# Biomass and carbon of Eucalyptus spp. genotypes estimated with UAV-LIDAR using area-based approach and machine learning

Carla Talita Pertille, Alexandre Behling, Carlos Roberto Sanquetta, Otávio

Camargo Campoe, Guerric Le Maire, Joannès Guillemot, Geovanni Malatesta

Barros, Emerson Roberto Schoeninger, Ana Paula Dalla Corte

► To cite this version:

Carla Talita Pertille, Alexandre Behling, Carlos Roberto Sanquetta, Otávio Camargo Campoe, Guerric Le Maire, et al.. Biomass and carbon of Eucalyptus spp. genotypes estimated with UAV-LIDAR using area-based approach and machine learning. International Journal of Forestry Research, 2025, 2025, pp.5516907. 10.1155/ijfr/5516907. hal-05382639

HAL Id: hal-05382639

https://hal.science/hal-05382639v1

Submitted on 26 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Research Article

# Biomass and Carbon of *Eucalyptus* spp. Genotypes Estimated With UAV-LIDAR Using Area-Based Approach and Machine Learning

Carla Talita Pertille<sup>1</sup>,<sup>1</sup> Alexandre Behling<sup>1</sup>,<sup>1</sup> Carlos Roberto Sanquetta<sup>1</sup>,<sup>1</sup> Otávio Camargo Campoe<sup>2</sup>,<sup>2</sup> Guerric Le Maire<sup>3</sup>,<sup>3</sup> Joannès Guillemot<sup>3</sup>,<sup>3</sup> Geovanni Malatesta Barros<sup>4</sup>,<sup>4</sup> Emerson Roberto Schoeninger,<sup>4</sup> and Ana Paula Dalla Corte<sup>1</sup>

Correspondence should be addressed to Carla Talita Pertille; carlatpertille@gmail.com

Received 12 June 2025; Revised 3 September 2025; Accepted 19 September 2025

Academic Editor: Poulami Jha

Copyright © 2025 Carla Talita Pertille et al. International Journal of Forestry Research published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

This study aimed to quantify aboveground biomass and aboveground carbon in the total, trunk, and canopy compartments of *Eucalyptus* spp. using UAV-LiDAR data, using modeling techniques. The study area is part of the EUCFLUX project located in São Paulo, Brazil, and consists of five experimental blocks with 25 *Eucalyptus* spp. genotypes. Forest inventory and destructive sampling to assess biomass took place in May 2023. UAV-LiDAR data were collected with a Zenmuse L1 sensor on a DJI Matrice 300 aircraft, and predictive models were developed based on the metrics selected through principal component analysis. RF obtained an adjusted *R*<fnref n="2" /> of 0.952 and 0.959 for total and trunk aboveground biomass, respectively, and an RMSE of 5.42% for total aboveground biomass and 4.59% for trunk biomass. The adjusted *R*<fnref n="2" /> values for total aboveground carbon and trunk carbon were 0.947 and 0.959, with RMSE of 5.38% and 4.54%, respectively. The XGB algorithm outperformed the other methods in the canopy compartment, with adjusted *R*<fnref n="2" /> of 0.765 and 0.707 and RMSE of 13.49% and 15.06%, respectively. This study highlights the potential of UAV-LiDAR and nonparametric algorithms for accurately estimating carbon stock in *Eucalyptus* spp., contributing to climate change mitigation strategies.

**Keywords:** climate change; ecosystem services; nonparametric modeling; UAV-LiDAR metrics

## 1. Introduction

Brazil has one of the largest planted forest covers in the Earth with 10.2 million hectares [1], which equates to 12% of the total [2]. *Eucalyptus* spp. stands out as the most planted among the main types of trees planted in the country, with approximately 7.83 million hectares, representing 76% of the area of planted forests in the country [1]. Given this vast number, the leading role of forests in mitigating climate change stands out, especially through the conversion of

atmospheric carbon into forest biomass (a process carried out by forests) [3], in addition to their importance in the global carbon cycle [4].

Carbon stock in forest areas is mainly concentrated in the forest biomass, divided into aboveground biomass (AGB), belowground biomass (BGB), necromass (trunks, stumps, and dead branches), litter, and stable organic matter distributed in the soil profile [5]. The largest concentration of forest biomass is in the AGB [6], especially in tropical forests, and can be obtained by direct methods (forest

<footnote n="1"><sup>1</sup>Department of Forest Engineering, Federal University of Paraná-UFPR, Curitiba, Paraná, Brazil</footnote>

<footnote n="2"><sup>2</sup>Department of Forest Sciences, Federal University of Lavras-UFLA, Lavras, Minas Gerais, Brazil</footnote>

<footnote n="3"><sup>3</sup>Le Centre de Coopération Internationale en Recherche Agronomique Pour le Développement-CIRAD, Montpellier, Occitanie, France</footnote>

<footnote n="4"><sup>4</sup>Bracell, Lençóis Paulista, São Paulo, Brazil</footnote>
inventory and destructive sampling of individuals), and/or
estimated indirectly (from allometric equations and data
derived from remote sensing technologies) [7].

Given the spatial limitations of direct methods, especially
due to the cost and execution time (since the tree is felled,
sectioned and the components are weighed in situ) [8],
nondestructive, continuous, and spatial approaches have
stood out in AGB estimates, especially due to the application
of remote sensing data, such as light detection and ranging
(LiDAR) [9].

LiDAR is an active laser scanning sensor (light ampli-
fication by stimulated emission of radiation). Its operation is
based on emitting laser beams at a specific wavelength with
a high repetition frequency rate that penetrates forests, thus
obtaining three-dimensional information with high reso-
lution [10]. According to [11], these systems can be divided
according to the pulse signal forms (discrete or continuous),
the type of platform (aerial, terrestrial, or orbital), and the
footprint size (small (~1 m), medium (~10–30 m), or large
(~50 m)).

According to these sensor characteristics, two ap-
proaches can be used to estimate AGB using LiDAR tech-
nology: (i) individual-based approach or individual tree
detection (ITD) and (ii) area-based approach (ABA). In this
last method, attributes at the plot or stand level are extracted
and applied in creating predictive AGB models [12].

This ABA enables calculating metrics which characterize
the point cloud of an area of interest, which can be used in
different predictive statistical models [13], such as traditional
techniques (i.e., multiple linear regression) and machine
learning (ML) algorithms. Estimating AGB in forest plan-
tations using multiple linear regression has limitations, such
as the difficulty in treating complex and nonlinear re-
relationships or multicollinearity problems [14]. This fact
motivated the search for ML techniques for predicting
biomass and carbon stock, such as artificial neural networks
(ANNs) [15], k-nearest neighbor (kNN) [16], random forest
(RF) [14], support vector regression (SVR) [17], and extreme
gradient boosting (XGB) [18].

In this context, robust estimates of carbon stock stored in
the biomass of *Eucalyptus* spp. forests are becoming in-
creasingly necessary and urgent, especially in addressing the
climate emergency. In turn, this study is justified by esti-
mating the biomass and carbon stock in multilayers (i.e., in
different compartments) by combining a high-precision data
source (unmanned aerial vehicle (UAV) LiDAR or UAV-
LiDAR platform, with high point density) and ML tech-
niques (such as ANN, kNN, RF, SVR, and XGB).

Although there are many studies using UAV-LiDAR to
estimate AGB in *Eucalyptus* spp. plantations, there are still
some limitations to consider. Most of these studies present
insufficient field validation or require extensive field sam-
pling to obtain a larger number of samples [19]. Another
challenge concerns the need for allometric models calibrated
according to species, age, and site conditions [19, 20].
Furthermore, the lack of reliable models to predict biomass
by compartment (total, trunk, and canopy) hinders accurate
estimation of carbon stocks in different parts of the tree.
Other constraints involve variation in point cloud density

and quality, effects of flight conditions on data acquisition,
low spatial and temporal transferability of models, and the
cost and complexity of processing large volumes of UAV-
LiDAR data [21, 22]. These gaps highlight the importance of
this study, as improving more accurate and detailed methods
helps optimize forest carbon monitoring, support climate
change mitigation policies, and provide essential data for the
sustainable management of *Eucalyptus* spp. plantations.
Along these lines, this work aimed to estimate the AGB and
aboveground carbon stock in different compartments (total,
trunk, and canopy) focusing on unit area and with different
modeling methods in *Eucalyptus* spp. genotypes using high-
density UAV-LiDAR data.

**2. Materials and Methods**

2.1. *Area Description.* The area evaluated is located in the municipality of Itatinga, in the state of São Paulo, Brazil. The farm belongs to a forestry company in the pulp and paper industry and has an experimental area of 192.54 ha, in which this study was conducted (Figure 1). This area is linked to the Cooperative Program on Productivity and Carbon and Water Fluxes in *Eucalyptus* project—Phase 2 (*Programa Cooperativo sobre Produtividade e Fluxos de Carbono e Água em Eucalyptus*—*EUCFLUX*) developed in partnership with the Forestry Research Institute (Instituto de Pesquisas Florestais—IPEF) [23] and CIRAD (*Le Centre de coopération Internationale en Recherche Agronomique pour le Développement*).

The region's climate is classified as Cfa according to the Köppen climate classification system, meaning a humid and hot subtropical climate with hot summers and dry winters [24]. It is located at an altitude of 845 m, with an average annual temperature of 19.4°C and an average annual precipitation of 1319 mm [20]. The relief is mostly flat, with soils classified as typical dystrophic red latosol (Latossolo Vermelho Distrófico Típico-LVd4) and typical dystrophic red-yellow latosol (Latossolo Vermelho-Amarelo Distrófico Típico-LVAd4), with a medium sandy texture and moderate A horizon, and typical dystrophic red latosol (Latossolo Vermelho Distrófico Típico-LVd2), with a clayey texture and moderate A horizon [25].

The experimental area comprises a plantation of 5 blocks containing 25 genotypes of the genus *Eucalyptus* spp. planted at the end of 2018 with spacing of 3 × 2 m, totaling 6 m² per tree and 1666 trees.ha⁻¹. The high-trunk management system was implemented. Planting with a manual planter was performed after the harvest of the first rotation (8.5 years) in 2018, and replanting was carried out 30 days later. Planting management involved base and top-dressing fertilization over time, in addition to weed control through chemical weeding and leaf-cutter ant control with ant baits at different times of the cycle according to the climatic conditions and the plant development stage [26].

Each block contains approximately 2.80 ha and contains
25 plots representing the 25 genotypes planted. The total area
of the block is 180 × 160 m, containing 25 plots of 36 × 32 m
(12 rows × 16 trees), totaling 192 trees per plot. However, the
useful area or the effective internal measurement area is
composed of 30 × 20 m, totaling 600 m² or 0.06 ha,
<figure><img src="image_2.png" /><figcaption>FIGURE 1: Experimental area with *Eucalyptus* spp. in Itatinga, São Paulo, Brazil.</figcaption></figure>

containing 10 rows × 10 trees, meaning 100 trees per plot for each of the blocks. Of the 125 plots in the experimental area, only 124 plots were used in this study, since genotype 23 planted in block 10 did not present field measurements due to the high mortality rate after planting.

**2.2. Forest Inventory.** The forest inventory was conducted in the existing plots in the experiment in May 2023. The diameter at breast height (DBH) was measured in each tree with a caliper, and the total height of the trees located in the fourth and fifth rows was obtained using a Haglof Clinometer. The height of the remaining trees in each plot was subsequently determined from the Trorey hypsometric model (equation (1)), with an adjusted coefficient of determination (adjusted R²) of 0.724, standard error of the estimate (Syx) of 2.36 m, and standard error of the percentage estimate (Syx (%)) of 11.29%:

$$TH = (-4.7496 + 2.9752 * \text{DBH} + -0.0728 * \text{DBH}^2), \quad (1)$$

where TH is total height (m); DBH is diameter at breast height (cm).

