Estimation of the Aboveground Carbon Storage of Dendrocalamus giganteus Based on Spaceborne Lidar Co-Kriging

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript addresses a very innovative topic, with the potential to contribute to the process of quantifying carbon stocks in bamboo forests in China. It is important to emphasize that quantifying these stocks requires a large amount of labor and resources. Therefore, the use of new technologies that can contribute to this task are of great relevance to the scientific community.

The manuscript is well structured and presents a wealth of details in almost its entirety. Below are some points that could be improved.

Variables used in the study: The nomenclature used in the variables presents long names, with repetitive use of the characters “_” and “&”. Throughout the manuscript, the variables were presented in a standard used in database organization. Therefore, I suggest the use of acronyms. If necessary, in the material and methods section, you can add a table containing the acronyms and their meaning.

Introduction: - Explain the main procedures currently used to quantify forest production, including carbon stock, in bamboo forests in China. How can the knowledge generated in this study contribute to the optimization of this process?

- Could you better specify the main hypotheses of the study?

Lines 159-165: Indicate the precision of the equations used.

Lines 279-281: I suggest including the formulas used, as you did for the other formulas.

Lines 351-357: Explain in more detail how RR was used. Used before or after adjusting the models? How? Is there no same data set? How does RR influence the results of the other models?

Conclusion: Lines 611-621 summarized the results. Present more objective presentations, aligned with the specific objectives and hypotheses of the study.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Overall, the original manuscript follows the scope of the journal. The author has presented the article very well and accurately according to academic standards, starting from the Introduction, Materials and Methods, Research Methods, Results (consistent with the objectives and accurate findings), and Discussion and Conclusion. 

There are only a few things that I would suggest to be revised, such as the names of the Modeling factors that appear in the original manuscript, such as h_mean_canopy_abs&b7, h_min_canopy&b7, and h_median_canopy_abs&b7, which are too long and can cause confusion when reading.

The author should consider naming or finding shorter and easier-to-read ways to refer to these Modeling factors.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The research demonstrates the potential of integrating GEDI and ICESat-2/ATLAS data with advanced machine learning models to estimate AGC more accurately. The Stacking-RR model showed the best performance, indicating the effectiveness of combining multiple data sources and modeling techniques. The study resulted in surprisingly high accuracy of the modelling results; however it also points out significant positional errors in the GEDI and ICESat-2/ATLAS data, which can affect the accuracy of AGC estimation. Future corrections are suggested to address these errors, while it is not clear if authors recommends the elaborated method for large scale biomass assessment, particularly in fragmented landscape with relatively small forest compartments. This aspect should be discussed in article and reflected in the conclusions to highlight practical value of the article, if authors considers that the elaborated method can be applied or adopted to certain end uses, e.g., forest inventory or GHG reporting.

The reliance on remote sensing data, despite its advantages, also introduces challenges such as data saturation in dense forests and sensitivity to environmental factors. These limitations need to be addressed in future studies and reflected in the discussion section, and pointed out as limitation if necessary.

Regarding uncertainty assessment it is not clearly stated if the uncertainty of the field measurements and models (biomass and carbon content) are considered in estimation of the modelling uncertainty. This should be clarified and, if necessary, calculations of the uncertainty corrected.

It is not clear, why above-ground biomass is estimated and not total biomass. Considering that the model is used to calculate biomass, application of the total biomass model would increase value of the model for the GHG inventory teams requiring total biomass.

I did not found evaluation of effect of the height of bamboo shoots on the assessment, respectively, what is limit value for the detection. Figure 8 shows that the accuracy is still high with young shoots, but it also clearly visible that the accuracy is far lower than the average value. This should be discussed in the article and pointed out as limitation, if necessary.

However, in spite of the above mentioned shortcomings the article demonstrates very interesting and cost efficient solution for modelling of carbon stock and monitoring of temporal carbon stock changes. It would be interesting to see the modelling results with forest tree species.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf