|
COLLABORATIVE MONITORING OF DIFFERENT HAZARDS AND ENVIRONMENTAL IMPACT DUE TO HEAVY INDUSTRIAL ACTIVITY AND NATURAL PHENOMENA WITH MULTI-SOURCE REMOTE SENSING DATA
|
The industrial district of Shenyang and Anshan plays an important role in the economic and social development of Northeast China. The mining activities strongly impact local environment due to ground excavations of coal and iron extraction. [...] |
Dr. Cristiano Tolomei, Ist. Naz di Geofisica e Vulcanologia - INGV, ITALY |
Dr. Lianhuan Wei, Northeastern University, CHINA |
Solid Earth |
The industrial district of Shenyang and Anshan plays an important role in the economic and social development of Northeast China. The mining activities strongly impact local environment due to ground excavations of coal and iron extraction. Anshan and Shenyang are subjected to multi-hazard including subsidence, landslides, and building damages. Results from the Dragon-4 project revealed landslides around open-pit mines, building instability and structural damages. In addition, the tunnel construction of underground lines at Shenyang has triggered surface fissuring, subsidence and sinkholes. The monitoring of such hazards is of fundamental importance to minimize and prevent the risks. In this proposal, we foresee to continue the monitoring activities started with the Dragon-4 project by means of multi-source remote sensing data at Shenyang and Anshan. Our Dragon-5 proposal will also consider a new study site, the Changbaishan active volcano (Jilin Province, ~300 km east from Shenyang). This volcano last erupted in 1903 and was responsible for the largest eruption of the last millennium in 946 CE. Changbaishan is affected by landslides, earthquakes and ground deformation. Deformation phenomena occurred during the 2002-2006 unrest episode and in 2017, when a nuclear test in North Korea triggered landslides. The multi-hazard exposure of Changbaishan is high because a population of ~135000 in China and 31000 in North Korea lives within 50 km from the volcano. In addition 2000000/yr tourists visit the Changbaishan volcano UNESCO National Reserve. We choose the topic ‘Solid Earth’ and the following sub-topics: 1.2-Monitoring of surface deformation and large landslides for the Shenyang, Anshan and Changbaishan sites 1.1-Seismic deformation monitoring for the Changbaishan site 1.4-Subsurface target detection for the Changbaishan hydrothermal and/or magma reservoir. The main goals of this proposal are to take advantage of the availability of remote sensing data to: 1) monitor and analyze the different hazards and environmental impact due to heavy industrial activity at Shenyang and Anshan areas and to natural phenomena at Changbaishan; 2) identification and modeling of single and multiple hazards, identifying the cross-related influence and causing factors; 3) forecast when and how hazards might happen, generate hazard scenarios, and provide support for disaster prevention and damage reduction to Authorities. The methodology to achieve the above objectives is the collaborative analysis of multi-source EO data, by means of InSAR time-series, VNIR optical data series, seismic, geochemical, laser scanning data and modeling. Time series InSAR allow to analyze the spatial and temporal deformation over large areas. Using both ascending and descending orbits, we will monitor such phenomena considering different sensors and different band frequency. We will decompose the LoS deformation into the Up and E-W directions to better constrain the deformation field. Volcanic deformation and landslide movements occur in both vertical and horizontal directions. We will adopt a multi-orbit InSAR time-series fusion approach with the assistance of high resolution DEMs generated by laser scanning. The deformation patterns will be validated with leveling and geodetic data. The volcanic source inversion will also be carried out by means of modeling. The hazards in traditional industrial regions and volcanic areas may be due to multiple causes. Instead of only monitoring a single hazard, this project aims to detect the spatio-temporal evolution of processes causative of multiple hazards via data modeling and assimilation techniques. The proposed research also foresees the exchange of young scientists. We apply for funding for the young scientist within this project and agencies in both Countries. Funding are also available from INGV and internal active projects.
