Remote Sensing-Based LULP Change and Its Effect on Ecological Quality in the Context of the Hainan Free Trade Port Plan

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The present research presents a procedure to study land use and landscape patterns using remote sensing imagery. 

It takes advantage of the google earth engine (GEE) platform for the generation of vegetation indices. 

In a personal opinion, it seems to me that the methodological tools used are adequate, the results are interesting.

Some observations on the study. 

The author could add how the study would contribute to the decision making of governmental actors (public policies), in order to have a more sustainable city, and if so, how it would complement the present study. 

What is the advantage of using Google Earth Engine over other platforms?

What would be your recommendations to a user who wishes to replicate the present procedure?

Author Response

Author’s Response to All the Comments

Dear Editor and Reviewers:

We are particularly grateful for your careful reading, and for giving us such constructive comments on this work! Those comments are all valuable and very helpful for revising and improving our manuscript, as well as the important guiding significance to our research. We have studied the comments carefully and have made revisions which we hope meet with approval.

According to the comments and suggestions, we have tried our best to improve the previous manuscript sustainability-3024729 (“Remote sensing based LULP change and its effect on ecological quality in the context of the Hainan Free Trade Port Plan”). Here is a summary of the major changes to the revised manuscript, ant then answer the reviewer's questions one by one.

We have made significant adjustments and revisions to the content of the manuscript as requested. The main changes revisions include, (1) the introduction was revised to highlight the significance of the research and the advantages of the Google Earth Engine (GEE) platform; (2) new content about Producer’s Accuracy (PA) and User’s Accuracy (UA) was added; (3) the language and grammar of the article was polished. All changes and author responses are marked in blue font in the manuscript.

Once again, we are particularly grateful for your careful reading and constructive comments. Thank you very much for your time. 

Best regards,

 

Pei Liu and all co-authors

 

2024-5-31

 

 

 

 

 

 

 

 

 

 

Author’s Response to the Comments of Reviewer #1

1. The present research presents a procedure to study land use and landscape patterns using remote sensing imagery. It takes advantage of the google earth engine (GEE) platform for the generation of vegetation indices. In an opinion, it seems to me that the methodological tools used are adequate, the results are interesting.

Responses: Many thanks for your positive comments.

 

2. The author could add how the study would contribute to the decision making of governmental actors (public policies), to have a more sustainable city, and if so, how it would complement the present study. 

Responses: Thank you very much for your valuable comments and suggestions. In this study, we analyzed LULP change and corresponding ecological effect under the context of HFTP with the help of remote sensing technology and GEE cloud platform. Research outcomes can significantly enhance decision-making for governmental actors striving for more sustainable cities. By utilizing historical satellite imagery and geospatial data, from 2016 to 2021 in this research, governmental actors can understand urban growth patterns, identify areas of rapid development, and predict future expansion. This information aids in formulating land use policies, zoning regulations, and urban planning strategies to promote compact, efficient development, preserve natural areas, and minimize sprawl [1]. Geospatial analysis enables the monitoring of environmental indicators such as air quality, vegetation health, and land cover changes. By assessing environmental trends and identifying pollution hotspots, policymakers can implement targeted interventions to improve environmental quality, mitigate pollution, and protect ecosystems, thus contributing to a healthier and more sustainable urban environment [2]. Remote sensing data provides insights into the impacts of climate change on urban areas, including changes in temperature, precipitation patterns, and sea levels. By understanding these risks, governmental actors can develop adaptation strategies such as green infrastructure, flood mitigation measures, and coastal protection to enhance urban resilience and minimize vulnerabilities to climate-related hazards [3]. Geospatial analysis can inform transportation planning by identifying transportation corridors, traffic congestion hotspots, and opportunities for transit-oriented development. By promoting sustainable transportation modes such as public transit, cycling, and walking, policymakers can reduce reliance on private vehicles, alleviate traffic congestion, and decrease greenhouse gas emissions, thus fostering more sustainable urban mobility [4].  Geospatial analysis facilitates the identification of areas experiencing socio-economic disparities and inequalities in access to resources and services. By mapping socio-economic indicators such as income levels, educational attainment, and access to healthcare, governmental actors can target investments and interventions in underserved communities, promote social inclusion, and advance equitable development goals, thereby fostering more resilient and cohesive cities [6].  In summary, the research overcomes the spatial and temporal limitations of traditional methods and can better reflect the landscape pattern information of surface cover, provide important reference for ecological environmental protection and restoration, and further promote the application and development of remote sensing technology in urban planning and environmental management.

