LiDAR and UAV Photogrammetry for Three-Dimensional Canopy Reconstruction: A Comparative Study for Precision Agriculture Under Mediterranean Conditions

1. Introduction

Agriculture is currently undergoing a profound technological transformation driven by the need to increase productivity while reducing environmental impacts and optimizing the use of natural resources. Climate change, water scarcity, and the growing global demand for food require agricultural systems capable of ensuring both efficiency and sustainability. In this context, digital technologies are playing a central role in the transition toward data-driven farming systems, enabling more precise, adaptive, and site-specific crop management. Precision agriculture has emerged as a key paradigm supporting this transition through the integration of advanced sensing technologies, geospatial analysis, and decision-support systems [1,2,3,4,5,6,7,8,9,10].

The development of precision agriculture has been strongly supported by the adoption of remote sensing technologies, which enable high-resolution spatial monitoring of crop conditions and environmental variability [11,12,13]. Among these technologies, unmanned aerial vehicles (UAVs) have gained widespread use due to their operational flexibility, relatively low cost, and ability to acquire ultra-high-resolution imagery at field scale [14,15,16]. UAV platforms equipped with RGB, multispectral, or hyperspectral sensors allow the generation of orthomosaics, digital surface models, vegetation maps, and canopy height models, supporting crop monitoring, phenotyping, and spatial variability analysis. Vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red-edge index (NDRE) are widely used to assess plant vigor, chlorophyll content, and stress conditions, enabling targeted agronomic interventions [17,18,19].

UAV photogrammetry based on Structure-from-Motion (SfM) algorithms has therefore become an important tool for reconstructing canopy surfaces and describing spatial variability at field scale. This approach is particularly useful for rapid and cost-effective monitoring, especially when the objective is to map external canopy geometry, crop cover, plant height variability, or vegetation vigor. However, photogrammetric reconstruction is based on passive optical imagery and is therefore strongly influenced by visibility, image texture, illumination conditions, and canopy occlusion. As a result, UAV photogrammetry mainly represents the upper and external canopy surface and may provide incomplete descriptions of internal canopy architecture, particularly in dense, multilayered, or structurally complex vegetation [20,21].

The structural characterization of plant canopies is essential for understanding crop growth dynamics and improving management strategies. Parameters such as canopy height, canopy volume, and vegetation density directly influence key physiological processes, including light interception, transpiration, microclimate regulation, and biomass accumulation, and are closely linked to crop productivity [22,23]. In Mediterranean agricultural environments, where crops are frequently exposed to drought, high solar radiation, heterogeneous soils, and irregular canopy development, accurate structural information is particularly relevant for irrigation scheduling, biomass estimation, pruning management, yield prediction, and decision-support applications. However, traditional field-based measurements of these traits are labor-intensive, time-consuming, and limited in spatial representativeness, highlighting the need for non-destructive and scalable monitoring techniques.

In this context, Light Detection and Ranging (LiDAR) technology has emerged as a powerful tool for three-dimensional vegetation analysis. LiDAR systems actively emit laser pulses and measure the return time of reflected signals, enabling the acquisition of dense and accurate point clouds describing vegetation structure [24,25]. Unlike passive optical sensors, LiDAR can partially penetrate vegetation canopies and capture multiple returns from different canopy layers, allowing the reconstruction of both external and internal plant structures [26,27]. This capability makes LiDAR particularly suitable for estimating canopy height, canopy volume, vegetation density, and vertical structural heterogeneity, especially when combined with voxel-based modelling approaches.

Recent advances in LiDAR technology have expanded its applications in agricultural environments, including terrestrial laser scanning (TLS), airborne laser scanning (ALS), mobile laser scanning (MLS), and UAV-mounted LiDAR systems [28,29]. UAV-LiDAR systems, in particular, combine high spatial resolution with operational flexibility, enabling detailed three-dimensional canopy analysis at field scale [30,31]. Previous studies have demonstrated strong correlations between LiDAR-derived metrics and field-measured crop parameters, with coefficients of determination often exceeding 0.88, confirming the suitability of this technology for non-destructive crop monitoring [32]. Moreover, LiDAR-derived structural metrics have been shown to outperform photogrammetric approaches in canopy height and volume estimation, especially under dense vegetation conditions where occlusion limits optical reconstruction [33].

Although LiDAR provides more reliable information for three-dimensional canopy characterization, UAV photogrammetry remains highly valuable in precision agriculture because it offers broader spatial coverage, lower operational cost, simpler acquisition workflows, and useful information on external canopy morphology and spectral variability. Therefore, these two approaches should not be considered as competing alternatives only, but rather as complementary sensing technologies. UAV photogrammetry can efficiently support large-scale mapping of canopy surface and crop variability, whereas LiDAR can provide more accurate structural information, including internal canopy organization and volumetric traits. Their integration may thus improve the reliability of crop monitoring systems and support more robust decision-making in heterogeneous Mediterranean agroecosystems.

