Conference Agenda

Traditional climate risk and impact assessments typically consider a single extreme event, a fact that leads to the underestimation of risks, as such events are often interdependent. The principal aim of this study is to evaluate the current state of the climate in Beijing, China, and Athens, Greece in terms of droughts and heatwaves, focusing on their compound effects (CDHW) and examining their association with urban form and fabric factors. The term compound events describe the combined effect of multiple climate factors (processes, variables, phenomena including feedback mechanisms) or climate hazards. In urban areas, these compound events can lead to other challenges, such as increased energy demand for cooling, higher air pollution levels, and impacts on critical infrastructure which can be associated with urban morphology. The determination of the CDHW climatology is carried out through the joint use of an Excess Heat Factor (EHF) and a Standardized Precipitation Index (SPI), according to the general definition of CDHW events (heat waves occurring during the period of drought events), using the high-resolution state-of-the-art ERA5-Land reanalysis product along with ground-based climate data, while Earth Observation (EO) imagery is used to extract land cover information from visible and near-infrared sensors. The study addresses the challenges of CDHW in cities and a range of strategies is proposed that include climate-resilient infrastructure, nature-based solutions, and heat warning systems.

While more and more people migrate to cities, uncontrolled urban growth poses pressing threats such as poverty and environmental degradation. Although sustainable urban planning can mitigate these threats, the lack of timely information on the sprawl of settlements hampers ongoing sustainability efforts. Multi-modal deep learning offers new opportunities for timely and accurate urban mapping and change detection by exploiting the complementary information acquired by Synthetic Aperture Radar (SAR) and optical sensors. In particular, the Copernicus Program's Sentinel-1 SAR and Sentinel-2 MultiSpectral Instrument (MSI) missions play a crucial role in multi-modal remote sensing research. For example, our previous work demonstrated that the complementary information in Sentinel-1 SAR and Sentinel-2 MSI data can be utilized to improve the transferability of deep learning models for urban extraction at a global scale (Hafner et al., 2022). However, the optical modality may not always be available due to cloud cover or other atmospheric conditions, which is particularly relevant for multi-temporal urban mapping and change detection. Although a limited number of studies have addressed this so-called missing modality problem (e.g., Zheng et al., 2021, Saha et al., 2022, and Li et al., 2022), multi-modal methods that are robust to a missing modality are still under-researched in remote sensing. Here, we propose a novel multi-temporal urban mapping approach that uses multi-modal satellite data from the Sentinel-1 SAR and Sentinel-2 MSI missions. In particular, our approach focuses on the problem of a partly missing optical modality due to clouds. The proposed model utilizes two networks to extract features from each modality separately. In addition, a reconstruction network is utilized to approximate the optical features based on the SAR data in case of a missing optical modality. Our experiments on a multi-temporal urban mapping dataset with Sentinel-1 SAR and Sentinel-2 MSI data demonstrate that the proposed method outperforms a multi-modal approach that uses zero values as a replacement for missing optical data, as well as a uni-modal SAR-based approach. Therefore, the proposed method effectively exploits multi-modal data, if available, but it also retains its effectiveness when the optical modality is missing.

Self-supervised learning methods are gaining popularity in remote sensing community due to their ability to utilize unlabeled data for representation learning. These representations can then be adapted to downstream tasks through pre-training and fine-tuning. Masked Autoencoder (MAE) is a concise self-supervised learning method that learns better semantic representations by masking most of the content in the input image. However, MAE was originally designed for natural images and may not be the best choice for remote sensing images. We propose a masking method to enhance correlation feature extraction capability. Our proposed model surpasses state-of-the-art contrastive learning and MAE-based models on land-cover classification tasks and reduces input data volume, achieving a more efficient model. Additional experiments demonstrate that the proposed model has good generalization performance and maintains good representation learning capabilities on small-scale data.

Artificial Intelligence (AI) is currently studied mainly for optical imagery, i.e. photography. Earth Observation (EO) images are basically different and much more complex. AI for EO requires specific methods for the full information extraction from spatial, temporal or spectral information at global scale. This involves new paradigms to analyze jointly multimodal sensor records as the EO multi-sensor data optical, IR or microwaves. EO records data of high complexity, physically-based, dynamic, non-linear coupled Earth System. We need to develop new AI paradigms with integrated physical principles into the learning mechanism. These are well beyond and do not emerge form the present cats and dogs recognition techniques. Thus, there is a huge motivation in developing AI for EO methods and exploiting the results.

Eddy interactions, including typical splitting and merging processes, are a popular research focus in oceanography. Automatic splitting and merging identification algorithms are crucial for global eddy interaction research. This study proposes an algorithm for identifying and tracking global mesoscale eddy splitting and merging events based on sea level anomaly (SLA) data. For identification, we present a multilevel eddy detection method that introduces eddygroups and eddytrees to describe the complicated spatial and topological relationships between different levels of closed SLA contours. For tracking, we define an eddy segment, eddy branch and eddy directed acyclic graph (eddy-DAG) to describe the complex topological trajectory of eddies that include at least one splitting or merging event. Only eddies contained within a common eddygroup and with the same polarity can be tracked as sources for merging events or sinks during splitting events. The Global Eddy Graph dataset (DOI: 10.12237/casearth.63369940819aec34df2674d8) extracted 1,905,742 splitting events as well as 1,790,266 merging events from CMEMS’s SLA data (1993-2020). Based on the typical events extracted from the Global Eddy Graph, the normalized results of different remotely sensed sea surface parameters (SSTA, SSSA) or in situ data (drifters) verify the reliability of the dataset and the effect of the interaction between eddies on marine material distribution.

As an important place for shipbuilding enterprises to manufacture and repair ships, docks and berths are the most critical components of shipbuilding enterprises.In the shipyard scene, the dock and berth are closely related to the production status of the shipyard. They are the core land types in the shipyard production status monitoring. Therefore, the production status of the shipyard can be inferred by monitoring the dock and berth in the satellite remote sensing image.In this paper, based on the characteristics that shipyards with different production states differ greatly in remote sensing images, five deep learning networks ( GoogLeNet, integrated network, Xception, VGG and Alexnet ) are used to train and predict the dock data set, and the accuracy and effect of the evaluation model are compared. Then, combined with the shipyard vector data, the production state activity of the shipyard 3km along the coastline is counted. The experiment adopts cross-time series statistics, and selects the areas with different production state activity across time series as the research area ( the research area chooses to avoid factories and many housing construction areas ). Finally, the Sentinel-2A image data of the selected study area in the cross-temporal period was obtained, and the water body was extracted by MNDWI. The water color index (FUI), turbid water index (TWI), cyanobacteria and macrophytes Index (CMI), river pollution index (RPI) were calculated to evaluate the water pollution situation, and the correlation analysis between the activity of the shipyard and the water pollution situation was established.

Landslides are one of the most serious geological disasters in the world, which seriously damage people's property and safety. In this paper, an object-oriented segmentation method is proposed, which combines spectral, terrain and texture features. The Lengqu basin, a tributary of the Nujiang River on the south side of the Tanggula Mountains in China, and parts of the Hunza basin in Pakistan were selected as the study areas. Landslides in the study area were identified using 12.5 m elevation data and Sentinel-2 data. The identification results were validated against images on Google Earth and collected landslide data. The results show that the object-oriented method can extract the landslide boundary accurately. The research results have great scientific significance for disaster prevention and mitigation, line planning and site selection and follow-up maintenance of the Sichuan-Tibet transportation corridor and the Karakorum line.

