Assessing 30-Year Land Use and Land Cover Change and the Driving Forces in Qianjiang, China, Using Multitemporal Remote Sensing Images
, Meng Mu 2, Yunbing Liu 1,*, Zheng Zhou 1,*, Haihua Zhuo 1, Guangsheng Qiu 1, Jie Chen 1, Mingjun Lei 1, Xiaolong Huang 1, Yichi Zhang 1 and Zheng Ren 1Round 1
Reviewer 1 Report
Assessing aquaculture areas is probably a worthy thing to do but I am not at all sure that it is “vital” since previous generations seemed to have survived without doing it. The paper is a good example of land use analysis using satellite imagery, but I don't think most readers will have much interest in it - probably more suited to a local (Chinese) journal than an international one.
I did ponder the interpretation of the transition matrices (I think the problem is with me rather than the authors) but they might give a little more information on their interpretation to help readers.
I do congratulate the authors on the quality of writing and presentation - excellent overall.
Page 2, Line 65: Bangladesh or Bangladeshi?
Somewhat spurious precision (e.g. 2030.34 mm2). Rounding off to the nearest square kilometre would be better.. Sunlight hours is presumably sunlight per year?
Could we have a sentence or two explaining what a “transition matrix” is please? I should know but struggled with it and many others will be in the same boat. I am puzzled by the absence of negative values (except in the totals) so I think it is my problem, not the authors. However, a little explanation here would help.
The term “area ratio” is used a few times but is not defined. So what does an area ratio of 70% mean?
Well written. Perhaps an occasional editing correction may help but the authors are to be congratulated on the overall quality of English.
Author Response
Thank you for your kind comments. After your reminder, we also realized that the manuscript needs to be improved. We have made detailed readings of your comments and we made careful amendments to our manuscript. We sincerely hope that our explanations and modifications can meet your requirements.
- 1. Assessing aquaculture areas is probably a worthy thing to do but I am not at all sure that it is “vital” since previous generations seemed to have survived without doing it. The paper is a good example of land use analysis using satellite imagery, but I don't think most readers will have much interest in it - probably more suited to a local (Chinese) journal than an international one.
Response 1:
Thank you for your comment. I understand your concern and would like to address it. First of all, on the issue of aquaculture zones being worth assessing, I agree that there may be subjectivity in this view. In this paper, we provide a method for land use analysis through satellite imagery to provide a feasible scheme for assessing aquaculture areas. However, I understand that the reviewer may be referring to the fact that this assessment may not be the highest priority in the context of more pressing issues. I will further emphasize the potential implications and possible implications of this research in a revised paper to better explain its value and importance.
I also agree with the reviewer's opinion on the appropriate publishing location of the paper. Although I wish the study had been published in an international journal, it is reasonable for the reviewers to think that the paper would be more appropriate in a local (Chinese) journal. In the revised paper, I will focus on the specific application of this research to the aquaculture industry in China, in order to demonstrate the practical value of this paper to the local and domestic.
At the same time, I will listen carefully to other reviewers' comments on the paper and incorporate them into the revised draft. The ultimate goal is to further refine the paper based on reviewer feedback, making it academically valuable and useful in any publication location.
Thanks again for your review comments. I will try my best to respond and improve the paper.
- I did ponder the interpretation of the transition matrices (I think the problem is with me rather than the authors) but they might give a little more information on their interpretation to help readers.
Response 2:
We are very sorry; this is our negligence. Taking LULC transition matrix from 1990 to 2006 in Table 2 as an example, we give a detailed explanation of transition matrices, including why there is no negative value. We have made some explanations in the manuscript to help readers understand the meaning of the numbers.
The major land use conversions from 1990 to 2006 in Table 2 as follows:
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Table 2. LULC transition matrix from 1990 to 2006 in Qianjiang city.
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First, numbers on the diagonal (marked in yellow, such as 45.09, 122.57, and so on) indicate that the LULC type has not changed from 1990 to 2006.
Secondly, the numbers marked by green only represent the area transformed by different LULC types, not the difference in numbers, so there are no negative values. For examples, the meaning of the number 28.28 is as follows: there are area of 28.28 km2 of the LULC type of “built-up” in 1990 converted to type “farmland” in 2006; the meaning of the number 330.32 is as follows: there are area of 330.32 km2 of the LULC type of “forest/grass” in 1990 converted to type “farmland” in 2006.
