Deformation Slope Extraction and Influencing Factor Analysis Using LT-1 Satellite Data: A Case Study of Chongqing and Surrounding Areas, China

Abstract

The use of satellite imagery for surface deformation monitoring has been steadily increasing. However, the study of extracting deformation slopes from deformation data requires further advancement. This limitation not only poses challenges for subsequent studies but also restricts the potential for deeper exploration and utilization of deformation data. The LT-1 satellite, China’s largest L-band synthetic aperture radar satellite, offers a new perspective for monitoring. In this study, we extracted deformation slopes in Chongqing and its surrounding areas of China based on deformation data generated by LT-1. Twelve factors were selected to analyze their influence on slope deformation, including elevation, topographic position, slope, landcover, soil, lithology, relief, average rainfall intensity, and distances to rivers, roads, railways, and active faults. A total of 5863 deformation slopes were identified, covering an area of 140 km², mainly concentrated in the central part of the study area, with the highest area density reaching 0.22%. Among these factors, average rainfall intensity was found to have the greatest impact on deformation slope. These findings provide valuable information for geological disaster early warning and management in Chongqing and surrounding areas, while also demonstrating the practical value of the LT-1 satellite in deformation monitoring.

1. Introduction

Landslides are a common natural disaster, occurring frequently around the world each year, causing significant economic losses and casualties [1,2,3]. However, the occurrence of a disaster does not always result in catastrophic consequences. Research by Basher [4] suggests that a disaster’s severity depends on the community’s preparedness and response capabilities. In the face of most natural disasters, if communities can construct sturdy buildings in safe zones, maintain the health of ecosystems, establish and effectively operate early warning and evacuation systems, and raise public awareness of disaster prevention, the potential losses can be significantly reduced [5]. Further research by Padli, et al. [6] reveals that economic development level is a critical factor influencing mortality rates during natural disasters. Economically developed countries typically have stronger resources and technologies to cope with natural disasters, thus reducing casualties. These countries effectively mitigate the social impacts of natural disasters through investments in disaster relief centers, preparedness plans, early warning systems, and the enforcement of building regulations.

In recent years, Chongqing has enjoyed sustained economic development as a multidisciplinary center of economy, finance, arts, culture, education, and technology in China’s upper reaches of the Yangtze River, as well as a water, land, and air transportation hub (Chongqing Municipal People’s Government Work Report (2024), http://admin.cq.gov.cn/zwgk/zfxxgkml/zfgzbg/202401/t20240129_12872692.html, accessed on 30 September 2024). Along with the rapid economic growth, the residents’ sense of well-being has continued to improve. Given the national strategies and rapid economic development, it is particularly crucial to conduct disaster monitoring in Chongqing and its surrounding areas. This necessity stems from both economic and social development demands and the region’s complex geological environment. Chongqing is characterized by complex geology and frequent natural disasters, among which landslides are particularly frequent in the region. There have been many major landslides, including the Jiweishan landslide [7], Zhujiatang landslide [8], Guang’an Village landslide [9], Shiquan landslide [10], etc. These disasters have caused significant local damage and sparked widespread attention. Especially in the Three Gorges Reservoir area, as the water storage process progresses, geological changes exacerbate the risk of landslides and other geological disasters, such as the Outang landslide [11], Longshuigou landslide [12], Gongjiafang landslide [13], etc. These disasters emphasize the necessity of strengthening surface deformation monitoring in order to detect potential hazards and take effective preventive measures in time.

In the field of surface monitoring, although there are various methods available, such as ground-based synthetic aperture radar systems (GBSAR) [14,15], GNSS measurements [16,17], total stations measurements [18], airborne LiDAR [19,20], and UAV photogrammetry [21,22], these techniques are less commonly used for large-scale, long-term monitoring due to their high costs, low efficiency, or limited application in complex terrains. Surface monitoring using optical satellite imagery [23,24] and SAR imagery [25,26] has been widely applied in geological hazard monitoring due to its excellent timeliness, broad spatial coverage, and low cost. Many disaster-prone areas are located in mountainous regions with frequent cloud cover, and optical remote sensing images are easily affected by these. In contrast, Interferometric Synthetic Aperture Radar (InSAR) technology, with its all-weather capability, high resolution, and ability to monitor even small deformations, has become a powerful tool for early detection and long-term monitoring of geological hazards.

