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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72391
10.1038/s41598-024-72391-2
Article
Identification of potential landslide in Jianzha county based on InSAR and deep learning
Yang Xianwu xianwu82@163.com

123
Chen Dannuo 1
Dong Yihang 1
Xue Yamei 1
Qin Kexin 1
1 https://ror.org/0190x2a66 grid.463053.7 0000 0000 9655 6126 School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
2 https://ror.org/0190x2a66 grid.463053.7 0000 0000 9655 6126 Henan Key Technology Engineering Research Center of Microwave Remote Sensing and Resource Environment Monitoring, Xinyang Normal University, Xinyang, 464000 China
3 https://ror.org/0190x2a66 grid.463053.7 0000 0000 9655 6126 Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
12 9 2024
12 9 2024
2024
14 2134626 6 2024
6 9 2024
© The Author(s) 2024
2024
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Landslide disasters have characteristics of frequent occurrence, widespread impact, and high destructiveness, posing serious threats to human lives, property, and the ecological environment. Timely and accurate early identification of landslides remains an urgent issue within the disaster prevention field. This study focuses on Jianzha County, Qinghai Province, integrating PS-InSAR, SBAS-InSAR, and optical remote-sensing techniques to delineate potential landslide-prone areas. Utilizing Google Earth imagery and existing landslide datasets, potential landslide points were identified through a deep learning model. Results indicate the following: (1) In Jianzha County, the variation trend of the average surface velocity monitored by PS-InSAR and SBAS-InSAR technology is consistent, and the deformation monitoring results are reliable. (2) Utilizing the deep learning model, 56 potential landslide points were identified, comprising 39 high-risk points and 17 medium-risk points. By integrating the spatial distribution data of historical geological disaster points, 10 out of 13 previously occurred landslide disaster points were found to be located at the identified high-risk landslide points, achieving a detection accuracy of 76.92%. (3) The spatial distribution of landslide points exhibits clustering, with slopes ranging from 10° to 40°, elevations between 15 and 30 m, and slope orientations predominantly toward the northeast. (4) Landslide formation is correlated with seasonal precipitation concentrations and temperature fluctuations. This method can provide a crucial basis for large-scale surface deformation monitoring and early identification of landslide risks.

Keywords

InSAR
Landslide identification
Visual analysis
Deep learning
Subject terms

Natural hazards
Environmental impact
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Geological hazards such as landslides occur frequently in Jianzha County, Qinghai Province, severely impacting the ecological environment and social stability. Consequently, landslides must be promptly and accurately identified early, and effective preventive and responsive measures should be implemented. Owing to various inducing factors of landslide disasters, such as river scouring, heavy rainfall, earthquake, human activities, and so on, it will not only cause serious casualties, property losses, traffic interruption, environmental damage, and other direct hazards but also produce secondary disasters such as debris flow and dammed lake1, which highly restricts the development and utilization of resources2. Landslides are characterized by sudden onset, high difficulty of management, and frequent occurrences in groups3, all of which complicate the early identification of landslide hazards. Timely, accurate, and efficient early identification of mountain landslides has become a pressing issue in the field of disaster prevention.

Field surveys and regular updates of landslide inventory maps are conventional methods for investigating potential landslides. However, relying solely on traditional manual field surveys or expert interpretation introduces subjective biases and consumes considerable resources. Additionally, these methods exhibit inefficiencies and inaccuracies in identifying and monitoring potential or subtly moving landslides4–6, making widespread implementation challenging. By contrast, optical remote-sensing data offer extensive coverage and high identification accuracy, enabling the identification of landslide locations and areas with considerable deformations, thereby improving the accuracy of potential landslide monitoring. Consequently, landslide monitoring using optical imagery has gained substantial attention. However, optical remote-sensing data alone are insufficient for detecting potential landslides with minor deformations and no destructive hazards7.

Radar data exhibit features of continuous operation, all-weather capability, and high precision. Research has illustrated their expansive application prospects and growth potential in surface deformation detection. Interferometric synthetic aperture radar (InSAR) techniques, a novel spatial surface measurement technology, remain unaffected by weather conditions and offer benefits such as high precision, low cost, extensive coverage, and continuous operation, rendering it widely adopted for automated landslide hazard monitoring. Persistent Scatterer InSAR (PS-InSAR) and small baseline subset InSAR (SBAS-InSAR) are commonly used in long time series InSAR Technology8–18. PS-InSAR mitigates temporal-spatial baseline self-coherence and atmospheric interference in surface deformation monitoring over extended periods. SBAS-InSAR inherits the advantages of D-InSAR technology and enables the acquisition of extensive and contiguous surface deformation information. Relying solely on PS InSAR or SBAS InSAR technique for potential landslide hazard identification entails uncertainties. Integrating both techniques can somewhat enhance identification outcomes. Elevation data are utilized in InSAR data processing for interferogram unwrapping, image registration, and geocoding, to name a few; they are also employed for extracting subsequent terrain factors to further delineate landslide-prone areas. Utilizing multi-source remote-sensing technology to study the dynamic changes in the development of landslide-type debris flows19,20. Wu21 utilized InSAR technology and optical remote sensing for early identification and monitoring of landslides in Guizhou Province, a crucial step in improving the area's geological disaster prevention and control capabilities. Piroton22 utilized a combination of drone-based imagery, radar, and optical remote-sensing technologies to detect terrain elevation changes associated with rapid and slow surface displacements, along with meteorological analyses, to identify triggering conditions leading to slope instability. Casagli23 described the application of remote-sensing techniques (RSTs) in landslide analysis and management; the analysis showed that remote-sensing technology has a huge potential in landslide detection, monitoring, and prediction. Bouali24 utilized Synthetic Aperture Radar (SAR), optical remote sensing, and Global Positioning System (GPS) to quantify the incremental and average deformation of the Portuguese Bend landslide in California from 2007 to 2017.

While machine learning methods are widely applied in landslide modeling owing to their simplicity and strong interpretability25, their limited capability to explore correlations between input variables hampers their ability to extract deep features from the data. Furthermore, these models lack the autonomy to perform feature learning26. Different from traditional machine learning methods, Convolutional Neural Networks (CNNs) are machine learning model containing multiple convolution layers and pooling layers, which reflect the structure of human brain neural network. By emulating the neural networks of the human brain, CNNs can comprehend learning objectives and automatically analyze data, facilitating the identification of landslide-prone areas in larger scenes without the requirement for visual interpretation27–34. Through extensive training of a large number of data sets, deep features can be obtained from complex data.