Despite these advances, comparative analyses between LiDAR-derived voxel models and UAV photogrammetric reconstructions remain limited, especially under Mediterranean conditions and for vegetation types characterized by contrasting canopy architectures. This gap limits the definition of operational criteria for selecting the most appropriate sensing technology according to crop structure, monitoring objective, spatial scale, and required level of structural detail.

Within this context, this study aims to reconstruct three-dimensional canopy models, estimate key structural parameters, including canopy height, canopy volume, and vegetation density, compare LiDAR and UAV photogrammetric approaches, and assess their suitability for precision agriculture applications. The analysis focuses on two species with contrasting canopy architectures, Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris. Moringa oleifera represents an emerging nutraceutical crop of increasing interest in Mediterranean environments, characterized by a relatively open and regular canopy, whereas Ficus macrophylla was included as a structural benchmark to test sensor performance under highly complex canopy conditions.

Unlike previous studies that have analyzed LiDAR or photogrammetry independently, this research provides a systematic comparative evaluation of both approaches and introduces a voxel-based framework for quantifying canopy structure. The central hypothesis is that LiDAR and UAV photogrammetry provide complementary information for canopy monitoring, but LiDAR is more reliable for detailed three-dimensional structural characterization and voxel-based internal canopy analysis, particularly under complex Mediterranean canopy conditions. By identifying the strengths and limitations of each method, this study contributes to defining optimal sensing strategies for precision agriculture and supports the development of multi-sensor monitoring frameworks for more efficient, sustainable, and data-driven crop management.

2. Materials and Methods

The methodological framework adopted in this study integrated LiDAR sensing, UAV photogrammetry, and voxel-based three-dimensional modelling to characterize canopy architecture and spatial variability under Mediterranean conditions. The workflow, illustrated in Figure 1, was designed to compare active and passive remote sensing approaches for estimating canopy height, canopy volume, and vegetation density distribution. LiDAR-derived point clouds were used to reconstruct detailed three-dimensional canopy structures, including internal vegetation organization, whereas UAV photogrammetry was used to generate orthomosaics, dense point clouds, digital surface models, and canopy height models through Structure-from-Motion processing. The outputs from both approaches were compared with ground-based measurements to evaluate their accuracy, complementarity, and suitability for precision agriculture applications. Statistical indicators, including RMSE, MAE, R², and relative error, were used to assess the performance of the proposed multi-sensor workflow (Figure 1).

Figure 1 illustrates the integrated workflow adopted in this study for canopy structural analysis using LiDAR sensing and UAV photogrammetry. The workflow combines LiDAR-derived point cloud acquisition and preprocessing with UAV-based RGB and multispectral image acquisition and Structure-from-Motion photogrammetric processing. The resulting datasets are used to extract key canopy structural parameters, including canopy height, canopy volume, and vegetation density distribution.

These outputs support several precision agriculture applications, such as crop monitoring, canopy management, biomass estimation, yield prediction, irrigation optimization, and plant health assessment.

2.1. Study Area

The study was conducted in Palermo, Sicily, Italy, under Mediterranean environmental conditions (Figure 2). The first experimental site was located at the experimental farm of the University of Palermo, Department of Agricultural, Food and Forestry Sciences, within the Fossa della Garofala agricultural area (38°06′26″ N, 13°20′56″ E; approximately 30 m a.s.l.). The site is characterized by a Mediterranean climate, classified as Csa according to the Köppen–Geiger system, with mild and wet winters and hot, dry summers. Average annual precipitation ranges between 400 and 500 mm and is mainly concentrated during autumn and winter. The summer period is typically characterized by prolonged drought and high temperatures, which strongly influence plant growth dynamics and water availability.

The experimental field covered approximately 410 m² and had relatively flat topography. The plantation consisted of Moringa oleifera Lam. trees arranged with a spacing of 3 m between rows and 1 m between plants within the row. The field was equipped with a micro-irrigation system used to provide supplemental irrigation during the dry season.

A second case study was included to evaluate sensor performance under conditions of high canopy complexity. This consisted of a mature specimen of Ficus macrophylla subsp. columnaris located in Piazza Marina, Palermo, Italy. The tree reaches approximately 21 m in height and has an extensive and complex canopy, with a trunk circumference of approximately 36 m. Although Ficus macrophylla is not an agricultural crop, it was included as a structural benchmark to test the robustness of LiDAR and UAV photogrammetry under highly complex canopy conditions. This comparison allowed sensor performance to be evaluated across contrasting canopy architectures, from the relatively open and regular structure of Moringa oleifera to a dense, multilayered, and highly heterogeneous tree canopy.

The inclusion of these two contrasting case studies allowed a comprehensive evaluation of LiDAR and UAV photogrammetric approaches under different structural conditions representative of Mediterranean environments. This experimental design was intended to assess the complementarity of UAV photogrammetry for external canopy mapping and LiDAR sensing for detailed three-dimensional structural characterization.