Chongqing has made some progress in using InSAR technology for landslide monitoring. However, most research still focuses on individual landslide or large-scale surface deformations. Systematic methods for deformation results processing have not yet been established. This limitation has led to the failure of utilizing their potential in disaster early warning and risk assessment. For example, Cui, et al. [27] used SBAS-InSAR technology to monitor potential landslides in Yunyang County; Xiao, et al. [28] applied SBAS technology to process Sentinel-1A data and, in conjunction with ground-based radar systems (GB-InSAR), analyzed the movement of the Zhongbao landslide; Gao, et al. [29] used UAV photography, field surveys, geological condition analysis, and InSAR monitoring, combined with PFC^3D numerical simulation, to explore the disaster-causing mechanism of the Niuerwan landslide. While these studies provide detailed deformation data for specific areas and landslide events, most research [30,31,32] offers only brief descriptions of methods for deformation slope extraction without systematically explaining the extraction methods and the scientific significance behind them. There is an urgent need to strengthen the post-processing of surface deformation data, with the aim of systematically applying it to deformation slope assessment. By developing a systematic method for deformation slope extraction, our study provides robust technical support for regional risk assessment, significantly enhancing the scientific rigor and effectiveness of geological disaster prevention and mitigation. The advantages of this systematic method lie in its ability to improve monitoring efficiency and accuracy, enabling the rapid deployment of monitoring equipment based on extracted deformation results. This strengthens the capability to monitor potential high-risk areas. The approach reduces unnecessary monitoring costs, achieving optimal resource allocation. More importantly, the systematic method for deformation slope identification and extraction greatly enhances the scientific value of InSAR technology in surface monitoring, driving advancements in related disciplines. The application of this method offers more precise and robust decision-making support for governments and relevant departments, ensuring greater focus and effectiveness in policy formulation and implementation.

The LT-1 is currently China’s largest SAR satellite in orbit and has demonstrated excellent performance in practical applications. This study, based on LT-1 deformation results processed using differential interferometric synthetic aperture radar (D-InSAR) technology, successfully extracted deformation slopes in Chongqing and its surrounding areas in China. Building on this, 12 factors were selected to explore their influence on the deformation slopes. Our study significantly enhanced the accuracy and reliability of deformation slope data through the integration of multi-source data fusion techniques, providing comprehensive and reliable data support for disaster prevention and mitigation. Additionally, we proposed a systematic method for identifying and extracting deformation slopes, greatly improving the ability to extract key information from monitoring data. This introduces new scientific approaches to the application of InSAR technology in disaster early warning and risk assessment, thereby increasing its scientific value in the field of disaster prevention and mitigation. It not only provides important scientific reference for the safe management of urban infrastructure but also offers a new perspective for assessing potential environmental risks. Moreover, it is also a preliminary exploration of utilizing LT-1-derived data in the field of disaster mitigation and prevention, showing its great potential for application.

2. Study Area

Chongqing, the largest city in Southwest China [33], is located in the transition zone between the Qinghai-Tibet Plateau and the middle and lower reaches of the Yangtze River. Geographically, it spans from 28°10′ to 32°13′N and 105°17′ to 110°11′E, covering an area of 82,400 km². Chongqing features numerous Quaternary active faults of varying scales, among which four regional basement faults stand out for their significant scale: the Huayingshan basement faults, the Qiyaoshan-Jinfoshan basement faults, the Changshou-Zunyi basement fault, and the Pengshui basement faults. These faults play a critical role in controlling the region’s geological structure, sedimentary formations, and seismic activity. The city is characterized by a rich network of rivers, with the main stream of the Yangtze River running through the whole territory from west to east, with a total length of 691 km. The watershed area of the Yangtze River within the territory of Chongqing is 82,370 km², forming a rich network of water systems. There are 274 rivers with a watershed area of 100 km² or more in the entire Chongqing, including 42 rivers with a watershed area of 1000 km² or more. The eastern, southern and southeastern parts of Chongqing are high. The terrain is mainly mountainous and hilly. Karst landforms are widely distributed, creating unique geological landscapes. The climate type is subtropical humid monsoon climate, characterized by insufficient sunshine, low wind speed, and frequent cloud cover. The average annual temperature is 17.7 °C, with an annual precipitation of 1136.5 mm. In addition, as an important part of the Three Gorges Reservoir Area, the Daba Mountains, the Wuling Mountains, and the Dalou Mountains in Chongqing are not only important for biodiversity conservation but also key areas for water resource conservation, which is pivotal to the maintenance of ecological balance. Due to the interplay of multiple factors, the geological environment in Chongqing and its surrounding areas is complex and dynamic. Conducting deformation slope extraction is essential for understanding and predicting geological hazard risks in this region.

