A</th><th>Site B</th><th>Site C</th><th>Site D</th></tr></thead><tbody><tr><td colspan="2">Forest type</td><td>Celtis spp.</td><td>Celtis spp.</td><td>Celtis spp.</td><td>Marantaceae spp.</td><td>Okoumé</td></tr><tr><td colspan="2">Plot design</td><td>1 x 4 ha</td><td>/</td><td>2 x 9 ha</td><td>/</td><td>4 x 4 ha</td></tr><tr><td rowspan="5">Field</td><td>Inventory before logging</td><td>July 2019</td><td>/</td><td>2018</td><td>/</td><td>2019</td></tr><tr><td>Inventory after logging</td><td>October 2019</td><td>/</td><td>2019</td><td>/</td><td>2020</td></tr><tr><td>Number of trees recorded</td><td>1877</td><td>/</td><td>6510</td><td>/</td><td>6117</td></tr><tr><td>Number of trees with height measurement</td><td>438</td><td>/</td><td>1054</td><td>/</td><td>106</td></tr><tr><td>UAV flights</td><td>UAV flights area</td><td>504 ha</td><td>964 ha</td><td>528 ha</td><td>3312 ha</td><td>629 ha</td></tr><tr><td rowspan="2">UAV flights</td><td>Flight before logging</td><td>/</td><td>/</td><td>June 2018</td><td>February 2019</td><td>June 2020</td></tr><tr><td>Flight after logging</td><td>July 2019</td><td>August 2021</td><td>January 2019</td><td>September 2019</td><td>September 2020</td></tr></tbody></table>

to logging, all trees with a diameter at breast height (DBH) ≥ 10 cm were identified by field botanists, measured and recorded (figure 2(a)). Each tree was geolocated using the *x* and *y* coordinates within the 20 × 20 m quadrats (see Picard and Gourlet-Fleury 2008, p 81), then calibrated against UAV images based on the emerging tree crowns to minimize geolocation errors. DBH was measured using a tape at a height of 1.3 m for regular stems, or 30 cm above the top of the buttresses, and then converted to DBH using a taper model for irregular stems (Bauwens et al 2021). In most 1 ha subplots (figure 1), at least 50 trees including the 10 largest trees and 10 trees randomly selected in 10 cm wide diameter classes to cover the diameter range, were selected for height measurement (Sullivan et al 2018). The total height of these trees (*H* in m) was measured using a VERTEX Haglöf ultrasonic meter or a Nikon Forestry laser rangefinder. The social status (understory, canopy or emergent) was observed in the field for all trees in sites A and D. After logging, each tree was revisited and categorized into one of four damage categories based on the impact of logging activities: alive, when it was intact; damaged, when the tree was impacted by logging but could still live (i.e. debranched, broken, barked, leaning tree); harvested, when it was cut by the logging company; dead, when it was dead or had no chance

to survive because of logging activities (i.e. uprooted tree or lying on the ground). The general category ‘impacted’ gathers all trees that are damaged, harvested or dead (figure 2(a)). In addition, in the UAV covered areas, we recorded the location and the DBH of all harvested trees (6503 trees in total, figure 2(b)). The recorded DBH corresponded to the midpoint of the diameter class estimated by operators in site A, and to the DBH measured with a tape in sites B, C and D.

## 2.3. Canopy gap identification on UAV images

RGB images, where each pixel is defined by the amount of red, green, and blue color, were obtained from UAV flights performed before and after logging, except for site A for which flights were performed only after logging (figure 1). Technical information can be found in table S1. GPS data with RTK/PPK corrections were processed using RTKLIB to ensure accurate georeferencing of the images (Takasu and Yasuda 2009, Cledat et al 2020). Orthomosaic and digital surface model (DSM) at 10 cm resolution were generated using photogrammetry in *Metashape* software (Lisein et al 2013). Using before and after orthomosaic and DSM canopy gaps caused by logging activities were photo-interpreted, manually digitalized in QGIS and categorized into felling gaps, roads, skid
<figure><img src="image_7.png" /><figcaption>**Figure 2.** Example of plot data (a) and UAV images (b) used to assess the impact of logging on forest structure from site D. Example of 4 ha plots and inventoried trees are shown in (a). Impacted trees correspond to all trees that are not intact after logging activities. Locations of harvested trees provided by logging companies, UAV images taken after logging and canopy openings categorized in different types of gaps are shown in (b). ‘Others’ refers to gaps where the cause of damage was not clearly identifiable.</figcaption></figure>