2.2. Plant Material and Sampling Design

Two plant types with contrasting canopy architectures were considered in this study. Moringa oleifera Lam., a fast-growing tree species belonging to the family Moringaceae, was selected as an emerging nutraceutical crop adapted to semi-arid and Mediterranean environments. Native to the sub-Himalayan regions of northern India, the species is now widely cultivated in tropical and subtropical areas due to its high nutritional value, rapid biomass production, and adaptability to water-limited conditions [8,9,10,34,35]. Under favorable conditions, M. oleifera can reach heights between 5 and 15 m, although regular pruning is commonly applied to control plant architecture and facilitate harvesting. Its canopy is relatively open and composed of compound tripinnate leaves, producing a light and discontinuous structure that is suitable for precision agriculture monitoring.

Ficus macrophylla subsp. columnaris was included as a structurally complex tree case study. This species is characterized by dense foliage, numerous aerial roots, and a multilayered canopy architecture that forms an intricate three-dimensional structure. Although it is not an agricultural crop, F. macrophylla was used as a structural benchmark to evaluate the robustness of LiDAR sensing and UAV photogrammetry under conditions of strong canopy occlusion and high architectural complexity.

For the Moringa oleifera field, a total of 120 plants were sampled for ground-truth validation. Sample plants were selected to represent the variability observed in the field in terms of plant height, canopy development, and row position. For each sampled plant, canopy height and canopy width were measured in the field and used as reference data for validating LiDAR- and UAV-derived structural metrics. For Ficus macrophylla, the analysis was performed on the whole canopy structure as a descriptive structural benchmark, without replicated agronomic sampling.

2.3. LiDAR Data Acquisition

LiDAR surveys were conducted using a Hovermap ST-X LiDAR sensor (Emesent Pty Ltd., Brisbane, Australia), operated as a mobile laser scanning (MLS) system equipped with simultaneous localization and mapping (SLAM) technology. The system integrates a multi-beam laser scanner, an inertial measurement unit (IMU), a GNSS receiver, and an onboard processing unit, allowing real-time estimation of the sensor position and orientation during data acquisition.

The LiDAR sensor operates at a wavelength of approximately 905 nm, with a scanning frequency of 200–300 kHz and a measurement range of up to 100 m. The acquisition generated point cloud densities higher than 200 points m⁻², enabling detailed reconstruction of vegetation structure. These technical specifications allowed the capture of canopy elements such as stems, branches, foliage layers, and internal structural discontinuities.

During data acquisition, the LiDAR system continuously emitted laser pulses toward the surrounding vegetation while recording the return signals reflected by canopy surfaces. The SLAM algorithm simultaneously estimated the trajectory of the sensor and generated a three-dimensional point cloud of the scanned environment.

LiDAR acquisition was carried out under stable weather conditions, with low wind speed and adequate visibility, in order to minimize canopy movement and reduce noise in the point cloud. During the survey, the operator followed predefined scanning trajectories around the target vegetation. For the Moringa oleifera plantation, the acquisition trajectory consisted of walking along the inter-row spaces and around the external perimeter of the experimental field, maintaining an approximate scanning distance of 1.5 m from the plants. For Ficus macrophylla subsp. columnaris, the scanner was moved along a circular or semi-circular trajectory around the tree, maintaining an approximate distance of 6 m from the trunk and canopy projection.

This acquisition strategy allowed canopy information to be collected from multiple viewing angles, improving the completeness of the reconstructed point clouds and reducing occlusion effects. The resulting LiDAR datasets were used for three-dimensional canopy reconstruction, canopy height estimation, voxel-based canopy volume calculation, and vegetation density analysis.

2.4. UAV Photogrammetric Survey

To compare LiDAR-derived structural models with photogrammetric reconstruction, aerial imagery was acquired using a multirotor UAV platform equipped with a high-resolution RGB camera. The UAV survey was designed to obtain overlapping images suitable for Structure-from-Motion (SfM) processing and three-dimensional canopy reconstruction.

Flights were conducted under stable weather conditions, with low wind speed and homogeneous illumination, in order to minimize image blur, shadow effects, and radiometric variability. The flight plan was defined to ensure adequate overlap between consecutive images and flight strips.

The main flight parameters were as follows:

• flight altitude: 20–30 m above ground level;
• forward overlap: 80%;
• side overlap: 70%;
• ground sampling distance (GSD): approximately 1.5–2.5 cm pixel⁻¹.

The ground sampling distance was estimated using Equation (1):

$$GSD={\displaystyle \frac{H\times S}{f\times I}}$$

(1)

where:

H = flight altitude above ground (m)

S = sensor pixel size (mm)

f = focal length of the camera lens (mm)

I = image width in pixels.

The acquired imagery was processed using Structure-from-Motion photogrammetric algorithms to reconstruct three-dimensional surface models of the study area.

