InSAR Research Articles

Volcanic Process Ground Deformation Using DInSAR Observations: The Case Study of the Sabancaya Volcano

Abstract. Remote sensing is one of the most useful tools when it comes to studying earth phenomena along large terrain extensions. Within this broad field, Differential Interferometric Synthetic Aperture Radar (DInSAR) has been evolving, and at this point is able to provide terrain deformation in millimetre-level precision. This analysis was conducted over the Sabancaya Volcano, in Perú, which is part of the Ampato-Sabancaya Volcanic Complex. This volcano has been active and continuously erupting since 2016, with various fault systems surrounding the volcanic complex. The main objective of this work was to map deformation caused by the volcanic system, processing Sentinel-1 products from 2016 and an interval that started in 2014 to 2017 to be able to study the effects of temporal decorrelation on interferometric products, being Sentinel-1 the first of the Copernicus Program satellite constellations conducted by the European Space Agency. The analysis showed deformation in the interval in which the eruption started and a deformation centre in the northern region of the volcanic complex, although the last one was found when studying a longer temporal baseline. Overall, the experiment shows that DInSAR is an effective tool to study volcanic systems.

Landslide Thickness Estimated from InSAR-Derived 2D Deformation: Application to the Xiongba Ancient Landslide, China

The thickness estimation of landslides is crucial for better landslide evaluation. Traditional non-contact mass conservation methods using 3D deformation may be unsuitable due to observation limitations. This study proposes a more feasible approach based on 2D deformation from two-track Interferometric Synthetic Aperture Radar (InSAR) observations, applied to the Xiongba landslide. The comparison with geological and drilling measurements confirms the reliability of this method. The mapped InSAR LOS deformation rate fields reveal two regions: a significantly deformed frontal zone and a relatively stable zone. Analysis suggests that surface uplift at the Xiongba-H2 landslide’s front edge results from rock–soil mass pushing in high-deformation areas. The estimated thickness ranges from 10 to 100 m, with an active volume of 6.17 × 107 m3. A thicker region is identified at the front edge along the Jinsha River, posing the potential for further failure. This low-cost, easily implemented approach enhances InSAR’s applicability for landslide analysis and hazard assessment.

Multidimensional Small Baseline Subset (Msbas) Software for Constrained and Unconstrained Deformation Analysis of Partially Coherent Dinsar and Speckle Offset Data

Differential Interferometric Synthetic Aperture Radar (DInSAR) and speckle offset data are used to measure ground deformation. Computing deformation time series from these data can be challenging due to varying radar line-of-sight acquisition geometry, rapid coherence loss, and the large datasets involved requiring computationally intensive processing. The Multidimensional Small Baseline Subset (MSBAS) Software is designed to compute deformation time series from these data, and its parallelized version is optimized to handle large datasets from modern SAR satellites. The software can produce 1D, 2D, and 3D Surface-Parallel and Aspect-Parallel Flow constrained and 3D unconstrained or 4D deformation time series. It runs on workstations and clusters utilizing OpenMP and MPI/OpenMP. This study presents novel results demonstrating MSBAS capabilities, such as landslides in Northwest Territories, Argentina, and Colombia, as well as tectonic deformation and subsidence in Turkey and Yemen. A 4D deformation time series of glacier flow and surge at the Malaspina Glacier in Alaska is showcased. Equations for solving multidimensional problems are included along with processing parameters and supplemental data used to create constraints. The manuscript serves as a comprehensive manual for the MSBAS Software Version 10. The source code is available.

Multi-scale monitoring and safety assessment of a prefabricated subway station integrating space-borne InSAR, ground-based machine vision and internal-distributed fiber optic sensors

ABSTRACT Safety monitoring of prefabricated subway stations is essential for early detection of potential damages to mitigate major accidents. This study developed an integrated multi-scale safety assessment and monitoring strategy for a prefabricated subway station by combining space-borne synthetic aperture radar interferometry (InSAR), ground-based machine vision, and internal-distributed fiber optic sensing (DFOS) technologies. Compared to the traditional single-data-source-monitoring method, the proposed space-ground-internal integration strategy is designed to monitor the multi-scale damages of prefabricated subaway stations. This includes the mm level of ground subsidence, submm level of structural surface deformation, and microstrain level of structural internal strain field. Taking the prefabricated Shapu Station in Shenzhen as the research site, the spatiotemporal evolution of land subsidence, the underground structural settlement and convergence deformation, as well as the structural internal strain changes were monitored. Then, a multi-scale data fusion methodology combining the space-borne InSAR, the ground-based machine vision, and the internal DFOS measurements was presented for effective safety assessment of the subway station. The monitoring results indicated that deformation occurred during the backfill period, and the settlement of the dangerous assembly ring increased (the seventh ring was at the lowest safety level) when the backfill of the assembly section was completed.

