the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Mar 2026
Revealing coastal vegetation structural diversity through LiDAR-derived relative entropy
Abstract. Coastal wetlands are among the most valuable ecosystems globally, due to the high ecological function of their structurally complex vegetation communities. However, there remains a lack of vegetation structural complexity (VSC) indicators tailored for coastal wetland applications. Here, we developed a new VSC index, vegetation structure relative entropy (VSRE), based on a measure of the asymmetry of the difference between two probability distributions. While the currently used VSC index all fail to capture the discrete and continuous complexity gradients of coastal vegetation communities, VSRE demonstrated ideal performance in these applications, exhibiting strong robustness across varying point cloud densities. By applying this indicator to 1,337 LiDAR samples of natural coastal vegetation, we used Alpha Earth Foundation data and a deep learning model to create a seamless VSRE spatial map of coastal wetlands in China, with high spatial resolution (10 m) and accuracy (R2 = 0.96). VSRE mapping provides crucial ecosystem structural information beyond vegetation classification data and conventional optical indices, highlighting the high spatial heterogeneity of VSC in coastal wetlands. This study offers a valuable foundation for prioritizing conservation areas and enhancing the resolution and accuracy of coastal zone ecological modelling.
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Status: closed
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RC1: 'Comment on essd-2026-68', Anonymous Referee #1, 05 Apr 2026
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AC1: 'Reply on RC1', Jun Ma, 26 May 2026
We sincerely thank you for your careful reading of our manuscript and for the constructive and insightful comments. We greatly appreciate your recognition of the importance of quantifying vegetation structural complexity in coastal ecosystems, as well as the concerns raised regarding the conceptual basis of VSRE, the fairness of metric comparisons, and the manuscript's overall focus and presentation.
In response to these comments, we have carefully revised the manuscript at both the conceptual and structural levels. First, we have clarified that VSRE is not intended to represent all dimensions of vegetation structural complexity, but rather to quantify a specific, ecologically meaningful dimension: spatial configurational heterogeneity. Second, we have substantially strengthened the description of the experimental basis for the low-, medium-, and high-VSC groups, and further clarified the role of the biomass-based Shannon index as an independent ecological proxy rather than a direct ground truth of VSC. Third, we have reorganized the manuscript to emphasize better the main storyline of this study: the development of VSRE, its validation using fixed plots and field LiDAR samples, and its first wall-to-wall application along the coast of China. In addition, we have revised the title, improved the structure of the Introduction and Methods, clarified the distinction between fixed-plot validation and nationwide field sampling, strengthened the presentation of classification and mapping results, and added or redesigned key figures to improve clarity and transparency.
We believe that these revisions have substantially improved the manuscript and have made our study more focused, rigorous, and accessible. Below, we provide a detailed, point-by-point response to each comment. Once again, we sincerely thank you for your valuable comments.
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RC2: 'Reply on AC1', Anonymous Referee #1, 09 Jun 2026
I highly appreciate the authors’ efforts in addressing my previous concerns. I am not sure whether I used the review system correctly, but I was unable to locate the revised manuscript in the system. Therefore, my evaluation is based solely on the authors’ response letter. Fortunately, the authors provided a detailed and well-structured response letter with clear explanations of how the manuscript was revised. Based on these responses, I believe that most of my previous concerns have been adequately addressed. However, I still have a few comments that the authors may wish to consider during further revision.
First, I continue to hold the view that the proposed index, relative entropy, primarily characterizes structural dissimilarity among vertical canopy layers. I do not dispute that this can serve as an indicator of one aspect of the vegetation structural complexity. However, I am concerned that its applicability may be limited in forest types with more homogeneous vertical structural distributions. I suggest that the authors explicitly acknowledge this potential limitation in the manuscript.
Second, because relative entropy appears to capture a different aspect of canopy structural complexity than existing metrics, I do not believe that extensive comparisons with other complexity metrics are particularly necessary. Instead, I suggest that the authors clearly state that the proposed metric is designed to quantify structural dissimilarity among vertical layers and explain why this characteristic makes it especially suitable for assessing canopy structural complexity in mangrove forests (possibly due to their relatively short but dense canopy structure). Accordingly, I recommend reducing the lengthy comparisons with other metrics and placing greater emphasis on the national-scale mapping of mangrove canopy structural complexity across China.
Third, the above comments naturally lead to my final suggestion. I encourage the authors to expand their discussion and interpretation of the national-scale mangrove canopy structural complexity map. After all, this topic is highlighted in the title of the paper and represents one of its most important contributions.