Next, 150 trees (6 trees per genotype) not belonging to the plots were selected by diameter class using the standard deviation method. Tree volume was estimated using Smalian's method to obtain the plot volume, with measurements taken at 0.10, 0.30, 1, and every 3 m up to the tree top. The Schumacher-Hall model (equation (2)) was adopted for the volumetric quantification of the genotypes whose fit presented an adjusted coefficient of determination (adjusted R²) of 0.985 and a standard error of the estimate in percentage (Syx (%)) of 6.47%:

$$\ln = -10.2992 + 1.7642 * \ln + 1.2262 * \ln, \quad (2)$$

where ln is natural logarithm; v is total volume (m³); TH is total height (m); and DBH is diameter at breast height (cm).

**2.3. Quantification of AGB and Aboveground Carbon.** The AGB in the field was determined considering the above-ground tree components (wood, bark, live branches, dead branches, and leaves) per tree unit (kg.tree⁻¹) using the 150 trees measured during the forest inventory.

After the trees were felled, the components were separated and weighed to determine the wet weight. Thus, 2.5-cm-thick discs were removed from the positions corresponding to the base (10 cm), at DBH (1.30 m), and every 3 m of height of the felled tree to collect the wood samples.

The samples were transported to the Forestry and Forest Hydrology Laboratory, located at the Itatinga Experimental Station of Forest Sciences, São Paulo, Brazil. The samples were initially subjected to the drying process at 65°C to obtain the dry weight, requiring 4 days for leaves, dead branches and bark, 7 days for live branches, and 3 weeks for wood. After obtaining the respective dry weights, the total dry matter percentage (%) (equation (3)) of each evaluated sample was calculated, and then, the total dry weight or total dry biomass (equation (4)) of each tree component is as follows:

$$DM_i = \frac{DW_i}{WW_i}, \quad (3)$$

$$TDM_i = DM_i * TWW_i, \quad (4)$$

where DMᵢ is dry matter of the ith tree component (%), DMᵢ is dry weight of the sample of the ith tree component (g), WWᵢ is wet weight of the sample of the ith tree component (g), TDMᵢ is total dry mass of the ith tree component (kg), and TWWᵢ is total wet weight of the ith tree component (kg).
Then, the average carbon content of 46.08% derived from the study of [27] in *Eucalyptus* spp. aged 4.7 years was adopted to obtain the aboveground carbon stock. The same value of carbon content was applied to all compartments, since [27] analyzed multiple *Eucalyptus* species—consistent with our dataset comprising 25 genotypes of *Eucalyptus* spp.—and the tree age was close to that of the present study (~4 years). The dry biomass of each compartment analyzed was multiplied by the average carbon content, resulting in the carbon stock per component. Thus, the sum of the carbon stock per tree component resulted in the total aboveground carbon stock (kg.tree⁻¹).

In turn, the Schumacher-Hall model was used with the inclusion of the genotypes as dummy variables to quantify the AGB per compartment (total, trunk, and canopy) per genotype (equations (5)–(7)). The sum of the AGB per compartment of all trees belonging to each genotype was subsequently divided by 1000 and multiplied by the proportionality factor of 16.67 (10,000/600 m²), resulting in the AGB of each compartment in t·ha⁻¹:

$$ \ln \text{tb} = \beta_0 + \beta_1 * \ln \text{DBH} + \beta_2 * \ln \text{HT} + \text{genotype} + \varepsilon, \quad (5) $$

$$ \ln \text{trb} = \beta_0 + \beta_1 * \ln \text{DBH} + \beta_2 * \ln \text{HT} + \text{genotype} + \varepsilon, \quad (6) $$

$$ \ln \text{tcb} = \beta_0 + \beta_1 * \ln \text{DBH} + \beta_2 * \ln \text{HT} + \text{genotype} + \varepsilon, \quad (7) $$

where tb is total AGB (kg), trb is AGB of trunk (kg), tcb is AGB of canopy (kg), βi is model coefficient, TH is total height (m), DBH is diameter at breast height (cm), and ε is the error associated with the model.

Finally, the AGB (t·ha⁻¹) of each compartment per genotype was multiplied by the average carbon content [23], resulting in the aboveground carbon stock per compartment (total, trunk, and canopy) per tree.

## 2.4. UAV-LiDAR Scanning.
The UAV-LiDAR data were acquired using a DJI Matrice 300 UAV [24] and a Zenmuse L1 sensor [28] in May 2023. The flight altitude was 80 m, with a speed of 9 m·s⁻¹ and a coverage swath width of 450 m. The Zenmuse L1 sensor can record up to 3 returns, collecting data with a wavelength of 905 nm, a scan angle of 45°, a field of view of 70.4° (horizontal) and 77.2° (vertical), and a vertical accuracy of 5 cm at 50 m and a horizontal accuracy of 10 cm at 50 m [28], resulting in an average point density of 389.30 points·m⁻².

The point cloud derived from the UAV-LiDAR survey was processed in R software, Version 4.2.0 [29] with the aid of the lidR library [13]. The ground returns were initially classified to only represent the ground [26], for which the cloth simulation filter (CSF) was used. After classifying the

points, the returns extracted as ground were used to generate the digital terrain model (DTM) using the kNN algorithm with inverse distance weighting (IDW) [30]. The digital surface model (DSM) was obtained by the p2r (point to raster) algorithm, which assigns the elevation of the highest point to each pixel [13].

Next, the point cloud was normalized using the normalize_height function [13]. After normalization, the canopy height model (CHM) was extracted using the p2r (point to raster) method [13, 30]. All digital models (DTM, DSM, and CHM) were extracted with 0.5 m resolution. The normalized point cloud and the CHM were subsequently extracted considering the parcel boundaries of each sub-block. The metrics related to height (maximum, mean, standard deviation, skewness, kurtosis, and entropy) in addition to the height percentiles and the cumulative return percentages were extracted using the lidR library [13] in R Version 1.0. 4.2.0 software [29].

## 2.5. Modeling of AGB and Aboveground Carbon.
First, the UAV-LiDAR metrics were selected from principal component analysis (PCA) to build the models using multiple linear regression. After selecting the metrics, the dataset was divided, considering 70% (88 data) for the formation of a training set and 30% (36 data) for a validation set using the “caret” package [31] in the R Version 4.2.0 software [29].

Then, the 5 candidate metrics for building the models were selected by the stepwise method, adding or removing variables by the “lmStepAIC” method in the “caret” package [31]. The models derived from the multiple linear regression technique were evaluated for residual normality (Shapiro-Wilk test) [32], independence of the residuals (Durbin-Watson test), and homogeneity of variances (Breusch-Pagan test) [10]. All analyses were performed in the R environment [29] at 95% probability. In addition, traditional fitting parameters were also considered, such as adjusted coefficient of determination (adjusted R²), absolute (Syx t·ha⁻¹) and percentage (Syx %) standard error of the estimate, and absolute (t·ha⁻¹) and percentage (%) root mean square error (RMSE).

The parameters of the ML algorithms tested are shown in Table 1. All algorithms were tested with the expand.grid function of the caret package [31] to create a grid of hyperparameter combinations to be tested. The ANNs were applied using the nnet method (nnet package) [33]. The other algorithms were tested in the caret package [31] using the knn, rf, svmLinear, and xgbTree methods. The model fit and parameter optimization steps described above were performed in the R software [29].

The following agreement measures were considered to validate and select the best predictive model for AGB and aboveground carbon: coefficient of agreement (A) (equation (8)), measure of agreement (p) (equation (9)); improved concordance correlation coefficient (CC2) (equation (10)), and agreement parameter (equation (11)) [34–38]:
<table><caption>TABLE 1: Machine learning algorithms tested for predicting aboveground biomass and aboveground carbon in different compartments of *Eucalyptus* spp. genotypes in São Paulo, Brazil.</caption><thead><tr><td>Algorithm</td><td>Parameter</td><td>Variation</td><td>Description</td></tr></thead><tbody><tr><td>ANN</td><td>Size</td><td>1 to 10</td><td>Number of neurons in the hidden layer</td></tr><tr><td>ANN</td><td>Decay</td><td>0.1 to 1</td><td>Regularization term (weight penalty)</td></tr><tr><td>kNN</td><td>k</td><td>10, 12, and 14</td><td>Number of neighbors</td></tr><tr><td>RF</td><td>mtry</td><td>10, 12, 14, and 16</td><td>Number of randomly selected predictor variables</td></tr><tr><td>SVR</td><td>Sigma</td><td>0.05 to 0.1</td><td>Radial kernel function parameter</td></tr><tr><td>SVR</td><td>C</td><td>5, 7, and 10</td><td>Regularization parameter</td></tr><tr><td>XGB</td><td>max_depth</td><td>3, 5, and 7</td><td>Maximum tree depth</td></tr><tr><td>XGB</td><td>eta</td><td>0.01, 0.05, and 0.1</td><td>Learning rate</td></tr><tr><td>XGB</td><td>Gamma</td><td>0, 0.01 and 0.02</td><td>Penalty for node splitting</td></tr><tr><td>XGB</td><td>colsample_bytree</td><td>0.2, 0.4, and 0.6</td><td>Percentage of columns sampled per tree</td></tr><tr><td>XGB</td><td>min_child_weight</td><td>1, 3, and 5</td><td>Minimum number of samples for node splitting</td></tr><tr><td>XGB</td><td>Subsample</td><td>0.7, 0.8, and 0.9</td><td>Percentage of data used in each tree</td></tr></tbody></table>

Abbreviations: ANN, artificial neural network; kNN, k-nearest neighbor; RF, random forest; SVR, support vector regression; XGB, extreme gradient boosting.

$$A = 1 - \frac{\sum (y_i - k_i)^2 + \sum (x_i - k_i)^2}{\sum (y_i - k)^2 + \sum (x_i - k)^2}, \qquad (8)$$

$$p = 1 - \frac{\text{MSE}}{\left(1/(\text{n}^2)\right) \sum_{i=1}^{n} (y_i - x_i)^2}, \qquad (9)$$

$$CC2 = r * A_p, \qquad (10)$$

$$\lambda = 1 - \frac{\sum (y_i - x_i)^2}{\sum (y_i - y_i)^2 + \sum (x_i - \bar{x})^2 + n (\bar{y} - \bar{x})^2} + k, \qquad (11)$$

where A: coefficient of agreement; $y_i$: AGB estimated and aboveground carbon estimated in the different compartments observed in the field (t·ha⁻¹); $x_i$: AGB observed and aboveground carbon observed in the different compartments (t·ha⁻¹); $k_i$: mean of $y_i$ and $x_i$ for tree i; k: mean of all $y_i$ and $x_i$ values; p: measure of agreement; n: number of observations; MSE: mean square error; CC2: improved concordance correlation coefficient; r: correlation coefficient; $A_p$: modified measure of accuracy; λ: agreement parameter; y: mean of y; and x: mean of x.

Estimates of AGB and aboveground carbon per compartment were analyzed using the Bland-Altman plot [39] in R software [29], with the blandr package [40].

The complete sequence of methodological steps described is summarized in the flowchart in Figure 2.

## 3. Results

### 3.1. Destructive Sampling: Total AGB and Total Aboveground Carbon.
With the hypsometric relationship adjusted by the Trorey model and the volumetric quantification by the Schumacher-Hall model, the biometric variables of the evaluated genotypes (Table 2) presented an average diameter of 12.86 cm and an average height of 20.63 m. Block 7 presented the tallest trees (31.10 m), while the thickest trees were predominant in block 10 (13.35 cm). The largest

volumetric stock occurred in block 10 (282.21 m³·ha⁻¹). In turn, block 2 had the smallest volume (236.65 m³·ha⁻¹). It is worth mentioning that there was no data collection in block 10 for genotype 23 due to its high mortality rate.

Regarding the components evaluated in the scaled trees, the highest biomass concentration on average was noted in the wood, with 64.20 kg·tree⁻¹ (78.96%), followed by the bark with 8.17 kg·tree⁻¹ (10.05%), live branches with 4.74 kg·tree⁻¹ (5.83%), leaves with 2.80 kg·tree⁻¹ (3.45%), and dead branches with 1.39 kg·tree⁻¹ (1.71%) (Figure 3).

