Spatial Sensor Network Research Articles

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.

Some applications of 2-D and 3-D photogrammetry during laboratory experiments for hydrogeological risk assessment

Scaled-down flume tests are largely used to support investigations for the assessment of hydrogeological risk. Achieved outcomes can be integrated to numerical analyses for the study of unstable slope collapse, debris transport, and hydrological models in general. In the set-up of such simulation platforms, a relevant role has to be given to the Spatial Sensor Network (SSN) which is in charge of collecting geo-referenced, quantitative information during experiments. Photogrammetry (including 3-D imaging sensors) can play an important role in SSN because of its capability of collecting information covering wide surfaces without any contact. The aim of this paper is to give an overview and some examples of the potential of photogrammetry in hydrogeological simulation experiments. After a general introduction on a few preliminary issues (sensors, calibration, ground reference, usage of imaging or ranging sensors), potential applications are classified into 2-D and 3-D categories. Examples are focused on a scaled-down landslide simulation platform, which has been developed at Tongji University (Shanghai, P.R. China).

PHOTOGRAMMETRY IN EXPERIMENTS FOR HYDROGEOLOGICAL RISK ASSESSMENT

Abstract. The construction of scaled-down simulation platforms is largely used to support investigations for the assessment of hydrological risk. Achieved outcomes can be integrated and assimilated to numerical analyses for the study of unstable slope collapse, debris transport, and hydrological modeling in general. During design of such simulation platforms, a relevant role has to be given to the spatial sensor network (SSN) to deploy, which is in charge of collecting geo-referenced, quantitative information during experiments. Photogrammetry (including 3D imaging sensors) can play an important role in SSN owing to its capability of collecting 2D images and 3D point clouds data covering wide surfaces without any contact. Different kinds of metric measurements can be then extracted from datasets. The aim of this paper is to give an overview and some examples on the potential of photogrammetry in hydrogeological experiments. After a general introduction on a few preliminary issues (sensors, calibration, ground reference, usage of imaging or ranging sensors), potential applications are classified into 2D and 3D categories. Examples are focused on a scaled-down landslide simulation platform developed at Tongji University (Shanghai, P. R. China).