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Article

Remote Sensing-Based Detection and Analysis of Slow-Moving Landslides in Aba Prefecture, Southwest China

by
Juan Ren
1,2,
Wunian Yang
1,*,
Zhigang Ma
2,
Weile Li
3,
Shuai Zeng
2,
Hao Fu
2,
Yan Wen
2 and
Jiayang He
2
1
College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China
2
Sichuan Institute of Land and Space Ecological Restoration and Geological Hazard Prevention, Chengdu 610081, China
3
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(8), 1462; https://doi.org/10.3390/rs17081462
Submission received: 26 February 2025 / Revised: 16 April 2025 / Accepted: 16 April 2025 / Published: 19 April 2025
(This article belongs to the Section Engineering Remote Sensing)
Browse Figures
Versions Notes

Abstract

:
Aba Tibetan and Qiang Autonomous Prefecture (Aba Prefecture), located in Southwest China, has complex geological conditions and frequent seismic activity, facing an increasing landslide risk that threatens the safety of local communities. This study aims to improve the regional geohazard database by identifying slow-moving landslides in the area. We combined Stacking Interferometric Synthetic Aperture Radar (Stacking-InSAR) technology for deformation detection, optical satellite imagery for landslide boundary mapping, and field investigations for validation. A total of 474 slow-moving landslides were identified, covering an area of 149.84 km2, with landslides predominantly concentrated in the river valleys of the southern and southeastern regions. The distribution of these landslides is strongly influenced by bedrock lithology, fault distribution, topographic features, proximity to rivers, and folds. Additionally, 236 previously unknown landslides were detected and incorporated into the local geohazard database. This study provides important scientific support for landslide risk management, infrastructure planning, and mitigation strategies in Aba Prefecture, offering valuable insights for disaster response and prevention efforts.

1. Introduction

Landslides are among the most significant geological hazards, causing widespread infrastructure damage and posing severe threats to human lives [1,2,3,4]. China is one of the most severely affected countries by landslides [5]. Between 1940 and 2020, a total of 1470 catastrophic landslides occurred in China, resulting in 14,394 fatalities [6]. Despite a decline in annual landslide-related fatalities and an improved understanding of regional landslide distribution patterns, the sudden occurrence of landslides still presents significant challenges for risk management [7,8].
Among different types of landslides, slow-moving landslides provide valuable cases for studying failure mechanisms, monitoring, and early warning strategies [9]. These landslides typically creep at rates of a few centimeters to meters per year, with deformation processes lasting for years or even decades [10]. However, some slow-moving landslides may suddenly accelerate and fail catastrophically, leading to casualties [11]. A classic example is the Vajont landslide in Italy, which underwent years of slow deformation before suddenly sliding into a reservoir, generating a massive wave that killed thousands of people downstream [12]. Additionally, slow-moving landslides in high-elevation or concealed areas pose a significant risk, as they are difficult to detect using traditional survey and monitoring techniques. Once triggered, such landslides can cause substantial casualties [13,14,15]. As a result, slow-moving landslides are considered potential geohazards that require effective identification and monitoring approaches.
Traditional methods for monitoring landslide deformation, such as total stations [16], GNSSs [17], crack meters [18], and borehole inclinometers [19], have proven effective. However, their applicability is often constrained by limited spatial coverage and high operational costs, reducing their suitability for large-scale or long-term monitoring [20]. In recent years, the application of remote sensing technologies has significantly advanced landslide detection, providing valuable datasets and methodologies [21,22,23]. Optical satellite imagery can visually capture landscape changes, making it particularly suitable for observing gradual landslide deformation and large-scale deposit movement [18,23]. Furthermore, large landslides often exhibit distinct surface deformation features, such as cracks and localized rockfalls, during their evolution [24]. However, the accuracy of optical satellite imagery for landslide interpretation is constrained by weather conditions and resolution limitations, as cloud cover and low resolution can affect data quality. In contrast, Synthetic Aperture Radar (SAR) imagery is not affected by weather conditions and can provide consistent surface phase information [22,25]. Interferometric SAR (InSAR) technology, in particular, enables millimeter-scale surface deformation measurements through radar signal comparisons [26,27,28,29,30]. Researchers have successfully integrated the advantages of InSAR and optical imagery by first identifying deformation zones with InSAR and then mapping slow-moving landslides using optical satellite images, yielding promising results [31,32,33,34,35].
Aba Tibetan and Qiang Autonomous Prefecture (Aba Prefecture) is characterized by rugged terrain, complex geological conditions, and a high frequency of natural disasters, particularly landslides [36]. In recent years, several major landslides have occurred in this region [37,38,39,40,41], the most notable being the Xinmo landslide on 24 June 2017, which resulted in 83 fatalities [42,43]. Optical satellite imagery and InSAR technology played a crucial role in understanding the failure mechanism of the Xinmo landslide [24,30]. Following this event, researchers have increasingly combined remote sensing techniques with field investigations to identify potential slow-moving landslides in geologically active areas of Aba Prefecture and to gain deeper insights into landslide mechanisms [44,45]. However, the majority of previous studies have focused on specific landslide cases rather than establishing a comprehensive regional-scale inventory and conducting a systematic analysis of slow-moving landslides in such a geologically complex environment.
This study aims to address these gaps by integrating optical satellite imagery, Stacking-InSAR, and field investigations to systematically identify slow-moving landslides across Aba Prefecture. The primary objectives are to understand the spatial distribution of these landslides and to enhance the existing geohazard database of Aba Prefecture. Additionally, this study analyzes the relationships between slow-moving landslides and key environmental factors, including geological structures and topographical features. The findings will contribute to a better understanding of landslide susceptibility in the region and provide scientific support for disaster risk management, infrastructure planning, and decision-making.

