Estimation of Tree Diameter at Breast Height (DBH) and Biomass from Allometric Models Using LiDAR Data: A Case of the Lake Broadwater Forest in Southeast Queensland, Australia

Round 1

Reviewer 1 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

In this second review, authors clarify a controversial point related to the taken of lidar data during the winter; author explained that Australian forests are not deciduous trees that shed leaves in winter. As well, authors reduced the manuscript avoiding the repetition of the information. My advice is the publication of this manuscript in Remote Sensing.

Author Response

Thank you for your review comments. Please see the attachment for specific  response.

Author Response File: Author Response.pdf

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

This manuscript explores the use of airborne LiDAR data and established pantropical allometric models to estimate tree DBH and AGB in the Lake Broadwater Forest of Queensland. The authors evaluate the performance of three widely used models—Jucker (2016) and two Gonzalez-Benecke variants—against field-measured DBH data and apply the Chave AGB equation to derive forest carbon metrics. The study has practical relevance, particularly in the context of carbon credit schemes in Australia, and touches on an important operational challenge: inferring DBH from LiDAR-derived crown metrics. However, several aspects of the manuscript remain problematic, both in terms of methodological execution and interpretation of results.

 

The evaluation of model performance rests heavily on a small validation dataset derived from a limited number of field plots. Only one plot (Plot 6) appears to have been used for full statistical comparison of measured and modeled DBH at the tree level. The other plots contribute averaged values, but these do not allow for proper inference about model behavior across the full range of tree sizes or species. Treating these plot-level means as independent validation points obscures within-plot variance and inflates the apparent sample size. If the authors have tree-level validation data for other plots, these should be included. If not, the current evaluation lacks the statistical depth needed to make claims about relative model performance.

 

The paper repeatedly refers to heteroscedasticity in the regression plots but does not engage with its implications. In several figures, model residuals clearly vary with DBH, yet the analysis does not examine whether model errors are size-dependent or species-dependent. This is particularly important in mixed-species forests where allometric scaling may differ systematically across functional types. A breakdown of model performance by species or DBH class would have been more informative than simply reporting global RMSE and MAE values.

 

The choice of the three DBH models is reasonable given their wide usage, but their application here lacks nuance. The authors rely on pan-tropical models developed in very different ecological settings, with no local calibration or species-specific parameterization. While the manuscript states that the dominant species in Lake Broadwater are "similar" to those used in the source models ("closely resemble the predominant species found in the study area"), this similarity is never quantified. Wood density, branching architecture, and canopy allometry vary significantly across these genera. No wood density values are presented, nor is there a discussion of whether local parameter adjustments were attempted. The Chave AGB equation, for example, requires ρ, but it is unclear where the authors sourced ρ for each species or whether species-specific values were applied consistently.

 

The LiDAR processing section is too brief for a remote sensing journal. The methods used to extract tree height, crown diameter, area, and volume are not well documented. What segmentation algorithm was used? Was a local maxima filtering approach applied? Were the CHMs smoothed before crown delineation? Were tree tops identified automatically or manually? What was the horizontal resolution of the CHM? Without these details, it is impossible to assess the reliability of the derived canopy metrics, which underpin all subsequent DBH and biomass estimates.

 

In several cases, figure content is redundant or poorly justified. For example, Figures 6&7 present nearly identical regression plots with swapped axes, producing visually repetitive content that does little to enhance the argument. Instead of showing six scatterplots per model, the authors could have focused on residual distribution, bias per DBH class, or species-specific deviations. Table 2 attempts to present plot-level residuals, but again, only averaged values are shown, which masks tree-level variation.

 

The use of R² as a key validation metric is problematic given the heteroscedasticity and lack of cross-validation. The paper also fails to clarify whether regression models were fitted using tree-level data or aggregated summaries. In cases where model inputs are derived from remote sensing and compared to field data, errors can be spatially correlated. No attention is paid to this issue.

 

Finally, the implications for carbon estimation are overstated. The authors compare AGB estimates based on LiDAR-derived DBH to those from field-measured DBH and interpret the differences as underestimation caused by model bias. However, this difference could also be due to error propagation from LiDAR-based canopy metrics, or species-specific misrepresentation in the allometric models. No uncertainty analysis is provided for AGB or CO₂ estimates, yet these are central to the conclusions about carbon sequestration potential and carbon credit eligibility.