2.5. Data Processing

LiDAR datasets were processed using the Hovermap ST-X SLAM processing software and CloudCompare v. 2.12.4, an open-source software distributed under the GNU General Public License, for point-cloud inspection, filtering, and segmentation. The LiDAR processing workflow included point cloud registration, noise filtering, outlier removal, ground point classification, terrain normalization, and vegetation segmentation.

First, raw LiDAR point clouds were registered and inspected to verify data completeness and remove acquisition artefacts. Noise filtering was performed using statistical outlier removal algorithms to eliminate isolated points and spurious returns caused by sensor noise, moving vegetation elements, or environmental disturbances. Ground points were then identified using progressive morphological filtering and used to normalize the point cloud relative to the terrain surface. This step allowed canopy height to be expressed as height above ground level. Finally, vegetation segmentation was performed to isolate canopy points from non-vegetated elements, producing a clean dataset suitable for structural and voxel-based analysis.

UAV images were processed using Agisoft Metashape Professional (version 1.7.3). The photogrammetric workflow included image alignment, sparse point cloud generation, dense point cloud reconstruction, digital surface model generation, RGB orthomosaic production, and canopy height model extraction. Image alignment was performed using high-accuracy settings, and tie points were automatically detected and matched across overlapping images. After bundle adjustment, a dense point cloud was generated using high-quality settings. The dense point cloud was then used to produce a digital surface model and an RGB orthomosaic.

A Canopy Height Model (CHM) was derived by subtracting the digital terrain model (DTM) from the digital surface model (DSM) according to the equation (2):

$$CHM=DSM-DTM$$

(2)

where

DSM = digital surface model

DTM = digital terrain model.

The CHM represents vegetation height above ground level and was used to estimate canopy height and compare UAV-derived structural information with LiDAR-derived metrics.

2.6. Voxel-Based Canopy Modelling

To quantitatively analyse canopy structure, the normalized LiDAR point cloud was converted into a voxel-based three-dimensional model.

Voxelization subdivides the canopy volume into regular three-dimensional grid cells. The volume of each voxel was calculated according to Equation (3):

$$v=\mathsf{\Delta }x\times \mathsf{\Delta }y\times \mathsf{\Delta }z$$

(3)

where

$\mathsf{\Delta }x$, $\mathsf{\Delta }y$, and $\mathsf{\Delta }z$ represent the spatial resolution of the voxel grid along the X, Y, and Z axes.

Each voxel was classified as occupied or empty according to the presence or absence of LiDAR points. Occupied voxels were used to reconstruct the three-dimensional canopy model and to describe the spatial distribution of vegetation within the canopy.

Canopy volume was calculated according to Equation (4):

$$CV=N_v\times v$$

(4)

where

$N_v$= number of occupied voxels

$v$= voxel volume.

This approach allows detailed analysis of vegetation density and spatial distribution within the canopy.

Vegetation density distribution was calculated as the number of occupied voxels or LiDAR points within each vertical canopy layer. This approach allowed the analysis of canopy compactness, vertical structure, internal vegetation distribution, and structural heterogeneity. The voxel-based metrics were then used to compare LiDAR-derived three-dimensional canopy information with UAV-derived photogrammetric products.

2.7. Statistical Validation

Statistical analyses were performed using R software (v. 4.3.2; R Core Team, Vienna, Austria). The accuracy of LiDAR- and UAV-derived canopy metrics was evaluated by comparing the estimated values with ground-truth measurements collected in the field. The agreement between datasets was assessed using the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and relative error.

The root mean square error (RMSE) was calculated according to Equation (5):

$$RMSE=\sqrt{{\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n}(P_i-O_i{)}^2}$$

(5)

where Pi is the predicted value, Oi is the observed value, and n is the number of observations.

The mean absolute error (MAE) was calculated according to the equation (6) as:

$$MAE={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n}\mid P_i-O_i\mid$$

(6)

The coefficient of determination (R²) was used to evaluate the strength of the relationship between LiDAR-derived and UAV-derived measurements according to the following equation (7):

$$R^2=1-{\displaystyle \frac{\sum (O_i-P_i{)}^2}{\sum (O_i-\bar{O}{)}^2}}$$

(7)

where $\bar{O}$ represents the mean of the observed values.

Relative error was calculated according to Equation (8):

$$\mathrm{R}\mathrm{e}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{E}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{r}\left(\mathrm{\%}\right)={\displaystyle \frac{\mid \mathrm{P}\mathrm{i}-\mathrm{O}\mathrm{i}\mid }{\mathrm{O}\mathrm{i}}}\times 100$$

(8)

where Pi is the predicted value and Oi is the observed value.

These metrics were used to assess the accuracy of canopy height and canopy volume estimates obtained from LiDAR and UAV photogrammetry. Normality of the data was assessed using the Shapiro–Wilk test. When the assumptions of normality were satisfied, paired comparisons between LiDAR- and UAV-derived estimates were performed using paired t-tests. When normality assumptions were not satisfied, the Wilcoxon signed-rank test was used. Statistical significance was assessed at p<0.05.