Remote sensing techniques are widely used for identification of ancient landslides and monitoring their activity In the present study, we used the Hunza valley basin in Pakistan as the study area, and enhanced the DEM (Digital elevation model) based on RRIM (Red relief image map) to identify the ancient landslides the SBAS InSAR (Small baseline subset synthetic aperture radar) technique was also used to monitor the surface deformation rate in the study area from 2004 to 2022 and then the histograms of the radar line of sight deformation rate results were used to categorize the deformation rate results In this research, a total of 157 ancient landslides with activity characteristics were identified It is found that the RRIM method supplemented with InSAR technology can effectively monitor the ancient landslides and avoid the risk by monitoring the hidden ancient landslides in a long time series.

The Karakoram region is located on the tectonic belt and is also a high-risk area for geological disasters. Due to the complex terrain, high mountains and deep valleys, geological disasters such as landslides are prone to occur, and traditional monitoring is extremely difficult to carry out, which hinders the understanding of landslides in the region and leads to a lack of disaster prevention and reduction measures for local landslide disasters. This study is based on the 2021 Sentinel-1A data along the Karakoram Highway, and starts from the identification results of Stacking InSAR technology, focusing on analyzing typical landslides along the Karakoram Highway. Utilizing Small Baseline Subset Synthetic Aperture Interferometric Radar (SBAS InSAR) technology to monitor the displacement characteristics of landslides, and analyzing the causes of landslides in conjunction with the environment in which they occur. The research results are as follows: (1) Based on Stacking InSAR technology, 7 potential landslides along the Karakoram Highway were obtained, all of which are in an unstable state. (2) In 2021, landslides occurred frequently along the Karakoram Highway, and the displacement data of the landslide line of sight showed significant deformation of the Mostag landslide, with a maximum deformation rate of 94 mm/a. The research results are of great significance to the prevention and control of geological disasters along the Karakoram Highway and to serving the national "the Belt and Road" strategy.

A good-quality landslide inventory map is necessary for assessing landslide hazard. However, it remains difficult and time-consuming to produce and update landslide inventories in most regions of the world, especially in mountainous areas with high extension and poor accessibility. Moreover, the inventoried landslides are usually the most morphologically visible on the landscape, while other typologies of large dimensions and more diffuse boundaries are often overlooked. Therefore, new technologies such as satellite remote sensing or advanced landscape analysis are gaining prominence to optimise landslide mapping at regional scale, in terms of time-consuming and cost-effectiveness. In this study, we performed a combination of two well-implemented techniques to improve landslide detection in a mountainous area. These techniques are Differential Interferometric Synthetic Aperture Radar (DInSAR) and Landscape Analysis through the double normalised channel steepness (k_sn) geomorphic index. The southwestern sector of Sierra Nevada mountain range (Granada, Southern Spain) was selected as the case study.

We derived DInSAR mean displacement or velocity maps from Sentinel-1 images through the P-SBAS automated and un-supervised processing chain, that is implemented on the European Space Agency (ESA)’s Geohazard Exploitation Platform (GEP) (https://geohazards-tep.eu/#!). Ascending and descending orbit data was obtained with spanning times from September 2016 to March 2020 and December 2014 to March 2020, respectively, with temporal sampling up to 6 days. The k_sn index was computed through the open Python library ‘landspy’ (https://github.com/geolovic/landspy). The only needed input was a 10 m resolution Digital Elevation Model to extract the drainage network and the k_sn index from rivers.

We identified the unstable areas from the DInSAR ground displacement maps and the k_sn anomalous values from the k_sn map to associate them with large landslides. To delimit the landslides’ boundaries as accurately as possible, it was essential an exhaustive examination of morphologies in the field, as well as the examination of products derived from high-resolution Digital Elevation Models (e.g. hillshade, slope, aspect, rugosity). This work conducted us to provide an updated inventory of 28 landslides, what implies the 33.5% of the analysed area. Most of the identified landslides are large Deep-Seated Gravitational Slope Deformations (DGSDs), that have not been discovered in the Sierra Nevada until this study. This new inventory has relevant implications as landslides are larger and more abundant than previously considered. Our work also emerges the potential of integrating data from DInSAR techniques and Landscape Analysis to detect large landslides and provide updated inventories in mountainous areas. Moreover, we proved that some limitations of both techniques could be well-compensated.

Monitoring mining subsidence dynamically offers valuable opportunities for exploring and examining the directional changes in surface displacement resulting from underground resource extraction. These changes can be significantly influenced by both natural geological environmental factors and human activities. LuTan-1(LT-1) mission is the first L-band bistatic spaceborne SAR mission for civil application in China which provides continuous DInSAR ground deformation results. Although orbital determination accuracy of LT-1 is 5 cm, we conducted a linear fitting and removed the orbital-induced phase ramp by means of Kriging’s interpolation method in this work. The subsidence bowl results derived from LT-1 show good agreement with the results derived from Sentinel-1 between March and April 2022. Furthermore, due to the scarcity of GNSS points and the irregular mining deformation, it is difficult to obtain high precision 3D deformation through GNSS and InSAR. Therefore, we projected continuous 3D GNSS to LOS direction to validate the DInSAR results derived from LT-1 and Sentinel, respectively. Finally, we observed the dynamic process associated to mining activities in this area by using four DInSAR results from different dates. InSAR results revealed obvious directional changes of the spatial location of ground surface displacements, with maximum horizontal displacement of the subsidence bowl of about 1.26 km during the observation time lag of approximate one year. This approach opens the door to the dynamic analysis of mining subsidence by DInSAR method.

Opencast mining, which involves huge quantities of overburden removal, dumping and backfilling in excavated areas, is a classical operation mode of large coal mines worldwide. With the continuous expansion of open pit mining areas, the mining angle has also increased sharply, resulting in frequent landslide disasters and significant safety threats to mining production operations. Therefore, it is of vital significance for the safety of personnel, mining operation equipment and infrastructures to perform continuous displacement monitoring of opencast mines and their surroundings. In recent decades, with the continuous enrichment of satellite Synthetic Aperture Radar (SAR) data resources, Multi-temporal SAR Interferometry (MT-InSAR) technique has become a fundamental tool to estimate surface displacements with high spatial resolution, short temporal revisit interval, wide coverage and millimeter accuracy.

In this paper, multi-temporal InSAR technology is adopted to monitor the line of sight (LOS) displacement of Fushun West Opencast Coal Mine (FWOCM) and its surrounding areas in Northeast China using Sentinel-1 SAR images acquired from 2018 to 2022. The spatial-temporal evolution of urban subsidence and the south-slope landslide are both analyzed in detail. Comparison with ground measurements and cross-correlation analysis via cross-wavelet transform with monthly precipitation data are also conducted to analyze the influence factors of displacements in FWOCM. The monitoring results show that a subsidence basin appeared in the urban area near the eastern part of the north slope in 2018, with the settlement center located at the intersection of E3000 and fault F1. The Qian Tai Shan (QTS) landslide on the south slope, which experienced rapid sliding from 2014 to 2016, presents seasonal deceleration and acceleration with precipitation, with the maximum displacement in the vicinity of the Liushan Paleochannel. The results of this paper have fully taken into account the complications of large topographic relief, geological conditions, spatial distribution, and temporal evolution characteristics of surface displacements in opencast mining areas. The wide range and long time series dynamic monitoring of opencast mines is of great significance to ensure mine safety, production, and geological disaster prevention in the investigated mining area.

