The Performance of a Novel Automated Algorithm in Estimating Truckload Volume Based on LiDAR Data
, Cătălin Cucu-Dumitrescu 2,4, Bogdan Candrea 2,3, Bogdan Grama 2, Iulian Iuga 2 and Stelian Alexandru Borz 1,*
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsGENERAL COMMENTS
The paper covers and interesting and relevant topic. Timber tracking and efficient estimation of truck volume is a crucial point for future development of wood supply chains. The paper shows a good approach but some more specification is necessary for explaining the method and interpreting the results.
One of the most critical points of this paper is that not all measurement alternatives were used for the same truck loads? This makes the results hardly comparable.
Data sourcing and experimental design should be described in more detail. It I not enough to refer to other papers. A general description of data collection and processing should be also provided within the paper.
The presentation of the results needs better cross-references between figures and text. The type of evaluation should be consistent for both alternatives.
ABSTRACT
The abstract is a little bit wordy and could be shortened to increase readability.
Line 16-18: First sentence in the abstract does not contain valuable information and can be deleted.
Line 20: Maybe change into “efficient and sustainable wood supply chain”.
Line 20: What do you understand by a sustainable wood supply chain? The word sustainable is mentioned in your paper but what do you understand by sustainability and more important which criteria do you use to measure the improvement of sustainability? You should explain more the context to sustainability or you should delete “sustainability” and focus on “efficiency improvement”.
Line 21-23: Sentence can be shortened: The study compares two scanning platforms, highlighting the influence of the tool choice on the accuracy, and other factors, that are important for an efficient digital flow of information.
Line 29: I would delete “implemented as a Telegram bot” because this is a specific detail which is not important to mention in the abstract.
Line 36: What do you understand by agreement levels?
It would be good to show in the abstract and in the results section the absolute and relative deviations of your measurements. So far, we don’t know how much one approach is better than another and how big are the over- or underestimations.
INTRODUCTION
The first two paragraphs of the introduction are very vague and should be more focused on the topic of the paper and describe the challenges in the forest-based supply chain more specifically.
Line 49-50: Please specify what is procured, produced, distributed and sold.
Line 51-53: Please specify or give examples how the local legal framework, types of products delivered to the market, and the economic context affects the overall performance.
Line 54: Please provide references for the statement “the degree of using digital solutions is low or lacking” as this is a very relevant statement.
You could mention some legal frameworks to show the importance of you work, e.g. Regulation on Deforestation-free Products (EUDR), Corporate Sustainability Due Diligence Directive 2024.
Line 64-69: The first part of this sentence was already mentioned in previous sentences. The second part is not clear and needs to be rewritten.
Line 76-77: Please explain why timber traceability is important in the context of sustainable forest management.
Line 99-124: Please check if all the advantages and weaknesses of automated and manual measurements have been included comprehensively.
You mention that automation promises a higher accuracy (line 100) but on the other hand one of the key challenges is the accurate measurement of individual logs (line 106) within a truckload and improved accuracy with 3D point cloud technology (line 109). Please explain in more detail if we can expect a higher or lower accuracy.
Line 108: What is included in current measurement systems?
Line 110: Are the high prices expected for the equipment or for data processing or both of them?
Line 118: It should be mentioned that manual measurements need probably lower equipment cost and cheaper (as less qualified) workers.
MATERIALS AND METHODS
Why did you not compare all measurement alternatives (manual, Zeb 176 Revo Horizon professional LiDAR scanner, iPhone 14 Pro Max, 3D scanner app, Microtec system) for the same truck loads? This would make the alternatives better comparable. This is one weak aspect of your approach.
Line 180: Does (1 to 4) mean that 4 different trucks were used?
Line 176-177: Can you describe in a few words how the scanning was performed? Was the scan performed in a stationary position? Were there multiple setups? If applicable, would a sketch of the setups be helpful?
Line 176-177: Is the Zeb Revo Horizon professional LiDAR scanner the same as the MLS GeoSLAM platform? If yes, please don’t use different names for the same device. Otherwise, explain the difference between these platforms.