2.8. Potential Applications for Precision Agriculture

The extracted canopy structural parameters were evaluated in relation to their potential use in precision agriculture. The integration of LiDAR-derived structural information and UAV photogrammetric products enables a comprehensive characterization of vegetation architecture and spatial variability within agricultural systems (Figure 3). In particular, canopy height, canopy volume, and vegetation density distribution provide useful indicators for biomass estimation, crop monitoring, canopy management, irrigation planning, yield prediction, and plant health assessment.

LiDAR-derived point clouds and voxel-based models allow detailed analysis of internal canopy structure, vertical heterogeneity, and vegetation density distribution. This information is particularly relevant for identifying differences in canopy compactness, biomass accumulation, and structural complexity. Conversely, UAV photogrammetry provides complementary information on external canopy morphology, canopy surface variability, and spatial patterns at field scale.

The integration of both datasets supports a multi-sensor monitoring framework for data-driven crop management in Mediterranean agricultural systems. By combining the detailed three-dimensional information provided by LiDAR with the spatial coverage and operational flexibility of UAV photogrammetry, the proposed approach can improve the interpretation of canopy development and support more precise agronomic decision-making.

Figure 3 summarizes the operational workflow used to extract and compare canopy structural metrics from LiDAR and UAV photogrammetric datasets. LiDAR data were first acquired through mobile laser scanning and processed to generate filtered point clouds suitable for voxel-based canopy modelling. In parallel, UAV photogrammetric surveys were used to derive complementary information on canopy surface structure. The resulting datasets were used to estimate canopy height, canopy volume, and vegetation density distribution and were subsequently compared through statistical analysis to assess their accuracy and consistency.

This workflow highlights the complementary role of the two sensing approaches. LiDAR-derived point clouds and voxel-based models provide detailed information on internal canopy structure and vertical vegetation distribution, whereas UAV photogrammetry supports external canopy surface mapping and spatial variability assessment. The integration of both data sources enables a more comprehensive characterization of canopy architecture and provides operational information for precision agriculture applications under Mediterranean conditions.

3. Results

The results provide a comprehensive comparative assessment of LiDAR and UAV photogrammetric approaches for canopy structural characterization, highlighting significant differences in accuracy, structural detail, and representation of vegetation architecture. The analysis focuses on canopy height estimation, canopy volume reconstruction, and the ability to capture internal canopy structure across species with contrasting morphological complexity.

3.1. Three-Dimensional Canopy Reconstruction

The comparative analysis of LiDAR sensing and UAV photogrammetry enabled the reconstruction of canopy structure for the two investigated species, Moringa oleifera and Ficus macrophylla, highlighting substantial differences in structural detail and representation accuracy between the two approaches. Figure 4 and Figure 5 specifically illustrate the LiDAR-derived point clouds and the corresponding voxel-based canopy models for the two case studies.

As shown in Figure 4 and Figure 5, LiDAR-derived point clouds provide a highly detailed three-dimensional representation of vegetation architecture. The high density of laser returns allows accurate reconstruction of canopy elements, including stems, branches, and foliage layers. This capability is particularly evident in the voxel-based models, which describe not only the external canopy envelope but also the internal spatial distribution of vegetation.

In the case of Moringa oleifera (Figure 4), characterized by a relatively regular and open canopy structure, LiDAR data enabled a precise reconstruction of canopy geometry and vertical development. The voxel-based model highlights a relatively homogeneous distribution of vegetation density, with a clear differentiation between lower and upper canopy layers. This structural organization is consistent with the growth pattern of the species and facilitates the estimation of key parameters such as canopy height and canopy volume.

In contrast, the reconstruction of Ficus macrophylla (Figure 5) reveals a much more complex canopy architecture. The LiDAR-derived point cloud captures the dense foliage and intricate arrangement of branches and aerial roots, providing a detailed representation of internal canopy heterogeneity. The voxel-based model clearly illustrates variations in vegetation density across different canopy layers, with higher density zones concentrated in the central and upper canopy regions. These results highlight the capability of LiDAR to effectively characterize structurally complex tree species.

UAV photogrammetric reconstruction, based on Structure-from-Motion techniques, provided reliable representations of the external canopy surface for both species. However, its ability to describe internal canopy structure was more limited. The resulting dense point clouds and derived surface models mainly represented the upper canopy layer, with reduced penetration into inner canopy zones due to occlusion effects caused by dense foliage.

These limitations were particularly evident in Ficus macrophylla, where the high canopy complexity reduced the effectiveness of photogrammetric reconstruction. Although the overall external canopy shape could be reconstructed, internal structural features were not adequately captured, resulting in a simplified representation of vegetation architecture. In contrast, the limitations of UAV photogrammetry were less pronounced in Moringa oleifera, owing to its more open and discontinuous canopy structure.

