Wing Yip Lau

Landslide Recognition and Prediction using Spaceborne Multispectral Data

Wing Yip Lau sq
University
University of NSW
Supervisor (Academic)
A/Prof Linlin Ge, University of NSW
Supervisor (Industry)
Hemayat Hussain, Vic Dept Primary Industries
Projects
mysite
Employment
Intergraph, Hong Kong
Thesis Abstract

Landslides are severe environmental hazards in mountainous areas. Nowadays, the threat of landslides to public safety has become more pronounced resulting from the burgeoning development and the increase of deforestation in hilly areas, and the increase of regional precipitation caused by global climate change.

Traditional landslide risk assessment requires immense physical power to assemble different in-situ data, such as identification of landslide location and land-cover classification. This traditional data collection technique is very time consuming, and thus impossible to be applied for the large scale assessment. Remote sensing techniques, therefore, are the solutions for providing fast and up-to-date landslide assessments. This thesis focuses on the applications of multispectral Landsat data for landslide recognition. Wollongong of Australia was chosen as a test bed for this analysis.

For landslide recognition analysis, three change detection techniques were employed, which were image differencing, bi-temporal linear data transformation and post-classification comparison. For the first two change detection methods, a new landslide identification procedure was developed by integrating surface change information of greenness, brightness and wetness. During the image differencing, the three surface change components were derived from Vegetation Indices (VIs), in which four different surface change composites were generated. Each composite contained three surface change bands which were greenness, brightness and wetness. For bi-temporal linear data transformation, multitemporal Kauth-Thomas (MKT) transformation was adopted for providing the three types of surface change information.

In the landslide recognition analysis, the best mapping performance is yielded by the image differencing method using brightness and wetness components of Kauth-Thomas transformation and NDVI. Its omission error (i.e. percentage of actual landslide pixels which were not detected) and commission error (i.e. percentage of change pixels identified which were not landslide) are 14.4% and 3.3%, respectively, with a strong agreement (KHAT = 88.8%).