Fitting the regression model considering the genotypes as dummy variables for estimating AGB in different compartments (Table 3) obtained coefficients of determination higher than 0.95 and errors of approximately 10%, except for the canopy compartment (branches, dead branches, and leaves), whose fit had an adjusted R² of 0.8076 and errors close to 30%.

Table 4 summarizes the determination of AGB in different compartments by the Schumacher-Hall model and the estimate of the aboveground carbon stock present in the *Eucalyptus* spp. genotypes. The mean total AGB was 111.22 t·ha⁻¹, and the total aboveground carbon stock had an average of 51.25 t·ha⁻¹. The trunk compartment had, on average, an AGB of 100.01 t·ha⁻¹ and an aboveground carbon of 46.09 t·ha⁻¹. The canopy compartment had a mean AGB of 10.79 t·ha⁻¹ and aboveground carbon of 4.97 t·ha⁻¹. It is noted that genotype 21 presents the highest biomass and carbon concentrations in all compartments. In contrast, genotype 25 has the lowest amounts of biomass and carbon.

### 3.2. Modeling of AGB and Aboveground Carbon by Compartment.
First, the 36 extracted UAV-LIDAR metrics were subjected to an exploratory analysis to identify outliers and select the variables using PCA. Thus, 8 metrics that presented values equal to 0 were excluded. PCA was subsequently applied, which identified that the first two principal components can explain 81% of the data variation. Next, the remaining 28 metrics were analyzed again and subjected to a correlation analysis to identify multicollinear metrics, or highly correlated. In turn, 20 metrics related to the mean height, percentiles of the height
<figure><img src="image_3.png" /><figcaption>FIGURE 2: Flowchart of UAV-LiDAR data processing for estimating aboveground biomass and aboveground carbon at the compartment level in *Eucalyptus* spp. in Itatinga, São Paulo, Brazil.</figcaption></figure>

<table><caption>TABLE 2: Biometric variables of *Eucalyptus* spp. genotypes in an experimental area in Itatinga, São Paulo, Brazil.</caption><thead><tr><th>Block</th><th>N.trees.ha</th><th>md (cm)</th><th>G (m<sup>2</sup>·ha<sup>-1</sup>)</th><th>maxh (m)</th><th>mh (m)</th><th>V (m<sup>3</sup>·ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td>2</td><td>1398</td><td>12.71</td><td>23.27</td><td>26.60</td><td>20.37</td><td>236.65</td></tr><tr><td>4</td><td>1481</td><td>12.72</td><td>24.64</td><td>28.20</td><td>20.56</td><td>252.07</td></tr><tr><td>7</td><td>1538</td><td>12.69</td><td>25.45</td><td>31.10</td><td>20.80</td><td>265.15</td></tr><tr><td>8</td><td>1544</td><td>12.84</td><td>26.21</td><td>27.80</td><td>20.72</td><td>270.61</td></tr><tr><td>10</td><td>1436</td><td>13.35</td><td>27.02</td><td>30.20</td><td>20.70</td><td>282.21</td></tr><tr><td>Mean</td><td><strong>1480</strong></td><td><strong>12.86</strong></td><td><strong>25.32</strong></td><td><strong>31.10</strong></td><td><strong>20.63</strong></td><td><strong>261.34</strong></td></tr></tbody></table>

Note: Em que: N.trees: number of trees measured per hectare; md: mean diameter (cm); G: basal area (m²·ha⁻¹); maxh: maximum total height (m); mh: mean total height (m); V: average total volume (m³·ha⁻¹). The bold values refer to the mean of each variable.

distribution located between 30% and 65%, cumulative percentages of returns between 10% and 90% of the height, and the percentage of returns above the average were excluded. Of the remaining 8 metrics, the 3 metrics with the lowest weight in the PCA were excluded, namely, mean height, standard deviation of height, and 95% height percentile. As a result, 5 metrics were used in creating the predictive models of AGB and aboveground carbon in the different compartments using ML and regression models, including height percentile at 70%, 75%, 80%, 85%, and 90%, whose model performed well in cross-validation (R² of 0.73 and an RMSE of 3.05).

Fitting of the models constructed by stepwise and ML for predicting AGB and aboveground carbon in the different compartments is summarized in Table 5. The models fitted by the stepwise method demonstrated an explanatory power (adjusted R²) which varied between 61% and 78% for the response variables analyzed. The RMSE percentage varied from 10.18% to 18.73%, with the highest value observed in the canopy compartment.

As a result, the RF and XGB techniques generally stood out among the tested ML methods, while ANN remained in third place. RF presented adjusted R² values above 0.95 for

predicting AGB and aboveground carbon in the total compartment, indicating excellent predictive capacity. Next, XGB stood out with an adjusted R² of 0.893 for total AGB and total aboveground carbon.

The RF algorithm again had the best fit in estimating biomass and carbon located in the trunk with an adjusted R² of 0.959 and 0.961, respectively, and errors below 5%, surpassing the results obtained with XGB and ANN. However, the XGB algorithm presented the best fit statistics in the canopy compartment for both biomass and carbon, with an adjusted R² of 0.844 for biomass and 0.924 for carbon. RF also presented an equally satisfactory adjustment, but slightly lower than XGB, with adjusted R² of 0.765 for biomass and 0.707 for carbon. In contrast, the models originating from the kNN and SVR techniques presented an adjusted R² lower than 0.782 for all compartments, with the lowest value observed in predicting canopy biomass, which was 0.285. These methods also presented the highest prediction errors (between 9.94% and 27.06%), especially in the canopy compartment.

The models derived from multiple linear regression for predicting total aboveground and trunk biomass, as well as total aboveground and trunk carbon, presented residuals
<figure><img src="image_4.png" /><figcaption>FIGURE 3: Percentage of total aboveground biomass per compartment in *Eucalyptus* spp. trees in Itatinga, São Paulo, Brazil.</figcaption></figure>

<table><caption>TABLE 3: Estimation of aboveground biomass in different compartments (total, trunk, and canopy) by the Schumacher-Hall model in *Eucalyptus* spp. in Itatinga, São Paulo, Brazil.</caption><thead><tr><th>Compartment</th><th>R<sup>2</sup> adj</th><th>Syx (kg.tree<sup>-1</sup>)</th><th>Syx (%)</th><th>RMSE (kg.tree<sup>-1</sup>)</th><th>RMSE (%)</th></tr></thead><tbody><tr><td>Total</td><td>0.960</td><td>8.65</td><td>10.64</td><td>7.84</td><td>9.65</td></tr><tr><td>Canopy</td><td>0.808</td><td>3.01</td><td>33.72</td><td>2.61</td><td>29.23</td></tr><tr><td>Trunk</td><td>0.961</td><td>7.46</td><td>10.31</td><td>6.77</td><td>9.35</td></tr></tbody></table>

Note: R² adj: adjusted coefficient of determination; Syx: standard error of the estimate (kg.tree⁻¹); Syx (%): standard error of the percentage estimate (%); RMSE: root mean square error (kg.tree⁻¹); RMSE (%): root mean square error percentage (%).

with normal distribution, although these were independent in all compartments. Only the predictive models for biomass and carbon in the canopy compartment showed homoscedasticity of the variances.

Figure 4 illustrates the graphical distribution of the model residuals built with ML and multiple linear regression for the basis used in validating the models. The residual heteroscedasticity is noted in all the algorithms tested; however, to a lesser extent in the random forest model in the total aboveground and trunk compartments, and in the XGB model in the canopy compartment.

Table 6 contains the agreement measures evaluated for the models tested in the prediction of AGB and aboveground carbon in the compartments. Considering the total basis, fit basis, and validation basis, the RF algorithm presented the best agreement measures (closest to 1) in the total aboveground and trunk compartments. Next, there are XGB, ANN, SVR, and kNN. In turn, the XGB algorithm was the best for the canopy compartment, followed by RF, ANN, SVR, and kNN.

Figure 5 illustrates the Bland-Altmann concordance plot for the predictive models of AGB and aboveground carbon of the total, trunk, and canopy compartments in *Eucalyptus* spp. considering the validation base.

Based on the fit and validation metrics, the agreement measures and the Bland-Altmann plots, the RF algorithm was elected as the best for estimating total aboveground and trunk biomass, as well as for evaluating total aboveground carbon stock and trunk carbon, while the models built by XGB were more efficient in predicting biomass and carbon in the canopy compartment in *Eucalyptus* spp. genotypes from UAV-LiDAR metrics.

## 4. Discussion

This study aimed to quantify biomass and carbon stock considering the total, trunk, and canopy compartments of *Eucalyptus* spp. genotypes using the ABA approach based on integrating UAV-LiDAR data and different modeling techniques.
<table><caption>TABLE 4: Average aboveground biomass (t·ha⁻¹) and average aboveground carbon (t·ha⁻¹) per compartment in *Eucalyptus* spp. genotypes in São Paulo, Brazil.</caption><thead><tr><th rowspan="2">Genotype</th><th colspan="3">Aboveground biomass (t·ha⁻¹)</th><th colspan="3">Aboveground carbon (t·ha⁻¹)</th></tr><tr><th>Total</th><th>Trunk</th><th>Canopy</th><th>Total</th><th>Trunk</th><th>Canopy</th></tr></thead><tbody><tr><td>1</td><td>110.68</td><td>100.21</td><td>10.10</td><td>51.00</td><td>46.18</td><td>4.65</td></tr><tr><td>2</td><td>117.39</td><td>106.62</td><td>11.11</td><td>54.09</td><td>49.13</td><td>5.12</td></tr><tr><td>3</td><td>114.26</td><td>103.43</td><td>10.97</td><td>52.65</td><td>47.66</td><td>5.06</td></tr><tr><td>4</td><td>131.72</td><td>115.87</td><td>14.65</td><td>60.70</td><td>53.39</td><td>6.75</td></tr><tr><td>5</td><td>119.30</td><td>107.62</td><td>10.71</td><td>54.98</td><td>49.59</td><td>4.93</td></tr><tr><td>6</td><td>105.90</td><td>94.35</td><td>11.26</td><td>48.80</td><td>43.48</td><td>5.19</td></tr><tr><td>7</td><td>121.67</td><td>107.93</td><td>13.78</td><td>56.06</td><td>49.73</td><td>6.35</td></tr><tr><td>8</td><td>93.80</td><td>86.52</td><td>7.39</td><td>43.22</td><td>39.87</td><td>3.41</td></tr><tr><td>9</td><td>126.32</td><td>112.59</td><td>12.08</td><td>58.21</td><td>51.88</td><td>5.56</td></tr><tr><td>10</td><td>120.75</td><td>107.68</td><td>12.89</td><td>55.64</td><td>49.62</td><td>5.94</td></tr><tr><td>11</td><td>122.54</td><td>109.14</td><td>13.14</td><td>56.47</td><td>50.29</td><td>6.05</td></tr><tr><td>12</td><td>112.07</td><td>104.29</td><td>7.51</td><td>51.64</td><td>48.06</td><td>3.46</td></tr><tr><td>13</td><td>105.33</td><td>97.22</td><td>8.50</td><td>48.54</td><td>44.80</td><td>3.92</td></tr><tr><td>14</td><td>117.32</td><td>109.75</td><td>7.40</td><td>54.06</td><td>50.57</td><td>3.41</td></tr><tr><td>15</td><td>127.02</td><td>112.62</td><td>14.63</td><td>58.53</td><td>51.90</td><td>6.74</td></tr><tr><td>16</td><td>86.02</td><td>81.41</td><td>5.19</td><td>39.64</td><td>37.51</td><td>2.39</td></tr><tr><td>17</td><td>125.29</td><td>111.82</td><td>13.32</td><td>57.73</td><td>51.53</td><td>6.14</td></tr><tr><td>18</td><td>124.00</td><td>112.25</td><td>10.38</td><td>57.14</td><td>51.72</td><td>4.78</td></tr><tr><td>19</td><td>119.18</td><td>105.82</td><td>13.17</td><td>54.92</td><td>48.76</td><td>6.07</td></tr><tr><td>20</td><td>124.16</td><td>113.32</td><td>9.77</td><td>57.22</td><td>52.22</td><td>4.50</td></tr><tr><td>21</td><td>137.61</td><td>117.36</td><td>17.44</td><td>63.41</td><td>54.08</td><td>8.04</td></tr><tr><td>22</td><td>128.80</td><td>116.32</td><td>12.43</td><td>59.35</td><td>53.60</td><td>5.73</td></tr><tr><td>23</td><td>80.96</td><td>67.65</td><td>11.90</td><td>37.31</td><td>31.17</td><td>5.48</td></tr><tr><td>24</td><td>70.14</td><td>63.69</td><td>6.41</td><td>32.32</td><td>29.35</td><td>2.95</td></tr><tr><td>25</td><td>32.31</td><td>28.34</td><td>3.89</td><td>14.89</td><td>13.06</td><td>1.79</td></tr></tbody><tfoot><tr><td>Mean</td><td>111.22</td><td>100.01</td><td>10.79</td><td>51.25</td><td>46.09</td><td>4.97</td></tr><tr><td>SD</td><td>25.40</td><td>22.57</td><td>3.46</td><td>11.70</td><td>10.40</td><td>1.60</td></tr></tfoot></table>

Note: Mean: mean. The bold values refer to the mean of each variable.