Currently, identifying landslide disasters through intelligent means has become a prevailing trend. However, research scarcely focuses on using deep learning networks to identify landslides by combining InSAR technology and optical remote-sensing technology. Leveraging multi-source databases and intelligent methods is crucial to construct high-precision models tailored for potential landslide disaster identification in this region, thereby enabling the intelligent identification of large-scale landslide distribution.

This study, grounded in terrain visibility analysis, aims to integrate PS-InSAR, SBAS-InSAR, and optical remote-sensing technologies to delineate potential landslide areas. Subsequently, by employing Google imagery in conjunction with deep learning models, it seeks to identify potential landslide points. Finally, the study integrates terrain, precipitation, and temperature data to analyze the distribution patterns and underlying causes of landslide development. This research provides novel approaches for the large-scale, automated early identification of mountain landslides and landslide susceptibility mapping, largely contributing to disaster prevention and mitigation efforts.

Materials and methods

Study area

Jianzha County (101°38′–102°06′E, 35°40′–36°10′N) is located in the southeast of Qinghai Province, with a total area of 1,714 km2 and an altitude of 1995–4263 m. It is located in the transition zone between the Qinghai Tibet Plateau and the Loess Plateau in China35. The Yellow River traverses the county from north to south, extending 96 km within the county boundaries; it also exhibits multiple river terraces and basin-hill landforms characterized by complex geological conditions. The terrain of the study area is high in the South and North, low in the middle, high in the West, and low in the East. The unique geological conditions, landform environment, climatic influences, and issues of land desertification in the upper reaches of the Yellow River have led to the development of numerous landslide hazards in this region. Landslide hazards in Jianzha County are predominantly found near the Yellow River, particularly in Kanbula, Nengke, Cuozhou, and Jianzha Township. Figure 1 illustrates the study area in Jianzha County.Fig. 1 (a) Location of Jianzha County in Qinghai Province. (b) Image, water system, and highway distribution of the study area; (c) digital elevation model of the study area. The figure was created using ArcGIS 10.6 (https://www.esri.com/en-us/home).

Data collection and processing

The Sentinel-1A data utilized in this study were sourced from NASA ASF (https://search.asf.alaska.edu/), while the Precise Orbit Data (POD) were procured from the Copernicus Data Space Ecosystem (https://dataspace.copernicus.eu/). A dataset comprising 60 monthly images, spanning from January 2018 to December 2022, was selected as the primary data source. All data were acquired along the same ascending orbit, featuring a spatial resolution of 5 m × 20 m, a 12-day revisit period, C-band (wavelength 5.6 cm), an incidence angle of 37.13°, and the Interferometric Wide (IW) swath imaging mode with VV polarization. The precise orbit data effectively eliminate the orbital errors associated with Sentinel-1A. High-resolution Google satellite images (3 m spatial resolution) from 2020 to 2022 were employed to construct a landslide recognition model using a deep learning network, thus providing substantial support for feature learning with an extensive landslide sample dataset. The dataset used for deep learning examples is the Bijie_Landslide_Dataset, curated by Wuhan University. The dataset data are obtained from TripeSat satellite data with 0.8 m resolution in panchromatic band and 3.2 m resolution in multispectral band. This study primarily utilizes 770 landslide images and their corresponding binary mask data derived from satellite imagery. In these masks, landslide sample values are uniformly set to 0, while non-landslide sample values are set to 1.

The SRTM DEM was obtained from USGS (https://earthexplorer.usgs.gov/), utilizing SRTM1 data with a spatial resolution of 30 m as elevation reference data. These data were used for geographic coordinate reference in surface deformation and extraction of environmental factors influencing landslide development. Historical landslide validation data were sourced from the Spatial Distribution of Geological Disaster Points dataset provided by the Chinese Academy of Sciences' Resource and Environmental Science and Data Center (https://www.resdc.cn/). Lithological data were obtained from ISRIC (https://www.isric.org/) for the lithological analysis of landslide hazard point distribution. Precipitation and temperature data were obtained from the National Earth System Science Data Center (https://www.geodata.cn/) with a spatial resolution of 1 km. The possible effects of precipitation and air temperature on landslide surface deformation were investigated using monthly data from 2018 to 2022.

Methodology

This paper proposes a method for identifying potential landslide points based on InSAR technology and deep learning techniques. The main approach is as follows: First, the visibility of the area is analyzed by combining SAR imagery and DEM data to verify the feasibility of InSAR technology. Second, the PS-InSAR technology and SBAS-InSAR technology were used to delineate a large area of surface deformation from sentinel-1a images of 60 scenes in Jianzha County from January 2018 to December 2022. Given the close correlation between surface deformation areas and potential landslide hazards, deep learning and visual interpretation are applied to Google imagery to identify potential landslide distribution points within these areas. Finally, the topographical features of landslide development in the region are extracted using known landslide points. The topographical characteristics of historical landslide points serve as the basis for assessing landslide hazard levels. Points matching InSAR surface deformation characteristics, deep learning identification features, and topographical attributes are classified as high-hazard landslide points; meanwhile, the remaining points are categorized as medium-hazard landslide points. Finally, the factors contributing to landslide deformation are analyzed by integrating topographical, precipitation, and temperature data. Figure 2 illustrates the technical workflow as follows:Fig. 2 Flowchart of the methodology.

InSAR deformation monitoring

InSAR is an advanced space-to-earth observation technology. The radar system interferes with the changing phase information generated by more than two multi-temporal SAR images in the same area and can finally obtain a large range, high precision, and high-resolution surface deformation information in the time series9,21,36. Owing to the side-looking imaging of SAR sensors, the incident angle formed when the radar hits ground objects affects the image quality. As the incident angle decreases, the image's echo signal is enhanced, resulting in brighter pixels37. This unique imaging method makes the radar images highly susceptible to displaying anomalies. When the slope substantially changes, illuminating the back slope is difficult for the beam, consequently causing difficulty for the sensor to receive the reflection of the ground object on the back slope, and the shadow area will be shown on the map. In mountainous regions with considerable relief degree, these phenomena will become increasingly pronounced, potentially leading to visual blind spots. To mitigate the impact of these phenomena on landslide point identification, a visibility analysis of the area must be conducted prior to detecting potential landslides using the PS-InSAR and SBAS-InSAR technologies.

