Conference Agenda

The geological conditions along the Karakoram Highway are extremely complex, with crisscrossing valleys and severe river cutting. Historical and ancient landslides and debris flows are well developed; There are still a large number of potential landslides along the highway that are difficult to identify. Potential landslides have a certain critical height, deformation without damage or damage without landslide, and pose a risk to the safe operation of highways. It is of great practical significance to carry out the identification and monitoring of potential landslides along the highway. Traditional landslide susceptibility mapping are static, without considering the change of the susceptibility mapping considering the activity of historical, ancient and potential landslides.. In this paper, one of the most serious nodes of disaster along the Central Karakorum Highway, Gilgit segment of Hunza river, North of Pakistan, is selected as the study area, and the landslide surface deformation rate monitored by the Interferometric Synthetic Aperture Radar (InSAR) small-baseline set technique developed using interferometry is used to represent the landslide activity, and the landslide susceptibility mapping is performed based on a logistic regression model. Based on the logistic regression model for landslide susceptibility assessment, the results of landslide activity and landslide susceptibility assessment are further coupled to obtain a comprehensive landslide susceptibility assessment map. The results of this study can help disaster prevention and mitigation on the Karakorum Highway.

The accurate estimation of daily evapotranspiration is pivotal for effective water management in agriculture, particularly in crops like vineyards, where maintaining a precise water balance is imperative for grape quality. With climate change intensifying water scarcity, optimizing water usage has become crucial for ensuring both profitability and sustainability in agricultural production. Thus, there's a need to enhance monitoring and quantification of water usage in this sector.

The integration of spatial data from Earth observation, notably Synthetic Aperture Radar (SAR), has gained prominence in monitoring environmental changes, including agricultural water usage. SAR's capacity to provide insights into vegetation status, soil moisture levels, and water usage characteristics has proven invaluable. By integrating SAR imagery with optical images and ground sensor data, decision-makers can refine irrigation practices and minimize environmental impact.

This study focuses on utilizing SAR (Sentinel-1), optical (Sentinel-2), and in-situ soil moisture data from vineyards in northern Portugal to predict soil moisture values through remote sensing. The SAR dataset comprises 60 series of Interferometric Wide (IW) level-1 Ground Range Detected (GRD) data from three acquisition tracks. SAR backscatter data, known for its sensitivity to soil moisture, were processed to derive synthetic bands based on various polarizations from Sentinel-1. Additionally, optical data were used to compute indices such as the Normalized Difference Water Index (NDWI), Normalized Difference Infrared Index (NDII), and Normalized Difference Vegetation Index (NDVI). These values were extracted using a 3x3 window centered at each geographical coordinate, corresponding to the locations of sensors in two vineyards.

The combined SAR, optical, and sensor data resulted in 174 samples for the year 2023, each containing 28 features. Subsequently, an artificial neural network model with 6 hidden layers was trained, tested, and evaluated using repeated k-Fold cross-validation. Performance metrics including Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Percentage Error (MAPE) were employed, yielding an R2 of 0.857, MAPE of 6.199%, and RMSE of 1.515 on the evaluation dataset. Figure 1 illustrates the graphical analysis of the model's predictions compared to sensor measurements.

Future research endeavors will focus on expanding sensor data collection across various crops, augmenting sample size, incorporating topographic features, and enhancing model performance.

Multi-agent Awareness No-retracing Integrated Q-Learning (or MANIQ) for Anomaly Detection utilises multiple agents to select from possible legal actions using a boolean screening process. Increased awareness is supplied by a kernel and an awareness field with padding = 1. The kernel allows for 8 agents in 2 dimensions; each of which are assigned an instance with their designation; path; state; awareness field and activity status (TRUE or FALSE). While agents remain active in the field, they execute either explorative or exploitative policies on the environment. The status of an agent is determined by the dot product of the goal and the number of agents still active in the field divided by the total number of instances. Once an agent reaches its goal, it is summarily deactivated. Once all agents are deactivated, a new round begins. At each round, the start point of the agent, the field and legal actions and screenings are reset. A significant barrier in all earlier versions of Q-Learning anomaly detection was accounting for multiple rewards (of the same value) on the same Q-Table. MANIQ AD solves this by segmenting each datapoint into its own environment. The start point for each agent is the data point under consideration. The agent works backwards towards the zero-line and backpropagates the reward value, which is equal to the square of the normed and rounded value of the data point. If the integral of the Q-Learning Surface is greater than a predefined value, the data point is classified as an anomaly; otherwise normal. The MANIQ algorithm is a significant improvement in terms of both time complexity and accuracy when compared against earlier versions of the algorithm, including; AKAD, MRQL, MRQLV2, FUQL and IQAD, and its performance is comparable to state-of-the-art algorithms such as Matrix Profiles and HBOS. The algorithm has been tested on VFM SWARM data. Its goal is to detect anomalies in the Earth’s geomagnetic field in the months and years preceding several seismic events including the Aegean Sea (2020), Croatia (2020) and Mexico (2017) earthquakes.

