Evaluating LiDAR technology for accurate measurement of tree metrics and carbon sequestration

Abstract

Carbon credits play a crucial role in mitigating climate change by incentivizing reductions in greenhouse gas emissions and providing a measurable way to balance carbon dioxide output, fostering sustainable environmental practices. However, conventional methods of measuring carbon credits are often time-consuming and lack accuracy. This research examines carbon credit measurement in a 40 × 40 m² rubber forest, evaluating the effectiveness of LiDAR technology in measuring Tree Height (TH) and Diameter at Breast Height (DBH) using a dataset of 100 samples. The method is as follows:

• •Three measurement methods were compared: conventional techniques using diameter tape and hypsometers, manual LiDAR measurements, and automated measurements using 3D Forest Inventory software with the CloudCompare plugin.
• •The Mean Absolute Percentage Error (MAPE) for carbon sequestration was 4.276 % for manual LiDAR measurements and 6.901 % for the 3D Forest Inventory method.
• •Root Mean Square Error (RMSE) values for carbon sequestration using LiDAR measurements were 33.492 kgCO₂e, whereas RMSE values for the 3D Forest Inventory method were significantly higher. This indicates that manual LiDAR measurements are more accurate and consistent, while the higher RMSE in the 3D Forest Inventory method reflects greater variability and potential estimation errors.

The findings suggest that LiDAR technology, particularly manual measurements, provides a reliable and efficient alternative for carbon sequestration assessments in forest management.

Graphical abstract

Specifications table

Subject area:	Engineering
More specific subject area:	Measurement and instrument
Name of your method:	Tree Metrics and Carbon Sequestration using LiDAR Technology
Name and reference of original method:	T-VER-S-TOOL-01–01 Calculation for Carbon Sequestration in tree
Resource availability:	Thailand Greenhouse Gas Management Organization (Public Organization), T-VER-S-TOOL-01–01 Calculation for Carbon Sequestration in tree, Thailand Greenhouse Gas Management Organization (Public Organization), 2023.

Background

In recent years, excessive carbon dioxide emissions have significantly accelerated global warming and climate change, leading to more frequent natural disasters, including heat waves, droughts, and glacier melting. These environmental changes impact human health, economic stability, and infrastructure, attracting global attention and fostering international cooperation [1,2,3]. To support this mission, the concept of carbon credits was introduced, enabling entities to offset emissions by investing in eco-friendly projects [4].

Carbon credits are tradable permits representing one ton of carbon dioxide or equivalent greenhouse gases. These credits are typically generated by activities that decrease greenhouse gases, such as reforestation [5]. Thailand has actively joined global carbon reduction efforts, establishing the Thailand Greenhouse Gas Management Organization (TGO), which encourages tree planting and conservation projects through the Thailand Voluntary Emission Reduction Program (T-VER) [6]. T-VER enables landowners to measure carbon sequestration, allowing them to calculate and trade credits based on their trees' carbon absorption [7].

However, assessing carbon sequestration under T-VER accurately requires detailed data on each tree's Diameter at Breast Height (DBH) and total height, which is often time-consuming [3,7,8]. Recent advancements in remote sensing and LiDAR technology present more efficient alternatives for gathering this data [3,9,10]. LiDAR, using laser scanning, generates high-resolution, three-dimensional point cloud data of trees, providing the precise measurements essential for calculating carbon sequestration [10].

Two primary LiDAR scanning types are Static and Mobile. Research indicates Static LiDAR provides slightly more accurate data, while Mobile scanning (Hand-Held Mobile Laser Scanning) is faster and easier, though slightly less precise. Despite this, Mobile scanning significantly expedites data collection [8]. However, handheld scanners face limitations in evaluating tall trees due to canopy density and vertical range. Airborne Laser Scanning, which mounts LiDAR on drones, overcomes this limitation by collecting data from above, offering better coverage for tall trees [3,9]. The collected data is then processed with software like CloudCompare, which enables the separation of individual trees, measurement of DBH and height, and integration of handheld and airborne data [3,10]. CloudCompare's 3D Forest Inventory (3DFIN) plugin further refines these measurements, enhancing the accessibility and accuracy of carbon sequestration assessments [3].