In recent years，thermal anomalies prior to large and hazardous earthquakes have been extensively detected by microwave remote sensing techniques. In order to effectively detect microwave brightness temperature (MBT) anomalies caused by seismic factors, a wavelet-based two-step difference (WTSD) method is proposed in this paper. In the WTSD method, the radiation received by the microwave sensor comprises of two components if no earthquakes happen, which are stable radiation and random radiation respectively. Since the radiation caused by topography, surface coverage and seasonal change has strong regularity and varies little over the years, it is therefore considered as stable contribution to the microwave radiation. On the other hand, radiation caused by meteorological conditions (e.g., precipitation and temperature change, etc.) frequently changes within several days, which has no regularities, and it is therefore considered as random contribution to the microwave radiation. The stable components and random components are removed step by step in the WTSD method. The key steps prior to difference calculation rely on reliable retrieval of the stable component (which is the background MBT), and on successful elimination of the random component (which is the meteorological factor) as well, which is realized by adopting the hierarchical clustering and wavelet analysis. Then, the proposed WTSD method was used to detect seismic MBT anomalies prior to three strong earthquakes happened in the Central and Eastern Qinghai-Tibet Plateau, including the Ms 7.1 earthquake in Yushu in 2010, the Ms 5.5 earthquake in Dingqing in 2016 and the Ms 7.4 earthquake in Maduo in 2021. Surprisingly, the MBT anomalies prior to the three earthquakes are generally similar in terms of location, shape and evolution characteristics. Preliminary mechanistic analysis suggests that the pre-earthquake MBT anomalies are consistent with spatial distribution of the NE-oriented normal faults and geothermal activities in this region. The pre-earthquake thermal anomalies may becaused by intensified extrusion of the Indian plate to the Eurasian plate and the increased crustal stress in this area

Assessing the impact of the February 6^th earthquake, which occurred in South-eastern Turkey near the Turkey-Syria border, on cultural heritage sites is crucial to ascertain the cultural and historical cost of the disaster. These twin quakes, which had a magnitude of 7.8 and an after-shock magnitude of 6.7, resulted in widespread damages with the official death toll figure rising to 55,000+ and over 107,000 injured across the eleven cities most affected. The zone of occurrence of this earthquake is a hotbed for seismic activity because of the complicated network of plate boundaries underlying the area. This zone is under-laid by three major plate boundaries namely the Anatolian plate, the Arabian plate and the African plate. It is characterized by series of lateral strike-slip fault movement which ultimately results in series of frequent earthquakes of varying magnitude.

The aim of this study is to detect damaged cultural heritage sites in the earthquake zone in Turkey, by using SAR (Synthetic-Aperture Radar) images. The affected cities are home to some of Turkey’s most iconic heritage sites. In this study, TerraSAR-X high-resolution X-band data and open access Sentinel-1 data was used. At some locations we also used Google Earth images as a reference images.

To detect damages on cultural heritage sites, two methods were adopted. First, since TerraSAR-X have high resolution spotlight mode, we tried to ‘visually recognize’ damages on historical buildings by comparing SAR images with terrestrial and UAV photos from the area taken by locals, archaeologists, and reporters. Second, we processed open access Sentinel-1 data of different dates, before and after the earthquake using ‘coherence change detection’ to detect the changes in specific structures in the city. The research will focus to a large extent the cultural and historical cost of the impact of earthquake and also highlight the further impacts of after-shock on damaged cultural heritage sites through time series analysis of images. We realized that some damaged buildings continued to collapse several days later as a result of subsequent aftershocks which shows the need to initiate mitigative measures as fast as possible to save what is left of the important monuments.

Looting is an ongoing global threat to cultural heritage. Detecting looting activities is therefore of the utmost importance. Remote sensing offers a possibility to detect looting in remote and inaccessible areas. The all-weather and continuous observation capabilities of SAR would be extremely beneficial for any practical implementation. However, SAR data is difficult to interpret and suffers from speckle noise, making the detection of small changes challenging.

The detectability of large-scale looting activity in high-resolution SAR images, for example in the context of the Syrian civil war, has been shown before. Many other looting activities are rather small scale and do not reach the almost industrialized looting activities witnessed in this conflict.

Therefore, the detectability of small-scale looting will be analyzed in this work. Based on an experimental setup with two different sized artificial looting holes, we analyze the detectability of these activities in SAR images of different resolution, polarization, looking angle, orbit, etc. Detectability in amplitude and coherence are being analyzed.

The results will provide deeper insight into the requirements in terms of resolution and other imaging parameters for looting detection.

Regular and continuous monitoring of surface deformation and structural instability is crucial for cultural heritage protection. Increasing urbanization and development are one of the causes of ground subsidence. In the last decade, the city of Wuhan (China) has experienced major threats in urban areas due to rapid expansion and ground deformation, as revealed by recent studies published prior to the Dragon-5 SARrchaeology project by the ASI, Wuhan University and CNR-ISAC team in the framework of the WUHAN-CSK project (Jiang et al., 2021, Tapete et al., 2021) and the follow-on research within SARrchaeology (Jiang et al., 2023). While the whole InSAR literature on Wuhan so far has focused on the relationships between urbanization and land subsidence, as well as on impacts on modern structures and infrastructures, no studies have been undertaken to assess the effect on the conservation of heritage buildings spread across the city.

To fill this gap, high-resolution COSMO-SkyMed and TerraSAR-X satellite imagery is used in this work for assessing potential deformation of cultural heritage in Wuhan using the PSInSAR technique, which allows object subsidence monitoring up to millimeter-level accuracy. However, for long-term observations of a highly dynamic urban environment, such as Wuhan, several assumptions of PSInSAR, like PS stability over the acquisition period or linear deformation, are unsuitable. Changes to the processing framework are therefore necessary and are tested in this work.

In the final paper, we will demonstrate the effectiveness of applying a modified PSInSAR technique for the analysis of high-resolution SAR images for long-term monitoring of subsidence. The effect and potential damage to different cultural heritage sites will be discussed.

References

Jiang H., Balz T., Cigna F., Tapete D. (2021) Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data. Remote Sens., 13, 1256. https://doi.org/10.3390/rs13071256

Jiang H., Balz T., Cigna F., Tapete D., Li J., Y. Han (2023). Multi-Sensor InSAR Time Series Fusion for Long-term Land Subsidence Monitoring. Geo-spatial Information Science. https://doi.org/10.1080/10095020.2023.2178337

Tapete D., Cigna F., Balz T., Tanveer H., Wang J., Jiang H. (2021) Multi-Temporal InSAR and Target Detection with COSMO-SkyMed SAR Big Data to Monitor Urban Dynamics in Wuhan (China). 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 3793-3796, doi: 10.1109/IGARSS47720.2021.9554360

Groundwater overexploitation and its resulting surface subsidence have been critical issues in the North China Plain (NCP) for the last half-century. This problem, however, is being alleviated by the implementation of the South-to-North Water Diversion (SNWD) Project since 2015. Here, we monitor surface deformation and investigate aquifer physical properties in NCP by combining Interferometric Synthetic Aperture Radar (InSAR), Global Positioning System (GPS), and hydraulic head data observed during 2015-2019.

