Influencing Factors Analysis and Optimization of Land Use Allocation: Combining MAS with MOPSO Procedure
Round 1
Reviewer 1 Report
The research is of limited value.
The innovation of the paper is not too high.
The mathematical model in the paper isnot clear. The paper lacks the interval of calculation parameters.
The calculation program is not listed.
If this paper is published, the author still needs to do a lot of work.
Comments for author File: 
Comments.pdf
Author Response
Response to Reviewer 1 Comments
Dear reviewer,
On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript and let us give an explanation on our amendment on manuscript. After carefully going through your comments of our manuscript, we revised manuscript as your kind comment. All authors are appreciated your excellent work and thank you very much for your consideration. Thank you in advance for evaluating our manuscript.
Sincerely yours,
Dr. Jingjie Liu
College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou 239000, China
College of Land Management, Nanjing Agricultural University, Nanjing 210095, China
Tel. +86-25-8439-5700 , Fax. +86-25-84395700
E-mail: [email protected]
Point 1: The research is of limited value.
Response 1: Thanks for your comment. At present, there have been relatively few studies on optimal land use allocation at a township scale considering the preferences and decisions of main actors, such as governments, entrepreneurs, town residents, and farmers. Therefore, the research comprehensively considered these stakeholders' decision-making behaviors and factors, and optimized the layout of rural land use. This idea has an innovative certain contribution to science. Therefore, we think that the research is valuable.
Point 2: The innovation of the paper is not too high.
Response 2: Thanks for your comment. We comprehensively considered the preferences and decisions of main actors in the optimization of land use allocation. In addition, the representation of agent behavior is crucial within the optimal allocation of land-use. We built the optimal land use allocation model including MAS and MOPSO. MAS represents the decision-making behaviors of the agents involved in rural land use allocation. Therefore, we think that there are some innovations in the paper.
We carefully supplemented the content in r.53-61. “Nevertheless, there have been relatively few studies on optimal land use allocation at a township scale considering the preferences and decisions of main actors, such as governments, entrepreneurs, town residents, and farmers. Therefore, these stakeholders' decision-making behaviors and factors would be comprehensively considered in the context. On this basis, the representation of agent behavior is crucial within the optimal allocation of land-use. The optimal land use allocation model was built including MAS and MOPSO. MAS represents the decision-making behaviors of the agents involved in rural land use allocation. This idea has an innovative certain contribution to science.”
Point 3: The mathematical model in the paper is not clear. The paper lacks the interval of calculation parameters. The calculation program is not listed.
Response 3: Thanks for your comment. Figure 2 in the paper shows the framework of the analysis adopted in the present study. We described the process of the model. Two modelling layers were embodied in entire model, namely MAS and MOPSO. The general procedure of MOPSO was referenced References 35, and the key modules of MOPSO was described in “2.5. Expression of the objective functions and constraints”. The general procedure of MAS was referenced References 36, and the key modules of MOPSO was described in “2.4. The Probabilities Calculation of Rural Land Use Conversion”.
The process of the model as follow: “Two modelling layers were embodied in entire model, namely MAS and MOPSO. MOPSO expresses the agents’ adaptability and get their best location of the optimization procedure [35], whereas MAS represents the decision-making behaviors of the agents involved in rural land use allocation [36].”
Author Response File: 
Author Response.docx
Reviewer 2 Report
Abstract: rewrite and clarify the terminology: rural – urban/town/city. Also rewrite because is only a “result” section.
Introduction: r.50-51 add references. Extend the section, add the general context of your study and the gaps in the field. Present the novelty and necessity of your study
Chapter 2. Overview of the study area will be part of Chapter 3 (will be 2) Data sources and Methods as “2.1.Study area”. Renumbering the chapters and subchapters
R.68-69 please rewrite, is general (what means good climatic condition, abundant water resources ....
Figures: improve the quality of all figures.
R. 93-94 add info about the sources of land-use data of satellite imagery (2006-2015)
Discussion: rewrite – it is a mixt to results and conclusion. Present the limit of your study.
Conclusion: is very short and general, move some info from section discussion
Author Response
Response to Reviewer 2 Comments
Dear reviewer,
On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript and let us give an explanation on our amendment on manuscript. After carefully going through your comments and suggestions of our manuscript, we revised manuscript as your kind comment. All authors are appreciated your excellent work and thank you very much for your consideration. Thank you in advance for evaluating our manuscript.