This research also examines the 3DFIN plugin's ability to accurately calculate DBH and tree height, highlighting its utility for future assessments [3]. The study combines traditional field measurements with advanced LiDAR data from handheld and airborne sources, focusing on rubber tree plantations. By using the ICS LiPix handheld laser scanner for initial data and supplementing it with drone-mounted LiDAR for comprehensive coverage, the study aims to advance carbon sequestration assessments [3].

Method details

The methodological framework for assessing tree parameters and calculating carbon sequestration involves both conventional and LiDAR-based approaches. The process begins by splitting into two primary measurement methods: the conventional method and the LiDAR-based method. In the conventional method, Diameter at Breast Height (DBH) is measured using a diameter tape, and tree height (TH) is measured with a hypsometer. These values are directly used for further calculations without additional processing.

In contrast, the LiDAR-based method involves collecting 3D point cloud data through both handheld and airborne LiDAR devices. This data enables two pathways for parameter measurement: (1) manual measurement of each parameter using CloudCompare, and (2) automated measurement utilizing CloudCompare with the 3DFIN plugin. These measurements from both the conventional and LiDAR-based approaches are subsequently used to calculate carbon sequestration values. The final step involves comparing the results to evaluate the accuracy and reliability in carbon assessment across the two methodologies, as shown in Fig. 1. A detailed explanation of each step is provided in the following section.

Fig. 1.

Flowchart of the Research Methodology for Assessing Carbon Sequestration in Rubber Tree Plantations.

Calculation for carbon sequestration in tree

Carbon credit evaluation methods by the Thailand greenhouse gas management organization (TGO)

The Thailand Greenhouse Gas Management Organization (TGO) has defined four methods for evaluating carbon credits: tree counting method, tree measurement method, remote sensing method, and a method that combines TGO-approved technologies.

Carbon Credit Evaluation by Tree Counting

This method is employed when the maximum area of the sub-land does not exceed 30 Rai (48,000 m²). The growth rate of carbon credits is assumed to be linear, with an annual increase of 9.5 kg per tree.

Carbon Credit Evaluation by Remote Sensing and Artificial Intelligence Technology

This method uses remote sensing technology in conjunction with algorithms implemented in programs or applications approved by TGO to evaluate carbon credits.

Carbon credit evaluation by tree measurement

This method utilizes allometric equations to evaluate the biomass of trees, which consists of aboveground biomass (AGB) and belowground biomass (BGB).

In this research, the tree measurement method is chosen, necessitating the use of allometric equations.

Allometric equations

Allometric equations from TGO [7] express the relationship between the diameter at breast height (DBH) of a tree, measured at 1.3 m above ground, and tree height (TH). These equations are used to calculate the dry mass of trees. According to Kira and Shidel, the relationship between the square of the DBH and the TH forms a parabolic volume, allowing for the estimation of tree volume and biomass:

$$Y=a{X}^b$$ (1)

Where:

• •Y is the biomass of the trunk, limbs, leaves, and roots (kg),
• •X is the independent variable,
• •a and b are constants.

Biomass

The biomass consists of both above ground and underground components. Above-ground biomass is calculated from the biomass of the trunk, branches, and leaves, while underground biomass is estimated using the ratio of the dry weight of roots to the dry weight of the trunk. The specific allometric equations used to calculate these biomass components were selected for their proven accuracy and relevance to the species and forest types in our study area. These equations have been widely applied in similar research, ensuring reliable estimates of biomass and carbon sequestration in comparable ecosystems. These biomass components can be calculated using the following equation:

$$TrunkBiomass\left(W_S\right)=0.0396{\left(DB{H}^{2}TH\right)}^{0.933}$$ (2)

$$BranchBiomass\left(W_B\right)=0.003496{\left(DB{H}^{2}TH\right)}^{1.030}$$ (3)

$$LeaveBiomass\left(W_L\right)={\left(\frac{28}{\left(W_S+W_B+0.025\right)}\right)}^{-1}$$ (4)

$$AboveGroundBiomass\left(W_T\right)=W_S+W_B+W_L$$ (5)

$$UnderGroundBiomass\left(W_U\right)=0.27\left(W_T\right)$$ (6)

