Dynamic Analysis of Spartina alterniflora in Yellow River Delta Based on U-Net Model and Zhuhai-1 Satellite

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. Based on the title and research content of the article, it is recommended to reorganize the introduction section, emphasizing the key and difficult issues of the article, and clarifying the contribution of this article.

2. In section 2.2 Data and Processing, in order to obtain real ground samples, on-site investigations were conducted in 2023 and 2024. Please provide detailed information on the collection process and corresponding photos.

3. It is necessary to supplement the criteria and basis for selecting spectral indices. Additionally, in the process of calculating spectral indices, specific bands should be identified, as hyperspectral images have a large number of bands, and adjacent bands may correspond to the same visible light band range.

4. Regarding the feature selection process, the author should provide quantitative evaluation criteria. It is not clear from Figure 2 that the top 20 features are the most important.

5. It is recommended to supplement the process of generating training samples to ensure their rationality and reliability.

6. It is suggested to supplement the comparison algorithms, one of which is to add SVM+Relief-F RF+Relief-F， Another is to supplement new deep learning algorithms.

7. It is suggested to optimize the title. The content of the article is about the monitoring and analysis of Spartina alterniflora in the Yellow River Delta from 2020 to 2023 based on zhuhai-1 hyperspectral images and Unet model, which is inconsistent with the core of the title.

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The article addresses an important environmental issue, namely the presence of the invasive plant Spartina alterniflora (S. alterniflora). Using remote sensing, the work recovers its dynamics in space and time. It presents fluent reading and easy understanding of the information presented. The following are observations from the point of view of the form and approach to the topic.

As for the form, it is suggested:

- Improve the quality of figures 2, 3 and 8 (text).

- Figure 2 should be referred to earlier in the text before its presentation.

- Table 5 should indicate the meaning of PA, UA and OA. (PA = producer’s accuracy, UA = user’s accuracy, OA = Overall accuracy, KC = Kappa coefficient.

- Tables 1 and 2 should be cited before their presentations.

- Place Table 2 on a single page.

 

Regarding the approach to the topic:

- The article values the use of remote sensing as a technique for the spatiotemporal evaluation of S. alterniflora, but in the discussions and conclusions this resource is underexplored. In several parts of the article the importance of remote sensing appears, which justifies adherence to the scope of the journal. The methodology focuses on procedures performed using products and techniques for digital processing of images and remote sensing information.

Below are some examples of this procedure:

The abstract states “This study evaluates the effectiveness of management efforts targeting S. alterniflora in the Yellow River Delta (YRD) using Zhuhai-1 hyperspectral imagery and the U-Net method. The U-Net model, coupled with the Relief-F algorithm, achieved superior extraction accuracy (Kappa > 0.9, overall accuracy 93%) compared to traditional machine learning methods”. The introduction highlights aspects such as “Recent innovations, including multispectral and hyperspectral imaging, have further enhanced the capacity to distinguish S. alterniflora from native vegetation and accurately monitor its distribution…; hyperspectral image… Zhuhai hyperspectral satellite represents a significant milestone in this progression”.

Therefore, it is suggested that the authors delve deeper into the discussions and conclusions on the use of remote sensing for the spatial and temporal monitoring of S. alterniflora.

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

 

This manuscript evaluated the effectiveness of management efforts targeting S. alterniflora in the Yellow River Delta (YRD) using Zhuhai-1 hyperspectral imagery and the U-Net method. The study is interesting and the objectives are essential for the conservation and sustainable utilization of coastal wetlands. The manuscript is well-written, with clear and precise expressions, and the logical flow of the content makes the research objectives easy to understand. However, some issues should be addressed before the manuscript can be accepted for publication. Detailed comments for improvement are provided below:

Major comments

1. Lack of detailed technical steps in method description

For Training Data Generation: Although it is mentioned that object-oriented segmentation combined with Random Forest (RF) was used for training data generation, the specific steps of this process are not clearly explained. For instance, how was object segmentation conducted? Were specific algorithms or threshold settings used? Were there precision checks applied during segmentation?

2. Deeper explanation and analysis of results needed

The results section provides a comparison of classification accuracy for different models (SVM, RF, and U-Net) but lacks a deeper analysis of why U-Net performs better. For example, while the results show that U-Net outperforms SVM and RF, the underlying reasons are not fully explored. Is it due to U-Net’s better ability to capture spatial-contextual information? How exactly does feature selection improve model performance? These aspects should be discussed in more detail.

 Minor comments

1. Occasionally long and complex sentences

Some sentences in the method section are quite long, which might make it harder for readers to follow. For example, the description of the Relief-F feature selection algorithm could be simplified.

2. Clarity of figures and image quality

Some figures (such as Figures 4, 6, 7, and 8) may lack clarity or have small text that could be difficult to read, especially in print or on high-resolution images.

3. Specific data references in spatial change descriptions

While trends are mentioned when describing the spatial changes of S. alterniflora, there is a lack of specific data references, making it difficult for readers to associate the changes with the numbers directly.

4. Page 4, line 170. “Specifically, the study utilized the GLCM method to extract eight texture features from Zhuhai-1 hyperspectral images, including mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation.” In which band do these 8 texture features apply?

5. Page 5, line 194~201. “As shown in Figure 2......to differentiating S. alterniflora communities from native plant communities.” This can be included in the Results section 3.1.

6. Page 6, line 202~205. “To further evaluate feature significance, the ranked features were grouped into six categories.” Please specify the six groups.

7. Please change "in YRD" to "in the YRD". For example, in Table 3, section 3.2 and section 4.2 headings.

8. In Table 3, it is recommended to specify the data sources used by the image example and the bands shown.

9. In Table 3, unify the text fonts in the image feature of tidal flat.

10. Page 7, line 230. "Three parameters should be set before training......". Please replace the comma at the end of this sentence with a period.

11. Please put a space between the number and the unit. For example, in line 291, line 444.

12. In the title of Figure 9, please capitalize the first letter of the province.

Author Response

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Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

Minor Revision

 

This manuscript employed the hyperspectral image time series for invasive species, Spartina alterniflora, as a practical means for invasion management in the Yellow Rive Delta. It is a significant contribution to mangrove conservation. The use of high spatial resolution hyperspectral image time series with U-Net and Relief-F is also innovative. However, there are certain aspects that require attention before publication.

 

1. Why is feature selection necessary for U-Net when classifying invasive species? It is well-known that U-Net can handle raw data.

2. On page 8: For a fair performance comparison, it is suggested to use the selected features rather than raw data for classification among SVM, RF, and U-Net. To demonstrate the effect of feature selection on classification, U-Net with raw data can be included for comparison.

3. On page 6: What criteria were employed to group the ranked features?

4. Table 4 on page 8: The title should explicitly indicate the classification method used.

5. It would be more informative to demonstrate the sample distribution over time, specifically listing the number of samples collected in different years.

6. For the remaining Spartina alterniflora in 2023, is it left or regrowing?

 

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I have no other suggestions.