Citation: https://doi.org/10.5194/essd-2026-68-RC2 -
AC3: 'Reply on RC2', Jun Ma, 07 Jul 2026
We sincerely thank the reviewer for the continued careful evaluation of our manuscript and for the constructive suggestions provided in this round of review. We are grateful that the reviewer found that most previous concerns had been adequately addressed based on our response letter. We also note the reviewer’s comment that the revised manuscript was not available in the review system. According to the ESSD interactive review procedure, our current response is provided as a structured author response rather than as a separately uploaded revised manuscript. Therefore, in the present response letter, we describe the corresponding manuscript changes as clearly and specifically as possible.
The reviewer’s remaining comments are highly valuable for further improving the focus and interpretation of the manuscript. In the subsequent revision stage permitted by the journal workflow, we will further clarify the conceptual scope and limitations of VSRE, streamline the comparison with existing structural-complexity metrics where appropriate, and strengthen the discussion of national-scale mapping results, especially for mangrove canopy structural complexity. These revisions will help make the manuscript more focused and better aligned with its core contribution.
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AC3: 'Reply on RC2', Jun Ma, 07 Jul 2026
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RC2: 'Reply on AC1', Anonymous Referee #1, 09 Jun 2026
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AC1: 'Reply on RC1', Jun Ma, 26 May 2026
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RC3: 'Comment on essd-2026-68', Anonymous Referee #2, 29 Jun 2026
General comments:
Coastal wetlands fulfil important ecosystem services such as carbon sequestration, shoreline protection and water purification. Since these services depend on vegetation structure, quantifying structural complexity of coastal wetlands allows estimating and predict ecosystem service provision. The manuscript introduces a new LiDAR-based vegetation structural complexity (VSC) index called Vegetation Structural Relative Entropy (VSRE) and presents a VSRE map for the coast of China. The authors state that the existing VSC metrics are not tailored to coastal wetlands, which differ from forest ecosystems for which these metrics have been typically developed.
The authors introduce the VSRE and compute it for LiDAR data of coastal wetlands across the coast of China. Based on Alpha Earth Foundation data, they further use an MLP deep learning model to produce a VSRE map for the entire coast of China (for 2022). They compare how both the VSRE and five other VSC metrics capture different levels of VSC or biodiversity and different plant communities, and how robust they are to varying point densities.
The dataset is interesting, and the index could be very valuable. However, after reading the manuscript, many aspects are still not clear to me. Most importantly, I am not sure if the manuscript in its current form fits the scope of ESSD.
ESSD states that “manuscripts that focus on methodology and model development should seek publication elsewhere”, that “extensive analysis and interpretation of data is considered out of scope” and that “any comparison to other methods is beyond the scope of regular articles”. Since the manuscript introduces a new method/index and has quite substantial analysis in the form of comparison to other metrics (and a lot of Supplementary Data), I feel like the focus of the paper does not match a data description article. In addition, it seems like some key content typical for data description articles is missing: rigorous quality assessment (of the actual VSRE map), presentation of use cases of the dataset, more in-depth discussion of its limitations, etc. I agree with Reviewer 1 here and would also recommend dividing the work into two separate papers or rewrite the paper so it is appropriate as data description article.
Specific comments:
- The dataset is a single tiff file with the VSRE map. Since there is no “usage” section, it is not clear how other researchers benefit the dataset. For which studies could it be used? Furthermore, it would be extremely valuable to the scientific community if the UAV-LiDAR data and ideally also the field data would be included in the dataset.
- How were these three qualitative VSC groups classified (cf. L80f.)? I could not find this in the paper (other than “human-defined complexity classification” in L390). Given that VSC is difficult to measure directly (as already stated by Reviewer 1), I think it is absolutely crucial for the manuscript and interpretation of the results and figures to explain this.
- You state that you use the Shannon Diversity Index as a “quantitatively objective VSC” (L391), but this index quantifies the diversity in terms of biomass but not in terms of structure, right? Please better explain the rationale here.
- The manuscript would benefit from a central workflow figure. From the text alone, it is difficult to follow the steps you took to prepare the dataset.
- Related to the previous point, the organization of the manuscript should be improved. Key information is difficult to find or overly vague and processing details are mentioned before an appropriate general overview is provided. Many data acquisition or processing steps remain unclear to me.
- General question: Is VSRE supposed to be a specific VI tailored for coastal wetlands or is it applicable to all kinds of vegetation ecosystems?
- L86: In other locations in the manuscript, you state 1,337 sites, not 1,336? Also, are these 1,337 sites included in the acquisitions described in 1.2.1 or is this another dataset?