The fundamental principle of PS-InSAR involves statistically analyzing the amplitude information of radar images from the same area and identifying permanent scatterers that remain unaffected by temporal and spatial baseline decorrelation and atmospheric delays to perform phase modeling and deformation calculation12,38. For N + 1 SAR images of the study area, the primary image is selected on the basis of image quality and the distribution of spatial and temporal baselines, and differential interferometric processing is performed on the registered images to generate N interferograms. However, this technique may result in poor coherence for some interferometric pairs due to long spatial and temporal baselines when selecting one image as the common primary image and using the remaining images as secondary images. In mountainous regions with substantial relief degree land surface and low coherence, supplementary techniques are required to validate the deformation monitoring results of PS-InSAR. SBAS-InSAR is an InSAR time series method based on multiple master images. Using the singular value decomposition (SVD) method, the spatial small baseline subset data are combined into a time series to compute the least squares solution within the subset11,39. Finally, the residual phase is used to invert the atmospheric phase and non-linear phase to derive the deformation time series for the given time phase. If the study area has N + 1 SAR images, the registered images are arranged in chronological order and divided into several sets based on spatial–temporal baselines for differential interferometric processing, resulting in M interferograms. The resulting M interferograms must satisfy the following constraint:1 N + 12≤M≤N (N + 1)2.

The expression for the interferometric phase of the i-th interferogram pair is2 φi=φtopo+φflat+φorb+φdef+φatm+φscat+φnoise,

3 φtopo=-4πB⊥hλRsinθ,

4 φflat=-4πB⊥λ,

In the equation, φtopo represents the topographic phase; φflat represents the flat-earth phase; φorb represents the orbital error phase; φdef represents the deformation phase, which includes both deformation and non-deformation components; φatm represents the atmospheric error phase; φscat represents the phase due to changes in point target scattering characteristics; and φnoise represents the noise phase. B┴ represents the perpendicular baseline length, h represents the elevation error, λ represents the radar wavelength, R represents the slant range, and θ represents the incidence angle.

Landslide recognition based on deep learning

This study utilizes a deep learning framework based on the open-source Python machine learning library PyTorch to build and train a ResNet50 network model for learning landslide features. Figure 3 shows the model structure of ResNet50 residual network. The process from input image to output result goes through 5 stages and 50 layers. There are 49 convolution layers, 1 fully connected layer, and 2 pooling operations. After extracting classification information, the trained network is validated using test data. Landslide classification divides the sample set into landslide and non-landslide targets, i.e., a binary classification, and learns to delineate landslide boundaries. During the adjustment of learning rate and loss weight, the optimal parameter combination is iteratively updated to obtain the best-trained network. Finally, the landslide recognition model is evaluated using binary classification metrics: Precision, Accuracy, and Recall. Precision represents the accuracy of the positive part predicted by the model; Accuracy refers to the accuracy of the model. The higher the accuracy, the better the model effect; Recall rate is the correct proportion predicted by the model. The calculation formulas are as follows:5 Precision = TP/(TP + FP),

6 Accuracy = (TP + TN)/(TP + TN + FP + FN),

7 Recall = TP/(TP + FN),

where True Positive (TP) represents the number of samples correctly predicted as positive by the model; False Positive (FP) represents the number of samples incorrectly predicted as positive by the model; True Negative (TN) represents the number of samples correctly predicted as negative by the model; and False Negative (FN) represents the number of samples incorrectly predicted as negative by the model.Fig. 3 ResNet50 network structure diagram.

Results

Visibility analysis

Spaceborne radar satellites, due to their side-looking imaging mode, often result in images with layover, shadow, and foreshortening effects when observing the ground using radar beams. Based on elevation data of the study area, the county exhibits considerable elevation differences, with variations reaching over 20,00 m. The dramatic topographical relief makes the area highly susceptible to tropospheric atmospheric delays when monitored using InSAR technology. In this region, the satellite incidence angle is 37.13°40. The visibility classification standards are based on Sentinel-1A's incidence angle and topographical factors, dividing the area into the following three categories: non-visibility, low sensitivity, and high visibility. Non-visibility areas are primarily located on slopes facing east, southeast, and northeast. When the slope angle is less than 37.13°, perspective shrinkage will occur; when the slope angle is greater than 37.13°, overlap will occur. Visibility areas include high visibility and low sensitivity regions. High visibility areas are primarily on slopes facing west, southwest, and northwest, where shadowing occurs when the slope angle exceeds 52.87°. Low sensitivity areas are primarily on the south and north slopes, which are less sensitive to surface deformation. Figure 4 illustrates the classification of visibility types and the geometric distortion areas of the study region. From the mapping results, visibility areas cover 1252.42 km2, accounting for 73.07% of the area. Layover and shadow areas are sparse, covering only 6.14 km2 (0.36%). Foreshortening areas are mostly on southeast-facing back slopes, covering 455.41 km2 (26.57%). Historical landslide point distributions reveal that landslides frequently occur in valleys and low mountain hills near the Yellow River. These areas are minimally affected by layover, shadow, and foreshortening, indicating that the selected ascending radar imagery has good visibility and high reliability for landslide monitoring.Fig. 4 Geometric distortion map of study area.

PS-InSAR deformation monitoring results

The image from January 4, 2020 was selected as the primary image, with the remaining 59 images serving as secondary images. They were used to generate SAR data pairs and connection graphs for subsequent differential interferometry processing. The temporal baseline between the primary image and all secondary images ranges from − 133 to 131 days, and the spatial baseline ranges from − 92 to 131 m, both below the critical baseline. The longest temporal baseline is 133 days, corresponding to the image from June 10, 2022. PS-InSAR involves individually processing each interferometric pair for registration and interferometry, resulting in unwrapped phase maps of the residual phase. During registration, the secondary images are aligned with the primary image, with a range-to-azimuth ratio set at 4:1. After completion, quick-look images are examined to verify the registration and interferometry results of all pairs. Interferometric pairs with poor unwrapping or coherence are excluded, ensuring that only correctly processed pairs are used. The first inversion method automatically selects the reference points with minimal deformation and high coherence and then analyzes the phase changes over the time series to obtain displacement rates and residual elevation. The second inversion converts the phase shifts from the first inversion results into deformation information in the geographic coordinate system, yielding the final deformation rates. The deformation data from the first and second inversions are geocoded to produce a PS point vector file, which is then interpolated to generate a point target deformation rate map (Fig. 5).Fig. 5 PS-InSAR deformation rate distribution.

The line-of-sight (LOS) deformation rate in the study area, as measured by PS-InSAR technology, ranges from − 68 to 28 mm/a. From the deformation rate distribution map, the PS-InSAR deformation monitoring area is observed to be primarily located in relatively flat valley areas near the Yellow River channel. Fewer valid PS points are extracted from densely vegetated areas, valleys with foreshortening effects, and mountainous regions, rendering the assessment of surface deformation a challenge. Considerable subsidence is observed in the northern and eastern directions in towns such as Kanbula, Kangyang, Dangshun, and Maketang, while localized uplift is evident in the Cuozhou Township. These contrasting phenomena arise due to the concentrated rainfall in the study area, leading to extensive overall subsidence caused by water erosion. Additionally, the expansion of human activities prompts the construction of production-friendly structures near residential areas, resulting in scattered local uplift.