Line 182-190: Also here, a more detailed description of the scanning procedure would be helpful.
Line 183-184: Please explain of the LiDAR sensor (iPhone 14 Pro Max) and the 3D scanner app should be used in combination or if they are two different approaches.
Line 209: What is meant with loads 1 to 4?
Line 230: Where the white dotted surfaces created manually or automatically?
Line 232: How many cross projections were generated for each pile and where are they located (beginning, center, end, intervals)?
Line 257: Can you explain why you used the Telegram bot? What about alternatives?
Line 257: As you mention “users”, is the developed routine open to several users and was it used by different persons for your analysis?
RESULTS
It would be good to show in the abstract and in the results section the absolute and relative deviations of your measurements. So far, we don’t know how much one approach is better than another and how big are the over- or underestimations.
Line 286-294: In this paragraph you describe the data processing of T1 but what is with the data processing of T2? In figure 3 we can see also some results of T2, so you should also describe the processing of T2.
Please provide some cross-references of Figure 4a, 4b, 4c and 4d in the text, otherwise it is difficult to see the connection between written explanations and the figures.
Please mention in the results or in the discussion the effort for data post-processing for T1 and T2. As you mentioned in line 446, this is a critical and important point to choose the adequate technology.
From my point of view, it makes hardly sense to compare T1 and T2 if you measured different truck loads. It would have been much better if you compare all measurement alternatives for the same truck loads.
Figure 4a: If you compare computation time for different truck loads, you should at least, set the truck load volume or the number of point in relation to the computation time. Absolute numbers make no sense for different truck loads.
Figure 4b and 4c: I don’t know exactly what figure 4b and 4c will show us. Please specify. Maybe, a histogram is also not a good choice of a figure. The overlapping of geoslam and iphone is also not perfect because we don’t know what is hiding behind the iphone data.
Please provide some cross-references of Figure 5a, 5b, 5c and 5d in the text, otherwise it is difficult to see the connection between written explanations and the figures.
Line 344: What do you mean with units? Is it m³? Otherwise, please give an example.
Line 345: Change into “two observation methods (manual vs. geoslam)”
Figure 5a: The concept of load levels is not clear as already mentioned in line 209.
Figure 5b: Please explain the meaning of the figures 28,35 and 35,42 and 42,29 and 49,56.
Figure 5d: The term “algorithm measurement” is new. Is it the same as automated measurement? If yes, please do not switch the naming.
Please provide some cross-references of Figure 6a, 6b, 6c and 6d in the text, otherwise it is difficult to see the connection between written explanations and the figures.
Figure 6a: Why is the analysis done for T2 on truck level but for T1 on load level?
Figure 6b: Please explain the meaning of the figures 24,30 and 30,36 and 36,42 and 42,48.
Line 374-375: “A lower MAE indicates a better overall accuracy.” could be shifted to Methodology.
Line 376-377: “RMSE penalizes larger errors more heavily than MAE.” could be shifted to Methodology.
Line 389: The term “MLS GeoSLAM” was never mentioned before.
Line 390-392: “These findings support the reliability and accuracy of the Automated measurement algorithm in estimating truckload volumes when compared to manual of factory-based measurements.” could be shifted to Discussion.
DISCUSSION
Please estimate the effort for post-processing, training with the tools (to get knowledge and skills) and equipment costs. Especially the time for post-processing is a critical point and the information should be available from your assessment.
Line 463: Please specify or give examples of environmental conditions
Line 463: Please specify or give examples of truck configurations
Line 463: Please specify or give examples of timber characteristics
Line 475: Please provide references, if available, for roadside inventories of piles.
CONCLUSION
Line 485: Delete “In conclusion,”
SUPPLEMENTARY MATERIAL
It seems, that in Supplementary materials, the segmentation of the timber loads was not correct for IP1, IP6, IP7, IP8, IP10, IP11, IP14 and IP19. Please check if this was the case. How did you solve that problem and what effect has it on the results?
Comments on the Quality of English LanguageEnglish language is ok ,but some parts are a little bit wordy (especially the abstract) and can be improved to increase readability.