- Section 1.2.1: When was the data acquired? Did the flights use parallel flight strips, or, e.g., a criss-cross pattern? What was the flight speed? How did the overlap rates differ between flights? Please add more details.
- L138: Which algorithm was used exactly for individual tree segmentation? Li et al. 2012? This part is very vague and should be explained better.
- L140f.: How was it possible to measure DBH from UAV-LiDAR data? Doesn’t such data usually have too low point density for that? Or was DBH measured in the field? Please explain.
- L143f.: As someone who has not used allometric models yet, this part seems very vague. Can you add the equation for the allometric model?
- L181: What voxel size was used in this study and why?
- L216ff.: These details on terrain normalisation seem to better fit into a different section.
- Section 1.2.4: After reading this section, it was not clear to me which existing metrics you used. I would recommend adding a table here for those metrics, possibly with additional explanations, typical applications of the metric, references, etc.
- Section 1.2.5: Here, it is not clear to me what your target variable is. What are the class labels you want to predict? What are the features you use? I would recommend a table here as well.
- L258-267: You repeat a statement on resampling here: “The results were resampled using triple convolution […]”, “The results are resampled into various vegetation indices through cubic convolution …”. Which one is right?
- The Introduction and Section 1.2.3 should include a better high-level explanation of the index (cf. 1.3.1 in the results)
- Fig. 4: I do not understand where the aspect and the “varying PCD” are contained in this figure. Please explain this better.
- Fig. 5: What does each circle represent in this figure? A UAV-LiDAR point cloud of a plot?
Technical corrections:
- Guo et al. (2020) is cited in the text but missing in the references
- “L1 LiDAR sensor” -> DJI Zenmuse L1 LiDAR system?
- Consistency: You sometimes used points/m2 and other times points m-2. You also sometimes used “formula” and sometimes “equation”. Please make this consistent.
- VSRE is sometimes misspelled as VSER
- Please check if the manuscript structure is according to the guidelines, e.g., should “Conclusions” be placed after “Data availability”?
- It seems that your Supplementary Material would be better placed in the Appendix. Please read the “Submission” page (https://www.earth-system-science-data.net/submission.html) and evaluate whether “Appendix” or “Supplementary Material” is suitable for this material.
Some comments may be similar to those raised by Reviewer 1. In case you have already replied to them and revised the manuscript accordingly, you can of course simply refer to those replies.
Please note that I did not review the Supplementary Material in more detail.
Citation: https://doi.org/10.5194/essd-2026-68-RC3 -
AC2: 'Reply on RC3', Jun Ma, 07 Jul 2026
Dear reviewer,
We sincerely thank you for your careful reading of our manuscript and for your constructive and insightful comments. We greatly appreciate your recognition of the potential value of the dataset and the proposed VSRE index, as well as your important concerns regarding the scope of ESSD, the conceptual basis of VSRE, the rationale for the validation design, and the overall organization and presentation of the manuscript.
In response to these comments, we have substantially revised the manuscript to make its data-descriptor focus clearer and to improve its logical structure, methodological transparency, and readability. Most importantly, we have clarified that the primary contribution of this study is the ChinaTidalVSC dataset, a spatially continuous 10 m vegetation structural complexity product for China’s tidal wetlands, rather than the development of a general-purpose methodological framework. Accordingly, VSRE is now presented more explicitly as a necessary data-generation and standardization procedure for converting UAV-LiDAR point clouds into an ecologically interpretable and reusable structural-complexity variable.
We have also strengthened the explanation of the experimental and ecological basis for the low-, medium-, and high-complexity groups, and clarified that the biomass-weighted Shannon index was used as an independent ecological proxy associated with community structural differentiation, rather than as a direct ground truth of vegetation structural complexity. In addition, we have reorganized the Methods and Results sections to better follow the logic of a data-description article, added a central workflow figure, improved the description of LiDAR data acquisition and processing, clarified the distinction between fixed-plot validation and nationwide field samples, and reframed the comparisons with existing metrics as data validation and quality assessment rather than broad methodological benchmarking.
To further improve the reusability of the dataset, we have added clearer guidance on potential applications, data format, value interpretation, and limitations. We have also expanded the data repository from a single GeoTIFF file to a more complete data package, including metadata, sample-level information, validation data, and VSRE calculation code where appropriate. We have revised the title, Abstract, Introduction, Methods, figure captions, and Data availability section accordingly, and have checked the manuscript for terminology consistency and typographical errors.