trails, log yards and others when the cause was not
clearly identifiable (figures 2(b) and S1). In site A,
where only post-logging images were available, errors
in photointerpretation were minimized by utilizing
the locations of harvested trees and by recognizing
the clear distinctions between natural gaps and log-
ging gaps, characterized by the absence of logs and
associated damages (figure S2). This dataset is avail-
able at (Dupuis et al 2024). In site A, the length of
41 felling gaps were measured in the field using a
VERTEX Haglöf ultrasonic meter. The length from
the stump to the outer edge of the crown within each
gap was recorded in the field, accounting for damage
delineated under the canopy, as well as assessed on
UAV images to capture damages occurring within the
canopy (figure S3). The difference between field and
UAV measurements was assessed by calculating the
mean absolute error (MAE), the root mean squared
error (RMSE) and the relative RMSE to the mean
(rRMSE).

**2.4. Biomass and volume estimates at the tree level**

The AGB (in kg) of all trees in the inventory
plots was calculated using the pantropical allometric
equation of Chave *et al* (2014) that was earlier val-
idated for Central Africa (Fayolle *et al* 2018). Site-
specific height-diameter allometric equations were
first developed to estimate the height of all trees in the
plots fitting non-linear Michaelis–Menten model as
recommended (Molto *et al* 2013, Fayolle *et al* 2016).
The BIOMASS package in R (Réjou-Méchain *et al* 2017) was used to extract wood density values (spe-
cies average) and to compute AGB estimates (res-
ults shown in tons). The commercial timber volume
(in m³), i.e. the over-bark volume, of all harvest-
ted trees was estimated using DBH measurements
and species-specific allometric equations (table S4)

compiled from Gourlet-Fleury *et al* (2013), Henry
*et al* (2013) and Ligot *et al* (2019).

**2.5. Data analyses**

Forest structural attributes were firstly assessed at the 1 ha-subplot level computing for example AGB (in t·ha⁻¹), BA (in m²·ha⁻¹), and NHA (in number of trees·ha⁻¹). The choice of 1 ha subplots is recommended for upscaling field data to larger areas (Réjou-Méchain *et al* 2019), and allows for covering a gradient of logging intensity within the study sites. The reduction in these attributes (delta AGB, BA and NHA), called logging impact (3.1 in figure 3), was assessed computing the metrics by damage categories (‘intact’ or ‘impacted’ trees) and social status (understory, canopy, emergent) before and after logging inventories. Logging intensity was quantified as the number (N harvested·ha⁻¹) and commercial timber volume (V harvested in m³·ha⁻¹) of harvested trees per hectare, both using the inventory plots and the harvested trees (figure 3, Paragraph 2.2). At the UAV-flight level and using gap photo-interpretation, canopy opening was evaluated according to different types of logging operations (felling gaps, logging roads, log yards, skid trails, others).

At the 1 ha-subplot level, we explored the rela-
tionships between logging impact, logging intensity
and canopy opening (3.2 and 3.3 in figure 3). Logging
impact was modeled using an exponential decay
model including logging intensity as an explanatory
variable (equation (1)). As intensity increases, dam-
ages tend to overlap, resulting in a decrease in damage
per tree (Durrieu de Madron et al 2000, Guitet et al
2012). Logging impact was modeled using an expo-
nential growth model including canopy opening as an
explanatory variable (equation (2)), indicating that
<figure><img src="image_8.png" /></figure>

larger openings result in a greater impact on forest structure.

