Study on Urban Expansion and Population Density Changes Based on the Inverse S-Shaped Function
Round 1
Reviewer 1 Report
Typos and minor spelling erros should be made by authors.
Better presentation between results, figures and text is needed.
Future research perspectives should be demonstrated to stakeholders.
Similarity found in text should be reduced.
1. Authors should properly demonstrate the main question addressed by the research in introduction as well as in main results analysis.
2. Authors should update relative references as well as better presentation in text showing that exists topic investigation originality, addressing based on their results and relative literature review that it addresss an inovation in the field.
3. Authors should explain in text what does it add to the subject area compared with other published material found from the literature.
4. Authors should improve the results that present innovation based on literature gaps following the methodology in relation to validated relative references that control the new innovative presented investigations with their results that should be considered in presented improved text analysis.
5. The conclusions should be consistent with the evidence and arguments presented in article and they should be clear to present to the reader
that they address the main question posed in relative results analysis.
6. The references should be updated and validated.
7. Relative additional tables and figures should be presented based on relations found in datasets analysis with good linkage at text.
Typos and minor spelling erros should be made by authors.
Better presentation between results, figures and text is needed.
Future research perspectives should be properly presented to readers.
Similarity found in text should be reduced .
Author Response
Please see the attachment
Response to Reviewer 1 Comments
Thank you very much for the comments and suggestions from the reviewer. We have made revisions to the paper, and the following is our response.
I am very sorry that some contents have been modified too much and the modified parts are scattered, please check in the revised paper.
Point 1: Authors should properly demonstrate the main question addressed by the research in introduction as well as in main results analysis.
Response 1: We have added this section
In the past few decades, China's economic growth has provided strong support for urbanization[ [54-56]. The process of land urbanization and population urbanization in China is rapidly advancing[ [57-59]. This study uses remote sensing data to analyze the impact of urban spatial expansion patterns and rates on urban population density, providing a reference for high-quality urban development. To reveal the relationship between urban expansion and population density changes in the development process of China, this paper selected 34 cities from over 300 cities in China. Based on land use data from 2000 to 2020, the spatial and temporal differences of urban expansion were analyzed using a reverse S-curve function model and Pearson correlation coefficient. On this basis, the correlation between urban expansion and population density changes was explored, providing a reference for sustainable urban development in China.
Point 2: Authors should update relative references as well as better presentation in text showing that exists topic investigation originality, addressing based on their results and relative literature review that it addresss an inovation in the field.
Response 2: We have added this section in the introduction and ab.
As the introduction contains more content, please check the revised paper
For decades, the continuous advance of urbanization has led to the continuous expansion of urban land and rapid increase in the total area of cities. The phenomenon of urban land expan-sion faster than population growth has become widespread. High population density can lead to problems such as traffic congestion, exacerbated air pollution, and hinder sustainable develop-ment, affecting the quality of life of urban residents. China is currently in a phase of rapid ur-banization, with high urban population density and rapid decline in urban population density. The decrease in urban population density is conducive to promoting sustainable urban devel-opment. This study selected 34 cities in China as sample cities and analyzed the spatial expan-sion and population density changes using land use and population density data from 2000, 2005, 2010, 2015, and 2020 in order to provide reference for controlling population density and pro-moting sustainable urban development. The conclusions of the study are as follows:
Point 3: Authors should explain in text what does it add to the subject area compared with other published material found from the literature.
Response 3: We have revised the introduction according to the reviewer's requirements.