SBAS-InSAR deformation monitoring results

To ensure the highest quality of interferograms and improve result accuracy, the maximum temporal baseline threshold was set to 120 days and the maximum spatial baseline threshold to 2%. The image from August 7, 2020 was selected as the super master image, with the remaining 59 images serving as secondary images, resulting in 228 freely combined interferometric pairs. The temporal baseline between the super master image and all secondary images ranges from − 116 to 106 days, and the spatial baseline ranges from − 110 to 152 m. Image pairs with short temporal and spatial baselines undergo SBAS-InSAR inversion after interferometric processing. Statistical analysis of the connected pairs reveals that the maximum number of pairs for a single image is 11; whereas the minimum is 1, which is sufficient to meet the landslide identification requirements for Jianzha County. The SBAS-InSAR interferometric processing can flatten, filter, and unwrap the phase of the image pairs. Similarly, the ratio of distance direction to azimuth direction is set to 4:1. Subsequently, the topographic phase is removed on the basis of DEM data, and a polynomial model (Goldstein) is employed for filtering. Given that the study area is a mountainous area with low coherence, 3D unwrapping is not carried out, and the minimum cost flow is selected. Finally, interference pairs with poor unwrapping effect and coherence are eliminated.

Using the selected control points as a reference, residual phase and phase ramps remaining after unwrapping are removed, and finally, the unwrapped phase is converted into elevation or deformation values. The introduction of precise ground control points can effectively remove the residual constant phase and flat-earth effect, thereby enhancing the validity of the results. The selection of ground control points should first ensure uniform coverage over a wide area with high coherence, good unwrapping results, and stability; residual topographic areas, phase jump regions, and deformation stripe areas should be avoided as much as possible12. Given that the GCP files obtained from PS-InSAR cannot be directly used for SBAS-InSAR processing, automatically selected and geocoded GCP points from the PS-InSAR technique are chosen here. This process reduces subjective errors in manually selecting control points in the SBAS-InSAR method while enhancing the accuracy of deformation estimation. Figure 6a shows the distribution of GCP points. The distribution of GCP points indicates that the 266 selected GCP targets are uniformly distributed within the study area and are mostly located in high-coherence urban regions, which align with the actual conditions of the study area. The deformation data obtained from SBAS-InSAR are geocoded to produce a deformation rate map (Fig. 6b).Fig. 6 (a) GCP point selection of SBAS-InSAR. (b) SBAS-InSAR deformation rate distribution.

Apart from the mountainous regions with substantial topographic relief in the southwest and northwest of Jianzha County, the coverage extracted using this monitoring method shows a substantial improvement compared with the deformation rate results of PS-InSAR. Deformation information can be extracted even in some mountainous areas. The LOS deformation rate of SBAS-InSAR ranges from − 62 to 37 mm/a. Using SBAS-InSAR technology, apart from the slight uplift observed in Cuozhou Township, local uplift also occurs in Maketang Township and Angla Township. The reason is that the number of permanent scatterers extracted by the PS-InSAR technology is limited by the environment, leading to a limited amount of deformation data obtained38. Consequently, meeting the requirements becomes difficult for extracting large-area continuous surface deformation. By contrast, SBAS-InSAR compensates for this deficiency, making it more effective for extracting surface deformation information within the study area. Therefore, subsequent surface deformation information will primarily utilize SBAS-InSAR technology, supplemented by PS-InSAR technology, to better identify potential landslide hazards.

To evaluate the reliability of the combined results from PS-InSAR and SBAS-InSAR technologies, 400 identical points were selected from the annual average deformation rates obtained using Sentinel-1A data for both technologies. A statistical analysis of the linear relationship between the annual average deformation rates (Fig. 7) reveals that the deformation rate distribution of identical points in both technologies is essentially consistent, with an R2 greater than 0.91. This finding indicates a high correlation between the two technologies, demonstrating the validity of combining the results from PS-InSAR and SBAS-InSAR technologies.Fig. 7 Linear relationship diagram of annual average deformation rate at the same point of track lifting.

Landslide detection results

The number of samples is limited in the landslide dataset, and convolutional neural networks require a large number of samples to extract feature information. To improve the accuracy of landslide sample recognition and achieve the best recognition results, data augmentation of the landslide samples is necessary. The samples in the data set were randomly rotated 90°, 180°, and 270°, horizontally and vertically mirrored to increase the quality of the landslide data set. The image data after data amplification are 3,850 pieces, and then the training set and the verification set are randomly selected according to the ratio of 7:3. Any landslide sample is selected to show the effect of data expansion process as shown in the figure. Figure 8 shows the processing examples of original sample, rotation 270°, rotation 270° + horizontal flip, vertical mirroring, and 90° rotation. The landslide prediction samples are created using Google satellite images with a spatial resolution of 3 m. The Google images of Jianzha County are preprocessed by cropping them into 256 × 256 pixels. The landslide hazard areas in the prediction samples are then identified and annotated sequentially, and finally, the annotated samples are merged to obtain the landslide prediction distribution map of the study area.Fig. 8 Amplification processing image of a sample data and corresponding mask.

Influenced by the topography, the surface deformation is mostly in the negative direction of LOS and has a large deformation rate, which can very easily cause landslide disasters. Based on the surface deformation information extracted using the aforementioned PS-InSAR and SBAS-InSAR technologies, areas with considerable deformation, reaching the threshold of 20 mm/a in the LOS direction, are delineated as landslide hazard zones. By integrating Google Earth images, the areas with considerable deformation from 2020 to 2022 are summarized to obtain the distribution of landslide hazard zones. The model training is set to 1,500 iterations, with a batch size of 32 and an initial learning rate of 0.0001. For the prediction part of the model, the potential landslide areas delineated in the Google images are cropped into 256 × 256-pixel files. The landslide boundaries are then mapped on the subdivided local images.

Combining the augmented dataset, the test accuracy curve and training loss curve (Fig. 9) reveal that the test accuracy of the model rapidly increases from 43.6% at the beginning of training, reaching 85% for the first time after 80 iterations. The fluctuation amplitude decreases after 800 iterations, stabilizing at roughly 91% after 1500 iterations. Training loss decreases from 0.52 at the beginning of training and stabilizes at 0.0557 after 1500 iterations. Considering both the test accuracy and iteration number as evaluation metrics for the weight files, the 770th weight file (test accuracy 94.4%, test precision 98.7%, recall 84.2%) is selected for subsequent landslide hazard monitoring. A total of 56 potential landslide areas are identified. As of 2020, 10 of the 13 historical landslide disaster points are located at the identified high hidden danger landslide points, and 3 landslides are missed, with the monitoring accuracy of 76.92%. For easier statistical analysis, the potential landslide hazard points are represented as dots. Figure 10 illustrates the distribution of historical and potential landslide monitoring points. The topographic features of historical landslides are extracted to assess the 56 identified landslide hazard points, revealing 39 high-risk and 17 medium-risk landslide points.Fig. 9 Test accuracy and training loss of augmented dataset.