Author Response
Response to Reviewer 1
We thank Reviewer 1 for the thorough and constructive comments that have significantly improved the clarity and quality of our manuscript. Below we provide detailed responses to each comment.
GENERAL COMMENTS
R1: Not all measurement alternatives were used for the same truck loads? This makes the results hardly comparable.
Response: We acknowledge this limitation and clarified it in the revised manuscript (Section 2.1 and Discussion, lines 505–511). Due to practical constraints and operational flow, the same truckloads could not be scanned with both devices. However, we ensured that the truckloads used across both tools were similar in terms of species, load structure, and volume range. We now explicitly discuss this limitation and its implications for comparability.
R1: Data sourcing and experimental design should be described in more detail.
Response: We expanded Section 2.1–2.2 to provide clearer explanations of the experimental setup, including how the scanning was performed, how truckloads were selected, and how the reference data were obtained.
R1: Better cross-referencing between figures and text; evaluation type should be consistent for both alternatives.
Response: We have revised the Results section to better reference subplots in Figures 4, 5, and 6 and to explain each evaluation consistently across T1 and T2. We now specify load vs. truck-level comparisons (see Section 3).
ABSTRACT
R1: Abstract is wordy; consider deleting the first sentence.
Response: The first sentence has been removed, and the abstract has been revised for conciseness and clarity.
R1: Clarify “sustainable wood supply chain” or remove the term.
Response: The term "sustainable" has been removed to avoid ambiguity. The abstract now focuses on efficiency and traceability.
R1: Delete "Telegram bot" detail.
Response: Removed as suggested.
R1: Clarify "agreement levels" and include absolute/relative deviations.
Response: The term “agreement levels” was clarified and replaced with statistical metrics (e.g., MAE, RMSE, correlation coefficient). Absolute and relative deviations are now explicitly mentioned in both abstract and results.
INTRODUCTION
R1: First two paragraphs are vague; focus more on forest supply chain challenges.
Response: Revised to clearly outline traceability, legality, and measurement bottlenecks in the timber supply chain.
R1: Line 49–50: specify what is procured/produced/distributed.
Response: Rewritten to clarify timber-related operations in the forest-to-mill supply chain.
R1: Line 54: provide references for low use of digital tools.
Response: References were added to support this claim (now in revised lines 60–65).
R1: Mention legal frameworks (e.g., EUDR).
Response: Added discussion of the EUDR and Corporate Sustainability Due Diligence Directive (lines 70–75).
R1: Clarify why timber traceability matters; clarify accuracy expectations with automation.
Response: These were expanded in lines 80–90 and 100–110, emphasizing traceability’s link to legality and the potential trade-off between speed and precision.
R1: Clarify what's included in current measurement systems and cost drivers.
Response: Additional details added regarding current commercial systems (line 108–110).
R1: Mention that manual methods require less qualified staff and are cheaper.
Response: Added in the end of the introduction.
MATERIALS AND METHODS
R1: Why not compare all methods for the same truck?
Response: Clarified in Section 2.1 and Discussion. Due to the dynamic field operations, repeated scanning was not feasible.
R1: Line 176–177: Describe scanning procedure (stationary or mobile). Add sketch?
Response: Clarified scanning procedure for both T1 and T2. Sketch added as Supplementary Figure S1.
R1: Clarify if MLS GeoSLAM and Zeb Revo Horizon are the same.
Response: Confirmed and now consistently refer to the tool as MLS GeoSLAM throughout.
R1: Line 183–184: Clarify iPhone LiDAR vs. 3D app.
Response: Clarified that these were used in combination (see Section 2.2).
R1: Line 230, 232: Were white dots/manual or automated? Where were cross sections taken?
Response: Clarified these were generated automatically; cross-sections were placed at beginning, middle, and end (Section 2.3).
R1: Telegram bot—why used? Is it multi-user?
Response: The Telegram bot was chosen for speed and field accessibility. We added explanation and noted its multi-user capability in Section 2.4.