Abbreviation: SD, standard deviation.

According to the dendrometric variables (Table 2) from the field inventory, variations in the growth of genotypes between blocks are noted, evidencing the genotype-environment interaction and its impact on biomass production, which was greater in genotypes 20 and 18 and lower in genotypes 25 and 16 (Table 4). The trunk (wood and bark) represented 89% of the total AGB of the genotypes in the AGB distribution in the tree components. The greater biomass accumulation in the wood is due to the age of the genotypes, since there was a reduction in the canopy biomass and an increase in the biomass proportion in the wood and bark with the development of these plantations, in addition to genetic improvement which indirectly selects plants based on their biomass allocation in the trunk. Factors such as site conditions, species and genetic materials, climatic conditions, planting density, and rotation duration can also influence the biomass distribution in storage reservoirs [41].

The wood was also the compartment with the greatest contribution to the biomass of plantations aged between 4 and 6 years of *Eucalyptus urophylla* × *Eucalyptus grandis* located in Brasília and Paranoá, in the Federal District, Brazil. Wood accounted for between 69% and 86% of the biomass in the four analyzed areas, followed by the other

components: bark (6%–11%), roots (1%–10%), branches (1%–9%), and leaves (1%–4%) [42].

The AGB and carbon in different compartments (Table 3), as estimated by the Schumacher-Hall model with the inclusion of genotypes as dummy variables, were consistent with research conducted on similar genotypes under different conditions, according to [42]. In this study, 4-year-old *E. urophylla* × *E. grandis* plantations stored an average of 41.75 kg.tree⁻¹ of carbon in the aboveground compartments [39]. The results in another study conducted in two experimental sites with contrasting climatic conditions belonging to the Cooperative Research Program for Tolerance of *Eucalyptus* spp. to Water, Thermal and Biotic Stresses (TECHS) of IPEF indicated that the *E. urophylla* and *E. grandis* × *E. camaldulensis* plantations stored on average 38.7 t·ha⁻¹ of carbon at 3 years of age and 81.2 t·ha⁻¹ of carbon at the end of the rotation (6 years) [43].

The results of this study regarding total AGB (111.22 t·ha⁻¹ at 4.5 years old) are in agreement with the literature. For example, [44] quantified the total AGB of five 4-year-old *Eucalyptus* spp. genotypes located in Eldorado do Sul in Rio Grande do Sul (Brazil), finding similar values for similar genotypes: *E. benthamii* (92.19 t·ha⁻¹), *E. benthamii* (area 2) (102.04 t·ha⁻¹), *E. saligna* (89.25 t·ha⁻¹), *E. dunnii* (68.40 t·ha⁻¹), *E. uroglobulus* (117.52 t·ha⁻¹), and *E. urograndis* (101.80 t·ha⁻¹).

The disparities found among the algorithms used in this study in biomass and carbon estimates resulting from the use of each algorithm are directly related to four main factors: (i) characteristics of the plantation and experimental area (spacing, age, and topography); (ii) UAV-LiDAR dataset (point density, interaction of laser returns with the canopy, and type of metrics used) and ABA approach; (iii) genotype-environment interactions (nutritional efficiency, sunlight absorption and photosynthetic capacity, resistance, and tolerance to adverse conditions); and (iv) optimization parameters of each algorithm.

The ABA approach is widely used in the forestry area, especially for inventory and mapping, but it is limited to the use of metrics derived from the vertical distribution of LiDAR returns [12]. Thus, the metrics contained in the cell size used comprise the main source of analysis for this approach [12].

In this regard, the influence of the plots should also be considered, since the number, size, shape, and accuracy of their geolocation interfere with the accuracy of biomass estimates [45]. Only 124 plots were used in this study—plots of 0.06 ha and rectangular shape. Reference [46] recommends that a greater number of plots and larger dimensions can reduce geolocation errors and consequently the biomass estimation process.

Genotype and environment interactions also influenced biomass production and carbon stock, since nutritional efficiency among these interactions can vary for each species [47] due to morphological characteristics and physiological characteristics (photosynthetic rate and the ability to maintain normal metabolism with low nutrient content in the tissues) [47].
<table><caption>TABLE 5: Fitting parameters of the predictive models of aboveground biomass and aboveground carbon (t·ha⁻¹) in different compartments (total, trunk, and canopy) of *Eucalyptus* spp.</caption><thead><tr><th rowspan="2">Compartments</th><th rowspan="2">Algorithm</th><th colspan="4">Fitting (70% of the data)</th><th colspan="5">Validation (30% of the data)</th></tr><tr><th>R<sup>2</sup><br>adj.</th><th>Syx<br>(t·ha<sup>-1</sup>)</th><th>Syx<br>(%)</th><th>RMSE<br>(t·ha<sup>-1</sup>)</th><th>RMSE (%)</th><th>MAE</th><th>MPE</th><th>MSE</th><th>RMSE<br>(t·ha<sup>-1</sup>)</th><th>RMSE (%)</th></tr></thead><tbody><tr><td rowspan="5">Total aboveground<br>biomass</td><td>Stepwise</td><td>0.744</td><td>13.30</td><td>12.04</td><td>13.14</td><td>11.90</td><td>9.08</td><td>8.64</td><td>134.69</td><td>11.61</td><td>10.25</td></tr><tr><td>ANN</td><td>0.829</td><td>10.55</td><td>9.56</td><td>10.43</td><td>9.45</td><td>9.59</td><td>8.89</td><td>159.50</td><td>12.63</td><td>11.44</td></tr><tr><td>kNN</td><td>0.758</td><td>12.54</td><td>11.36</td><td>12.40</td><td>11.23</td><td>10.51</td><td>11.09</td><td>174.47</td><td>13.21</td><td>11.96</td></tr><tr><td>RF</td><td><strong>0.952</strong></td><td><strong>6.05</strong></td><td><strong>5.48</strong></td><td><strong>5.99</strong></td><td><strong>5.42</strong></td><td><strong>9.12</strong></td><td><strong>8.48</strong></td><td><strong>133.64</strong></td><td><strong>11.56</strong></td><td><strong>10.47</strong></td></tr><tr><td>SVR</td><td>0.719</td><td>13.94</td><td>12.62</td><td>13.45</td><td>12.18</td><td>8.66</td><td>7.63</td><td>130.72</td><td>11.43</td><td>10.10</td></tr><tr><td></td><td>XGB</td><td>0.893</td><td>8.33</td><td>7.54</td><td>8.23</td><td>7.46</td><td>9.06</td><td>8.22</td><td>137.47</td><td>11.72</td><td>10.36</td></tr><tr><td rowspan="5">Total aboveground<br>carbon</td><td>Stepwise</td><td>0.744</td><td>6.13</td><td>12.04</td><td>6.06</td><td>11.90</td><td>4.18</td><td>8.64</td><td>28.60</td><td>5.35</td><td>10.25</td></tr><tr><td>ANN</td><td>0.807</td><td>5.16</td><td>10.15</td><td>5.11</td><td>10.03</td><td>4.41</td><td>8.77</td><td>33.34</td><td>5.77</td><td>11.35</td></tr><tr><td>kNN</td><td>0.758</td><td>5.78</td><td>11.36</td><td>5.71</td><td>11.23</td><td>4.84</td><td>11.09</td><td>37.05</td><td>6.09</td><td>11.96</td></tr><tr><td>RF</td><td><strong>0.952</strong></td><td><strong>2.77</strong></td><td><strong>5.44</strong></td><td><strong>2.74</strong></td><td><strong>5.38</strong></td><td><strong>4.21</strong></td><td><strong>8.47</strong></td><td><strong>28.15</strong></td><td><strong>5.31</strong></td><td><strong>10.17</strong></td></tr><tr><td>SVR</td><td>0.719</td><td>6.42</td><td>12.62</td><td>6.20</td><td>12.18</td><td>3.99</td><td>7.63</td><td>27.76</td><td>5.27</td><td>10.10</td></tr><tr><td></td><td>XGB</td><td>0.893</td><td>3.84</td><td>7.56</td><td>3.80</td><td>7.47</td><td>4.20</td><td>8.28</td><td>29.43</td><td>5.43</td><td>10.66</td></tr><tr><td rowspan="5">Trunk biomass</td><td>Stepwise</td><td>0.780</td><td>10.39</td><td>10.42</td><td>10.15</td><td>10.18</td><td>8.97</td><td>10.48</td><td>136.20</td><td>11.67</td><td>11.59</td></tr><tr><td>ANN</td><td>0.853</td><td>8.24</td><td>8.26</td><td>8.15</td><td>8.17</td><td>8.34</td><td>8.56</td><td>130.44</td><td>11.42</td><td>11.45</td></tr><tr><td>kNN</td><td>0.782</td><td>10.03</td><td>10.06</td><td>9.92</td><td>9.94</td><td>10.09</td><td>13.91</td><td>162.93</td><td>12.76</td><td>12.80</td></tr><tr><td>RF</td><td><strong>0.959</strong></td><td><strong>4.63</strong></td><td><strong>4.64</strong></td><td><strong>4.57</strong></td><td><strong>4.59</strong></td><td><strong>8.18</strong></td><td><strong>8.90</strong></td><td><strong>116.14</strong></td><td><strong>10.78</strong></td><td><strong>10.81</strong></td></tr><tr><td>SVR</td><td>0.763</td><td>10.78</td><td>10.81</td><td>10.41</td><td>10.44</td><td>8.59</td><td>8.64</td><td>135.90</td><td>11.66</td><td>11.58</td></tr><tr><td></td><td>XGB</td><td>0.927</td><td>5.82</td><td>5.83</td><td>5.75</td><td>5.77</td><td>8.14</td><td>8.46</td><td>121.42</td><td>11.02</td><td>10.94</td></tr><tr><td rowspan="5">Trunk carbon</td><td>Stepwise</td><td>0.780</td><td>4.79</td><td>10.42</td><td>4.68</td><td>10.18</td><td>4.13</td><td>10.48</td><td>28.92</td><td>5.38</td><td>11.59</td></tr><tr><td>ANN</td><td>0.848</td><td>3.86</td><td>8.39</td><td>3.81</td><td>8.30</td><td>3.89</td><td>8.81</td><td>28.01</td><td>5.29</td><td>11.52</td></tr><tr><td>kNN</td><td>0.782</td><td>4.62</td><td>10.06</td><td>4.57</td><td>9.94</td><td>4.65</td><td>13.91</td><td>34.60</td><td>5.88</td><td>12.80</td></tr><tr><td>RF</td><td><strong>0.961</strong></td><td><strong>2.11</strong></td><td><strong>4.59</strong></td><td><strong>2.09</strong></td><td><strong>4.54</strong></td><td><strong>3.81</strong></td><td><strong>9.16</strong></td><td><strong>25.29</strong></td><td><strong>5.03</strong></td><td><strong>10.84</strong></td></tr><tr><td>SVR</td><td>0.763</td><td>4.97</td><td>10.81</td><td>4.80</td><td>10.44</td><td>3.96</td><td>8.64</td><td>28.86</td><td>5.37</td><td>11.58</td></tr><tr><td></td><td>XGB</td><td>0.932</td><td>2.58</td><td>5.61</td><td>2.55</td><td>5.54</td><td>3.73</td><td>8.37</td><td>26.31</td><td>5.13</td><td>11.16</td></tr><tr><td rowspan="5">Canopy biomass</td><td>Stepwise</td><td>0.610</td><td>2.23</td><td>20.71</td><td>2.02</td><td>18.73</td><td>2.24</td><td>25.55</td><td>9.31</td><td>3.05</td><td>28.22</td></tr><tr><td>ANN</td><td>0.609</td><td>1.90</td><td>17.60</td><td>1.88</td><td>17.40</td><td>2.18</td><td>23.29</td><td>8.24</td><td>2.87</td><td>26.62</td></tr><tr><td>kNN</td><td>0.564</td><td>2.29</td><td>21.26</td><td>2.27</td><td>21.02</td><td>2.58</td><td>27.78</td><td>9.54</td><td>3.09</td><td>28.65</td></tr><tr><td>RF</td><td>0.844</td><td>1.54</td><td>14.29</td><td>1.52</td><td>14.13</td><td>2.64</td><td>28.34</td><td>8.89</td><td>2.98</td><td>27.66</td></tr><tr><td>SVR</td><td>0.285</td><td>3.02</td><td>28.04</td><td>2.92</td><td>27.06</td><td>2.22</td><td>23.56</td><td>6.45</td><td>2.54</td><td>23.49</td></tr><tr><td></td><td>XGB</td><td><strong>0.765</strong></td><td><strong>1.47</strong></td><td><strong>13.64</strong></td><td><strong>1.45</strong></td><td><strong>13.49</strong></td><td><strong>1.81</strong></td><td><strong>18.65</strong></td><td><strong>5.39</strong></td><td><strong>2.32</strong></td><td><strong>21.46</strong></td></tr><tr><td rowspan="5">Canopy carbon</td><td>Stepwise</td><td>0.610</td><td>1.03</td><td>20.71</td><td>0.93</td><td>18.73</td><td>1.03</td><td>25.53</td><td>1.98</td><td>1.41</td><td>28.22</td></tr><tr><td>ANN</td><td>0.609</td><td>0.87</td><td>17.61</td><td>0.86</td><td>17.41</td><td>1.00</td><td>23.16</td><td>1.66</td><td>1.29</td><td>25.97</td></tr><tr><td>kNN</td><td>0.557</td><td>0.93</td><td>18.73</td><td>0.92</td><td>18.52</td><td>0.99</td><td>23.58</td><td>1.48</td><td>1.22</td><td>24.48</td></tr><tr><td>RF</td><td>0.924</td><td>0.55</td><td>11.17</td><td>0.55</td><td>11.04</td><td>0.91</td><td>21.16</td><td>1.21</td><td>1.10</td><td>22.07</td></tr><tr><td>SVR</td><td>0.416</td><td>1.26</td><td>25.34</td><td>1.07</td><td>21.44</td><td>1.01</td><td>23.77</td><td>1.54</td><td>1.24</td><td>24.92</td></tr><tr><td></td><td>XGB</td><td><strong>0.707</strong></td><td><strong>0.76</strong></td><td><strong>15.24</strong></td><td><strong>0.75</strong></td><td><strong>15.06</strong></td><td><strong>0.88</strong></td><td><strong>20.31</strong></td><td><strong>1.17</strong></td><td><strong>1.08</strong></td><td><strong>21.79</strong></td></tr></tbody></table>