The collapse of buildings is the main cause for casualties after earthquakes. Real-time and accurate positioning of the building areas are crucial to make an effective implementation of emergency rescue after an earthquake. Synthetic Aperture Radar (SAR) possesses advantages such as all-weather and all-day capabilities, as well as resilience to lighting and weather conditions. Therefore, the use of SAR imagery has garnered significant attention in various fields, including post-earthquake rescue, damage estimation, and urbanization studies, and so on. The single-polarimetric spaceborne SAR data are very difficult to decipher, because their information is simple and abstract, and the spatial resolution of radar satellite data is limited. In this case, the accuracy of recognizing building earthquake damage information using only one post-earthquake single-phase SAR data is low. In order to ensure the accuracy of building damage information extraction as much as possible, the project utilizes deep learning network to fuse multiple feature parameters to identify building damage from post-earthquake SAR images. By comparing the classification accuracies of deep learning networks with different data and feature parameters, we found that the deep learning method combining spatial domain and frequency domain texture features can more accurately recognize collapsed buildings and non-collapsed buildings. The method demonstrates a robust capability to identify collapsed and intact buildings. Taking the area of Kahramanmaras, Turkey hit by the 6 February, 2023 Turkey-Syria earthquake as the case study, the region severely affected by the earthquake, this project incorporates both spatial and frequency domain features into the deep learning network for classification. Experimental results show that the proposed method achieves a classification accuracy of 80.98%, significantly surpassing the classification accuracy of 47.84% for the original SAR image. Moreover, the accuracy of 80.98% is higher than using only spatial domain features (73.30%) or only frequency domain features (73.42%). The proposed method in this study can provide fundamental support for post-earthquake disaster assessment and situational awareness.

The Tianshan orogenic belt (TSOB) represents one of the most dynamically active regions in Eurasia. The distal effects stemming from the late Cenozoic collision between the Indian and Eurasian tectonic plates have triggered renewed tectonic activity within the TSOB, fostering intracontinental orogenesis. Concurrently, the TSOB has extended into the foreland basins flanking its peripheries, giving rise to multiple series of fold belts associated with décollements and faults at the basin-mountain interface. Global Positioning System (GPS) data indicates a gradual reduction in the north-south shortening rate across the TSOB, diminishing from approximately 20 mm/yr in the western segment to around 8 mm/yr in the eastern part. Nevertheless, the internal distribution of deformation within the TSOB remains a subject of debate. In this study, we ascertain the contemporary kinematics of the principal structural zones by leveraging Interferometric Synthetic Aperture Radar (InSAR) data acquired from the Sentinel-1 satellite constellation.

Our analysis incorporates Synthetic Aperture Radar (SAR) data gathered from 7 ascending tracks (T27;T129;T56;T158;T85;T12;T114) and 6 descending tracks (T107;T34;T136;T63;T165;T92) of the Sentinel-1A/1B satellites spanning the period between November 2014 and December 2020. Through the utilization of Gamma software, we generated a total of 1509 single-reference interferometric pairs, covering a geographical extent of 1290 km in length and 490 km in width within the TSOB. Subsequently, the InSAR time series data underwent processing using the StaMPS software package. Long-wavelength atmospheric errors and elevation-dependent variations were mitigated through the application of the TRAIN package in conjunction with the ECWMF ERA5 models.