Pre-eruption Deformation Prior to 2018 Eruption of Anak Krakatau Volcano

Abstract Anak Krakatau is an active volcano situtated in Sunda Strait between Sumatera and Java that is frequently erupts. In 2018, Anak Krakatau eruupted where its eruptions destroys its western and south western flank that created a tsunami that swept across western Banten Province and southern Lampung Province, killed hundreds of casualties and destroyed homes in the shorelines. With the use of Differential InSAR analysis, we observe the dynamics of activity of Anak Krakatau starting from January 2018 until December 2019. To observe the unrest of Anak Krakatau prior and after the eruption, we’re using radar satellite imagery from Sentinel 1 from European Space Agency with temporal baseline every 12 days with total of 60 images being used. These images then processed using differential Interferometric Synthetic Aperture Radar (DInSAR) method and then being analysed the ups and downs patterns between 12-day-pairs. From our observation, deformation signals appeared in some interferogram pairs prior to eruption in December 2018. We also find that the activity of Anak Krakatau still rising after the eruption of 2018, showed by the upward trend that happened in the point samples near the vent. The graph analysed from InSAR pairs also show that there is a pattern of inflation and subsidence that shows the activity of Anak Krakatau. It is suggested that the supply from magma chamber played a role in the uplift of Anak Krakatau that happened in the time of our observation. We conclude that the activity of Anak Krakatau was detected prior the eruption and still not decline and will going to be active in some more time.

Analyzing the geomorphometric changes of Dana County due to the Sisakhat earthquake using the Differential Synthetic Aperture Radar Interferometry (D-InSAR) method.

Analyzing the geomorphometric changes of Dana County due to the Sisakhat earthquake using the Differential Synthetic Aperture Radar Interferometry (D-InSAR) method.

Evaluation of effects on a viaduct affected by a snowmelt-induced landslide using AI-Driven Pattern Recognition

Over recent years, the worldwide increase in infrastructure failures due to natural hazards such as landslides and earthquakes has raised significant concern. Aggravated by the effects of climate change, this issue has had a considerable impact, particularly in Italy. This study focuses on the "Villa Ilii" Viaduct on the A24 highway to analyze landslide-structure interactions, emphasizing the significant deformation risks. The methodology includes comprehensive geological, morphometric, and geophysical analyses, with findings showing cumulative deformation rates up to +2.5 cm/year at critical points. Field surveys and high-resolution SAR data analysis have identified deformation patterns linked to seasonal snow melt and temperature variations, adding a quantitative dimension to the hazard assessment. A novel component of this study is the application of Gradient Boosting Machines (GBM) for automated pattern recognition within SAR data processed using the Differential Interferometry SAR (DInSAR) technique. By leveraging datasets on snow melting patterns and cumulative radar target deformation, GBMs uncover intricate, non-linear relationships between snowmelt dynamics and ground deformation. This integration of GBMs and DInSAR data advances landslide hazard assessment by providing a robust 87% accuracy predictive model, effectively capturing environmental triggers that impact infrastructure stability. These findings contribute to more effective hazard management strategies and inform resilient infrastructure design, highlighting the study's significance in addressing climate-driven geohazards.

Mapping of highway slope hazard susceptibility based on InSAR and machine learning: a pilot study toward road infrastructure resilience

Highway slope hazard susceptibility monitoring and prediction is of great importance for highway disaster risk management which corresponds to highway infrastructure resilience. This paper proposes a method for predicting slope hazard susceptibility within the highway area. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), geospatial analysis and machine learning algorithms are employed for achieving the purpose. Specifically, 11 influencing factors of the study area are selected and the data are collected. Sensitivity of the influencing factors is verified using matrix scatter plot, Spearman coefficient test and one-way ANOVA test. The ANN, SVM and CatBoost models are constructed to predict the slope hazard susceptibility. Among them, CatBoost shows the best performance on the testing set with an AUC value of 0.85. The findings of this study assist in evaluating the resilience of road infrastructure and hazard susceptibility of highway slopes.