We believe that these revisions have substantially improved the focus, rigor, transparency, and accessibility of the manuscript. We hope that the revised version more clearly demonstrates the suitability of this work as an ESSD data-description article and provides sufficient information for users to understand, evaluate, and reuse the ChinaTidalVSC dataset.
Status: closed
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RC1: 'Comment on essd-2026-68', Anonymous Referee #1, 05 Apr 2026
Comments on “Revealing coastal vegetation structural diversity through LiDAR derived relative entropy” by Qi et al.
General comments
Vegetation structural complexity (VSC) is increasingly recognized as a key attribute influencing ecosystem processes and functions. The quantification of VSC from 3D LiDAR data has therefore become an important area of research to support such studies. In this work, the authors investigate methodologies for quantifying VSC specifically in coastal ecosystems, propose a new index, namely VSRE, and demonstrate that this metric outperforms existing approaches. They further combine this metric with the latest Google AEF dataset to produce a nationwide VSC product. Overall, this is a timely study for advancing VSC research in coastal ecosystems, and the resulting dataset is valuable. However, I have several major concerns that prevent me from recommending its publication in its current form.
First, I have difficulty understanding the rationale for using relative entropy to quantify VSC. Fundamentally, relative entropy measures the dissimilarity of LiDAR point distributions between different voxels. It is unclear how this directly reflects vegetation structural complexity. By definition, structural complexity relates to both the randomness of canopy element distribution and the extent of space occupation. In this sense, VSC should capture at least two components: the abundance of structural elements and the variability in their spatial arrangement. However, strong dissimilarity between layers does not necessarily indicate high structural complexity. For instance, in tropical forests, the canopy is often densely filled due to complementary layering, resulting in relatively similar element distributions across layers. Despite this similarity, such forests exhibit high structural complexity. Therefore, I remain fundamentally uncertain about the appropriateness of using relative entropy as a metric for quantifying VSC.
Second, the comparison among metrics appears methodologically unclear and potentially unfair. The authors used three groups of plots—categorized as low, medium, and high VSC—to evaluate the performance of the proposed metric against existing ones. However, it is not clear how these groups were defined in the field. Were they based on visual assessment or some quantitative criteria? Given that VSC is inherently difficult to measure directly, establishing reliable ground-truth data is challenging without the use of simulation or well-defined structural proxies. Furthermore, I do not consider the comparison between VSC and species richness to be a robust or appropriate way to demonstrate the superiority of the proposed metric. The relationship between VSC and species richness remains debated in the literature and is not necessarily positive or consistent. For example, in northern temperate forests, single-layer, monocultural stands can still exhibit relatively high VSC due to selection effects and high space occupation.
Third, the overall writing of the manuscript requires improvement. The current version attempts to integrate a wide range of content, which reduces its focus, while several important components lack sufficient detail. For instance, the classification of coastal vegetation types is itself a substantial task, yet it is only briefly described. A similar issue arises with the nationwide VSC mapping, where methodological and implementation details are limited. I therefore suggest that the authors consider dividing the work into two separate papers: one focusing on the technical development and validation of the proposed VSC metric, and another dedicated to its application in national-scale mapping.
Below are some specific comments for the authors to consider.
Specific comments
- Title: The current title doesn’t seem like a data paper.
- Line 38: “the their”. Typo.
- Line 58-59: Numerous studies have already addressed this question in terrestrial ecosystems. Therefore, the discussion should be more focused on the functional and structural characteristics of coastal ecosystems.
- Line 68-71: What are the unique challenges associated with quantifying canopy structural complexity in coastal ecosystems? It would be helpful to include one or two sentences to clarify this point.
- Line 72-90: This content belongs in the Methods section. In the Introduction, you may briefly summarize the data and overall approach of the study, but detailed procedural steps should not be included.
- Line 92: To be honest, this section is somewhat disorganized. The descriptions of fixed experimental plot data and drone-based LiDAR data should be clearly separated. At present, key information regarding the drone LiDAR data used for national mapping is missing. For example, the manuscript does not specify where these plots are located or what their spatial extent is.
- Line 93: This sentence is misleading, as it suggests that the entire study was conducted at a single site. In fact, the site was only used for method validation.
- Line 104-105: Beyond point density, the vertical penetration capability of the LiDAR sensor is critical for accurately measuring canopy structural complexity. To my knowledge, the DJI L1 sensor has limited ability to penetrate dense canopies. It would therefore be helpful to include a vertical profile visualization to illustrate this limitation, allowing readers to better understand the characteristics and potential constraints of the data
- Line114: “numerous” It would be better to provide a specific number.
