An Integrated Framework for Landscape Indices’ Calculation with Raster–Vector Integration and Its Application Based on QGIS
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe authors created a front-end tool for QGIS that allows people interested in landscape ecology to compute and display a large number of metrics both in tabular form and in spatial form in the GIS. This is useful. Their front end can import both raster and vector data and can convert between them if need be. The user can then compute a large number of metrics and display the results like typical symbology, i.e., annotating or colorizing features on the map. It's easy to see how to do this for vector data because the features are discrete. It is not so clear to me how the tool does this for raster data sets. I want to see more examples with rasters.
Does/can the tool add the metric values to the features, probably by adding a column to the attribute data and populating the column, when appropriate? (Not all metrics are feature specific, so this might not always be possible.) I can think of arguments why it should and also why it should not. Having the metric values in the features allows the user to employ the full power of the GIS for further analyses; however, these metric values could be quite numerous and cumbersome. The proper compromise might be to allow the user to add whichever metric values they choose to the feature classes, if possible. This should be discussed.
The scientific value of the paper is sound. Such work should become known to the community, and the community will decide the worth of the work by either choosing to use the tool or to not use it. Either way, the community should be made aware of this tool.
Comments for author File: 
Comments.pdf
I found a couple minor mistakes. See attached comments
Author Response
Response to Reviewer 1 Comments
Dear Prof.,
Thank you for dedicating your time to review our manuscript. We genuinely appreciate your insightful comments and valuable suggestions. Please find our detailed responses to your feedback below, along with the revisions in the re-submitted files.
Thanks again!
Comment 1: The authors created a front-end tool for QGIS that allows people interested in landscape ecology to compute and display a large number of metrics both in tabular form and in spatial form in the GIS. This is useful. Their front end can import both raster and vector data and can convert between them if need be. The user can then compute a large number of metrics and display the results like typical symbology, i.e., annotating or colorizing features on the map. It's easy to see how to do this for vector data because the features are discrete. It is not so clear to me how the tool does this for raster data sets. I want to see more examples with rasters.
Answer 1: Thank you for the suggestion! We fully agree with your recommendations and apologize for the oversight in the visualization of raster data in Section 2.3 of the manuscript. We have revised this section to include both graphical representations and textual descriptions of raster data visualization. We acknowledge that the visualization methods for raster data differ from those for vector data. Inspired by your suggestions, we have stored the results of the raster data index calculations in the form of raster bands and visualized them accordingly. In the re-submitted manuscript, we have added a sentence in line 214 - line 217: “In VARLI, the calculation results of landscape indices for vector data can be added to its attribute table, enabling feature-based visualization of the landscape indices. Additionally, for raster data, the calculation results of landscape indices are stored as bands, allowing for spatial visualization of the indices in the form of a raster matrix.”.
Comment 2: Does/can the tool add the metric values to the features, probably by adding a column to the attribute data and populating the column, when appropriate? (Not all metrics are feature specific, so this might not always be possible.) I can think of arguments why it should and also why it should not. Having the metric values in the features allows the user to employ the full power of the GIS for further analyses; however, these metric values could be quite numerous and cumbersome. The proper compromise might be to allow the user to add whichever metric values they choose to the feature classes, if possible. This should be discussed.
Answer 2: Thank you for your suggestion. In fact, the principle of the VARLI tool for visualization is as follows: for vector data, a column for feature IDs is first added. The feature is then located based on the ID value to retrieve the calculated index result, and the rendering color of the feature is determined according to the numerical range or color specified by the user. For raster data, an ID label matrix is added to each patch, the landscape index for each patch is calculated based on the ID, and the rendering color of the patch is determined according to the numerical range specified by the user. Your suggestion can be implemented by assigning the landscape index calculation result to the corresponding feature based on the feature ID and adding landscape index bands to the raster data. Your suggestion is very practical and will provide broader opportunities for the use of the index. Indeed, adding all the results to the features or bands might overwhelm users (as they would spend unnecessary time searching for the index they wish to analyze). Allowing users to select the required landscape index to add to the feature class or band is a better solution. We have updated the description of this output result in the manuscript. Please refer to the revised paper from line 214-line 217, line 223-line 230 and line 237-line 242.