Note: R² adj.: adjusted coefficient of determination; Syx: standard error of the estimate (t·ha⁻¹); Syx (%): standard error of the percentage estimate (%); RMSE: root mean square error (t·ha⁻¹); RMSE (%): root mean square error percentage (%); MAE: mean absolute error (t·ha⁻¹); MPE: mean percentage error; MSE: mean squared error (t·ha⁻¹). The bold values refer to the mean of each variable.

Abbreviations: ANN, artificial neural network; kNN, k-nearest neighbor; RF, random forest; SVR, support vector regression; XGB, extreme gradient boosting.

The modeling by biomass and carbon compartment revealed that in the total aboveground and trunk compartments, random forest was the best algorithm; in the canopy compartment, it was XGB. However, the application of the ML algorithms for estimating aerial biomass and aerial carbon in multicompartments (as in this research) of clonal plantations of *Eucalyptus* spp. in Brazil using UAV-LiDAR data is still in its infancy.

The RF algorithm combines predictions from multiple decision trees using bootstrap sampling, randomly selecting subsets of variables at each split. This reduces variance in complex predictions [14, 48], thereby mitigating the risk of overfitting and increasing accuracy [49]. [50] emphasize

that, for data obtained through remote sensing techniques, RF's high performance is due to its ability to handle high-dimensional data, such as variables derived from LiDAR data (mean height, height percentiles, point density, intensity metrics). However, its performance depends on hyperparameter settings (number of trees, mtry, minimum node size) and the validation method, since random validation can lead to overestimation in spatially correlated data. Therefore, spatial cross-validation is recommended [48].

RF has demonstrated consistency in estimating biomass from LiDAR data in forestry applications. For instance, [17] compared multiple algorithms and found that RF outperformed multiple linear regression in predicting forest
<figure><img src="image_5.png" /><figcaption>FIGURE 4: Predictive models of total aboveground biomass (a), aboveground carbon total (b), biomass trunk (c), carbon trunk (d), biomass canopy (e), and carbon canopy (f) by stepwise; artificial neural network, k-nearest neighbor, random forest, support vector regression, and extreme gradient boosting in *Eucalyptus* spp. in Itatinga, São Paulo, Brazil.</figcaption></figure>

structural variables, precisely because it can capture non-linear relationships. [49] also reported high accuracy in estimating AGB of *Eucalyptus globulus* in Peru using LiDAR data collected with a DJI Zenmuse L1. The RF model achieved an $R^2$ of 0.76 on the training set and 0.66 on the test set. In contrast, multiple linear regression had an $R^2$ of 0.647, while elastic net reached an $R^2$ of 0.58 during training and 0.57 during testing [49]. However, in complex scenarios

involving large data volumes, algorithms such as XGB have outperformed RF in terms of precision and efficiency. A recent review on forest carbon stocks indicated that, although RF is widely used (around 88% of studies), XGB performed better in 75% of direct comparisons [51].