As the introduction contains more content, please check the revised paper
The expansion of urban land area is the main indicator of urban spatial expansion [13]. There are three main categories of assessment methods for urban expansion: the first is the boundary assessment method, which defines the "city" based on administrative division data. This method can evaluate urban expansion over a long period of time, but cannot distinguish between urban and rural areas within the "city" [14]. The second is the threshold method, which uses nighttime light data to build a threshold segmentation model, dividing urban and rural areas in remote sensing images, evaluating the urban extent, and calculating the expansion intensity. This method can distinguish between urban and rural areas within the "city," but has limitations in data availability for conducting long-term urban expansion assessments and low segmentation accuracy [15]. The third is the function equation method, which is based on land use data, establishes a function model, defines the urban boundary, and analyzes urban spatial form. Compared to the other two methods, this approach is relatively accurate and suitable for long-term urban expansion assessments. Xuecao Li et al. used global impervious surface data to build a global urban boundary dataset, employing kernel density analysis and domain expansion algorithm [16]. Jianxin Yang et al. explored the process of urban expansion and analyzed urban spatial form based on land use data and Gaussian function model [17]. Jiao Limin constructed the urban land density function based on land use data for 1900, 2000, and 2010 to define the urban scope [18].
Point 4: Authors should improve the results that present innovation based on literature gaps following the methodology in relation to validated relative references that control the new innovative presented investigations with their results that should be considered in presented improved text analysis.
Response 4:
We analyze regional urban expansion and urban population density changes in China, and reveal the relationship between the two.
Some of the revisions are shown below:
In the past few decades, China's economic growth has provided strong support for urbanization[ [54-56]. The process of land urbanization and population urbanization in China is rapidly advancing[ [57-59]. This study uses remote sensing data to analyze the impact of urban spatial expansion patterns and rates on urban population density, providing a reference for high-quality urban development. To reveal the relationship between urban expansion and population density changes in the development process of China, this paper selected 34 cities from over 300 cities in China. Based on land use data from 2000 to 2020, the spatial and temporal differences of urban expansion were analyzed using inverse s-shaped function model and Pearson correlation coefficient. On this basis, the correlation between urban expansion and population density changes was explored, providing a reference for sustainable urban development in China.
Point 5: The conclusions should be consistent with the evidence and arguments presented in article and they should be clear to present to the reader that they address the main question posed in relative results analysis.
Response 5: We have revised our conclusions
- The decline in urban population density is not only caused by urban expansion but also by a decrease in population growth rate. This decrease can be attributed to economic development, increased salaries, job opportunities and higher living standards. As re-gions become more developed, they may become less attractive due to lower salary levels and fewer protections for employees, resulting in a decrease in cross-city commuting pop-ulations. Furthermore, the decline in population growth rate can be linked to rising hous-ing and commodity prices, which increase the cost of living and having children in cities. This factor leads people to reconsider their attitudes towards having children and can ul-timately impact natural population growth rates.
- There are notable disparities in the rates of expansion among different cities. Temporally, the primary factor contributing to the variance in urban radii growth rates is due to early-stage cities having small radii, abundant undeveloped land, and low con-struction expenses, which results in faster growth. As cities continue to develop and ex-pand their radii, the availability of unused land decreases while construction costs soar, leading to a significant decline in the rate of urban radius growth. In order to increase revenue and improve resource utilization efficiency, many industries choose to locate in underdeveloped areas. From a spatial perspective, the uneven socioeconomic foundations of each city constitute the main cause for different rates of urban radius expansion. Cities with prosperous economic conditions generally possess robust infrastructure, active commercial activities, and abundant employment opportunities, all of which attract a large number of immigrants and promote rapid urban expansion.
Point 6: The references should be updated and validated.
Response 6: We have added and updated some references as requested by the reviewer.
Point 7: Relative additional tables and figures should be presented based on relations found in datasets analysis with good linkage at text.
Response 7: We changed some pictures. Some link statements have been added to indicate the relationship between pictures, tables and the analysis section.
The following is part of the revised picture, due to the modification of more content, other content please check in the revised paper
Author Response File: 
Author Response.docx
Reviewer 2 Report
The authors evaluate the" study on urban expansion and population density changes based on the inverse s-shaped function". It is an interesting and significant contribution to the scientific community; however, the paper's abstract, material method, discussion, and conclusions should be improved. Numerous errors in the manuscript should be clearly discussed and the manuscript needs some revisions, as given below:
- The abstract should be enhanced.