We process data from the ascending track 142 of the Sentinel-1A/1B satellites, with a total of 92 acquisitions among 5 consecutive frames during 4 years. The InSAR time series are generated using the StaMPS software package, and all of the interferograms are formed with respect to one reference image. By dividing the study area into overlapping patches, we use parallel computing algorithms and cluster job management system to reduce the computational overburden. With this method, we effectively reduce computation time and successfully obtain the InSAR time series in NCP with full resolution for the first time. The atmospheric phase screen (APS) is estimated and reduced using a combined method, in which the first-order APS is estimated using the ERA5 global atmosphere model, and the residual APS is estimated using the Common Scene Stacking method.

Geodetic observations reveal widespread and remarkable subsidence in the NCP, with an average rate of ~30 mm/yr, and ~100 mm/yr for the maximum. We successfully extract seasonal and long-term deformation components caused by different hydrogeological processes. By joint analysis of the seasonal deformation and hydraulic head changes, we estimate the storativity of 0.07~12.04*10^-3 and the thickness of clay lenses of 0.08~2.00 m for the confined aquifer system, and attribute their spatial distribution patterns to the alluvial and lacustrine sediments of the subsystem layers. Our study also reveals fulfilment of the SNWD Project in alleviating the groundwater shortage. About 57% of the NCP is found to have experienced subsidence deacceleration, mostly along the SNWD aqueduct lines, by a total of 37.0 mm on average during 2015-2019. The subsidence was reduced by 4.1 mm on average for the entire NCP, suggesting that although subsidence was still ongoing, the trend was reversed, particularly for some major cities along the routes of the SNWD Project. A distinct difference in subsidence rates is found across the borderline between the Hebei and Shandong Provinces, resulting from differences in groundwater use management. Our study demonstrates that the integration of geodetic and hydrological data can be effectively used for the assessment of groundwater circulation and to assist groundwater management and policy formulation.

With fast shale gas exploitation in Sichuan basin in China in recent years, numerous micro-seismicities and even some medium-sized earthquakes occurred. Some studies show that shale gas exploitations can generate detectable surface deformation. We used ALOS-2 InSAR data to measure the surface deformation over the Changning shale gas block and find significant ground deformation that may be caused by massive shale gas production. Meanwhile, we also did time-series analysis of Sentinel-1 satellite radar data to measure the surface deformation of the Sichuan basin during the active periods of shale gas exploitation, which shows strong correlations between the surface deformation and three major shale gas blocks, namely the Changning, Weiyuan, and Fulin blocks. So the observed InSAR deformation in the tectonic-stable Sichuan basin is probably caused by hydraulic fracturing for shale gas production.

Some speculations on deformation sources could be made based on such deformation patterns. Firstly, the surface deformation could be caused by long-term fluid injection or pumping which lasted several months in a poroelasticity medium. Secondly, such deformation may be due to multiple induced seismicities or fault creeping caused by pore pressure diffusion or fluid migration to vulnerable faults. Thirdly, the long-term shale gas development in the Sichuan basin could change the underground fluid mass. Injection or pumping of fluids into the crust would change upper crustal gravity and produce the elastic response of the crust, called the mass loading effect. We test these hypotheses based on numerical analysis of surface deformation patterns from InSAR data.

To quantitatively interpret the surface deformation with shale gas production, we model the deformation sources as multiple fluid injection and pumping processes in a poroelasticity layer by spatiotemporal Green’s function method, rather than the simple elastic volcanic-like sources, which may misinterpret the physical parameters of the shale gas production. Then we invert for the production parameters in a least-squares solution and compare our results with limited open production data as a verification. The details will be reported in the meeting.

In 2013, the European Space Agency (ESA) launched a constellation of three satellites: known collectively as the SWARM satellites. Their mission is to monitor variations in the Earth’s magnetic field. It has long been theorised that anomalous fluctuations in the Earth’s ionosphere could herald the beginnings of major earthquakes. However, the ability to accurately capture the frequency and extent of these anomalies has proven to be a persistent challenge to the scientific community. Anomalies are defined as data points which lie outside of the scope of normal data. High-intensity anomalies are comparatively easy to detect, but it is difficult to distinguish low-intensity anomalies from normal data, using purely mathematical or statistical means. The aim of this research is to apply Q-Learning and Deep Q-Networks to SWARM (and possibly CSES) satellite data and solve this problem. The proposed method uses kNN machine learning algorithms; a modified version of Matrix Profiles and planar wave functions to construct a Q-Learning Table for our agent. Double Deep Q Networks could also be trained using the kNN and modified Matrix Profiles. This method eliminates the need for Active Learning (or human feedback) when training such algorithms. Reinforcement Learning could be the key to unlocking the Earth’s magnetic field, predicting earthquakes and saving countless lives.

The Tianshan orogenic belt (TSOB) is one of the most active regions in Eurasia. The far-range effect of the collision between the Indian and the Eurasian plates in the late Cenozoic led to the reactivation of the TSOB and the occurrence of intracontinental orogeny. At the same time, the TSOB expanded to the foreland basins on its both sides, forming multiple rows of décollement- and fault-related fold belts in the basin-mountain boundary zone. Global Positioning System (GPS) observations show that the shortening rate in the north-south direction across the TSOB gradually decreases from ~ 20 mm/yr in the west to ~ 8 mm/yr in the east. However, how the deformation is distributed inside the TSOB is controversial. Here, we determine the present-day kinematics of the major structural belts based on the Interferometric Synthetic Aperture Radar (InSAR) data of the Sentinel-1 satellites.

We process Synthetic Aperture Radar (SAR) data from 5 ascending tracks (T27;T129;T56;T158;T85) and 4 descending tracks (T107;T34;T136;T63) of the Sentinel-1A/1B satellites recorded between November 2014 and December 2020. We constructed a total of 1074 single-reference single-look interferometric pairs based on Gamma software covering a 790-km-length and 520-km-width area of the TSOB. Finally, the InSAR time series are processed using the StaMPS software package. The long-wavelength and elevation-dependent atmospheric errors from each date are mitigated using the TRAIN package and ECWMF ERA5 models.

Combining InSAR and GPS measurements, we show that the tectonic deformation is not evenly distributed in the TSOB. The convergence across the Tianshan ranges is approximately 15–24 mm/yr; the deformation gradient in the junction area between South Tianshan and Pamir is the largest and adjusts ∼68% of the total convergence deformation. South Tianshan is relatively stable without sharp gradients, and the remaining deformation is distributed in the intermontane faults and basin systems in the north of South Tianshan. We also find that the Kashi fold-thrust belt is the most active unit in this area, and the deformation is mainly concentrated on a series of folds: the Mushi, Kashi, and Atushi folds, and the faults between the folds, such as the Kashi, Atushi, and Toth Goubaz faults. As the boundary fault between the South Tianshan and the Tarim basin, the Maidan fault shows a clear deformation gradient. In the Keping nappe, the deformation is mainly concentrated on the Keping hill and Kepingtag fault in the front of the nappe. There are several remarkable deformation zones in the Kuche foreland. The deformation in the north of South Tianshan is dispersed in a series of intermountain active structures and the depression basins, unlike in the south side, where the deformation is mainly concentrated on the thrust folds. Furthermore, our study can provide constraints for deformation and slip partitioning patterns associated with the ongoing India-Eurasia collision in the TOSB.