Comment 3: The scientific value of the paper is sound. Such work should become known to the community, and the community will decide the worth of the work by either choosing to use the tool or to not use it. Either way, the community should be made aware of this tool.
Answer 3: Thank you for your thoughtful feedback. We're pleased to hear that you find the scientific value of our work to be sound. We agree that it's important for the community to be aware of this tool, and we hope that by sharing our research, researchers will have the opportunity to evaluate its usefulness in their own work.
Comment 4: Line 64-65: “Once the utility of quantifying landscape patterns exponentially has been acknowledged, it becomes necessary to acquire a suitable tool for quantifying various aspect of the landscape”. The usage of “exponentially” and “aspect” is incorrect.
Answer 4: Thank you for your suggestion. This sentence is rephrased to “Recognizing the importance of quantifying landscape patterns, it becomes necessary to acquire a suitable tool for quantifying various aspects of the landscape.”
Comment 5: Line 89: “After delineating the commonly utilized tools, …”. It is suggested to change "delineating" to "enumerating".
Answer 5: Thank you for your suggestion. The word "delineating" in this sentence has been changed to "enumerating".
Comment 6: Line 90-92: “(1) The program's existence as an independent application proves more efficient than those reliant on desktop GIS plugins, as it eliminates the need for prior proficiency in GIS desktop software." It is suggested not to mention the phrase "as it eliminates the need for prior proficiency in GIS desktop software" in the sentence.
Answer 6: Thank you for your suggestion. The phrase "as it eliminates the need for prior proficiency in GIS desktop software" has been removed from the paper.
Comment 7: Line 93: “(2) It should be freely available as open-source software to minimize programming time, thereby fostering sustained development in the future". It’s not clear on why it being free (without cost) minimizes programming time. It seems like being free just makes it universally accessible.
Answer 7: Thank you for pointing out this issue. I apologize for the inaccuracies in this section. Given the current state of tool development, it is only available for free use and is universally accessible, but does not minimize programming time. If there is an opportunity in the future to make the code openly available, it could serve as a reference for others and potentially minimize programming time for their development. Therefore, the sentence in the paper has already been revised to “It should be freely available for use, accessible to anyone who wishes to obtain and utilize it”.
Comment 8: Line 96: “Index calculation results should be presented spatially …". It is suggested that it be expressed as "the option to be presented spatially".
Answer 8: Thank you for your suggestion. Some indices are better presented without spatialization. The sentence has been revised to: "The option to present index calculation results spatially should be considered."
Comment 9: Line 100: “QGIS (Quantum Geographic Information System)” It is suggested to state the words first and then define the acronym.
Answer 9: Thank you for your suggestion. In the paper, it has been revised to "Quantum Geographic Information System (QGIS)".
Comment 10: Line 148-150: “raster data organizes the landscape into regular grids, with each grid cell assigned a corresponding attribute value to represent geographical entities. Each grid cell corresponds to one attribute, representing a discrete numerical value in space.” Not necessarily just one attribute. How about a 7-band digital image augmented with height data from LiDAR?
Answer 10: Thank you for pointing out this issue. Sometimes raster data has more than one band. The sentence has been revised to: "raster data organizes the landscape into regular grids, with each grid cell assigned at least one corresponding attribute value to represent geographical entities. Each grid cell corresponds to one attribute in a band, representing a discrete numerical value in space."
Comment 11: Line 153: “Landscape indices calculated from vector data tend to be more precise, …” More precise for features with definite boundaries, like buildings or roads. I'm not sure they're more precise for organic features.