Sincerely yours,
Dr. Jingjie Liu
College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou 239000, China
College of Land Management, Nanjing Agricultural University, Nanjing 210095, China
Tel. +86-25-8439-5700 , Fax. +86-25-84395700
E-mail: [email protected]
Point 1: Abstract: rewrite and clarify the terminology: rural – urban/town/city. Also rewrite because is only a “result” section.
Response 1: Thanks for your comment. We carefully studied the differences between urban, town, and city. Chinese administrative system divides land use planning into five hierarchical levels: national, provincial, municipal, county, and township. In Chinese cities or urbanized areas, governments above the county level are stationed, a large number of non-agricultural industries and population are gathered. In Chinese towns, governments in the township level are stationed, and agriculture is the main industry. The present study adopted Guanlin town, Yixing City, China as a case study, we have revised and adopted the words such as town, township and town residents and so on in the manuscript.
At the same time, we also modified the “Abstract”, and supplemented the result and discussion.
Point 2: Introduction: r.50-51 add references. Extend the section, add the general context of your study and the gaps in the field. Present the novelty and necessity of your study.
Response 2: Thanks for your comment. The novelty and necessity of our study should be improved. Therefore, we carefully revised this sentence in r.50-51. “Nevertheless, there have been relatively few studies on optimal land use allocation at a township scale considering the preferences and decisions of main actors, such as governments, entrepreneurs, town residents, and farmers. Therefore, these stakeholders' decision-making behaviors and factors would be comprehensively considered in the context. On this basis, the representation of agent behavior is crucial within the optimal allocation of land-use. The optimal land use allocation model was built including MAS and MOPSO. MAS represents the decision-making behaviors of the agents involved in rural land use allocation. This idea has an innovative certain contribution to science.”
Point 3: Chapter 2. Overview of the study area will be part of Chapter 3 (will be 2) Data sources and Methods as “2.1. Study area”. Renumbering the chapters and subchapters.
Response 3: Thanks for your comment. We have renumbered the chapters and subchapters.
Point 4: R.68-69 please rewrite, is general (what means good climatic condition, abundant water resources ....
Response 4: Thanks for your comment. We carefully revised this sentence in R.68-69. “Based on the various advantages of good climate conditions, abundant water resources and a developed transportation system, Guanlin Town has a rapid growth of aquaculture industries.”
Point 5: Figures: improve the quality of all figures.
Response 5: Thanks for your comment. The quality of all figures should really be improved. We re-output the all figures, enlarged the legend’ sizes, unified the scale bars. The graphics accuracy has been improved.
Point 6: R. 93-94 add info about the sources of land-use data of satellite imagery (2006-2015)
Response 6: Thanks for your comment. The rural land-use data in the manuscript included two parts: 1) The rural land-use data of 2015 for the study area were visually interpreted from QuickBird satellite images (spatial resolution of 2.44 m), and then digitized in eCogniton Developer 8.7. Based on this data, we obtained the results for optimal land use allocation for 2030. 2) The rural land-use data of 2006-2015 for the study area were summarized from the land use change survey carried out by the Natural Resources Bureau of Yixing City, Jiangsu Province, China. We predicted the areas of various land-use types in 2015 and 2030 based on land-use data of 2006–2012 and 2006–2015, respectively.
Therefore, we carefully revised this sentence in R.93-94. “The rural land-use data of 2006-2015 for the study area were summarized from the land use change survey carried out by the Natural Resources Bureau of Yixing City, Jiangsu Province, China.”
Point 7: Discussion: rewrite – it is a mixt to results and conclusion. Present the limit of your study.
Response 7: Thanks for your comment. We carefully revised the Discussion. The limit of our study was added. Some discussions were drawn as follows.
“4. Discussion
4.1. Complexity of Rural Land Use – Based Structure and Layout
The land use structure of Guanlin Town was calculated according to land use survey data for 2015. Agricultural land accounted for the largest proportion of total area of Guanlin Town (42.37%), with cumulative areas of cultivated land and aquaculture of 44.86 km2. Ecological land showed the second largest area at 25.73 km2. The area of un-used land was relatively small, accounting for only 1.81% of total area, lower than the average level of Wuxi City of 20.8%. This result indicates a relative shortage of land resources. The numbers of patches per km2 of cultivated land, ecological land, and “other” land were 10.832, 13.058, and 31.984, respectively, far exceeding those for aquaculture area, rural residential land, town residential land, and enterprise land of 3.064, 4.123, 0.481, and 1.896, respectively. On the other hand, the degrees of proximity of aquaculture area and cultivated land were relatively low at 58.245 and 62.301, respectively.