2. Study Area

Aba Prefecture is situated on the east edge of Qinghai-Tibet Plateau in the northest of Sichuan Province, China (Figure 1). It borders on Qinghai and Gansu provinces to the north and spans approximately 83,000 km2. The prefecture is composed of 13 administrative counties: Maerkang, Rangtang, Ruoergai, Jiuzhaigou, Songpan, Hongyuan, Heishui, Mao, Jinchuan, Wenchuan, Li, Xiaojin, and Aba.
Aba Prefecture forms a transition region between the Sichuan Basin and the Qinghai-Tibet Plateau, with an elevation varying from 788 m to 6250 m. Diverse terrains form a significant geographical and climatic variation. The northern part of the region features plateau landscapes with a continental plateau climate, while most of the southern part is covered with steep valleys and thus has significant vertical climatic differences, with rainfall concentrated in summer months. The study area hydrologically lies within the upper reaches of the Yellow River and Yangtze River basins. The northern region belongs to the Yellow River basin, while the western, eastern, and southern areas are part of the Yangtze River basin, including major rivers such as the Min River and Dadu River.
Geologically, Aba Prefecture is located on the eastern margin of the Qinghai–Tibet fault block. Most part of the region lies within the Bayan Har fault zone, the remaining is situated in the eastern boundary fault zone. Seismic activity is frequent in this area due to the dense distribution of active faults. According to earthquake data from the USGS, a total of 22 earthquakes with a magnitude greater than 6.0 have been recorded in this region since 1900, including the Diexi earthquake in 1933 [46], the Wenchuan earthquake in 2008 [47,48], and the Jiuzhaigou earthquake in 2017 [49]. The complex geological conditions have led to frequent landslides in Aba Prefecture. Currently, 1962 known landslides pose risks to local residents and infrastructure. These landslides have been incorporated in the geohazard database by the authorities and are under real-time monitoring.

3. Data and Methods

The major goal of this study is to identify and analyze slow-moving landslides. The process is summarized in Figure 2.
Step 1: Deformation Detection. Sentinel-1 SAR images are processed to calculate the average deformation phase, and deformation areas are then identified through manual and visual interpretation.
Step 2: Landslide Mapping. Based on the identified deformation areas, landslide characteristics are recognized by analyzing variations in hue, texture, and shadow patterns in optical satellite imagery. Landslide boundaries are then delineated accordingly.
Step 3: Field Investigation. Landslide features are verified through ground surveys and unmanned aerial vehicle (UAV) imagery. Additionally, interviews with local residents provide further insights into historical deformation events. These field data are compiled to develop a comprehensive landslide catalogue.
Step 4: Influencing Factor Analysis. Statistical analysis is conducted to examine the relationships between landslides and environmental variables such as elevation, slope, aspect, proximity to faults, and distance to rivers. This analysis helps to understand the spatial distribution and driving mechanisms of slow-moving landslides.

