Early Identification and Influencing Factors Analysis of Active Landslides in Mountainous Areas of Southwest China Using SBAS−InSAR

Round 1

Reviewer 1 Report

Landslides are natural hazards that cause losses in the natural environment, infrastructure and construction. Early identification of deformation, high-frequency monitoring and knowledge of hydrogeological conditions of a given terrain are the basis for forecasting the occurrence of these hazards and risk evaluation. The Authors have recommended  SBAS-InSAR technology in the early identification of mass movements danger on the base of investigations in southwest mountainous areas in China. The Authors took into account tectonic, lithological and meteorological conditions in the process of landslide activation. Especially, the precipitation has a significant influence on mass movement dynamics. The emergence of new or the renewal of older landslides takes place when precipitation, usually in a short time (1–3 days), exceeds the threshold (critical) values [Skrzypczak et al. Landslide Hazard Assessment Map as an Element Supporting Spatial Planning: The Flysch Carpathians Region Study. Remote Sens. 2021, 13, 317. https://doi.org/10.3390/rs13020317]. These critical precipitation values are characteristic for a given region, characterized by a specific ground state (with its lithology, morphology, slope angle etc.). For example, analysis of hydrometeorological conditions for the development of Carpathian landslides in Europe caused by heavy rains, confirm that the number of landslides is increasing when total precipitation over roughly 20–40 days preceding the formation of a landslide exceeds 200–250 mm. The amount of critical rainfall for the Carpathian region, approximately 100 mm, is usually reported within a few days before a landslide activation.

Note: It should be emphasized in the paper that also the daily rainfall intensity expressed in [mm/day] is very important. Can the authors indicate the critical value of precipitation for the analyzed region?

Author Response

Early Identification and Influencing Factors Analysis of Active Landslides in Mountainous Areas of Southwest China Using SBAS-InSAR

Peilian Ran¹, Shaoda Li^1,*, Guanchen Zhuo¹, Xiao Wang², Mingjie Meng³, Liang Liu¹, Youdong Chen¹, Huina Huang⁴, Yu Ye⁵ and Xiangqi Lei¹

(Manuscript ID: sustainability-2160023)

Dear Reviewer #1

Many thanks for all the constructive advice and comments to our manuscript. According to your comments and suggestion in the first review, we have carefully revised our manuscript. In this response letter, we provide an item-by-item response to your comments. Hope this manuscript could be considered for the further publication. Thank you for reviewing this manuscript and considering the paper positively for publication.

Note: Reviewer’s comments are in “times new roman” black font, and our responses in “times new roman” blue text for clarity. “Italic times new roman” blue text is extracted from the revised manuscript. All the revision in the manuscript were highlighted with blue text.

 

Reviewer #1

Landslides are natural hazards that cause losses in the natural environment, infrastructure and construction. Early identification of deformation, high-frequency monitoring and knowledge of hydrogeological conditions of a given terrain are the basis for forecasting the occurrence of these hazards and risk evaluation. The Authors have recommended  SBAS-InSAR technology in the early identification of mass movements danger on the base of investigations in southwest mountainous areas in China. The Authors took into account tectonic, lithological and meteorological conditions in the process of landslide activation. Especially, the precipitation has a significant influence on mass movement dynamics. The emergence of new or the renewal of older landslides takes place when precipitation, usually in a short time (1–3 days), exceeds the threshold (critical) values [Skrzypczak et al. Landslide Hazard Assessment Map as an Element Supporting Spatial Planning: The Flysch Carpathians Region Study. Remote Sens. 2021, 13, 317. https://doi.org/10.3390/rs13020317]. These critical precipitation values are characteristic for a given region, characterized by a specific ground state (with its lithology, morphology, slope angle etc.). For example, analysis of hydrometeorological conditions for the development of Carpathian landslides in Europe caused by heavy rains, confirm that the number of landslides is increasing when total precipitation over roughly 20–40 days preceding the formation of a landslide exceeds 200–250 mm. The amount of critical rainfall for the Carpathian region, approximately 100 mm, is usually reported within a few days before a landslide activation.