4.2. Accuracy of Model – Based combining MAS and MOPSO
The optimal allocation of land use is a complex, multi-objective decision-making process. The model used in the present study was established by combining MAS and MOPSO and was based on the analysis of decision-making behaviors of agents, the influencing factors, optimal objectives, and constraints in the study area. A comparison of the model results with land use data for 2015 provided a model accuracy index of 0.8226, suggesting a good optimization effect. The model not only accurately reflected the environmental conditions and the impacts of agents on rural land-use allocation, but also further improved the representation of the interaction among the agents and the environment, thus providing an improved simulation of land use change. The model is suitable for representing the interactions between individual agents and environment and for solving multi-objective optimal allocation of rural land use.
4.3. Rationality of Land Function Categories – Based Optimal allocation of land use
The present study reclassified seven land-use types into four function categories: (1) ecological land (E), including rivers and lakes; (2) ecological productive land (EP), which refers to the aquaculture area; (3) productive ecological land (PE), referring to cultivated land, and; (4) living productive land (LP), representing rural residential land, town residential land, and enterprise land. There is a need for government to strengthen the protection of land E. Land development and enterprise construction should be prevented in ecologically fragile and sensitive areas. EP land such as “Ring Dang” and the “Ring Lake” aquaculture belts should be constructed for protection of the ecological environment and aquaculture development of the region with Dushandang, Linjindang, and Gehu as the center. The area of the PE land can be stabilized and agricultural modernization can be realized by splitting agricultural land structure into two parts: (1) characteristic agriculture in the south of the town and; (2) traditional agriculture in the north of the town. The Dushan concentrated residential area could be added based on the existing planned village clusters. This would provide an accommodation solution for the large number of migrant workers employed by chemical enterprises. Moreover, the LP land in the middle of the town could be reclassified into four residential areas, namely Old town, Donghong, Jinghu, and Situ. This approach would decrease the scattered distribution of residential areas. The formation of three industrial zones, including for the chemical industry, high-end industry research and development, and traditional wire and cable industry would achieve optimized land use for industrial clusters and decrease the impact of industrial development on cultivated land. In contrast with approaches by General Land Use Planning, Town and Village Planning, and the High-Tech Industrial Development Zone Plan, the results obtained in the present study are backed up by objective research and are focused on maximization of benefits.
4.4. Limitations of the influencing factors and the Model
Rural land use is affected by macro policies and the human environment. The decisions and factors influencing the different levels of governments and types of farmers, town residents, and the entrepreneurs also vary. Future studies should formulate more detailed and reasonable rules to achieve more accurate modelling, and the quantitative expressions of constraint indicators, such as institutional policies and cultural deposits, should be further considered. The model represents many interactions be-tween individual agents, and PSO remains in an immature stage. The calculation overhead of the model increases with increasing spatial resolution. Therefore, the computational limits placed on the model is an urgent problem that needs to be solved. Further studies may explore the synthesis of high convergence computing and multi-agent modelling to provide efficient operational support for the model. In addition, the particular characteristics of the rural land system in China and the complexity of multi-agent interactions indicates the need for future work to focus on the decision rules for multiple agents, including land policy and supervision. In addition, although the present study was initiated in 2016, the remote sensing data used only covers the period up to 2015. Subsequent studies should use more updated datasets.”
Point 8: Conclusion: is very short and general, move some info from section discussion
Response 8: Thanks for your comment. We carefully revised the Conclusion, as follows:
“5. Conclusions
Taking Guanlin Town, Yixing City, China as an example, this study analyzed the factors by agents effecting rural land use conversion probability, identified the objectives and the constraints within the optimization of rural land use allocation, simulated the optimal land use allocation for 2030 combining MAS with MOPSO procedure. Some conclusions can be drawn as follows.
(1) The land use structure of Guanlin Town in 2015 was complex and indicated a relative shortage of land resources. The numbers of patches per km2 and the degrees of proximity of various types of land reflected that land-use types in Guanlin Town had a low intensification and an unreasonable structure, indicating the urgent need for land use optimization.
(2) The decision-making behaviors of agents (government, entrepreneurs, town residents, and farmers) and the influencing factors adjusted the spatial distribution of rural land use. The agent types and their decisions were different among the different land types. Government determined the conversion between land use types. The preferences of the entrepreneurs resulted in the distribution of industrial land. Town residents made a high contribution to the configuration of the urban town residential land by considering some factors. Rural families influenced land-use allocation by considering the quality of cultivated soils, and the optimal spatial location of aquaculture systems.
(3) The four objectives to be maximized were selected according to the questionnaire survey. These were economic revenue, basic living security, employment security, and the ecosystem services offered by each land use type. The most relevant constraints were the upper and lower limits of each land-use type.
