Multi-source Satellite Remote Sensing Techniques for Landslide Monitoring and Characterization

Landslides are natural geological hazards that pose significant threats, resulting in economic losses and casualties worldwide. Effective monitoring and characterization of landslides are crucial for understanding their evolution mechanisms and preventing catastrophic failures. While conventional field surveying methods provide accurate measurements of surface deformation, they are limited by high costs in terms of labor and time and uncertainties of arrangement for the ground-based equipment. The Satellite Interferometric Synthetic Aperture Radar (InSAR) technique has proven its application in landslide monitoring, offering advantages such as all-weather operations, wide spatial coverage, high spatial resolution, and high accuracy. InSAR can measure subtle changes along the SAR line-of-sight (LOS) direction but is not sensitive to movements along the north-south direction. Additionally, rapid movements during the failure stage can cause high decorrelation. On the other hand, satellite optical remote sensing data, combined with pixel offset tracking (POT) techniques, can measure large displacements in the horizontal plane. Moreover, multi-spectral analysis of optical images can offer insights into the spatial evolution of landslides. Therefore, the joint use of satellite InSAR and optical remote sensing techniques is complementary in landslide monitoring and characterization. However, the joint utilization of these techniques for capturing the long-term evolutions of landslides, particularly at their different stages using multi-source data, remains relatively unexplored. This dissertation aims to optimize and demonstrate the approaches for the joint use of satellite SAR and optical data in landslide monitoring and characterization across three distinct stages: pre-failure, failure, and post-failure. Three major landslides were studied in this dissertation. Firstly, the surface deformation of the 2017 Maoxian landslide during the pre-failure stage was captured using time series InSAR, while pre-failure slope features were detected from optical images. Secondly, the joint utilization of time series InSAR observations and optical analysis facilitated the monitoring of the pre-failure, failure, and post-failure stages of the 2020 Aniangzhai landslide. Lastly, the long-term post-failure deformation of the Huangtupo landslide in the Three Gorges Reservoir region was mapped using multi-source satellite SAR data, while the multi-temporal optical images were employed to investigate the long-term evolution of surface covers over the slope

CCTSDB 2021: A more comprehensive traffic sign detection benchmark

Traffic signs are one of the most important information that guide cars to travel, and the detection of traffic signs is an important component of autonomous driving and intelligent transportation systems. Constructing a traffic sign dataset with many samples and sufficient attribute categories will promote the development of traffic sign detection research. In this paper, we propose a new Chinese traffic sign detection benchmark, which adds more than 4,000 real traffic scene images and corresponding detailed annotations based on our CCTSDB 2017, and replaces many original easily-detected images with difficult samples to adapt to the complex and changing detection environment. Due to the increase of the number of difficult samples, the new benchmark can improve the robustness of the detection network to some extent compared to the old version. At the same time, we create new dedicated test sets and categorize them according to three aspects: category meanings, sign sizes, and weather conditions. Finally, we present a comprehensive evaluation of nine classic traffic sign detection algorithms on the new benchmark. Our proposed benchmark can help determine the future research direction of the algorithm and develop a more precise traffic sign detection algorithm with higher robustness and real-time performance

Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset

On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1A/B satellite and optical images captured by the PlanetScope satellites were jointly used to analyze and explore the deformation characteristics and the Spatial-Temporal evolution of the ANZ landslide before and after the multi-hazard chain. Several areas of pre-failure movements were found from the multi-temporal optical images analysis before the reactivation of the ANZ landslide. The large post-failure surface deformation over the ANZ slope was also retrieved by the optical pixel offset tracking (POT) technique. A major northwest movement with the maximum horizontal deformation of up to 14.4 m was found. A time-series InSAR technique was applied to analyze the descending and ascending Sentinel-1A/B datasets spanning from March 2018 to July 2020, showing that the maximum magnitudes of the Line of Sight (LoS) displacement velocities were −70 mm/year and 45 mm/year, respectively. The Spatial-Temporal evolution over the ANZ landslide was analyzed based on the time-series results. No obvious change in acceleration (precursory deformation) was detected before the multi-hazard chain, while clear accelerated deformation can be observed over the slope after the event. This suggested that heavy rainfall was the most significant triggering factor for the generation and reactivation of the ANZ landslide. Other preparatory factors, including the deformation behavior, the undercutting and erosion of the river and the outburst flood, the local terrain conditions, and earthquakes, might also have played an important role in the generation and reactivation of the landslide

Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset

On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1A/B satellite and optical images captured by the PlanetScope satellites were jointly used to analyze and explore the deformation characteristics and the Spatial-Temporal evolution of the ANZ landslide before and after the multi-hazard chain. Several areas of pre-failure movements were found from the multi-temporal optical images analysis before the reactivation of the ANZ landslide. The large post-failure surface deformation over the ANZ slope was also retrieved by the optical pixel offset tracking (POT) technique. A major northwest movement with the maximum horizontal deformation of up to 14.4 m was found. A time-series InSAR technique was applied to analyze the descending and ascending Sentinel-1A/B datasets spanning from March 2018 to July 2020, showing that the maximum magnitudes of the Line of Sight (LoS) displacement velocities were −70 mm/year and 45 mm/year, respectively. The Spatial-Temporal evolution over the ANZ landslide was analyzed based on the time-series results. No obvious change in acceleration (precursory deformation) was detected before the multi-hazard chain, while clear accelerated deformation can be observed over the slope after the event. This suggested that heavy rainfall was the most significant triggering factor for the generation and reactivation of the ANZ landslide. Other preparatory factors, including the deformation behavior, the undercutting and erosion of the river and the outburst flood, the local terrain conditions, and earthquakes, might also have played an important role in the generation and reactivation of the landslide

Integration of DInSAR and GPS for Co-seismic Modelling and Assessment of Potential Seismic Hazard

Earthquakes often cause the collapse of buildings and other structures over a large area, which severely threaten life and properties on the ground. Aftershock sequences or further triggered seismic events on other faults could continuously bring damage or threat to the vicinity of the epicentre area. GPS/GNSS can provide accurate measurements of surface deformation in three directions but only with point-based spatial coverage. Interferometric Synthetic Aperture Radar (InSAR), as a space-born imaging technique, can map ground deformation along the radar line-of-sight (LOS) direction over a large area with denser observations at centimetre level accuracy. With the recent development of new SAR satellite systems, more SAR acquisitions with higher spatial resolution and larger ground coverage can be obtained within a shorter period. However, InSAR is also limited by the insensitivity of measurements in the north-south direction due to the near-polar orbits of SAR satellites. Therefore, the integration of GPS and Differential Interferometric Synthetic Aperture Radar (DInSAR) allows us to obtain more information of the co-seismic deformation caused by earthquakes and to further optimize the co-seismic earthquake source modelling. In addition, large earthquakes always perturb the stress conditions of the surrounding fault systems, and the estimation of stress changes based on the source model is an essential hint of potential seismic hazard. This dissertation focuses on co-seismic modelling from the combination of DInSAR and GPS measurements, and the assessment of potential seismic hazard.Geodetic data from DInSAR and GPS measurements are used to invert source parameters and slip distribution of an earthquake based on the finite rectangle source fault in a homogeneous half-space. Furthermore, Coulomb stress change on the source fault and neighbouring active faults are estimated to evaluate the risk of seismic hazards. In this dissertation, three most recent large earthquakes were studied. First, the best-fit source models for the 2015 Mw 7.8 Nepal Earthquake and the following Mw 7.2 aftershock were inverted based on DInSAR and GPS data, revealing two NW-SE striking faults with low dips. The estimation of Coulomb stress change demonstrates that the Mw 7.2 event occurred on the high stress-loaded areas of the source fault for the main shock. Second, a normal single fault with a small left-lateral component was determined by co-seismic DInSAR and GPS measurements for the 2016 Mw 6.2 Amatrice (Central Italy) Earthquake. The stress changes induced by this event on the fault planes of the followingtwo major shocks reveal that the triggering relationship between the Amatrice Earthquake and the following two events. Third, for the 2017 Mw 7.3 Kermanshah (Iran-Iraq border) Earthquake the optimised source model shows a blind reverse fault with a relatively large right-lateral component. The high spatial resolution images from SuperView-1 satellite reveal that most linear surface features mapped by DInSAR measurements are gravitational deformation. Also, the Coulomb stress change on the neighbouring active faults agrees with the occurrence of major aftershocks and suggests the risk of future earthquakes. This research reveals the better performance of the new generation of SAR satellites and demonstrates the seismic implication from co-seismic modelling

Joint Use of Optical and Radar Remote Sensing Data for Characterizing the 2020 Aniangzhai Landslide Post-Failure Displacement

