2019 Vol. 44, No. 7
Since 2017, many serious geological disasters have been reported, including the 2017 mountain collapse at high altitudes in Xinmo Village in Mao County, Sichuan Province, and the 2018 Baige landslide in Jinsha River, most of which are of great destructive power and hard to detect in advance. It is worth noting that although the geohazard prevention has been carried out extensively across the whole country which is supported by the state, many of these geological disasters occur outside the potential geohazard points estimated in advance. The early identification of these undetectable geohazards points remains a big challenge and a crucial task in current geohazard prevention work. In this paper, the characteristics of interferometric synthetic aperture radar (InSAR) and its inherited limitations are summarized. Based on the integrated remote sensing technologies (including optical, SAR/InSAR and LiDAR), the key observation concept with three forms "morphology, deformation, situation" is proposed. Through the integration of a range of remote sensing technologies, the locations of potential geohazards will be identified qualitatively, and their associated movements will be monitored quantitatively. Finally, a series of thoughts and recommendations are provided to guide our future work for the early detection of serious geological hazards.
In China, traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space-air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar (InSAR) data from satellites are employed to obtain a global panorama of a region, highlighting these problematic locations; yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence, it makes the compilation of long displacement time-histories feasible, contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then, detailed assessments can be done through the deve-lopment of unmanned aerial vehicles (UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging (LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system (GNSS) and crack gauges, validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention.
Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously, which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements, and the geometric distortion introduced by the side-looking orbit. In this paper, from landslide detection and monitoring perspective, the four major challenges of satellite radar technologies are discussed:①The phase decorrelation caused by heavy vegetation can be weakened by use of synthetic aperture radar (SAR) imagery with a long radar wavelength (e.g. S-band or L-band), a short temporal resolution, and/or a high spatial resolution (e.g. 1 m or even higher), and/or advanced interferometric SAR (InSAR) time series, and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range split-spectrum interferometry techniques.②Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The generic atmospheric correction online service (GACOS) which is developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis.③The geometric distortions such as shadows and layovers can be pre-analyzed using an external digital elevation model (DEM) for medium-spatial-resolution SAR data; in contrast, for high-resolution SAR data, a machine learning approach can be used to identify water bodies, shadow and layover areas without a requirement of a high-spatial-resolution DEM.④Residual topographic phase exhibits in areas with high buildings or steep slopes, which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e.g. ground-based radar, LiDAR and GNSS) to develop an automated landslide detection and monitoring system. It is expected that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development.
Due to the influence of global climate change, most glaciers in southeastern Tibet and Hengduan Mountains in recent years have been losing weight, deteriorating and thinning, which has caused the variation of glacier movement characteristics, resulting in frequent disasters such as debris flows and landslides. In order to break through the bottleneck of optical remote sensing restricted by climatic conditions, this paper combines satellite and ground-based synthetic aperture radar (SAR) technology and selects Hailuogou Glacier (HLG) basin as a typical research area to carry out time series monitoring and analysis. Firstly, by using 38 SAR images acquired by PALSAR-1/2 satellites from 2007 to 2018, the temporal and spatial variations and local surface displacements of HLG in Gongga Mountain are monitored by using the pixel offset tracking (POT) method. The average velocity of HLG No.1 is slowed down by 7.27% per year in recent years, and the slow-down rate reaches 15.57% per year in the ablation areas. At the same time, several unstable landslides are detected by POT and Stacking-InSAR methods at the moraine embankment on the side of the glacier. Statistical analysis confirms that the movement of such landslides is strongly correlated with the melting of the glacier. The sliding speed reaches its peak in summer every year. The maximum sliding speed in 2018 was 100 mm/d in the north-south direction and 50 mm/d in the east-west direction. Subsequently, by utilization of the high-frequency real-time monitoring data of ground-based radar, it is further determined that the sliding speed reaches its peak value of 150 mm/d on July 9, 2018, and abnormal fluctuations occur with the subsequent collapse, which shows in detail the whole process of landslide creep to result in disasters. Relevant research data and the monitoring results can provide a reference for the study of the cryosphere and mountain hazards.
The interferometric synthetic aperture radar (InSAR) technique is used over Heifangtai loess terrace, Gansu province of China to map the distribution of potential loess landslides, the evolution of landslide deformation and the failure mode. Firstly, the archived synthetic aperture radar (SAR) datasets with different spatial resolutions and wavelengths from December 2006 to November 2017 are used to identify the potential landslides. Tens potential landslide areas are identified from December 2006 to March 2011 and from January 2016 to November 2016. Field investigation and optical remote sensing images validate the reliability and accuracy of the identified landslides. Then, the TerraSAR-X data with high spatial and temporal resolution are used to monitor the time series deformation of the typical unstable slopes. Results demonstrate that the landslides with the large accumulative deformation all occur in the following time, and the acceleration dates of failed landslides are successfully captured by InSAR time series results. Finally, two-dimensional deformation monitoring of loess landslide is conducted by combining with ascending and descending SAR datasets. The landslide failure mode are analyzed in depth according to the obtained two-dimensional deformation results, topographic map and remote sensing images. The accuracy of the obtained result is verified by field investigation.