The numbers marked by blue represent the increase or decrease of LULC type from 1990 to 2006. For example, the meaning of the number -281.58 is as follows: the area of the LULC type of “forest/grass” in 1990 was 281.58 km2 less than that in 2006. This is why only numbers marked with blue have negative values.
Thank you again for your comments again.
We have revised Table 2 in line 238 as follows:
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Table 2. LULC transition matrix from 1990 to 2006, 2006 to 2017, 2017 to 2022 in Qianjiang city. (“45.09” means there are 45.09 km2 of the LULC type of "built up" in 1990 converted to type " built up " in 2006; “28.28” means there are 28.28 km2 of the LULC type of "built up" in 1990 converted to type "farmland" in 2006; “-281.58” means that the area of the LULC type of “Forest/grass” in 1990 was 281.58 km2 less than that in 2006)
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- Page 2, Line 65: Bangladesh or Bangladeshi?
Response 3:
Thank you for your comment, this is our negligence. It would be Bangladesh.
We have revised the word in line 68 as follows:
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Results showed that standing water area within Bangladesh have expanded from less than 300 km2 in 1990 to over 1400 km2 in 2015, mainly caused by the increased inter-connected networks of flooded areas associated with aquaculture. |
- Somewhat spurious precision (e.g. 2030.34 mm2). Rounding off to the nearest square kilometre would be better. Sunlight hoursis presumably sunlight per year?
Response 4:
Thank you for your comment. We have modified the spurious precision in the manuscript. In addition, “Sunlight hours” is the average annual sunlight hours.
We have revised the first expressions in line 86 as follows:
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The study area is Qianjiang city (112°29′39″~113°01′27″E, 30°04′53″~30°38′53″N), a sub-prefecture-level city of south-central Hubei province, China. The city spans an area of 2030 km2. |
We have revised the second expressions in line 87 as follows:
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Qianjiang has a humid subtropical climate, with the annual (1988-2017) temperature of 16.6 ℃, precipitation of 1162 mm, and the average annual sunlight hours of 1949–1988 hours. |
- Could we have a sentence or two explaining what a “transition matrix” is please? I should know but struggled with it and many others will be in the same boat. I am puzzled by the absence of negative values (except in the totals) so I think it is my problem, not the authors. However, a little explanation here would help.
Response 5:
Thank you for your comment. Since the Comment 5 is consistent with the Comment 2, we provided a detailed answer in 'Response 2'.
- The term “area ratio”is used a few times but is not defined.So what does an area ratio of 70% mean?
Response 6:
Thank you for your comment. The term “area ratio” means the ratio of the particular category’ area to the study area. We modified its description in the manuscript.
We have revised the description in Abstract in lines 20-21 as follows:
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Results showed that the main type LULC type was farmland, which accounted for more than 70% of the study area. |
We have revised the description in Conclusions in lines286- 287 as follows:
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The results showed that the main type LULC type in the study area was farmland, which accounted for more than 70% of the Qianjiang City. |
Author Response File: 
Author Response.docx
Reviewer 2 Report
The study is very interesting, however, I have few concern before accepting it. My comments as follows:
The Abstract has not covered the findings of the study clearly. The authors should include it in the revised version and state it clearly.
“Remote sensing technique provides an effective tool to evaluate LULCC at different scales (Powell et al. 2007; Pouliot et al. 2014; Li, Gong, and Liang 2015).” The authors are encouraged to avoid citing many studies in one sentence. Also, the authors are encouraged to update the studies.
For urban land cover impact, the authors can refer/cite this study : Analysis of urban heat islands with landsat satellite images and GIS in Kuala Lumpur Metropolitan City
Why only 1990 to 2022 have been used, why not earlier.
The authors should improve the Methodology section. The current section is very short.
Table 2,3 and 4 maybe can be combined.
The conclusion should be revised by include the limitations of the study.
Author Response
Thank you for your kind comments. After your reminder, we also realized that the manuscript needs to be improved. We have made detailed readings of your comments and we made careful amendments to our manuscript. We sincerely hope that our explanations and modifications can meet your requirements.
- 1. The Abstract has not covered the findings of the study clearly. The authors should include it in the revised version and state it clearly.
Response 1:
Thank you for your comment. We revised the Abstract in the manuscript.