(4) The results of optimizing the spatial layout of rural land use indicated that the spatial distributions of different land-use types in rural land tend to remain concentrated. Running the optimization model after optimal land-use allocation was reached allowed the optimal weights to be attributed to the decisions of agents. The values of these weights after this process were different from the starting values.
(5) The present study reclassified seven land-use types into four function categories: ecological land (E); ecological productive land (EP); productive ecological land (PE); living productive land (LP). In contrast with approaches by General Land Use Planning, Town and Village Planning, and the High-Tech Industrial Development Zone Plan, the results obtained in the present study are backed up by objective re-search and are focused on maximization of benefits.”
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
We put forward 9 suggestions, and the author only replied to 3 of them.
We are not satisfied with the reply,
Since the author mentioned in his reply that references and calculation processes have been listed, so that methods and frameworks have been published in relevant literature, what is the innovation of this paper?
Therefore, the innovation is very low.
The author's attitude towards scientific research is poor.
Author Response
Response to Reviewer 1 Comments
Dear reviewer,
Thank you for your decision and constructive comments on our manuscript. On behalf of my co-authors, we were really sorry for our previous attitudes. It was our mistake to give a unified reply to several suggestions and comments. We are very sorry for our negligence of expert opinion in the peer-review-24796348.v2.pdf. Now, we have carefully considered every comment of Reviewer and have tried our best to improve and made some make some changes and explanations. All authors are appreciated your excellent work and thank you very much for your consideration. And we sincerely hope the correction will meet with approval. Thank you in advance for evaluating our manuscript.
Merry Christmas to you and your family!
Sincerely yours,
Dr. Jingjie Liu
College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou 239000, China
College of Land Management, Nanjing Agricultural University, Nanjing 210095, China
Tel. +86-25-8439-5700 , Fax. +86-25-84395700
E-mail: [email protected]
Thank you for the comments concerning our manuscript. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We carefully arranged these comments as follows:
The comments in the webpage:
1) The research is of limited value. 2) The innovation of the paper is not too high. 3) The mathematical model in the paper is not clear. The paper lacks the interval of calculation parameters. 4) The calculation program is not listed. 5) If this paper is published, the author still needs to do a lot of work.
The comments in the peer-review-24796348.v2.pdf:
1) Line 80-81 The spatial resolution of the images is 2.44 m. Line 90-91 The present study extracted altitude and slope data from the ASTER Global Digital Elevation Model (30 m resolution) obtained from the Geospatial Data Cloud. Line 99 The above data were normalized and then converted to 98 raster maps with a resolution of 5 m.
The precision of the 2.44m, 30m and 5m are different. How to reconcile this precision?
2) In Figure 3, the land use maps for 2015 and 2030 are listed respectively. How is this map predicted in 2030? What is the size of the smallest computing unit in the figure? Is it calculated by pixels or other smallest units? The author needs to give a clear explanation. In Line179-184, cells are used as the minimum allocation units. The distribution map of the minimum units needs to be listed by the author. The author should clarify the parameter characteristics and laws of these minimum elements.
3) How is the 2015 optimization result chart in Figure 3 (b) optimized? Throughout the paper, we found that Formula (1) (2) (3) (4) lists some optimization goals and formulas, but it is necessary to define the interval of each variable. The entire paper does not see the interval and range of all parameters, so the author needs to define the interval range of these parameters.
4) The whole paper is an optimization problem. Take the cell as the minimum unit to calculate the corresponding income, and then conduct the overall accumulation to find the best one. It is obvious from Figure 3 that the function expansion and reduction of each small unit are not disordered and random, but have certain special conditions, which need to be clarified by the author and expressed in mathematical form.
5) The overall idea and idea of the paper is OK, but the mathematical model lacks detailed and accurate expression, and each parameter has no corresponding interval range. In particular, how is the ecological effect evaluated? Finally, a diagram and some calculation results are given. The reviewers have no way to know the calculation process, and the final calculation results can not give an accurate judgment.
6) Finally, the paper gives the calculation weight table 9. How is this result different from other existing literature? They are different. Is this different result universal? How to refer to this paper for other future research or engineering applications lacks practical significance.
7) The error between the evaluation and calculation based on the data obtained from remote sensing may be large. Since a large number of questionnaires can be conducted while sitting, why don't the author go directly to survey and divide the area to obtain direct data? （don't directly use remote sensing image ）
8) The specific equation, parameter interval, calculation process, input data of remote sensing calculation, manual survey data, comparison of calculation results, and the practical application reference value of the optimization model in this paper have some doubts or deficiencies.
