MENT 
a - Legon for supporting 
to enable me attend the 
Australia to present this 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
SUSCEPTIBILITY EVALUATION AND MAPPING OF CHINA'S LANDSLIDE 
DISASTER BASED ON MULTI-TEMPORAL GROUND AND REMOTE SENSING 
SATELLITE DATA 
Chun Liu®®, Weiyue Li***, Ping Lu®, Kai Sang, Yang Hong? and Rongxing Li" 
^Center for Spatial Information Science and Sustainable Development, Tongji University, China 
"Department of Surveying and Geo-Informatics, Tongji University, China 
Commission VIII, WG VIII/1 
KEY WORDS: landslide, empirical model, ANN learning, susceptibility, hazard mapping 
ABSTRACT: 
Under the circumstances of global climate change, nowadays landslide occurs in China more frequently than ever before. The 
landslide hazard and risk assessment remains an international focus on disaster prevention and mitigation. It is also an important 
approach for compiling and quantitatively characterizing landslide damages. By integrating empirical models for landslide disasters, 
and through multi-temporal ground data and remote sensing data, this paper will perform a landslide susceptibility assessment 
throughout China. A landslide susceptibility (LS) map will then be produced, which can be used for disaster evaluation, and provide 
basis for analyzing China’s major landslide-affected regions. Firstly, based on previous research of landslide susceptibility 
assessment, this paper collects and analyzes the historical landslide event data (location, quantity and distribution) of past sixty years 
in China as a reference for late-stage studies. Secondly, this paper will make use of regional GIS data of the whole country provided 
by the National Geomatics Centre and China Meteorological Administration, including regional precipitation data, and satellite 
remote sensing data such as from TRMM and MODIS. By referring to historical landslide data of past sixty years, it is possible to 
develop models for assessing LS, including producing empirical models for prediction, and discovering both static and dynamic key 
factors, such as topography and landforms (elevation, curvature and slope), geologic conditions (lithology of the strata), soil type, 
vegetation cover, hydrological conditions (flow distribution). In addition, by analyzing historical data and combining empirical 
models, it is possible to synthesize a regional statistical model and perform a LS assessment. Finally, based on the lkmx1km grid, 
the LS map is then produced by ANN learning and multiplying the weighted factor layers. The validation is performed with reference 
to the frequency and distribution of historical data. 
This research reveals the spatiotemporal distribution of landslide disasters in China. The study develops a complete algorithm of data 
collecting, processing, modelling and synthesizing, which fulfils the assessment of landslide susceptibility, and provides theoretical 
basis for prediction and forecast of landslide disasters throughout China. 
landslide, debris flow, typhoon, flood, soil erosion, 
1. INTRODUCTION desertification, water pollution, and so on. These disasters are 
complicated and closely connected with environmental 
Natural disasters are abnormal and inevitable phenomena, from : . ; 
degradation and human life (Henderson, 2004). The casualties 
the nature on which human beings depend to live. It did harm ; ; ; 
and property losses caused by landslide are often listed first in 
to the human society, mainly including earthquake, volcano, : piens 
the natural disaster damage. Landslide is defined as the 
  
* Weiyue Li(1983-), male, PhD candidate, main research interests-laser scanning data processing and application. 
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