RESULTS
R1: Include absolute/relative deviations.
Response: These were included in Tables and discussed in the text for both T1 and T2.
R1: Line 286–294: Also explain data processing for T2.
Response: Added explanation of T2 data processing under Section 3.1.
R1: Cross-reference all figure subpanels more clearly.
Response: Cross-references to Figures 4a–4d, 5a–5d, 6a–6d were inserted and explained in Results text.
R1: Mention post-processing effort.
Response: Addressed in Discussion section (lines 446–456) comparing effort and processing load for both systems.
R1: Figure 4a: relate computation time to volume or point size.
Response: We now mention that computation time does not correlate strongly with point cloud size or volume, as shown in Figure 4a and described in text.
R1: Clarify unclear figure labels and terms: “units,” “load levels,” “algorithm measurement.”
Response: Labels clarified in Figure captions and Results text; “algorithm measurement” was replaced consistently with “automated measurement.”
R1: Figures 5b, 6b: explain the intervals and labels.
Response: Explanation added in figure captions and results subsection.
R1: Why load-level for T1, truck-level for T2?
Response: Clarified that this choice was based on data availability; added to Section 3.2 and Discussion.
DISCUSSION
R1: Estimate post-processing, training time, and equipment cost.
Response: These estimates are now included in Discussion, lines 446–458. We discuss trade-offs between MLS and iPhone tools.
R1: Specify environmental/truck/timber conditions.
Response: Examples now provided in lines 460–465.
R1: Provide references for roadside inventories.
Response: Added references [37]–[41], including [38–39] on harvester data and roadside inventory practices.
CONCLUSION
R1: Line 485: Delete “In conclusion,”
Response: Removed as requested.
SUPPLEMENTARY MATERIAL
R1: IP1, IP6, IP7, IP8, IP10, IP11, IP14 and IP19 segmentation seems incorrect. How was it handled?
Response: We reviewed these cases and confirmed that some segmentation inaccuracies occurred due to occlusions or partial scans. These were excluded from validation analysis and are now noted in Supplementary Table S2 and referenced in the Methods section. An explanation was added to the main manuscript (lines 248–250 and Discussion).
Reviewer 2 Report
Comments and Suggestions for AuthorsI would like to thank the authors for their work and the effort invested in preparing this manuscript. The study is well-conceived, methodologically sound, and clearly written. The topic is relevant and timely, and the manuscript demonstrates a high level of scientific rigor and professionalism. Overall, this is a well-executed piece of research that only requires minor revisions to enhance clarity in a few sections.
Please, make some changes about the next sections.
- Please, use the same name for MLS GeoSLAM and iPhone LIDAR approaches in the entire MS for text and all figures. Please, use uppercase or lowercase in all MS.
- Please, include the specific site location in Abstract and Materials and methods sections. If a figure is included, please ensure it provides meaningful information.
- Please, include a table about the dataset in Materials and methods sections.
- Please, use the same name for MLS GeoSLAM and iPhone LIDAR approaches in all figures.
- Please use mean absolute error (MAE) and root mean square error (RMSE) for the first time and after use MAE and RMSE in the entire MS.
- In abstract, a paragraph about the results of the MS could improve the MS,
- Please, try to use more references in the Discussion section.
- In conclusion, which approach was better?
Thanks a lot and good job.
Author Response
Response to Reviewer 2
We thank the reviewer for their positive evaluation of our work and for the constructive comments aimed at improving the clarity and consistency of the manuscript. We have addressed all suggestions carefully, as detailed below.
- Consistent Naming for MLS GeoSLAM and iPhone LiDAR
Comment: Use the same name for MLS GeoSLAM and iPhone LiDAR approaches in the entire manuscript and all figures. Ensure consistent use of uppercase/lowercase.
Response: We have revised the entire manuscript and all figure captions to use consistent terminology. The MLS-based approach is now consistently referred to as MLS GeoSLAM, and the mobile device-based approach as iPhone LiDAR. We also ensured uniform formatting (uppercase capitalization) throughout the manuscript and figures.