Overall, the results demonstrate that LiDAR-based approaches provide a more comprehensive and accurate representation of canopy structure than UAV photogrammetry, particularly under conditions of high canopy density and structural complexity. The voxel-based modelling approach further enhances this capability by enabling the quantification of vegetation distribution within the canopy volume.

The comparison between the two species also confirms that the performance of sensing technologies is strongly influenced by canopy architecture. LiDAR consistently outperformed photogrammetric approaches in capturing internal structural variability, whereas UAV-based methods remained effective for external canopy mapping and spatial variability analysis. These findings highlight the importance of selecting appropriate sensing technologies according to crop structure and monitoring objectives, particularly in precision agriculture applications where accurate canopy characterization is essential for improving crop management and decision-making.

3.2. Canopy Height and Canopy Volume Estimation

The quantitative analysis of canopy structural parameters confirms the differences observed in the three-dimensional reconstructions (Figure 4 and Figure 5), highlighting the superior performance of LiDAR-based approaches for both canopy height and canopy volume estimation.

Canopy height is a key structural parameter for evaluating plant growth and biomass development. LiDAR-derived canopy height models (CHMs) provided highly accurate and spatially consistent measurements across both species. The high density of laser returns and the ability to penetrate the canopy enabled reliable detection of both upper and intermediate vegetation layers, ensuring accurate height estimation at both individual plant and plot scales.

UAV-derived CHMs also provided consistent estimates, particularly for Moringa oleifera, where the relatively open canopy reduces occlusion effects. However, as observed in Figure 4, UAV-based estimates tend to underestimate canopy height, especially in dense vegetation. This limitation is more evident in Ficus macrophylla (Figure 5), where complex canopy architecture restricts the capability of photogrammetric reconstruction to capture lower canopy layers.

The statistical comparison supports these observations. Differences between LiDAR- and UAV-derived estimates were evaluated using paired statistical tests for Moringa oleifera. Normality was assessed using the Shapiro–Wilk test, and significance was evaluated at p < 0.05.

As reported in Table 1, LiDAR-based canopy height estimation achieved lower root mean square error (RMSE = 0.19–0.21 m) and higher coefficients of determination (R² = 0.94–0.96) compared with UAV-based estimates (RMSE = 0.52–0.60 m; R² = 0.82–0.87). The relationship between LiDAR- and UAV-derived estimates indicated good agreement, although UAV-derived values consistently underestimated canopy height under high-density conditions.

Canopy volume estimation further emphasizes the advantages of LiDAR-based analysis. The voxel-based modeling approach applied to LiDAR point clouds enabled a detailed characterization of vegetation distribution within the canopy, as illustrated in Figure 4 and Figure 5. This approach allows the quantification of canopy compactness and spatial variability by subdividing the canopy into volumetric elements.

LiDAR-derived canopy volume estimates showed substantially lower relative errors (3.5–4.2%) compared with UAV photogrammetric estimates (13.7–16.1%), as reported in Table 1. These differences are mainly related to the limited ability of photogrammetric approaches to capture internal canopy structure, particularly in dense and heterogeneous vegetation.

The comparison between species highlights the influence of canopy architecture on model performance. In Moringa oleifera, UAV-based estimates show relatively good agreement with LiDAR data due to the more regular canopy structure. In contrast, in Ficus macrophylla, the dense foliage and complex branching system lead to significant underestimation of both canopy height and volume when using UAV photogrammetry.

Overall, these results confirm that LiDAR provides a more accurate and comprehensive representation of canopy structural parameters, particularly for metrics depending on the three-dimensional distribution of vegetation. The integration of voxel-based modeling further enhances structural analysis, reinforcing the suitability of LiDAR for precision agriculture applications requiring detailed canopy characterization.

3.3. Comparison Between LiDAR and UAV-Based Reconstruction

The comparative analysis between LiDAR-based scanning and UAV photogrammetric reconstruction highlights clear differences in structural accuracy, data resolution, and operational performance.

LiDAR systems provide higher structural accuracy due to their ability to generate high-density point clouds and capture multiple returns from different canopy layers. This capability enables the reconstruction of internal canopy architecture and supports detailed analysis of vegetation density distribution, as observed in Figure 4 and Figure 5. In contrast, UAV photogrammetry is primarily limited to the reconstruction of the external canopy surface, with reduced capability to detect internal vegetation layers due to occlusion effects.

These differences are particularly evident in Ficus macrophylla, where the dense canopy structure significantly limits the performance of photogrammetric reconstruction. While UAV-based models accurately describe the external canopy geometry, they fail to capture internal structural variability. LiDAR-based models, on the other hand, provide a comprehensive representation of canopy architecture, including internal elements that are not visible in photogrammetric data.

As summarized in Table 2, LiDAR outperforms UAV photogrammetry in canopy height and volume estimation, structural detail, and robustness to canopy occlusion. However, UAV-based approaches offer advantages in terms of spatial coverage, operational cost, and processing simplicity, making them suitable for large-scale monitoring applications.