3.1. Identification of Slow-Moving Landslide

3.1.1. Deformation Detection with InSAR Technology

The study area was monitored using Sentinel-1A radar imagery collected between 1 October 2020 and 1 November 2023. In mountainous regions, the combination of ascending and descending orbit images significantly enhances result visibility and maximizes landslide detection [45]. During this period, a total of 723 ascending orbit images and 714 descending orbit images were considered, as shown in Table 1 and Figure 3a,b. To minimize external errors, precise orbit determination (POD) data corresponding to the acquisition time of the radar images are downloaded. Additionally, high-resolution ALOS DEM data (12.5 m) of the study area are utilized to remove topographic phase effects during the InSAR processing, so as to facilitate geographic encoding, and identify masked and shadowed areas.
Surface deformation was detected using the Stacking-InSAR technique, which derives the average deformation phase through filtering and phase unwrapping processes [28]. Compared to differential InSAR, Stacking-InSAR offers superior suppression of atmospheric and topographic errors [50]. Additionally, its computational efficiency and suitability for large-scale landslide detection make it an optimal choice for this study [51].
Radar image processing was carried out using GAMMA software https://gamma.app/ (accessed on 15 April 2025), with all computational codes and models sourced from the software system. The analysis was conducted on a path basis, involving interferometric processing, atmospheric correction, and phase unwrapping. Multiple differential interferograms were then stacked linearly to obtain the average deformation phase across the study area.
Given the extensive spatial coverage of the study area, a temporal baseline threshold of 72 days and a coherence coefficient threshold of 0.2 were applied to ensure reliable detection results. To generate a comprehensive deformation map, the Seamless Mosaic function in ENVI (Version 5.3) was used to merge multiple paths: Path 26, Path 128, and Path 55 were combined to produce the ascending-track average phase map (Figure 3a), while Path 33, Path 135, and Path 62 were merged to create the descending-track average phase map (Figure 3b). These maps serve as fundamental datasets for identifying surface instabilities and detecting slow-moving landslides. The spatial distribution of surface deformation is visually distinguishable based on color variations in the average deformation phase maps, as illustrated in Figure 3c,d.

3.1.2. Landslide Mapping Based on Optical Satellite Images

While InSAR analysis provides valuable insights into surface instability by identifying deformation areas, it cannot directly delineate precise landslide boundaries. This limitation arises because surface deformation is not exclusively caused by landslides; human engineering activities, glacial movements, and vegetation growth can also contribute to such changes [33,52,53,54]. These types of “non-landslide deformations” are difficult to distinguish using Stacking-InSAR maps alone [33].
Additionally, the landslide catalog used in this study is limited to slow-moving landslides that impact surrounding infrastructure. Landslides in remote areas without residents, roads, or other infrastructure are therefore excluded from consideration. To improve the accuracy of landslide identification and boundary delineation, high-resolution optical satellite imagery is essential for verification.
The Google Earth platform provides global access to high-resolution optical imagery, often achieving sub-meter accuracy. Additionally, historical images of certain locations across different time periods are available, enabling temporal analysis. By leveraging optical satellite imagery, landslide boundaries can be accurately delineated through visual interpretation. This method is based on the principle that landslides cause significant spectral changes, which can be detected in optical imagery. Key indicators such as variations in hue, texture, shadow patterns, and vegetation cover are analyzed to identify landslide morphology. Specific features, including distinct landslide backwalls, cracks, and localized displacement, can also be visually identified in high-resolution images [23,55]. Figure 4 presents optical satellite images of typical landslides, including ancient landslides with visible deformation (Figure 4a–d) and recent landslides (Figure 4e–h).

3.1.3. Field Investigation

Field investigation plays a critical role in landslide identification and monitoring. Once potential landslides are identified with remote sensing technologies, a more detailed and comprehensive ground investigation is conducted to verify the remote sensing results and gather additional data.
To begin with, we use UAVs to capture an overall view of the landslide area (Figure 5a). For certain landslides, we conduct more detailed aerial photography and use Pix4D to generate digital orthophotos and surface elevation information. During the flight, we adjust the altitude according to the height of the landslide to ensure an image resolution of less than 0.1 m. Following the aerial survey, we carry out a ground investigation focusing on several key aspects:
(1)
Identifying deformation features of the landslide, such as local sliding failures and surface cracks (Figure 5b).
(2)
Evaluating infrastructure damage, including its impact on residential areas and transportation networks (Figure 5c,d).
(3)
Assessing the effectiveness of existing landslide control measures, such as drainage systems and stabilization works (Figure 5b).