By integrating InSAR observations with GPS measurements, our study demonstrates a heterogeneous distribution of tectonic deformation across the TSOB. The convergence rate along the Tianshan ranges between 15–24 mm/yr, with the most pronounced deformation gradient localized at the junction between South Tianshan and Pamir, accounting for approximately 68% of the overall convergence deformation. South Tianshan exhibits relative stability with limited deformation gradients, while residual deformation is discernible within the intermontane faults and basin systems north of South Tianshan. Notably, the Kashi fold-thrust belt emerges as a highly active unit, with deformation predominantly concentrated in the Mushi, Kashi, and Atushi folds, as well as the interfold faults like Kashi, Atushi, and Toth Goubaz faults. The Maidan fault, serving as the boundary fault separating South Tianshan and the Tarim basin, exhibits a discernible deformation gradient. Within the Keping nappe, deformation primarily manifests around the Keping hill and Kepingtag fault at the nappe's forefront. The Kuche foreland hosts several prominent deformation zones, with deformation patterns in the northern sector of South Tianshan dispersed across a series of active intermontane structures and depression basins, contrasting the southern region where deformation primarily concentrates on thrust folds.

Strain rate analyses within the Tianshan Zone highlight prominent strain accumulation zones at the pre-subduction front of the Pamir, followed by the southern margin of Issyk-Kul lake and the southern periphery of the eastern segment of South Tianshan. The Mw7.1 earthquake on January 23, 2024, in Wushi, Xinjiang, coincided with areas of heightened surface contraction strain and shear strain. Using InSAR data, we derived and inverted the fault slip distribution associated with the coseismic deformation field of this event, revealing it to be a thrust and strike-slip earthquake in alignment with the regional strain context.

The goal of this work is to examine the applicability of Generative Adversarial Networks (GANs) Neural Networks (NNs) in creating synthetic geomagnetic residual spatial temporal datasets, comparable to real data, as acquired by ESA’s SWARM A and C satellites. The long-term aim is to utilize the synthetic data for detecting potential geomagnetic pre-earthquake anomalies and investigate to which extent this generation can help in addressing the problem of sparsely collected real data points. Geomagnetic data collected in a period of one year before and during a major M6.0 earthquake episode in Arzak, China, on 19^th January 2020, was used for this study. Geomagnetic datasets collected by SWARM A and C have been merged for this period, and a 5-day window has been found to provide the optimal spatial coverage. A Feedforward Discriminator and Generator and Long Short Term (LSTM) GAN NN architectures have been used for the purpose of generating synthetic 5-day data blocks. Initial results appear promising, and a comparison study is currently underway.

The Li-Kan Road landslide (LKRL)，located on the right bank of the Lijia Gorge Reservoir (LJGR) in northwestern China, is a typical active slope induced by a road engineering project. The long-term creep of the landslide has resulted in frequent repairs to the infrastructure. More significantly, it poses a severe threat to the safety of the nearby hydropower station located approximately one kilometer away. In this study, Sentinel-1 datasets are utilized to analyze the spatiotemporal evolution characteristics of surface deformation in the LKRL since 2014. Additionally, geometric parameters of the landslide are inverted to provide an estimation of its thickness and volume. Firstly, line-of-sight (LOS) surface displacement velocities are derived from both ascending and descending orbit images in the LJGR area using time series InSAR technology. This approach reveals the presence of multiple active slopes in the region. Subsequently, by integrating dual-orbit observations with the aspect-parallel flow model, three-dimensional (3D) displacement time series of the LKRL are retrieved. The results indicate that the maximum cumulative horizontal and vertical displacements of LKRL during the period from 2014 to 2023 exceed 3.2 m and 1.0 m, respectively. Additionally, the landslide exhibits spatial heterogeneity in its deformation pattern. Following this, using the 3D displacement rate and the mass conservation equation, the sliding geometry of the landslide is inverted, revealing a sliding surface with an average depth of 10.6 m and a volume of approximately 1.45×107 m3. The landslide is characterized by an uneven thickness distribution. Finally, the deformation mechanism of the LKRL is discussed and it is concluded that the landslide is currently in a phase of constant motion without an imminent risk of catastrophic failure. This study represents the first systematic mapping and analysis of LKRL movement using satellite imaging geodesy methods. The findings contribute to the mechanistic interpretation and risk management of the LKRL.