3. Materials

3.1. LT-1 Deformation Data

The LT-1 01 satellite group (Figure 1) consists of two SAR satellites (named A and B), which share identical design parameters. It has six imaging modes, with a maximum resolution of 3 m and a maximum observation width of 400 km. The L-band SAR payload is characterized by its long wavelength, wide coverage, high-density measurement points, high repeat observation frequency and strong vegetation penetration capability, allowing ground investigations under complex conditions. Given the extensive coverage of the study area and the availability of LT-1 SAR imagery, our team prioritized the use of D-InSAR technology for processing the LT-1 SAR data. It not only balances the data processing efficiency and the demand for computational resources but also ensures high accuracy and timeliness of deformation monitoring. For landslide monitoring, the selection of suitable SAR image datasets is the key foundation for D-InSAR, which takes into account not only the topographic characteristics of slopes but also the state of landcover, as well as buildings and infrastructures in the area [34,35]. To ensure the accuracy and reliability of the results, our team carefully selected LT-1 SAR imagery in the preliminary work, considering all relevant factors and image processing methods, and ensured that the images covered the geographic extent of Chongqing. On this basis, the selected images were processed using D-InSAR technology, and a dataset comprising 56 deformation images was successfully constructed. The spatiotemporal distribution characteristics of this dataset are detailed in Figure 2 (e.g., “20230703–20240216” indicates that the data was derived from the processing of imageries captured on 3 July 2023, and 16 February 2024).

3.2. Optical Imagery and 3D Terrain Model Data

The Google Earth platform, with its excellent visualization, data exploration, and powerful data collection and integration capabilities, as well as good interoperability and ease of use [38,39,40], has led to a wide range of applications in various fields [41,42,43]. The satellite images of this platform have sub-meter resolution and can provide rich surface information, which makes this platform a powerful tool for target feature extraction. Different terrains can significantly affect the kinematic processes of landslides, potentially exacerbating their consequences [44,45,46]. A 3D terrain model, on the other hand, more comprehensively reflects topographic characteristics. The combination of satellite imagery and 3D terrain model can enhance the accuracy of extracting deformation slopes. These advantages make these essential tools in modern geological disaster research.

3.3. Influencing Factor Data

Surface deformation is influenced by various factors, including topographic features [47,48], landcover and soil properties [49,50,51], geological factors [52,53], and human activities [54,55]. Additionally, rainfall, as a significant driving force of surface deformation, should not be overlooked [56,57]. Finally, a comprehensive analysis of 12 factors, including elevation, slope position, slope, landcover, soil, lithology, relief, average rainfall intensity, distance to rivers, roads, railways, and active faults (data sources shown in

Table 1. Data source.

Data	Resolution	Source
Active fault	-	Wu et al. [37]
SRTMTPI 90 m resolution topographic position data product	90 m	The data set is provided by Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences. (http://www.gscloud.cn, accessed on 22 August 2024).
Landcover product	30 m	Zhang, et al. [58]
Lithology	-	Dijkshoorn, et al. [59]
Soil
Slope	-	Calculated from DEM.
Relief
Copernicus Global Digital Elevation Model	30 m	European Space Agency [60]
Rainfall data	0.1 degree × 0.1 degree	Huffman, et al. [61]
Railway	-	1:1 million public version of basic geographic information data (2021) (https://www.webmap.cn/commres.do?method=result100W, accessed on 29 August 2024)
Road
River

), will help to assess their impact on deformation slopes.

4. Methods

4.1. Deformation Slope Extraction

Landslides often begin with localized deformations on the slopes. During surface deformation monitoring, if the deformation of a particular area is significantly different from that of surrounding regions, it usually indicates a high-risk potential landslide area. Therefore, we define slopes with unusually high deformation as “deformation slopes”. In this study, we conducted the extraction of deformation slopes based on the obtained LT-1 deformation data. During the extraction process, we combined deformation data with satellite imagery and 3D terrain models from the Google Earth platform, using human–computer interaction methods to identify and extract deformation slopes (Figure 3). Our team has already developed a mature technique for interactive object extraction using satellite imagery and 3D terrain models [62,63,64], and the database established by this method has shown outstanding results in geological disaster research [65,66]. In this study, we overlaid LT-1 deformation data with satellite imagery and 3D terrain models from the Google Earth platform. First, we identified potential deformation slopes (areas with significantly higher deformation than surrounding regions), initially without considering terrain and surface features. Next, we evaluated the surface features using optical imagery and analyzed their terrain characteristics with the 3D terrain model to assess the validity of defining them as deformation slopes. Using geomorphological knowledge, we precisely delineated the boundaries of deformation slopes with satellite imagery and 3D terrain models on the Google Earth platform.

4.2. Importance Ranking of Impact Factors

Literature reviews [67,68,69] indicate that potential landslide source areas generally exhibit subsidence between the initial stage and the actual occurrence of landslides. Based on this, this study uses the maximum subsidence rate on each deformation slope as the key indicator for subsequent analysis, as this indicator is closely related to the critical deformation that triggers landslides. Compared to the mean value, it more effectively represents extreme deformation conditions. We selected the daily rainfall data from GPM IMERG Final Run, and the cumulative rainfall in each area was calculated separately and processed into the average rainfall intensity in millimeters/day for the monitoring period. The Random Forest model [70] was applied to assess the importance of 12 factors affecting deformation slopes. The whole research process is shown in Figure 4.