XGB's performance is attributed to its additive learning technique, which involves a learning process adjusted according to input data, followed by adaptation to residuals
<table><caption>TABLE 6: Agreement measures of the aboveground biomass and aboveground carbon (t·ha⁻¹) models by compartment in *Eucalyptus* spp.</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Algorithm</th><th colspan="3">Total basis</th><th colspan="3">Fit</th><th colspan="3">Validation</th></tr><tr><th>A</th><th>P</th><th>CC2</th><th>Λ</th><th>A</th><th>P</th><th>CC2</th><th>Λ</th><th>A</th><th>P</th><th>CC2</th><th>Λ</th></tr></thead><tbody><tr><td rowspan="6">Total aboveground biomass</td><td>Stepwise</td><td>0.93</td><td>0.85</td><td>0.85</td><td>0.92</td><td>0.93</td><td>0.86</td><td>0.85</td><td>0.92</td><td>0.93</td><td>0.85</td><td>0.84</td><td>0.92</td></tr><tr><td>ANN</td><td>0.95</td><td>0.89</td><td>0.89</td><td>0.94</td><td>0.96</td><td>0.91</td><td>0.91</td><td>0.95</td><td>0.91</td><td>0.82</td><td>0.81</td><td>0.90</td></tr><tr><td>kNN</td><td>0.92</td><td>0.84</td><td>0.80</td><td>0.91</td><td>0.93</td><td>0.85</td><td>0.82</td><td>0.92</td><td>0.88</td><td>0.77</td><td>0.72</td><td>0.87</td></tr><tr><td>RF</td><td><strong>0.97</strong></td><td><strong>0.94</strong></td><td><strong>0.94</strong></td><td><strong>0.97</strong></td><td><strong>0.99</strong></td><td><strong>0.97</strong></td><td><strong>0.97</strong></td><td><strong>0.99</strong></td><td><strong>0.93</strong></td><td><strong>0.85</strong></td><td><strong>0.84</strong></td><td><strong>0.92</strong></td></tr><tr><td>SVR</td><td>0.93</td><td>0.86</td><td>0.86</td><td>0.92</td><td>0.93</td><td>0.86</td><td>0.85</td><td>0.92</td><td>0.93</td><td>0.86</td><td>0.86</td><td>0.93</td></tr><tr><td>XGB</td><td>0.96</td><td>0.92</td><td>0.92</td><td>0.96</td><td>0.97</td><td>0.95</td><td>0.94</td><td>0.97</td><td>0.92</td><td>0.84</td><td>0.83</td><td>0.92</td></tr><tr><td rowspan="6">Total aboveground carbon</td><td>Stepwise</td><td>0.93</td><td>0.85</td><td>0.85</td><td>0.92</td><td>0.93</td><td>0.86</td><td>0.85</td><td>0.92</td><td>0.92</td><td>0.85</td><td>0.84</td><td>0.92</td></tr><tr><td>ANN</td><td>0.94</td><td>0.88</td><td>0.87</td><td>0.94</td><td>0.95</td><td>0.90</td><td>0.89</td><td>0.95</td><td>0.91</td><td>0.82</td><td>0.80</td><td>0.90</td></tr><tr><td>kNN</td><td>0.92</td><td>0.84</td><td>0.80</td><td>0.91</td><td>0.93</td><td>0.85</td><td>0.82</td><td>0.92</td><td>0.88</td><td>0.77</td><td>0.72</td><td>0.87</td></tr><tr><td>RF</td><td><strong>0.97</strong></td><td><strong>0.95</strong></td><td><strong>0.94</strong></td><td><strong>0.97</strong></td><td><strong>0.99</strong></td><td><strong>0.97</strong></td><td><strong>0.97</strong></td><td><strong>0.99</strong></td><td><strong>0.93</strong></td><td><strong>0.85</strong></td><td><strong>0.84</strong></td><td><strong>0.92</strong></td></tr><tr><td>SVR</td><td>0.85</td><td>0.71</td><td>0.71</td><td>0.83</td><td>0.93</td><td>0.86</td><td>0.85</td><td>0.92</td><td>0.59</td><td>0.18</td><td>0.18</td><td>0.31</td></tr><tr><td>XGB</td><td>0.96</td><td>0.92</td><td>0.91</td><td>0.96</td><td>0.97</td><td>0.94</td><td>0.94</td><td>0.97</td><td>0.92</td><td>0.84</td><td>0.83</td><td>0.91</td></tr><tr><td rowspan="6">Trunk biomass</td><td>Stepwise</td><td>0.94</td><td>0.87</td><td>0.86</td><td>0.93</td><td>0.94</td><td>0.88</td><td>0.88</td><td>0.94</td><td>0.92</td><td>0.85</td><td>0.83</td><td>0.92</td></tr><tr><td>ANN</td><td>0.95</td><td>0.91</td><td>0.90</td><td>0.95</td><td>0.96</td><td>0.93</td><td>0.92</td><td>0.96</td><td>0.93</td><td>0.86</td><td>0.85</td><td>0.93</td></tr><tr><td>kNN</td><td>0.92</td><td>0.85</td><td>0.81</td><td>0.92</td><td>0.94</td><td>0.87</td><td>0.84</td><td>0.93</td><td>0.89</td><td>0.79</td><td>0.71</td><td>0.88</td></tr><tr><td>RF</td><td><strong>0.97</strong></td><td><strong>0.95</strong></td><td><strong>0.94</strong></td><td><strong>0.97</strong></td><td><strong>0.99</strong></td><td><strong>0.98</strong></td><td><strong>0.97</strong></td><td><strong>0.99</strong></td><td><strong>0.94</strong></td><td><strong>0.87</strong></td><td><strong>0.86</strong></td><td><strong>0.93</strong></td></tr><tr><td>SVR</td><td>0.94</td><td>0.87</td><td>0.87</td><td>0.93</td><td>0.94</td><td>0.88</td><td>0.88</td><td>0.94</td><td>0.93</td><td>0.86</td><td>0.85</td><td>0.93</td></tr><tr><td>XGB</td><td>0.97</td><td>0.94</td><td>0.93</td><td>0.97</td><td>0.98</td><td>0.96</td><td>0.96</td><td>0.98</td><td>0.94</td><td>0.87</td><td>0.86</td><td>0.93</td></tr><tr><td rowspan="6">Trunk carbon</td><td>Stepwise</td><td>0.94</td><td>0.87</td><td>0.86</td><td>0.93</td><td>0.94</td><td>0.88</td><td>0.88</td><td>0.94</td><td>0.92</td><td>0.85</td><td>0.83</td><td>0.92</td></tr><tr><td>ANN</td><td>0.95</td><td>0.90</td><td>0.90</td><td>0.95</td><td>0.96</td><td>0.92</td><td>0.92</td><td>0.96</td><td>0.93</td><td>0.86</td><td>0.85</td><td>0.92</td></tr><tr><td>kNN</td><td>0.92</td><td>0.85</td><td>0.81</td><td>0.92</td><td>0.94</td><td>0.87</td><td>0.84</td><td>0.93</td><td>0.89</td><td>0.79</td><td>0.71</td><td>0.88</td></tr><tr><td>RF</td><td><strong>0.97</strong></td><td><strong>0.95</strong></td><td><strong>0.94</strong></td><td><strong>0.97</strong></td><td><strong>0.99</strong></td><td><strong>0.98</strong></td><td><strong>0.97</strong></td><td><strong>0.99</strong></td><td><strong>0.94</strong></td><td><strong>0.87</strong></td><td><strong>0.86</strong></td><td><strong>0.93</strong></td></tr><tr><td>SVR</td><td>0.94</td><td>0.87</td><td>0.87</td><td>0.93</td><td>0.94</td><td>0.88</td><td>0.88</td><td>0.94</td><td>0.93</td><td>0.86</td><td>0.85</td><td>0.93</td></tr><tr><td>XGB</td><td>0.97</td><td>0.94</td><td>0.93</td><td>0.97</td><td>0.98</td><td>0.97</td><td>0.96</td><td>0.98</td><td>0.94</td><td>0.87</td><td>0.86</td><td>0.93</td></tr><tr><td rowspan="6">Canopy biomass</td><td>Stepwise</td><td>0.86</td><td>0.71</td><td>0.70</td><td>0.83</td><td>0.90</td><td>0.81</td><td>0.80</td><td>0.89</td><td>0.70</td><td>0.39</td><td>0.39</td><td>0.56</td></tr><tr><td>ANN</td><td>0.87</td><td>0.74</td><td>0.72</td><td>0.85</td><td>0.91</td><td>0.83</td><td>0.81</td><td>0.90</td><td>0.72</td><td>0.44</td><td>0.42</td><td>0.61</td></tr><tr><td>kNN</td><td>0.82</td><td>0.64</td><td>0.63</td><td>0.78</td><td>0.87</td><td>0.74</td><td>0.72</td><td>0.85</td><td>0.65</td><td>0.29</td><td>0.29</td><td>0.45</td></tr><tr><td>RF</td><td>0.88</td><td>0.76</td><td>0.73</td><td>0.86</td><td>0.94</td><td>0.88</td><td>0.85</td><td>0.94</td><td>0.65</td><td>0.29</td><td>0.28</td><td>0.45</td></tr><tr><td>SVR</td><td>0.76</td><td>0.51</td><td>0.48</td><td>0.68</td><td>0.76</td><td>0.53</td><td>0.51</td><td>0.69</td><td>0.73</td><td>0.46</td><td>0.38</td><td>0.63</td></tr><tr><td>XGB</td><td><strong>0.92</strong></td><td><strong>0.83</strong></td><td><strong>0.80</strong></td><td><strong>0.91</strong></td><td><strong>0.95</strong></td><td><strong>0.90</strong></td><td><strong>0.87</strong></td><td><strong>0.95</strong></td><td><strong>0.79</strong></td><td><strong>0.59</strong></td><td><strong>0.53</strong></td><td><strong>0.74</strong></td></tr><tr><td rowspan="6">Canopy carbon</td><td>Stepwise</td><td>0.86</td><td>0.71</td><td>0.70</td><td>0.83</td><td>0.90</td><td>0.81</td><td>0.80</td><td>0.89</td><td>0.70</td><td>0.39</td><td>0.39</td><td>0.56</td></tr><tr><td>ANN</td><td>0.87</td><td>0.75</td><td>0.73</td><td>0.85</td><td>0.91</td><td>0.83</td><td>0.81</td><td>0.91</td><td>0.72</td><td>0.44</td><td>0.42</td><td>0.61</td></tr><tr><td>kNN</td><td>0.87</td><td>0.74</td><td>0.72</td><td>0.85</td><td>0.90</td><td>0.80</td><td>0.78</td><td>0.89</td><td>0.65</td><td>0.29</td><td>0.29</td><td>0.45</td></tr><tr><td>RF</td><td>0.93</td><td>0.85</td><td>0.81</td><td>0.92</td><td>0.96</td><td>0.93</td><td>0.90</td><td>0.96</td><td>0.65</td><td>0.29</td><td>0.28</td><td>0.45</td></tr><tr><td>SVR</td><td>0.84</td><td>0.68</td><td>0.66</td><td>0.81</td><td>0.87</td><td>0.73</td><td>0.72</td><td>0.85</td><td>0.73</td><td>0.46</td><td>0.38</td><td>0.63</td></tr><tr><td>XGB</td><td><strong>0.90</strong></td><td><strong>0.80</strong></td><td><strong>0.76</strong></td><td><strong>0.89</strong></td><td><strong>0.93</strong></td><td><strong>0.87</strong></td><td><strong>0.83</strong></td><td><strong>0.93</strong></td><td><strong>0.79</strong></td><td><strong>0.59</strong></td><td><strong>0.53</strong></td><td><strong>0.74</strong></td></tr></tbody></table>

Note: Λ: coefficient of agreement; p: measure of agreement; CC2: improved concordance correlation coefficient; λ: agreement parameter. The bold values refer to the mean of each variable.

Abbreviations: ANN, artificial neural network; kNN, k-nearest neighbor; RF, random forest; SVR, support vector regression; XGB, extreme gradient boosting.

[52]. This adjustment process is repeated until stopping criteria are met. Thus, the algorithm is based on gradient-boosted decision trees, employing supervised ML with additive training strategies. This minimizes overfitting, optimizing the loss function not via first-order derivatives, but through an efficient second-order expression [52].

In forestry applications using UAV-LiDAR data, XGB can capture complex nonlinear relationships between height, point density, and intensity metrics, often out-performing RF in situations with high spatial heterogeneity or multilayered tree compartments [51]. However, its performance is strongly dependent on hyperparameter tuning (e.g., number of trees, maximum depth, learning rate) [52], requiring more computational resources than RF.

In the present study, XGB outperformed in the crown compartment (composed of leaves, live branches, and dead branches), whereas RF proved more efficient in the stem and

total compartments. XGB's ability to model complex nonlinear relationships may explain this result. These characteristics are present in the crown structure, where leaf and branch distribution creates variable height and density patterns captured by UAV-LiDAR metrics. Conversely, stem and total volume exhibit more linear relationships with UAV-LiDAR metrics, benefiting RF performance.

Studies on *Eucalyptus* plantations using UAV-LiDAR confirm these findings. For instance, [53] used XGB to estimate AGB of *Eucalyptus globoidea* and *Eucalyptus bosistoana*, employing structural crown metrics derived from LiDAR. The model achieved an *R*² of 0.903, out-performing RF and SVR. In previous studies using XGB, the combination of Landsat-8 and Sentinel-1A imagery as predictors for biomass estimation reached an *R*² of 0.75 and RMSE (%) of 38.28 in subtropical forests in China [54]. AGB in forests of Jharkhand, India, was estimated with an *R*² of
<figure><img src="image_6.png" /><figcaption>FIGURE 5: Bland–Altman plot for the predictive models of aboveground biomass and aboveground carbon in the total, trunk, and canopy compartments in *Eucalyptus* spp., generated by multiple linear regression and machine learning models. The colored bands represent the limits of agreement that encompass 95% of the data variation.</figcaption></figure>

0.83 using vegetation indices derived from Sentinel imagery [55].

Overall, ANN was the third-best performing method. While it captured nonlinear relationships, the model showed high sensitivity to data quantity. Furthermore, [56] note that factors such as the size of test and validation sets (since ANNs require large datasets), distributed parallel processing, and adaptive nonlinear learning can affect ANN performance. Some of these characteristics make ANNs “black boxes,” complicating variable interpretation and function approximation convergence [56].

According to the literature, estimating AGB and carbon in *Eucalyptus* using UAV-LiDAR and ANNs yielded results similar to those of this study, as shown by [17]. In that study, AGB of *Eucalyptus globulus* was estimated with $R^2$ ranging from 0.85 to 0.92, depending on LiDAR point density and canopy characteristics.

However, despite efficiently modeling nonlinear patterns, ANNs are often outperformed by algorithms such as XGB in compartments with high spatial heterogeneity, like the crown. This is due to XGB's superior ability to optimize the loss function and adapt to complex residuals.

As with other algorithms, there is a gap in studies using ANNs and UAV-LiDAR metrics for predicting biomass and carbon in clonal *Eucalyptus* plantations in Brazil. Using other remote sensing data, [57] applied multispectral and SAR data to estimate biomass in *Eucalyptus urophylla* × *Eucalyptus grandis* in Minas Gerais, Brazil. Data from 206 inventory plots were used, and ANN-derived models achieved an $R^2$ of 0.95 and RMSE of 2.87%, with errors ranging from -8% to 4% [57].

Results for predicting biomass and carbon in the total and stem compartments of Eucalyptus genotypes indicate that, although linear regression outperformed some ML
methods (kNN and SVR), it relies on linear relationships between biomass or carbon and predictor variables, failing to capture the complex relationships present between biomass/ carbon and UAV-LiDAR-derived variables. However, [49] note that multiple linear regression can provide a baseline for comparison with more complex methods, such as ML algorithms or nonlinear models.

In the present study, regression did not yield adequate estimates, unlike in other studies where it performed excellently in estimating biomass and carbon in *Eucalyptus* spp. In *Eucalyptus urophylla* × *Eucalyptus grandis* clones, models derived from this technique estimated carbon stock with adjusted R² above 0.98 and RMSE (%) below 12.86% [42]. In 6.5-year-old *Eucalyptus grandis*, simple linear regression models built using LiDAR metrics for carbon estimation achieved an adjusted R² of 0.97 [27].

Regarding algorithms with the highest errors, for SVR, the lack of a standardized approach to parameter optimization, as discussed by [58], may have resulted in inaccurate estimates. For kNN, optimization of the number of nearest neighbors, distance metrics, and weighting methods can improve performance [59], since the distance metric determines which training observations are closest to the target value based on predictor variables [60].

kNN showed consistent but lower performance compared to other algorithms, except SVR. This is due to its nonparametric nature and strong dependence on the choice of neighbors and distance metric. In forested regions, structural diversity directly affects neighbor proximity, which can increase variation in biomass estimates. Studies such as [54] indicate that kNN is sensitive to high-dimensional datasets and performs worse in the absence of significant spatial or structural correlation among neighboring plots.