|
|
EARTH OBSERVATION FOR SEISMIC HAZARD ASSESSMENT AND LANDSLIDE EARLY WARNING SYSTEM
|
Landslides are a major global hazard, controlled by geology, weather and land-use, and also a major secondary hazard in most continental earthquakes. Recent catastrophic landslides in China and elsewhere have demonstrated the importance of [...] |
Professor Roberto Tomas Jover, University of Alicante, Spain |
Prof. Qiming Zeng, Peking University, CHINA |
Solid Earth |
Landslides are a major global hazard, controlled by geology, weather and land-use, and also a major secondary hazard in most continental earthquakes. Recent catastrophic landslides in China and elsewhere have demonstrated the importance of understanding this hazard and of developing early-warning systems. Developing and validating Earth Observation (EO) technologies for the detection and monitoring of landslide hazards meets the Sentinel mission objective of geological hazard mapping. EO allows hazard assessments to be made and enable improved planning, design and early warning systems. In our Dragon-1/2/3/4 projects, we have successfully employed InSAR to map a range of active landslides in different regions of China, e.g. the Badong, Xintan, Shuping, Heifangtai and Maoxian landslides. In this project, we aim to further develop advanced SAR and optical techniques to detect potential landslides across the whole Jinsha River region, and demonstrate EO-based landslide early warning system over selected landslides. The main objectives of the project are as follows: O1. Integrate various SAR/InSAR/Optical techniques to generate surface deformation maps for extremely-slow to very-slow moving to slow-moving landslides. O2. Combine various SAR and optical datasets to generate surface deformation maps for slow-moving to fast-moving landslides. O3. Utilise deep learning techinques to automatically detect landslides based on surface deformation maps. O4. Determine the geophysical mechanisms responsible for landslides and provide a quantitative risk assessment along the Jinsha River region. O5. Demontrate GNSS-based landslide early warning system on selected sites. We expect that this project will lead to: (1). A processing chain to integrate Conventional InSAR, pixel offset tracking of radar and optical amplitude measurements and a time series tool. (2). Optimized ways to combine satellite radar and optical images for automatic detection of fast-moving landslides (3). Demonstration of landslide early warning system (4). Around 15 young researchers in China and Europe trained in the landslide field by the end of this Dragon-5 project (5). Regular academic exchanges between China and Europe (6). Joint workshops with young researchers involved (7). Joint publications in high impact journals This project is a collaboration among eleven institutions in China and the EU/UK. We will take advantage of the opportunity offered by the Dragon framework for Chinese-European exchange. We plan joint workshops in China for young postdoctoral scientists and students. Young scientists will also take part in exchanges, e.g. visits by Chinese scientists to work on InSAR at the UK universities, field visits to China by UK scientists. This project will be supported by (1) National Natural Science Foundation of China (NSFC) [41571337] (PI: Qiming Zeng) (2) China Earthquake Administration [ZDJ2018-16] (PI: Jingfa Zhang) (3) National Natural Science Foundation of China (NSFC) [41874005] (PI: Chaoying Zhao) (4) National Natural Science Foundation of China (NSFC) [41941019] (Co-I: Wu Zhu) (5) UK Natural Environment Research Council [NE/K010794/1] (Newcastle PI: Zhenhong Li) (6) UK Natural Environment Research Council [COMET] (Newcastle PI: Zhenhong Li)
|
|
INTEGRATION OF MULTI-SOURCE REMOTE SENSING DATA TO DETECT AND MONITORING LARGE AND RAPID LANDSLIDES AND USE OF ARTIFICIAL INTELLIGENCE FOR CULTURAL HERITAGE PRESERVATION
|
Remote sensing (RS) data is successfully applied since decades for the identification and monitoring of landslide phenomena at different spatio-temporal scales. However, limitations associated with data availability/accessibility (spatial [...] |
Prof. Joaquim Sousa, University of Trás-os-Montes and Alto Douro, UTAD, PORTUGAL |
Prof.. Fan Jinghui, China Aero Geophysical Survey & Remote Sensing Center for Natural Resources, CHINA |
Solid Earth |
Remote sensing (RS) data is successfully applied since decades for the identification and monitoring of landslide phenomena at different spatio-temporal scales. However, limitations associated with data availability/accessibility (spatial coverage, low temporal revisit time, high costs) might hampered the development of operational tools.The results and analyses retrieved in the framework of D4 32365 have shown the great benefits of RS in monitoring multi-hazards. The wide spatial and temporal data availability allowed a detailed description of landslide histories even of remote regions. Therefore, the continuous monitoring of such hazards, namely large landslides, is of fundamental importance to minimize and prevent the actual and future risks. In this D5 proposal, we foresee to continue the monitoring activities started with the D4 project mainly by means of multi-source RS data at diverse areas located in different countries.Our D5 proposal would also consider monitoring structures of great heritage and historical value, more quickly and effectively, as these structures are continuously subject to deformations caused by internal and external factors, especially when located in high risk areas. The availability of SAR data with spatial and temporal resolutions at an unprecedented level, associated with the new methods of SAR time series processing, allow us to think in the development of active systems for structural risks detecting and alerting. However, only the use of Artificial Intelligence techniques will allow to deal with the huge amount of data that will be generated. The Vilari├ºa Valley, located in the north of Portugal is crossed by an active fault and will be used as test site