We have revised the Abstract in lines 20-26 as follows:
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Abstract Assessing Land Use and Land Cover Change (LULCC) related with aquaculture areas is vital for evaluating the impacts of aquaculture ponds on environment and developing a sustainable aquaculture production system. Most studies analyze changes in aquaculture land in coastal area, and few research focuses on the inland area, where the conversions between agriculture and aquaculture land is primarily driven by socioeconomic factors. This study assessed LULCC related to aquaculture areas in Qianjiang City, China from 1990 to 2022, using multitemporal Landsat images and a combination of decision tree classifier and visual interpretation. The LULCC was analyzed by the transition matrix. Results showed that the main type LULC type was farmland, which accounted for more than 70 % of the study area from 1990 to 2022. The built-up and aquaculture land showed an increasing trend year by year. In contrast, there was a gradual decline in Forest/Grass land from 1990 to 2016, and then its area increased slightly from 2016 to 2022 due to the policy of returning farmland to forest. Water areas were mainly composed of rivers and ponds, with subtle changes during the study period. The main driving forces of LULCC in Qianjiang City were economic and policy factors, with rapid GDP growth and government policies being the dominant factors. |
- The authors are encouraged to avoid citing many studies in one sentence. Also, the authors are encouraged to update the studies.
Response 2:
Thank you for your reminder. we have modified and updated the references. In addition, we try to avoid citing many studies in one sentence, but it has to be said that there are many papers expressing the same contribution. We tried to explain everyone's conclusions clearly in the manuscript.
We have added three new papers in lines 32-33 as follows:
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These changes may occur in the form of deforestation, urbanization, urban heat islands, water resources imbalance, agriculture expansion, or other alterations in land use and land cover (LULC) [1-3]. |
We have added one new paper in line 38 as follows:
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Assessment of LULCC, particularly the evolution of aquaculture, helps to detect the impact of human activities on natural environment [6]. |
We have added the paper in lines 52-55 as follows:
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Ottinger et al. mapped aquaculture ponds in major river deltas in China and Vietnam using Sentinel-1 time series images [14]. The aquaculture ponds map reached the accu-racy of 83%. Meng et al. monitored LULCC in Nansi Lake using Landsat 5/8 images and found that the aquaculture area showed an increasing trend from 1987 to 2017, which overall accuracy of each type was mostly above 80% [15]. |
We have added the paper in lines 59-62 as follows:
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Assessment of the LULCC change related with aquaculture areas is vital for evaluating the impacts of aquaculture ponds on environment and developing a sustainable aqua-culture production system. Meena monitored Kolleru Lake's ecosystem through mapped the distribution of illegal fishponds around the lake area over the past five decades. The results indicated that the area of illegal fishponds has decreased slightly through policy intervention [16]. |
- For urban land cover impact, the authors can refer/cite this study : Analysis of urban heat islands with landsat satellite images and GIS in Kuala Lumpur Metropolitan City。
Response 3:
Thank you for your comment. We read this paper carefully, which is really very worthy of reference.
We have added the paper in lines 32-33 as follows:
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These changes may occur in the form of deforestation, urbanization, urban heat islands, water resources imbalance, agriculture expansion, or other alterations in land use and land cover (LULC) [1-3]. |
- Why only 1990 to 2022 have been used, why not earlier.
Response 4:
Thank you for your comment. Due to the advantages of long time series, we chose the Landsat series images (image time can be traced back to 1987). Taking into account the correspondence with other data and the cover of the clouds, we chose the time of the images to 5 years as a unit, so the images of 1987, 1988 and 1999 were not selected.
- The authors should improve the Methodology section. The current section is very short.
Response 5:
Thank you for your comment. In fact, the manuscript mainly uses four methods: (1) decision tree classifier, which obtains preliminary LULC results; (2) artificial visual interpretation method, which corrects the misclassified LULC type and finally obtains fine LULC results; (3) transition matrix analysis, which is used to analyze LULCC, is used to clarify the conversion of various LULC types; (4) the Pearson correlation coefficient analysis, which is used to analyze the driving force of LULCC.
In the manuscript, we describe the decision tree classifier in detail. Due to the universality of the Pearson correlation coefficient, we do not spend too much words.
It is undeniable that we ignore the artificial visual interpretation, resulting in the Methodology section looks too short and too simple. Due to the influence of water depth, pixel scale and other factors, the spectral characteristics of aquaculture areas are too complex. The LULC type of aquaculture are often divided into farmland or built-up area. Importantly, this misclassification cannot be solved by the decision tree method. Therefore, artificial visual interpretation almost runs through the whole classification process, and its importance to the LULC results is obvious. Therefore, we supplement the visual interpretation method in the method part.