Mapping Surface Deformation in Rwanda and Neighboring Areas Using SBAS-InSAR

Surface deformation poses significant risks to urban infrastructure, agriculture, and the environment in many regions worldwide, including Rwanda and the neighboring areas. This study focuses on surface deformation mapping and time series analysis in Rwanda and the neighboring areas from 2 July 2016 to 8 June 2023 using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The influence of atmospheric delay error is effectively reduced by integrating the Generic Atmospheric Correction Online Service (GACOS), which provides precise atmospheric delay maps. Then the SBAS-InSAR method is utilized to generate surface deformation maps and displacement time series across the region. The results of this study indicated that the maximum deformation rate was −0.11 m/yr (subsidence) and +0.13 m/yr (uplift). Through time series analysis, we quantified subsidence and uplift areas and identified key drivers of surface deformation. Since subsidence or uplift varies across the region, we have summarized the different deformation patterns and briefly analyzed the factors that may lead to deformation. Finally, this study underscores the importance of SBAS-InSAR for tracking surface deformation in Rwanda and the neighboring areas, which offers valuable perspectives for sustainable land utilization strategizing and risk mitigation.

Unveiling subsidence patterns: Time series analysis for land deformation investigation in the west-Qurna oil field, Iraq

Land subsidence is a worldwide geological and environmental risk caused by natural occurrences and human actions. Its effects include a range of socio-economic, environmental, and hydrogeological consequences, such as damage to infrastructure like buildings, roads, bridges, and pipelines, as well as increased flooding and reduced groundwater storage capacity. Due to these diverse impacts, it is crucial to monitor the spatial and temporal scope of land subsidence. This study presents an investigation into land subsidence within the West-Qurna oil field, a large oil reservoir situated in Iraq's Basrah governorate. The study employs Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) analysis from the European Space Agency Sentinel 1A over six years, from June 2017 to May 2023. The Stanford Method for Persistent Scatterers (StaMPS) has been utilised to assess the scale and magnitude of land deformations in this region. Results revealed a notable subsidence within the central urban area of the oil field, forming an ellipsoidal subsidence bowl spanning 86 km2. The peak subsidence rate is identified at −13.2 ± 0.4 mm/yr within this bowl, with a cumulative vertical displacement of 75 mm throughout the six-year observation period. Furthermore, uplifting phenomena are also detected at the study area's peripheries, reaching a maximum rate of 12 ± 0.4 mm/yr and a cumulative shift of 54 mm. Temporal analysis showcases a significant alteration in subsidence rates, with rates of −18 mm/yr observed between 2017 and 2020, followed by −5 mm/yr post-2020. This change is attributed to COVID-19-related oil production reductions enacted by the government to boost prices. Our analysis points toward oil extraction as a probable primary driver of subsidence in the studied area, although a deeper probe into the impact of groundwater extraction for reservoir injection remains essential.

Ground moving target indication of polarimetric interferometric synthetic aperture radar using joint scattering vector

AbstractGround moving target indication (GMTI) plays a critical role in both civilian applications and military applications. The accuracy of complex images coregistration is a major factor for multichannel synthetic aperture radar (SAR) GMTI. Moreover, polarimetric interferometric SAR (PolInSAR) represents a growing strength in multi‐function radar systems. In response to this, we propose a new SAR‐GMTI approach tailored for the PolInSAR system. To address the deterioration in clutter suppression performance caused by the coregistration errors, we construct a joint scattering vector (JSV) for polarimetric SAR (PolSAR) by incorporating the neighbouring vectors of the central pixel. Subsequently, the covariance matrix of JSV is estimated and subjected to eigen‐decomposed. Clutter suppression of the PolInSAR system for GMTI is performed using eigen‐subspace projection, where the JSV is projected onto noise subspace. This method is robust against coregistration errors since all ground moving target information is encapsulated within the JSV. Furthermore, the improvement factors with different clutter backgrounds (by changing the signal‐to‐clutter‐plus‐noise‐ratio, SCNR) are analysed. The effectiveness of the proposed JSV‐based method is validated using simulated PolInSAR data.