 

Updated references:

1. Seto, K. C., et al. Urban land teleconnections and sustainability. Proceedings of the National Academy of Sciences, 2012, 109(20): 7687-7692.
2. Jenerette, G. D., et al. Urban ecology in a developing world: why advanced socioecological theory needs Africa. Frontiers in Ecology and the Environment, 2016,14(8): 367-375.
3. Solecki, W., et al. Urbanization and climate change: An assessment of stakeholders’ capacity and needs for adapting to extreme events in China. Climate Risk Management, 2019,24: 100202.
4. Cervero, R., et al. Transit-oriented development and joint development in the United States: A literature review. Research in Transportation Economics, 2013, 39(1): 50-59.
5. Higgs, G., et al. Spatial and spatiotemporal models for exploring urban land use dynamics in shrinking cities: A case study of Liverpool. Applied Geography, 2019, 109: 102015.

 

3. What is the advantage of using Google Earth Engine over other platforms?

Responses: Thank you for your valuable questions. Google Earth Engine (GEE) offers several advantages over other platforms for geospatial analysis and remote sensing. The advantages of GEE can be summarized as follow, (1) GEE hosts a vast collection of satellite imagery and geospatial datasets, including Landsat, Sentinel, MODIS, and more. This extensive archive allows users to access petabytes of data without worrying about storage or data management [1]. (2) The Earth Engine is built on Google's cloud infrastructure, which enables scalable and parallelized processing of large-scale geospatial data. Users can analyze terabytes of data in a fraction of the time it would take using traditional computing resources [2]. Earth Engine also provides a wide range of analysis tools and functions for image processing, time series analysis, machine learning, and statistical modeling. These tools are integrated into the platform, making it easier for users to perform complex analyses without switching between different software packages [3]. (3) Earth Engine offers a Code Editor with a JavaScript-based integrated development environment (IDE) that allows users to write, test, and execute code directly in the browser. Additionally, Earth Engine provides APIs for Python and JavaScript, enabling seamless integration with existing workflows and programming languages [4]. (4) Earth Engine facilitates collaboration and sharing of geospatial data, algorithms, and analysis results through its cloud-based platform. Users can share their code, scripts, and visualizations with others, fostering a collaborative and open-source approach to geospatial research [5]. (5) In my opinion, the most import advantage is that GEE provides free access to its platform for non-commercial and educational use. This lowers the barrier to entry for researchers, educators, and students who want to explore and analyze geospatial data for various applications, including environmental monitoring, land cover mapping, disaster response, and more [6]. These advantages make GEE a powerful platform for geospatial analysis and remote sensing applications, suitable for a wide range of users from researchers and scientists to educators and students.

 

References:

1. "Google Earth Engine Data Catalog." Earth Engine - Google Developers, https://developers.google.com/earth-engine/datasets.
2. Gorelick, Noel et al. "Google Earth Engine: Planetary-scale geospatial analysis for everyone." Remote Sensing of Environment, 2017, 202: 18-27.
3. "Earth Engine Documentation." Earth Engine - Google Developers, https://developers.google.com/earth-engine/.
4. "Earth Engine Code Editor." Earth Engine - Google Developers, https://developers.google.com/earth-engine/playground.
5. "Earth Engine User Forum." Earth Engine - Google Groups, https://groups.google.com/g/google-earth-engine-users.
6. "Google Earth Engine Terms of Service." Earth Engine - Google Developers, https://earthengine.google.com/terms/.