 

Note: It should be emphasized in the paper that also the daily rainfall intensity expressed in [mm/day] is very important. Can the authors indicate the critical value of precipitation for the analyzed region?

Thank you very much for your positive comments and support for our manuscript. We are sorry that we do not express the daily rainfall intensity in [mm/day]. There are mainly three reasons for this point, first, the daily rainfall intensity varies with year, besides, the weather in the study area is changeable due to the complex terrain and specific geological background. Second, the daily rainfall intensity is a sufficient condition but not a necessary condition in this area, the emergence of new or the renewal of older landslides takes place when precipitation or earthquake or other tectonic movements. Third, the origin precipitation data we got from Sichuan Meteorological Bureau is expressed in [mm/month], so it is hard to quantify the critical value of precipitation in [mm/day] for the analyzed area.

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper utilizes SBAS-InSAR technology to process ascending and descending Sentinel-1 datasets and identify active landslides located in the mountainous areas of southwest China. Although the authors utilize a mature InSAR technique to prove nice deformation maps, some conclusions of this paper are well known to researchers, and some other conclusions could not be deduced from the experimental results. A major revision is necessary.

1.      As the authors state, Keren Dai et al. [17] identified 41 active landslides in the whole area of Maoxian County, Sichuan Province using SBAS-InSAR technology. Many other researchers have also validated the ability of early identification of active landslides using InSAR technology. However, this paper still claims that ‘it is of great significance to further analyze the appropriateness and accuracy of early identification using InSAR technology in the mountainous areas of southwest China.’ The research significance is inadequate and doubtful.

2.      A simple quantitative explanation about how to determine the active landslides form the deformation measurement results should be made in the Section 4.2.

3.      No information about the method or relevant references to determine the landslide boundary of Google earth optical image. It seems that the authors just manually mark the boundary on the Google map and keep the same with the deformation area.

4.      It is a well-known and obvious conclusion that combining the ascending and descending datasets benefits reducing the geometric distortion due to radar observation sights. In my opinion, there is no need to overemphasize this point.

5.      From Fig. 13 to Fig 15, how to determine that the deformation is closely related with rainfall? The deformation kept almost linearly increasing, while the rainfalls were seasonal.

6.      Considering that the authors just analyze three factors affecting the development and distribution of landslides, I think that it is not reasonable to deduce that these three factors are the three main factors, although this conclusion is probably correct judging from experience.

 

Author Response

Early Identification and Influencing Factors Analysis of Active Landslides in Mountainous Areas of Southwest China Using SBAS-InSAR

Peilian Ran¹, Shaoda Li^1,*, Guanchen Zhuo¹, Xiao Wang², Mingjie Meng³, Liang Liu¹, Youdong Chen¹, Huina Huang⁴, Yu Ye⁵ and Xiangqi Lei¹

(Manuscript ID: sustainability-2160023)

Dear Reviewer #2

Many thanks for all the constructive advice and comments to our manuscript. According to your comments and suggestion in the first review, we have carefully revised our manuscript. In this response letter, we provide an item-by-item response to your comments. Hope this manuscript could be considered for the further publication. Thank you for reviewing this manuscript and considering the paper positively for publication.

Note: Reviewer’s comments are in “times new roman” black font, and our responses in “times new roman” blue text for clarity. “Italic times new roman” blue text is extracted from the revised manuscript. All the revision in the manuscript were highlighted with blue text.

Reviewer #2

This paper utilizes SBAS-InSAR technology to process ascending and descending Sentinel-1 datasets and identify active landslides located in the mountainous areas of southwest China. Although the authors utilize a mature InSAR technique to prove nice deformation maps, some conclusions of this paper are well known to researchers, and some other conclusions could not be deduced from the experimental results. A major revision is necessary.

Thank you very much for your positive comments and support for our manuscript. We are sorry that we did not make the research significance clear enough. After carefully read your advice and deep thinking, we think there are scientific novelty and important findings in our study and we will try our best to make the major revision.