Where:

• •W_S is the Trunk Biomass,
• •W_B is the Branch Biomass,
• •W_L is the Leave Biomass,
• •W_T is the Above Ground Biomass
• •DBH is the Diameter of the tree measured at breast height, which is standardized at 1.3 meter from ground
• •TH is the Tree height.

Carbon sequestration

Carbon sequestration is the process of capturing and storing atmospheric carbon dioxide CO₂ to mitigate climate change, and it directly relates to carbon content, which quantifies the amount of carbon stored in biological materials and can be calculated from ratio of average carbon from TGO [7] as:

$$Carboncontent=0.47\left(Biomass\right)$$ (7)

$$Carbonsequestration\left(peryear\right)=\frac{44}{12}\left(Carboncontent\right)$$ (8)

Light detection and ranging (LiDAR)

LiDAR (Light Detection and Ranging) operates by emitting a laser beam towards a surface and then calculating the distance based on the time taken for the laser to travel from the device to the surface and back. This distance is determined using the following equation:

$$D=c\left(\frac{\Delta T}{2}\right)$$ (9)

Where:

• •D is distance,
• •C is the speed of light,
• •∆T is Time that light travels.

The distance measurements collected can be processed using algorithms to create 3D point cloud data. This data can then be used to calculate the quantity and height of trees.

SLAM (Simultaneous Localization and Mapping) is a technique used for both localization and mapping in real-time. It uses the environmental data gathered by the LiDAR system to create a map and estimate the current position of the LiDAR device simultaneously. This allows for accurate mapping and navigation in real-time.

Data acquisition

Conventional measurements

According to the 'Carbon Credit Evaluation Methods' by the Thailand Greenhouse Gas Management Organization under the Thailand Voluntary Emission Reduction Program (T-VER), the calculation of Carbon Credits requires the collection of DBH (Diameter at Breast Height) and tree height data. These data are used in the calculation process. The T-VER document explains that these two measurements are used because the diameter is the easiest dimension of the tree to measure and has relatively lower measurement errors compared to other dimensions of the tree. Measuring the diameter at breast height, or DBH, is convenient and is above the buttress roots, which can otherwise cause measurement errors. Therefore, T-VER designates DBH as the standard measurement point. Additionally, tree height is an important factor in assessing the tree volume and the quality of the forest's location. Hence, tree height is another necessary piece of data for evaluating Carbon Credits [7]. This research references the method for calculating Carbon Credits from T-VER. Consequently, this research also references the methods for measuring DBH and tree height from T-VER, which are described in sections 3.1.1 and 3.1.2 as follows.

Diameter at breast height

The Diameter at Breast Height (DBH) is measured at 1.3 m above the ground, as shown in Fig. 2. Measurements are taken three times using a diameter tape, and the mean of these three measurements is calculated.

Fig. 2.

DBH Measuring at 1.3 meter.

Tree height

To measure Tree Height (TH), laser rangefinder/hypsometer is utilized. This involves measuring the angle of elevation and the distance between the observer and the tree using a measuring tape. The TH is then calculated using trigonometric equations, a method known as the Errors Tangent Method. Fig. 3 demonstrates how the tree height is measured using a hypsometer.

Fig. 3.

Measuring tree height with Error tangent method.