- Line 119: This represents a very wide range of point densities. Additional information is needed to justify the experimental design and the selection of point density levels.
- Line 141: Did you measure tree height manually in the field? If so, what is the purpose of using LiDAR-segmented individual trees?
- Line 155: I am not convinced that this index effectively quantifies canopy structural complexity. Rather, it appears to primarily capture the dissimilarity in vertical canopy distribution. Please see my general comment.
- Fig. 2: This figure is not an effective illustration. It does not clearly convey how the metric is designed or calculated, and instead only shows a set of voxels without sufficient explanation.
- Line 233: Five? But you only mentioned four here.
- Line 239-306: This is an interesting study; however, the manuscript does not allocate sufficient space to adequately present the results. Key outcomes, such as classification and mapping accuracy, are only mentioned in the Methods section, which is inappropriate. Please refer to my general comments for further details.
- Line 305: This level of accuracy suggests potential overfitting, which is a known issue for MLP models when trained on a limited number of samples.
- Line 332: you have defined the abbreviation of VSC before.
- Fig. 3: How were the three VSC groups defined? I could not find sufficient details on this. Notably, most existing metrics indicate that the “medium” group corresponds to relatively low VSC, whereas only your metric suggests otherwise. This raises questions about the criteria used to establish these groupings, particularly considering the difficulty to quantify VSC in real world.
- Line 362: Point density is not the only factor that should be considered (see my previous comments). It would be helpful to include a vertical profile of the LiDAR data to better illustrate its structural characteristics. To my knowledge, the DJI L1 sensor primarily captures returns from the upper canopy and has limited penetration into lower layers. As a result, even with increasing point density, the additional points may still be disproportionately concentrated in the upper canopy. This could artificially increase relative entropy by enhancing apparent dissimilarity between layers, without necessarily reflecting changes in VSC.
- Line 386: High species diversity does not necessarily mean high canopy structural complexity. Please see my general comment.
- Line 471-484 and Fig. 9: I may be misunderstanding something here, but I am not clear how these conclusions are derived. Wouldn’t a mature forest with strong canopy complementarity exhibit a near-random distribution of canopy elements, and therefore correspond to high entropy?
- Line 493-494: I am lost again here. What do you mean by structural reproducibility?
- Line 507: I did not put too much focus on the discussion on the national mapping here, because I think this should be put in a separate paper.
Citation: https://doi.org/10.5194/essd-2026-68-RC1 -
AC1: 'Reply on RC1', Jun Ma, 26 May 2026
We sincerely thank you for your careful reading of our manuscript and for the constructive and insightful comments. We greatly appreciate your recognition of the importance of quantifying vegetation structural complexity in coastal ecosystems, as well as the concerns raised regarding the conceptual basis of VSRE, the fairness of metric comparisons, and the manuscript's overall focus and presentation.
In response to these comments, we have carefully revised the manuscript at both the conceptual and structural levels. First, we have clarified that VSRE is not intended to represent all dimensions of vegetation structural complexity, but rather to quantify a specific, ecologically meaningful dimension: spatial configurational heterogeneity. Second, we have substantially strengthened the description of the experimental basis for the low-, medium-, and high-VSC groups, and further clarified the role of the biomass-based Shannon index as an independent ecological proxy rather than a direct ground truth of VSC. Third, we have reorganized the manuscript to emphasize better the main storyline of this study: the development of VSRE, its validation using fixed plots and field LiDAR samples, and its first wall-to-wall application along the coast of China. In addition, we have revised the title, improved the structure of the Introduction and Methods, clarified the distinction between fixed-plot validation and nationwide field sampling, strengthened the presentation of classification and mapping results, and added or redesigned key figures to improve clarity and transparency.
We believe that these revisions have substantially improved the manuscript and have made our study more focused, rigorous, and accessible. Below, we provide a detailed, point-by-point response to each comment. Once again, we sincerely thank you for your valuable comments.
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RC2: 'Reply on AC1', Anonymous Referee #1, 09 Jun 2026
I highly appreciate the authors’ efforts in addressing my previous concerns. I am not sure whether I used the review system correctly, but I was unable to locate the revised manuscript in the system. Therefore, my evaluation is based solely on the authors’ response letter. Fortunately, the authors provided a detailed and well-structured response letter with clear explanations of how the manuscript was revised. Based on these responses, I believe that most of my previous concerns have been adequately addressed. However, I still have a few comments that the authors may wish to consider during further revision.