5. Results

In this study, we extracted a total of 5,836 deformation slopes, covering an area of 140 km², with the largest slope measuring 1.2 km². It was found that the distribution of deformation slopes in Chongqing and its neighboring areas exhibits significant regional differences, as shown in Figure 5. To quantify the concentration trends, we applied the Kernel Density method with a search radius of 100 km. As indicated in Figure 6, deformation slopes are primarily concentrated in the central region of the study area, with a density as high as 0.22%. Additionally, the northeast and southwest regions of the study area also show noticeable concentration trends, although their density is slightly lower than that of the central region. In the western region, deformation slopes are predominantly distributed along the mountains in parallel range-valley area. These spatial distribution patterns reveal the close correlation between surface stability and specific geographic conditions.

As shown in Figure 7, the average daily deformation rate varies across different geographical locations. However, except for Group 20230823–20230920 (in the northeast) and Group 20240224–20240323 (in the southwest), the average daily deformation rates for the remaining groups are relatively low. It is noteworthy that the deformation in Group 20230823–20230920 is particularly significant in a short monitoring period. To better understand the reasons for this phenomenon, we used the GPM IMERG Final Precipitation daily rainfall data to investigate the precipitation from 1 April 2023, to 31 March 2024. Although the monitoring time span of Group 20230823–20230920 is not the longest, the deformation intensity is significantly high. This is closely related to the area’s large terrain relief and high elevation, and it may also be associated with three major rainfall events that occurred during this period (Figure 8). Daily average rainfall intensity of this group revealed a range of 8.6 mm/day to 13.7 mm/day, indicating generally high rainfall during this period. It likely led to soil saturation, which in turn exacerbated landslides and other surface deformation phenomena. In addition, the coverage for this group is mainly located in a geological background of limestone and other carbonate rocks, which are commonly found in Karst landscapes. Karst is prone to the formation of underground cavities and surface cracks [71,72]. A comprehensive consideration of the geological background, rainfall intensity, and topographic features enables a more complete understanding of the mechanisms of deformation phenomena in the region.

The impact of landcover on landslides has been widely studied [73]. As shown in Figure 9A, the deformation phenomenon of subsidence is most prominent in closed evergreen broadleaved forests, which account for the highest proportion of deformation slopes, reaching 50.12%. In contrast, herbaceous cover has the lowest proportion, at just 0.05%. The significant differences in the frequency of deformation slopes across different landcover types can be explained by four key factors: water management, root distribution, biomass, and soil disturbance. Landcover types with high deformation slope frequency (closed evergreen broadleaved forest, closed deciduous broadleaved forest, closed evergreen needle-leaved forest, and rainfed cropland) generally have dense canopy layers that can intercept a large amount of rainwater, causing more water to infiltrate into the soil. This is particularly true during continuous rainfall periods, where the soil is more likely to become saturated and shear strength decreases. Despite the strong transpiration in these forests, the soil moisture remains high in humid climates, which increases the landslide risk. Although the roots of these forests extend deep into the soil, the root density may not be as high as in shrublands, especially in the surface soil, where the stabilizing effect may be insufficient. The roots may be concentrated in certain soil layers, leaving other layers poorly stabilized. In terms of biomass, the decomposition of tree roots after tree death is slow, which may lead to gradual deterioration of soil structure. Additionally, tree death reduces the stabilizing effect of roots on the soil, compromising soil stability. Furthermore, the growth and toppling of trees can disturb the soil structure significantly, potentially triggering landslides. For the rainfed cropland, the soil moisture content is too high, and human agricultural activities also disturb the soil, further reducing its stability. In contrast, landcover types with low deformation slope frequency (shrubland, evergreen shrubland, open evergreen needle-leaved forest, herbaceous cover, irrigated cropland, grassland, and impervious) tend to have less canopy interception, leading to lower water interception, making surface runoff more likely to form and reducing rainwater infiltration. Meanwhile, human water management in irrigated croplands and impervious urban surfaces helps maintain soil stability. In terms of root distribution, shrubland and herbaceous vegetation typically have denser roots, especially in the surface soil, providing better stabilization, and their root distribution is more even, which helps to improve overall soil stability. The biomass of shrubs and herbaceous plants is relatively low, and their roots decompose quickly after death, which has a smaller impact on soil structure. Additionally, their short growth cycles and rapid root turnover enable them to quickly adapt to environmental changes.