to develop the AI-based risk alert system. In this region there is a high number of buildings with historical and patrimonial interests that may be at risk. In order to cover most situations, the following Chinese sites will be also included: (1) Hani Rice Terraces; (2) Fushun West Open pit Coal Mine and (3) Shuping-Fanjiaping. Besides, the Central Karakoram Range in the Northern Pakistan, exposed to a variety of natural hazards including devastating landslides, would be an important study area on the Belt and Road.Based on the above selected areas of investigation and their multi-hazard exposure, we refer to the ÔÇÿAcross topicsÔÇÖ option. We choose the topics ÔÇÿ1. Solid EarthÔÇÖ and ÔÇÖ10. Data AnalysisÔÇÖ, with the following sub-topics:ÔÇó 1.2 – ÔÇ£Monitoring of surface deformation of large landslidesÔÇØÔÇó 1.3 – ÔÇ£Infrastructure health diagnosis and safety monitoringÔÇØÔÇó 10.2 – ÔÇ£Artificial Intelligence and Machine LearningÔÇØMain goals:- detect and analyze recent rapid landslide events with satellite and ground based EO data- evaluate the stability of landslides, forecast when and how hazards might happen in future, generate future hazard scenarios and provide support for disaster prevention and damage reduction to authorities- use latest EO technologies for monitoring historical structures leading to the early detection of potential risks and thus making it possible to increase security and significantly reduce maintenance costs- develop an AI system to process and analyze huge amount of dataThe methodology to achieve the previous objectives is the collaborative analysis of multi-source EO data, by means of satellite SAR and InSAR time-series, S2 optical images, VHR and stereo optical images, GBSAR data, GNSS and CR measurements, geological and environmental data, and data modeling.The proposed research also foresees the exchange of YS in the scope of projects 41941017 and 41877522 funded by National Natural Science Foundation of China, 07-Y30B03-9001-19/21 funded by State Administration of Science, Technology and Industry for National Defense of China´╝îDD20190514 funded by China Geological Survey, and 1/SAMA/2020/2019 (POCI-62-2019-04) funded by AMA. We also apply for funding for the YS within this project and national agencies
|
|
SARCHAEOLOGY: EXPLOITING SATELLITE SAR FOR ARCHAEOLOGICAL PROSPECTION AND HERITAGE SITE PROTECTION
|
Archaeological prospection and the protection of cultural and natural heritage sites are important applications of remote sensing. In the past, they have been underrepresented in the Dragon programme. In our proposed project, we intend to work [...] |
Dr. Francesca Cigna, National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), ITALY |
Prof. Timo Balz, Wuhan University, CHINA |
Solid Earth |
Archaeological prospection and the protection of cultural and natural heritage sites are important applications of remote sensing. In the past, they have been underrepresented in the Dragon programme. In our proposed project, we intend to work on archaeological prospection and heritage protection with SAR remote sensing. With the upcoming wider availability of long-wavelength data from various L-band missions and ESA’s BIOMASS P-band mission, sub-surface target detection is becoming possible. This opens new perspectives for the use of SAR for the support of archaeological prospection, and this will be the main research focus of this project. Although the spatial resolution of these long-wavelength sensors will be too low for many archaeological applications, we expect the data to be useful for landscape archaeological analyses, especially with respect to hidden paleo-channels and hidden linear structures. This research will focus on arid areas in China, e.g. paleo-channels around Lop-Nor, as well as the larger province of Rome, including sub-urban and rural expanses with partly buried archaeological ruins. SAR data to be exploited for archaeological prospection will include ALOS-1 L-band, as well as shorter wavelengths, namely ERS-1/2, ENVISAT, RADARSAT-1/2 and Sentinel-1 C-band, and potentially Iceye and Paz X-band data, in order to test signal penetration capabilities at the different wavelengths and spatial resolutions. The identification of objects of archaeological interest from SAR is also an on-going research hotspot. Based on the team’s previous research on Kurgans (Iron Age burial mounds), we plan to work on the detection of Kurgans in Copernicus Sentinel-1 images. Generally, the resolution of Sentinel-1 is too low for the clear identification of Kurgans. However, using multi-temporal despeckling, Kurgans can become distinguishable in Sentinel-1. Even more so, with the correct combination of seasonal images, e.g. only summer images, and polarization.Subsidence can damage cultural heritage sites and various surface motion related disasters, e.g. landslides, can endanger natural and cultural heritage sites. Measuring surface motion with SAR is therefore an important part of heritage protection. In this regard, we focus our research on the long-term surface motion monitoring. Due to changes in the environment, as well as changes in the availability of sensors, a true long-term surveillance, covering decades, is a challenging task. The research in this project will therefore focus on the long-term surveillance, mainly with Sentinel-1. Looting is another on-going problem in archaeology and in the protection of our heritage. Remote sensing can play an important role in the detection of looting and we intend to further investigate looting detection with SAR data. In the proposed project, we present a new research direction for Dragon, with a new team, while keeping a degree of continuity with previous Dragon programs as well. The European partners will support the project with in-kind contribution of their work time and in-house computing resources. The funding from China will be coming from internal funding of LIESMARS and Wuhan University. The exchange of the Chinese students will be supported by the Chinese Scholarship Council.