Author Response

Thank you for your review comments. Please see the attachment for specific response.

Author Response File: Author Response.pdf

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

General comments

From the Response to Editor’s Comment, it can be seen that both reviewers mentioned the length of the manuscript, and the length has been significantly revised and shortened. However, it is still not concise enough, or rather, it still fails to focus on the core of the problem. As proposed by Reviewer 2, " I respectfully point out that it is possible that authors organized a thesis and did this manuscript. I recommend that the authors review published research papers to understand how scientific papers are condensed and written." In the discussion section, Section 4.2 spends a large amount of space discussing indicators such as RMSE and MAE, which is not the key of this study. We do not intend to discuss the advantages and disadvantages of the indicators, but the performance of the model reflected by the indicators. My suggestion is that it can be discussed, but not too much. Therefore, taking all factors into consideration, although you have made relevant modifications and the research content is innovative, this manuscript still has a certain gap from the standard format of journal papers, so I have to give you a major revision. It is suggested that you refer to the relevant paper format or journal article norms and make the necessary revisions. If you can do well in this regard, I will be very happy to accept your paper.

 

Special comments

1. The letters in Figure 2 are too small, and the title text in Figures 4/5 is of inconsistent size.
2. Figure 3 is made too simply.
3. Line 322: R2 → R²; As a manuscript for the second review, I think this kind of problem should not occur.
4. Line 435: Should it be 3.3.2 here?

Author Response

Thank you for your review comments. Please see the attachment for specific response.

Author Response File: Author Response.pdf

Reviewer 4 Report (New Reviewer)

Comments and Suggestions for Authors

Paper entitled Estimation of tree diameter at breast height (DBH) and biomass from allometric models using LiDAR data: A case of the Lake Broadwater Forest in southeast Queensland, Australia is an interesting paper, but it requires significant modifications.

Comment 1: Figure 1 (line 143) illustrates both the intended and the surveyed plots. Please explain why some of the planned plots were not included in the final data collection. Moreover, the visualization of the plots in Figure 1 should be improved for greater clarity.

omment 2: In line 176, you mention a point density of 4 points per square meter used for the analysis. Could you elaborate on whether this density is adequate for achieving sufficiently precise results? Considering that current airborne LiDAR technologies offer much higher point densities, how might the use of such data influence your findings or improve the accuracy of your results?

Comment 3: In lines 195–196, you report that the positions of individual trees were recorded with an accuracy of 2–3 meters. This level of accuracy appears rather imprecise. Considering the potential for spatial error, could you elaborate on how you ensured that the data being compared referred to the same tree?

Comment 4: In line 206, you mention the use of satellite imagery for identifying tree locations. Could you clarify which specific satellite data (e.g., provider, resolution, acquisition dates) were used for this purpose?

Comment 5: Lines 206–208 do not appear to be directly related to subsection 2.3 Field Measurements and may be better placed elsewhere in the manuscript.

Comment 6: The content in lines 209–216, along with Table 1, seems more suitable for inclusion in the Results section rather than under Methods.

Comment 7: The Results section should be limited to presenting the outcomes of the research conducted and should not include references to the literature.

Comment 8: The Conclusions section should be restricted to summarizing the findings of the study itself and should not contain references to the literature.

Comment 9: All Latin names of species (genus and species) should appear in italics throughout the manuscript.

Comment 10: Correct the literature citations according to the instructions for the authors.

Comment 11: The overall quality of all figures in the manuscript should be improved to ensure clarity and readability.

Author Response

Thank you for your review comments. Please see the attachment for specific response.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

The manuscript appears only marginally revised, and many of the key concerns raised previously have not been meaningfully addressed. Unfortunately, the authors did not highlight all the changes in the revised text, which makes it unnecessarily difficult to track modifications and assess their responses.

First, the manuscript presents itself as a model evaluation study, but it’s never entirely clear how rigorously that evaluation has been carried out. The authors reference multiple plots and claim to use tree-level data, yet in the main text, what we mostly see are plot-level averages and scatterplots that don't reveal much about within-plot variation or model behavior across the full range of tree sizes. 