The influence of soil properties on surface deformation cannot be ignored [74,75], and its mechanism is complex and geographically significant. Dystric Cambisols have the highest proportion, accounting for 42.2%. This type of soil is located on or beneath the surface of the parent rock, lacking organic matter and biological activity, with a typically high pH and solid structure. The combination of insufficient organic matter, which is not conducive to plant growth, and high water storage capacity but poor drainage makes this type of soil susceptible to deformation. This is followed by Haplic Alisols, which make up 25.5%. These soils have a B layer with low base saturation (the soil with high base saturation has a large buffering capacity for acid and a small buffering capacity for alkali), and their pH is relatively low. In strongly acidic soils, the solubility of elements like aluminum, manganese, and iron increases, which can be toxic to plant roots, inhibiting plant growth. Its soil nutrients are easily lost, leading to low nutrient content and decreased availability of trace elements like phosphorus and potassium, which affects plants’ ability to absorb and utilize nutrients, thus weakening the fixation of soil by plant roots. The solubility of clay particles leads to a loose soil structure, decreased permeability, and increased susceptibility to soil erosion, compaction, and hardening. Highly active leached soils, such as Chromic Luvisols and Haplic Luvisols, show a higher percentage of deformation slopes, possibly due to their high cation exchange capacity and base saturation. Albic Lixisols, although they have lower base saturation, maintain high cation exchange capacity, which helps retain nutrients and facilitates plant absorption, promoting plant growth and improving soil stability. Eutric Cambisols exhibited the most extensive range of deformation, which corresponds to the characteristics of initial soils, as their moisture content is higher than that of Dystric Cambisols, resulting in larger deformation, as expected. These areas are mainly distributed in the mountainous regions of northeastern Chongqing, where high subsidence rates were frequently observed during the monitoring period from 23 August 2023, to 20 September 2023. This indicates a close correlation between the soil type and the deformation (Figure 9C). In rock outcrop areas, due to the thin or absent soil layers, soil is less likely to experience landslides or deformation slopes. Other soil types exhibited lower frequencies of deformation slopes, which may be due to their smaller distribution areas in the study region or their physical and chemical properties making them relatively stable.

Deformation phenomena exhibit significant imbalance in both frequency and intensity across different lithologies. Specifically, as shown in Figure 9B, limestone and other carbonate rocks, due to their porosity and solubility, are more prone to dissolution and erosion under the influence of groundwater, leading to a higher proportion (32%). Following this is shale (25.3%), which is primarily composed of fine clay minerals. These tightly packed mineral particles result in high porosity and low permeability. Additionally, the strength of shale is generally low, especially when saturated with water, making it more prone to deformation under external pressure or vibrations. Sandstone, greywacke, and arkose are in third place with a share of 20.5%. These rocks have a loose structure and are highly permeable, and their stability depends to a large extent on the solidity and uniformity of the cementing material. If the cementing material is not strong enough or is unevenly distributed, the strength of these rocks decreases, which makes them more susceptible to deformation under water or load. Quartzite, with its high chemical stability, dense structure, and strong resistance to weathering, has the lowest proportion (only 0.02%). Although quartzite is extremely hard, it is also brittle, meaning it may fracture suddenly when subjected to stress exceeding its breaking strength rather than undergoing gradual deformation.

Surface deformation is primarily concentrated on the middle slope (Figure 9D,

Table 2. Topographic position data product description (https://www.gscloud.cn/sources/details/308?pid=302, accessed on 24 December 2024).

Topographic Position	Explanation
Ridge	TPI > 1 SD
Upper slope	0.5 SD < TPI ≤ 1 SD
Middle slope	−0.5 SD < TPI < 0.5 SD, slope > 5°
Flat slope	−0.5 SD < TPI < 0.5 SD, slope ≤ 5°
Lower slope	−1 SD < TPI ≤ −0.5 SD
Valley	TPI < −1 STDV

), which accounts for 60.3%, significantly higher than other types. The lower slope follows with 18%, while the flat slope has the lowest proportion at only 1.7%, indicating the stability of flat terrain. Although ridges are generally considered topographic highland, their proportion is relatively low at just 3.3%. This suggests that deformation slopes are not solely controlled by terrain but the combined effects of various factors. There is a significant positive correlation between relief and landslide magnitude [76]. This finding highlights the importance of topographic features in influencing surface stability, revealing the complex relationship between topography and surface deformation processes. According to Figure 9E, deformation slopes are primarily concentrated in areas with a terrain relief between 80 m and 180 m. Additionally, within this range, the frequency of larger daily deformation rates significantly increases. Higher terrain relief typically indicates more active geological movements and more vulnerable geological structures. The concentration of precipitation due to steep terrain not only increases the saturation of the soil but reduces the surface’s resistance, thus exacerbating the risk of deformation. The surface deformation is prominent in the slope of 20~40° (Figure 9F). The deformation risk increases with slope, but the relationship between them is not simply positive. In the 400 m to 1200 m elevation range, deformations are more concentrated. It was observed that larger daily deformation rates frequently appear within the 700 m to 800 m elevation range (Figure 9G), suggesting that these regions may be potential high-risk zones for landslides.