9) It is suggested that the author refine the whole process, especially the mathematical model, parameter interval and known data, which need to be further added and supplemented.
First, we have revised and elaborated item by item in the webpage:
Point 1: The research is of limited value.
Response 1: Thanks for your comment. At present, there have been relatively few studies on optimal land use allocation at a township scale considering the preferences and decisions of main actors, such as governments, entrepreneurs, town residents, and farmers. Therefore, the research comprehensively considered these stakeholders' decision-making behaviours and factors, and optimized the layout of rural land use. This idea has an innovative certain contribution to science. Therefore, we think that the research is valuable.
Point 2: The innovation of the paper is not too high.
Response 2: Thanks for your comment. We comprehensively considered the preferences and decisions of main actors in the optimization of land use allocation. In addition, the representation of agent behaviour is crucial within the optimal allocation of land-use. We built the optimal land use allocation model including MAS and MOPSO. MAS represents the decision-making behaviours of the agents involved in rural land use allocation. Therefore, we think that there are some innovations in the paper.
We carefully supplemented the content in Line 53-61. “Nevertheless, there have been relatively few studies on optimal land use allocation at a township scale considering the preferences and decisions of main actors, such as governments, entrepreneurs, town residents, and farmers. Therefore, these stakeholders' decision-making behaviours and factors would be comprehensively considered in the context. On this basis, the representation of agent behaviour is crucial within the optimal allocation of land-use. The optimal land use allocation model was built including MAS and MOPSO. MAS represents the decision-making behaviours of the agents involved in rural land use allocation. This idea has an innovative certain contribution to science.”
Point 3: The mathematical model in the paper is not clear. The paper lacks the interval of calculation parameters. The calculation program is not listed.
Response 3: Thanks for your comment. Figure 2 in the paper shows the framework of the analysis adopted in the present study. We described the process of the model. Two modelling layers were embodied in entire model, namely MAS and MOPSO. The general procedure of MOPSO was referenced References 35, and the key modules of MOPSO was described in “2.5. Expression of the objective functions and constraints”. The general procedure of MAS was referenced References 36, and the key modules of MOPSO was described in “2.4. The Probabilities Calculation of Rural Land Use Conversion”.
The process of the model as follow: “Two modelling layers were embodied in entire model, namely MAS and MOPSO. MOPSO expresses the agents’ adaptability and get their best location of the optimization procedure [35], whereas MAS represents the decision-making behaviours of the agents involved in rural land use allocation [36].”
What's more, we have revised and elaborated item by item in the pdf:
Point 1: Line 80-81 The spatial resolution of the images is 2.44 m. Line 90-91 The present study extracted altitude and slope data from the ASTER Global Digital Elevation Model (30 m resolution) obtained from the Geospatial Data Cloud. Line 99 The above data were normalized and then converted to 98 raster maps with a resolution of 5 m.
The precision of the 2.44m, 30m and 5m are different. How to reconcile this precision?
Response 1: Thanks for your comment. In our manuscript, the spatial resolutions of the land-use images, and the altitude and slope data were different. And it is our negligence of unclearly descripting the coordination between the different data. First, land-use data for the study area were visually interpreted and then digitized from the QuickBird satellite images in 2015. The land-use data was the basis of our study. Because the study area was small, the accuracy of data should be high. We purchased the remote sensing data in 2015, and interpreted it to obtain land-use data. However, we have carefully confirmed that the altitude and slope data were extracted from STER GDEM data (30 m) obtained from the geospatial data cloud platform (http://www.gscloud.cn). The accuracy of data was 30m. It is difficult to improve the accuracy of elevation and slope data. What's more, the study area was divided into many grids (the scale of 5m * 5m) in the optimization process. Each land use grid should only be allocated with one land use type and one agent. Therefore, the above data were normalized and then converted to raster maps (a resolution of 5 m) to ensure the optimal allocation of rural land use.
In Line 111-114, we have revised the content, “The study area was divided into many grids (the scale of 5m * 5m) in the optimization process. Each land use grid should only be allocated with one land use type and one agent. Therefore, the above data were normalized and then converted to raster maps (a resolution of 5 m) to ensure the optimal allocation of rural land use.”
Point 2: In Figure 3, the land use maps for 2015 and 2030 are listed respectively. How is this map predicted in 2030? What is the size of the smallest computing unit in the figure? Is it calculated by pixels or other smallest units? The author needs to give a clear explanation. In Line179-184, cells are used as the minimum allocation units. The distribution map of the minimum units needs to be listed by the author. The author should clarify the parameter characteristics and laws of these minimum elements.