At the UAV flight level, logging intensity was modeled using grids of various sizes, ranging from 1 ha to 100 ha (see example in white grid lines in figure 3 with a grid size of 500 m). A linear model was used to estimate logging intensity, with canopy opening as the explanatory variable (3.4 in figure 3, equation (3)). By varying grid sizes, we aimed to identify the scale at which these attributes could be most reliably estimated. The data were collected from various sites and forest types, leading to inherent clusters within the dataset, and thus non-independent data. Non-linear (equations (1) and (2)) and linear (equation (3)) mixed models were used to address this issue as they can accommodate the dataset structural complexity. These models include random effects to account for sites and forest types (α and β in equations (1)–(3) but these models can be used to predict new observations considering only the fixed effects (marginal model with α = 0 and β = 0 in equations (1)–(3)). Due to the limited size of the dataset (38 ha), the MAE, RMSE, and rRMSE of the marginal models for equations (1) and (2) were calculated on the entire dataset. For equation (3), as the dataset was bigger (6000 ha of UAV flights), a k-fold cross-validation with k = 5 was performed. Each site corresponds to a fold and site C was divided into two

folds representing areas of 6500 × 2000 m separated by 200 m. Equal numbers of points were randomly selected in each fold during cross-validation,

$$y = 1 - \exp((b + \beta)x) + \varepsilon_{ij} \quad (1)$$

where y = Delta AGB, BA or NHA, x = V or N harvested

$$y = \left( \frac{1}{1-x} \right)^{1/(b+\beta)} - 1 + \varepsilon_{ij} \quad (2)$$

where y = Delta AGB, BA or NHA, x = canopy opening

$$y = (a + \alpha) + bx + \varepsilon_{ij} \quad (3)$$

where y = V or N harvested, x = canopy opening

# 3. Results

## 3.1. Impact of logging on forest structure

Before logging, the mean estimated average AGB was 435.4 t·ha⁻¹, with 381.7 trees·ha⁻¹ and a BA of 32.8 m²·ha⁻¹ (table 1). Only slight variations across sites were observed with lower AGB and BA in Okoumé forests than in Celtis forests (figure S4). Understory trees were more abundant (75% of NHA) and emergent/canopy trees stored a greater proportion of biomass (75% AGB and 78% BA, table 1).
<table><caption>**Table 1.** Impact of logging on the aboveground biomass (AGB), the number of trees (NHA) and the basal area (BA) for trees with DBH > 10 cm, categorized by damage status and social position of the trees.</caption><thead><tr><th rowspan="2">Structural attribute</th><th rowspan="2">Before logging</th><th colspan="4">After logging</th></tr><tr><th>Damaged</th><th>Dead</th><th>Harvested</th><th>All impacted trees</th></tr></thead><tbody><tr><td>AGB (t·ha<sup>-1</sup>)</td><td>435.4<br>sd = 83.9</td><td>10.3<br>sd = 31.9</td><td>5.9<br>sd = 12.1</td><td>22.0<br>sd = 28.1</td><td>38.2 (–8.8%)<br>sd = 49.8</td></tr><tr><td>% in emergent</td><td>39.5</td><td>37.5</td><td>13.5</td><td>90.4</td><td>68.1</td></tr><tr><td>% in canopy</td><td>35.3</td><td>38.2</td><td>29.0</td><td>9.6</td><td>19.2</td></tr><tr><td>% in understory</td><td>25.2</td><td>24.3</td><td>57.5</td><td>0.0</td><td>12.7</td></tr><tr><td>AGB (t per m<sup>3</sup> harvested)</td><td>/</td><td>0.6</td><td>0.3</td><td>1.2</td><td>2.1</td></tr><tr><td>NHA (# trees·ha<sup>-1</sup>)</td><td>381.7<br>sd = 42.1</td><td>13.3<br>sd = 47.6</td><td>10.3<br>sd = 9.5</td><td>1.1<br>sd = 1.2</td><td>24.7 (–6.5%)<br>sd = 33.9</td></tr><tr><td>% in emergent</td><td>5.9</td><td>3.7</td><td>1.5</td><td>91.4</td><td>7.3</td></tr><tr><td>% in canopy</td><td>18.9</td><td>20.1</td><td>7.9</td><td>8.6</td><td>14.9</td></tr><tr><td>% in understory</td><td>75.3</td><td>76.2</td><td>90.5</td><td>0.0</td><td>77.8</td></tr><tr><td>NHA (# trees per m<sup>3</sup> harvested)</td><td>/</td><td>0.72</td><td>0.56</td><td>0.06</td><td>1.34</td></tr><tr><td>BA (m<sup>2</sup>·ha<sup>-1</sup>)</td><td>32.8<br>sd = 4.86</td><td>0.8<br>sd = 2.33</td><td>0.5<br>sd = 0.76</td><td>1.5<br>sd = 1.87</td><td>2.8 (–8.5%)<br>sd = 3.44</td></tr><tr><td>% in emergent</td><td>33.4</td><td>26.3</td><td>9.9</td><td>91.3</td><td>62.6</td></tr><tr><td>% in canopy</td><td>34.6</td><td>37.4</td><td>25.4</td><td>8.7</td><td>18.8</td></tr><tr><td>% in understory</td><td>32.0</td><td>36.3</td><td>64.8</td><td>0.0</td><td>18.6</td></tr><tr><td>BA (m<sup>2</sup> per m<sup>3</sup> harvested)</td><td>/</td><td>0.04</td><td>0.03</td><td>0.08</td><td>0.15</td></tr></tbody></table>