Fig. 10 Distribution of historical and potential landslide points.

Discussion

Influence of topography on landslide distribution

The frequent occurrence of landslides is closely related to geological structures. By integrating slope topography factors, the environmental elements for landslide development are extracted, including slope, aspect, relief degree, and lithology, to analyze the control conditions of the landslide development environment (Fig. 11). Given that steep slopes increase the risk of soil or rock instability, combined with the differences in slope topography, humidity, and vegetation cover due to different slope aspects, slope angle is often considered an important factor in landslide prevention and mitigation planning. Using the equal interval method, the slope is divided into six categories to count the number of landslides: 0°–10°, 10°–20°, 20°–30°, 30°–40°, 40°–50°, and above 50°. Aspect determines the wind direction, precipitation, surface water flow direction, and vegetation growth, which all affect slope stability. To visually display the relationship between landslides and aspect, the slope aspects are categorized into eight directions for statistical analysis. Relief degree refers to the vertical distance between the highest and lowest points within a specific area and is typically used to measure the unevenness of the terrain, reflecting the slope characteristics that foster landslide occurrences. Statistics reveal that the relief degree of landslide disaster points in this area is concentrated between 0 and 90 m. Using the equal interval method, the relief degree is divided into six levels: 0–15 m, 15–30 m, 30–45 m, 45–60 m, 60–75 m, and 75–90 m. Research indicates that the distribution of landslides is inseparable from the characteristics of rock and soil masses. The rock or soil types and structures in landslide areas substantially differ from those in surrounding non-sliding slopes. Previously sliding rock layers or soils usually have a looser structure, making areas with frequent fault activities prone to landslides. Therefore, lithology is selected as one of the topographic factors for analyzing landslide development.Fig. 11 Terrain factors and potential landslide points: (a) slope (b) aspect (c) relief degree (d) lithology.

Analyzing the development and distribution characteristics of potential landslide points using slope terrain factors. As illustrated in Fig. 12a, potential landslides are predominantly distributed within the 10°–40° slope range, comprising 76.8% of the total. This figure indicates no significant positive correlation between landslide distribution and slope steepness. Landslides are likely to occur on natural slopes where environmental factors such as wind direction, precipitation, lithology, and soil quality are favorable. The relief degree statistics indicate that the relief degree in this area is largely influenced by lithology. Potential landslide points are predominantly distributed at elevations of 15–45 m, primarily in areas where rainfall and unsound engineering activities have caused environmental damage. 98.21% of landslide points exhibit a relief degree of less than 60 m, and as relief degree increases above 60 m, a worsening landslide severity trend is not observed. Lithological statistics indicate that landslide points are predominantly found in areas composed of sandstone, mudstone, feldspathic sandstone, and aeolian deposits. These lithological formations belong to a group prone to sliding and, upon contact with water, form impermeable layers within the slope, thereby creating potential sliding surfaces. Figure 12b shows the statistical results of slope aspect. Potential landslides are concentrated on the northeast and Southeast slopes, and the probability of occurrence of the facing slope is significantly higher than that of the back slope. Combining the analysis of slope aspect characteristics, the terrain causing windward slopes to receive more rainfall than leeward slopes has a high likelihood of occurring, thus reducing slope stability and loosening the soil, making precipitation a dominant factor in landslide occurrence. Additionally, the study area's topography, characterized by higher elevations in the west and lower elevations in the east, provides natural east-facing slopes with effective free faces, thereby raising the tendency for landslides to occur.Fig. 12 Statistics of landslide development and distribution characteristics: (a) relationship between potential landslide distribution and slope, relief degree, (b) relationship between potential landslide distribution and aspect.

From the observed landslide points, the aforementioned four types of terrain factors were extracted as potential landslide development elements. The thresholds for landslide-prone factors were identified as follows: elevation between 2066 and 2839 m for river terraces and low hills, slope gradient of 5.19°–39.78°, aspect of 39.69°–358.53°, surface relief degree of 5–54 m, with no significant differentiation in lithological factors. Therefore, landslides tend to occur when the aforementioned slope terrain factors fall within the appropriate threshold ranges, combined with environmental triggers such as rainfall. Not all potential landslide points are equally prone to landslide disasters.

Impact of precipitation and temperature

Precipitation is a critical factor influencing landslide deformation41. Owing to the influence of terrain and the southeast monsoon, precipitation is higher in the southwestern part of Jianzha County, characterized by mid- and high-mountain areas. In 2018, Jianzha County received an annual precipitation of 570.9 mm, with summer precipitation averaging 323.7 mm, comprising 56.7% of the annual total. The precipitation in spring and autumn were 101.5 mm and 138.7 mm, respectively. The precipitation in winter is only 7 mm. Synthesis of the monitoring data from the past five years indicates that the average downward deformation rates at the landslide points are consistently 0.17 mm • (30 d)−1, 0.79 mm • (30 d)−1, 3.71 mm • (30 d)−1, 3.04 mm • (30 d)−1, − 7.05 mm • (30 d)−1, 6.09 mm • (30 d)−1, 6.52 mm • (30 d)−1, 4.04 mm •(30 d)−1, and 5.86 mm • (30 d)−1, respectively. From July to September 2018, the deformation rates at P2, P3, P4, and P7 reached 6–10 mm•(30 d)−1, which were much higher than the average deformation rate. From June to September 2019, the deformation rates at P4, P6, P8, and P9 reached 3–5 mm • (30 d)−1. From August to September 2020, the deformation rates at P6, P7, and P9 reached 3–5 mm • (30 d)−1. From August to September 2021, the deformation rate at P4 reached 3 mm • (30 d)−1. From June to September 2022, P1, P3, P4, and P5 experienced sudden deformation changes, reaching 4–7 mm • (30 d)−1 (Fig. 13). These peak deformation periods coincided with times of higher annual precipitation, indicating a correlation between landslides and rainfall and suggesting that areas with past landslides are at risk of recurring events.Fig. 13 Relationship between landslide deformation and precipitation monitoring.