Response 2: Thanks for your comment. It's our fault that we didn't explain clearly the process of optimization results. The land use map in 2015 was the basis of the study. To verify the validity of the model, we obtained the optimal allocation results in 2015 after a few steps including running related files, loading the basic data, initializing parameters, using Raster to ASCII in ArcMap. Then, we obtained the optimal allocation results of rural land use in the study area in 2030 by using the optimal land use allocation model.
In Line 339-345, we have carefully revised the content, “The optimal allocation results of land use were obtained after a few steps including running related files, loading the basic data, initializing parameters, using Raster to ASCII in ArcMap. The land use map for 2015 is shown in Figure 3(a). Based on the land use map in 2015, optimal allocation results in 2015 and 2030 were shown in Figure 3(b), Figure 3(c) and Table 8.”
It's our fault that we didn't describe clearly the size of the smallest computing unit in the figure. The size of the smallest computing unit was 5m * 5m. The all data were normalized and then converted to raster maps (a resolution of 5 m). Because the total grid number of land use in the study area was 4,236,083, it was difficult to display the minimum units. We supplemented the sentence in Line 111-114 to explain the scale of the smallest unit, “The study area was divided into many grids (the scale of 5m * 5m) in the optimization process. Each land use grid should only be allocated with one land use type and one agent. Therefore, the above data were normalized and then converted to raster maps (a resolution of 5 m) to ensure the optimal allocation of rural land use.”
Point 3: How is the 2015 optimization result chart in Figure 3 (b) optimized? Throughout the paper, we found that Formula (1) (2) (3) (4) lists some optimization goals and formulas, but it is necessary to define the interval of each variable. The entire paper does not see the interval and range of all parameters, so the author needs to define the interval range of these parameters.
Response 3: Thanks for your comment. It's our negligence that we didn't explain clearly the 2015 optimization result. To verify the validity of the model, we obtained the optimal allocation results in 2015 after a few steps including running related files, loading the basic data, initializing parameters, using Raster to ASCII in ArcMap. We have carefully revised the content in Line 339-345, and we made some explanations in Point 2.
In the optimal land use allocation model, each agent has lived in a grid of rural land use environment (the scale of 5m * 5m, a cell). the conversion probability of a cell (i, j) was expressed by Formula (1) and (2).
(1)
(2)
In Formula (1),
Pij: is the land use conversion probability determined by agents and ai denotes the weight of an agent, and they are the real numbers in the range of 0-1; Cij represents the effectiveness of the decision of an agent in influencing the probability of land use conversion. In Line 182-184, we supplemented the sentence, “Pij is the land use conversion probability determined by agents and ai denotes the weight of an agent [subscript “i” (1 to 4) indicates the agent, as shown in Table 1, and they are the real numbers in the range of 0-1).”
In Formula (2),
Cij: When i=1, C1j is an integer in the range of 0–3.
[farmland], [built-up], and [ecology]: When a cell corresponding to cultivated land within the protection zone (farmland), built-up area (built-up), or ecological protection area (ecology), [farmland], [built-up], and [ecology] is 1, otherwise 0. So C1j =0 indicates that the cell can be converted to another land-use type. The C1j value assigned to a cell corresponding to cultivated land within the protection zone (farm-land), urban town built-up area (built-up), or ecological protection area [ecology] was 1, 2, and 3, respectively, otherwise 0 was assigned to C1j. Therefore, C1j is an integer in the range of 0–3.
When i=2, 3, or 4, Cij is obtained by Formula (2). wk and dk represent the decision parameters standardized index value and the variables. Cij, wk, and dk are the real number in the range of 0-1. In Line 190-193, we supplemented the sentence, “C1j is an integer in the range of 0–3; C1j =0 indicates that the cell can be converted to an-other land-use type. The C1j value assigned to a cell corresponding to cultivated land within the protection zone (farmland), town built-up area (built-up), or ecological protection area [ecology] was 1, 2, and 3, respectively, otherwise 0 was assigned to C1j.” In Line 196-197, we added the sentence, “Cij, wk, and dk are the real number in the range of 0-1.”
(3)
In Formula (3), Fi represents the total regional land-use benefit, “i” (1 to 4) represents economic revenue, basic living security, employment security, or the ecosystem services offered by each land use type, respectively. Wik represents the benefit value coefficient of land use type k [“i” (1 to 4) represents the average revenues of various land use types, town endowment insurance, the incomes per unit area of employees, the ecosystem service values of various land types per unit area, respectively]. Sk represents the areas of seven land use types, respectively. However, it is difficult that the interval and range of four parameters are unified due to different units of coefficients. But the total regional land-use benefits and the benefit value coefficients should be described in our manuscript. In Line 207-212, we supplemented some content, “Fi represents the total benefit [“i” (1 to 4) represents the economic revenue, basic living security, employment security, and the ecosystem services offered by each land use type.” “Wik represents the benefit value coefficient of land use type k, i.e., the average revenues of various land use types, town endowment insurance, the incomes per unit area of employees, the ecosystem service values of various land types per unit area.”