For an average logging intensity of 1.1 trees harvested per hectare corresponding to an exploitable volume of 18.4 m³·ha⁻¹, AGB, NHA and BA were respectively reduced by an average of 8.8%, 6.5% and 8.5% (table 1). Most impacted trees were understory trees (77.8% of NHA), but canopy trees (14.9%) and emergent trees (7.2%) were also impacted. The DBH of harvested trees ranged between 80 and 200 cm, resulting in a significant reduction in AGB at the 1 ha subplot level (table 1, figure S5). In Okoumé forests (site D), 71% of the harvested trees were *Aucoumea klaineana* trees, while in *Celtis* forests (site A and B) *Entandrophragma cylindricum* and *Erythrophleum suaveolens* accounted for 75% of the harvested trees (table S3).

Over the 5937 ha covered by the UAV flights, 4.4% of the canopy area was impacted by logging activities. Felling gaps had a mean area of 577.7 m² (median = 448.2 m², sd = 531.1 m², N = 2443) (figure S6) corresponding to 217 m² of gap per harvested tree. Felling gaps were the most important disturbance accounting for 56% of the impacted area, followed by logging roads (23%), skid trails (10%), the category ‘others’ (8%) and log yards (3%), with only slight differences between sites (table S2). For site A, the lengths of felling gaps measured on UAV images (mean = 37.8 m, sd = 8.2 m) were smaller than those measured on the ground (mean = 43.2 m, sd = 5.7 m), with a MAE of 6.2 m and a RMSE of 7.6 m (figure S7).

## 3.2. Relationships between logging impact and logging intensity at 1 ha level

Relationships were identified to predict logging impact (delta AGB, NHA, BA) based on logging intensity expressed in harvested volume (V harvested, figure 4) and number of harvested trees (N harvested, figure S8) at the 1 ha-subplot level. V harvested was a better explanatory variable of logging impact than N harvested, and a different response was observed depending on the forest type. For the same logging intensity, the logging impact was more severe in *Celtis* forests than in Okoumé forests (figures 4 and S8). Random effect values associated with different sites and types of forest for conditional models are presented in table S5. Marginal models were nearly identical between types and sites, with a rRMSE ranging between 0.128 and 0.164 across variables (figures 4 and S8).

## 3.3. Relationships between logging impact and canopy opening on the 1 ha scale

Relationships were identified for the prediction of logging impact (delta AGB, NHA, BA) based on canopy opening, with a rRMSE ranging between 0.119 and 0.150 for marginal models (figure 5). Three 1 ha-subplots in site B showed a reduction in AGB and BA with not much canopy opening because of skid trails under the canopy that are not visible on UAV images.
<figure><img src="image_9.png" /><figcaption>**Figure 4.** Prediction of logging impact based on logging intensity in terms of volume (V) harvested per hectare within 1 ha-subplots (n = 38), according to the different sites and types of forest. Marginal and conditional models for each site and type of forests are shown on the graphs. The table contains the mean absolute error (MAE), the root mean squared error (RMSE) and the relative root mean squared error (rRMSE) for the marginal models, as well as the fixed effects (b) and the standard deviations of the mixed model residuals (error) for equation (1). Values of random effects for the conditional models are in table S5.</figcaption></figure>

