Investigating the Identification and Spatial Distribution Characteristics of Camellia oleifera Plantations Using High-Resolution Imagery
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsDear Editor(Author(s)
"Investigating the Identification and Spatial Distribution Characteristics of Camellia Oleifera Plantations using High-Resolution Imagery" is an original study reviewed by me. Some suggestions are given below.
Best Wishes...
The introductory part of the study is sufficient and its originality has been revealed.
In the material method, it should be stated how the data set was grouped as training, testing and validation.
What was used as input data, and which activation functions were used in the hidden layer should be specified.
Calculating the uncertainty of the model will make the study more effective.
shannon entropy, confusion index.... in usable obscurity.
The discussion and conclusion sections were found to be sufficient.
Author Response
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Reviewer 2 Report
Comments and Suggestions for AuthorsPlease find the pdf file attached below.
Comments for author File: 
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Author Response
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Reviewer 3 Report
Comments and Suggestions for AuthorsI have carefully read your manuscript entitled “Investigating the Identification and Spatial Distribution Characteristics of Camellia Oleifera Plantations using High-Resolution Imagery” and I would like to share my comments and suggestions with you. I appreciate the originality and significance of your work related to camellia oleifera plantation detection and analysis, as well as the clarity and rigor of your presentation. However, I also have some concerns and questions that need to be addressed before I can recommend your paper for publication.
First, I would like to commend you on highlighting the importance and challenges of camellia oleifera plantation monitoring. You have provided a clear motivation and background for your research problem, and explained how your proposed method can contribute to the field.
Second, I would like to suggest that you improve the discussion part, because your manuscript only performs a very simple topographic analysis and spatial distribution characteristic analysis of camellia oleifera plantations, without providing more insights or implications for future research. You should discuss the limitations of your method, the potential sources of error or uncertainty, the comparison with other existing methods, and the possible applications or extensions of your work.
Finally, please revise the following details:
1 The affiliation of the third author is not used, please check.
2 The keywords are too long, please revise.
3 The Latin name of camellia oleifera should be italicized.
4 The literature review part lacks sufficient discussion on the research development of camellia oleifera plantation mapping. Although there are few studies based on deep learning in this field, you should mention the application of traditional methods in deep learning, or the challenges of extracting camellia oleifera plantations compared to other plants or crops.
5 In the paper, does “high-resolution” mean “high spatial resolution”? 61. Line 140, how does the forest resource map improve the accuracy of spatial distribution analysis by excluding non-forest interference?
6 In the first half of the paper, you have repeatedly stated the distribution characteristics of camellia oleifera without any literature support. For example, line 150, you pointed out that camellia oleifera is mainly distributed in hilly and mountainous areas. What is the significance of the subsequent spatial distribution analysis?
7 Figure 1 does not clearly mark the area of Hunan Province on the map of China.
8 Line 170, “The optimal model was selected” appears abruptly, without introducing how to select the best model and lacking connection with the previous experimental steps.
9 Figure 3 does not show well the role of supplementary data, please modify.
10 In section 2.3.1, why are U-net++ and Efficientnet-b0 most suitable? You should provide some discussion or literature support. If this is a conclusion drawn from your experimental results, it should not be discussed here.
11 In section 2.3.3, the accuracy evaluation includes confusion matrix based on pixel number rather than pixel area.
12 In section 3.3 Accuracy Validation, the definition or formula of OA and UA should be presented to explain their meaning.
13 Table 1, is Google Earth Image’s spatial resolution 0.15 or 0.5m?
14 The first row of Table 2 should place “Hengnan County” in the correct position in the table.
Author Response
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Reviewer 4 Report
Comments and Suggestions for AuthorsThis manuscript presents a deep learning-based method that utilizes GF-2 remote sensing imagery to achieve precise mapping and efficient monitoring of Camellia Oleifera plantations in Hengyang City, China. The manuscript is interesting and well written. My specific comments are as follows:
1. In the introduction, page 2, the authors mentioned two strategies: field investigation performed manually and interpretation of remote sensing data. It must be clarified why the second approach is more convenient and efficient. Even in large scale investigation, the first strategy can be used efficiently using any sampling scheme that can provide accurate results.
2. The authors should detail the reasons for choosing the specific site.
3. The data description should be more detailed. Additionally, the availability of data will add chances of result reproducibility.
4. Please improve the figure's quality.
5. The authors should give the details on how to extend the current model. How can exogenous information affect the current models' performance?
6. What are the study limitations? How the work can be extended in the future? Please add it to the manuscript.
Comments on the Quality of English Languagefine
Author Response
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Author Response File: Author Response.docx
Round 2
Reviewer 4 Report
Comments and Suggestions for AuthorsAs the authors addressed my concerns, I recommend the paper for publication in its present form.
Comments on the Quality of English LanguageFine