(4)
In Formula (4), f(i) is the fitness value, and was calculated by the probability of conversion, the values of the objective functions, and the constraint value of land use unit x. Pij denotes the probability of conversion of the j-th land-use type, and has been calculated according to Formula (1). z(x) represents the values of the objective functions of land use unit x, and was obtained by handling the total regional land-use benefit (MaxFi). We adopted the adaptive weight approach to calculate the z(x). p(x) is the constraint value of land use unit x using the penalty function to obtain the unconstrained optimal problems. β is a constant fixed at (1,2). Because the adaptive weight approach and the penalty function are the basic treatment methods, we did not elaborate the process referred to the 44th literature. In Line 235-240, we supplemented some sentence, “Pij denotes the probability of conversion of the j-th land-use type determined by the i-th agent, and has been calculated according to Formula (1)”, “z(x) represents the values of the objective functions of land use unit x by handling the total regional land-use benefit (MaxFi)”, “β is a constant fixed at (1,2)”.
Because not all parameters have intervals to determined clearly, we have tried our best to add intervals for each parameter.
Point 4: The whole paper is an optimization problem. Take the cell as the minimum unit to calculate the corresponding income, and then conduct the overall accumulation to find the best one. It is obvious from Figure 3 that the function expansion and reduction of each small unit are not disordered and random, but have certain special conditions, which need to be clarified by the author and expressed in mathematical form.
Response 4: Thanks for your comment. The optimal land use allocation is a combinatorial and optimization problem including multiple objectives, discontinuous, and high-dimensional objective. In the optimal land use allocation model, the study area was divided into many grids (the scale of 5m * 5m) in the optimization process. Each land use grid should only be allocated with one land use type and one agent. one agent contains two data: speed and location. Agents could sense the information around their local environment, and improve their adaptability through competition, cooperation and self-learning with agents' neighbourhoods. In addition, agents selected the best position of the group and the individual by the global and local searching strategies independently. The key to solve the optimal problem was processing efficiently the objectives and constraints, and measuring the adaptability of agents in a grid or a cell.
In Line 147-157, we supplemented some content, “In the optimal land use allocation model, the study area was divided into many grids (the scale of 5m * 5m) in the optimization process. Each land use grid should only be allocated with one land use type and one agent. one agent contains two data: speed and location. The key to solve the optimal problem was processing efficiently the objectives and constraints, and measuring the adaptability of agents in a grid or a cell.”, “Agents could sense the information around their local environment, and improve their adaptability through competition, cooperation and self-learning with agents' neighbourhoods. In addition, agents selected the best position of the group and the individual by the global and local searching strategies independently.”
In addition, we developed nine source program files to achieve above goals, including “fitnessfunction.m”, “muloptfunction.m”, “yushufunction.m”, “compfunction.m”, “MaxMinfunction.m”, “neiborfunction.m”, “selflearnfunction.m”, and “Lijfunction.m”. We also set some constants: 1) To obtain the global optimal solution and prevent particles (agents or cells) from falling into local minima, the inertia constants were set to 0.4 and 0.9; 2) The probability of the cooperation and competition with the agents' neighbourhoods were both 0.85. 3) The size of self-learning with agents' neighbourhoods (sL size) was 6; 4) The radius of local search (sR) was 0.2; 5) The number of maximum iteration (SGen) was 10; 6) The precision or the minimum error value (Amin) was 10-6.
In Line 163-173, we supplemented some content, “Nine source program files were developed to achieve above goals, including “fitness-function.m”, “muloptfunction.m”, “yushufunction.m”, “compfunction.m”, “MaxMin-function.m”, “neiborfunction.m”, “selflearnfunction.m”, and “Lijfunction.m”. In addi-tion, we also set some constants were set: 1) To obtain the global optimal solution and prevent particles (agents or cells) from falling into local minima, the inertia constants were set to 0.4 and 0.9; 2) The probability of the cooperation and competition with the agents' neighbourhoods were both 0.85. 3) The size of self-learning with agents' neighbourhoods (sL size) was 6; 4) The radius of local search (sR) was 0.2; 5) The number of maximum iteration (SGen) was 10; 6) The precision or the minimum error value (Amin) was 10-6.”