## 3.4. Relationships between the logging intensity and canopy opening on different scales

Relationships were identified to predict logging intensity based on canopy opening on different scales ranging between 1 and 100 ha (figure 6, see example in white grid lines in figure 3 with a grid size of 500 m). Marginal and conditional models for different sites and types of forest were very similar and provided a good fit to the data, thus only marginal models are presented in figure 6. The average MAE ranged between 2.4 and 8.1 m³ for predicting V harvested, and between 0.3 and 0.86 trees for predicting N harvested. However, below the grid size of 500 × 500 m, there was significant noise in the distribution of points, with extreme values of logging intensity. Upon observing the data, this noise is partly explained by the mislocation of harvested trees or by edge effects, which are smoothed from a grid size of 500 × 500 m.

## 4. Discussion

Our results show that the selective logging applied in FSC-certified concessions in Central Africa has a small direct impact on the forest structure. On average, logging reduced AGB by 8.8%, NHA by 6.5% and BA by 8.5% which is analogous to previous results (Pinard et al 1995, Sist 2000). Our estimates align with

other studies conducted in Central Africa, estimating direct AGB losses ranging between 7.1% and 13.4% depending on whether concessions are FSC-certified or not (Ngueguim et al 2009, Medjibe et al 2013). There is no significant difference in carbon emissions between FSC-certified and non-certified logging concessions. However, Reduced Impact Logging practices can effectively reduce emissions by approximately half without compromising timber yields (Umunay et al 2019). Felling gaps exhibited the most important impact on the canopy cover, which is consistent with earlier results found in south-east Cameroon (Dupuis et al 2023) and in French Guiana (Guitet et al 2012). The mean size of canopy opening (578 m² with N harvested = 2443 and 217 m² per harvested trees) was nevertheless smaller than earlier measurements in Gabon (mean = 787 m², N harvested = 12) (Medjibe, et al 2013), but close to the estimation of Doucet et al (2009) in Cameroon, where the average gap area was 265.8 m² (N harvested = 174). Concerning canopy opening, our estimates show that low-intensity logging activities (1–2 trees·ha⁻¹) cause an opening of 4.4% of the canopy which is lower than the 11% measured in south-east Cameroon (Ngueguim et al 2009). This difference can be partly explained by the resolution of images used to measure the canopy opening, i.e. 10 cm for the UAV images (this study) and 30 m for the Landsat images (Ngueguim et al
<figure><img src="image_10.png" /><figcaption>**Figure 5.** Prediction of logging impact based on canopy opening within 1 ha-subplots (n = 38), according to the different sites and types of forest. Marginal and conditional models for each site and type of forests are shown on the graphs. The table contains the mean absolute error (MAE), the root mean squared error (RMSE) and the relative root mean squared error (rRMSE) for the marginal models, as well as the fixed effects (b) and the standard deviations of the mixed model residuals (error) for equation (2). Values of random effects for the conditional models are in table S5.</figcaption></figure>

2009), suggesting that, depending on data resolution, measurements may be overestimated. In this study, we demonstrated that the impact of logging varies by forest type. One possible explanation is that in forests dominated by a single species, such as Okoumé forests (Guidosse et al 2022), the average crown size of the harvested trees tends to be smaller compared to the larger crowns of species like *Meliaceae* (e.g. *Entandrophragma cylindricum* in *Celtis* forests, see table S3). This difference in crown size may lead to less canopy opening in Okoumé forests compared to *Celtis* forests, where larger-crowned trees are harvested. Second, certain exploited species, like Okoumé, tend to be more gregarious (Quentin et al 2022), leading to concentrated exploitation in specific areas. This can result in overlapping damages and reduce the need for operators to penetrate deep into the forest to find valuable trees, thereby minimizing the impact caused by roads and skid trails, and leading to reduced canopy opening per harvested tree. Finally, we showed that logging practices in Central Africa have low impact on canopy compared to other continents. In French Guiana, a logging intensity of 3.5 trees·ha⁻¹ resulted in a canopy opening of 20% (measured on SPOT images, resolution = 4–20 m) (Guitet et al 2012), whereas forest in Malaysia, a logging intensity of 27 trees·ha⁻¹ led to a canopy opening

of 21% (measured with fish eye photographs from the ground) (Saiful and Latiff 2019).