First, I continue to hold the view that the proposed index, relative entropy, primarily characterizes structural dissimilarity among vertical canopy layers. I do not dispute that this can serve as an indicator of one aspect of the vegetation structural complexity. However, I am concerned that its applicability may be limited in forest types with more homogeneous vertical structural distributions. I suggest that the authors explicitly acknowledge this potential limitation in the manuscript.
Second, because relative entropy appears to capture a different aspect of canopy structural complexity than existing metrics, I do not believe that extensive comparisons with other complexity metrics are particularly necessary. Instead, I suggest that the authors clearly state that the proposed metric is designed to quantify structural dissimilarity among vertical layers and explain why this characteristic makes it especially suitable for assessing canopy structural complexity in mangrove forests (possibly due to their relatively short but dense canopy structure). Accordingly, I recommend reducing the lengthy comparisons with other metrics and placing greater emphasis on the national-scale mapping of mangrove canopy structural complexity across China.
Third, the above comments naturally lead to my final suggestion. I encourage the authors to expand their discussion and interpretation of the national-scale mangrove canopy structural complexity map. After all, this topic is highlighted in the title of the paper and represents one of its most important contributions.
Citation: https://doi.org/10.5194/essd-2026-68-RC2 -
AC3: 'Reply on RC2', Jun Ma, 07 Jul 2026
We sincerely thank the reviewer for the continued careful evaluation of our manuscript and for the constructive suggestions provided in this round of review. We are grateful that the reviewer found that most previous concerns had been adequately addressed based on our response letter. We also note the reviewer’s comment that the revised manuscript was not available in the review system. According to the ESSD interactive review procedure, our current response is provided as a structured author response rather than as a separately uploaded revised manuscript. Therefore, in the present response letter, we describe the corresponding manuscript changes as clearly and specifically as possible.
The reviewer’s remaining comments are highly valuable for further improving the focus and interpretation of the manuscript. In the subsequent revision stage permitted by the journal workflow, we will further clarify the conceptual scope and limitations of VSRE, streamline the comparison with existing structural-complexity metrics where appropriate, and strengthen the discussion of national-scale mapping results, especially for mangrove canopy structural complexity. These revisions will help make the manuscript more focused and better aligned with its core contribution.
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AC3: 'Reply on RC2', Jun Ma, 07 Jul 2026
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RC2: 'Reply on AC1', Anonymous Referee #1, 09 Jun 2026
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RC3: 'Comment on essd-2026-68', Anonymous Referee #2, 29 Jun 2026
General comments:
Coastal wetlands fulfil important ecosystem services such as carbon sequestration, shoreline protection and water purification. Since these services depend on vegetation structure, quantifying structural complexity of coastal wetlands allows estimating and predict ecosystem service provision. The manuscript introduces a new LiDAR-based vegetation structural complexity (VSC) index called Vegetation Structural Relative Entropy (VSRE) and presents a VSRE map for the coast of China. The authors state that the existing VSC metrics are not tailored to coastal wetlands, which differ from forest ecosystems for which these metrics have been typically developed.
The authors introduce the VSRE and compute it for LiDAR data of coastal wetlands across the coast of China. Based on Alpha Earth Foundation data, they further use an MLP deep learning model to produce a VSRE map for the entire coast of China (for 2022). They compare how both the VSRE and five other VSC metrics capture different levels of VSC or biodiversity and different plant communities, and how robust they are to varying point densities.
The dataset is interesting, and the index could be very valuable. However, after reading the manuscript, many aspects are still not clear to me. Most importantly, I am not sure if the manuscript in its current form fits the scope of ESSD.
ESSD states that “manuscripts that focus on methodology and model development should seek publication elsewhere”, that “extensive analysis and interpretation of data is considered out of scope” and that “any comparison to other methods is beyond the scope of regular articles”. Since the manuscript introduces a new method/index and has quite substantial analysis in the form of comparison to other metrics (and a lot of Supplementary Data), I feel like the focus of the paper does not match a data description article. In addition, it seems like some key content typical for data description articles is missing: rigorous quality assessment (of the actual VSRE map), presentation of use cases of the dataset, more in-depth discussion of its limitations, etc. I agree with Reviewer 1 here and would also recommend dividing the work into two separate papers or rewrite the paper so it is appropriate as data description article.
Specific comments:
- The dataset is a single tiff file with the VSRE map. Since there is no “usage” section, it is not clear how other researchers benefit the dataset. For which studies could it be used? Furthermore, it would be extremely valuable to the scientific community if the UAV-LiDAR data and ideally also the field data would be included in the dataset.