Point 5: The overall idea and idea of the paper is OK, but the mathematical model lacks detailed and accurate expression, and each parameter has no corresponding interval range. In particular, how is the ecological effect evaluated? Finally, a diagram and some calculation results are given. The reviewers have no way to know the calculation process, and the final calculation results can not give an accurate judgment.
Response 5: Thanks for your comment. It's our fault that we didn't accurately express the mathematical model in detail. We are deeply sorry for lacking calculation process. In Line 147-157 and Line 163-173, we supplemented some content about the model. Because not all parameters in Formulas have intervals to determined clearly, we have tried our best to add intervals for each parameter.
We have specially revised the ecological effect assessment. We evaluated the ecological effect by the Formula (3).
(3)
When i = 4, Fi represents the ecosystem services of total regional land-use benefit. Wik denotes the benefit value coefficient of land use type k, and represents the ecosystem service values of various land types per unit area. Sk represents the areas of seven land use types, respectively. The ecosystem service values of various land types per unit area was calculated according to previous analyses, and seen in the last line of Table 6 (in Line 315-316).
Point 6: Finally, the paper gives the calculation weight table 9. How is this result different from other existing literature? They are different. Is this different result universal? How to refer to this paper for other future research or engineering applications lacks practical significance.
Response 6: Thanks for your comment. The calculation weight in table 9 is different from other existing literature. First, the starting set of weights was assessed according to the initial survey performed on the agents. Second, the land use conversion probability (Pij) determined by agents and the fitness value of an agent (f(i)) were calculated based on the starting weights. Third, by running the MOPSO of the optimal land use allocation model continuously and setting terminal condition, the final weights of the various agents’ decisions and the optimal allocation scheme of rural land use fulfilling the requirements was generated.
Therefore, the weights of agents’ decisions or influencing factors were determined by the analytic hierarchy process or other subjective methods in other existing literature. However, the final weights of the various agents’ decisions were obtained through the subsequent optimal allocation model of land-use in our manuscript. Because the method calculating the weights was less affected by subjective factors. So, we think that the result is universal and the method is practical significance for other future research or engineering applications.
Point 7: The error between the evaluation and calculation based on the data obtained from remote sensing may be large. Since a large number of questionnaires can be conducted while sitting, why don't the author go directly to survey and divide the area to obtain direct data? （don't directly use remote sensing image）
Response 7: Thanks for your comment. Your opinion is very constructive for future research. To get the results corresponding to the agent' s decision, the error between the evaluation and calculation was exit based on the data obtained from remote sensing image. However, the direct data from the survey included the decision-making behaviours of land use agents and the objectives and constraints to be applied in the optimization of rural land allocation. It is difficult to achieve a one-to-one correspondence between the agent and the land. Agents were divided into groups, and a stratified random sampling was conducted to improve the representativeness of sampling. It was too hard to divide the area and identify the type and location of each land based on the survey. Finally, the meaning of an agent cannot be understood as an individual (person or enterprise). It represents the average number of population or enterprises on a 5 miles grid cell. In our manuscript, the agent in the optimal land use allocation model was abstract, and the remote sensing images was chosen as the base images.
Point 8: The specific equation, parameter interval, calculation process, input data of remote sensing calculation, manual survey data, comparison of calculation results, and the practical application reference value of the optimization model in this paper have some doubts or deficiencies.
Response 8: Thanks for your comment. It's our negligence that we didn't accurately express the specific equation, parameter interval, calculation process, input data of remote sensing calculation. In Line 147-157 and Line 163-173, we supplemented some content and intervals for each parameter about the model. And the manual survey data and the incomes of employees per unit area were obtained through the questionnaire surveys. The comparison of calculation results between the optimal allocation results in 2015 and the land use map in 2015 were analysed in “4.2. Accuracy of Model – Based combining MAS and MOPSO”. A comparison of the model results with land use data for 2015 provided a model accuracy index of 0.8226, suggesting a good optimization effect.
Point 9: It is suggested that the author refine the whole process, especially the mathematical model, parameter interval and known data, which need to be further added and supplemented.
Response 9: Thanks for your suggestion. We have tried our best to refine the whole process, especially the mathematical model, parameter interval and known data. Sincere thanks again for your comments and suggestions.
Author Response File: Author Response.docx
Reviewer 2 Report
Agree
Author Response
Dear reviewer,
Thank you for your comments and suggestions on our manuscript.
Round 3
Reviewer 1 Report
The author carefully revised and responded all the questionsa and comments.
It is recommended to accept and publish.