- English should be enhanced.
- Please keep a space before citations, check, and revise the whole manuscript.
- The research gap should be enhanced.
- Map readability should be enhanced (check and revise the whole manuscript.)
- The data section should be explained clearly—especially LULC and Worldpop data.
- Explain the resolutions of each data variable.
- Equation 1 should be corrected.
- "Based on the land cover data from China’s Land Cover Dataset in 2000, 2005, 2010, 2015, and 2020, the impervious surface density was calculated for each layer by taking the city center as the starting point and buffering outward every 1km." How many rings were generated? Why did the authors use 1 km for this study?
- Further clarification is needed.
(ii) Table 1 - Impervious to Cropland 21538.71 km2?
(iii) Table 3 - Forest cover has increased during the study period?
11. Discussion section should be enhanced. Please explain the causes profoundly.
12. conclusion section should be enhanced.
Please check the references, for example, on page 2, line numbers 61 to 63. "Xu et al. used the geographic detector to analyze the driving factors of urban spatial expansion and proposed that population is a key factor affecting urban expansion[22,28].
22 reference is "Liu, J.; Xu, Q.L.; Yi, J.H.; Huang, X. Analysis of the heterogeneity of urban expansion landscape patterns and driving factors 437 based on a combined Multi-Order Adjacency Index and Geodetector model. Ecol. Indic. 2022, 136, 15, 438 doi:10.1016/j.ecolind.2022.108655. "
not Xu et al.
Moderate editing of English language required.
Author Response
Please see the attachment
Response to Reviewer 2 Comments
Thank you very much for the comments and suggestions from the reviewer. We have made revisions to the paper, and the following is our response.
I am very sorry that some contents have been modified too much and the modified parts are scattered, please check in the revised paper.
Point 1:The abstract should be enhanced.
Response 1: We have made modifications to the abstract.
For decades, the continuous advance of urbanization has led to the continuous expansion of urban land and rapid increase in the total area of cities. The phenomenon of urban land expansion faster than population growth has become widespread. High population density can lead to problems such as traffic congestion, exacerbated air pollution, and hinder sustainable development, affecting the quality of life of urban residents. China is currently in a phase of rapid urbanization, with high urban population density and rapid decline in urban population density. The decrease in urban population density is conducive to promoting sustainable urban development. This study selected 34 cities in China as sample cities and analyzed the spatial expansion and population density changes using land use and population density data from 2000, 2005, 2010, 2015, and 2020 in order to provide reference for controlling population density and promoting sustainable urban development. The conclusions of the study are as follows:
In the 34 sample cities, the average urban radius was only 11.61km in 2000, but reached 17.98km in 2020, with an annual growth rate of 2.5%. There were significant spatial differences in urban expansion. Beijing and Shanghai, as the most developed cities in China, had urban radii exceeding 40km, while the less developed cities of Liaoyang and Suzhou had urban radii of only 9km.
Although the population density decreased in most cities, the population density values in first-tier cities in China, such as Tianjin, Beijing, and Shanghai, continued to rise.
Cities with loose spatial expansion patterns had faster decreases in population density than compact-type cities. The rate of urban spatial expansion was negatively correlated with changes in population density, with cities that had faster urban spatial expansion also having faster declines in artificial ground density.
Point 2:English should be enhanced.
Response 2: We have made modifications to multiple sections of the paper including the discussion and conclusions.
I am very sorry for this, because the modified content is too much and scattered, please check in the modified version, the following is part of the modified content:
The impervious surface area is rapidly increasing, which is leading to the occupation of a significant amount of agricultural land. However, due to China's substantial investment in ecological conservation, the forest area has increased by 2329.42 km2. There has also been a conversion between four types of land cover: forest, grassland, water, and cropland.