Unwrap-Net: A deep neural network-based InSAR phase unwrapping method assisted by airborne LiDAR data

In Interferometric Synthetic Aperture Radar (InSAR) data processing, accurately unwrapping the phase is crucial for measuring elevation or deformation. DCNN models such as PhaseNet and PGNet have improved the efficiency and accuracy of phase unwrapping, but they still face challenges such as incomplete multiscale feature learning, high feature redundancy, and reliance on unrealistic datasets. These limitations compromise their effectiveness in areas with low coherence and high gradient deformation. This study proposed Unwrap-Net, a novel network model featuring an encoder-decoder structure and enhanced multiscale feature learning via ASPP (Atrous Spatial Pyramid Pooling). Unwrap-Net minimizes feature redundancy and boosts learning efficiency using SERB (Residual Convolutional with SE-block). For dataset construction, airborne LiDAR terrain data combined with land cover data from optical images, using the SGS (Sequential Gaussian Simulation) method, are used to synthesize phase data and simulate decorrelation noise. This approach creates a dataset that closely approximates real-world conditions. Additionally, the introduction of a new high-fidelity optimization loss function significantly enhances the model’s resistance to noise. Experimental results show that compared to the SNAPHU and PhaseNet models, the SSIM of the Unwrap-Net model improves by over 13%, and the RMSE is reduced by more than 34% in simulated data experiments. In real data experiments, SSIM improves by over 6%, and RMSE is reduced by more than 49%. This indicates that the unwrapping results of the Unwrap-Net model are more reliable and have stronger generalization capabilities. The related experimental code and dataset will be made available at https://github.com/yangwangyangzi48/UNWRAPNETV1.git.

Object oriented data analysis of surface motion time series in peatland landscapes

Abstract Peatlands account for 10% of UK land area, 80% of which are degraded to some degree, emitting carbon at a similar magnitude to oil refineries or landfill sites. A lack of tools for rapid and reliable assessment of peatland condition has limited monitoring of vast areas of peatland and prevented targeting areas urgently needing action to halt further degradation. Measured using interferometric synthetic aperture radar (InSAR), peatland surface motion is highly indicative of peatland condition, largely driven by the eco-hydrological change in the peatland causing swelling and shrinking of the peat substrate. The computational intensity of recent methods using InSAR time series to capture the annual functional structure of peatland surface motion becomes increasingly challenging as the sample size increases. Instead, we utilize the behaviour of the entire peatland surface motion time series using object oriented data analysis to assess peatland condition. Bayesian cluster analysis based on the functional structure of the surface motion time series finds areas indicative of soft/wet peatlands, drier/shrubby peatlands, and thin/modified peatlands. The posterior distribution of the assigned peatland types enables the scale of peatland degradation to be assessed, which will guide future cost-effective decisions for peatland restoration.

Active Gully Head Erosion Rates Characteristics on the Loess Plateau: InSAR‐Based Calculation and Response to Extreme Rainfall

ABSTRACTQuantitative analysis and prediction of gully head erosion hold paramount importance for terrain evolution studies and risk mitigation effort. However, previous findings could not reconcile high temporal resolution with long time series coverage, particularly at the watershed scale. In this study, a calculation method for the active gully head erosion rate (AGHER) was proposed on the basis of interferometric synthetic aperture radar (InSAR) technology, thereby combining the historical climate data to estimate the annual AGHER. Additionally, we investigated the relative importance of extreme rainfall events on erosion rates. The results indicated that the long‐term annual AGHER in the study area ranged from 51.83 to 89.50 mm year−1 from 1980 to 2023 on the Dongzhi Plateau (DZP). Extreme rainfall events (rainfall amount ≥ 150 mm) emerged as the foremost erosion‐causing factor, accounting for a contribution rate ranging from 69.8% to 75.0%. Furthermore, through field surveys, we identified 21 gully heads that represent potential hazards to high‐speed railways (HSRs). Although the proportion of gullies affecting rail infrastructure may be relatively modest in century‐long projections, it notably increases against the background of worldwide escalation due to the occurrence of extreme precipitation events. This study establishes a robust foundation for gully erosion management and risk assessment on the Loess Plateau.