Jianzha County is characterized by a plateau cool temperate semi-arid climate with frequent spring droughts, hail, and frost events, large diurnal temperature variations, long sunshine duration, and intense solar radiation. The annual average temperature in the region is 7.9 °C, with an extreme annual maximum temperature of 34.1 °C, and annual sunshine duration totaling 4,432.3 h. The relationship between monitoring points and temperature indicates that from October 2018 to January 2019, the deformation rates at P1, P3, P4, and P6 reached 3–6 mm • (30 d)−1. From October 2019 to January 2020, the deformation rates at P4 and P7 reached 4–7 mm • (30 d)−1. From October 2020 to January 2021, the deformation rates at P1 and P5 reached 2–7 mm • (30 d)−1, significantly higher than the average deformation rates at these points (Fig. 14). These periods of substantial deformation coincide with the winter months, characterized by low precipitation and large temperature variations, indicating that, apart from rainfall, temperature changes also affect the deformation rates of landslides.Fig. 14 Relationship between landslide deformation and temperature monitoring.

Conclusions

This paper combines InSAR technology with deep learning methods, utilizing InSAR technology to monitor surface deformation and convolutional neural networks to identify potential landslides in Jianzha County based on known landslide characteristics. The main conclusions are as follows:PS-InSAR and SBAS-InSAR technologies exhibit a high correlation. Time series analysis of extracted feature points confirms the consistency in the surface deformation trends detected by both InSAR technologies. In this study, SBAS-InSAR technology is primarily used for surface deformation information, supplemented by surface subsidence data extracted by PS-InSAR, allowing for better identification of potential landslide hazards.

Based on the deep learning model and combined with existing landslide data, potential landslide points can be quickly and accurately identified, particularly the 39 high-risk landslide points, which have a high probability of experiencing actual landslide events in a short period.

Landslide points in Jianzha County are concentrated on the northeast and southeast slopes with angles ranging from 10° to 40°, and the relief degree is concentrated in river terraces and low hilly areas with elevations ranging from 15 to 30 m. The left bank of the Yellow River is a key area for landslide occurrences. The formation of landslides is also related to precipitation and temperature.

These studies demonstrate that combining InSAR technology with deep learning methods can quickly and accurately identify early potential landslide points, providing scientific evidence for landslide monitoring and management. Future research should focus on improving the landslide dataset, particularly by incorporating historical landslide data from the study area, to establish more targeted landslide identification results.

The research was sponsored by the National Natural Science Foundation of China (No. 41701449 and No. 41930102), Nanhu Scholars Program for Young Scholars of XYNU. Postgraduate Education Reform and Quality Improvement Project of Henan Province (HNYJS2020JD14).

Author contributions

Conceptualization, X.Y. and D.C.; methodology, X.Y. and D.C.; software, Y.D.; validation, Y.X.; formal analysis, K.Q.; investigation, Y.D.; resources, Y.X. and K.Q; data curation, D.C.; writ-ing—original draft preparation, D.C.; writing—review and editing, X.Y.; visualization, D.C.; project administration, X.Y. All authors have read and agreed to the published version of the manuscript. All authors agree to submit this manuscript and affirm that it has not been pub-lished or submitted to any other journal.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to the funding responsibility but are available from the corresponding author on reasonable request.