In the context of climate change, rainfall has become an increasingly significant factor influencing geological hazards [77,78,79]. The data show that the highest number of deformation slopes were produced when the average daily rainfall intensity reached 2–4 mm/day (Figure 9H), showing that this rainfall intensity is usually more likely to trigger deformation. In contrast, smaller daily rainfall intensities result in lower daily deformation rates. Higher rainfall intensities did not lead to a higher frequency of deformations, likely because larger rainfall events are relatively rare and their impact on deformation is not as significant as that of moderate-intensity rainfall. Under the influence of topography, rainfall significantly impacts deformation. On windward slopes, as altitude increases, air is lifted by the terrain, causing it to cool and condense, forming precipitation. This intense rainfall effect not only increases soil moisture but can also weaken the cohesion between soil particles, triggering deformations. Within a given elevation, rainfall on windward slopes is typically positively correlated with altitude, and the resulting moist environment further exacerbates soil instability. In contrast, leeward slopes often exhibit characteristics opposite to those of windward slopes due to the Foehn effect. When air descends along the leeward slope after crossing a mountain, it warms rapidly, and humidity drops significantly. This dry environment helps mitigate soil erosion and reduce the risk of landslides. The differences between these two types of slope aspects highlight the role of topography in regulating rainfall distribution and intensity and reveal the complex driving mechanism of rainfall in geologic deformation. The permeability, porosity, and water absorption capacity of rocks significantly influence deformation under rainy conditions. Highly permeable rocks, such as sandstone and conglomerate, allow rainwater to infiltrate rapidly into the ground, reducing surface runoff and soil erosion. However, this infiltration can also raise the groundwater level, increase soil pore water pressure, and decrease slope stability. Conversely, impermeable rocks like shale retain water, leading to its accumulation. This water softens the soil at contact layers, dramatically reducing the shear strength at rock layer interfaces. High-porosity rocks can absorb more water, increasing their weight while reducing their strength, thereby promoting deformation. Some rocks undergo significant volumetric expansion after water absorption, which can disrupt their structure and trigger deformation. Non-expansive rocks (like quartzite) exhibit minimal volumetric changes after water absorption and therefore have less influence on deformation. Additionally, the chemical composition and structure of rocks can change under the influence of rainfall. Rocks containing easily soluble minerals may become more fragile due to chemical erosion from rainwater, further affecting their stability and tendency to deform.

The number of deformation slopes shows a significant correlation with the distances to active faults, rivers, roads, and railways, a finding that aligns with previous research [80,81,82], highlighting the key role of geological tectonic movements, river erosion, and human engineering activities in surface deformation. As these distance decreases, the number of deformation slopes gradually increases, especially in areas close to active faults, rivers, and roads, where the highest daily average deformation rates are observed (Figure 9I–K). This suggests that these regions have poorer surface stability and higher deformation risk. Maximum daily deformation rates were observed in areas farther from railways (Figure 9L), further proving that surface deformation is a complex phenomenon influenced by multiple factors.

In the analysis of deformation slope data, a systematic ranking of factor importance was conducted. The Increase in Mean Squared Error (Increase in MSE) metric was used, which indicates that when a variable is removed, if the accuracy of the target prediction significantly decreases, it can be inferred that the variable contributes highly to the accuracy of the prediction. Considering both statistical significance and Increase in MSE, this study found that average rainfall intensity has a particularly significant impact on deformation slope (Figure 10). Additionally, landcover, soil, lithology, distance to rivers, distance to roads, distance to railways, and distance to active faults also showed statistical significance. These factors work together through various physical processes, such as tectonic movements, fluvial erosion and deposition, human engineering activities, and climate change, and are key to predicting and managing surface deformation. Both variables of distance to roads and railways contribute to slope stability by affecting the stability of the foot of the slope. However, we found that the distance to railway had a more prominent effect on slope stability. This difference may be attributed to the fact that more stringent and extensive terrain modification and stabilization measures are generally implemented during railway construction, and these engineering activities may have a more profound effect on the stability of adjacent slopes. The higher frequency of heavy loads in rail transportation and the vibrations and pressures generated by trains on the tracks far exceed the effects of road vehicles on the roadbed, which undoubtedly exacerbates the risk of deformation of slopes. At the same time, railway maintenance work is usually more frequent and the implementation of standards is more stringent, while maintenance process operations, such as blasting operations, may cause additional vibration and deformation of the slope, further affecting its stability.