Since the implementation of China's reform and opening-up policy, there has been rapid development in both the country's economy and society. One of the resulting effects has been the swift expansion of urban impervious land. A study conducted across 34 sample cities reveals that the average radius of urban areas increased from 11.61 km in 2000 to 17.98 km in 2020, representing an annual growth rate of 2.5%. It is worth noting that the city radius varies considerably across these cities, as exemplified by Beijing and Shanghai's city radius of over 40 km, while Liaoyang and Suzhou(b), cities with lower development levels, have a radius of 9 km. Half of the sample cities measured had a radius of 15 km. Alongside the size differences between sample cities, the urban expansion rates also differ noticeably. For instance, Hefei and Suzhou exhibited annual average urban spatial expansion rates of 16.7% and 13.8%, respectively, whereas Jinan and Liaoyang grew at only 4.8% and 4.7% each year.
The urban spatial form is predominantly characterized by the loose type, and the average Kp continues to decline. China's urban population density has been consistently decreasing, which has a negative correlation with the expansion rate of urban areas. It is worth noting that cities featuring the loose type of spatial form tend to exhibit a faster decrease in population density compared to other types of cities.
Point 3:Please keep a space before citations, check, and revise the whole manuscript.
Response 3: Sorry for our negligence, we have now made improvements according to the requirements of reviewer 2.
Point 4:The research gap should be enhanced.
Response 4: We have amended this in the introduction as requested
The expansion of urban land area is the main indicator of urban spatial expansion [13]. There are three main categories of assessment methods for urban expansion: the first is the boundary assessment method, which defines the "city" based on administrative division data. This method can evaluate urban expansion over a long period of time, but cannot distinguish between urban and rural areas within the "city" [14]. The second is the threshold method, which uses nighttime light data to build a threshold segmentation model, dividing urban and rural areas in remote sensing images, evaluating the urban extent, and calculating the expansion intensity. This method can distinguish between urban and rural areas within the "city," but has limitations in data availability for conducting long-term urban expansion assessments and low segmentation accuracy [15]. The third is the function equation method, which is based on land use data, establishes a function model, defines the urban boundary, and analyzes urban spatial form. Compared to the other two methods, this approach is relatively accurate and suitable for long-term urban expansion assessments. Xuecao Li et al. used global impervious surface data to build a global urban boundary dataset, employing kernel density analysis and domain expansion algorithm [16]. Jianxin Yang et al. explored the process of urban expansion and analyzed urban spatial form based on land use data and Gaussian function model [17]. Jiao Limin constructed the urban land density function based on land use data for 1900, 2000, and 2010 to define the urban scope [18].
………………………………..
In the past few decades, China's economic growth has provided strong support for urbanization [54-56]. The process of land urbanization and population urbanization in China is rapidly advancing [57-59]. To reveal the relationship between urban expansion and population density changes in the development process of China, this paper selected 34 cities from over 300 cities in China. Based on land use data from 2000 to 2020, the spatial and temporal differences of urban expansion were analyzed using inverse s-shaped function model and Pearson correlation coefficient. On this basis, the correlation between urban expansion and population density changes was explored, providing a reference for sustainable urban development in China.
………………………………..
Point 5:Map readability should be enhanced (check and revise the whole manuscript.)
Response 5: We have made modifications to the figures in the paper.
We changed some pictures. Some link statements have been added to indicate the relationship between pictures, tables and the analysis section.
The following is part of the revised picture, due to the modification of more content, other content please check in the revised paper
Point 6:The data section should be explained clearly—especially LULC and Worldpop data.
Response 6: We have made modifications to the introduction of our data.
Point 7:Explain the resolutions of each data variable.
Response 7: We added Table 1 to explain the source, resolution, and other details of the data.
Point 8:Equation 1 should be corrected.
Response 8: We have corrected equation 1.
Calculate the population density of each city by obtaining population density data.
(1)
represents the population density of a city. represents the population density value corresponding to the i region. represents the area corresponding to the i region. refers to the total area of the city.