- How were these three qualitative VSC groups classified (cf. L80f.)? I could not find this in the paper (other than “human-defined complexity classification” in L390). Given that VSC is difficult to measure directly (as already stated by Reviewer 1), I think it is absolutely crucial for the manuscript and interpretation of the results and figures to explain this.
- You state that you use the Shannon Diversity Index as a “quantitatively objective VSC” (L391), but this index quantifies the diversity in terms of biomass but not in terms of structure, right? Please better explain the rationale here.
- The manuscript would benefit from a central workflow figure. From the text alone, it is difficult to follow the steps you took to prepare the dataset.
- Related to the previous point, the organization of the manuscript should be improved. Key information is difficult to find or overly vague and processing details are mentioned before an appropriate general overview is provided. Many data acquisition or processing steps remain unclear to me.
- General question: Is VSRE supposed to be a specific VI tailored for coastal wetlands or is it applicable to all kinds of vegetation ecosystems?
- L86: In other locations in the manuscript, you state 1,337 sites, not 1,336? Also, are these 1,337 sites included in the acquisitions described in 1.2.1 or is this another dataset?
- Section 1.2.1: When was the data acquired? Did the flights use parallel flight strips, or, e.g., a criss-cross pattern? What was the flight speed? How did the overlap rates differ between flights? Please add more details.
- L138: Which algorithm was used exactly for individual tree segmentation? Li et al. 2012? This part is very vague and should be explained better.
- L140f.: How was it possible to measure DBH from UAV-LiDAR data? Doesn’t such data usually have too low point density for that? Or was DBH measured in the field? Please explain.
- L143f.: As someone who has not used allometric models yet, this part seems very vague. Can you add the equation for the allometric model?
- L181: What voxel size was used in this study and why?
- L216ff.: These details on terrain normalisation seem to better fit into a different section.
- Section 1.2.4: After reading this section, it was not clear to me which existing metrics you used. I would recommend adding a table here for those metrics, possibly with additional explanations, typical applications of the metric, references, etc.
- Section 1.2.5: Here, it is not clear to me what your target variable is. What are the class labels you want to predict? What are the features you use? I would recommend a table here as well.
- L258-267: You repeat a statement on resampling here: “The results were resampled using triple convolution […]”, “The results are resampled into various vegetation indices through cubic convolution …”. Which one is right?
- The Introduction and Section 1.2.3 should include a better high-level explanation of the index (cf. 1.3.1 in the results)
- Fig. 4: I do not understand where the aspect and the “varying PCD” are contained in this figure. Please explain this better.
- Fig. 5: What does each circle represent in this figure? A UAV-LiDAR point cloud of a plot?
Technical corrections:
- Guo et al. (2020) is cited in the text but missing in the references
- “L1 LiDAR sensor” -> DJI Zenmuse L1 LiDAR system?
- Consistency: You sometimes used points/m2 and other times points m-2. You also sometimes used “formula” and sometimes “equation”. Please make this consistent.
- VSRE is sometimes misspelled as VSER
- Please check if the manuscript structure is according to the guidelines, e.g., should “Conclusions” be placed after “Data availability”?
- It seems that your Supplementary Material would be better placed in the Appendix. Please read the “Submission” page (https://www.earth-system-science-data.net/submission.html) and evaluate whether “Appendix” or “Supplementary Material” is suitable for this material.
Some comments may be similar to those raised by Reviewer 1. In case you have already replied to them and revised the manuscript accordingly, you can of course simply refer to those replies.
Please note that I did not review the Supplementary Material in more detail.
Citation: https://doi.org/10.5194/essd-2026-68-RC3 -
AC2: 'Reply on RC3', Jun Ma, 07 Jul 2026
Dear reviewer,
We sincerely thank you for your careful reading of our manuscript and for your constructive and insightful comments. We greatly appreciate your recognition of the potential value of the dataset and the proposed VSRE index, as well as your important concerns regarding the scope of ESSD, the conceptual basis of VSRE, the rationale for the validation design, and the overall organization and presentation of the manuscript.
In response to these comments, we have substantially revised the manuscript to make its data-descriptor focus clearer and to improve its logical structure, methodological transparency, and readability. Most importantly, we have clarified that the primary contribution of this study is the ChinaTidalVSC dataset, a spatially continuous 10 m vegetation structural complexity product for China’s tidal wetlands, rather than the development of a general-purpose methodological framework. Accordingly, VSRE is now presented more explicitly as a necessary data-generation and standardization procedure for converting UAV-LiDAR point clouds into an ecologically interpretable and reusable structural-complexity variable.