The pixel offset tracking method has been widely applied to monitor the horizontal displacement of landslides through orthophotos. However, it predominantly relies on cross-correlation matching and sliding windows for offset estimation, which may introduce bias or boundary effects due to lower cross-correlation. In this paper, we utilized a two-stage method that combines cross-correlation matching with the pyramid Lucas-Kanade optical flow method to retrieve the horizontal displacement from optical images. The former ensures the initial global alignment of images, while the latter calculates the local horizontal displacement. In this work, we chose Upper Tena Valley in Spain as the study area because many landslides were developed in this place, and open-access orthophotos covering the region were also collected. First, the horizontal surface displacements of landslides in the area were recovered through the two-stage method. Subsequently, the three-dimensional displacement was retrieved based on a prior model assumption. Our results demonstrate the effectiveness of this method in mitigating the boundary effect associated with traditional cross-correlation offset estimation methods, providing robust technical support for landslide deformation monitoring.

In large hydropower projects, first impoundment leads to substantial increases in reservoir water levels, significantly transforming the geoenvironment and activating associated geohazards. The Baihetan Hydropower Station, ranked as the world's second-largest hydropower project, beginning its first impoundment on April 6, 2021. Unlike other large hydropower projects, the first impoundment of the Baihetan Hydropower Project has altered the geoenvironment more drastically, experiencing a rise in reservoir water levels of approximately ~120 m in just seven months. To reveal the impact of extensive and first impoundment on the dynamics of reservoir slopes in Baihetan, we employed an integrated spaceborne time series interferometric SAR (InSAR), optical remote sensing, Airborne UAV Photogrammetry, and field investigation. This approach led to the detection of 24 unstable landslides. We also introduced an innovative method for identifying displacement patterns of detected slopes, including acceleration and deceleration patterns. Our findings indicate that subsequent to the first impoundment, numerous reservoir slopes experienced acceleration, primarily due to the buoyancy effect of the reservoir water on their resisting sections. Additionally, the observed transition from acceleration to deceleration in the displacement of some reservoir slopes can be primarily linked to the buoyancy effect within the driving sections of these slopes. Our research has shed light on the profound impact exerted by both extensive and first impoundment on the dynamic behavior of reservoir slopes. Moreover, our study highlights the remarkable capability of the time series InSAR technique in accurately identifying and characterizing the displacement patterns of reservoir slopes. This knowledge is crucial for geohazard prevention and geoenviroment protection, especially in the context of constructing large hydropower projects.

Floods is a natural risk that threat with significant impacts in flood-prone regions, particularly those with complex geomorphological and hydrological characteristics. The present study investigates the flood dynamics in the Alto Guadalentín valley, an orogenic tectonic depression experiencing flash floods and land subsidence due to groundwater withdrawal. Employing 2D flood event models, two scenarios for 1992 and 2016 were simulated and compared, using the HEC-RAS 2D hydraulic model. Differential SAR Interferometry (DInSAR) techniques allowed quantifying ground deformations for the period 1992-2016; such data were combined with the Digital Elevation Model (year 2009) in order to obtain the topography for the two scenarios. The study demonstrates that in regions with simultaneous land subsidence and flooding, flood risks can be significantly altered. The hydraulic modelling results indicate a considerable rise in flood depth and in the extent of inundation in 2016 when compared to 1992. There were notable alterations in the water surface elevation throughout the 14-year span, resulting in a 2.04 km² expansion of areas experiencing water depths surpassing 0.7 m and indicating a critical interaction between subsidence and flooding. Additionally, changes in flood propagation patterns, supported by velocity maps, indicate a migration of inundation areas, influencing flood risk distribution. The study also discusses the economic implications of subsidence-induced flood risk changes, highlighting the importance of considering subsidence in flood risk evaluations. The impact on drainage capacity and economic costs emphasises the need for comprehensive flood risk management strategies. Flood hazard maps, integrating subsidence data, provide essential information for local authorities to identify high-risk areas, formulate effective management strategies, and evaluate hazard and economic costs.

Keywords: floodplain dynamics, hydrogeological modelling, InSAR, land subsidence, risk evaluation.