Point 9:"Based on the land cover data from China’s Land Cover Dataset in 2000, 2005, 2010, 2015, and 2020, the impervious surface density was calculated for each layer by taking the city center as the starting point and buffering outward every 1km." How many rings were generated? Why did the authors use 1 km for this study?
Response 9: To better determine the city radius, we calculated the city radius twice according to this formula. The first calculation used multiple ring buffers with radii of 15km and intervals of 1km. For the second calculation, we used multiple ring buffers with a radius of 20km for city radii exceeding 15km, a radius of 25km for city radii exceeding 20km, a radius of 30km for city radii exceeding 25km, and so on. The formula was proposed by Limin Jiao, who used a 1km interval, so we used the same interval of 1km.
Point 10:Further clarification is needed.
(ii) Table 1 - Impervious to Cropland 21538.71 km2?
(iii) Table 3 - Forest cover has increased during the study period?
Response 10:
(ii)
During the process of urban expansion, impervious surfaces have occupied a large amount of cropland. This phenomenon is particularly significant in China, which is in the rapid urbanization stage. Therefore, we have added some analysis content to address this issue：
Table S1. Land Use Transfer Matrix from 2000 to 2005(km2)
|
Cropland |
Forest |
Shrub |
Grassland |
Water |
Sonw |
Barren |
Impervious |
Wetland |
|
|
Cropland |
246867.28 |
2130.69 |
4.47 |
1597.37 |
541.99 |
0.00 |
8.69 |
2.42 |
7.08 |
|
Forest |
1724.91 |
67888.57 |
129.92 |
503.57 |
3.79 |
0.00 |
0.01 |
0.01 |
0.43 |
|
Shrub |
1.29 |
88.90 |
445.49 |
187.23 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
Grassland |
1930.82 |
8.81 |
42.01 |
23130.22 |
8.43 |
0.00 |
33.38 |
0.02 |
0.01 |
|
Water |
1324.78 |
0.76 |
0.00 |
15.21 |
8500.74 |
0.00 |
4.69 |
256.62 |
0.26 |
|
Sonw |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.03 |
0.00 |
0.00 |
0.00 |
|
Barren |
0.57 |
0.00 |
0.00 |
23.21 |
3.31 |
0.00 |
66.38 |
0.10 |
0.00 |
|
Impervious |
5005.60 |
27.37 |
0.00 |
93.99 |
162.33 |
0.00 |
12.99 |
37445.78 |
0.00 |
|
Wetland |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
21.12 |
Table S2. Land Use Transfer Matrix from 2000 to 2005(km2)
|
Cropland |
Forest |
Shrub |
Grassland |
Water |
Sonw |
Barren |
Impervious |
Wetland |
|
|
Cropland |
240353.32 |
2129.90 |
2.39 |
1282.18 |
812.37 |
0.00 |
7.25 |
3.82 |
3.29 |
|
Forest |
1929.86 |
68017.84 |
131.39 |
566.18 |
11.90 |
0.00 |
0.02 |
0.01 |
0.13 |
|
Shrub |
1.54 |
44.68 |
445.00 |
53.49 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
Grassland |
1891.92 |
8.65 |
144.11 |
23062.55 |
6.88 |
0.01 |
20.40 |
0.00 |
0.00 |
|
Water |
985.75 |
1.47 |
0.00 |
11.84 |
9014.13 |
0.00 |
3.40 |
234.04 |
0.12 |
|
Sonw |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.02 |
0.03 |
0.00 |
0.00 |
|
Barren |
0.71 |
0.00 |
0.00 |
19.81 |
8.96 |
0.00 |
52.57 |
0.04 |
0.00 |
|
Impervious |
5996.88 |
48.66 |
0.00 |
157.64 |
248.81 |
0.00 |
9.88 |
42510.15 |
0.00 |
|
Wetland |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
17.57 |
Table S3. Land Use Transfer Matrix from 2000 to 2005(km2)
|
Cropland |
Forest |
Shrub |
Grassland |
