Monitoring Spatiotemporal Variation of Individual Tree Biomass Using Multitemporal LiDAR Data

Round 1

Reviewer 1 Report

General Comments:

In this study, the authors used multi-temporal LiDAR data to quantify the spatial and temporal changes of AGB in the tree layer of boreal forests, explored the accuracy of different individual tree segmentation (ITS) algorithms for stand metrics, and provided the important factors affecting the growth of plot-level AGB. The temporal variation of forest AGB is difficult to describe quantitatively, and airborne LiDAR and the corresponding single-tree segmentation algorithms provide us with a broad application prospect. The overall idea of the experiments in the paper is clear, but there are some errors that need to be checked and corrected. In addition, the English language of the whole paper should be greatly improved. Therefore, I recommend that it be accepted for publication with minor revisions.

Specific Comments:

1. In the abstract section, the methodology or empirical model applied needs to be described in detail, and the description of the results should highlight the focus of the experiment in order to improve the overall logic, and at the same time refine some statements and pay attention to the professional terminology.

2. In the introduction section, the canopy structure is mentioned as affecting the accuracy of the individual tree segmentation (ITS) algorithm, and it is suggested that the description of the factors affecting the estimation of tree metrics using LiDAR could be added, in particular the pulse density.

3. In section 3.4, the validation accuracy of the LIDAR estimates of plot-level AGBs for 2012, 2016, and 2022 is gradually increasing, but 2016 has the highest number of measured plots, so please explain why the validation accuracy is the highest in 2022 and consider whether a relevant explanation needs to be added to the text.

4. Please check the manuscript carefully for grammar, spaces, and definite articles.

 

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

General comments:

 

This paper monitored a spatiotemporal variation of individual tree biomass using multiple temporal LiDAR data, improved the accuracy of the individual tree segmentation algorithm, and successfully corrected the underestimation of tree-level metrics.

Amount of work was conducted and Massive in situ measurements were valuable. I believe the results of this study are full of meaning. This study needs to emphasise the highlight, especially in the abstract. I would recommend a minor revision before accepting it for publishing.

Specific suggestions:

 

1. Considering that there are a lot of noun abbreviations in the text, and some of them are not common. I suggest that the authors consider adding a table to summarize the explanation of the abbreviations and parameters. 

 

2. As in situ measurements in this study are valuable, I suggest that if possible, the author could make the data publicly available.

 

3. L13 What is the shortcoming of existing studies is suggested to be added in the abstract. 

 

4. Variables in the whole manuscript need to be italic.

 

5. L248, L276 Some typeset problems exist.

 

6. L345 “where” is missing.

 

L620 “map” > “mapped”.

Minor editing of English language required

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

 

It is necessary to monitor the spatiotemporal variation of forest biomass, whether for forest ecology or remote sensing community. The manuscript proposed something new but not necessarily a strict method. I understand the author's efforts and heavy workload, however, some major issues should be properly resolved and explained before making a further decision.

 

How does the author consider monitoring individual tree biomass and what can the research do for forest ecosystem? For both remote sensing and ecological applications, we should pay more attention to the larger scale, which is different from the survey of a tree. In the current version, it seems to deviate from the research and argument to some extent, where is the gap in the field at present, and how to improve it? Therefore, the innovation and significance of the research should be first highlighted.

 

From a technical point of view, I think it is counterintuitive to explore a tree-based method rather than an area-based approach with the aim of forest biomass monitoring. How can this method be applied on a large scale when the segmentation accuracy seems lower in such forest stands than in others?

 

More importantly, changes in tree biomass seem to be a pseudo-proposition, unlike other directly measurable parameters such as tree height or crown width, which cannot be measured once a tree has fallen down. The use of predicted biomass seems to be the only approach, but this often involves a large margin of error. I can't find hope to overcome these difficulties in the manuscript.

Author Response

Please see the attachment.

Author Response File: Author Response.docx