- Site Location in Abstract and Materials & Methods
Comment: Include the specific site location in the Abstract and Materials and Methods.
Response: The scanning location (Brașov County, Romania) has been added to both the Abstract and the Materials and Methods section (lines 31 and 145–147, respectively). Additional context about the working conditions and setting was also introduced for clarity.
- Include a Dataset Summary Table
Comment: Include a table summarizing the dataset in Materials and Methods.
Response: A new Table 1 has been added to Section 2.1 summarizing the dataset for both scanning tools. It includes truck IDs, number of loads, point count ranges, and volume ranges. This improves transparency and supports easier comparison between T1 and T2.
- First Use of MAE and RMSE
Comment: Use full terms (mean absolute error, root mean square error) on first use, then use acronyms throughout.
Response: This has been corrected. We now use the full terms the first time they appear in the Abstract, Methods, and Results, followed by the acronyms (MAE, RMSE), which are then used consistently in the rest of the manuscript.
- Add a Results Paragraph in the Abstract
Comment: Include a paragraph in the Abstract summarizing the results.
Response: A short paragraph summarizing key results—such as accuracy metrics and main conclusions about tool performance—has been added to the Abstract (lines 33–38).
- Use of References in the Discussion Section
Comment: Use more references in the Discussion.
Response: We have added four new references [37–41] to support our discussion of harvester data, roadside inventories, and international practices in truckload volume measurement. These enhance the relevance of our findings and connect our results to broader wood supply chain applications.
- Clarify Conclusion: Which Approach Was Better?
Comment: State in the conclusion which approach was better.
Response: We clarified in the Conclusion that, while both tools showed high potential, the MLS GeoSLAM provided better agreement with reference data and required less user-dependent effort, making it more accurate. However, the iPhone LiDAR demonstrated advantages in speed, usability, and lower cost, which may be preferable in certain operational contexts (see lines 495–503 in revised Conclusion).
Reviewer 3 Report
Comments and Suggestions for AuthorsDear author,
The manuscript titled ‘Performance of a novel automated algorithm in estimating 2
truckload volume based on LiDAR data’ by Mihai Daniel NIŢĂ et al.
The author explores the potential of automaton algorithms and 3D point cloud processing in auto-matching truck load measurements, which are crucial for sustainable timber supply chains. Their findings contribute to improving forest management practices and emphasize the importance of regularly verifying algorithm reliability in practical applications.
Minor revisions:
- Figure 3: The author provides 292 visual examples. How were these images acquired? Were they captured via cameras? Specific acquisition parameters should be provided, requiring further clarification from the authors.
- Section 3.2 (Agreement): The authors describe a discrepancy of approximately 343 units between automated and manual measurements, equivalent to 2.06 units. What is the unit of measurement (e.g., point grid, quantitative value)? The authors should explain this unit.
- "Lion iPhone LiDAR": The manufacturer of the LiDAR device should be specified (note: likely a typo for "iPhone LiDAR," which is produced by Apple Inc.).
Author Response
Response to Reviewer 3
We sincerely thank the reviewer for their thoughtful feedback and for acknowledging the contribution of our work to the field of automated volume estimation and sustainable forest supply chains. We address each point of feedback below:
- Clarification on Figure 3 Visual Examples
Comment: Figure 3 presents visual examples—how were these images acquired? Were they captured by cameras? Please clarify the acquisition parameters.
Response: We clarified in the caption and corresponding text in Section 3.1 that the visualizations in Figure 3 were not captured by cameras, but were generated directly from the 3D point cloud data using the automated algorithm. The images represent algorithm-processed outputs: side views and top-down segmentation derived from LiDAR scans. We now specify that these are synthetic visualizations created programmatically from the point cloud, not photographs.
- Clarification on Unit of Measurement in Section 3.2
Comment: In Section 3.2, a discrepancy of 2.06 units is reported—what is the unit (e.g., m³)? Please clarify.
Response: Thank you for pointing this out. The discrepancy refers to cubic meters (m³), which is the unit of truckload volume used throughout the study. We have revised the text in Section 3.2 to explicitly state this unit wherever applicable (e.g., “2.06 m³”).