Water |
Sonw |
Barren |
Impervious |
Wetland |
|
|
Cropland |
232900.36 |
2298.06 |
1.79 |
1675.89 |
782.50 |
0.00 |
6.29 |
2.17 |
4.29 |
|
Forest |
2782.19 |
68175.97 |
65.58 |
494.44 |
5.19 |
0.00 |
0.00 |
0.01 |
0.05 |
|
Shrub |
1.99 |
130.33 |
400.53 |
105.73 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
Grassland |
1346.04 |
2.96 |
76.80 |
22658.22 |
3.41 |
0.01 |
18.78 |
0.01 |
0.01 |
|
Water |
863.55 |
0.42 |
0.00 |
8.40 |
9171.61 |
0.00 |
4.53 |
199.33 |
0.11 |
|
Sonw |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.02 |
0.00 |
0.00 |
0.00 |
|
Barren |
0.43 |
0.00 |
0.00 |
10.31 |
9.40 |
0.03 |
41.71 |
0.17 |
0.00 |
|
Impervious |
6699.98 |
49.59 |
0.00 |
181.54 |
278.66 |
0.00 |
10.79 |
48770.34 |
0.00 |
|
Wetland |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
13.12 |
Table S4. Land Use Transfer Matrix from 2000 to 2005(km2)
|
Cropland |
Forest |
Shrub |
Grassland |
Water |
Sonw |
Barren |
Impervious |
Wetland |
|
|
Cropland |
229976.63 |
1731.96 |
2.04 |
2227.28 |
905.53 |
0.00 |
5.04 |
3.87 |
1.06 |
|
Forest |
1984.43 |
69665.83 |
91.97 |
728.41 |
3.72 |
0.00 |
0.03 |
0.00 |
0.11 |
|
Shrub |
1.38 |
87.64 |
437.45 |
43.65 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
Grassland |
1300.22 |
3.24 |
107.10 |
20931.80 |
3.55 |
0.00 |
10.75 |
0.05 |
0.00 |
|
Water |
541.80 |
0.82 |
0.00 |
6.80 |
9168.66 |
0.00 |
4.34 |
188.75 |
0.11 |
|
Sonw |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.01 |
0.08 |
0.00 |
0.00 |
|
Barren |
0.63 |
0.00 |
0.00 |
12.98 |
4.76 |
0.00 |
31.26 |
0.15 |
0.00 |
|
Impervious |
3866.25 |
33.93 |
0.01 |
154.93 |
161.64 |
0.00 |
10.55 |
55798.09 |
0.00 |
|
Wetland |
0.01 |
0.00 |
0.00 |
0.37 |
0.10 |
0.00 |
0.00 |
0.00 |
11.83 |
According to the land use transfer matrix (Table S1-S4), it is found that Cropland area has decreased the most and Impervious area has increased the most. Specifically, from 2000 to 2005, Cropland area decreased by 9,987.97 KM2, and in the periods of 2005-2010, 2010-2015, and 2015-2020, it decreased by 10,806.67, 11,694.18, and 7,694.71 KM2, respectively. These areas were mainly converted to Forest, Grassland, Water, and Impervious land. The growth of Impervious area mainly came from the conversion of Cropland, Forest, Grassland, and Water, increasing by 5,302.29, 6,461.88, 7,220.57, and 4,227.32 km2 during the periods of 2000-2005, 2005-2010, 2010-2015, and 2015-2020, respectively.In addition, since 2015, the change values of Cropland and Impervious area have been increasing year by year but started to decrease after 2015. The types of land use, including Cropland, Forest, Shrub, Grassland, and Water, are interconverted, while Barren and Wetland areas are decreasing each year, with Snow having the smallest proportion.
(iii)
China is one of the main driving forces behind the global increase in green area, accounting for 25% of the world's net leaf area increase. Forests are the main growth point of China's green area (42%). To achieve the goals of peak carbon emissions, carbon neutrality, and global ecological security, China has invested heavily in ecological protection, resulting in continuous "double growth" of forest area and forest stock volume for 30 consecutive years.