• As the authors state, Keren Dai et al. [17] identified 41 active landslides in the whole area of Maoxian County, Sichuan Province using SBAS-InSAR technology. Many other researchers have also validated the ability of early identification of active landslides using InSAR technology. However, this paper still claims that ‘it is of great significance to further analyze the appropriateness and accuracy of early identification using InSAR technology in the mountainous areas of southwest China.’ The research significance is inadequate and doubtful.

Thanks for your advice. In this study, we make the scientific contributions for the following two questions,

1. How to early identify active landslides in the mountainous areas of southwest China by InSAR? How to ensure the accuracy of early identification results using InSAR?
2. What factors affect and how affect active landslide?

We solve these problems from the following two aspects:

1. The InSAR deformation results were verified by geometric distortion analysis, optical remote sensing interpretation and field investigation to determine the active landslides.

2.The influencing factors of identified landslides were analyzed with fault, stratigraphic lithology and rainfall data in detail.

There are not straightforward answers for the above two questions before this revision. According to your advice and comments, we have revised the research significance in the Introduction section, hope it emphasize the scientific novelty and important findings, and meet your requirement.

The new research significance added in the Introduction as:

“However, due to the complex geomorphology and geological conditions in the mountainous areas, the early identification of landslides using InSAR is still challenging and is limited by many factors, such as serious geometric distortions exist in the mountainous areas [15-18]. Therefore, it is necessary to analyze the spatial distribution of geometric distortions and the influence on the early identification results, verifying the accuracy of the early identification results using InSAR. In addition, it is also of great significance to know about the influencing factors of active landslides for the purpose of prevention of landslides in mountainous areas.”

In term of ‘it is of great significance to further analyze the appropriateness and accuracy of early identification using InSAR technology in the mountainous areas of southwest China.’, the authors want to validate the gotten results by using SAR images with different bands and variable spacing pixels when conditions permit. The noted validation can prove the efficiency of early identification using InSAR technology well, besides, the appropriateness and accuracy of early identification using InSAR technology had been proved in lots of studies, especially in the mountainous areas of southwest China.

• A simple quantitative explanation about how to determine the active landslides form the deformation measurement results should be made in the Section 4.2.

Thanks for your advice. The explanation added in the Section 4.2 as:

“The potential active landslides were determined using shape, size and magnitude of deformation from the deformation measurement results, especially -10cm/year for an active landslide. During this process, the deformation measurement results were used for location the active landslides, the Google earth optical images were utilized for determining the active landslides boundaries [14], and the field investigation was applied for verifying the identified results.”

• No information about the method or relevant references to determine the landslide boundary of Google earth optical image. It seems that the authors just manually mark the boundary on the Google map and keep the same with the deformation area.

Thanks a lot for your comments. We do not manually mark the boundary on the Google map and keep the same with the deformation area, as replied in question 2, we first got the locations of the obvious active landslides from the InSAR monitoring results, then, using the Google map to determine the boundary of the active landslides and verifying with the field investigation. The method or relevant references for determining the landslide boundary of Google earth optical image can be seen from [14].

[14]. Liu, X., Zhao, C., Zhang, Q., Lu, Z., Li, Z., Yang, C., ... & Liu, C. (2021). Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China. Engineering Geology, 284, 106033.

• It is a well-known and obvious conclusion that combining the ascending and descending datasets benefits reducing the geometric distortion due to radar observation sights. In my opinion, there is no need to overemphasize this point.

Many thanks to your comments. As you said, reducing the geometric distortion with combining the ascending and descending datasets is a well-known and obvious conclusion. However, the degree of reducing the geometric distortion with combining the ascending and descending datasets is different for different study areas. Thus, we further analyze and discuss this phenomenon in section 5.1.

• From 13 to Fig 15, how to determine that the deformation is closely related with rainfall? The deformation kept almost linearly increasing, while the rainfalls were seasonal.

Thanks for your comments. As you said, the deformation trend of the given landslide was almost linearly increasing From Fig. 13 to Fig 15, while the rainfalls were seasonal. Thus, it can be observable from Fig. 13 to Fig 15 that the deformation of landslide is obviously increasing when the precipitation is large or the rainfall is heavy, while it’s decreasing when the precipitation is small, so the deformation is closely related with rainfall.