These results demonstrate that the choice of sensing technology should be guided by the specific objectives of the analysis. LiDAR is more suitable for detailed structural characterization, while UAV photogrammetry represents an efficient solution for large-scale monitoring of canopy variability.

The table summarizes the main differences between the two approaches in terms of structural accuracy, canopy volume estimation, spatial resolution, and operational requirements. LiDAR systems provide higher accuracy in canopy height and volume estimation and allow the reconstruction of internal vegetation layers through high-density point clouds and voxel-based modeling. UAV photogrammetry offers wider spatial coverage and lower operational costs but is more affected by canopy occlusion and limitations in reconstructing internal canopy structure.

3.4. Implications for Precision Agriculture Applications

The results obtained in this study highlight the strong potential of LiDAR technology for advanced crop monitoring applications within precision agriculture frameworks. The high structural accuracy of LiDAR-derived models, as demonstrated in canopy height and volume estimation (Table 1), makes this approach particularly suitable for applications requiring detailed three-dimensional characterization of vegetation, including biomass estimation, canopy management optimization, and yield prediction.

The ability of LiDAR systems to capture internal canopy structure, as observed in Figure 4 and Figure 5, provides a significant advantage for analyzing vegetation density distribution and structural heterogeneity. This information is essential for understanding plant growth dynamics and supports the development of more accurate decision-support systems for site-specific crop management.

UAV photogrammetry, although less effective in capturing internal canopy structure, remains a valuable tool for large-scale crop monitoring due to its operational flexibility, lower cost, and capacity to rapidly acquire high-resolution spatial data. As shown in the results, UAV-based approaches provide reliable information on canopy surface variability, making them suitable for applications such as crop vigor assessment, field-scale variability mapping, and early detection of stress conditions.

The integration of LiDAR-derived structural information with UAV-based spatial datasets represents a promising multi-sensor approach for precision agriculture. This combined methodology enables the simultaneous analysis of canopy architecture and spatial variability, improving the accuracy and reliability of crop monitoring systems. Such integration is particularly relevant in Mediterranean environments, where heterogeneous growing conditions and water limitations require precise and adaptive management strategies.

Overall, the findings demonstrate the complementary nature of LiDAR sensing and UAV photogrammetry. While LiDAR provides high-resolution structural information, UAV-based approaches ensure efficient large-scale monitoring. The integration of these technologies offers significant potential for improving data-driven decision-making processes and advancing sustainable agricultural practices.

4. Discussion

The results of this study demonstrate the superior capability of LiDAR sensing for high-resolution three-dimensional characterization of vegetation canopy structure, particularly when compared with UAV photogrammetric approaches. The differences observed in canopy reconstruction (Figure 4 and Figure 5) and in the quantitative analysis of canopy height and volume (Table 1) highlight the fundamental advantages of active sensing systems for structural vegetation analysis.

The higher accuracy achieved by LiDAR in canopy height estimation (RMSE = 0.19–0.21 m) compared with UAV photogrammetry (RMSE = 0.52–0.60 m) confirms the robustness of laser-based measurements for capturing vegetation geometry. This performance is primarily related to the active sensing mechanism of LiDAR, which directly measures distances through laser pulse return time, enabling precise geometric reconstruction of vegetation elements. Moreover, the ability of LiDAR sensors to generate multiple returns from different canopy layers allows partial penetration of dense foliage, resulting in a more complete representation of canopy architecture. These findings are consistent with previous studies reporting the high accuracy of LiDAR for vegetation structural analysis [28,29,32].

In contrast, UAV photogrammetry showed lower accuracy in both canopy height and volume estimation, with systematic underestimation observed in dense canopy conditions. This limitation is directly related to the nature of Structure-from-Motion (SfM) reconstruction, which relies on visible surface features and is therefore affected by canopy occlusion. As a result, photogrammetric models primarily represent the outer canopy surface, as also observed in Figure 4 and Figure 5, where internal structural elements are not captured. Similar limitations have been reported in previous studies, highlighting the reduced capability of photogrammetric approaches in dense vegetation environments [20,21,31].

The influence of canopy architecture on sensing performance is particularly evident when comparing the two study species. In Moringa oleifera, characterized by a relatively open canopy structure, UAV-based estimates show relatively good agreement with LiDAR-derived measurements. In contrast, in Ficus macrophylla, the dense and complex canopy architecture leads to significant discrepancies between the two approaches, with UAV photogrammetry failing to capture internal canopy variability. This confirms that the effectiveness of remote sensing techniques is strongly dependent on vegetation structure and canopy complexity.

