Weian Wang

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.

Assessment of Geo-positioning Capability of High Resolution Satellite Imagery for Densely Populated High Buildings in Metropolitan Areas

This paper analyzes the geo-positioning capability of high-resolution satellite images in a very special metropolitan environment where within a relatively small region there are a large number of densely populated skyscrapers and high buildings. A pair of QuickBird stereo images covering downtown Shanghai were used for analysis. Multi-source data including GPS survey data, aerial images, and lidar data collected in the same area were used for Ground Control Points (GCPS) and Independent Check Points (ICPS) measurements and accuracy analysis. The vendor-provided Rational Function Model followed by a translation and scale correction model in image space was used to introduce the ground control and improve geo-positioning accuracy. The experimental results revealed that there is a clear dependency of geo-positioning accuracy on elevation where the GCPS are placed. Using the vendor-provided RFM and several GCPS across all the elevation ranges improves the overall geo-positioning accuracy.

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.

An intelligent visualization system for landslide monitoring with sensor network

This paper is dedicated to an intelligent visualization system for landslide monitoring, whereas wireless sensor network is deployed for observing and detecting the dynamic changes of the landslide body at an experiment site. The visualization system is designed and implemented to visualize the spatial distribution of the employed sensors and the data of these sensors. Through the visualization-supported data analysis, the system will be capable of alert for possible critical events such as landslide surface failure. The experimental results show the initial functions and prototype capabilities of the system.

3D geo-positioning based on bias-compensated rational function model for QuickBird imagery

This paper first discusses the background and details of Rational Function Model (RFM) which describes the coordinates in image space and object space, and then by incorporating additional parameters into the basic RFM, the bias-compensated RFM is introduced. With different additional parameters, four kinds of bias-compensated RFM are put forward and discussed. Finally an experimental test in Shanghai, China, is carried out with the Bias-compensated Rational Function Model using QuickBird across-track stereo imagery and highly accuracy Ground Control Points (GCP) as data source. Conclusion is drawn that with more well distributed GCPs, high 3D geo-positioning accuracy can be obtained up to about 0.6 meter in plane direction and 0.7 meter in height direction.

Assessment of vulnerability to sea-level rise for China's Coast

Coastal systems are characterized by high geophysical and biophysical sensitivity due to their special environment. The ecosystems along Chinas coast are especially vulnerable to the accelerated sea-level rise and associated multi-hazards such as storm surges and coastal flooding due to their low-lying topography, highly developed economy, and high-density population. To ensure a better management of Chinas coastal areas, it is crucial to develop adequate nationwide coastal vulnerability assessment models. This paperpresents our design and procedure of assessing the vulnerability of Chinas entire coast using eight factors. Techniques of geographic information systemand remote sensing were used to integrate the best available, spatially-disaggregated national data on critical impact elements. A comprehensive coastal vulnerability index(CVI) was then calculated by integrating the differentially weighted rank values of the variables. In the finalstep, the coastline was segmented into 4vulnerability levels: low, moderate, high and very high. The results generated from the segmentation show that 35% of the entire coast has high or very high vulnerability, mostly along Bohai Bay, the North Jiangsu coastal plain, the Yangtze River and the Yellow River Delta, and the western coast of Taiwan. The geographical distribution of the vulnerability grade proves to be reasonable. Findingsfurtherreveal that large amounts of land and population wouldbe vulnerable to inundation by coastal flooding from sea level rise and storm surge without effective measures. Finally, some suggestions are presented for decision makers and other concerned stakeholders to develop appropriate coastal zonemanagement and mitigation measures.

Rupture terrain characteristic extraction from DEM based on variable-scale grads

DEM (Digital Elevation Model) is a commonest basic data in different geospatial applications. It is one of the fundamental issues for geo-science to extract the different kinds of terrains waving characteristic in DEM self-adaptively and automatically. This paper presented our research on the basic mathematical model for rupture terrain characteristic extraction with variable-scale method. After the rupture edge characteristic point is obtained, the irregular line of rupture edge is generated in Terrain Characteristic Hypergraph Relationship (TCHR) [1] method, then combined into ruptures region object. Through experiment about submarine grid DEM, the comprehensive terrain characteristic of rupture can be obtained accurately and effectively, and its result matched the submarine terrain precisely.

Research of integration for coastal belt multi-source spatial data

To meet the need of the research and application of topographic changes on coastal belt, multi-source data of coastal belt, which are spatial and non-spatial, are collected, analyzed and managed. Ideas and methods on the integration of multiple sources spatial data, coming from GIS, RS and GPS, are briefly expatiated based on the work just mentioned and have been verified theoretic correctness and practical feasibility. The organization method of spatial information based on ArcSDE is efficient and adaptive to integration of multi-source. What we have done makes for the implementation of establishment of coastal belt database by Oracle and the study of data mining on the topographic changes of coastal belt.