3.2. Analysis of Influencing Factors

Landslides are caused by multiple factors [56]. Six critical environmental factors that influence landslide distribution in the study area are identified, on the basis of geological environment, previous research, and data availability [57].
Bedrock lithology is a key factor influencing landslide occurrence, as unfavorable strata can significantly promote landslide development. The lithological data come from the 1:200,000 geological maps provided by the Geoscientific Data and Discovery Publishing System (http://dcc.ngac.org.cn, accessed on 27 November 2024). The complex lithology composition of Aba Prefecture was simplified into three basic types for analysis purpose: sedimentary rocks, igneous rocks, and metamorphic rocks (Figure 6a).
Faults are tectonic stress release zones, where intense compression and fracturing lead to the formation of joints and fissures in rocks. Fault grids are quite dense in Aba Prefecture, one of them being the famous Longmenshan Fault Belt. Fault data were obtained from the 1:250,000 structural geological maps provided by the Geoscientific Data and Discovery Publishing System. With the help of ArcGIS, buffer zones can be generated around faults to evaluate those areas where landsides are likely to happen (Figure 6b).
Terrain plays a significant role in landslide distribution, slope in particular. Elevation data were obtained from the Advanced Land Observing Satellite (ALOS) Global Digital Surface Model “ALOS World 3D-30 m” (https://earth.jaxa.jp/, accessed on 22 October 2022) (Figure 6c). These Digital Surface Model (DSM) data are processed to generate slope maps (Figure 6d) and aspect maps (Figure 6e) by means of ArcGIS. The aspect is classified into nine categories: flat, north, northeast, east, southeast, south, southwest, west, and northwest.
Valley locations can be marked by rivers, where fluvial erosion influences the stress distribution on slopes, thereby decreasing slope stability. River data are obtained from the 1:250,000 National Fundamental Geographic Information Database (National Geographic Information Resources Directory Service System, https://www.webmap.cn/, accessed on 27 November 2024). Buffer zones around rivers are generated using ArcGIS to assess the relation between landslides and river systems (Figure 6f).

4. Results

4.1. Result of Slow-Moving Landslides Catalogue

A total of 474 slow-moving landslides were identified within the study area, covering an approximate area of 149.84 km2 (Table 2). The size of the landslides varies significantly, ranging from 65.22 m2 to 3.0 × 106 m2, with an average area of approximately 3.16 × 105 m2. The average density of landslides is 5.7 per 1000 km2. However, the spatial distribution of the landslides is uneven, with most landslides concentrated in the river valleys of the southern and southeastern regions of Aba Prefecture. These disparities are clearly depicted in the spatial distribution map (Figure 7). Using ArcGIS, a Gaussian kernel density analysis is conducted with a search radius of 1000 km, revealing that landslides are mainly clustered in four distinct regions. These regions have 334 landslides in total, accounting for 70.5% of all identified landslides.
Zone I is located around Xiaojin, it covers an area of approximately 822.5 km2 and contains 48 landslides, accounting for 10.1% of the total landslides. Zone II is located around Wenchuan and Wenchuan-Li area, it covers about 1643.3 km2 and contains 112 landslides, representing 23.6% of the total. Zone III is located in Mao to Heishui area, spans about 1931.0 km2 and contains 128 landslides, accounting for 27.0% of the total. Zone IV is located around Songpan, covers approximately 656.2 km2 and contains 46 landslides, representing 9.7% of the total.
Among these four zones, Zone IV has the highest landslide density, with 70.1 landslides per 1000 km2, but the average landslide area and elevation difference here is the smallest. Zone III has the largest number of landslides; it also boasts the largest average landslide area of approximately 46.7 × 104 m2 and an average elevation difference of 465 m.

4.2. Influencing Factors of Landslide Distribution

4.2.1. Influence of Lithology

A statistical analysis of landslide occurrences reveals that most landslides (427, or 90.1%) occurred in metamorphic rocks, while 8 landslides (1.7%) occurred in igneous rocks and 39 landslides (8.2%) in sedimentary rocks. Metamorphic rocks are particularly common in landslide concentration zones, indicating that they play a critical role in landslide development and spatial distribution (Figure 8a).