6. Discussion

6.1. Differences Between Deformation Slopes, Potential Landslide Hazards, and Slope Deformation

This study focuses on the extraction of deformation slopes in Chongqing and its surrounding areas, providing preliminary identification of potential landslide zones, with the goal of exploring slope stability in this region. This work holds significant practical implications for disaster early warning. Through comprehensive analysis of deformation slopes, we can not only reveal the underlying mechanisms influencing slope stability but also provide crucial data support for risk management. Potential landslide hazards are typically considered as slopes that are undergoing deformation and pose a potential threat to human. It is important to note that the identification of potential landslide hazards largely takes into account the extent of human activities, and the direct use of such data in geohazard studies may introduce biases. Slope deformation extraction, on the other hand, primarily focuses on specific regions where deformation occurs on the slope. This type of data is more suitable for exploring the mechanisms behind surface deformation and providing theoretical support for geological disaster research. However, since deformation does not always correlate with landslides, such data has limitations in landslide early warning field. In engineering site planning, deformation slope data and slope deformation data can be used in combination. During the early stage, deformation slope data helps quickly eliminate high-risk areas, providing an initial safety filter for the overall layout. Slope deformation data is more suitable for smaller-scale assessments, providing a basis for detailed site selection and design. Future research can explore on this basis from multiple perspectives in order to improve the understanding and early warning capability of geohazards.

6.2. Challenges and Application Potential of LT-1 Deformation Data

This study is based on deformation data generated by LT-1 SAR data. We noticed that LT-1 data could be affected by orbital instability or other reasons, resulting in occasional missing data for certain dates within the same orbit, as well as inconsistencies in the image coverage. To some extent, these issues have resulted in image vacancies in certain regions. These areas account for less than 10% of Chongqing and are primarily located in flatter terrain. Therefore, the impact of these vacancies across Chongqing is within an acceptable range. Some of the LT-1 data have large errors due to orbital instability, which in turn cause some bias in the geocoding process. However, data with stable orbits did not exhibit such matching issues. Despite the challenges encountered in using LT-1 data for monitoring, LT-1 still holds great potential.

The deformation data used in this study were obtained through D-InSAR technology, which measures centimeter-level surface deformation by processing phase information from two SAR images covering the same area at different times. However, it is susceptible to temporal and spatial phase uncorrelation in regions with large displacement gradients, leading to measurement errors. Additionally, D-InSAR is less sensitive to slow deformations, and weak signals may be obscured by atmospheric interference. Influenced by topographic and atmospheric noise, errors may be introduced [83,84], reducing the precision of results. If multi-period data is available to meet the requirements of time-series InSAR monitoring over time, the problems of spatio-temporal incoherence and errors caused by topography and atmosphere will be effectively mitigated, and more reliable deformation monitoring results can be provided [85,86,87].

In the application of geological disaster monitoring, the L-band of the LT-1 satellite demonstrates superior vegetation and soil penetration capabilities compared to the C-band of Sentinel-1, making it more suitable for large-scale surface deformation monitoring. Wang, et al. [88] utilized 31 LT-1 images and 12 Sentinel-1 images from the same time period to monitor surface deformation in Maoxian County, Sichuan Province, China. The results showed that both LT-1 and Sentinel-1 data aligned well with ground leveling measurements, though some discrepancies existed. As deformation increased, the monitoring results of Sentinel-1 SBAS-InSAR gradually became distorted, while LT-1 data remained consistent with the leveling measurements. In the high-altitude forested areas of Maoxian County, the deformation detection accuracy of LT-1 data was approximately 64.7% higher than that of Sentinel-1. Zhang, et al. [89] used four LT-1 satellite Stripmap Mode 2 (12 m resolution) images to monitor surface deformation in the permafrost regions of the Tuotuo River Basin on the Qinghai-Tibet Plateau. The results revealed that LT-1 SAR successfully detected deformation differences between alpine meadows and alpine deserts. Compared to Sentinel-1A data, LT-1 SAR data achieved higher monitoring point density, particularly in alpine meadows, alpine wetlands, and thermokarst terrains. The integration of L-band and C-band for surface deformation monitoring introduces a new direction for remote sensing geological disaster research. Combining the unique advantages of both bands allows the results to complement and validate each other, significantly improving monitoring accuracy. This comprehensive approach enables more precise detection and quantification of surface deformation, facilitating broader and more detailed monitoring. It provides strong technical support for the early warning and prevention of geological disasters.