In addition, we have added some interpretation about transition matrix in Table 2(see Response 6), considering Comment 5, we added some sentences in the Methodology section to help readers understand our methods.
We have added the description of artificial visual interpretation method in line 114, and lines 121-126 as follows:
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2.3.1. LULC classification and validation LULC types in the study area was classified using the decision tree classifier and artificial visual interpretation. The decision tree algorithm is a non-parametric machine learning algorithm that has been widely used for remote sensing applications (Pal and Mather, 2003; Schneider et al. 2010). It builds a flowchart-like tree structure by recur-sively partitioning the data based on the values of input features. Each internal node represents a decision based on a specific feature, and each leaf node represents the pre-dicted class or regression output. This classification method can handle large datasets with noisy or missing data and capture complex non-linear relationships between fea-tures and classes (Roberts 2002; Pal and Mather 2003). Artificial visual interpretation combines remote sensing images with human knowledge and experience, and then carries out the process of reasoning and judgment, which is an effective auxiliary means of artificial intelligence. Artificial visual interpretation can correct the misclassified pixel types through position, shape and logical relationship. The visual interpretation process is completed in ArcGIS10.8. |
We have added the description of transition matrix in lines 157-161 as follows:
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2.3.2. LULCC Analysis The characteristics of land use/cover change in Qianjiang was comprehensively analyzed using the land use/cover type transition matrix. The transition matrix not only reflects the initial and final land use type but also captures the transitional changes of different land use/cover types during a certain study period. This facilitates an understanding of the land loss at the beginning of the study period, as well as the sources and compositions of land use/cover types at the end of the study period [24]. The LULC transition matrices were established for 1990-2006, 2006-2017, 2017-2022. In each transition matrix, the area of LULCC from one type to another was computed by multiplying the area of a pixel (30 m×30 m) by the quantity of pixels where the LULCC transitioned from type i to type j [25]. The data in the transfer matrix represent the transfer intensity of LULC type. Transfer matrix analysis can identify the key transfer points and discover the main trend of LULC. These changes may be due to policy adjustments, natural disasters, economic development and other factors, from which we can find out the main driving forces affecting land use change. |
- Table 2,3 and 4 maybe can be combined.
Response 6:
Thank you for your comment. We combined the three tables and found that the one did easier to understand.
We have combined the tables in line 238 as follows:
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Table 2. LULC transition matrix from 1990 to 2006, 2006 to 2017, 2017 to 2022 in Qianjiang city. (“45.09” means there are 45.09 km2 of the LULC type of "built up" in 1990 converted to type " built up " in 2006; “28.28” means there are 28.28 km2 of the LULC type of "built up" in 1990 converted to type "farmland" in 2006; “-281.58” means that the area of the LULC type of “Forest/grass” in 1990 was 281.58 km2 less than that in 2006)
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- The conclusion should be revised by include the limitations of the study.
Response 7:
Thank you for your comment. We added the limitations of the study in the “Conclusions” section.
We have added the limitations in lines 292-298 as follows:
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4. Conclusions This study used multitemporal Landsat imagery to detect LULCC in Qianjiang City from 1990 to 2022, particularly the change in aquaculture areas. LULC classification was conducted using a combination of decision tree classifier and visual interpretation, and LULCC were analyzed via transition matrices. The results showed that the main type LULC type in the study area was farmland, which accounted for more than 70% of the Qianjiang City. This was followed by built-up land, aquaculture land, and water bodies, etc. From 1990 to 2022, there were drastic LULCC in Qianjiang City, with a rapid de-crease in farmland area and an increase in aquaculture zone. The area of aquaculture land rose from 35.98 km2 in 1990 to 240.83 km2 in 2017. However, after 2017, due to the new policy regarding protecting farmland, the area of aquaculture land slightly declined. To clarify the driving mechanism of LULCC, comprehensive analysis of various natural, social and economic factors is necessary. However, this study only discusses 4-years LUCC in the past 30 years due to data limitations, and draws a rough conclusion: the rapid GDP growth and government policies were the main driving forces of LULCC in Qianjiang City, which provides data support for the government in making decisions and policies.
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Author Response File: Author Response.docx
