Peter Tian-Yuan Shih

Topographic Correction of Wind-Driven Rainfall for Landslide Analysis in Central Taiwan with Validation from Aerial and Satellite Optical Images

Rainfall intensity plays an important role in landslide prediction especially in mountain areas. However, the rainfall intensity of a location is usually interpolated from rainfall recorded at nearby gauges without considering any possible effects of topographic slopes. In order to obtain reliable rainfall intensity for disaster mitigation, this study proposes a rainfall-vector projection method for topographic-corrected rainfall. The topographic-corrected rainfall is derived from wind speed, terminal velocity of raindrops, and topographical factors from digital terrain model. In addition, scatter plot was used to present landslide distribution with two triggering factors and kernel density analysis is adopted to enhance the perception of the distribution. Numerical analysis is conducted for a historic event, typhoon Mindulle, which occurred in 2004, in a location in central Taiwan. The largest correction reaches 11%, which indicates that topographic correction is significant. The corrected rainfall distribution is then applied to the analysis of landslide triggering factors. The result with corrected rainfall distribution provides better agreement with the actual landslide occurrence than the result without correction.

A GEOMORPHOLOGICAL MODEL FOR LANDSLIDE DETECTION USING AIRBORNE LIDAR DATA

This study analyzes multi-temporal LiDAR data of high accuracy and high resolution by installing a geomorphometric model for extracting landslides. First, two sets of LiDAR data were acquired for before and after a heavy rainfall event. The landslides which took place from 2005 to 2009 were classified automatically by satellite images, and subsequently the landslides were interpreted and edited manually. Geomorphometric parameters including slope, curvature, OHM, OHM roughness, and topographic wetness index were then extracted using stencils of landslide polygons overlaid on respective thematic maps derived from LiDAR, DEM and DSM. The ranges of every parameter were derived from the statistics of the landslide area. Some selected non-morphometric parameters were also included in a later stage to account for all possible features of landslides, such as vegetation index and geological strength. The ranges of the parameters of landslides were optimized for the model by the statistics of the landslide area. The overall accuracy predicted by the model was 64.9%. When the buffer zones of old landslides and riverside areas were included, the overall accuracy was 64.4%, showing no improvement. When landslides smaller than 50 m^2 were filtered, the overall accuracy reached 76.6% and 72.5% for 2005 and 2009, respectively. The results show that the geomorphological model proposed in this research is effective for landslide extraction.

UNDERWATER LINEAR FEATURE EXTRACTION WITH MULTISPECTRAL BAND IMAGES: AN EVALUATION WITH LEVEL-SET METHOD IN DONGSHAATOLL AND ZENGMU SHOAL

Optical remote sensing satellite images are a useful and convenient source to provide underwater features, particularly for shallow water areas because light, dependent on wavelength, has the capability to penetrate water. In this study, the information richness of underwater features is investigated for each spectral band of the optical images, and also several derived bands. This assessment is performed with the level-set method for segmentation. Two cases are analyzed in this study. The first study site is the Dongsha atoll, which is composed of Dongsha island, lagoon, and surrounding reefs. The water depth ranges from zero to less than 3 m at the outer ring and down to a depth of 20 m in the lagoon. The images were acquired with WorldView-2 in October 2013 and covered the entire atoll. The second study site is Zengmu shoal, an underwater feature. The image used is a scene acquired with Landsat 8. These images demonstrate high water clarity in both sites. For the Dongsha atoll, both the reflectance of each spectral band, the NDWI, and bands processed with Principle Component Transformation (PCT) are analyzed. The assessment is made based on the number of segments identified. The more segments identified, subsequently the more information, we assume, is provided. In order to remove those caused by noise, only the segments larger than 100 m were counted. Based on this, PCT band 1 performs the best, and followed by green, yellow, coastal, blue, red, and fewer features from red-edge NIR and NIR2 bands when the objects in the scene are completely submerged underwater. For the Zengmu shoal, the boundary of the object identified is used for the assessment. The one closest to the manually digitized imaged boundary would be recognized as having the best performance. Among the spectral bands, coastal/ aerosol (CA) and blue perform the best. The four bands, coastal, blue, green, and red, are projected with PCT. The boundary resulting from the first principle component resembles most the one identified by a human operator on a QuickBird image.