As one important part of post-earthquake loss assessment, building damage detection has important significance for understanding the disaster in time, implementing the emergency response, and post-disaster reconstructing. In recent years the development of the multi-platform remote sensing and the high-resolution remote sensing technology have provided opportunities to building damage detection with high accuracy. In this paper, firstly, the basic building damage characteristics and the concrete classification criteria are introduced. Then, the common damage detection methods at home and abroad based on the remote sensing technology are concluded, including the single-temporal image detection of the post-earthquake, the change detection between the pre-earthquake and the post-earthquake images, and the damage detection based on the multi-sources data. Limitations and drawbacks of these methods are proposed. Finally, combining with some cutting-edge technologies in remote sensing domain, the latest research progress of building damage detection based on the high-resolution remote sensing technology, synthetic aperture radar technology, light detection and ranging technology, and oblique aerial photography technology are summarized in detail.
On the basis of the second generation explicit statistical early warning model, based on the geological conditions and rainfall conditions of Zhongshan, a statistical early warning model for the area is constructed. On this basis, the zoning map of geological hazard is superimposed. At the same time, the plain area with elevation less than 10 meters is directly judged as no risk in the calculation process, so that it can reduce the amount of calculation and realize the more precise geological hazard warning in the study area. Compared with the early warning method of single point installation monitoring equipment in Zhongshan, the research area is divided into some small regions according to different terrain and geomorphology, and then the early warning formula of each region is derived. Finally, the early warning map of geological hazards is obtained by combining different regional formulas and geological hazard prone zoning maps. The experiment shows that the regional early warning method can be more accurate in positioning than the previous method, and the levels of early warning become more intuitive.
Persistent agricultural irrigation makes loess terrace prone to landslide hazards. Therefore, it is necessary to identify and monitor the potential landslides with effective ways. In this paper, the time series interferometric synthetic aperture radar (InSAR) analysis technique is utilized to analyze both descending and ascending Sentinel-1 data stacks covering the period from January 2016 to August 2018. Active slopes on the Heifangtai terrace and surrounding area in northwest China are identified. Comparison between time series InSAR measurements and GPS measurements is carried out. Time series displacement analysis reveals that agricultural irrigation poses a great threat to the stability of slopes at the terrace edge. Meanwhile, temperature variation related displacements are also identified on bridges across the Yellow River with high correlation. Our study confirms the effectiveness of time series InSAR method, which can play a significant role in the identification and prevention of loess landslides.
By using Sentinel-1A ascending and descending synthetic aperture radar (SAR) data, this paper extracts the high-precision interferometric SAR (InSAR) coseismic deformation of the 2016 Menyuan Mw5.9 earthquake, inverts the fault geometry and slip distribution using simplex method and nonnegative least squares method, and constructs the deep geometry model of regional fault zone. The results show that coseismic deformation of the Menyuan Mw5.9 earthquake is dominated by surface uplift, and the maximums along the line-of-sight direction of ascending and descending tracks are 5.3 cm and 7.1 cm respectively. The fault strike and dip angles are 133° and 43° respectively. The seismic slip is dominated by thrust component, mainly occurring in 6.14-12.28 km underground. The maximum slip is about 0.5 m, the average slip angle is 66.85°, and the earthquake moment is 1.0×1018 N·m (Mw5.94). The fitting residual root mean square of deformation observations is 0.36 cm. The deep geometry of the regional fault zone is characterized by flower structure, which inclines to the south-west as a whole. The Menyuan earthquake rupture is a blind fault which does not appear on the surface in the flower structure. Relevant results can provide the reference for studying regional crustal movement and deformation, active fault and earthquake preparation and occurrence.
Large-scale rocky landslides usually have the characteristics of high-altitude, strong concealment, high-speed and long-runout, which often lead to serious casualties and property losses. Revealing its deformation history and evolution rules can provide a reference for early detection of similar landslide disasters. Five typical large-scale rocky landslides in China in recent years are collected. Through visual interpretation of multi-temporal high-resolution remote sensing images, their deformation signs are identified and their evolution rules are analyzed. It is found that large-scale rocky landslides will produce significant surface deformation signs in the process of development and evolution. This deformation information can be identified by high-resolution (sub-meter level) optical remote sensing images. The incubation and evolution time of deformation can reach several years or even decades. Large-scale rocky landslides often do not have the "chair-like" geomorphological characteristics. The early identification signs of the landslides using optical remote sensing images are mainly whether there are tension cracks at the back of the slope and whether there are local slides at the front of the slope.