References：
Satellite data show increasing leaf area of vegetation due to direct factors (human land-use management) and indirect factors (such as climate change, CO2 fertilization, nitrogen deposition and recovery from natural disturbances). Among these, climate change and CO2 fertilization effects seem to be the dominant drivers. However, recent satellite data (2000–2017) reveal a greening pattern that is strikingly prominent in China and India and overlaps with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programmes to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly owing to an increase in harvested area through multiple cropping facilitated by fertilizer use and surface- and/or groundwater irrigation. Our results indicate that the direct factor is a key driver of the ‘Greening Earth’, accounting for over a third, and probably more, of the observed net increase in green leaf area. They highlight the need for a realistic representation of human land-use practices in Earth system models.
Source：https://www.nature.com/articles/s41893-019-0220-7/
China's forest area and forest stock volume have maintained "double growth" for 30 consecutive years. It is reported that of the newly added green areas globally from 2000 to 2017, approximately 1/4 came from China, with China ranking first in terms of its contribution proportion. In 2020, China completed afforestation of 6.77 million hectares, forest nurturing of 8.37 million hectares, and grassland improvement of 2.83 million hectares.
Source：http://www.forestry.gov.cn/main/216/20210615/145354484186543.html
Thanks to China's massive investment in ecological protection, a total of 3.83 million hectares of afforestation, 3.214 million hectares of grassland improvement, and 1.8473 million hectares of desertification control were completed in 2022 alone. Currently, China's forest area is 231 million hectares, with a forest coverage rate of 24.02%, and the grassland area is 265 million hectares, with a comprehensive vegetation coverage of 50.32%.
Source：http://www.forestry.gov.cn/main/586/20230312/095048980343075.html
Point 11: Discussion section should be enhanced. Please explain the causes profoundly.
Response 11: We revised the discussion according to the reviewer's comments
Point 12:conclusion section should be enhanced.
Response 12: We revised the conclusion according to the reviewer's comments
Point 13:Please check the references, for example, on page 2, line numbers 61 to 63. "Xu et al. used the geographic detector to analyze the driving factors of urban spatial expansion and proposed that population is a key factor affecting urban expansion[22,28].
22 reference is "Liu, J.; Xu, Q.L.; Yi, J.H.; Huang, X. Analysis of the heterogeneity of urban expansion landscape patterns and driving factors 437 based on a combined Multi-Order Adjacency Index and Geodetector model. Ecol. Indic. 2022, 136, 15, 438 doi:10.1016/j.ecolind.2022.108655. "
not Xu et al.
Response 13: I'm sorry for causing a misunderstanding to the reviewer. The studies of both Liu et al. and Xu et al. support this viewpoint. However, due to the author's native writing habits., only Xu et al. was listed. It has now been corrected to Liu et al. and Xu et al.
Author Response File: Author Response.docx
Reviewer 3 Report
This manuscript analyzes urban spatial expansion and changes in population density in selected cities of China, providing insight into the development of big cites. The results will be interested by many scholars, hence I recommend the paper should be accepted after minor revision.
1. Why such 34 cities were selected? what's the criteria?
2. The format of abstract should be improved.
Most of parts of the text is smooth.
Author Response
Thank you very much for the comments and suggestions from the reviewer. We have made revisions to the paper, and the following is our response.
Response to Reviewer 3 Comments
Point 1: Why such 34 cities were selected? what's the criteria?
Response 1: The inverse s-shaped function is suitable for single-centered cities. However, urban expansion is limited by various factors such as nature and economy, resulting in diverse urban forms. Therefore, we selected 34 single-centered cities from more than 300 cities in China based on land use data and satellite remote sensing images.
Point 2: The format of abstract should be improved.
Response 2: We have revised the abstract of the paper according to the comments and suggestions of the reviewer.