We have also strengthened the explanation of the experimental and ecological basis for the low-, medium-, and high-complexity groups, and clarified that the biomass-weighted Shannon index was used as an independent ecological proxy associated with community structural differentiation, rather than as a direct ground truth of vegetation structural complexity. In addition, we have reorganized the Methods and Results sections to better follow the logic of a data-description article, added a central workflow figure, improved the description of LiDAR data acquisition and processing, clarified the distinction between fixed-plot validation and nationwide field samples, and reframed the comparisons with existing metrics as data validation and quality assessment rather than broad methodological benchmarking.
To further improve the reusability of the dataset, we have added clearer guidance on potential applications, data format, value interpretation, and limitations. We have also expanded the data repository from a single GeoTIFF file to a more complete data package, including metadata, sample-level information, validation data, and VSRE calculation code where appropriate. We have revised the title, Abstract, Introduction, Methods, figure captions, and Data availability section accordingly, and have checked the manuscript for terminology consistency and typographical errors.
We believe that these revisions have substantially improved the focus, rigor, transparency, and accessibility of the manuscript. We hope that the revised version more clearly demonstrates the suitability of this work as an ESSD data-description article and provides sufficient information for users to understand, evaluate, and reuse the ChinaTidalVSC dataset.
Data sets
VSRE mapping for “Revealing coastal vegetation structural diversity through LiDAR-derived relative entropy” Guanpu Qi et al. https://doi.org/10.6084/m9.figshare.30588722
Model code and software
VSRE Calculator for LiDAR Point Clouds Guanpu Qi et al. https://github.com/EmpTyset-phi/VSRE
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Comments on “Revealing coastal vegetation structural diversity through LiDAR derived relative entropy” by Qi et al.
General comments
Vegetation structural complexity (VSC) is increasingly recognized as a key attribute influencing ecosystem processes and functions. The quantification of VSC from 3D LiDAR data has therefore become an important area of research to support such studies. In this work, the authors investigate methodologies for quantifying VSC specifically in coastal ecosystems, propose a new index, namely VSRE, and demonstrate that this metric outperforms existing approaches. They further combine this metric with the latest Google AEF dataset to produce a nationwide VSC product. Overall, this is a timely study for advancing VSC research in coastal ecosystems, and the resulting dataset is valuable. However, I have several major concerns that prevent me from recommending its publication in its current form.
First, I have difficulty understanding the rationale for using relative entropy to quantify VSC. Fundamentally, relative entropy measures the dissimilarity of LiDAR point distributions between different voxels. It is unclear how this directly reflects vegetation structural complexity. By definition, structural complexity relates to both the randomness of canopy element distribution and the extent of space occupation. In this sense, VSC should capture at least two components: the abundance of structural elements and the variability in their spatial arrangement. However, strong dissimilarity between layers does not necessarily indicate high structural complexity. For instance, in tropical forests, the canopy is often densely filled due to complementary layering, resulting in relatively similar element distributions across layers. Despite this similarity, such forests exhibit high structural complexity. Therefore, I remain fundamentally uncertain about the appropriateness of using relative entropy as a metric for quantifying VSC.
Second, the comparison among metrics appears methodologically unclear and potentially unfair. The authors used three groups of plots—categorized as low, medium, and high VSC—to evaluate the performance of the proposed metric against existing ones. However, it is not clear how these groups were defined in the field. Were they based on visual assessment or some quantitative criteria? Given that VSC is inherently difficult to measure directly, establishing reliable ground-truth data is challenging without the use of simulation or well-defined structural proxies. Furthermore, I do not consider the comparison between VSC and species richness to be a robust or appropriate way to demonstrate the superiority of the proposed metric. The relationship between VSC and species richness remains debated in the literature and is not necessarily positive or consistent. For example, in northern temperate forests, single-layer, monocultural stands can still exhibit relatively high VSC due to selection effects and high space occupation.
Third, the overall writing of the manuscript requires improvement. The current version attempts to integrate a wide range of content, which reduces its focus, while several important components lack sufficient detail. For instance, the classification of coastal vegetation types is itself a substantial task, yet it is only briefly described. A similar issue arises with the nationwide VSC mapping, where methodological and implementation details are limited. I therefore suggest that the authors consider dividing the work into two separate papers: one focusing on the technical development and validation of the proposed VSC metric, and another dedicated to its application in national-scale mapping.
Below are some specific comments for the authors to consider.
Specific comments