- Manufacturer of the iPhone LiDAR
Comment: Clarify that the iPhone LiDAR used is manufactured by Apple Inc. There may be a typo ("Lion").
Response: We have corrected the typo from “Lion iPhone LiDAR” to “iPhone LiDAR (Apple Inc.)” throughout the manuscript. The clarification that the LiDAR sensor is embedded in the iPhone 14 Pro Max produced by Apple Inc. has also been added in Section 2.2 (lines 183–185 of the revised manuscript).
Reviewer 4 Report
Comments and Suggestions for AuthorsThe paper discusses various digital tools and algorithms based on LIDAR data but does not extensively gather and analyze data from multiple sources beyond the primary study
. The question of why the presented method is needed should be clearer. Other methods are almost skipped completely. I mean regarding literature review, you should provide a detailed analysis of available measurement techniques (manual, photogrammetry, other LiDAR-based systems) with their advantages and disadvantages.
About your methodology, what specific gaps does this method address that existing solutions like Loadmon, Microtec, Logmeter, and Timspect cannot as you mentioned? And I don't understand how the algorithm might perform with different truck configurations and timber species.
In the paper, there are claims without proper analysis or validation such as "The scanning tool choice significantly impacts the agreement levels." Please provide deeper analysis of how the choice of scanning tool affects measurement accuracy and operational efficiency.
Conclusion should be better formulated. Why Geoslam and iPhone were used/tested, the final conclusion is missing. What this paper brings is not well clarified.
The method is tested with one sample, how is this method to be generalized, not clear. You should mention this limitation more explicitly and discuss how it might affect the extended applicability of your findings. As I have not seen, the specific parameters, thresholds for color-based labeling, plane cutting are not mentioned. On the other hand, measurement accuracy for different log sizes and arrangements should be explained.
The results should be presented in better way, especially statistics, maybe in tables. E.g. statistical metrics for easy comparison between T1 and T2 nor presented. There is no info given regarding outliers.
The data preprocessing steps and algorithm implementation parts are not detailed. Better to write again by providing more detailed explanations.
Figures 5 and 6 show some assessment results, the presentation is not done well I also recommend you to include more recent and directly relevant studies on LiDAR-based measurement in forestry,
Author Response
Response to Reviewer 4
We thank the reviewer for the critical and constructive feedback, which has helped us improve the clarity, depth, and scientific rigor of the manuscript. Below, we provide point-by-point responses to each comment.
- Limited Review of Existing Measurement Techniques
Comment: The literature review should include a more detailed analysis of existing techniques (manual, photogrammetry, other LiDAR-based systems) with pros and cons.
Response: We agree with this comment and have substantially revised the Introduction (lines 85–125) to include an expanded literature review. This now includes comparative details of manual measurements, close-range photogrammetry, and established systems such as Microtec, Loadmon, Logmeter, and Timspect. We have outlined their respective strengths and limitations, emphasizing where our method adds value, particularly in cost-efficiency, deployment flexibility, and real-time integration potential.
- Clarify the Need for the New Method
Comment: Why is this method needed? What specific gaps does it address compared to existing solutions?
Response: In the revised Introduction and Discussion (lines 130–150 and 460–480), we clearly state the methodological gap: most existing systems are either prohibitively expensive, infrastructure-dependent, or proprietary. Our method fills the niche for a cost-effective, portable, and semi-automated solution usable by small- and medium-sized enterprises or mobile operators in the field. The Telegram bot implementation allows rapid feedback without specialized software or training.
- Lack of Analysis on Scanning Tool Influence
Comment: The claim that “scanning tool choice significantly impacts agreement” is not supported with proper analysis.
Response: We have revised the Results (Section 3.2) and Discussion to support this claim with more robust comparison. Specifically:
- We now present absolute and relative errors in text for both T1 and T2.
- We describe differences in occlusion patterns, point density, and field of view, which lead to divergence in load envelope detection (see lines 320–330 and 475–490).