In this report we will present the latest development of anomaly detection algorithms underpinned with Deep Neural Networks (DNN), which focuses on predicting and generating electromagnetic data from the Swarm historic data. We report our investigation into the two architectures of Recurrent Neural Networks (RNN) and generative adversarial network (GAN), particularly illustrating the development of Long-Short Term Memory (LSTM) based architectures and a flow-based generative model. The first RNN architecture is modelling with a stacked LSTM layers. There are several variations of this architecture, however our empirical analysis that the best result achieved is three LSTM layers structure. The second architecture is an architecture on three RNN models, called Encoder-Predictor-Decoder that is inspired by the work of Multi-head CNN–RNN for multi-time series anomaly detection. We will present the design of the architectures and their implementation, and compare the predicted and generated results of applying these approaches to the Swarm historic data. Based on the predicted and generated results, we will describe error metrics that can be used to measure the accuracy of reconstructed Swarm data reconstruction. Finally we will present our methods of detecting anomalies in the synthesized and true Swarm data along with possible applications in detecting seismic precursors from the synthesized and true Swarm data.

Building damage assessment is one of the most important parts of the earthquake damage assessment, the rapid and accurate damage assessment can help to reduce the disaster loss. A method built on using SAR images for building damage assessment is independent of weather conditions, and also using one post-earthquake SAR image to assess building damage is much quicker and more convenient than using the multi-source or multi-temporal data. PolSAR (fully-polarimetric SAR) data contain much more information than single- or dual-polarization SAR data, and the texture features extracted are very useful for recognizing ground objects in SAR image. However with PolSAR images, building damage recognition results directly generated by a polarimetric decomposition method always give rise to excessive assessment of damaged buildings. To overcome this deficit and improve the identification accuracy of building earthquake damage, we developed the two new texture feature parameters CV_AFI in the frequency domain and MSD in the spatial domain.
In SAR imagery, the scattering intensity of collapsed buildings is weaker than that of standing buildings, as the dihedral structures in collapsed buildings are destroyed. The standing oriented buildings (whose orientation is not parallel to the flight direction) have strong depolarization effect. Therefore, both collapsed buildings and oriented buildings are dominated by volume scattering, and they are easily misclassified. The oriented buildings always show banded textures with consistent arrangement, but collapsed buildings often show more random textures with a disordered distribution. The spatial frequency of SAR images can be clearly rendered in the frequency domain. For comparing the classification performance of texture features in the frequency domain and the spatial domain, we proposed the variable coefficient of angle domains based on the Fourier amplitude spectrum parameter (CV_AFI) and the mean standard deviation (MSD) parameter based on the statistical characteristics to discriminate oriented buildings and collapsed buildings.
We used CV_AFI and MSD, and combined the Yamaguchi four-component polarimetric decomposition method to extract building earthquake damage information, respectively. The double-bounce scattering components generated from Yamaguchi four-component decomposition are directly regarded as the intact buildings which is called DB intact buildings for short. The total power image as the intensity image of PolSAR data is used to compute CV_AFI and MSD. According to the threshold values of CV_AFI and MSD, the volume scattering components generated from the Yamaguchi four-component decomposition are classified into two categories of the collapsed buildings and the oriented buildings. The volume-dominated buildings corresponding to the CV_AFI or MSD values which are bigger than the thresholds are classified as the collapsed buildings, and the remaining volume-dominated buildings are classified as the oriented buildings. Finally, the oriented buildings are incorporated into the intact buildings.
The experimental results show that the overall correct building damage recognition accuracies of CV_AFI and MSD are 84.45% and 80.65%, respectively. The correct identification rates of CV_AFI and MSD for the collapsed buildings are 82.95% and 82.43%, respectively. The undamaged building correct identification accuracies of CV_AFI and MSD are 85.20% and 80.30%, respectively. The correct recognition accuracies of collapsed buildings and undamaged buildings and the building damage recognition overall accuracy with CV_AFI are higher than those of MSD. Especially, more oriented buildings are correctly detected using CV_AFI, that is, less oriented buildings are misclassified as collapsed buildings. Thus the identification accuracy of intact buildings is higher than that of collapsed buildings. This result well confirmed that the texture features in the frequency domain can better reflect the difference in the spatial distribution between oriented buildings and collapsed buildings. Therefore, the texture features in frequency domain are more effective for building damage recognition, and they should be given more consideration when developing earthquake damage assessment methods, in addition to applying the texture features in spatial domain.

The pace of urbanization has been unprecedented. Rapid urbanization poses significant social and environmental challenges, including sprawling informal settlements, increased pollution and urban heat island effects, loss of biodiversity and ecosystem services, and increased vulnerability to disasters. Therefore, timely and accurate information on urban change patterns is crucial to support sustainable and resilient urban planning and monitoring of the UN 2030 Urban Sustainable Development Goal (SDG). The overall objective of this project is to develop innovative, robust and globally applicable methods, based on Earth observation (EO) big data and AI, for urban land cover mapping and urbanization monitoring.

Using ESA Sentinel-1 SAR, Sentinel-2 MSI and Chinese GaoFen-1 images, the EO-AI4Urban team has developed varous deep learning-based methods for urban mapping and change detection. For urban mapping, a novel Domain Adaptation (DA) approach using semi-supervised learning has been developed for urban extraction. The DA approach jointly exploits Sentinel-1 SAR and Sentinel-2 MSI data to improve across-region generalization for built-up area mapping [1]. For urban change detection, several novel methods have been developed including a dual-stream U-Net [2] and a Siamese Difference Dual-Task network with Multi-Modal Consistency Regularization [3]. Further, a high-resolution feature difference attention network (HDANet) is proposed to detect changes using the Siamese network structure [4]. Another novel procedure was designed to search for built-up changing patterns with the joint use of temporal and spatial properties, starting from high-frequency SAR time series. The methodology has been tested on the city of Wuhan and considering a SAR series from March 2018 to March 2021 [5] [6]. Additionally, a novel automatic deep learning-based binary scene-level change detection method that trains a Scene Change Detection Triplet Network (SCDTN) using the automatically selected scene-level training samples was proposed [8]. A machine learning method was also developed using Landsat time series, to map built-up areas and to analyze changes during 2000 to 2020 [9]. Finally, to identify similar urban areas quickly and to reduce the cost of manually labeled data, a multisource data reconstruction-based deep unsupervised hashing method was proposed for unisource remote sensing image retrieval, called MrHash, which consists of a label generation network and a deep hashing network [9].