Answer 11: Thank you for pointing out this issue. It is a challenge currently faced by the Patch Matrix Model (PMM). To address this, some scholars have proposed Gradient Model (GM), which use percentages to represent the proportion of organic features in raster grids. Since this article calculates landscape indices based on the discrete patch model, and I haven't yet found a better solution to this problem, therefore, this sentence is revised to: “In PMM, landscape indices calculated from vector data tend to be more precise, …”
Comment 12: Line 158: “raster data structures are comparatively simpler and facilitate faster computations”. Raster typically occupy a lot more disk space than vector data, and so raster analyses have to process a large data set. This can be slower even though the algorithm is simpler.
Answer 12: Thank you for pointing out this issue. I apologize for not expressing it clearly in the article. You pointed out that raster data takes up more disk space, so raster analyses have to handle a large data set. Although the algorithms are simpler, the speed may be slower. I acknowledge this viewpoint. Raster data is stored in a grid format, its data structure is simple. However, if the grid pixels are small, it can result in a large amount of data, taking up more disk space. Nevertheless, the simplicity of the raster data structure allows for quick reading and access. Parallel computing can also process multiple pixels simultaneously, significantly reducing the time required. On the other hand, vector data is typically composed of points, lines, and polygons. When performing calculations, it needs to handle the spatial topology of geometric objects, which can increase processing time.
Some might question that as the raster data resolution increases, the data volume will be very large, leading to long computation times, possibly even exceeding the processing time of vector data. I completely agree with this viewpoint. However, in some cases, it is not necessary to process highly resolution raster data. If the goal is to achieve calculation accuracy, it would be better to convert the high-resolution raster into vector data for processing. Conversely, if accuracy is not a priority, low-resolution raster data can suffice. If the processing time for this low-resolution raster is shorter compared to vector data, due to its lower resolution and simpler matrix structure, and faster than processing high-precision vector data, then why not choose this data structure?
I apologize once again for not expressing the article clearly and scientifically enough. Without providing the necessary preconditions, our absolute descriptions may have misled readers and resulted in differing opinions. Therefore, we have revised this part of the article to ensure that the viewpoint is valid under specific conditions. Please see line 156-line 166 in the re-submitted manuscript
Comment 13: Line 166: I don't agree with the unilateral efficiency statements; however, are they needed for this article?
Answer 13: Thank you for pointing out this issue. Regarding the response to “the unilateral efficiency statements”, please see Answer 12. In my opinion, this passage is necessary for the article, as the topic revolves around an integrated vector-raster landscape index calculation framework. Highlighting the strengths and weaknesses of both vector and raster data in processing helps underscore the value of the article. Therefore, I revised this passage to make it clearer.
Comment 14: Line 199: “but it also provides a choice: For large datasets of vector data”. It is suggested to lower “For” to “for”.
Answer 14: Thank you for pointing out this issue. The correction has been made in the article.
Comment 15: Line 259-263: “Users have the option to select from a variety of data formats, including vector data in shapefile (SHP), Keyhole Markup Language (KML), GeoJSON, and file geodatabase formats stored in databases. Additionally, raster data formats such as GeoTIFF, ASCII Grid, and IMG will be supported. The current version of the program only supports inputting vector data in shapefile format and raster data in GeoTIFF format”. It is suggested to put the former sentence after the next one.
Answer 15: Thank you for your suggestion. It is rephrased to “The current version of the program allows loading vector data in SHP format and raster data in GeoTIFF format. In future versions, vector data inputs data format will include shapefile (SHP), Keyhole Markup Language (KML), GeoJSON, and file geodatabase formats stored in databases. For raster data, inputs data format such as GeoTIFF, ASCII Grid, and IMG will also be supported.”
Comment 16: Line 300-301: “…exhibit a sawtooth pattern”. instead, consider "...become increasingly pixelated."
Answer 16: Thank you for your suggestion. The "Case study" section has been rewritten. Please refer to the re-submitted manuscript.
Comment 17: Line 316 and Line 322: “index such as PLAND” and “index like ED”. The word “index” should be plural.
Answer 17: Thank you for your suggestion. The "Case study" section has been rewritten. Please refer to the re-submitted manuscript.
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe manuscript developed a software on the basis of QGIS (Quantum Geographic Information System) platform and explored methods for integrating landscape indices with both raster and vector data.