- The discussion elaborates on how platform-specific characteristics, such as scanning angle and occlusion, affect results.
- Generalization and Sample Limitations
Comment: The method was tested on a limited sample. How can it be generalized?
Response: We have added a clear paragraph under Discussion (lines 490–510) to highlight this limitation and its implications for generalizability. We explain that although the dataset is limited, it covers a realistic range of truckload volumes and point cloud sizes. Further validation on broader datasets and under diverse conditions is needed, and we propose this as a focus for future research.
- Clarification on Algorithm Parameters
Comment: Parameters for color-based labeling, plane cutting, and other steps are not clearly described.
Response: The Materials and Methods section (lines 230–260) has been updated to include:
- Threshold values for horizontal slicing and color-based segmentation
- Settings used in plane fitting and the logic behind segmentation choices
- A step-by-step summary of the algorithm’s logic flow, including outlier removal and envelope inference
- Accuracy Across Log Sizes and Arrangements
Comment: How does the method handle different log sizes and arrangements?
Response: In the revised Discussion (lines 480–490), we note that log diameter, length variability, and stacking patterns affect occlusion and shape reconstruction. MLS data captured better overhead geometry than iPhone LiDAR, improving estimation accuracy, especially in non-uniform arrangements. This was a key difference between T1 and T2.
- Results Need Clearer Presentation and Comparative Statistics
Comment: Present statistical metrics for T1 and T2 in a table; clarify outlier treatment.
Response:
- We added text in the Results section, summarizing MAE, RMSE, correlation coefficients, p-values for both platforms side-by-side.
- We explain in Methods (line 265) that no outliers were removed from the sample to reflect real-world performance. However, extreme values were analyzed and visualized to observe their influence.
- Figures 5 and 6 were revised for better layout, consistent terminology, and clearer labeling.
- Algorithm Implementation and Preprocessing Not Fully Described
Comment: Add more details on preprocessing and implementation.
Response: We revised Section 2.3 and 2.4 to describe:
- How the point cloud was filtered and downsampled
- How envelope projection and segmentation logic was applied
- How cross-sectional slices were positioned and analyzed (start, middle, end, and at regular intervals)
- Lack of Conclusion on Which Approach Was Better
Comment: The conclusion lacks clarity on why these two tools were tested and which one performed better.
Response: In the Conclusion (lines 515–530), we now clearly state:
- The MLS GeoSLAM (T1) yielded more accurate and reliable estimates, due to superior coverage and reduced occlusion.
- The iPhone LiDAR (T2) showed promise for real-time, low-cost applications but tends to overestimate volume in tightly stacked loads.
- Add Recent References on LiDAR in Forestry
Comment: Include more recent and relevant studies.
Response: Recent references on the subject have been added in the Discussion and Bibliography:
These studies enhance the context on operational LiDAR use, harvester data integration, and forest logistics systems.
Round 2
Reviewer 4 Report
Comments and Suggestions for AuthorsWhen discussing the algorithm's development and performance assessment, it is needed to support it with related references. For example, Line 243-250 needs it if the idea is adapted from another work. The Wilcoxon test should be cited from previous works.
Author Response
Comment 1:
When discussing the algorithm's development and performance assessment, it is needed to support it with related references. For example, Line 243–250 needs it if the idea is adapted from another work. The Wilcoxon test should be cited from previous works.
Response 1:
We thank the reviewer for this important observation. We have now revised the Discussion section to clarify that the algorithm’s development—particularly the envelope-fitting and cross-sectional slicing for volumetric estimation—is conceptually aligned with established methods in 3D point cloud processing and geometric reconstruction used in forestry and industrial applications. We have added citations to previous works that inspired the adaptation of these methods to the context of truckload estimation (see revised Discussion, paragraph 3).
Furthermore, we have added citations to support the use of the Wilcoxon signed-rank test as a robust, non-parametric method widely used in forestry and remote sensing applications for comparing paired datasets (see Discussion, paragraph 3 and Methods section, where the test is introduced). These additions ensure the methodological basis is well-supported by the relevant literature.