In contrast, SVR exhibited superior fit under various conditions, corroborating the studies by [56, 61]. These studies highlighted SVR's ability to handle complex nonlinear relationships between UAV-LiDAR-derived variables and biomass. Using kernel functions, such as the radial basis function (RBF), allows SVR to detect nonlinear patterns more effectively than proximity-based methods like kNN. However, parameter calibration (C, ε, and γ) is essential and computationally expensive. Additionally, SVR is less intuitive and more restricted in interpretability compared to neighborhood-based methods.

Furthermore, some limitations of this study should be considered, such as the Zenmuse L1 sensor used. Sensor type, particularly pulse rate and return type, affects canopy penetration, point density, and flight altitude [21]. These factors may impact biomass estimation in very dense or heterogeneous crowns. Model performance may also be influenced by spatial heterogeneity of plots and limited data quantity.

Although conventional ML methods (RF, XGB, ANN, SVR, and kNN) were appropriate for this experimental study with a limited number of plots, recent literature indicates that modern deep learning (DL) techniques are more efficient for estimating AGB in complex scenarios with multimodal data. For instance, [62] applied adapted 3D convolutional neural networks (Minkowski CNNs) directly

to airborne LiDAR point clouds to estimate biomass and wood volume, observing considerable improvements over traditional statistical methods.

Similarly, [63] implemented ForestIQNet, a multimodal architecture integrating UAV multispectral images and 3D CHM models in a CNN-Transformer framework. This model achieved an R² above 0.89, compared to R² values of 0.78-0.82 obtained by RF and XGB. Reference [64] employed an attention-based DL model combined with GEDI, Sentinel-1, PALSAR-2, and Sentinel-2 data. This model outperformed RF in AGB estimation, with lower RMSE and improved spatial representation.

Therefore, it is recommended that future studies integrate additional LiDAR acquisition platforms, such as terrestrial laser scanning (TLS) and high-resolution UAV orthophotos. In this context, [17] showed that fusing airborne LiDAR and multispectral data allows for more accurate vegetation distribution, enhancing biomass estimation. Reference [22] proposed an automated framework for extracting individual tree attributes using high-density UAV-LiDAR, indicating that combining it with orthophotos increases inventory and monitoring accuracy. Complementarily, [65] used aerial digital camera and UAV-LiDAR fusion, demonstrating that integrating different data sources provides advantages for biomass estimation, suggesting potential for more detailed analyses across multiple tree compartments.

Finally, it should be emphasized that the use of the ABA approach applied in this research to *Eucalyptus* spp. plantations should consider the formation of strata (same species and planting date) and destructive sampling of AGB alongside seasonal UAV-LiDAR flights to obtain the metrics. It is recommended to follow a flight schedule, starting after planting and subsequently on an annual basis. With the proper application of these data in predictive models of AGB and carbon, the results generated can support decisions for monitoring the stock of these areas, as well as identifying their growth rate and role in addressing the climate emergency.

## 5. Conclusions

The RF algorithm was chosen from the modeling methods tested for quantifying AGB and aboveground carbon in different compartments (total, trunk, and canopy) of *Eucalyptus* spp. genotypes using UAV-LiDAR data to predict the total AGB (with mean of 110.97 t·ha⁻¹ at 4.5 years old) and total carbon (with mean of 51.16 t·ha⁻¹), as well as trunk biomass (with mean of 100.46 t·ha⁻¹) and trunk carbon (mean of 46.34 t·ha⁻¹). However, XGB was superior to the others for the canopy compartment, with mean of 10.72 t·ha⁻¹ of canopy biomass and 4.96 t·ha⁻¹ in mean of canopy carbon.

These results reinforce the effectiveness of UAV-LiDAR data associated with advanced modeling methods, such as ML algorithms, for quantifying AGB and carbon stocks in clonal *Eucalyptus* spp. plantations. The use of these tools can provide strategic information for sustainable management of forest resources. It is recommended to integrate this approach, considering the variability between genotypes, growth conditions, and plantation ages, in addition to
considering the seasonality of UAV-LiDAR data and dif-
ferent techniques for extracting UAV-LiDAR metrics,
aming to increase the accuracy of biomass and carbon
estimates to assist in decision-making related to climate
change mitigation.

Finally, for monitoring carbon stocks using UAV-LiDAR
data, it is recommended to establish a flight schedule—initially
postplanting and thereafter on an annual basis—integrated
with targeted field sampling to enhance the robustness of ML
models.

Data Availability Statement

Research data are not shared.

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

Conceptualization: Carla Talita Pertille, Alexandre Behling, Carlos Roberto Sanquetta, Otávio Camargo Campoe, Guerric Le Maire, Joannès Guillemot, Geovanni Malatesta Barros, Emerson Roberto Schoeninger, and Ana Paula Dalla Corte; methodology: Carla Talita Pertille, Alexandre Behling, Carlos Roberto Sanquetta, Otávio Camargo Campoe, Guerric Le Maire, Joannès Guillemot, Geovanni Malatesta Barros, Emerson Roberto Schoeninger, and Ana Paula Dalla Corte; software: Carla Talita Pertille, Alexandre Behling, and Ana Paula Dalla Corte; validation: Otávio Camargo Campoe, Guerric Le Maire, Joannès Guillemot, Geovanni Malatesta Barros, and Ana Paula Dalla Corte; formal analysis: Carla Talita Pertille, Alexandre Behling, and Ana Paula Dalla Corte; investigation: Carla Talita Pertille; resources: Otávio Camargo Campoe, Guerric Le Maire, Joannès Guillemot, Geovanni Malatesta Barros, Emerson Roberto Schoeninger; data curation: Carla Talita Pertille; writing—original draft preparation: Carla Talita Pertille, Alexandre Behling, Otávio Camargo Campoe, Guerric Le Maire, Joannès Guillemot, Geovanni Malatesta Barros, Emerson Roberto Schoeninger, and Ana Paula Dalla Corte; writing—review and editing: Carla Talita Pertille and Ana Paula Dalla Corte; visualization: Carla Talita Pertille; supervision: Ana Paula Dalla Corte; project administration: Otávio Camargo Campoe, Guerric Le Maire, Joannès Guillemot, Geovanni Malatesta Barros, Emerson Roberto Schoeninger.

Funding

This study was funded by the Coordination for the Im-
provement of Higher Education Personnel (Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior—CAPES)—
Finance Code 001.

Acknowledgments

The authors would like to thank the Bracell company for the
technical support and for providing the necessary resources
to perform the UAV-LiDAR flight and to the EUCFLUX

Cooperative Research Program, coordinated by the CIRAD (Le Centre de coopération Internationale en Recherche Agronomique pour le Développement, France) and IPEF (Forestry Science and Research Institute, Brazil).

References

[1] Indústria Brasileira de Árvores, *Relatório Anual IBÁ 2024* (São Paulo, 2023), https://iba.org/datafiles/publicacoes/relatorios/relatorio2024.pdf.

[2] Food and Agriculture Organization of the United Nations, "Global Forest Resources Assessment (FRA)," (2020), https://www.fao.org/3/ca9825en/ca9825en.pdf.

[3] R. R. Roubuste, G. Valcorte, N. H. Bianchini, and K. M. O. de Souza, “Mudanças Climáticas e o Mercado de Carbono,” *Iheringia* 77 (2022).

[4] Y. Zhang, J. Ma, S. Liang, X. Li, and J. Liu, “A Stacking Ensemble Algorithm for Improving the Biases of Forest Above-ground Biomass Estimations From Multiple Remotely Sensed Datasets,” *GIScience and Remote Sensing* 59, no. 1 (2022): 234–249, https://doi.org/10.1080/15481603.2021.2023842.

[5] Intergovernmental Panel on Climate Change, “IPCC Guidelines for National Greenhouse Gases Inventories,” (2006), https://www.ipcc-nggip.iges.or.jp/public/2006gl/.

[6] Intergovernmental Panel on Climate Change, “Climate Change 2013: The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,” (2014), https://www.ipcc.ch/report/ar5/wg1/.

[7] F. M. B. Romero, L. A. G. Jacovine, S. C. Ribeiro, et al., “Allometric Equations for Volume, Biomass, and Carbon in Commercial Stems Harvested in a Managed Forest in the Southwestern Amazon: A Case Study,” *Forests* 11, no. 8 (2020): 874, https://doi.org/10.3390/f11080874.

[8] E. D. G. da Silva, A. R. Alves, A. C. de Holanda, R. M. Bezerra, and A. S. Magalhães, “Biomassa e Nutrientes na Parte Aérea de Espécies Arbóreas da Caatinga, Rio Grande do Norte,” *Nativa* 10, no. 1 (2022): 131–137, https://doi.org/10.31413/nativa.v10i1.13093.

[9] W. Xu, Y. Cheng, M. Luo, et al., “Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review,” *Forests* 16, no. 3 (2025): 449, https://doi.org/10.3390/f16030449.

[10] A. P. Corte, E. N. Broadbent, L. H. Santos, et al., *Aplicações do Lidar Para o Inventário Florestal—Enfoque Unidade de Área* (Ponta Grossa: Editora Atena, 2022).

[11] C. Wang, X. Yang, X. Xi, S. Nie, and P. Dong, “Chapter 2: LiDAR Remote Sensing Principles,” in *Introduction to LiDAR Remote Sensing* (Taylor & Francis, 2024).

[12] J. C. White, M. A. Wulder, A. Varhola, et al., “A Best Practices Guide for Generating Forest Inventory Attributes From Airborne Laser Scanning Data Using an Area-Based Approach,” *The Forestry Chronicle* 89, no. 06 (2013): 722–723, https://doi.org/10.5558/tfc2013-132.

[13] J.-R. Roussel, D. Auty, N. C. Coops, et al., “lidR: An R Package for Analysis of Airborne Laser Scanning (ALS) Data,” *Remote Sensing of Environment* 251 (2020): 112061, https://doi.org/10.1016/j.rse.2020.112061.

[14] L. Torre-Tojal, A. Bastarrika, A. Boyano, J. M. Lopez-Guede, and M. Graña, “Above-Ground Biomass Estimation From LiDAR Data Using Random Forest Algorithms,” *Journal of Computational Science* 58 (2022): 101517, https://doi.org/10.1016/j.jocs.2021.101517.
[15] Y. Liu, P. Lei, Q. You, et al., "Individual Tree Aboveground Biomass Estimation Based on UAV Stereo Images in a Eucalyptus Plantation," *Forests* 14, no. 9 (2023): 1748, https://doi.org/10.3390/f14091748.

[16] A. Balazs, E. Liski, S. Tuominen, and A. Kangas, "Comparison of Neural Networks and K-Nearest Neighbors Methods in Forest Stand Variable Estimation Using Airborne Laser Data," *ISPRS Open Journal of Photogrammetry and Remote Sensing* 4 (2022): 100012, https://doi.org/10.1016/j.ophoto.2022.100012.

[17] Y. Li, R. Wang, W. Shi, Q. Yu, X. Li, and X. Chen, "Research on Accurate Estimation Method of Eucalyptus Biomass Based on Airborne LiDAR Data and Aerial Images," *Sustainability* 14, no. 17 (2022): 10576-18, https://doi.org/10.3390/su141710576.

[18] Y. Zhang and J. Liu, "Estimating Forest Aboveground Biomass Using Temporal Features Extracted From Multiple Satellite Data Products and Ensemble Machine Learning Algorithm," *Geocarto International* 38, no. 1 (2023): 2153930, https://doi.org/10.1080/10106049.2022.2153930.

[19] A. A. Borsah, M. Nazeer, and M. S. Wong, "LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Altimetric Equations," *Forests* 14, no. 10 (2023): 2095, https://doi.org/10.3390/f14102095.