The authors are suggested to conduct additional experiments to compare the reported software with FragStat and/or other open source software, especially in terms of the calculation results on the same input data. In this light, the accuracy and efficiency performance of the reported software could be validated.
Comments on the Quality of English LanguageMinor editing of English language required.
Author Response
Response to Reviewer 2 Comments
Dear Prof.,
Thank you for dedicating your time to review our manuscript. We genuinely appreciate your insightful comments and valuable suggestions. Please find our detailed responses to your feedback below, along with the revisions in the re-submitted files.
Thanks again!
Comment 1: The authors are suggested to conduct additional experiments to compare the reported software with FragStat and/or other open source software, especially in terms of the calculation results on the same input data. In this light, the accuracy and efficiency performance of the reported software could be validated.
Answer 1: Thank you for the suggestion! The re-submitted manuscript includes a "Discussion" section where the landscape metrics results of VARLI are compared with those from VecLI and Fragstats using three statistical measures: Standard Deviation of Absolute Error, Pearson Correlation, and t-test. Some metrics results are consistent with VecLI and Fragstats, while others show differences. The reasons can be summarized as follows: (1) VecLI provides reasonable corrections for vector-based landscape metrics computation. (2) Fragstats derives landscape metrics from raster data, whereas VARLI currently does not calculate the same number of landscape metrics as Fragstats. Additionally, some metrics in VARLI are calculated based on vector data, resulting in differences between them.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThe article presents a framework for calculating landscape indices integrating vector and raster data. The authors claim they are simultaneously addressing the limitations of existing tools or compute indices for most used data types. The framework has 3 main modules: data input (supporting vector, raster, and tabular data), landscape indices calculation (allowing selection of indices at patch, class, and landscape scales with 165 indices for vector data and 238 for raster data), and visualization (enhancing spatial visualization of indices).
Strengths:
- The framework provides a comprehensive set of 165 indices for vector data and 20 for raster data, allowing users to select the most appropriate index.
- The framework is well-structured into key modules (data input, indices calculation, visualization) as described in the class diagram in Figure 1.
Major issues:
- The article's objective must be clearly defined initially. Although a new framework for landscape indices calculation is mentioned, there is a lack of a clear and concise statement of the specific problem this framework aims to solve.
- It is unclear how the authors selected the 165 indices for vector data and the 20 for raster data. An explanation or justification for this choice is needed. (Lines 170-173)
The paper is missing a discussion section in which the authors should discuss how the spatial indicators can be useful for planners, policymakers, and territorial managers. They should explore how having a wide range of landscape indices calculated from both vector and raster data and spatial visualization capabilities can provide more comprehensive and intuitive information to guide decision-making in land planning and management. The discussion should also include specific examples of how these indices can assess landscape patterns, track changes over time, and identify areas that need conservation or development. By doing this, the authors can show the practical value of their framework in connecting academic research on landscape patterns to real-world applications. Without this discussion, the work might seem like a purely stylistic exercise.
Minor issues:
- The title could be more specific by mentioning "QGIS", as it is the platform on which the framework is developed.
- Some sentences are rather long and could be simplified to improve readability. (e.g., Lines 41-44, 104-110)
- The authors use "we" and "our" in some sentences that should be rewritten in a more impersonal manner. (e.g., Lines 88)
- The graphic style of the figures is not catchy and could be improved.
Overall, the article is of good quality, fluent and understandable scientific English. Most of the issues are minor and do not affect the clarity and rigour of the exposition. With some revisions mainly related to sentence length and repetitions, the linguistic quality of the text can be further raised.
Author Response
Response to Reviewer 3 Comments
Dear Prof.,
Thank you for dedicating your time to review our manuscript. We genuinely appreciate your insightful comments and valuable suggestions. Please find our detailed responses to your feedback below, along with the revisions in the re-submitted files.
Thanks again!
Comment 1: The article's objective must be clearly defined initially. Although a new framework for landscape indices calculation is mentioned, there is a lack of a clear and concise statement of the specific problem this framework aims to solve.