The ancient Aniangzhai (ANZ) landslide in Danba County, Sichuan Province of southwest China was reactivated after a series of complex hazard events that occurred in June 2020. Since then, and until June 2021, emergency engineering work was carried out to prevent the further failure of the reactivated landslide. This study investigates the potential of joint use of time series Interferometric Synthetic Aperture Radar (InSAR) and optical pixel offset tracking (POT) to assess deformation characteristic and spatial-temporal evolution of the reactivated ANZ landslide during the post-failure stage. The relationships between sun illumination differences, temporal baseline of correlation pairs and the uncertainties were deeply explored. Surface deformation along the line-of-sight (LoS) direction was retrieved by the time series InSAR processing with the two Sentinel-1 datasets, revealing a maximum deformation rate up to 190 mm/year. The large horizontal displacements were also detected from the POT processing using 11 optical images acquired by the PlanetScope satellite (3 m spatial resolution), showing a significant increase of about 24 m between 24 June 2020 and 11 June 2021. The time series analysis from the InSAR and optical POT results revealed that the reactivated ANZ landslide body is gradually slowing down to a steady deformation status since its occurrence in August 2020, indicating the effectiveness of engineering work on the prevention of further landslide. A slight acceleration was detected from both InSAR and optical POT time series analysis between May 2021 and June 2021, which could be caused by the increased rainfall in May 2021

Interface engineering in transition metal carbides for electrocatalytic hydrogen generation and nitrogen fixation

With an increasing energy consumption rate and rising global population, constructing sustainable energy technologies has become one of the major scientific challenges. Therefore, the development of electrocatalytic conversion technologies that can convert renewable resources, such as water and nitrogen, into value-added chemicals or fuels (e.g., hydrogen and ammonia) can be crucial. A number of transition metal carbides (TMCs) have been investigated over the past few years as effective electrocatalysts for various reactions. This is mainly owing to their unique electronic structures, which leads to high electrical conductivity and chemical stability. Moreover, the reactivity of TMC-based electrocatalysts is highly dependent on their surface and interfacial properties. This review focuses on tuning nanostructures and interfaces to enhance the electrocatalytic activity of TMC-based materials for hydrogen production and nitrogen fixation. The mechanisms behind the surface and interface engineering are discussed, including the synergy effects, facet binding energy, active defects, and low-coordinated sites. In particular, studies on activity enhancement through design of the interfacial phase, composition, and structure in TMC-based electrocatalysts are highlighted. The effective tuning strategies might pave the way for future development of highly active TMC-based electrocatalysts for sustainable energy-related conversion.MOE (Min. of Education, S’pore)Accepted versio

The role of hepatic stellate cells in the regulation of T-cell function and the promotion of hepatocellular carcinoma

Hepatic stellate cells (HSCs) have immunosuppressive abilities and may be responsible for the occurrence and development of hepatocellular carcinoma (HCC). However, the mechanisms through which HSCs affect T-cell-mediated immune responses remain unclear. The aim of this study was to elucidate these mechanisms. We examined the effect of HSCs on T-cell proliferation and apoptosis, regulatory T cells (Treg cells) and T-cell-mediated cytotoxicity using mixed leukocyte reactions (MLRs). Furthermore, we examined the cytokines present in the supernatant and the effect of this supernatant on the proliferation and migration of cancer cells. Finally, we examined the effect of HSCs on HCC cells in vivo. We found that activated HSCs induced T-cell hyporesponsiveness, accelerated activated T-cell apoptosis, increased the number of Treg cells and inhibited T-cell-mediated cytotoxicity. HSCs also enhanced the expression of some cytokines and promoted the proliferation and migration of cancer cells. Furthermore, activated HSCs were able to induce HCC proliferation and Treg cells expansion in vivo. Activated HSCs may induce T cell anergy, thereby facilitating the immunologic escape of HCC cells.National Key Sci-Tech Special Project of China [2012ZX10002011-005]; National Natural Science Foundation of China [81171967

Efficacy of Mesenchymal Stem Cells Derived from Human Adipose Tissue in Inhibition of Hepatocellular Carcinoma Cells In Vitro

National Natural Science Foundation of China [81171967]; National Key Sci-Tech Special Project of China [2012ZX10002011-005]; Projects of Xiamen Science and Technology Program [3502Z20114010]; Public Health Department of Fujian Foundation for Young Scientists [2009-2-65]Hepatocellular carcinoma (HCC) is often diagnosed at an advanced stage, and over the past several decades, many researchers have worked to develop novel effective therapies for HCC patients. The functional contributions of mesenchymal stem cells to human malignancies, including HCC growth and progression, are controversial, and the potential mechanisms underlying these effects are not clear. The aim of this study was to investigate the effect of adipose-derived mesenchymal stem cells (ADSCs) on the growth of HCC cells. In this study, a conditioned medium from ADSCs (ADSC-CM) efficiently inhibited HCC cell proliferation and division, and induced HCC cell death through the downregulation of Akt signaling. These findings indicated that the ADSC-CM could inhibit HCC growth. Thus, the ADSC-CM is a good candidate for the treatment of HCC patients for whom no effective therapy is available