• Considering that the authors just analyze three factors affecting the development and distribution of landslides, I think that it is not reasonable to deduce that these three factors are the three main factors, although this conclusion is probably correct judging from experience.

Many thanks to your comments. We analyze three factors affecting the development and distribution of landslides, which are tectonic, lithological and meteorological conditions in the process of landslide activation. Especially, the precipitation has a significant influence on mass movement dynamics, tectonic and lithological also show as trigger factors in the study area. Besides, these three factors are selected for analyzing truly because they are common factors affecting the activation of landslides from lots of studies related to this area. Thus, we concluded that these three factors are the three main factors.

Author Response File: Author Response.pdf

Reviewer 3 Report

1. This paper identifies landslides in mountainous areas and analyzes them from the perspectives of rainfall, fault zone and stratigraphic lithology. The analysis content is relatively comprehensive, and the analysis methods and field investigation are abundant. The analysis method of landslide in mountainous area is innovative, but the content of the article is not expressed.

2.Line48-51: How are they using the technology differently, how is the recognition different

3.Line75-76: There is a lack of relevant literature on stratigraphic analysis. Why is the fracture zone in the middle part of the study area relatively dense? The influence of different strata on landslide should be different, lack of analysis of this part.

4.Line98-99: Very few climate-related literature. Landslides are caused by rainfall. Landslides are caused by earthquakes.

5. FIG. 3 (a) The time interval of horizontal coordinate is wrong, and the time unit should be consistent with the coordinate. And the numbers in the graph are not consistent with the Y-axis.

6. In FIG. 8, the distance standards identified by different scales are different. If all figures use the same color bar, the scale standards should be the same.

7.Line302: The image recognition interval is a few years at most, a few months or even a few days, and there is no strong crustal movement.

8. FIG. 14 The cumulative deformation of time with low rainfall should be reduced relative to the time period with high rainfall.

Author Response

Early Identification and Influencing Factors Analysis of Active Landslides in Mountainous Areas of Southwest China Using SBAS-InSAR

Peilian Ran¹, Shaoda Li^1,*, Guanchen Zhuo¹, Xiao Wang², Mingjie Meng³, Liang Liu¹, Youdong Chen¹, Huina Huang⁴, Yu Ye⁵ and Xiangqi Lei¹

(Manuscript ID: sustainability-2160023)

Dear Reviewer #3

Many thanks for all the constructive advice and comments to our manuscript. According to your comments and suggestion in the first review, we have carefully revised our manuscript. In this response letter, we provide an item-by-item response to your comments. Hope this manuscript could be considered for the further publication. Thank you for reviewing this manuscript and considering the paper positively for publication.

Note: Reviewer’s comments are in “times new roman” black font, and our responses in “times new roman” blue text for clarity. “Italic times new roman” blue text is extracted from the revised manuscript. All the revision in the manuscript were highlighted with blue text.

Reviewer #3

1- This paper identifies landslides in mountainous areas and analyzes them from the perspectives of rainfall, fault zone and stratigraphic lithology. The analysis content is relatively comprehensive, and the analysis methods and field investigation are abundant. The analysis method of landslide in mountainous area is innovative, but the content of the article is not expressed.

Thank you very much for your positive comments and support for our manuscript. We are sorry that we did not express the content of the article well. After carefully read your advice and deep thinking, we think there are some contents needed to improve in our study and we will try our best to make the revision.

2- Line48-51: How are they using the technology differently, how is the recognition different.

Thanks for your comments. The main differences in technology they used may be the processing platform, data processing procedures, threshold settings in the data processing, SAR image processed and its revisit period and resolution, thus leading to the different recognition.

3- Line75-76: There is a lack of relevant literature on stratigraphic analysis. Why is the fracture zone in the middle part of the study area relatively dense? The influence of different strata on landslide should be different, lack of analysis of this part.