Answer 1: Thank you for pointing out this issue. The objective of the article and the description of the specific problem to be addressed have been included in the re-submitted manuscript. Please refer to lines 99 to 103 in the file.
Comment 2: It is unclear how the authors selected the 165 indices for vector data and the 20 for raster data. An explanation or justification for this choice is needed. (Lines 170-173)
Answer 2: Thank you for the suggestion! The explanation for selecting these metrics has been added to the manuscript between Line 188 and Line 189.
Comment 3: The paper is missing a discussion section in which the authors should discuss how the spatial indicators can be useful for planners, policymakers, and territorial managers. They should explore how having a wide range of landscape indices calculated from both vector and raster data and spatial visualization capabilities can provide more comprehensive and intuitive information to guide decision-making in land planning and management. The discussion should also include specific examples of how these indices can assess landscape patterns, track changes over time, and identify areas that need conservation or development. By doing this, the authors can show the practical value of their framework in connecting academic research on landscape patterns to real-world applications. Without this discussion, the work might seem like a purely stylistic exercise.
Answer 3: Thank you for the suggestion! The re-submitted manuscript has revised the "Case Study" section. In this part, we selected three landscape metrics as examples and calculated these metrics at three scales: landscape type, region, and grid. Then we analyzed the spatial distributions of these metrics. The purpose of this section is to demonstrate the effectiveness of visualizing landscape metrics. Please see the re-submitted manuscript for details.
Comment 4: The title could be more specific by mentioning "QGIS", as it is the platform on which the framework is developed.
Answer 4: Thank you for the suggestion! The title and abstract have already been updated to include "based in QGIS".
Comment 5: Some sentences are rather long and could be simplified to improve readability. (e.g., Lines 41-44, 104-110)
Answer 5: Thank you for pointing out this issue. The sentence from lines 41-44, "Landscape indices derived from the PMM offer statistical insights, enabling quantitative evaluation of landscape composition and configuration, thereby depicting land-scape structure and its patterns," has been revised to "Landscape indices derived from the PMM model exhibit specific statistical properties. They quantitatively delineate the structure of the landscape through assessment of its spatial configuration and composition. These indices are instrumental in landscape evaluation and planning processes." Moreover, some other longer sentences in the manuscript have been revised.
Comment 6: The authors use "we" and "our" in some sentences that should be rewritten in a more impersonal manner. (e.g., Lines 88)
Answer 6: Thank you for the suggestion! The pronouns "we" and "our" in the paper have been changed to an impersonal manner.
Comment 7: The graphic style of the figures is not catchy and could be improved.
Answer 7: Thank you for pointing out this issue. Figure 3 has been revised.
Author Response File: Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for AuthorsFile submitted.
Comments for author File: Comments.pdf
Minor edits needed. Some sentences could be more clear.
Author Response
Response to Reviewer 4 Comments
Dear Prof.,
Thank you for dedicating your time to review our manuscript. We genuinely appreciate your insightful comments and valuable suggestions. Please find our detailed responses to your feedback below, along with the revisions in the re-submitted files.
Thanks again!
Comment 1: Having experience writing similar papers I understand there can be doubts in what to include in a “program description" section versus “case study" because some contents may overlay but I suggest at least the name of the software should be included in the former. Notice the first, and only, time the name of your software appears in the paper is in the supplementary material.
Answer 1: Thank you for pointing out this question. The software name "VARLI" has been explicitly mentioned in the paper, and other sections referring to the program have been replaced with "VARLI".
Comment 2: Regarding the case study, it should be used to highlight the software capabilities (how easy it is to use, type of outputs, etc.) or to compare with other software's particular performance (e.g, processing time, more formats accepted, etc.).
Answer 2: Thank you for the suggestion! The re-submitted manuscript has revised the "Case Study" section. In this part, we selected three landscape metrics as examples and calculated these metrics at three scales: landscape type, region, and grid. Then we analyzed the spatial distributions of these metrics. The purpose of this section is to demonstrate the effectiveness of visualizing landscape metrics. Please see the re-submitted manuscript for details.