4.2.2. Influence of Fault

Approximately 32.5% of landslides are located within 1 km of a fault, and at least 57.8% are within 3 km. Overall, within a 10 km range, landslide frequency decreases as the distance from faults increases (Figure 8b). However, beyond 10 km, the number of landslides remains notably high, with 77 landslides recorded. Notably, in Zone I, 32 landslides (representing 66.7% of the total in this zone) still occur even when the distance to faults is more than 10 km. This suggests that in this region, landslide development is primarily influenced by factors other than fault proximity.

4.2.3. Influence of Topography and River

The study area has a wide range of elevations, from 788 m to 6250 m, with over 94.7% of landslides occurring between 1500 m and 3500 m. Landslide concentration zones have distinct elevation characteristics: landslides in Zone II are concentrated between 1000 m and 2500 m, while those in Zone IV are primarily located between 2500 m and 3500 m (Figure 8c).
The southeast of Aba Prefecture is characterized by steep terrain and river valleys, while the northwest is relatively flat and dominated by grasslands. Notably, nearly all landslides are concentrated within the river valley areas. In terms of slope gradient, 430 landslides (more than 90%) occurred on slopes of 20° to 50°. Landslide frequency sharply declines on slopes with gradients either less than 20° or greater than 50° (Figure 8d).
Analysis of aspect reveals that most landslides are concentrated on southeast-facing (SE) slopes, with 92 landslides (19.4%) recorded. Other prominent slopes face east (E), south (S), and southwest (SW), each with over 70 landslides. By contrast, north (N), northeast (NE), and west-facing (W) slopes have fewer landslides, more than 40 on each side. Each study zone shows the same kind of trend in terms of aspect (Figure 8e).
In terms of proximity to rivers, more than 94.7% of landslides are located within 2000 m from rivers, with landslide frequency decreasing as the distance from rivers increases (Figure 8f). This indicates that river system plays a significant role in the distribution of landslides, as slope stability is likely to be changed by fluvial erosion in river valleys.

5. Discussion

5.1. Structural Controls on Landslides in Zone I

Folding, like faulting, is a significant geological process that can lead to rock fracturing. During fold formation, uneven stress distribution generates joints and cleavages, weakening the structural integrity of the rock and creating favorable conditions for landslides [58].
Aba Prefecture is located within the Songpan-Garzê fold belt [59]. To evaluate the influence of fold structures on slow-moving landslides, fold hinge lines were extracted from the 1:250,000 structural geology map of Aba Prefecture. Buffer zones were created at 1 km intervals around these hinge lines, and the spatial distribution of landslides was analyzed (Figure 9a). The results indicate that 360 landslides, accounting for 75.9% of the total landslide area, are concentrated within 5 km of the hinge lines. The number of landslides gradually decreases with increasing distance from the hinge lines (Figure 9b). However, beyond 10 km, 42 landslides were still identified, suggesting that other factors—such as lithology, topography, and river erosion—may also contribute to landslide occurrence, as discussed earlier.
In Zone I, fold structures exert a significant influence on landslide distribution. Although no major faults are present in this area, a series of NW-SE-trending anticlines and synclines form a structurally complex setting (Figure 10a). Among these structures, the Dapusa Anticlinorium is particularly noteworthy, as it coincides with a large number of slow-moving landslides. Other prominent fold structures, including the Maanshan Overturned Anticline, Chaoshanping Overturned Anticline, and Zhuangfang Overturned Syncline, also contribute to the region’s geological instability. The limbs of these folds are primarily composed of strata from the Triassic Zhuwo and Zagunao Formations (Figure 10b).
A closer analysis of landslide distribution in Zone I further highlights the role of folding (Figure 9b). A total of 29 landslides are located within 1 km of the hinge lines, while all 48 identified landslides are concentrated within 4 km. These findings suggest that, compared to faulting, fold-related deformation is the dominant structural factor controlling landslide activity in this area. The combination of anticlinal and synclinal structures with unstable lithological formations provides a favorable environment for slow-moving landslides, emphasizing the critical role of folding in landslide susceptibility.