Logging impacts primarily depends on logging intensity. The reduction in BA has become a widespread indicator of logging intensity, it was also used in the M'Baïki forest experiment and related to the proportion of pioneer species (Ouédraogo et al 2011, Gourlet-Fleury et al 2013). The direct impact of logging on other structural attributes such as AGB and NHA has been proposed to evaluate the sustainability of logging activities (Sist et al 2021) and used to model forest recovery after logging (Rutishauser et al 2015, Elliott et al 2023). Tree information necessary to compute these structural attributes and, consequently, logging impacts is not commonly measured in tropical forests. This process requires inventory plots in the field, which can be expensive and time-consuming. Additionally, some measurements are challenging, such as tree heights, which are needed to calculate AGB (Larjavaara et al 2013). Here, we proposed to use more accessible measurements, i.e. logging intensity (V or N harvested per ha) and canopy opening as a starting point to predict logging impact (delta AGB, BA, NHA). Logging intensity is a commonly used metric (BAD 2018) and is recorded by logging companies for timber trackability, while canopy opening can be measured by remote sensing
<figure><img src="image_11.png" /></figure>

(Dupuis et al 2020). For example, logging companies could use their existing inventory data to evaluate the impact of logging on forest structure, enabling them to adapt their practices for improved sustainability. By employing these alternative metrics, we aim to provide a more practical approach for assessing logging impacts and facilitating sustainable practices in tropical forest management. The models used to assess the logging impact based on logging intensity and canopy opening were built using a dataset that covers a total area of 38 ha across 3 sites. Further validation in other locations and forest types should be carried out, such as *Marantaceae* forests. While waiting for these valuable data, the marginal models presented here could provide initial estimates of the logging impact on forest structural attributes. Concerning the models predicting logging intensity based on canopy opening, and using ~6000 ha of UAV flights and >6000 harvested trees, they could be utilized with high confidence, when measured over a grid of 500 × 500 m at least, due to the extensive dataset covering four sites and three types of forest.

In addition to this information on the direct impact of logging on forest structure, this study also confirms that UAV could act as a bridge between field inventories and satellite-based monitoring techniques. Logging impacts on forest structure are typically measured on a small scale, using inventory plots ranging from 1 to 10 ha. However, these observations may not be representative of the overall logging intensity due to spatial variations within Central African logging concessions, as shown in this study, and higher pressure on smaller concessions (Pérez et al 2005). Localized intensity can surpass the average of 0.7–4.0 trees per hectare, with certain areas experiencing up to 9–10 trees and 180–200 m³ harvested, along with 85% of canopy opening in 1 ha plots (Dupuis et al 2023, Welsink et al 2023). A larger-scale evaluation should be recommended, and using UAV RGB imagery as a bridge offers a cost-effective alternative to LiDAR, and can be repeated affordably to detect and quantify disturbances (Ota et al 2019, Réjou-Méchain et al 2019, McNicol et al 2021). In French Guiana, Bourgoin et al (2020) used texture indices to reveal disturbances on UAV images, while
we propose to use a simpler indicator here in Central Africa, the percentage of canopy opening after logging. Canopy opening is a key indicator of disturbance frequently measured by remote sensing (Dupuis et al 2020), and can be detected automatically on UAV images using a classification algorithm (Castillo et al 2022) or manually, as shown in this study. Moreover, canopy opening is a simple metric attributed to the fall of emergent and canopy trees, contributing significantly to the decrease in AGB and BA because large trees shape the structure of tropical moist forests (Bastin et al 2018). Canopy opening measured on UAV images can help detect small disturbances that are not identifiable using satellite data. This information can be used to accurately assess the extent of disturbances and calibrate satellite-based methods (Castillo et al 2022, Dupuis et al 2023, Heinrich et al 2023), and finally upscale the impact of logging on large scale (Réjou-Méchain et al 2019). Indeed, current large-scale disturbance detection methods such as the Tropical Moist Forest products (Vancutsem et al 2021) and the RADD system (Reiche et al 2021) have limited accuracy as shown in Cameroon (Dupuis et al 2023). Promising results indicate that it may soon be possible to detect forest disturbance caused by logging on a large scale (Carstairs et al 2022, Dupuis et al 2023, Welsink et al 2023) and the datasets created in this study could certainly contribute to this improvement. Similar to the Geo-Trees project (Chave et al 2019, Geo-Trees 2024), which was designed for biomass estimation, the protocol used in this study—combining inventory plots and UAV flights—could be replicated in various regions to establish reliable references for calibrating satellite-based methods.