Comment 3: Much of the text in subsection 4.2 is not clear and would need some clarification of concepts (pixel size/resolution, etc.).
Answer 3: Thank you for pointing out this question. This part has been revised, please see the re-submitted manuscript.
Comment 4: The conclusion section contains relevant information (e.g. “our tool includes visualization features for landscape indices, facilitating user understanding through ...") and should explore more of that and reinforce with more examples. I suggest to remove the conclusions on comparisons between indices/ metrics and pixel sizes/resolution because it was not the paper's goal. Although there's not an explicit goal mentioned in the Introduction, it can be understood that the paper is about introducing a new software to further expand the analytical capabilities. The conclusion must focus on that and not on parallel topics.
Answer 4: Thank you for your suggestions! The 'Case study' section has been rewritten in the re-submitted manuscript. It demonstrates the effectiveness of program visualization through examples of three landscape metrics. Additionally, the “Conclusion” section has also been rewritten. Please refer to lines 430 to 436 for details.
Comment 5: Maybe using bullets to refer to the pros/strengths of the VARLI may be useful.
Answer 5: Thank you for your suggestions! The advantages of VARLI have been listed in bullets in the re-submitted manuscript, please see lines 417 to 421.
Comment 6: Mention the weaknesses/limitations.
Answer 6: Thank you for your suggestions! The limitations of VARLI have been listed in bullets in the re-submitted manuscript, please see lines 423 to 428.
Comment 7: List of future work (Maybe bullets again?)
Answer 7: Thank you for your suggestions! The future work of VARLI have been listed in bullets in the re-submitted manuscript, please see lines 438 to 487.
Comment 8: Line 100 - Add QGIS reference as it is the first time the software is mentioned.
Answer 8: Thank you for your suggestions! The re-submitted manuscript has referenced QGIS in line 105.
Comment 9: Line 103-104 - These two sentences shouldn't belong to the final part of the introduction, where the focus should be on the goals of the work. These sound more like good practice in landscape analysis.
Answer 9: Thank you for your suggestions! The final part of the introduction has been revised After comprehensive analysis, the existing software for calculating landscape indices has the following issues: (1) Some of the software is not openly accessible. (2) it pro-cesses and computes landscape indices for either raster or vector data types but not both. (3) it mostly presents landscape characteristics in tabular form, lacking spatial data representation of landscape index distributions. To address the aforementioned issues, this study has implemented the following efforts:
- Based on the Quantum Geographic Information System (QGIS) [48] platform, this study conducts secondary development to explore methods for integrating vector and raster data for landscape indices. Users can choose to calculate landscape indices using either vector or raster methods according to their needs
- For vector data, 165 indices are available for selection, while for raster data, 20 indices are available.
- Three rendering methods are provided for visualizing landscape indices: unique value rendering, graduated rendering, and chart rendering. to:
Comment 10: Figure 1 - I suggest adding a brief (real short) definition of “composition" and “aggregation" in this context. For the audience that understands landscape concepts but not Python 00P language, it will be easy to misunderstand these terms.
Answer 10: Thank you for your suggestions! The caption of the figure has been updated to include footnotes explaining the definitions of "composition" and "aggregation".
Comment 11: Line 300 and Line 303 - do you mean cell or pixel size instead of raster size?
Answer 11: Thank you for pointing out this question. The 'Case study' section has been revised, please see the re-submitted manuscript.
Comment 12: Figure 4- No need to show the word "legend". Take it out or replace with something meaningful like “Land use types".
Answer 12: Thank you for pointing out this question. In the re-submitted manuscript, the legend has been changed to "Land use types" in Figure 4.
Comment 13: Line 315 - Three?
Answer 13: Thank you for pointing out this issue. This part has been revised, please see the re-submitted manuscript.
Comment 14: Line 316-317 - when the pixel size is smaller, the same extent contains more pixels and therefore the resolution is higher, not lower.
