Advancing Forest Degradation and Regeneration Assessment Through Light Detection and Ranging and Hyperspectral Imaging Integration

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper follows a very standard methodological approach, thus there is no novelty. There are plenty of papers already…

Comments on the Quality of English Language

Minor editing is needed.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dears Authors,  

     The manuscript “Advancing Forest Degradation and Regeneration Assessment through LiDAR and Hyperspectral Imaging Integration” assesses how drought-induced wildfires affect forest composition and structure, shedding light on ecological shifts in the Amazon rainforest.

      The manuscript presents a theoretical framework with multiple studies with applications related to the effects of drought and forest fires. The theoretical basis presented to justify the study is adequate and is supported by the theoretical framework.

       In order to contribute to the manuscript, I have highlighted recommendations for improving the writing in the comments of the digital file.

       I'd like to draw your attention to the following suggestions:

1) Figure 1: add a graphic scale bar to the map.

2) Detail the hardware resources used for the study.

3) The presentation of a flowchart can help in the understanding of the study developed.

4) Line 240: “The terrain roughness, defined as the difference between the highest and low-240 est altitude in a 3 × 3 moving window [43], was calculated from a 10-m DTM”. Explain the criteria used to define the spatial resolution used.

5) Line 307: “This function considers the absolute values of pairwise Pearson's correlations and...”. In this case, the normality of the data is assumed. Please elaborate on this choice and the inherent condition.

6) Line 367: “A two-way analysis of variance (ANOVA), followed by a Tukey test, was used to assess whether there were any differences in performance measures from cross-validation”. Could the Scott Knott test perform better in this case?

7) What recommendations do you have for future studies?

     I end my review by congratulating you on the study and the version of the manuscript.

 

 

Respectfully,

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The wording of the text needs minor adjustments, which have been noted in the comments on the digital file. Standardize: when introducing acronyms for the first time, their meaning should be presented.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

 

The manuscript with the title “Advancing Forest Degradation and Regeneration Assessment through LiDAR and Hyperspectral Imaging Integration”, presents a study that optimizes procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (1984-2017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. At first glance, the paper takes into a worthy topic and present valuable information in remote sensing for forest degradation assessment.

I believe the manuscript has merits for being published in Remote Sensing but, before further processing some revisions and improvements need to be made  

First the Introduction provides the context and the justification for the study but at the very end I feel that it lacks to highlight the unique contribution that the paper is making compared to was already available in literature. Also, I do not see the research question or the hypothesis driving the study.  It could be obvious, but it is always adequate to state the research question (or hypothesis) as a research paper.

Second, for materials and methods sections I have some revisions/questions.

a)     As the authors stated, the sites selected encompass a wide variety of anthropogenic, climatic, geological and edaphic conditions. These elements, presumably results in different vegetation types, with different states and conditions. It call my attention because in the description of the study sites (lines 130-154) there is a lot of attention on geological/soil conditions, but at the end the spectral response coming from vegetation (later) is the base for the forest degradation classification. My point is why not put more attention in the vegetation dynamics of each site?

b)     Lines 155-162 stated that much of the studied forest have been degraded by fire, selective logging and/or fragmented. But in my experience those events are disturbances, mainly manmade. It is very important to define clearly what is degradation, specially under the REDD+ context, and what are the drivers that lead to.

c)     According to the previous point, section 2.2 needs to be revised. The issue here is that the forest classification is subjective, or at least it is unclear. There should be metrics for the classification of forest degradation. For instance, my guess is the authors used something like LandTrend to identify the four stages mentioned (Figure 2). However, this approach that uses NBR  detects “breaks” in a pixel time series, that not necessarily fit one of the four classes defined. Perhaps I am understanding wrong, but in any case, some clarifications on how the forest classes were defined is required, especially in those sites where apparently forest vegetation types look different (Figure 1).

d)     I agree to the authors, there are a large amount of LiDAR and HIS metrics, and as it was expected there should be multicollinearity issues and it was correctly resolved; however, the question here is why not from the beginning eliminate the highly correlated metrics and avoid unnecessary work?

The results and discussion sections are adequate addressed.

For the conclusion section, be careful to not bring discussion here. Please revise this section. Conclusions need to be sound, concise and base on the findings of the manuscript.

Comments on the Quality of English Language

Moderate editing of English language required.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I was expecting to see some major methodological enhancements to support what author`s claim as a unique contribution, however, I didn`t see that. Author`s added some generalities and extra references in the main text. I am afraid that this manuscript is not suitable for the journal of Remote Sensing. I insist that there is no novelty, therefore, I would strongly recommend trying a lower impact journal.

Comments on the Quality of English Language

Still it needs a language review by a native person.

Reviewer 3 Report

Comments and Suggestions for Authors

Many thanks to the authors for carefully addressing all my revisions. After a deep reading, I concluded that the paper had improved substantially. It is well-written and has a strong structure. It would make a nice contribution to Remote Sensing. Congratulations to the author on a fine job.

Comments on the Quality of English Language

Minor editing of English language required.