D-InSAR-Based Analysis of Slip Distribution and Coulomb Stress Implications from the 2024 Mw 7.01 Wushi Earthquake

On 23 January 2024, an Mw 7.01 earthquake struck the Wushi County, Xinjiang Uygur Autonomous Region, China. The occurrence of this earthquake provides an opportunity to gain a deeper understanding of the rupture behavior and tectonic activity of the fault system in the Tianshan seismic belt. The coseismic deformation field of the Wushi earthquake was derived from Sentinel-1A ascending and descending track data using Differential Interferometric Synthetic Aperture Radar (D-InSAR) technology. The findings reveal a maximum line-of-sight (LOS) displacement of 81.1 cm in the uplift direction and 16 cm in subsidence. Source parameters were determined using an elastic half-space dislocation model. The slip distribution on the fault plane for the Mw 7.01 Wushi earthquake was further refined through a coseismic slip model, and Coulomb stress changes on nearby faults were calculated to evaluate seismic hazards in surrounding areas. Results indicate that the coseismic rupture in the Mw 7.01 Wushi earthquake sequence was mainly characterized by left-lateral strike-slip motion. The peak fault slip was 3.2 m, with a strike of 228.34° and a dip of 61.80°, concentrated primarily at depths between 5 and 25 km. The focal depth is 13 km. This is consistent with findings reported by organizations like the United States Geological Survey (USGS). The fault rupture extended to the surface, consistent with field investigations by the Xinjiang Uygur Autonomous Region Earthquake Bureau. Coulomb stress results suggest that several fault zones, including the Kuokesale, Dashixia, Piqiang North, Karaitike, southeastern sections of the Wensu, northwestern sections of the Tuoergan, and the Maidan-Sayram Fault Zone, are within regions of stress loading. These areas show an increased risk of future seismic activity and warrant close monitoring.

Coastal Reclamation Embankment Deformation: Dynamic Monitoring and Future Trend Prediction Using Multi-Temporal InSAR Technology in Funing Bay, China

Reclamation is an effective strategy for alleviating land scarcity in coastal areas, thereby providing additional arable land and opportunities for marine ranching. Monitoring the safety of artificial reclamation embankments is crucial for protecting these reclaimed areas. This study employed synthetic aperture radar interferometry (InSAR) using 224 Sentinel-1A data, spanning from 9 January 2016 to 8 April 2024, to investigate the deformation characteristics of the coastal reclamation embankment in Funing Bay, China. We optimized the phase-unwrapping network by employing ambiguity-detection and redundant-observation methods to facilitate the multitemporal InSAR phase-unwrapping process. The deformation results indicated that the maximum observed land subsidence rate exceeded 50 mm per year. The Funing Bay embankment exhibited a higher level of internal deformation than areas closer to the sea. Time-series analysis revealed a gradual deceleration in the deformation rate. Furthermore, a geotechnical model was utilized to predict future deformation trends. Understanding the spatial dynamics of deformation characteristics in the Funing Bay reclamation embankment will be beneficial for ensuring the safe operation of future coastal reclamation projects.

A GIS Plugin for the Assessment of Deformations in Existing Bridge Portfolios via MTInSAR Data

The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining the safety of these structures, such as aging, material degradation, and environmental factors. Although effective and standardized methodologies exist (e.g., structural monitoring, periodic onsite inspections), new emerging technologies could be employed to provide time- and cost-effective information on the current state of structures and to drive prompt interventions to mitigate risk. One example is represented by MTInSAR data, which can provide near-continuous information about structural displacements over time. To easily manage these data, the paper presents BAS-MTInSAR. The tool allows users to insert information of the focused bridge (displacement time series, structural information, temperature data) and, through a user-friendly GUI, observe the occurrence of abnormal deformations. In addition, the tool implements a procedure of multisource data management and defines proper thresholds to assess bridge behavior against current code prescriptions. BAS-MTInSAR is fully described throughout the text and was tested on a real case study, showing the main potentialities of the tool in managing bridge portfolios.

Utilizing LuTan-1 SAR Images to Monitor the Mining-Induced Subsidence and Comparative Analysis with Sentinel-1

The LuTan-1 (LT-1) satellite, launched in 2022, is China’s first L-band full-polarimetric Synthetic Aperture Radar (SAR) constellation, boasting interferometry capabilities. However, given its limited use in subsidence monitoring to date, a comprehensive evaluation of LT-1’s interferometric quality and capabilities is necessary. In this study, we utilized the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to analyze mining-induced subsidence results near Shenmu City (China) with LT-1 data, revealing nine subsidence areas with a maximum subsidence of −19.6 mm within 32 days. Furthermore, a comparative analysis between LT-1 and Sentinel-1 data was conducted focusing on the aspects of subsidence results, interferometric phase, scattering intensity, and interferometric coherence. Notably, LT-1 detected some subsidence areas larger than those identified by Sentinel-1, attributed to LT-1’s high resolution, which significantly enhances the detectability of deformation gradients. Additionally, the coherence of LT-1 data exceeded that of Sentinel-1 due to LT-1’s L-band long wavelength compared to Sentinel-1’s C-band. This higher coherence facilitated more accurate capturing of differential interferometric phases, particularly in areas with large-gradient subsidence. Moreover, the quality of LT-1’s monitoring results surpassed that of Sentinel-1 in root mean square error (RMSE), standard deviation (SD), and signal-to-noise ratio (SNR). In conclusion, these findings provide valuable insights for future subsidence-monitoring tasks utilizing LT-1 data. Ultimately, the systematic differences between LT-1 and Sentinel-1 satellites confirm that LT-1 is well-suited for detailed and accurate subsidence monitoring in complex environments.

Satellite Geodesy Uncovers 15 m of Slip on a Detachment Fault Prior to the 2018 Collapse at Anak Krakatau, Indonesia

AbstractOn 22 December 2018, parts of the Anak Krakatau edifice collapsed, triggering a deadly tsunami. To investigate pre‐collapse surface displacements, we analyzed Interferometric Synthetic Aperture Radar satellite geodetic data from 2006 to 2018, acquired from ALOS‐1 (2006–2011), COSMO‐SkyMED (2012–2018), and Sentinel‐1 (2014–2018). We identified line‐of‐sight displacements on the southwestern flank throughout the study period. Inversion of COSMO‐SkyMED data revealed a rectangular dislocation with a cumulative slip of 12 m from April 2012 to December 2018. Fixing the fault geometry, we found the optimal slip for time periods corresponding to slip rate changes, ranging from 1.2 to 3.1 m/yr. The slip estimates for ALOS‐1 and Sentinel‐1 data were 0.88 m/yr and 1.1 m/yr, respectively, over their individual time periods. Overall, the detachment fault experienced approximately 15 m of slip from 2006 to 2018 with acceleration and deceleration periods, and a notable acceleration prior to the 2018 collapse.

A non-contact quantitative risk assessment framework for translational highway landslides: Integration of InSAR, geophysical inversion, and numerical simulation

Landslides frequently disrupt highway networks in mountainous regions globally, presenting a grave threat to the safety of vehicles and pedestrians. A quantitative assessment of landslide risk within the highway network is crucial for the implementation of targeted monitoring, early warning, and engineering interventions. In this study, a non-contact quantitative risk assessment framework for translational landslides is proposed, which integrates Interferometric Synthetic Aperture Radar (InSAR), geophysical inversion, and numerical simulation techniques. The framework can reliably estimate the surface velocity, subsurface geometry, volume, and potential spatial consequences of landslides, and assess the potential damages and losses resulting from the landslide failure. Taking Lashagou L6 landslide in Linxia City, China as a case study, the results demonstrate a robust agreement between the InSAR-derived two-dimensional (2D) displacements and Global Navigation Satellite System (GNSS) observations, which are suitable for the inversion of active landslide thickness. The minimum mean error between the inverted landslide thickness and the observed borehole thickness is 0.8 m. The maximum thickness of the inverted basal sliding surface is 4.1 m, corresponding to a landslide volume of 1.25 × 104 m3. Fully considering the uncertainties within the technical framework, an estimated 1.2–1.7 vehicles are projected to be impacted, resulting in 5.3–7.7 casualties. The proposed non-contact framework demonstrates high reliability and considerable application versatility, particularly beneficial in inaccessible mountainous regions.