LiDAR-Based measurements

Handheld lidar

The ICS LiPix handheld LiDAR, as shown in Fig. 4, is a laser scanner device that utilizes Solid-state LiDAR SLAM technology to collect 3D point cloud data, with a demonstration of point cloud detection illustrated in Fig. 5. This method significantly reduces the time required for data collection compared to traditional methods, which typically require 10 to 30 mins to set up each scanning point. The handheld LiDAR is user-friendly and portable, making it ideal for use in forest environments. In this research, the handheld LiDAR is employed to gather distance and near-ground data efficiently.

Fig. 4.

ICS LiPix Handheld LiDAR.

Fig. 5.

Point Cloud Detection with LiDAR.

Airborne lidar

Airborne LiDAR, carried on an Unmanned Aircraft Vehicle (UAV), as shown in Fig. 6, offers rapid and efficient coverage of large areas, high resolution and accurate data from elevated positions, access to difficult terrains, comprehensive canopy analysis, reduced ground interference, and real-time data processing, making it ideal for environmental monitoring and forestry management.

Fig. 6.

Airborne LiDAR.

Data process and analysis

Point cloud data processing was conducted using the software “CloudCompare,” which involved several detailed steps to ensure accurate positioning and removal of outlier or unwanted data:

Point cloud registration

As shown in Fig. 7, this initial step involved aligning the point clouds using the Fine Registration (Iterative Closest Point, ICP) method. The registration process was fine-tuned by setting the final overlap to 99 %, a root mean square (RMS) error threshold of 1E-5, and a random sampling limit of 20,000,000 data points. These parameters helped achieve precise alignment between the different point clouds.

Fig. 7.

Point cloud registration.

Merge and alignment

Following registration, multiple 3D point clouds were merged into a single cohesive dataset using the “Merge Multiple” function as shown in Fig. 8. The merged data was then aligned along the standard axes, utilizing the “Alignment” function to ensure consistency in orientation and facilitate further analysis.

Fig. 8.

Merged and Aligned Point cloud data.

Segmentation

Specific sections of the point cloud, pertinent to the research objectives, were isolated using the “Segment” function. For this study, rows 1 to 9 were extracted to focus on relevant areas as shown in Fig. 9. This step enabled the precise measurement of various parameters without interference from extraneous data.

Fig. 9.

Point cloud Segmentation.

Tree separation

The separation of individual trees was accomplished by first removing ground data using the CFS (Cloth Simulation Filter) filter. This step was crucial for distinguishing trees from the ground surface. Subsequently, the “3DFIN” plugin was employed to isolate the shapes of tree crowns. The separation process was conducted for two rows at a time to manage the complexity of the data and maintain accuracy as shown in Fig. 10.

Fig. 10.

Point cloud Tree separation.

Measurement of tree diameter and height

In CloudCompare, the measurement of tree height and diameter from point cloud data involves a systematic approach. The process begins with importing the point cloud data, followed by selecting the relevant points within the area of interest using the “Segment” tool. To obtain the diameter, the “Distance” tool is utilized by selecting two points across the trunk, which provides the diameter measurement displayed in the properties panel. Fig. 11 demonstrates how to measure Diameter at Breast Height (DBH) using CloudCompare's tool, where the diameter is determined at 1.3 m above ground level by selecting appropriate points on the trunk. For height measurement, the vertical distance is determined by identifying the highest point of the tree and the ground level using the same “Distance” tool. As shown in Fig. 12, CloudCompare's tool allows accurate tree height measurements by calculating the vertical distance from the ground to the highest detected point of the tree.

Fig. 11.

Measuring Diameter at Breast Height (DBH) using CloudCompare's Distance Tool.

Fig. 12.

Measuring Tree Height using CloudCompare's Distance Tool.