Thanks a lot for your comments. We have collected few relevant literatures such as [18] on stratigraphic analysis and been quoted in related line. Besides, the fault zone data we originally got show the fracture zone in the middle part of the study area relatively dense, we did not modify the original data, thus retaining the authenticity in our study. As you said, the influence of different strata on landslide should be different, the weak strata usually lead to landslides, while hard strata often prevent the development of landslides to some extent. Especially in the Shangyan Formation of Maoxian Group, Silurian system, there are lots of potential landslides identified by InSAR which can be seen from Fig. 12.

[18] Zhao, S., Chigira, M., Wu, X. (2019). Gigantic rockslides induced by fluvial incision in the Diexi area along the eastern margin of the Tibetan Plateau. Geomorphology, 338, 27-42.

4- Line98-99: Very few climate-related literature. Landslides are caused by rainfall. Landslides are caused by earthquakes.

Many thanks to your comments. As you said, landslides are caused by rainfall or landslides are caused by earthquakes. Thus, we have noticed that the frequent earthquakes occurred in the surrounding areas always result in large numbers of landslides, such as the 2008 Wenchuan earthquake, the Lushan earthquake in 2013 and many earthquakes occur recently. In term of rainfall-related landslides, we have surveyed and collected the relevant literature about climate-related affecting the stability of landslides [14], and the literature about the meteorological conditions in the study area, [19], also have added in the corresponding line.

[14] Liu, X., Zhao, C., Zhang, Q., Lu, Z., Li, Z., Yang, C., ... & Liu, C. (2021). Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China. Engineering Geology, 284, 106033.

[19] Dong, J., Zhang, L., Li, M., Yu, Y., Liao, M., Gong, J., Luo, H. (2018). Measuring precursory movements of the recent Xinmo landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 datasets. Landslides, 15, 135-144.

5- FIG. 3 (a) The time interval of horizontal coordinate is wrong, and the time unit should be consistent with the coordinate. And the numbers in the graph are not consistent with the Y-axis.

Thanks for your comments. FIG. 3 (a) was generated automatically by the SAR data processing software. We are sincerely sorry that the time interval of horizontal coordinate is wrong, we have revised it correctly in (day-month-year) and the new FIG. 3 (a) can be seen from the following area, in the meantime, we also revised FIG. 3 (b). After correcting, the time unit can be consistent with the coordinate. Besides, the numbers in the graph are the number of images used in our study which are not consistent with the Y-axis due to effects of the X-axis.

Fig 3. Time-Position map of Sentinel-1A (a) ascending and (b) descending.

6- In FIG. 8, the distance standards identified by different scales are different. If all figures use the same color bar, the scale standards should be the same.

Many thanks to your comments. First, the color bar used in FIG. 8 is the same, while the scale standards are not the same truly because the shape of the landslides identified in our study is diverse, thus, we select different distance standards to show the detailed deformation characteristics of every single landslide.

7- Line302: The image recognition interval is a few years at most, a few months or even a few days, and there is no strong crustal movement.

Thanks a lot for your comments. As you said, the strong crustal movement takes lots of time to develop, and it is possible that a small magnitude of crustal movement near the fault zone occurs during the monitoring interval. We have revised this sentence “Because the strong crustal movement during the formation of the fault zone exceeds the maximum bearing capacity of the rock mass, the broken rock mass leads to poor structure of the strata near the fault zone.” into “A small magnitude of crustal movement near the fault zone occurs during the monitoring interval, the broken rock mass leads to poor structure of the strata near the fault zone.”

8- FIG. 14 The cumulative deformation of time with low rainfall should be reduced relative to the time period with high rainfall.

Thanks for your comments. As you said, the cumulative deformation of time with high rainfall should be increased relative to the time period with low rainfall. We have rechecked the deformation data and replotted the rainfall curve, the new relationship between slope deformation and precipitation for the Longshan landslide is shown as below.

Fig 14. Relationship between slope deformation and precipitation for the Longshan landslide with descending data.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors have well revised their manuscript. I have no further comments, and this manuscript can be accepted now.