 

4. What would be your recommendations to a user who wishes to replicate the present procedure?

Responses: Thank you for your valuable comments. If a researcher who wishes to replicate the present procedure for studying remote sensing-based LULP change and its ecological implications, here are some recommendations: Firstly, clearly define the research objectives and questions you want to address with the study. Determine the specific spatial and temporal scales, as well as the ecological indicators you aim to analyze. Then, choose a study area, considering factors such as ecological diversity, land use dynamics, and relevance to policy decisions. And obtain satellite imagery, such as Landsat, Sentinel, and MODIS imagery,  covering the study area for multiple time periods for LULC change analysis due to their availability and suitability for monitoring landscape dynamics. Next, preprocess the acquired imagery to correct for atmospheric effects, sensor distortions, and geometric inaccuracies. Conduct radiometric and geometric calibration, as well as atmospheric correction, to ensure data consistency and accuracy. Use supervised or unsupervised classification techniques to categorize land cover types in the study area. Consider incorporating spectral, spatial, and temporal features for improved classification accuracy. Compare LULC maps from different time periods to identify and quantify changes in land cover categories. Analyze the extent, direction, and spatial patterns of change, focusing on areas experiencing significant transformations. Evaluate the ecological impacts of LULC change by assessing changes in key ecological indicators. Utilize ecological models and spatial analysis tools to quantify these impacts. If researchers who are only interested in our current research, we are willing to share our code for GEE processing. The code link is as follows：
https://code.earthengine.google.com/6a1ad3a6ef5bd92b920cc1dfb0b0003f
https://code.earthengine.google.com/7d6b0ba9a15804c70cf8b743ee7fdf38

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study researched the spatio-temporal changes in land use and landscape patterns (LULP) and their impacts on the ecological quality of Haikou City. The study created a framework to monitor urban ecological security using multi-source data, including S2, DEM, NDVI, EVI, BSI, and NIMI. Maps of brightness, greenness, and humidity were created to enable ecological environment quality evaluation and analysis. The study found that construction areas decreased due to conversion to forest and farmland. Furthermore, the degree of landscape fragmentation decreased, and landscape distribution heterogeneity increased.

 

The study is well designed. However, it only covers a period of 5 years, which may not be sufficient to capture significant changes. Most importantly, the changes were based on annual classification maps, which heavily relied on the accuracy of these maps. The accuracy numbers of specific land cover types are missing, making it difficult to determine whether the areal changes in some land cover types are real changes. Additionally, the writing needs improvement as it is difficult to understand.

 

Line 303: “results are after removing small patches are shown in Figure 3.” This sentence is problematic.

Figure 3: The building land in the upper right region (beside the river) has many gaps in 2016. More than half of those gaps disappear in 2017. In 2018, the gaps reappear. It is very unlikely to have such huge changes within one year. The major reason is likely misclassification. If you show the classification accuracy of the building land in these years, you might find the reason.

Table 4: It only shows the overall accuracy of all land cover types without the accuracy information of specific land cover types, especially the accuracy of the building land. Because the conversion of building land to forest/farmland is one of your major findings, the accuracy information is very critical.

 

Line 308: Do you mean Table 5?

 

Table 5: Can the numbers be trusted? If you show the numbers for other years, such as 2019, you might find contradictory changing directions.

Comments on the Quality of English Language

The abstract is difficult to undertand.

Author Response

Author’s Response to All the Comments

Dear Editor and Reviewers:

We are particularly grateful for your careful reading, and for giving us such constructive comments on this work! Those comments are all valuable and very helpful for revising and improving our manuscript, as well as the important guiding significance to our research. We have studied the comments carefully and have made revisions which we hope meet with approval.