Forest INventory

3D Forest INventory (3DFIN) is a specialized plugin for CloudCompare that provides an alternative approach to 3D forest inventory by automating the analysis of point cloud data from LiDAR systems, contrasting with traditional manual measurement techniques. This tool enhances the detection and measurement of critical forest attributes, such as diameter at breast height (DBH), height, and crown dimensions, facilitating a quicker data collection process. Fig. 13 illustrates how DBH is automatically measured using the 3DFIN plugin, which detects and calculates the diameter at the standard height of 1.3 m, providing consistent results. For tree height measurement, as shown in Fig. 14, the 3DFIN plugin automates the calculation by identifying the highest point of the tree and measuring its vertical distance from the ground level. Unlike manual methods, which can be time consuming, 3DFIN enables rapid estimation of tree and stand volumes while delivering 3D visualizations of forest structures, making it a valuable resource for ecological research and sustainable forest management.

Fig. 13.

Automated Measurement of Diameter at Breast Height (DBH) using the 3DFIN Plugin.

Fig. 14.

Automated Measurement of Tree Height using the 3DFIN Plugin.

Noise filtering

In handling noise, the manual method employs only Statistical Outlier Removal (SOR), which analyzes the distances between each point and its neighbors. Points are identified as noise if their distance from neighboring points significantly deviates from the mean distance of all neighbors. In contrast, the 3DFIN method incorporates additional algorithms to manage noise, providing a more comprehensive approach to noise reduction.

Measurement comparison and carbon credit evaluation

Measurement comparison

In this section the results from the two methods of measurement are compared which include Diameter at breast height and Tree height as shown in Table 1, Table 2.

Table 1.

Comparison of Conventional and 3D Forest INventory software using LiDAR-Based Measurements.

Parameter	Conventional	3DFIN	MAPE	RMSE
TH	15.477 m	16.508 m	8.708 %	1.639 m
DBH	23.591 cm	22.613 cm	4.868 %	1.297 cm

Table 2.

Comparison of Conventional and Manual LiDAR-Based Measurements.

Parameter	Conventional	LiDAR	MAPE	RMSE
TH	15.477 m	15.327 m	1.572 %	0.301 m
DBH	23.591 cm	23.255 cm	1.734 %	0.537 cm

The results comparing the diameter at breast height (DBH) and tree height (TH) for 100 rubber trees measured using conventional methods and the 3D Forest Inventory (3DFIN) CloudCompare's plugin based on LiDAR are shown in Table 1. The findings indicate that the 3DFIN measurements tend to detect higher values than conventional measurements. This suggests an overestimation error, which is concerning compared to manual LiDAR-based methods, as it does not ensure that carbon sequestration is accurately represented within the specified criteria. The Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for tree height using LiDAR are 8.708 % and 1.639 m, respectively. This error may arise from the software's inability to effectively filter out noise or account for taller trees. For DBH, the MAPE and RMSE are 4.686 % and 1.297 cm, respectively. These results show that using automated measurement software can introduce errors from LiDAR data, potentially leading to overestimation of carbon sequestration.

The results comparing the diameter at breast height (DBH) and tree height (TH) for 100 rubber trees measured using conventional methods and LiDAR-based methods are shown in Table 2. The results indicate that the LiDAR-based measurements can detect lower values than conventional measurements. This suggests that there is no overestimation error, which is beneficial as it ensures that carbon sequestration is accurately represented within the criteria. The Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for tree height using LiDAR are 1.572 % and 0.301 m, respectively, while for DBH, the MAPE and RMSE are 1.734 % and 0.537 cm, respectively. Additionally, the LiDAR-based approach saves time compared to conventional methods while still maintaining accuracy comparable to traditional measurements.

Fig. 15 presents a comparison of Diameter at Breast Height (DBH) measurements for 100 sample trees using Conventional, LiDAR-based, and 3D Forest Inventory methods. The results indicate that both Conventional and automated software measurements yield higher DBH values. This discrepancy can be attributed to potential human errors during manual measurements, such as incorrect alignment, inconsistent tension, or environmental factors like uneven terrain. Conversely, errors from the software may arise due to noise in the data; however, these errors can be mitigated by manually filtering the noise.

Fig. 15.

Diameter at Breast Height comparison.