|
|
SEISMIC DEFORMATION MONITORING AND ELECTROMAGNETISM ANOMALY DETECTION BY BIG SATELLITE DATA ANALYTICS WITH PARALLEL COMPUTING (SMEAC)
|
The seismic deformation monitoring efforts using InSAR in the past 16 years gain fruitful achievements under the Dragon 1-4 cooperation projects. The seismic-related works using InSAR method include interseismic deformation monitoring along big [...] |
Dr. Yaxin Bi, University of Ulster, UK |
Prof.. Jianbao Sun, China Earthquake Administration, CHINA |
Solid Earth |
The seismic deformation monitoring efforts using InSAR in the past 16 years gain fruitful achievements under the Dragon 1-4 cooperation projects. The seismic-related works using InSAR method include interseismic deformation monitoring along big faults, regional-scale deformation detection, major earthquake deformation measurements and postseismic deformation analysis for rheology studies. In recent years, induced seismicity monitoring is also another important task to do for mines or shale gas production. In Dragon 5, we plan to continue our Dragon 1-4 works on seismic deformation monitoring, in conjunction with detecting abnormal changes of electromagnetic field in the lithosphere. However, new challenges appear on SAR data analysis itself and integration with electromagnetic field to interpret the mechanism of causing seismic deformation. In the past 5 years, Sentinel-1 satellites acquired high-quality data and are still accumulating with fast rate and require high capability for InSAR data processing. To overcome the issues, we developed parallel computation systems for this purpose, which also has a great storage system attached to it. Moreover, with the big forward on artificial intelligence (AI) and machine learning algorithms developed in recent years, we hope to integrate them into data processing system to improve deformation detection precision and data analysis process in aggregation with electromagnetic data. Another piece of work is to deal with the atmospheric delays on InSAR time-series analysis because the current methods all have various kinds of difficulties in the analysis, and prevent further improvements on precisions. The project proposes to use machine learning methods to construct models that could be used to accurately make predictions or simulations of atmospheric delays, as shown by some of the recent tries. The tectonic environment of China and surrounding regions depend mostly on the collision of Indo and Eurasia plates. In Dragon 5, we will still focus on faults, such as the Haiyuan, Kunlun, Altyn Tagh, Xianshuihe, Tianshan fault systems etc. In addition, we will also integrate InSAR and GPS data to invert for regional strain distribution in particular regions such as Tibet, North China Plain, to prepare for seismic hazard mitigation, and assess the risk for national key projects, such as the Sichuan-Tibet railway construction project. Moreover, the recent hot topic on induced seismicity is the new field for InSAR working with other traditional approaches, in particular for the Sichuan basin, so we will also address this new topic in our Dragon project. Since 2012, China Earthquake Administration has constructed a Control Source Extremely Low Frequency (CSELF) observatory network that is composed of more than 30 stations, covering the main seismic zones across the mainland of China. The network observes electromagnetic fields and ground resistivity from natural and artificial sources. By joint analysis of deformation data and any abnormal changes captured in electromagnetic field through CSELF, we expect to detect possible pre-slip events along particular faults. During Dragon 5, seismic events will certainly occur irregularly. We will investigate both the deformation produced by major earthquakes and their CSELF anomaly signals, then try to find the correlations between these two geophysical quantities. In the past 16 years, we had different projects, funded by the National Nature Science Foundation of China and China Earthquake Administration, focusing on geodetic strain measurements of specific faults or areas by InSAR and GPS data, monitoring earthquakes through analyzing the changes of electromagnetic field etc.
|
