Rongxing Li

Landslide Investigation with Remote Sensing and Sensor Network: From Susceptibility Mapping and Scaled-down Simulation towards in situ Sensor Network Design

This paper presents an integrated approach to landslide research based on remote sensing and sensor networks. This approach is composed of three important parts: (i) landslide susceptibility mapping using remote-sensing techniques for susceptible determination of landslide spots; (ii) scaled-down landslide simulation experiments for validation of sensor network for landslide monitoring, and (iii) in situ sensor network deployment for intensified landslide monitoring. The study site is the Taziping landslide located in Hongkou Town (Sichuan, China). The landslide features generated by landslides triggered by the 2008 Wenchuan Earthquake were first extracted by means of object-oriented methods from the remote-sensing images before and after the landslides events. On the basis of correlations derived between spatial distribution of landslides and control factors, the landslide susceptibility mapping was carried out using the Artificial Neural Network (ANN) technique. Then the Taziping landslide, located in the above mentioned study area, was taken as an example to design and implement a scaled-down landslide simulation platform in Tongji University (Shanghai, China). The landslide monitoring sensors were carefully investigated and deployed for rainfall induced landslide simulation experiments. Finally, outcomes from the simulation experiments were adopted and employed to design the future in situ sensor network in Taziping landslide site where the sensor deployment is being implemented.

Analysis of spatial sensor network observations during landslide simulation experiments

A spatial sensor network was tested during five experiments on a landslide simulation platform. Here, a landslide was triggered by means of simulated rainfall. The sensor network currently incorporates in situ sensors and two stereo imaging systems. In future, these sensors will be installed on a real-scene slopes in Sichuan Province (South-West China). The paper focuses on the results of two latest landslide simulation experiments. While one experiment ended with a partial failure, the second one showed a complete slope collapse. In the first part of the study, the full data series are investigated to perform correlations and common pattern analysis, as well as to link them to the physical processes. In the second part, four subsets of sensors located in neighbouring positions are analysed. Although the small scale of the simulated experiment probably influenced the results, these experiments allowed ascertaining which sensors could be more suitable to be deployed on the real-scene landslide sites.

Real-time landslide monitoring using close-range stereo image sequences analysis

Landslide is one of the major natural hazards, threatening human lives and properties. Real-time landslide monitoring is very important to disaster relief. In this paper, the effectiveness of real-time landslide monitoring method based on close-range photogrammetry is investigated in an on-campus landslide test site at Tongji University. Feature point tracking method using landslide image sequences is tested first, and then the elevation change detection method on the landslide surface is also tested. According to the experimental results, it is shown that both of these two methods can effectively monitor the landslide movements, which can provide important pre-experimental results for the real-time landslide monitoring in Hongkou Town, Sichun, China.

Model test study on monitoring dynamic process of slope failure through spatial sensor network

Landslides represent a major type of natural hazards worldwide. For development of risk mitigation capabilities, an effective system for monitoring dynamic process of slope failure, capable of gathering spatially distributed information before, during and after a landslide occurrence at real-time manner is essential. A spatial sensor network (SSN), which integrates the real-time communication infrastructure and observations from in situ sensors and remote sensing platforms, offers an efficient and effective approach for such purpose. In this paper, a SSN-based landslide monitoring system was designed and evaluated through a model test study conducted at Tongji University, China. This system, MUNOLD (MUlti-Sensor Network for Observing Landslide Disaster), has been designed as a comprehensive monitoring framework, including sensor observations, multi-channel wireless communication, remote data storage, visualization, data processing and data analysis. In this model test study, initial experimentation demonstrated the capabilities of the MUNOLD system for collecting real-time information about the dynamic process and propagation of slope failure. Innovatively, generated from the high-speed stereo images, the sequential surface deformation vector field can be created and may exhibit the dynamic process during the extremely critical and short period of the slope failure. After this model test study, the MUNOLD system is going to be further improved and extended in a landslide prone region in Sichuan Province, China.

Hybrid-SAR Technique: Joint Analysis Using Phase-Based and Amplitude-Based Methods for the Xishancun Giant Landslide Monitoring

Early detection and early warning are of great importance in giant landslide monitoring because of the unexpectedness and concealed nature of large-scale landslides. In China, the western mountainous areas are prone to landslides and feature many giant complex landslides, especially following the Wenchuan Earthquake in 2008. This work concentrates on a new technique, known as the “hybrid-SAR technique”, that combines both phase-based and amplitude-based methods to detect and monitor large-scale landslides in Li County, Sichuan Province, southwestern China. This work aims to develop a robust methodological approach to promptly identify diverse landslides with different deformation magnitudes, sliding modes and slope geometries, even when the available satellite data are limited. The phase-based and amplitude-based techniques are used to obtain the landslide displacements from six TerraSAR-X Stripmap descending scenes acquired from November 2014 to March 2015. Furthermore, the application circumstances and influence factors of hybrid-SAR are evaluated according to four aspects: (1) quality of terrain visibility to the radar sensor; (2) landslide deformation magnitude and different sliding mode; (3) impact of dense vegetation cover; and (4) sliding direction sensitivity. The results achieved from hybrid-SAR are consistent with in situ measurements. This new hybrid-SAR technique for complex giant landslide research successfully identified representative movement areas, e.g., an extremely slow earthflow and a creeping region with a displacement rate of 1 cm per month and a typical rotational slide with a displacement rate of 2–3 cm per month downwards and towards the riverbank. Hybrid-SAR allows for a comprehensive and preliminary identification of areas with significant movement and provides reliable data support for the forecasting and monitoring of landslides.

A New Analytical Method for Estimating Antarctic Ice Flow in the 1960s From Historical Optical Satellite Imagery

Ice flow velocity is used to estimate ice mass changes in glaciers and is a significant indicator of the stability of the Antarctica ice sheet in global change studies. The existing regional Antarctica ice flow speed maps are usually derived from radar or optical satellite observations of modern satellites since the 1970s. This paper presents a new analytical photogrammetric method for estimating Antarctica ice flow velocity fields by using film-based stereo ARGON photographs collected in the 1960s. The key of the proposed innovative method is a parallax decomposition that separates the effect of the terrain relief from the ice flow motion. An innovative implementation strategy is developed by using a framework that involves key techniques of hierarchical stereo image matching, ice flow direction determination, parallax decomposition, and ice flow speed estimation. This method is applied in the Rayner glacier in eastern Antarctica by using two sets of ARGON images with a two-month interval in 1963. The produced digital terrain model and speed map achieved a ground position accuracy of 61 m and a speed accuracy of 70 m

Experimental results of elevation change analysis in the Antarctic ice sheet using DEMs from ERS and ICESat data

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An intelligent visualization system for landslide monitoring with sensor network

. A comparison with recent products from 2000 to 2010 shows no significant topographic changes in the study area. Furthermore, the speed around the grounding line remained at the same level, while the speed in the ice shelf front decreased by 73 m

MUNOLD: Landslide Monitoring Using a Spatial Sensor Network

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Quality assessment of existing antarctic remote sensing products

. The ice shelf front advanced by approximately 7 km over more than 40 years. Overall, the observation results indicate favorable conditions for the stability of the Rayner glacier-ice shelf system.