5.2. Importance of Slow-Moving Landslides Identification for Disaster Risk Reduction

Except for the known landslides, another 236 landslides are newly identified, accounting for 49.8% of the total number documented in this study. The spatial distribution and statistical analysis of these new landslides are illustrated in Figure 11. Nearly all the counties in the study area (except for Ruo’ergai) are experiencing new landslide occurrence. Among these counties, Li has the highest number of newly identified landslides of 53, representing 22.27% of the total number of newly identified ones. Songpan followed with 43 landslides (18.07%), and Mao recorded 34 new landslides, accounting for 14.29% of the total.
Field investigations and interviews revealed that 1071 households with 4124 members are at risk due to the proximity of these new landslides. The identification of these slow-moving landslides plays a critical role in disaster risk reduction by facilitating the timely integration of newly detected landslides into the geohazard management system. This allows for targeted risk assessment and prioritization of mitigation efforts.

6. Conclusions

This study provides a comprehensive analysis of slow-moving landslides in Aba Prefecture, Sichuan, by integrating Stacking-InSAR, optical satellite imagery, and field investigations. A total of 474 slow-moving landslides are identified, most of which are concentrated in river valleys in the south and southeast of the prefecture. Our analysis reveals that these landslides are closely connected with bedrock lithology, faults, and topographical factors, including elevation, slope, and proximity to rivers. Additionally, this study demonstrates that, in addition to faults, folds play a critical role in landslide occurrence. Future research should further investigate these structural controls through geomechanical modeling and field monitoring, providing a more detailed understanding of landslide susceptibility in fold-dominated terrains.
Moreover, the identification of 236 previously unknown landslides, nearly half of the total documented in this study, highlights the dynamic and evolving nature of landslide hazards in the region. These findings emphasize the necessity of continuous monitoring and early detection for effective landslide risk management. By integrating these newly detected landslides into the existing geohazard database, local authorities can conduct more precise risk assessments and prioritize mitigation efforts. It is crucial to implement targeted disaster prevention measures, including enhanced slope monitoring networks, optimized early warning systems, and engineering stabilization efforts such as slope reinforcement and drainage improvements. Additionally, promoting community-based disaster preparedness programs will help mitigate the impact of landslides on local residents.

Author Contributions

Conceptualization: J.R.; Methodology: J.R. and W.Y.; Software: J.R.; Validation: J.R., W.Y. and Z.M.; Formal Analysis: J.R., W.Y. and Z.M.; Investigation: J.R., S.Z., H.F., Y.W. and J.H.; Resources: W.Y. and Z.M.; Data Curation: J.R., S.Z., H.F., Y.W. and J.H.; Writing—Original Draft Preparation: J.R.; Writing—Review and Editing: J.R., W.Y. and W.L.; Visualization: J.R.; Supervision: W.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work is co-funded by the Ministry of Natural Resources of the People’s Republic of China (Grant number: 2023ZRBSHZ049) and the Science and Technology Department of Sichuan Province (Grant number: 2024YFFK0108).