According to the comments and suggestions, we have tried our best to improve the previous manuscript sustainability-3024729 (“Remote sensing based LULP change and its effect on ecological quality in the context of the Hainan Free Trade Port Plan”). Here is a summary of the major changes to the revised manuscript, ant then answer the reviewer's questions one by one.

We have made significant adjustments and revisions to the content of the manuscript as requested. The main changes revisions include, (1) the introduction was revised to highlight the significance of the research and the advantages of the Google Earth Engine (GEE) platform; (2) new content about Producer’s Accuracy (PA) and User’s Accuracy (UA) was added; (3) the language and grammar of the article was polished. All changes and author responses are marked in blue font in the manuscript.

Once again, we are particularly grateful for your careful reading and constructive comments. Thank you very much for your time. 

Best regards,

 

Pei Liu and all co-authors

 

2024-5-31

 

 

 

 

 

 

 

 

 

 

Author’s Response to the Comments of Reviewer #2

 

1. This study researched the spatio-temporal changes in land use and landscape patterns (LULP) and their impacts on the ecological quality of Haikou City. The study created a framework to monitor urban ecological security using multi-source data, including S2, DEM, NDVI, EVI, BSI, and NIMI. Maps of brightness, greenness, and humidity were created to enable ecological environment quality evaluation and analysis. The study found that construction areas decreased due to conversion to forest and farmland. Furthermore, the degree of landscape fragmentation decreased, and landscape distribution heterogeneity increased.

 Responses: Thank you for your valuable comments. Regarding the research time span issue you mentioned, I agree with your concerns about the research time span. However, the 5-year time interval selected for this study is since it is an important starting point in 2018 for the construction of the Hainan Free Trade Port (HFTP). Although 5 years may not be enough to capture all significant changes, it does provide us with a critical perspective to observe and analyze the impact of the early stages of HFTP construction on Haikou city’s land use and landscape patterns.

 

2. The study is well designed. However, it only covers a period of 5 years, which may not be sufficient to capture significant changes. Most importantly, the changes were based on annual classification maps, which heavily relied on the accuracy of these maps. The accuracy numbers of specific land cover types are missing, making it difficult to determine whether the areal changes in some land cover types are real changes. Additionally, the writing needs improvement as it is difficult to understand.

 Responses: Thank you for your valuable comments and suggestions. In response to your question about the lack of accuracy figures for specific land cover types, we have made corresponding additions and revisions in the manuscript. We have added Producer’s Accuracy (PA) and User’s Accuracy (UA). The content “The accuracy of land use classification in Haikou City was verified by using the confusion matrix and sample points collected from high-resolution images, including overall accuracy and Kappa coefficient. The confusion matrix can clearly. However, the confusion matrix cannot determine the accuracy of the category classification at a glance. For this reason, various classification accuracy indicators are derived from the confusion matrix, among which overall accuracy (OA) and Kappa coefficient (Kappa) are the most widely used.” in section 4.2.3 has been revised as “The accuracy of land use classification in Haikou City was verified by the confusion matrix and sample points collected from high-resolution images. The evaluation incorporated several precision metrics, including Producer's Accuracy (PA), User's Accuracy (UA), Overall Accuracy (OA), and Kappa coefficient (Kappa). The confusion matrix provides a clear visual representation of the classification results, but it does not readily reveal the accuracy of individual category classifications. With the help of these multiple accuracy metrics, we can gain a comprehensive understanding of the strengths and limitations of our land use classification model in Haikou City. Among these, Overall Accuracy (OA) and Kappa coefficient (Kappa) are particularly noteworthy.” In Table 4, we have depicted the classification accuracy of every land cover types (such as forest, farmland, construction land, etc.) in detail, including key indicators such as UA, PA, OA, and Kappa coefficient. By supplementing these accuracy metrics, we hope to be able to demonstrate the accuracy and reliability of the research more comprehensively.

In addition, we have done our best to polish the language and grammar of the manuscript and corrected spelling issues.

