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Abstract. In this study we focused on identifying a geomorphological feature that controls the location of landslides. The representation of the feature is based on a high-resolution digital elevation model derived from the airborne laser altimetry (LiDAR) and evaluated by the statistical analysis of axial orientation data. The main principle of this analysis is generating eigenvalues from axial orientation data and comparing them. The planarity, a ratio of eigenvalues, would tell the degree of irregularity on the ground surface based on their ratios. Results are compared to the recent landslide case in Korea in order to evaluate the feasibility of the proposed methodology in identifying the potential landslide hazard. The preliminary landslide hazard assessment based on the planarity analysis discriminates features between stable and unstable domain in the study area well, especially in the landslide initiation zones. Results also show it is beneficial to build the landslide hazard inventory mapping, especially where no information on historical records of landslides exists. By combining other physical procedures such as geotechnical monitoring, the landslide hazard assessment using geomorphological features promises a better understanding of landslides and their mechanisms and provides an enhanced methodology to evaluate their hazards and appropriate actions.

As natural disaster occurs, the local and the central government should investigate the damaged fields promptly, analyze quantitatively the degree of damage, and establish an appropriate disaster recovery plan in accordance with Framework Act on the Management of Disasters and Safety in Korea. The purpose of this study is to assess the applicability of UAV photogrammetry for the management of natural disaster. First, we suggest small easy-to-use UAV-based investigation procedure for natural disaster damaged area in the phase of disaster recovery in Korea. Before drone-based aerial surveying, the field survey can be performed with DGPS RTK for GCPs setting-up around disaster site. In this paper, we generate three dimensional terrain information and high-resolution ortho-imagery and then analyse quantitatively damage degree by natural disaster using commercial UAVs and drone mapping technique. Finally, we evaluate the mapping accuracy and work efficiency of drone mapping for disaster investigation application through comparing with traditional investigation work process which was dependent on labour-intensive field survey. The resolution ortho-image map of within less 5cm of GSD generated by aerial photographs acquired from UAVs at the altitude of 100m~250m enabled us to check damage information such as facilities destroy or the trace of soil erosion around the river flooded and reservoir collapsed area. In addition, three dimensional point cloud data of landslide-damaged areas enabled us to more accurately measure the width and the depth of outflows caused by landslides, soil runoff distance, and landslide damage area. The photogrammetry-based drone mapping technology for the disaster damage investigation is expected to be an alternative approach to support or replace the labour-intensive disaster site survey that needs to investigate the disaster site quickly and timely.

As natural disaster occurs, the local and the central government should investigate the damaged fields promptly, analyze quantitatively the degree of damage, and establish an appropriate disaster recovery plan in accordance with Framework Act on the Management of Disasters and Safety in Korea. The purpose of this study is to assess the applicability of UAV photogrammetry for the management of natural disaster. First, we suggest small easy-to-use UAV-based investigation procedure for natural disaster damaged area in the phase of disaster recovery in Korea. Before drone-based aerial surveying, the field survey can be performed with DGPS RTK for GCPs setting-up around disaster site. In this paper, we generate three dimensional terrain information and high-resolution ortho-imagery and then analyse quantitatively damage degree by natural disaster using commercial UAVs and drone mapping technique. Finally, we evaluate the mapping accuracy and work efficiency of drone mapping for disaster investigation application through comparing with traditional investigation work process which was dependent on labour-intensive field survey. The resolution ortho-image map of within less 5cm of GSD generated by aerial photographs acquired from UAVs at the altitude of 100m~250m enabled us to check damage information such as facilities destroy or the trace of soil erosion around the river flooded and reservoir collapsed area. In addition, three dimensional point cloud data of landslide-damaged areas enabled us to more accurately measure the width and the depth of outflows caused by landslides, soil runoff distance, and landslide damage area. The photogrammetry-based drone mapping technology for the disaster damage investigation is expected to be an alternative approach to support or replace the labour-intensive disaster site survey that needs to investigate the disaster site quickly and timely.