In this study, we were unable to conduct field investigations to directly validate the deformation results obtained from remote sensing techniques. This was primarily due to the extensive scope of the study area, making comprehensive field surveys prohibitively expensive in terms of manpower and resources, which is unrealistic given the current research constraints and time limitations. Field validation is crucial in scientific research, and the absence of such investigations may have multiple implications for research. Without on-site data, we cannot directly compare the remote sensing results with real-world conditions, potentially leading to inaccuracies in the estimation of deformation magnitude. Moreover, field surveys provide critical information about soil properties, rock structures, and other factors essential for accurately interpreting the causes of landslides. D-InSAR technology, as a well-established remote sensing method, has been widely recognized and applied in the field of surface deformation monitoring. It offers high-precision deformation data over large areas, and our study leveraged this technology to its full advantage. During the extraction of deformation slopes, we not only utilized D-InSAR deformation data but also integrated high-resolution optical imagery and 3D terrain models. This multi-source data analysis significantly improved the accuracy and reliability of our results. We emphasize the value of remote sensing technology in detecting surface deformation and demonstrate how multi-source data fusion can enhance the confidence in the results. Despite its limitations, our methodological approach provides an efficient and cost-effective pathway for surface deformation monitoring. Future studies could build on this foundation by incorporating field survey data to further validate and deepen our findings.

6.3. Limitations of Data Processing Methods

Since the cumulative deformation spans different time periods, it is necessary to process these data appropriately. We drew valuable insights from time-series InSAR technology. Specifically, the mathematical models for Stacking [90], PS phase models, and SBAS function models [91] all suggest that deformation rates are closely related to the cumulative phase difference and the corresponding monitoring duration. Based on this idea, we chose to convert the cumulative deformation into (linear) deformation rates to more accurately reflect the dynamic changes in deformation slopes. We realized that the relationship between cumulative deformation and time is not simply linear [92,93,94]. Therefore, this study opted for daily average deformation rates rather than annual deformation rates. The core aim of this choice was to avoid larger errors introduced by a long duration. However, the limitations of this method should not be overlooked. We look forward to obtaining more LT-1 data in the future to conduct surface deformation studies with unified time scales, which will provide a more solid foundation for subsequent research.

6.4. Research Prospects

In this study, the extraction relied on manual identification. Although this method allows for an intuitive recognition of surface deformation features, its limitations are also quite evident. Manual extraction is susceptible to subjective factors, which may lead to the omission of certain regions, thus affecting the completeness and accuracy of the dataset. To address this issue, we formulated detailed operation guidelines and standards at the early stage, and the whole process was operated by only one person to ensure that the staff always followed rules. After that, we verified the data for consistency, and the results showed that all data exhibited high geographic plausibility. The manual extraction method becomes less efficient when applied to large-scale monitoring areas. The time and labor costs required for it are high, limiting its potential application in rapid response and real-time monitoring [95,96]. Therefore, more efficient and accurate automated extraction techniques are needed in future research [97,98].

In surface deformation studies, triggering events such as earthquakes [99,100,101] and rainfall [102,103,104] are typically considered as factors leading to deformation. These events often occur rapidly and can significantly affect the stability of slopes. Additionally, gravity, as a continuously acting influence, plays an essential role in geological environments. It not only affects slope stability but also has a profound impact on deformation characteristics and processes [105,106]. During the monitoring period, no seismic events occurred, so earthquake-related factors were not considered. Due to limitations in data availability and other technical constraints [107,108,109], gravity-related factors were also not included. Therefore, we focus on the effect of rainfall on surface deformation, aiming to reveal its key role in the entire process. In the future, we can integrate gravity and other potential factors to achieve a more comprehensive understanding.

7. Conclusions

In this study, based on the deformation data generated from LT-1 SAR data, the extraction of deformation slopes in Chongqing and its surrounding areas in China and the analysis of the influencing factors on deformation slopes were successfully realized. The results identified 5863 deformation slopes with a total area of 140 km², primarily concentrated in the central part of the study area, with an area density as high as 0.22%. The deformation intensity was especially high in the period from 23 August to 20 September 2023, likely related to three significant rainfall events, as well as the lithology and geomorphology of the region. Closed evergreen broadleaved forests had the highest number of deformation slopes, potentially due to factors such as humidity changes caused by dense canopies. Additionally, limestone and other carbonate rocks, due to their porosity and solubility, were significantly affected by groundwater, leading to notable deformation. The deformation slopes were mainly distributed in the middle slope and 20–40° slope, and the deformation rate was generally high at the elevation of 700–800 m. The number of deformation slopes was significantly associated with distances from active faults, rivers, and transportation networks. Using Random Forest model to rank the importance of these factors, we found that average rainfall intensity had the most significant impact on deformation slopes. In the future, acquiring more LT-1 imagery to establish long-term surface deformation monitoring will help reveal long-term deformation patterns, providing critical guidance for geological assessment, urban planning, land use, and disaster prevention. Moreover, the development of more efficient and accurate automated extraction techniques will improve the efficiency and precision of target feature extraction, providing technological support for disaster emergency response.