Experiments conducted on a test set comprised of sixty representative sites across the world showed that the proposed DA approach achieves strong improvements upon fully supervised learning. The fusion DA offers great potential to be adapted to produce easily updateable human settlements maps at a global scale [1]. Using the OSCD dataset, the results showed that the dual-stream U-Net outperformed other U-Net-based approaches together with SAR or optical data and feature level fusion of SAR and optical data [2]. Using bi-temporal SAR and MSI image pairs as input, the Siamese Difference Dual-Task network with Multi-Modal Consistency Regularization have been tested in the 60 sites of the SpaceNet7 dataset. The method achieved higher F1 score than that of several supervised models when applied to the sites located outside of the source domain [3]. Using several public building change detection datasets, the experimental results showed that the HDANet can achieve a high building change detection accuracy, compared with the current mainstream methods, with public building change detection datasets [4]. Using Landsat time series, the results show that machine learning method could extract built-up areas effectively. To analyze urbanization in 13 cities in the Beijing–Tianjin–Hebei region, SDG indicator 11.3.1, the ratio of land consumption rate to population growth rate (LCRPGR) is calculated and the results show that the LCRPGR in Beijing–Tianjin–Hebei region fluctuated significantly. Apart from the megacities of Beijing and Tianjin, after 2010, the LCRPGR values were greater than 2 in all the cities in the region, indicating inefficient urban land use [7]. The results for the scene-level changes between the bi-temporal VHR images showed that the proposed SCDTN method achieved the highest F1 score of 81.85% [8]. Conducting experiments on Sentinel-2 and GF-1 satellite images, the results showed that MrHash yielded the best performance among all methods [9].

References:

[1] Hafner, S., Y. Ban and A. Nascetti, 2022a. Unsupervised Domain Adaptation for Global Urban Extraction Using Sentinel-1 and Sentinel-2 Data. Remote Sensing of Environment. Volume 280, 113192.

[2] Hafner, S., A. Nascetti, H. Azizpour and Y. Ban, 2022b. Sentinel-1 and Sentinel- 2 Data Fusion for Urban Change Detection Using a Dual Stream U-Net. IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5.

[3] Hafner, S., Y. Ban and A. Nascetti, 2023. Multi-Modal Consistency Regular- ization Using Sentinel-1/2 Data for Urban Change Detection. International Journal of Applied Earth Observation and Geoinformation (under review).

[4] Wang, X., J. Du, K. Tan, J. Ding, Z. Liu, C. Pan, and B. Han, 2022. A high-resolution feature difference attention network for the application of building change detection, International Journal of Applied Earth Observation and Geoinformation, Volume 112, 102950.

[5] M. Che, A. Vizziello and P. Gamba, 2022. Spatio-temporal Urban Change Mapping with Time-Series SAR data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6] Che, M., A. Vizziello, P. Gamba. 2021. Spatio-temporal Change Mapping with Coherence Time-Series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7] Zhou, M., Lu, L., Guo, H., Weng, Q., Cao, S., Zhang, S., & Li, Q. (2021). Urban Sprawl and Changes in Land-Use Efficiency in the Beijing–Tianjin–Hebei Region, China from 2000 to 2020: A Spatiotemporal Analysis Using Earth Observation Data. Remote Sensing, 13(15).

[8] H. Fang, S. Guo, X. Wang, S. Liu, C. Lin and P. Du. 2023. Automatic Urban Scene-Level Binary Change Detection Based on a Novel Sample Selection Approach and Advanced Triplet Neural Network, IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18.

[9] Y. Sun, Y. Ye, J. Kang, R. Fernandez-Beltran, Y. Ban, X. Li, B. Zhang, and A. Plaza. 2022 Multisource Data Reconstruction-based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16.

Presenting Authors: Gao, Mingliang and Cartalis, Constantinos
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The scope of the work is to demonstrate the potential of Earth Observation to support urban security. The work is deployed in a two-fold manner.

At a first stage, the evolution of groundwater flow field and the corresponding response of land subsidence along Yongding River (Beijing section) were analyzed by performing spatio-temporal analysis, time series decomposition, based on the data sets covering traditional hydrogeological data, groundwater observation data, and satellite-based images. Results showed that, at present, ecological water replenishment of Yondding River has no obvious impact on the formation deformation, but the rising groundwater level and differential land subsidence in some regions will pose a great risk to the safety of coastal areas in the future. In addition, the Beijing section of the Yongding River crosses multiple subway lines, and the affected area is close to the Beijing Daxing International Airport. Local groundwater level rising may cause underground facilities damage, and uneven land subsidence may cause surface & underground structure break, as well as the stability of electronic equipment, which affect the safe operation of airports and rail transit.

At a second stage, the dynamics of urban heat risk were analyzed by means of a tool that is based on the use of high-resolution Earth Observation (EO), climate, and socioeconomic data and exploits the potential of machine learning. The tool is developed in the cloud-based Google Earth Engine (GEE) platform that effectively addresses the challenges of big data analysis in studying urban heat risk. Urban heat risk maps are created for Beijing and Athens using clustering algorithms, which group areas with similar characteristics and assign them to different heat risk categories based on the spatiotemporal patterns of the above-mentioned indicators. The results effectively identify vulnerable regions that experience significantly higher heat risk and constitute intracity thermal heat spots. To this end, scientific evidence may be used in support of spatially differentiated resilience plans for climate extremes at the city scale.

Recommendations on the use of Earth Observation for urban security will be provided along with a discussion on other urban challenges that may be addressed accordingly.

The Earth is facing unprecedented climatic, geomorphologic, environmental and anthropogenic changes, which require global scale observation and monitoring. The interest is in understanding involving Earth Observations (EO) of large extended areas, and long periods of time, with a broad variety of satellite sensors. The collected EO data volumes are thus increasing immensely with a rate of many Terabytes of data a day. With the current EO technologies these figure will be soon amplified, the horizons are beyond Zettabytes of data.

1）“Pre-trained and fine-tuning" is one of main paradigms that pre-train a fundamental model with large-scale unlabelled data in a unsupervised learning way and then retrain it with a small amount of labeled data for downstream tasks. Pre-trained models are demonstrated to be of strong generalization and adaptation to multi-tasks. To address the challenges of the difficulty and high cost of manual ground truth labeling, a three-dimensional masked autoencoder (MAE) self-supervised learning method is designed based on an improved masked autoencoder (MAE) self-supervised framework for SAR and optical image joint self-supervised learning to enhance the feature extraction ability in the vertical direction along modal channels. Experimental results show that the proposed method surpasses the state-of-the-art comparative learning and MAE-based models in land cover classification tasks and reduces data input through vertical masking to achieve a more efficient model. Furthermore, additional experiments show that the proposed model has good generalization and can maintain good representation learning capabilities on small-scale data.

2）A remote sensing image self-supervised learning method based on SimMIM is pre-trained , and a MIM-SwinUNet is fine-tuned for land cover classification model supervisedly. The experiment shows that the self-supervised pre-trained model can effectively extract generalizable image features, and when transferred to downstream land cover classification tasks, it can achieve similar classification performance with significantly reduced labeled training sample size. Based on self-supervised labeling learning methods, multi-temporal remote sensing image land cover classification and land use change analysis are carried out in the case of Shanghai area using Sentinel 1 and 2 data.