The increasing occurrence of natural disaster events and related damages have led to a growing demand for models that predict financial loss. Although considerable research has studied the financial losses related to natural disaster events, and has found significant predictors, there has not yet been a comprehensive study that addresses the relationship among the vulnerabilities, natural disasters, and economic losses of the individual buildings. This study identified hurricanes and their vulnerability indicators in order to establish a metric to predict the related financial loss. We identify hurricane-prone areas by imaging the spatial distribution of the losses and vulnerabilities. This study utilized a Geographical Information System (GIS) to combine and produce spatial data, as well as a multiple linear regression method, to establish a hurricane damage prediction model. As the dependent variable, we utilized the following ratio to predict the real pecuniary loss: the value of the Texas Windstorm Insurance Association (TWIA) claim payout divided by the appraised values of the buildings. As independent variables, we selected the hurricane indicators and vulnerability indicators of the built environment and the geographical features. The developed statistical model and results can be used as important guidelines by insurance companies, government agencies, and emergency planners for predicting hurricane damage.

Abstract. Assessment of geological hazards in urban areas must integrate geospatial and temporal data, such as complex geology, highly irregular ground surface, fluctuations in pore-water pressure, surface displacements and environmental factors. Site investigation for geological hazard studies frequently produces surface maps, geological information from borehole data, laboratory test results and monitoring data. Specialized web-based GIS tools were created to facilitate geospatial analyses of displacement data from inclinometers and pore pressure data from piezometers as well as geological information from boreholes and surface mapping. A variety of visual aids in terms of graphs or charts can be created in the web page on the fly, e.g. displacement vector, time displacement and summaries of geotechnical testing results. High-resolution satellite or aerial images and LiDAR data can also be effectively managed, facilitating fast and preliminary hazard assessment. A preliminary geohazard assessment using the web based tools was carried out for the Town of Peace River.

Superconductivity in the vicinity of a competing electronic order often manifests itself with a superconducting dome, centered at a presumed quantum critical point in the phase diagram. This common feature, found in many unconventional superconductors, has supported a prevalent scenario in which fluctuations or partial melting of a parent order are essential for inducing or enhancing superconductivity. Here we present a contrary example, found in IrTe2 nanoflakes of which the superconducting dome is identified well inside the parent stripe charge ordering phase in the thickness-dependent phase diagram. The coexisting stripe charge order in IrTe2 nanoflakes significantly increases the out-of-plane coherence length and the coupling strength of superconductivity, in contrast to the doped bulk IrTe2. These findings clarify that the inherent instabilities of the parent stripe phase are sufficient to induce superconductivity in IrTe2 without its complete or partial melting. Our study highlights the thickness control as an effective means to unveil intrinsic phase diagrams of correlated van der Waals materials. © 2021, The Author(s). ; The authors thank K.T. Ko, G.Y. Jo, Y.K. Bang for fruitful discussion. We also thank H. G. Kim in Pohang Accelerator Laboratory (PAL) for the technical support. This work was supported by the Institute for Basic Science (IBS) through the Center for Artificial Low Dimensional Electronic Systems (no. IBS-R014-D1), the National Research Foundation of Korea (NRF) through SRC (Grant No. NRF-2018R1A5A6075964), and the Max Planck-POSTECH Center for Complex Phase Materials (Grant No. NRF-2016K1A4A4A01922028). J.K., M.J.K. acknowledge the support from the NRF of Korea grant (Grant No. NRF-2017R1C1B2012729 and NRF-2020R1A4A1018935). K.W. and T.T. acknowledge support from the Elemental Strategy Initiative conducted by the MEXT, Japan, Grant Number JPMXP0112101001, JSPS KAKENHI Grant Numbers JP20H00354 and the CREST (JPMJCR15F3), JST. E. F. T. thanks financial support provided by the state assignment of Minobrnauki of Russia (theme Pressure No. AAAA-A18-118020190104-3) and by Act 211 Government of the Russian Federation, contract no. 02.A03.21.0006. S.K. acknowledges the support from the NRF of Korea grant (Grant No. NRF-2019R1F1A1052026) and KISTI supercomputing center (Grant No. KSC-2019-CRE-0172). K.K. acknowledges the support from the NRF of Korea grant (Grant No. 2016R1D1A1B02008461) and Internal R&D programme at KAERI (Grant No. 5244460-21). SWC was partially supported by the NSF under Grant No. DMR-1629059.