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Forests
Volume 14
Issue 3
10.3390/f14030484
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Article
Peer-Review Record

Classification of Individual Tree Species Using UAV LiDAR Based on Transformer

Forests 2023, 14(3), 484; https://doi.org/10.3390/f14030484
by Peng Sun 1, Xuguang Yuan 2 and Dan Li 1,*
Reviewer 1: Anonymous
Reviewer 2:
Anastasiya Popova
Reviewer 3: Anonymous
Forests 2023, 14(3), 484; https://doi.org/10.3390/f14030484
Submission received: 12 December 2022 / Revised: 17 February 2023 / Accepted: 22 February 2023 / Published: 28 February 2023
(This article belongs to the Special Issue Deep Learning Techniques for Forest Parameter Retrieval and Accurate Tree Modeling from Remote Sensing Data)

Round 1

Reviewer 1 Report

Dear Authors,

I have read with much interest your paper. I think it is very good but still, it would benefit from some improvements. Please check carefully the language and terminology.

The introduction is not too convincing and would need some improvement to more clearly give the gap in knowledge.

Paragraph 1: second sentence needs references to support the statements. There are plenty including in Remote Sensing and Forests journal published in the last couple of years. In general, the sentences from the first paragraph need to be much better referenced/cited.

Last paragraph: it is not too convincing as a goal and objectives. There are language problems and mistakes in the species names.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

The authors describe classification of tree species with UAV LiDAR and machine learning. They compare six different deep learning algorithm and Random Forest to classify the point clouds of three tree species. Tree species classification is actual complex task. UAV LiDAR data are more accurate and complete than spaceborne LiDAR or satellite images. The subject of the paper and the results obtained are quite interesting, the authors obtained sufficiently good accuracy with PCT networks training. I have a few comments to improve the presentation of the work:

1.      Section 2.2 - how long did the data acquisition take and what area was surveyed – all study area or part of it?

2.      Section 2.3.4 – Please add descriptions of PT1 and PT2 models here - their features, differences.

3.      I didn’t find the full writing of the PCT abbreviation

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

Dear Author,

You have done a good job in producing this article. However, there are several areas that need to be improved. Please find my comments in the word file.

Thank You

Comments for author File: Comments.docx

Author Response

Please see the attachment.

Author Response File: Author Response.docx

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