Competing interests

The authors declare no conflict of interest.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Flentje, P. & Chowdhury, R. Resilience and sustainability in the management of landslides. In Proceedings of the Institution of Civil Engineers-Engineering Sustainability, vol. 171 3–14 (Thomas Telford Ltd, 2016).
2. Constantin M Bednarik M Jurchescu MC Vlaicu M Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania) Environ. Earth Sci. 2011 63 397 406 10.1007/s12665-010-0724-y
Constantin, M., Bednarik, M., Jurchescu, M. C. & Vlaicu, M. Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ. Earth Sci. 63, 397–406 (2011).10.1007/s12665-010-0724-y
3. Chen S Huo A Zhang J Identification of potential landslides in the loess hilly area (Xiji County) of Ningxia with InSAR technology Sci. Technol. Eng. 2022 22 12 4721 4728
Chen, S. et al. Identification of potential landslides in the loess hilly area (Xiji County) of Ningxia with InSAR technology. Sci. Technol. Eng. 22(12), 4721–4728 (2022).
4. Jin Y Li X Zhu S Tong B Chen F Cui R Huang J Accurate landslide identification by multisource data fusion analysis with improved feature extraction backbone network Geomat. Nat. Haz. Risk 2022 13 1 2313 2332 10.1080/19475705.2022.2116357
Jin, Y. et al. Accurate landslide identification by multisource data fusion analysis with improved feature extraction backbone network. Geomat. Nat. Haz. Risk 13(1), 2313–2332 (2022).10.1080/19475705.2022.2116357
5. Pang D Liu G He J Li W Fu R Automatic remote sensing identification of co-seismic landslides using deep learning methods Forests 2022 13 1213 10.3390/f13081213
Pang, D., Liu, G., He, J., Li, W. & Fu, R. Automatic remote sensing identification of co-seismic landslides using deep learning methods. Forests 13, 1213 (2022).10.3390/f13081213
6. Kovács IP Czigány S Dobre B A field survey–based method to characterise landslide development: A case study at the high bluff of the Danube, south-central Hungary Landslides 2019 16 1567 1581 10.1007/s10346-019-01205-8
Kovács, I. P. et al. A field survey–based method to characterise landslide development: A case study at the high bluff of the Danube, south-central Hungary. Landslides 16, 1567–1581 (2019).10.1007/s10346-019-01205-8
7. Xin W Xuanmei F Qiang X Peijun D Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy ISPRS J. Photogram. Remote Sens. 2022 187 225 239 10.1016/j.isprsjprs.2022.03.011
Xin, W., Xuanmei, F., Qiang, X. & Peijun, D. Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy. ISPRS J. Photogram. Remote Sens. 187, 225–239 (2022).10.1016/j.isprsjprs.2022.03.011
8. Zhang T Zhang W Cao D Yi Y Wu X A new deep learning neural network model for the identification of InSAR anomalous deformation areas Remote Sens. 2022 14 2690 10.3390/rs14112690
Zhang, T., Zhang, W., Cao, D., Yi, Y. & Wu, X. A new deep learning neural network model for the identification of InSAR anomalous deformation areas. Remote Sens. 14, 2690 (2022).10.3390/rs14112690
9. Jia H Wang Y Ge D Deng Y Wang R InSAR study of landslides: Early detection, three-dimensional, and long-term surface displacement estimation—a case of Xiaojiang River Basin, China Remote Sens. 2022 14 1759 10.3390/rs14071759
Jia, H., Wang, Y., Ge, D., Deng, Y. & Wang, R. InSAR study of landslides: Early detection, three-dimensional, and long-term surface displacement estimation—a case of Xiaojiang River Basin, China. Remote Sens. 14, 1759 (2022).10.3390/rs14071759
10. Zhang R Zhao X Dong X Dai K Deng J Zhuo G Yu B Wu T Xiang J Potential landslide identification in baihetan reservoir area based on C-/L-band synthetic aperture radar data and applicability analysis Remote Sens. 2024 16 1591 10.3390/rs16091591
Zhang, R. et al. Potential landslide identification in baihetan reservoir area based on C-/L-band synthetic aperture radar data and applicability analysis. Remote Sens. 16, 1591 (2024).10.3390/rs16091591
11. Dong J Niu R Li B Xu H Wang S Potential landslides identification based on temporal and spatial filtering of SBAS-InSAR results Geom. Nat. Hazards Risk 2022 14 1 52 75 10.1080/19475705.2022.2154574
Dong, J., Niu, R., Li, B., Xu, H. & Wang, S. Potential landslides identification based on temporal and spatial filtering of SBAS-InSAR results. Geom. Nat. Hazards Risk 14(1), 52–75 (2022).10.1080/19475705.2022.2154574
12. Yao J Yao X Liu X Landslide detection and mapping based on SBAS-InSAR and PS-InSAR: A case study in Gongjue County, Tibet, China Remote Sens. 2022 14 4728 10.3390/rs14194728
Yao, J., Yao, X. & Liu, X. Landslide detection and mapping based on SBAS-InSAR and PS-InSAR: A case study in Gongjue County, Tibet, China. Remote Sens. 14, 4728 (2022).10.3390/rs14194728
13. Huang H Ju S Duan W Jiang D Gao Z Liu H Landslide monitoring along the Dadu River in Sichuan based on Sentinel-1 multi-temporal InSAR Sensors 2023 23 3383 10.3390/s23073383 37050447
Huang, H. et al. Landslide monitoring along the Dadu River in Sichuan based on Sentinel-1 multi-temporal InSAR. Sensors 23, 3383 (2023).37050447 10.3390/s23073383
14. Zhang J Gong Y Huang W Wang X Ke Z Liu Y Huo A Adnan A Abuarab ME-S Identification of potential landslide hazards using time-series InSAR in Xiji County, Ningxia Water 2023 15 300 10.3390/w15020300
Zhang, J. et al. Identification of potential landslide hazards using time-series InSAR in Xiji County, Ningxia. Water 15, 300 (2023).10.3390/w15020300
15. Yi Y Xu X Xu G Gao H Rapid mapping of slow-moving landslides using an automated SAR processing platform (HyP3) and stacking-InSAR method Remote Sens. 2023 15 1611 10.3390/rs15061611
Yi, Y., Xu, X., Xu, G. & Gao, H. Rapid mapping of slow-moving landslides using an automated SAR processing platform (HyP3) and stacking-InSAR method. Remote Sens. 15, 1611 (2023).10.3390/rs15061611
16. Hussain S Pan B Afzal Z Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan Sci. Rep. 2023 13 7485 10.1038/s41598-023-34030-0 37161025
Hussain, S. et al. Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan. Sci. Rep. 13, 7485 (2023).37161025 10.1038/s41598-023-34030-0
17. Lian B Wang D Wang X Tan W Early identification and dynamic stability evaluation of high-locality landslides in Yezhi Site Area, China by the InSAR Method Land 2024 13 569 10.3390/land13050569
Lian, B., Wang, D., Wang, X. & Tan, W. Early identification and dynamic stability evaluation of high-locality landslides in Yezhi Site Area, China by the InSAR Method. Land 13, 569 (2024).10.3390/land13050569
18. Kalavrezou I-E Castro-Melgar I Nika D Gatsios T Lalechos S Parcharidis I Application of time series INSAR (SBAS) method using sentinel-1 for monitoring ground deformation of the Aegina Island (Western Edge of Hellenic Volcanic Arc) Land 2024 13 485 10.3390/land13040485
Kalavrezou, I.-E. et al. Application of time series INSAR (SBAS) method using sentinel-1 for monitoring ground deformation of the Aegina Island (Western Edge of Hellenic Volcanic Arc). Land 13, 485 (2024).10.3390/land13040485
19. Guo H Martínez-Graña AM Susceptibility of landslide debris flow in Yanghe township based on multi-source remote sensing information extraction technology (Sichuan, China) Land 2024 13 206 10.3390/land13020206
Guo, H. & Martínez-Graña, A. M. Susceptibility of landslide debris flow in Yanghe township based on multi-source remote sensing information extraction technology (Sichuan, China). Land 13, 206 (2024).10.3390/land13020206
20. Albanwan H Qin R Liu J-K Remote sensing-based 3d assessment of landslides: A review of the data, methods, and applications Remote Sens. 2024 16 455 10.3390/rs16030455
Albanwan, H., Qin, R. & Liu, J.-K. Remote sensing-based 3d assessment of landslides: A review of the data, methods, and applications. Remote Sens. 16, 455 (2024).10.3390/rs16030455