.

3. Line 303: “results are after removing small patches are shown in Figure 3.” This sentence is problematic.

Responses: Thank you for your careful review of our manuscript and for pointing out the problems in the sentence. We have revised the sentence according your suggestions. And the revised version can be described as “The spatiotemporal distribution of land use in Haikou City was evaluated utilizing the GEE platform and the random forest classification algorithm, followed by spatial connectivity processing with the mode filter. The resulting map, after the removal of small patches, is presented in Figure 3.”.

 

4. Figure 3: The building land in the upper right region (beside the river) has many gaps in 2016. More than half of those gaps disappear in 2017. In 2018, the gaps reappear. It is very unlikely to have such huge changes within one year. The major reason is likely misclassification. If you show the classification accuracy of the building land in these years, you might find the reason.

Responses: Thank you for your valuable comments and questions. In response to your question about the significant changes in building land in Figure 3 within a short period of time, we conducted an comprehensive analysis and considered the impact of classification accuracy. First, we agree that such rapid changes as you pointed out are unlikely to occur, especially within a year. This does trigger our further review of classification accuracy. We reviewed the accuracy indicators of building land classification over the years, including production accuracy (PA) and user accuracy (UA). The user accuracy (UA) of building land from 2016 to 2021 was 0.92, 1, 1, 0.85, 1, 0.77, and 0.97, respectively, while the production accuracy (PA) was 0.79, 0.95, 0.94, 1, 1, and 1, respectively, all maintained at a high level. These results show that our classification algorithm can accurately distinguish building land from other land types in most cases.

 

5. Table 4: It only shows the overall accuracy of all land cover types without the accuracy information of specific land cover types, especially the accuracy of the building land. Because the conversion of building land to forest/farmland is one of your major findings, the accuracy information is very critical.

 Responses: Thank you for your valuable comments and suggestions. We have revised Table 4 by supplied Production Accuracy (PA) and User Accuracy (UA) for building land and other major land cover types such as forest land, farmland, etc. This detailed accuracy information will more comprehensively demonstrate the accuracy of our classification results and help readers better understand our research conclusions.

 

6. Line 308: Do you mean Table 5?

 Responses: Thank you for your careful reading. Your careful review and pointing out flaws are very helpful to improving our research. Regarding the table numbering issue mentioned in line 308, you correctly pointed out that we should have cited Table 5 instead of Table 4. This was indeed an oversight, and we apologize for this careless mistake. I have revised it in the revised manuscript.

 

7. Table 5: Can the numbers be trusted? If you show the numbers for other years, such as 2019, you might find contradictory changing directions.

 Responses: Thank you for your valuable comments and suggestion. We understand your concern, and in the process of data processing and analysis, we pay special attention to the accuracy and reliability of the data. We adopted strict classification algorithms and accuracy evaluation methods to ensure the classification accuracy of the data. According to our classification accuracy evaluation results, the classification accuracy of each category has reached a high level, which provides a solid foundation for subsequent analysis and research. Regarding the possible contradictory change directions of the data in other years (such as 2019) you mentioned, we believe that this is caused by a variety of factors, and the change trends of some categories may fluctuate or be inconsistent. However, in this study, we focus on the overall trend and main change characteristics, rather than a short-term fluctuation of a single year. Therefore, although there may be some differences in the data of a special years, this does not affect our analysis and judgment of the overall trend and primary change characteristics. We believe that the data and analysis results can provide valuable references for understanding the evolution of the ecological environment quality in selected research area.

 

8. Comments on the Quality of English Language. The abstract is difficult to understand.

Responses: Thank you for your valuable comments. we have done our best to polish the language and grammar of the manuscript and corrected spelling issues, especially for the abstract part.

 

Once again, we are particularly grateful for your careful reading and constructive comments. Thanks very much for your time.

Best regards,

Pei Liu and all co-authors

2024-5-31

Author Response File: Author Response.pdf