3）The challenge is the exploration of these data and the timely delivery of focused information and knowledge in a simple understandable format. In this context we envisage the monitoring of Danube Delta and Black see costal areal. The study is directed to the modeling and understanding of climate change effects, particularly droughts and maritime currents. Droughts are studied using multispectral Satellite Image Time Series (SITS) of Sentinel 2. The study case is focused on the ensemble of lakes between the Black Sea coast and Danube Delta for the period 2019 to 2022. The Sentinel 2 SITS are analyzed to quantitively measure the lakes water surface, as the case of lake “Nuntasi”. During the 2020 drought the lake was completely dearth, a channel was built connecting it to the neighboring larger lake and refilling it. The SITS characterizes both the water level and quality variation. The Black Sea surface current of in the coastline limitrophe area are analyzed using SAR SITS from Sentinel 2. The maritime surface currents are characterized estimating the Doppler frequency of the SAR images. The SITS data are used to predict current patterns

As the most common form of ocean movement, mesoscale eddies promote the redistribution of marine variables, such as temperature, salinity and nutrients, through the transport of material and energy. They have an important influence on the marine biogeochemistry cycle, marine ecosystem and marine heat balance etc. Through the 2D/3D structural visualization of multiple variables of mesoscale eddies, the motion patterns of mesoscale eddies are directly visual through graphics and images, greatly contributing to studying mesoscale eddies.

Under the Euler coordinate, various methods for extracting mesoscale eddies have been proposed based on their basic features, among which the sea level anomaly (SLA)-based methods have performed better because these methods are able to avoid extra noise and excess eddy detections. A previous SLA-based method has been provided to identify and track global eddies. This highly effective orthogonal parallel algorithm greatly improves the efficiency of recognition without reducing the accuracy of mesoscale eddy recognition. The global eddy identification and trajectory dataset is built with a total time span between 1993 and 2020, which provides a data foundation for the subsequent study of mesoscale eddies. Affected by the modulation of various physical mechanisms and the complex marine environments, there are also complex dynamic processes such as eddy splitting, eddy merging, and dipoles. In this case, an automatic recognition method of global eddy dipoles is developed in terms of the mesoscale eddy dataset and the transmission modes as well with the characteristics of dipoles are simultaneously analyzed. In addition, an algorithm named EddyGraph for tracking mesoscale eddy splitting and merging events is come up with based on multi-level topological relationships, which helps to analyze the statistical characterization of global eddy splitting and merging events.

Under the Lagrangian coordinate, eddies are the cumulative results of the state of the fluid within a given time scale, which can maintain material coherence over the specified time intervals. By using the elliptic Lagrangian coherent structures, a typical black-hole eddy is extracted based on the data of the geostrophic flow velocity field. Combined with multi-source satellite remote sensing data and in-situ data, it shows that the black-hole eddy boundary can describe material transport more objectively than the Euler eddy boundary on a longer time scale. On the regional scale, Lagrangian eddies in the Western Pacific are successfully extracted and their spatial and temporal variations are analyzed. Through normalized chlorophyll data, it is observed that Lagrangian eddies can cause chlorophyll aggregation and hole effects. These findings demonstrate the important role of Lagrangian eddies in material transport. Nevertheless, the high calculation cost during the integration process has become a bottleneck, especially when the data resolution is improved or the study area is enlarged. Therefore, SLA-based orthogonal parallel detection of global rotationally coherent Lagrangian eddies is built, whose runtime is much faster than the previous nonparallel method. Finally, a dataset of long-term global Lagrangian eddies is established.

Based on objective reference framework and criteria, the extraction and visualization of the mesoscale eddy coreline, an ocean three-dimensional structure, are achieved by extracting the valley line of the obtained from objective flow field calculations as the eddy coreline. At the same time, equipped with an integrated visualization system, named i4Ocean, a standard morphological model of the transfer function for ocean thermohaline anomaly data and pressure anomaly data is designed from the number of feature points, feature color mapping and the line shape. Volume rendering technology and spherical ray casting algorithm are utilized to more clearly and completely display the large-scale ocean 3D eddies under the condition of ensuring the rendering quality. Based on 2D and 3D flow field vector data, the spatio-temporal continuity of ocean flow field visualization is enhanced under the whole spatio-temporal continuous framework of pathline-pathline. The geometry-based visualization animation of trace becomes smoother and more stable after solving the problem of aliasing in previous visualized ocean flow fields. Applying region-based eddy detection techniques (ow method, Q method, and Ω method) to ocean flow fields, the extracted mesoscale eddies are more comprehensive. Based on the ow criterion, Q criterion, and Ω criterion, standard transfer functions are constructed to optimize the extraction effect of ocean mesoscale eddies, reduce the difficulty of analyzing ocean mesoscale eddies through user interaction transfer functions, and improve the efficiency of user interaction analysis of ocean mesoscale eddies.

Abstract

Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. Using satellite remote sensing data to monitor the production state of shipyard dynamically has the advantages of high efficiency and objectivity. Further, dock, shipway, assembly area, material storage area and other shipbuilding places are the indispensable part of shipbuilding industry, which can reflect the production activity of the shipyard. This study performs object detection for docks based on high-resolution remote sensing images and deep learning methods. Meanwhile, according to the imaging characteristics of optical remote sensing images and SAR images of shipbuilding places, we used satellite remote sensing data to dynamically monitor the shipyard production state from spatial and time series perspective.

The study firstly uses an object detection network based on the deformable spatial attention module (DSAM), which can be used to detect the docks on high spatial resolution remote sensing image. This network solve the object detection problems caused by the limit of actual docks and the diversity of docks. Secondly, since the backbone of the dock object detection network is with the excellent feature extraction capability for docks, this study connects the backbone with a lightweight status recognition network (Status Head) to determine the dock production status information based on the features extracted from the backbone. Thirdly, we analyzed the backscattered features of shipbuilding places on SAR satellite images, and proposed a method to monitor shipyard production state by using multi-time SAR data. This method can reduce the error caused by insufficient time resolution when using high resolution optical remote sensing data to monitor the production state. Finally, the proposed method can accurately recognize the shipyard production state through experimental verification, which reflects the potential of satellite remote sensing images in shipyard production state monitoring, and also provides a new research thought perspective for other coastal industrial production state monitoring.

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Geohazard monitoring is essential to anticipate and alleviate the hazards of natural disasters, safeguard human lives and critical infrastructure, and promote the sustainable growth of communities located in areas prone to such events. The increasing incidence of land subsidence and landslides poses a significant threat to human settlements and critical infrastructure worldwide, requiring urgent attention and mitigation measures. To effectively manage the risks associated with geohazards and minimize their impacts, it is of utmost importance to map their displacement rates and gain a comprehensive understanding of their mechanics. In this work, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. During last year, the research team has been mainly working on: a) EO monitoring, automatic mapping and classification of active displacement areas related to land subsidence and landslides on wide regions; and b) identification of triggering factors and modelling of specific landslides and land subsidence based on InSAR and in situ data. The results obtained from the study, which primarily concentrate on selected vulnerable areas in China and Spain, offer valuable insights for planning current and future scientific efforts aimed at monitoring landslides and land subsidence. The comprehensive analyses of these geohazards are essential for effective prevention and management, as well as enabling prompt response in the aftermath of their occurrence.

In the framework of the ESA-MOST Dragon-5 project, the National Institute of Geophysics and Volcanology (INGV) from Italy, Northeastern University (NEU) and Jilin Earthquake Agency from China conduct collaborative research on the multiple mining-induced geohazards in Northeast China using time Series SAR images. Moreover, we have also considered a new study site, the Changbaishan active volcano (Jilin Province, ~300 km east from Shenyang), which was responsible for the largest eruption of the last millennium in 946 CE.