Fig. 16 illustrates the discrepancy between Conventional and two LiDAR based measurement techniques. The data indicates that the Manual LiDAR based method yields lower tree height values. The inaccuracies in Conventional measurements are likely due to errors associated with Hypsometer use when aiming at the top branches. In contrast, the error from the automated software, 3DFIN (with an RMSE of 1.639 cm and MAPE of 8.708 %), is significantly higher than that of the Conventional method. This increased error can be attributed to noise in the data and the software's inability to segment the tree as effectively as the manual method.

Fig. 16.

Tree Height comparison.

Carbon credit evaluation

The measurement data from Table 1, Table 2 is employed to compute various parameters, including Trunk, Branch, and Leaf biomass. These parameters are subsequently used to derive Aboveground Biomass. Utilizing Aboveground Biomass is estimated, and both biomass values are then applied to calculate carbon content and carbon credits as in Table 3, Table 4.

Table 3.

Comparison of Biomass and Carbon Content Measurements between 3DFIN and Conventional Methods.

Parameter	Conventional	3DFIN	MAPE	RMSE
Trunk Biomass (kg)	200.655	196.116	6.811 %	15.252
Branch Biomass (kg)	43.581	42.511	7.504 %	3.646
Leave Biomass (kg)	6.879	6.745	5.867 %	0.437
Total Aboveground Biomass (kg)	251.115	245.371	6.901 %	19.327
Underground Biomass (ratio)	67.801	66.250	6.901 %	5.218
Total Biomass (kg)	318.916	311.621	6.901 %	24.546
Carbon content (kgC)	149.890	146.462	6.901 %	11.537
Carbon sequestration (kgCO2e)	549.598	537.027	6.901 %	42.301

Table 4.

Comparison of Biomass and Carbon Content Measurements between LiDAR-Based and Conventional Methods.

Parameter	Conventional	3DFIN	MAPE	RMSE
Trunk Biomass (kg)	200.655	192.792	4.222 %	12.038
Branch Biomass (kg)	43.581	41.669	4.649 %	2.966
Leave Biomass (kg)	6.879	6.671	3.550 %	0.306
Total Aboveground Biomass (kg)	251.115	241.132	4.276 %	15.303
Underground Biomass (ratio)	67.801	65.106	4.276 %	4.132
Total Biomass (kg)	318.916	306.238	4.276 %	19.435
Carbon content (kgC)	149.890	143.932	4.276 %	9.134
Carbon sequestration (kgCO2e)	549.598	527.750	4.276 %	33.492

After calculating the carbon sequestration, the errors associated with the carbon credit estimates derived from the three measurement methods were analyzed. The carbon sequestration values obtained from the 3D Forest Inventory software (3DFIN), as shown in Table 3, reveal a Mean Absolute Percentage Error (MAPE) of 6.901 %. This higher error rate indicates a lower level of precision compared to LiDAR-based measurements, which could affect the reliability of the carbon credit estimates. Furthermore, the Root Mean Square Error (RMSE) for the 3DFIN method is 42.301 kgCO2e, suggesting a greater degree of variability in the estimates and potentially leading to overestimation of carbon sequestration.

In contrast, the errors from the LiDAR-based measurements are relatively low. The MAPE for carbon sequestration derived from these measurements is 4.276 %, which is below the 5 % threshold, indicating a good level of ac curacy. Additionally, the RMSE is 33.492 kgCO2e, suggesting that these estimates are more reliable and do not lead to an overestimation of carbon sequestration.

Method validation

Cross Validation of LiDAR-Based Measurement

In this study, the accuracy of Diameter at Breast Height (DBH) and Tree Height (TH) measurements obtained through two LiDAR-based methods automated measurements using 3DFIN software and manual measurements using CloudCompare's distance tool was evaluated through cross-validation against conventional field measurements.

Cross validation of diameter at breast height measurements

The DBH values extracted automatically using the 3DFIN plugin in CloudCompare were compared with the conventional DBH measurements, as shown in Fig. 17. The coefficient of determination (R²) is 0.9543, indicating a high correlation between the two methods. Although this value suggests that the automated 3DFIN method provides accurate results, some deviations were observed, indicating that it may not be as precise as manual measurements in certain cases.