Affected by global climate change, most glaciers in southeastern Tibet have deteriorated more and more frequently. The risk of mountain disasters, such as floods, surges and debris flows, has increased sharply in recent years. This paper implements a research on glacial lakes classification by using multi-source synthetic aperture radar (SAR) images. According to the statistical analysis of intensity difference between signals coming from water and non-water objects, a method of glacial lake extraction and dynamic monitoring based on the intensity standardization ratio of sequential SAR images is proposed. Within a typical experimental area in Gongba Glacier basin, the image series of ALOS/PALSAR-1 of Japan Space Agency and Sentinel-1A of European Space Agency are selected to carry out dynamic extraction of glacial lakes and long-term change analysis over 11 years'time, just for validation purpose. The temporal and spatial variations of glacial lakes at the end of Gongba Glacier from 2007 to 2018 are successfully obtained. And the further analysis finds out that the volume of glacial lakes increase rapidly in the past ten years. This evidence can also confirm the current situation of aggravated melting of Gongba Glacier. In addition, the recent monitoring results in 2018 show that the expansion of local burst gushes has broken the inherent life cycle of glacial lakes, and even trigger secondary disasters of floods and debris flows. It is necessary to strengthen monitoring and prevention.
The landslide is one of the most common geological hazards in nature, the rapid development of unmanned aerial vehicle (UAV) technology and virtual reality (VR) technology provides important data resources and technical support for immersive simulation and visual analysis of the landslide. Firstly, this paper focuses on the dynamic construction and exploratory analysis of VR scene in landslide hazard, then some key technologies such as diversified organization of landslide data, dynamic fusion visualization of VR scene are addressed in detail, thus a VR scene interaction method based on handle ray is proposed. Finally, a prototype system is implemented and a case study region is selected for experimental analysis. The experimental results show that the proposed method can dynamically construct landslide VR scene based on UAV remote sensing data, and can support the user to carry out immersive interaction and disaster information analysis.
In order to promote the wider use of ground-based synthetic aperture radar (GB-SAR) in the field of surface micro-deformation monitoring, this paper summarizes the main types of GB-SAR and introduces the working principles and important parameters of some representative systems. By taking the S-SAR (slope-synthetic aperture radar) ground-based radar independently developed by the China Academy of Safety and Production Science as an example, the key processing techniques utilized by the GB-SAR are introduced. Experiments verify the accuracy of GB-SAR. It also shows the application of GB-SAR in open pit mine slope monitoring, mine dumping site and landslide emergency rescue. Finally, the problems and suggestions of GB-SAR in monitoring are proposed.
Based on the "Gaofen +", this paper explores the emergency analysis of the Jinsha River landslide disasters on October 11 and November 3, 2018, with the "Gaofen + geological disaster" model, and uses the high-resolution remote sensing image of the disaster area before and after the disaster to interpret the landslide. According to the basic disaster information of the disaster, the characteristics of pregnancy disaster and deformation and creep before and after the disaster are analyzed. The hidden dangers are investigated in the Baige Lake and surrounding areas after the disaster. The suspected cracks and secondary landslide hazard points on both sides of the barrier lake are identified, and road accessibility analysis of disaster areas after the second landslide is carried out. The results show that the "Gaofen +" model has great potential for the application of natural disasters such as the Jinsha River landslide, disaster monitoring and emergency rescue roads.
Landslide is the second only to earthquake as one of the most serious geological hazards, which occur most frequently and cause the most serious losses. It poses a great threat to people's lives and property safety. In order to monitor dangerous landslides dynamically, GPRI-Ⅱ ground-based radar system is used in this experiment to study the key technology of landslide deformation time series analysis, and the main process of ground-based radar data time series processing is formulated. It is applied to the stability monitoring of a road slope in Shaoxing, Zhejiang Province. The experimental results show that there is an obvious deformation area in the slope, which is located in the accumulation area of the construction site, and the maximum deformation is 3.5 mm/d. At present, ground-based radar has the ability to monitor deformation with sub-millimeter accuracy when the observation conditions are satisfied. Ground-based radar has become one of the important means of disaster early warning and control.
As a city with rapid economic development and urbanization in China's Pearl River Delta region, Foshan has been affected by land subsidence disasters for a long time due to its fragile geological and hydrological conditions. At the same time, the subway in the region is an important tool to alleviate the traffic pressure of the city. The ground subsidence caused by its construction and operation affects the safety of people's lives and property. However, there are not many systematic studies on this aspect in Foshan, and there is insufficient understanding of the settlement law along the subway. This paper uses fixed-point data to monitor the deformation information of Foshan from June 2015 to September 2018. The results show that the surface deformation of Foshan is sporadic, and there is no large-scale settlement funnel. The deformation rate is between 5 mm/a and -20 mm/a, and the settlement rate of local area is over -30 mm/a.Land subsidence is mainly related to unstable geological structures, groundwater extraction and local area construction. Based on the obtained deformation results, this paper also studies the deformation along the subway in Foshan city, and analyzes the settlement of the collapse section of Foshan subway in 2018, and expounds the cause of the difference in the space along the subway. In the time, the model parameters are inversed in the deformation along the subway. This paper provides a reference for the local government to carry out the survey of surface deformation and the early warning of subsidence disasters. And it provides a theoretical basis for the safety monitoring of the normal operation and maintenance of the subway.