Answer 14: Thank you for pointing out this question. You make a lot of sense. This part has been revised, please see the re-submitted manuscript.
Comment 15: Line 317-318 - Sentence not clear.
Answer 15: Thank you for the suggestion! This part has been revised, please see the re-submitted manuscript.
Comment 16: Line 332-33 - Basically, higher resolution is better for area calculation than lower resolution because the raster is accurate enough to follow the vectorial limits.
Answer 16: Thank you for pointing out this question. You make a lot of sense. This part has been revised, please see the re-submitted manuscript.
Comment 17: Line 349 - Good idea.
Answer 17: Thank you for your recognition.
Comment 18: Line 357 - You don't have to explain it extensively because this is the conclusion section, but perhaps you could provide a brief example. I understand the overall idea but what would be the concrete result of applying arithmetic operations (e.g. map algebra or similar) to different landscape indices? Are there benefits in adding or multiplying indices? Which ones? Some measure shape, others isolation, other contrast. They also vary in range. These are basic questions that emerge from not knowing exactly what you suggest.
I understand the value of indices assemblage. I've been working in combining indices that represent real world landscape transformations (types of dynamics, land use transformations patterns, etc.) but I'm not sure if that is what you suggest.
But anyway, if the proposal adds analytical value, I'm all for it. I just wanted to understand a bit more about it.
Answer 18: Thank you for pointing out this question. I apologize for the lack of clarity in that section of the article. The paragraph has been rewritten, and you can see the revised version in the updated manuscript. In the process of developing this software, we frequently encountered the calculation formulas for landscape indices and found that most landscape indices are composed of basic elements such as area, perimeter, number of patches, area proportion, etc. What I intended to convey is the use of arithmetic operations based on the combination of these fundamental elements. This includes performing arithmetic operations (addition, subtraction, multiplication, division operations, and relevant functions) on different landscape indices, since landscape indices are composed of these basic elements, and combinations of landscape indices can also be represented as combinations of these fundamental elements. Currently, most discussions focus on the utility of individual landscape indices. I believe it is also worthwhile to explore the utility of combining multiple landscape indices.
Comment 19: Line362 - You don't have to change it but the machine learning options you mention are more than spatial analysis tools. Perhaps they' re better classified as geospatial data mining.
Answer 19: Thank you for pointing out the issue. This paragraph has been rewritten, and the “spatial analysis tool” has now been changed to “geospatial data mining tool”.
s
Comment 20: Line 365 - the
Answer 20: Thank you for pointing out this question. This paragraph has been revised, please see the re-submitted manuscript.
Comment 21: Line 362-363 - Please specify. Which tool is it?
Answer 21: Thank you for pointing out the issue. This paragraph has been rewritten, and the “spatial analysis tool” has now been changed to “geospatial data mining tool”.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsImprovements in the paper are significant.
Comments on the Quality of English LanguageMinor editing of English language required.
Author Response
Comments 1: Minor editing of English language required.
Answer 1: Thank you for your suggestion. We have revised the article by replacing some words with simpler terms and breaking down some longer sentences. We have also corrected grammatical errors and spelling mistakes to make the article more accessible and easier to understand.
Reviewer 4 Report
Comments and Suggestions for AuthorsThe authors did a good job improving the manuscript. I think it's now better than before and potentially more appealing to the readers.
Congratulations and thank you for building VARLI.
Comments on the Quality of English LanguageI'd just recommend a last careful review of the text to prevent minor errors or typos.
e.g.
L315 ras-ter
L316 in-clude
L382 The landscape of Yaopu WAS measured
...
Author Response
Comments 1: I'd just recommend a last careful review of the text to prevent minor errors or typos.
e.g.
L315 ras-ter
L316 in-clude
L382 The landscape of Yaopu was measured
Answer 1: Thank you for recognition of this article and the revisions we have made.
We have carefully reviewed and revised the "-" errors in some words that you pointed out, and we have checked for similar errors throughout the manuscript. These corrections are highlighted in yellow in the revised document.