Fig. 17.

Cross Validation of DBH between Conventional and 3DFIN LiDAR-Based.

The manual DBH measurements were conducted using the same point cloud data in CloudCompare's distance tool to manually determine the DBH. Fig. 18 shows the comparison between these manual LiDAR-based measurements and conventional DBH measurements. The correlation between these two methods is exceptionally high, with an R² value of 0.9922, indicating near-perfect alignment. This suggests that the manual method is more accurate in measuring DBH.

Fig. 18.

Cross Validation of DBH between Conventional and Manual LiDAR-Based.

Despite the higher accuracy of the manual method, the R² value from the automated 3DFIN approach remains within an acceptable range for practical applications, offering a good balance between automation and accuracy.

Cross validation of tree height measurements

In the comparison of tree height (TH) measurements, the 3DFIN automated method (Fig. 19) showed significant discrepancies, with a negative coefficient of determination (R² =−0.2979) compared to conventional measurements. This discrepancy arises from differences in the reference points and measurement processes used by the two methods. The conventional method relies on a hypsometer to estimate the height from the tree base to the perceived top, where results may be influenced by obstructions such as branches or foliage. In contrast, the 3DFIN method utilizes point cloud data to construct a 3D tree model for height measurement. However, noise during data collection and analysis in the 3DFIN method led to errors in height estimates. These inaccuracies could result in overestimated values, ultimately impacting carbon sequestration calculations.

Fig. 19.

Cross Validation of TH between Conventional and 3DFIN LiDAR-Based.

In contrast, the manual LiDAR-based method (Fig. 20), which involved manual filtering of the same data, yielded a much higher (R² =0.9563), indicating strong alignment with conventional measurements. The filtering process helps mitigate the noise that was problematic in the automated method, ensuring more accurate tree height measurements.

Fig. 20.

Cross Validation of TH between Conventional and Manual LiDAR-Based.

For accurate carbon sequestration assessments, it is essential to apply noise filtering, as demonstrated by the manual method. Without this step, as seen with the 3DFIN results, errors in tree height estimation could lead to overestimations in carbon sequestration, affecting the overall reliability of forest carbon assessments.

Conclusion

This study highlights the effectiveness of LiDAR technology as a reliable method for measuring key tree metrics and carbon sequestration in a rubber forest. The analysis revealed that the Mean Absolute Percentage Error (MAPE) for tree height measurements using LiDAR was 1.572 %, demonstrating a high level of accuracy. The Root Mean Square Error (RMSE) for tree height measured by LiDAR was 0.301 m, indicating that LiDAR can effectively capture the vertical structure of the forest.

