Central Geological Survey, MOEA

About: Central Geological Survey, MOEA is a government organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Landslide & Fault (geology). The organization has 68 authors who have published 81 publications receiving 2037 citations. The organization is also known as: Jīngjì Bù Zhōngyāng Dìzhí Diàochá Suǒ.

Active interseismic shallow deformation of the Pingting terraces (Longitudinal Valley – Eastern Taiwan) from UAV high-resolution topographic data combined with InSAR time series

Abstract: We focus herein on the location, characterization and the quantification of the most active structural feature of Taiwan: the Longitudinal Valley Fault that corresponds to the suture in between the Philippine and Eurasian Plates. In order to determine and monitor its present inter-seismic deformation, we focus on the Pingting Terraces area, situated in the South Longitudinal Valley (Eastern Taiwan). We first determine the structural geometry issued from both photo-interpretation deduced from new unmanned aerial vehicle (UAV) high-resolution Digital Terrain Model data that we acquired (34.78 km2 with 7.73 cm ground sampling distance), combined with geological field work. In order to characterize and quantify the present deformational patterns over the Pingting terraces, we used an InSAR time series Interferometry algorithm (MT-InSAR) applied to nine L-band SAR images from ALOS satellite acquired over the period 2007–2010. The unprecedented density of measurements (about 120 points per km2 for a tot...

Methanoculleus sediminis sp. nov., a methanogen from sediments near a submarine mud volcano

Abstract: A mesophilic, hydrogenotrophic methanogen, strain S3FaT, was isolated from sediments collected by Ocean Researcher I cruise ORI-934 in 2010 near the submarine mud volcano MV4 located at the upper slope of south-west Taiwan. The methanogenic substrates utilized by strain S3FaT were formate and H2/CO2 but not acetate, secondary alcohols, methylamines, methanol or ethanol. Cells of strain S3FaT were non-motile, irregular cocci, 0.5–1.0 μm in diameter. The surface-layer protein showed an Mr of 128 000.The optimum growth conditions were 37 °C, pH 7.1 and 0.17 M NaCl. The DNA G+C content of the genome of strain S3FaT was 62.3 mol %. Phylogenetic analysis revealed that strain S3FaT was most closely related to Methanoculleus marisnigri JR1T (99.3 % 16S rRNA gene sequence similarity). Genome relatedness between strain S3FaT and Methanoculleus marisnigri JR1T was computed using both genome-to-genome distance analysis (GGDA) and average nucleotide identity (ANI) with values of 46.3–55.5 % and 93.08 %, respectively. Based on morphological, phenotypic, phylogenetic and genomic relatedness data, it is evident that strain S3FaT represents a novel species of the genus Methanoculleus, for which the name Methanoculleus sediminis sp. nov. is proposed. The type strain is S3FaT ( = BCRC AR10044T = DSM 29354T).

Adopting the I 3 – R 24 rainfall index and landslide susceptibility for the establishment of an early warning model for rainfall-induced shallow landslides

Abstract: . Rainfall-induced landslides number among the most devastating natural hazards in the world and early warning models are urgently needed to reduce losses and fatalities. Most landslide early warning systems are based on rainfall thresholds defined on the regional scale, regardless of the different landslide susceptibilities of various slopes. Here we divided slope units in southern Taiwan into three categories (high, moderate and low) according to their susceptibility. For each category, we established separate rainfall thresholds so as to provide differentiated thresholds for different degrees of susceptibility. Logistic regression (LR) analysis was performed to evaluate landslide susceptibility by using event-based landslide inventories and predisposing factors. Analysis of rainfall patterns of 941 landslide cases gathered from field investigation led to the recognition that 3 h mean rainfall intensity (I3) is a key rainfall index for rainfall of short duration but high intensity; in contrast, 24 h accumulated rainfall (R24) was recognized as a key rainfall index for rainfall of long duration but low intensity. Thus, the I3–R24 rainfall index was used to establish rainfall thresholds in this study. Finally, an early warning model is proposed by setting alert levels including yellow (advisory), orange (watch) and red (warning) according to a hazard matrix. These differentiated thresholds and alert levels can provide essential information for local governments to use in deciding whether to evacuate residents.