## 5. Conclusion

This study covers multiple inventory plots and UAV images in Central Africa, improving the representativeness of the results compared to local studies and providing a valuable reference to evaluate logging activities' impact on forest structure. Logging practices in Central Africa have small direct impact on forest structural attributes and on canopy compared to other continents. Logging impact is strongly related to logging intensity and canopy openings, but the models proposed in this study and based on a few plots should be further validated. Logging intensity can be predicted with high confidence based on canopy openings when measured over a grid of 500 × 500 m at least. By using UAV-measured canopy openings as a bridge, we show how field data can be connected to remote sensing measurements for large-scale monitoring of logging

impacts on forest structure. These findings have strong implications for forest disturbance monitoring systems, which is particularly important within the context of environmental crises, sustainable forest management, certification standards, and European regulations.

## Data availability statement

The dataset is available at this link: https://hdl.handle.net/2268/323683 (Dupuis et al 2024).

## Acknowledgments

We thank Pallisco-CIFM, CIB-Olam, Precious Woods-CEB, and IFO-Interholco, as well as their field research teams, for sharing their data, providing support, and being available throughout this work. We thank the Nature+ team for enabling us to collect field data and collaborate with forest concessions. We thank the botanists: Narcisse Kandem from the University of Yaoundé in Cameroon (site A), Isaac Nzombo and Jean-François Gillet in Congo (site B), Yves Issembé, Raoul Niangadouma of the National Herbarium and Jean-François Gillet in Gabon (site D). We are grateful to Jean-François Gillet and Robin Doucet for their assistance in identifying commercial species; Eric Forni, Valery Gond, Johan Oszwald, and Jan Bogaert for their advice on interpreting the results; Jérôme Perrin and Yves Brostaux for their statistical guidance; Sébastien Bauwens and Samuel Quevauvillers for their expertise in UAV acquisition; and Alain Monseur for his help with UAV images photo-interpretation. We also thank the master's students who assisted with data collection: Marie d'Aspremont, Fanny Hermand, Sophie Jeanmart, Michael Ronse, Cyril Cabrit, Sarah Tossens, Alexandre Ernst de Bunswyck, Laetitia Forget, and Aurélie Tock. We thank our financial supporters: the University of Liege; the iDROC project co-financed by the 'Programme de Promotion de l'Exploitation Certifiée des Forêts' (PPECF), the FEDER and the Occitanie Region, and implemented by the ASBL Nature+ in collaboration with the companies Sunbirds and TER-Consult, and the research organisations Gembloux Agro-Bio Tech, the 'Commissariat à l'énergie atomique' (CEA) and CIRAD. We also thank the French Global Environment Facility (FGEF) for the financial support of this study through the DynAffor (Dynamics of Central African Forests; conventions n° CZZ 1636.01 D and n° CZZ 1636.02 E) and P3FAC (Public-Private Partnership for the Sustainable Management of Central African Forests; convention n° CZZ201.01R) projects.
## Conflict of interest

The authors declare no conflict of interest.

## ORCID iDs

Chloé Dupuis https://orcid.org/0000-0003-1174-4286

Gauthier Ligot https://orcid.org/0000-0002-5508-4358

Jean-François Bastin https://orcid.org/0000-0003-2602-7247

Vivien Rossi https://orcid.org/0000-0001-5458-1523

Adeline Fayolle https://orcid.org/0000-0002-6770-0031

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