Abstract:
For decades, the continuous advance of urbanization has led to the continuous expansion of urban land and rapid increase in the total area of cities. The phenomenon of urban land expansion faster than population growth has become widespread. High population density can lead to problems such as traffic congestion, exacerbated air pollution, and hinder sustainable development, affecting the quality of life of urban residents. China is currently in a phase of rapid urbanization, with high urban population density and rapid decline in urban population density. The decrease in urban population density is conducive to promoting sustainable urban development. This study selected 34 cities in China as sample cities and analyzed the spatial expansion and population density changes using land use and population density data from 2000, 2005, 2010, 2015, and 2020 in order to provide reference for controlling population density and promoting sustainable urban development. The conclusions of the study are as follows:
In the 34 sample cities, the average urban radius was only 11.61km in 2000, but reached 17.98km in 2020, with an annual growth rate of 2.5%. There were significant spatial differences in urban expansion. Beijing and Shanghai, as the most developed cities in China, had urban radii exceeding 40km, while the less developed cities of Liaoyang and Suzhou had urban radii of only 9km.
Although the population density decreased in most cities, the population density values in first-tier cities in China, such as Tianjin, Beijing, and Shanghai, continued to rise.
Cities with loose spatial expansion patterns had faster decreases in population density than compact-type cities. The rate of urban spatial expansion was negatively correlated with changes in population density, with cities that had faster urban spatial expansion also having faster declines in artificial ground density.
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
Thank you very much for your response. With the author's modifications, the manuscript has been improved. Also, the paper is suitable for publication after minor revision.
- Point 10: Further clarification is needed.
(ii) Table 1 - Impervious to Cropland 21538.71 km2?
"During the process of urban expansion, impervious surfaces have occupied a large amount of cropland. This phenomenon is particularly significant in China, which is in the rapid urbanization stage. Therefore, we have added some analysis content to address this issue："
I understand what is happening. However, it is preferable to clarify why "impervious surfaces" were transformed to "cropland" during the study period (this is not cropland to the impervious surface). You can obtain an idea about this by looking at more documents and state projects.
2. Line number 355, please remove one full stop. Recheck the whole manuscript.
Author Response
Please see the attachment
Response to Reviewer 2 Comments
Point 1: Point 10: Further clarification is needed.
(ii) Table 1 - Impervious to Cropland 21538.71 km2?
"During the process of urban expansion, impervious surfaces have occupied a large amount of cropland. This phenomenon is particularly significant in China, which is in the rapid urbanization stage. Therefore, we have added some analysis content to address this issue："
I understand what is happening. However, it is preferable to clarify why "impervious surfaces" were transformed to "cropland" during the study period (this is not cropland to the impervious surface). You can obtain an idea about this by looking at more documents and state projects.
Response 1: I am truly sorry for my negligence and misunderstanding of the reviewer's comments. Please find my response to the reviewer's comments below:
The reasons for the conversion of cropland to impervious surfaces are as follows:
（1）China has implemented the "red line for arable land" policy, and many buildings that encroached upon arable land have been demolished.
Source:
http://www.fhzf.gov.cn/zwgk_49798/fdzdgknr/ywdt/fhsz/202303/t20230321_1996503.html
https://www.gov.cn/zhengce/zhengceku/2021-12/02/content_5655390.htm
（2）Due to a large population of rural laborers migrating to cities, many houses in rural areas were left vacant. These houses, due to their old age, poor quality, and lack of occupancy, were subsequently demolished. The areas where these buildings once stood were then turned into cropland for vegetable planting. As the author lives in a rural area and has observed this phenomenon, they took some pictures specifically to demonstrate this. The pictures are as follows:
（3）In order to protect the environment, some factories and small-scale breeding farms in rural areas with high pollution and excessive emissions have been demolished, and some factories in urban areas have also been demolished. In rural areas, vacant land is being used as cropland, as shown in the picture below:
Point 2: Line number 355, please remove one full stop. Recheck the whole manuscript.
Response 2: I'm sorry for overlooking these questions. They have now been corrected.
Author Response File: Author Response.docx