Our first study site is the Fushun west Opencast coal mine (FWOCM), located in the southwest of Fushun city, China, which is the largest opencast mine in Asia. Since the 1920s, more than 90 landslides have been reported in FWOCM, especially the huge landslide on the south slope, which named Qiantaishan landslide. The Qiantaishan landslide has experienced a fast moving period during 2013 to 2016, and has stabilized since 2017. During the fast moving period, the landslide mass has moved approximately 90 m. However, since 2017, displacements of the Qiantaishan landslide is less than 150 mm/year. In order to analyze the spatial pattern and temporal evolution of different periods of the Qiantaishan landslide, both MT-InSAR and multi-temporal pixel offset tracking have been performed. Multi-temporal pixel offset tracking has been conducted considering 53 Cosmo SkyMed SAR images collected from 2013-07-03 to 2016-12-18 for the descending track, to monitor displacement of the fast moving period of Qiantaishan landslide. The results show that the landslide moves very fast during 2014, and slows down during 2015 to 2016. The MT-InSAR analysis has been carried out based on Sentinel-1 images collected during 2017 to 2022, to track the slow-moving period of Qiantaishan landslide. MT-InSAR results highlight that the displacements rate of the Qiantaishan landslide is up to 150 mm/year, which has basically stabilized. Comparison with ground measurements and cross correlation analysis via cross wavelet transform with monthly precipitation data are also computed, to analyze the influence factors of displacements in FWOCM.

The second study site, Changbaishan volcano complex is affected by landslides, earthquakes, degassing, and ground deformation. Deformations occurred during the 2002-2006 unrest episode and in 2020-2022. Analysis on the multi-hazards of Changbaishan is very important because a population of ~135000 in China and 31000 in North Korea lives within 50 km far from the volcano. Using 33 Envisat ASAR images acquired during 2004-2010 along the descending orbit, the accurate surface deformation parameters of Changbaishan Tianchi volcano has been extracted through a modified multi-temporal InSAR approach which involves point selection based on the Normalized Difference Vegetation Index (NDVI), to minimize the volume decorrelation problem. Then, based on three-dimensional geometric relationship between the volcanic surface deformation field and the radar line of sight (LOS) deformation, Mogi point source modeling has been calculated, revealing the inflation-deflation-stabilization process of the magma chamber during the end of the 2002-2005 unrest episode. Furthermore, we analyze the deformation of Changbaishan volcano during 2018–2022 processing by means of the SBAS technique a dataset consisting of 23 ALOS-2 images (L-Band, StripMap mode), acquired along the ascending orbit and revealing a low-level unrest occurred during 2020.12-2021.6. Modeling results suggest that three active sources are responsible for the observed ground velocities: a deep tabular deflating source, a shallower inflating NW-SE elongated spheroid source, and a NW-SE striking dip-slip fault. The depth and geometry of the inferred sources are consistent with independent petrological and geophysical data.

Acknowledgments

The Sentinel-1 data are free of charge distributed by the European Space Agency.

The COSMO-SkyMed data are provided by ASI through the ASI-ESA Dragon5 Project ID. 58029.

In the Dragon-5 project SARchaeology, we are working on using satellite SAR data and developing methods to support archaeological prospections and heritage site protection. SAR offers unique advantages, but also several challenges in this field. During Dragon-5, our work focused so far on study sites in China, Russia, Italy, Norway, and Turkey. However, due to the sanctions imposed on Russia, the cooperation on this test area came to a stop, so that the team members who were working on this site (i.e. University of Sydney) are currently focusing their attention on the other areas. In terms of employed methodologies, a strong focus is on the use of long-term multi-baseline SAR interferometry for continuous surface motion stability analysis on cultural heritage sites, as well as change detection methods. In terms of change detection, various approaches are under development, ranging from PSInSAR-based detection of urban developments, automatic coherence and amplitude-based change detection for looting mapping, and coherence change detection for damage assessment. Additionally, multi-sensor / multi-angle image analysis for post-earthquake damage detection in high-resolution SAR images has been undertaken for damage detection after the devastating earthquake that hit Turkey and Syria on 2023-2-6, damaged a vast area and led to immense loss in lives. The area is also well-known for its richness in cultural heritage und unfortunately, widespread damages to cultural heritage has been witnessed. To support the identification of damages at sites of archaeological interest, the team used data from the Dragon-5 project as well as several Third-Party Mission (TPM) data sources. Not all damages to cultural heritage are clearly visible from remote sensing imagery and the situation gets significantly worse when using SAR data. Even using very high-resolution TerraSAR-X staring spotlight datasets, damages are often hard to identify without the availability of similar images acquired before the disaster, which are missing. This proves the importance of continuous observation missions, like Sentinel-1, from which (albeit the very low spatial resolution) damage maps can be derived through coherence change detection analysis, which than can be used as a starting point for visual inspection on high-resolution optical images or very-high resolution SAR images. With optical imagery, the weather conditions play a central role in the detectability of damages, while on SAR images the image configuration, for example orbit direction and looking angles, can determine if a damage is visible or not in a given image.

Looting provides a global threat to cultural heritage, but in the aftermath of natural or man-made disasters, looting unfortunately strives even more. Looting activities are detectable from remote sensing. However, small-scale looting pits/holes are not always identified as such. In an experiment on the detectability of looting activities within SAR data, we conduct an experiment in Wuhan (China), where we create an experimental looting site of two different sizes and monitor the area with SAR data before and after the ‘looting’. Based on the so generated data, we analyse the detectability of looting activity in TerraSAR-X imagery at different resolutions and analyse the influence of polarizations, looking angles, and other SAR acquisition parameters.

Within the experimental area of Wuhan, we are also interested in the threat that the fast urban development of Wuhan poses to cultural heritage sites in and around the city. The fast development of the urban area of Wuhan leads to encroachment of buildings on cultural heritage sites, that, although often protected, are in danger due to the economic pressure with rising property prices. Additionally, the urban development leads to subsidence, which can also threaten the stability of sites. Using long-term SAR interferometry, the subsidence affecting sites of cultural heritage are identified as well as possible endangerment from urban encroachment.

Using a similar approach, threats to cultural heritage assets in the capital city of Rome (Italy) and its surrounding rural landscape are characterised. Sentinel-1 image stacks acquired in 2018-2022 are processed with the SBAS method, and a series of urban sectors affected by ground instability are identified across the wider Province, such as in the area of Fiumicino international airport (representing a relatively young phase of urban development and associated land conversion) and along the Tiber River alluvium, involving monuments and heritage assets. Given the paucity of studies using multi-polarization datasets in InSAR deformation investigations, the performances of the SBAS chain using Sentinel-1 VV and VH cross-polarised channels were also trialled to identify the amount and quality of coherent targets that the method is capable to detect and track using the two polarisations.

So far, the team's work focuses on employing long-term multi-baseline SAR interferometry for continuous surface motion stability analysis on cultural heritage sites, as well as change detection methods. Furthermore, the project has also shown the importance of continuous observation missions, like Sentinel-1, for damage detection and mapping especially in the context of the earthquake in Turkey. The team's experiments on the detectability of looting activities within SAR data will be significant contributions to the field of archaeology and heritage site protection. With further development and collaborations, the SARchaeology project can continue to make significant contributions to the preservation and protection of cultural heritage sites globally.

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