• •For Diameter at Breast Height (DBH), the MAPE for LiDAR measurements was found to be 1.734 %, with an RMSE of 0.537 cm. This accuracy is essential, as DBH is a critical parameter in determining tree volume and biomass. In comparison, the 3D Forest Inventory method had a higher MAPE of 8.708 % for tree height and 4.868 % for DBH. The increased error in the 3D Forest Inventory measurements is attributed to the software's inability to effectively filter out noise, which impacts the accuracy of the results.
• •Cross-validation further emphasized the reliability of manual LiDAR based measurements compared to the 3D Forest Inventory method. The 3DFIN method produced an R-squared value of-0.2973 for tree height (TH) and 0.9543 for Diameter at Breast Height (DBH). This indicates that while DBH measurements are acceptable due to minimal noise and error—close to the accuracy of LiDAR—TH measurements are not acceptable because of excessive noise from leaves and branches of other trees.
• •In contrast, manual LiDAR measurements exhibited an R-squared value of 0.9563 for TH and 0.9922 for DBH. This suggests that DBH measurements are highly accurate, while TH results are also acceptable compared to conventional methods. This accuracy leads to reduced human resource requirements and decreased time consumption during data collection.
• •The automated method utilizing 3DFIN employs noise management through the Statistical Outlier Removal (SOR) technique. While this approach mitigates noise to some extent, residual noise continues to influence the accuracy of data analysis. To address this limitation, future work will focus on developing more robust and efficient noise management strategies. These will include optimizing LiDAR parameter settings for improved data collection, refining data collection processes to minimize noise generation, reducing noise during the integration of Handheld and Airborne LiDAR data, and implementing pre-processing noise management techniques, such as Manual Noise Removal, where automated methods fall short. These advancements are anticipated to enhance the accuracy of data analysis and effectively minimize noise-induced errors.
• •In terms of carbon sequestration, the MAPE for LiDAR measurements was 4.276 %, while the 3D Forest Inventory method yielded a higher MAPE of 6.901 %. The RMSE for carbon sequestration measured by LiDAR was 33.492 kgCO₂e, significantly lower than the RMSE values for the 3D Forest Inventory method.
• •Inaccuracies in carbon credit evaluations can lead to unfair allocation, undermine market trust, and hinder greenhouse gas reduction goals. To address these issues, it is crucial to develop methods that minimize errors, along with third-party validation to verify outcomes. These measures will enhance the effectiveness of carbon credit trading and support efficient environmental policies.

In conclusion, the findings underscore that manual LiDAR measurements not only provide high accuracy but also offer significant time savings compared to conventional measurement techniques. This efficiency, combined with reliable data quality, positions manual LiDAR as an optimal choice for forest metrics and carbon sequestration assessments. The results suggest that adopting LiDAR technology can enhance forest management practices while ensuring precise evaluation of tree metrics and carbon credits.

Limitations

Data requisition

LiDAR data collection presents specific limitations depending on the platform used. Handheld LiDAR devices are highly effective for capturing point cloud data close to the ground, which ensures accurate measurement of Diameter at Breast Height (DBH). However, this method is restricted by the terrain's accessibility; data can only be collected along paths where a human can walk, limiting acquisition in dense or rugged areas.

In contrast, airborne LiDAR, deployed via Unmanned Aerial Vehicles (UAVs), is advantageous for capturing accurate tree height from elevated positions. However, UAV operations are limited by environmental conditions, as flights cannot be conducted safely in hazardous winds or rain. Additionally, Thai regulations prohibit UAV flights at night, adding a further constraint on data collection under certain environmental and operational conditions.

Carbon credit evaluation

The principles of the Thailand Voluntary Emission Reduction Program (T-VER) are applied to calculate carbon credits in this study. However, carbon credit evaluation is influenced by the precision of LiDAR-derived metrics such as DBH and tree height. Differences in LiDAR technologies can lead to variations in data accuracy, which may impact the consistency of carbon sequestration estimates. Ensuring methodological consistency is essential to reliably assess carbon credits across diverse forest landscapes.

Cost

LiDAR technology, particularly airborne systems, can be expensive due to equipment, UAV operations, and data processing. Handheld devices are more affordable but limited by terrain accessibility. These costs can be a significant consideration in large-scale studies.

Requirement of expertise

LiDAR data collection and analysis require specialized training. Handheld LiDAR demands skill for accurate ground-level measurements, while airborne systems require expertise in UAV operation and data processing. The integration of additional remote sensing technologies also necessitates advanced knowledge in data management and analysis.

Ethics statements

Not applicable.

CRediT author statement

Suradet Tantrairatn: Conceptualization, Data curation, Investigation, Methodology, Resources, Software, Writing. Auraluck Pichitkul: Methodology, Investigation, Validation, Writing. Nutchanan Petcharat: Formal analysis, Visualization, Writing, Editing. Pawarut Karaked: Writing, Editing, Visualization. Atthaphon Ariyarit: Review and editing, Visualization, Methodology, Funding acquistion, Investigation, Resources, Project administration, Supervision, Validation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

No data was used for the research described in the article.