[20] T. Ma, C. Zhang, L. Ji, et al., "Development of Forest Aboveground Biomass Estimation, its Problems and Future Solutions: A Review," *Ecological Indicators* 159 (2024): 111653, https://doi.org/10.1016/j.ecolind.2024.111653.

[21] L. G. Poley and G. J. McDermid, "A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems," *Remote Sensing* 12, no. 7 (2020): 1052.

[22] M. Zhou, C. Li, and Z. Li, "Extraction of Individual Tree Attributes Using Ultra-High-Density Point Clouds Acquired by Low-Cost UAV-LiDAR in Eucalyptus Plantations," *Annals of Forest Science* 82, no. 1 (2025): 20, https://doi.org/10.1186/s13595-025-01291-w.

[23] "EUCFLUX-Fluxos de Carbono e Água em Eucalyptus," https://www.ipef.br/eucflux2/.

[24] C. A. Alvares, J. L. Stape, P. C. Sentelhas, J. L. de Moraes Gonçalves, and G. Sparovek, "Köppen's Climate Classification Map for Brazil," *Meteorologische Zeitschrift* 22, no. 6 (2013): 711-728, https://doi.org/10.1127/0941-2948/2013/0507.

[25] Empresa Brasileira De Pesquisa Agropecuária-Embrapa, *Sistema Brasileiro de Classificação De solos*, 2rd ed. (Embrapa Solos, 2006).

[26] M. Sbardella, *Capacidade de brotação e crescimento de espécies de Eucalyptus spp. em sistema de alto fuste e talhadia. Dissertação (Mestrado em Ciência Florestal) - Faculdade de Ciências Agronômicas da UNESP (Universidade Estadual Paulista Júlio de Mesquita Filho, Botucatu, 2021).*

[27] H. F. Silva, S. C. Ribeiro, S. A. Botelho, R. A. V. B. Faria, M. B. R. Teixeira, and J. M. Mello, "Estimativa do Estoque de Carbono por Métodos Indiretos em Área de Restauração Florestal em Minas Gerais," *Science Forum* 43, no. 108 (2015): 136-146, https://doi.org/10.18671/scifor.v43n108.18.

[28] "DJI. Zenmuse L1," https://enterprise.dji.com/pt-br/zenmuse-l1.

[29] R Core Team, *R: A Language and Environment for Statistical Computing* (R Foundation for Statistical Computing), https://www.R-project.org.

[30] M. Mohan, R. V. Leite, E. N. Broadbent, et al., "Individual Tree Detection Using UAV-Lidar and UAV-SfM Data: A Tutorial

for Beginners," *Open Geosciences* 13, no. 1 (2021): 1028-1039, https://doi.org/10.1515/geo-2020-0290.

[31] M. Kuhn, "Building Predictive Models in R Using the Caret Package," *Journal of Statistical Software* 28, no. 5 (2008): 1-26, https://doi.org/10.18637/jss.v028.i05.

[32] S. S. Shapiro and M. B. Wilk, "An Analysis of Variance Test for Normality (Complete Samples)," *Biometrika* 52, no. 3-4 (1965): 591-611, https://doi.org/10.1093/biomet/52.3.4-591.

[33] W. N. Venables and B. D. Ripley, *Modern Applied Statistics* (Springer, 2002).

[34] W. S. Robinson, "The Statistical Measurement of Agreement," *American Sociological Review* 22, no. 1 (1957): 17-25, https://doi.org/10.2307/2088760.

[35] S. Huang, M. Zaichkowsky, D. Weeks, et al., "Method Comparison and Method Calibration Applicable to Forest Measurements and Model Predictions," *Alberta Agriculture and Forestry* (2019): 132.

[36] P. W. Mielke, "34 Meteorological Applications of Permutation Techniques Based on Distance Functions," *Handbook of Statistics*, ed. P. R. Krishnaiah and P. K. Sen (Amsterdam, The Netherlands: Elsevier, 1984), 813-830, https://doi.org/10.1016/s0169-7161(84)04036-0.

[37] L. I. K. Lin and K. A. Lin, "A Concordance Correlation Coefficient to Evaluate Reproducibility," *Biometrics* 45, no. 1 (1989): 255-268, https://doi.org/10.2307/2532051.

[38] G. Duveiller, D. Fasbender, and M. Meroni, "Revisiting the Concept of a Symmetric Index of Agreement for Continuous Datasets," *Scientific Reports* 6, no. 1 (2016): 19401, https://doi.org/10.1038/srep19401.

[39] J. M. Bland and D. G. Altman, "Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement," *Lancet* 1, no. 8476 (1986): 307-310.

[40] D. Datta, "Blandr: A Bland-Altman Method Comparison Package for R".

[41] D. Olemberg and A. M. Lupi, "Economic Analysis of Eucalyptus Biomass Cultivation for Energy in Argentina," *Cleaner and Circular Bioeconomy* 8 (2024): 100085, https://doi.org/10.1016/j.clcb.2024.100085.

[42] F. P. Ribeiro, T. R. de Sousa, F. R. Silva, et al., "Individual Carbon Modeling in Eucalyptus Stands in the Cerrado Region," *Forests* 15, no. 8 (2024): 1332-13, https://doi.org/10.3390/f15081332.

[43] J. J. N. Basílio, O. C. Campoe, T. B. Queiroz, et al., "Fine Root Density Dynamics and Carbon Stock of Eucalyptus Spp.: Interplay of Age, Genotype, and Edaphoclimatic Conditions," *Plants* 13, no. 11 (2024): 1503-1515, https://doi.org/10.3390/plants13111503.

[44] K. F. Santos, M. V. Schumacher, A. A. Ludvichak, and E. F. De Araújo, "Biomass and Stock of Nutrients in Different Genotypes of Eucalypts in Southern Brazil," *Journal of Experimental Agriculture International* 34 (2019): 1-12, https://doi.org/10.9734/jeai/2019/v34i430178.

[45] J. Schäfer, L. Winiwarter, H. Weiser, et al., "Assessing the Potential of Synthetic and Ex Situ Airborne Laser Scanning and Ground Plot Data to Train Forest Biomass Models," *Forestry: International Journal of Financial Research* 97, no. 4 (2024): 512-530, https://doi.org/10.1093/forestry/cpad061.

[46] P. Packalen, J. Strunk, M. Maltamo, and M. Myllymäki, "Circular or Square Plots in ALS-Based Forest Inventories—Does it Matter?" *Forestry: International Journal of Financial Research* 96, no. 1 (2023): 49-61, https://doi.org/10.1093/forestry/cpac032.

[47] F. L. Cunha, E. M. Nieri, M. G. Dias, L. A. Melo, and N. Venturin, "Uso de Adubos de Liberação Lenta no
Crescimento Inicial de Clones de Eucalipto," *Ciência Florestal* 33, no. 2 (2023): e69118, https://doi.org/10.5902/1980509869118.

[48] L. Breiman, "Random Forests," *Machine Learning* 45, no. 1 (2001): 5-32, https://doi.org/10.1023/a:1010933404324.

[49] L. E. Pinedo, K. Ortega Quispe, D. Ccopi Trucios, et al., "Estimation of Height and Aerial Biomass in Eucalyptus Globulus Plantations Using UAV-LiDAR," *Trees, Forests and People* 19 (2025): 100763, https://doi.org/10.1016/j.tfp.2024.100763.

[50] M. Belgiu and L. Drăguț, "Random Forest in Remote Sensing: A Review of Applications and Future Directions," *ISPRS Journal of Photogrammetry and Remote Sensing* 114 (2016): 24-31, https://doi.org/10.1016/j.isprsjprs.2016.01.011.

[51] A. Nguyen and S. Saha, "Machine Learning and Multi-Source Remote Sensing in Forest Carbon Stock Estimation: A Review" (2024).

[52] T. Chen and C. Guestrin, "Xgboost: A Scalable Tree Boosting System," in *Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining* (2016), 785-794, https://doi.org/10.1145/2939672.2939785.

[53] N. Ye, E. Mason, C. Xu, and J. Morgenroth, "Estimating Individual Tree DBH and Biomass of Durable Eucalyptus Using UAV LiDAR," *Ecological Informatics* 89 (2025): 103169, https://doi.org/10.1016/j.ecoinf.2025.103169.

[54] Y. Li, M. Li, C. Li, and Z. Liu, "Forest Aboveground Biomass Estimation Using Landsat 8 and Sentinel-1A Data With Machine Learning Algorithms," *Scientific Reports* 10, no. 1 (2020): 9952, https://doi.org/10.1038/s41598-020-67024-3.

[55] K. Anandita, A. K. Sinha, and C. Jeganathan, "Machine Learning Approach to Biomass Estimation: Integrating Satellite and Ground Data in Sal Forests of Jharkhand," *Journal of the Indian Society of Remote Sensing* 53, no. 4 (2024): 1137-1153, https://doi.org/10.1007/s12524-024-02061-3.

[56] D. Lu, Q. Chen, G. Wang, L. Liu, G. Li, and E. Moran, "A Survey of Remote Sensing-Based Aboveground Biomass Estimation Methods in Forest Ecosystems," *International Journal of Digital Earth* 9, no. 1 (2016): 63-105, https://doi.org/10.1080/17538947.2014.990526.

[57] G. F. Domingues, V. P. Soares, H. G. Leite, et al., "Artificial Neural Networks on Integrated Multispectral and SAR Data for High-Performance Prediction of Eucalyptus Biomass," *Computers and Electronics in Agriculture* 168 (2020): 105089, https://doi.org/10.1016/j.compag.2019.105089.

[58] V. Cherkassky and Y. Ma, "Practical Selection of SVM Parameters and Noise Estimation for SVM Regression," *Neural Networks* 17, no. 1 (2004): 113-126, https://doi.org/10.1016/S0893-6080(03)00169-2.

[59] R. E. McRoberts, E. Næsset, and T. Gobakken, "Optimizing the K-Nearest Neighbors Technique for Estimating Forest Aboveground Biomass Using Airborne Laser Scanning Data," *Remote Sensing of Environment* 163 (2015): 13-22, https://doi.org/10.1016/j.rse.2015.02.026.

[60] D. N. Cosenza, L. Korhonen, M. Maltamo, et al., "Comparison of Linear Regression, K-Nearest Neighbour and Random Forest Methods in Airborne Laser-Scanning-Based Prediction of Growing Stock," *Forestry: International Journal of Financial Research* 94, no. 2 (2021): 311-323, https://doi.org/10.1093/forestry/cpa034.

[61] L. Gao and X. Zhang, "Above-Ground Biomass Estimation of Plantation With Complex Forest Stand Structure Using Multiple Features From Airborne Laser Scanning Point Cloud

Data," *Forests* 12, no. 12 (2021): 1713, https://doi.org/10.3390/f12121713.

[62] S. Oehmcke, L. Li, J. C. Revenga, et al., "Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass," in *Proceedings of the 30th International Conference on Advances in Geographic Information Systems*, 38 (2022), 1-4, https://doi.org/10.1145/3557915.3561471.

[63] F. Safarov, U. Khojamuratova, M. Komoliddin, X. Ibragim Ismailovich, and Y. I. Cho, "A Multimodal Deep Learning Framework for Accurate Biomass and Carbon Sequestration Estimation From UAV Imagery," *Drones* 9, no. 7 (2025): 496, https://doi.org/10.3390/drones9070496.

[64] W. Dong, E. T. A. Mitchard, H. Yu, S. Hancock, and C. M. Ryan, "Forest Aboveground Biomass Estimation Using GEDI and Earth Observation Data Through Attention-Based Deep Learning" (2023).

[65] R. Huang, W. Yao, Z. Xu, L. Cao, and X. Shen, "Information Fusion Approach for Biomass Estimation in a Plateau Mountainous Forest Using a Synergistic System Comprising UAS-Based Digital Camera and LiDAR," *Computers and Electronics in Agriculture* 202 (2022): 107420, https://doi.org/10.1016/j.compag.2022.107420.