Logging Pattern Detection by Multispectral Remote Sensing Imagery in North Subtropical Plantation Forests

Round 1

Reviewer 1 Report

Comment for remotesensing-1868975

This manuscript addressed using multispectral remote sensing imagery to detect logging pattern in north subtropical plantation forests. Although the topic is easy to understand, I still could not find a new finding or special scientific sound in this paper. In general, logging (especially, in clear cut) could be detect by not only multispectral remote sensing imagery but also by aerial photograph. Perhaps, using aerial photograph detects logging pattern is better than multispectral remote sensing. Moreover, I have some special comments for authors.

1.      Line 112-114, the study purpose is weakness. The answer of this question can be found in many papers of remote sensing fields.

2.      Every Table or Figure is individual. it must have notes. Such as, CK, SL and CC in Table 2 should add notes in this Table. Others, please refer to this pattern.

3.      In my viewpoint, using multispectral remote sensing imagery to detect clear cut, the technology is low. Audiences might interest in this technology used in selective logging. However, Lines 31-34 pointed out that “…the selective logging detection accuracy can be improved by adding more spectral bands.” I think that the problem is the key point and should be solved in this study not only give comment or suggestion here.

Based on the above reasons, in this stage, I could not recommend this manuscript for publication in Remote Sensing.

Author Response

Dear reviewer,

 Thank you for your comments on this paper. We believe that you have contributed to the improvement of this paper. We have made detailed changes based on your comments. Our response to each specific comment is listed in attachment. We think the most important innovation of this manuscript is that we want evaluate the spectral band information on detecting logging pattern, especially the selective logging pattern. So, we modified the object of this manuscript and the conclusion to make the strength of the spectral information on selective logging pattern detection. 

Thanks very much for your comments. If any questions, please help us to refine our manuscript.

Best wishes,

Dian Yuanyong

Author Response File: Author Response.pdf

Reviewer 2 Report

In the paper entitled " Logging Pattern Detection by Multispectral Remote Sensing imagery in North Subtropical Plantation Forests” the authors used random forest algorithm to detect logging patterns (unlogged, selective logging, clear-cutting), in the Taizi Mountains from Hubei Province. Their results are showing that the red-edge and short-wave infrared bands are improving the ability of conventional optical satellites to monitor forest harvesting patterns. The article is within the journal's scope providing interesting information for the readers. I recommend the publication after minor revisions.

1.    Abstract is written well. I suggest rephrasing the sentence: The problems that the high-resolution remote sensing images contain richer spectral bands can help us to promote the logging pattern detection and which spectral bands combination are more efficient in detecting logging patterns are still unsolved.

2.    In the introduction section the author/s review the state of the art regarding the works done in the past, their strengths, weaknesses (past contributions) and research gaps (current needs of improvement).

3.    Data collection, materials and the methodologies are adequate to the purpose of the paper. The datasets are covering the Jingshan County from Hubei province territory using Sentinel-2 images and logging samples from the Taizishan Forestry Administration Bureau.

4.    The results section has been written in a consistent manner and it looks convincing

5.       In the discussions the authors underlined the meaning of their research stating that the inclusion of the red-edge and short-wave infrared bands can significantly improve the accuracy of logging detection process.

6.       The conclusions are corelated to their research results, stating that the optical images have the potential ability to detect logging patterns especially for the clear cuttings.

7.       The references cited are up to date and relevant.

Author Response

Dear reviewer,

We appreciate the positive comments from the reviewer. The response was listed as follow.

Comment:

I suggest rephrasing the sentence: The problems that the high-resolution remote sensing images contain richer spectral bands can help us to promote the logging pattern detection and which spectral bands combination are more efficient in detecting logging patterns are still unsolved.

Response:

This sentence is indeed not very clear. It has been changed to: Although high-resolution remote sensing images contain richer spectral bands, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns remain unresolved. See lines 18-20 of the manuscript.

Thanks very much for your comments. If any questions, please help us to refine the manuscript.

Best wishes,

Dian Yuanyong

Reviewer 3 Report

A well presented paper.

A couple of suggestions;

1) how are the compartments defined?

2) what is the effect of topography? The impression I get is that this a hilly area; are shadows an issue?

3) is the "clear cut" almost immediately covered with herbaceous vegetation and grass? to what extent is the "brash" (small branches) removed when an area is clear cut?

4) as "selectively logged" is the most interesting category is it possible to "weight" your method to increase the accuracy of that category at the expense of the "clear cut" and "un-logged"?

5) what is the difference between "selectively logged" and poor growth / young growth?

6) some pictures of the categories taken from the ground would be useful

 

Author Response

Dear reviewer,

Thank you for the positive evaluation of this paper. We have made revisions to address all the comments. A list of our responses to each of the specific comments is listed in attachment. If any quesitons, please help us to refine the manuscript. 

Best wishes, 

Dian Yuanyong

Author Response File: Author Response.pdf

Reviewer 4 Report

The use of the alternative Sentinel-2 red-edge channels on selective logging detection is a relevant and scarcely explored field of research.

The research is pertinent, well executed and reported. 

Although the used English is not perfect, the authors successfully transmit their methods and results.

Some minor corrections and clarifications:

Table 2: The notation of the time lag is column nuclear. ‘0~81’ means 81 days after logging? This is more than the four weeks specified previously (line 160).

 Line 222: GLCM parametrization is arbitrarily set to a 7x7 window. Please discuss why this filter size was chosen. A reference to the level of quantization used during the GLCM computations is also desirable. There is no reference to the bands and parameters used on the (presumed) PCA transformation performed before the GLCM computation. Please elaborate on this.

 

Line 248: As far as I know, the default is the *square root* of the number of features.

Comments for author File: Comments.pdf

Author Response

Dear reviewer,

     Thanks very much for your positive evaluation of this manuscript. According to your comments, we have made a careful revision. Responses to the comments are in the attachment.  Thanks again for your review.

Best wishes, 

Dian Yuanyong

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

I review the revised manuscript and still maintain my previous recommendation.

Author Response

Dear reviewer, 

    Thanks very much for your comments on our manuscript. We think that logging pattern detection with remote sensing images is still a big problem. Some other RS data such as Lidar or SAR may have a powerful ability to detect little changes with logging, but Images are still a common and cheap way, especially in local forest administrator agencies. In this paper, we tried to analyze the potential of spectral bands on detection logging patterns, especially for selective logging. We think our results achieve a little progress in this field. Although you do not agree, we thank you very much for your comments which help us to get more achievement. 

Best wishes, 

Dian yuanyong