21. Wu L Wang J Fu Y Early identifying and monitoring landslides in Guizhou province with InSAR and optical remote sensing Bull. Surv. Map. 2021 7 98 102
Wu, L., Wang, J. & Fu, Y. Early identifying and monitoring landslides in Guizhou province with InSAR and optical remote sensing. Bull. Surv. Map. 7, 98–102 (2021).
22. Piroton V Schlögel R Barbier C Monitoring the recent activity of landslides in the Mailuu-Suu Valley (Kyrgyzstan) using radar and optical remote sensing techniques Geosciences 2020 10 5 164 10.3390/geosciences10050164
Piroton, V. et al. Monitoring the recent activity of landslides in the Mailuu-Suu Valley (Kyrgyzstan) using radar and optical remote sensing techniques. Geosciences 10(5), 164 (2020).10.3390/geosciences10050164
23. Casagli N Intrieri E Tofani V Landslide detection, monitoring and prediction with remote-sensing techniques Nat. Rev. Earth Env. 2023 4 1 51 64 10.1038/s43017-022-00373-x
Casagli, N. et al. Landslide detection, monitoring and prediction with remote-sensing techniques. Nat. Rev. Earth Env. 4(1), 51–64 (2023).10.1038/s43017-022-00373-x
24. Bouali EH Oommen T Escobar-Wolf R Evidence of instability in previously-mapped landslides as measured using GPS, optical, and SAR data between 2007 and 2017: A case study in the Portuguese Bend Landslide Complex, California Remote Sens. 2019 11 8 937 956 10.3390/rs11080937
Bouali, E. H., Oommen, T. & Escobar-Wolf, R. Evidence of instability in previously-mapped landslides as measured using GPS, optical, and SAR data between 2007 and 2017: A case study in the Portuguese Bend Landslide Complex, California. Remote Sens. 11(8), 937–956 (2019).10.3390/rs11080937
25. Chen C Shen Z Weng Y You S Lin J Li S Wang K Modeling landslide susceptibility in forest-covered areas in Lin’an, China, using logistical regression, a decision tree, and random forests Remote Sens. 2023 15 4378 10.3390/rs15184378
Chen, C. et al. Modeling landslide susceptibility in forest-covered areas in Lin’an, China, using logistical regression, a decision tree, and random forests. Remote Sens. 15, 4378 (2023).10.3390/rs15184378
26. Sheng Y Xu G Jin B Zhou C Li Y Chen W Data-Driven Landslide Spatial Prediction and Deformation Monitoring: A Case Study of Shiyan City, China Remote Sens 2023 15 5256 10.3390/rs15215256
Sheng, Y. et al. Data-Driven Landslide Spatial Prediction and Deformation Monitoring: A Case Study of Shiyan City, China. Remote Sens 15, 5256 (2023).10.3390/rs15215256
27. Xiao L Zhang Y Peng G Landslide susceptibility assessment using integrated deep learning algorithm along the China-Nepal highway Sensors 2018 18 12 4436 10.3390/s18124436 30558225
Xiao, L., Zhang, Y. & Peng, G. Landslide susceptibility assessment using integrated deep learning algorithm along the China-Nepal highway. Sensors 18(12), 4436 (2018).30558225 10.3390/s18124436
28. Xiong K Adhikari BR Stamatopoulos CA Zhan Y Wu S Dong Z Di B Comparison of different machine learning methods for debris flow susceptibility mapping: A case study in the Sichuan Province, China Remote Sens. 2020 12 295 10.3390/rs12020295
Xiong, K. et al. Comparison of different machine learning methods for debris flow susceptibility mapping: A case study in the Sichuan Province, China. Remote Sens. 12, 295 (2020).10.3390/rs12020295
29. Huang F Zhang J Zhou C A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction Landslides 2020 17 217 229 10.1007/s10346-019-01274-9
Huang, F. et al. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17, 217–229 (2020).10.1007/s10346-019-01274-9
30. Van Dao D Jaafari A Bayat M A spatially explicit deep learning neural network model for the prediction of landslide susceptibility Catena 2020 188 104451 10.1016/j.catena.2019.104451
Van Dao, D. et al. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena 188, 104451 (2020).10.1016/j.catena.2019.104451
31. Shahabi H Ahmadi R Alizadeh M Hashim M Al-Ansari N Shirzadi A Wolf ID Ariffin EH Landslide susceptibility mapping in a mountainous area using machine learning algorithms Remote Sens. 2023 15 3112 10.3390/rs15123112
Shahabi, H. et al. Landslide susceptibility mapping in a mountainous area using machine learning algorithms. Remote Sens. 15, 3112 (2023).10.3390/rs15123112
32. Huang W Ding M Li Z Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms Catena 2023 222 106866 10.1016/j.catena.2022.106866
Huang, W. et al. Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms. Catena 222, 106866 (2023).10.1016/j.catena.2022.106866
33. Ali N Chen J Fu X Ali R Hussain MA Daud H Hussain J Altalbe A integrating machine learning ensembles for landslide susceptibility mapping in Northern Pakistan Remote Sens. 2024 16 988 10.3390/rs16060988
Ali, N. et al. integrating machine learning ensembles for landslide susceptibility mapping in Northern Pakistan. Remote Sens. 16, 988 (2024).10.3390/rs16060988
34. Zhang Q Wang T Deep learning for exploring landslides with remote sensing and geo-environmental data: Frameworks, progress, challenges, and opportunities Remote Sens. 2024 16 1344 10.3390/rs16081344
Zhang, Q. & Wang, T. Deep learning for exploring landslides with remote sensing and geo-environmental data: Frameworks, progress, challenges, and opportunities. Remote Sens. 16, 1344 (2024).10.3390/rs16081344
35. Zhang X Yang L Song X Runoff and sediment load changes in the upper Yellow River and their influencing factors in recent 60 years J. Lake Sci. 2024 36 2 602 619 10.18307/2024.0243
Zhang, X., Yang, L. & Song, X. Runoff and sediment load changes in the upper Yellow River and their influencing factors in recent 60 years. J. Lake Sci. 36(2), 602–619 (2024).10.18307/2024.0243
36. Richard W Daniel A Mark F Volumetric interferometry for sparse 3D synthetic aperture radar with bistatic geometries Electron. Lett. 2023 59 12 12851 10.1049/ell2.12851
Richard, W., Daniel, A. & Mark, F. Volumetric interferometry for sparse 3D synthetic aperture radar with bistatic geometries. Electron. Lett. 59(12), 12851 (2023).10.1049/ell2.12851
37. Chen Y Xia J Yu C Chen B Editorial: InSAR crustal deformation monitoring, modeling and error analysis Front. Environ. Sci. 2022 10 1009492 10.3389/fenvs.2022.1009492
Chen, Y., Xia, J., Yu, C. & Chen, B. Editorial: InSAR crustal deformation monitoring, modeling and error analysis. Front. Environ. Sci. 10, 1009492 (2022).10.3389/fenvs.2022.1009492
38. Huang, X. et al. Study on Rock Strata Movement Deformation and Surface Subsidence in Mining Area Based on PS-InSAR Technology (Springer, 2023).
39. Karaca ŞO Erten G Ergintav S Khan SD Anthropogenic problems threatening major cities: Largest surface deformations observed in Hatay, Türkiye based on SBAS-InSAR Bull. Miner. Res. Explor. 2024 173 173 235 252
Karaca, ŞO., Erten, G., Ergintav, S. & Khan, S. D. Anthropogenic problems threatening major cities: Largest surface deformations observed in Hatay, Türkiye based on SBAS-InSAR. Bull. Miner. Res. Explor. 173(173), 235–252 (2024).
40. Guo R Li S Chen Y Yuan L A method based on SBAS-InSAR for comprehensive identification of potential landslide J. Geo-inf. Sci. 2019 21 7 1109 1120
Guo, R., Li, S., Chen, Y. & Yuan, L. A method based on SBAS-InSAR for comprehensive identification of potential landslide. J. Geo-inf. Sci. 21(7), 1109–1120 (2019).
41. She X Li D Yang S Xie X Sun Y Zhao W Landslide hazard assessment for Wanzhou considering the correlation of rainfall and surface deformation Remote Sens. 2024 16 1587 10.3390/rs16091587
She, X. et al. Landslide hazard assessment for Wanzhou considering the correlation of rainfall and surface deformation. Remote Sens. 16, 1587 (2024).10.3390/rs16091587
