Yixiang Tian

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.

An automatic multi-image procedure for accurate 3D object reconstruction

This paper presents an automated methodology for the accurate reconstruction of surfaces and 3D objects. This method was developed for close-range photogrammetric applications, with a particular attention to terrestrial free-form objects that can be modeled with point clouds extracted from high resolution images. The image orientation phase is carried out using an automatic procedure based on a rigorous bundle adjustment. Then, an approximate model is obtained using a patch-based matching strategy. Then, this initial model is refined using a Multi-Image Least Squares Matching (MGCM) implementation, increasing in this way the precision of the final model and giving the possibility to evaluate its consistency using a rigorous variance-covariance propagation. The multi-image matching result is a dense point cloud describing in an accurate way the object surface. The derived point cloud is used to obtain a mesh which is finally textured obtaining a photo-realistic result.

Quality assessment of existing antarctic remote sensing products

There are a variety of remote sensing products of Antarctica that cover different time periods and published by different research groups. The resolution and accuracy for these products are quite different due to original data sources, extraction methods, etc. The aim of this research is to assess the quality and investigate changes that can be detected in three main categories of Antarctic remote sensing products including groundling lines, coastlines and surface elevation models (DEM). All the products are available at National Snow and Ice Data Center (NSIDC). In order to identify changes and uncertainties of these products, cross-validation strategies are developed for the quality assessment when there is a lack of ground truth data. The quality assessment results for each remote sensing product are presented in the paper.

On the Conversion of Antarctic Ice-Mass Change to Sea Level Equivalent

This technical note aims to provide a quick reference and some computational examples for the conversion between Antarctic ice-mass changes and global sea level equivalent (SLE) changes using a few assumptions that computationally simplify this complex problem and that acknowledge gaps in our knowledge of the Antarctic environment. A number of factors involved in the conversion process are discussed, and the sensitivity of the conversion result to certain aspects is analyzed. It was found that the global ocean area calculated from a recently improved global shoreline dataset has little impact on the uncertainty of the SLE estimation. SLE estimation using satellite gravity observations, such as those by GRACE, are sensitive to glacial isostatic adjustment (GIA) models. One more important result from the computation is that the effective density of the volume that is gained or lost during mass change may greatly affect the outcome of the conversion if it differs greatly from the actual density of the firn/ice layers. Finally, a table of computational examples is provided for reference under some assumptions for simplifying the computation.