Another issue lies in the black-box treatment of the LiDAR-derived metrics. The study hinges on estimates of crown area, height, and volume, but the workflow by which these were extracted is poorly documented. Simply stating that LiDAR360 and ArcGIS Pro were used is not enough—what segmentation logic was followed? Were any filters applied to reduce false positives or merged crowns? Was there a minimum crown size threshold? These are not minor technicalities; they determine the accuracy of every downstream DBH prediction. Without transparency here, it's very difficult to evaluate how much trust we can place in the input data going into the allometric models.

Author Response

Thank you for your review comments. Please see the attachment for specific response.

Author Response File: Author Response.pdf

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

I agree with the publication of the manuscript.

Author Response

Thank you for your review comments. 

Reviewer 4 Report (New Reviewer)

Comments and Suggestions for Authors

The authors have adequately addressed the comments and suggestions provided in the previous review round. The manuscript entitled "Estimation of tree diameter at breast height (DBH) and biomass from allometric models using LiDAR data: A case of the Lake Broadwater Forest in southeast Queensland, Australia" has been substantially improved and revised accordingly. I consider the manuscript suitable for publication in Remote Sensing.

Author Response

Thank you for your review comments. 

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

General comment

Authors of this manuscript used lidar data to estimate some variables related to forest structure and subsequently forest aboveground biomass in a nature reserve in Australia. Authors emphasized in the estimation of Diameter at Breast Height (DBH) because it is a variable not usually estimated directly from lidar data. The objectives are well stated and methodologically well developed, supporting the results. However, the analysis has a serious methodological problem: authors used lidar data taken during winter for estimating leaf structure variables (crown diameter, for instance). The problem is that many trees lose their leaves during winter, leading to an underestimation of this variable. Authors will need to clarify this specific point of the methodology in the next submission.

On the other hand, I need to highlight the most serious problem of this manuscript: the current document is excessively long (41 pages), repetitive, and contains unnecessary information. For example, it has 15 figures and 8 tables (a serious scientific paper has less than 7 figures and no more than three tables). To correct this, authors must reduce the text, figures, and tables in all sections of the article. This manuscript cannot have more than 20 pages in total. I respectfully point out that it is possible that authors organized a thesis and did this manuscript. I recommend that the authors review published research papers to understand how scientific papers are condensed and written. Other specific comments can be seen below. In conclusion, I recommend that this article be rejected until all the points indicated in this review are corrected. When authors create the next submission, I will be able to evaluate better the academic details of the future manuscript.  

Introduction

Authors wrote the objectives in two parts of the introduction, first at L95 and later repeat them at L122. Authors must rewrite the introduction in order to write the objectives only in the last paragraph of the introduction.

Methodology

L194-199. Lidar survey corresponds to winter season when trees lose leaves, generating that Lidar cannot estimate crown diameter (CD). Thus, in my view, crown diameter could not be estimated in this work.

L200. Table 1 is not needed; authors can add the same information in the text.

L238. Table 2. What are the acronyms “Av Tree H”, “CD S_N”, “CD E_W, and “CV”? Please, review that all acronyms have been described in the text.

L364-402. The statistics of error and explained variation (RMSE, MAE, MBE, R2) are well known for specialized readers of Remote Sensing; therefore, authors do not need to give this detailed explanation. Only the general definition of each statistic is enough. 

Results

A scientific paper should have less than 6 figures and three tables. Authors must reduce the figures to a maximum of 7 (This manuscript has 15 figures) and tables to maximum of 4 (this manuscript has 8 tables). For instance, the figures from 8 to 12 can be moved as annexes as well as majority of tables.  

L412. Results do not need citation; these lines seem closer to the discussion section.

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors, 

I have read carefully your paper. Although it can hold some scientific merit, my most important concern is about the time difference between LiDAR data acquisition and the measurements used for comparison. Two years are indeed standing from some time that can introduce bias in the assessment of your residuals, therefore invalidating the accuracy of your goals. Therefore, I must question the validity of the comparison and I would advise for rejection.

I am sorry about this and good luck.

Rev.