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Study area location map.
Figure 1. Study area location map.
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Figure 2. Flowchart of identification and influencing factors of slow-moving landslide.
Figure 2. Flowchart of identification and influencing factors of slow-moving landslide.
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Figure 3. Average deformation phase by Stacking-InSAR. (a,b) The global view of ascending and descending orbit images. (c,d) The local view of ascending and descending orbit images.
Figure 3. Average deformation phase by Stacking-InSAR. (a,b) The global view of ascending and descending orbit images. (c,d) The local view of ascending and descending orbit images.
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Figure 4. Typical optical satellite images of landslides. (a–d) Ancient landslides. (e–h) Recent landslides. (a-1), (b-1), (c-1), (d-1), (e-1), (f-1), (g-1), and (h-1) show the average deformation phase, while (a-2), (b-2), (c-2), (d-2), (e-2), (f-2), (g-2), and (h-2) are the corresponding high-resolution satellite imagery.
Figure 4. Typical optical satellite images of landslides. (a–d) Ancient landslides. (e–h) Recent landslides. (a-1), (b-1), (c-1), (d-1), (e-1), (f-1), (g-1), and (h-1) show the average deformation phase, while (a-2), (b-2), (c-2), (d-2), (e-2), (f-2), (g-2), and (h-2) are the corresponding high-resolution satellite imagery.
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Figure 5. Field investigation of landslides. (a) Overall view of the landslide captured by a drone. (b) Local sliding failure and broken retaining wall. (c,d) Tension cracks on houses and roads.
Figure 5. Field investigation of landslides. (a) Overall view of the landslide captured by a drone. (b) Local sliding failure and broken retaining wall. (c,d) Tension cracks on houses and roads.
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Figure 6. Influencing factors of slow-moving landslides. (a) Rock units, (b) distance to fault, (c) elevation, (d) slope, (e) aspect, and (f) distance to river.
Figure 6. Influencing factors of slow-moving landslides. (a) Rock units, (b) distance to fault, (c) elevation, (d) slope, (e) aspect, and (f) distance to river.
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Figure 7. Spatial distribution and concentration zones of landslide.
Figure 7. Spatial distribution and concentration zones of landslide.
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Figure 8. Landslide distribution influenced by lithology (a), distance to fault (b), elevation (c), slope (d), aspect (e), and distance to river (f).
Figure 8. Landslide distribution influenced by lithology (a), distance to fault (b), elevation (c), slope (d), aspect (e), and distance to river (f).
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Figure 9. Spatial distribution (a) and statistics (b) of landslides at different distances to hinge lines in zone I.
Figure 9. Spatial distribution (a) and statistics (b) of landslides at different distances to hinge lines in zone I.
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Figure 10. (a) The 1:250,000 geological structure and formation map of zone I and (b) A-A’ profile map.
Figure 10. (a) The 1:250,000 geological structure and formation map of zone I and (b) A-A’ profile map.
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Figure 11. Spatial distribution of already-known and newly identified landslides in Aba Prefecture.
Figure 11. Spatial distribution of already-known and newly identified landslides in Aba Prefecture.
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Table 1. Coverage of Sentinel-1 SAR images used in the study.
Table 1. Coverage of Sentinel-1 SAR images used in the study.
Orbit DirectionPathFrameCollection DateScenes
Ascending2693/98/1031 October 2020–1 November 2023258
12894/99/104/1096 October 2020–27 October 2023332
55102/1071 October 2020–22 October 2023133
Descending3348211 October 2020–20 October 202391
135478/483/4886 October 2020–27 October 2023255
62479/484/4891 October 2020–22 October 2023368
Table 2. Statistical table of different zones of landslides.
Table 2. Statistical table of different zones of landslides.
ZoneArea (km2)NumberPercentage of Total LandslidesAverage Density (/103 km2)Mean Area (×104 m2)Mean Height (m)
Zone I822.54810.1%58.444.1448
Zone II1643.311223.6%68.226.8439
Zone III1931.012827.0%66.346.7465
Zone IV656.2469.7%70.16.4130
Others78,041.914029.5%1.825.7361
Total area83,095.0474100%5.731.6394
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MDPI and ACS Style

Ren, J.; Yang, W.; Ma, Z.; Li, W.; Zeng, S.; Fu, H.; Wen, Y.; He, J. Remote Sensing-Based Detection and Analysis of Slow-Moving Landslides in Aba Prefecture, Southwest China. Remote Sens. 2025, 17, 1462. https://doi.org/10.3390/rs17081462

AMA Style

Ren J, Yang W, Ma Z, Li W, Zeng S, Fu H, Wen Y, He J. Remote Sensing-Based Detection and Analysis of Slow-Moving Landslides in Aba Prefecture, Southwest China. Remote Sensing. 2025; 17(8):1462. https://doi.org/10.3390/rs17081462

Chicago/Turabian Style

Ren, Juan, Wunian Yang, Zhigang Ma, Weile Li, Shuai Zeng, Hao Fu, Yan Wen, and Jiayang He. 2025. "Remote Sensing-Based Detection and Analysis of Slow-Moving Landslides in Aba Prefecture, Southwest China" Remote Sensing 17, no. 8: 1462. https://doi.org/10.3390/rs17081462

APA Style

Ren, J., Yang, W., Ma, Z., Li, W., Zeng, S., Fu, H., Wen, Y., & He, J. (2025). Remote Sensing-Based Detection and Analysis of Slow-Moving Landslides in Aba Prefecture, Southwest China. Remote Sensing, 17(8), 1462. https://doi.org/10.3390/rs17081462

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