A key methodological contribution of this study is the implementation of voxel-based canopy modeling, which enables detailed analysis of vegetation distribution within the canopy volume. The voxel representation derived from LiDAR data allows the quantification of structural parameters such as vegetation density and canopy compactness, providing insights into vertical canopy organization that cannot be obtained from two-dimensional remote sensing approaches. Previous studies have demonstrated the effectiveness of voxel-based LiDAR analysis for characterizing vegetation structure and improving biomass estimation [36,37,38,39,40], and the present results further confirm its applicability in agricultural contexts.

From an applied perspective, the findings align closely with the implications identified in Section 3.4. The high structural accuracy of LiDAR-derived models makes this technology particularly suitable for precision agriculture applications requiring detailed canopy characterization, such as biomass estimation, canopy management optimization, and yield prediction. At the same time, UAV photogrammetry remains a valuable tool for large-scale monitoring due to its lower cost and operational flexibility, enabling rapid assessment of spatial variability across agricultural fields.

The integration of LiDAR structural information with UAV-derived spatial datasets therefore represents a promising multi-sensor approach. By combining detailed three-dimensional canopy characterization with high-resolution spatial monitoring, this methodology enables more accurate and scalable crop assessment, particularly in heterogeneous Mediterranean environments where structural variability strongly influences crop performance.

Despite these promising results, some limitations should be acknowledged. The study was performed using at a relatively limited spatial scale, which may affect the generalizability of the findings to larger agricultural systems. Furthermore, the analysis focused on two species with contrasting canopy structures, and additional studies are required to validate the proposed approach across a wider range of crops and environmental conditions.

Future research should focus on extending the application of LiDAR-based monitoring to larger spatial scales, integrating multi-temporal datasets, and combining LiDAR with additional sensing technologies such as multispectral, hyperspectral, and thermal sensors. The integration of machine learning and deep learning algorithms for automated point cloud analysis and structural trait extraction also represents a promising direction for improving the scalability and efficiency of LiDAR-based monitoring systems.

Overall, this study confirms that LiDAR technology represents a powerful tool for high-resolution canopy structural analysis and that its integration with UAV-based remote sensing approaches can significantly enhance precision agriculture monitoring systems. The proposed framework provides a robust basis for the development of advanced, data-driven crop management strategies within digital agriculture systems.

5. Conclusions

This study establishes a robust methodological framework for evaluating three-dimensional canopy reconstruction techniques within precision agriculture and provides clear evidence of the superior performance of LiDAR technology compared with UAV photogrammetry for structural vegetation analysis. The results consistently demonstrate that LiDAR-derived point clouds enable highly accurate reconstruction of canopy architecture, allowing reliable estimation of key structural parameters such as canopy height, canopy volume, and vegetation density distribution. The significantly lower estimation errors obtained with LiDAR confirm its capability to overcome the limitations of photogrammetric approaches, particularly those related to canopy occlusion.

The comparative analysis highlights that the effectiveness of remote sensing techniques is strongly influenced by canopy architecture and monitoring objectives. UAV photogrammetry proved to be effective for mapping the external canopy surface and capturing spatial variability at field scale, whereas its performance decreases in dense and structurally complex vegetation. In contrast, LiDAR technology, through its active sensing mechanism and capacity to capture multiple returns, provides a more complete representation of both external and internal canopy components. This advantage is particularly relevant for perennial crops and complex vegetation systems typical of Mediterranean environments.

The application of voxel-based modeling further enhances the analytical potential of LiDAR data by enabling the quantification of vegetation distribution within the canopy volume. These volumetric metrics provide detailed insights into structural heterogeneity and canopy organization, supporting the analysis of plant growth dynamics and biomass accumulation. The consistency of results obtained for both Moringa oleifera and Ficus macrophylla confirms the robustness of the proposed approach across contrasting canopy architectures, highlighting its applicability to both emerging nutraceutical crops and species characterized by high structural complexity.

From an agronomic perspective, the findings underline the importance of integrating structural information into precision agriculture workflows. Accurate characterization of canopy architecture can support improved crop management practices, including irrigation scheduling, pruning optimization, and biomass estimation. In line with the implications discussed in Section 3.4, the integration of LiDAR-derived structural metrics with UAV-based spatial monitoring represents a promising multi-sensor approach for enhancing data-driven decision-making processes.

Although the study was performed using at plot scale, the proposed framework represents a critical step toward the validation of advanced remote sensing methodologies prior to large-scale implementation. Future developments should focus on improving scalability through the use of UAV-mounted LiDAR platforms, multi-temporal monitoring, and the integration of complementary sensing technologies such as multispectral, hyperspectral, and thermal imaging. In addition, the application of machine learning techniques to three-dimensional point cloud data offers further opportunities for automating structural analysis and improving predictive modeling.

Overall, this work demonstrates that LiDAR technology represents a key tool for high-resolution canopy structural monitoring and provides clear guidelines for its integration within precision agriculture systems. By linking methodological validation with practical agronomic applications, the study contributes to the development of more efficient, sustainable, and data-driven agricultural practices, particularly in Mediterranean environments characterized by high variability and resource constraints.