Landslide Image Interpretation in Mountainous Areas with Complex Vegetation based on Airborne LiDAR

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Yin Chenfeng ◽  
Liu Guodong
Keyword(s):  
Image Interpretation ◽  
Airborne Lidar ◽  
Mountainous Areas

scholarly journals Derivation of Strike and Dip in Sedimentary Terrain Using 3D Image Interpretation Based on Airborne LiDAR Data

2014 ◽  
Vol 25 (6) ◽  
pp. 775 ◽  
Author(s):  
Chih-Hsiang Yeh ◽  
Yu-Chang Chan ◽  
Kuo-Jen Chang ◽  
Ming-Lang Lin ◽  
Yu-Chung Hsieh
Keyword(s):  
Image Interpretation ◽  
Airborne Lidar ◽  
Lidar Data ◽  
3D Image ◽  
Airborne Lidar Data

scholarly journals ASSIMILATING GEOSPATIAL METAMODEL AND INVENTORY MAPPING FOR NON-STRUCTURAL MITIGATION OF LANDSLIDE

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 217-228
Author(s):  
N. S. Nasir ◽  
M. F. Abdul Khanan ◽  
S. H. Othman ◽  
M. Z. Abdul Rahman ◽  
K. A. Razak ◽  
...  
Keyword(s):  
Disaster Management ◽  
Aerial Photograph ◽  
Image Interpretation ◽  
Digital Terrain Model ◽  
Shallow Landslide ◽  
Airborne Lidar ◽  
Landslide Inventory ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Using Data

<p><strong>Abstract.</strong> In Malaysia, issues related to disaster management are always given attention in society and by the responsible parties. However, in general, citizen do not think of the consequential impact of disaster due to less of knowledge regarding the early phase in disaster management. Therefore, citizen in those areas will be more vulnerable to landslide as the citizen face difficulties in identifying specific areas with the tendency of landslides. This paper presents a geospatial metamodel approach for non-structural mitigation of landslide using data from airborne LiDAR and aerial photograph. Disaster management metamodel with geospatial element combines activity for managing disaster along with geospatial database that makes it handy for appreciating the metamodel. On the other hand, the digital terrain model (DTM) from LiDAR and aerial photograph is required to produce landslide inventory mapping. The case study area is located in Kundasang, Sabah, where landslides occur frequently. In order to get better visual in identifying landslides in the study area, three types of data are required to carry out image interpretation. The three types of data are hillshade, topographic openness and colour composite. The result of the landslide inventory map shows that there are five types of landslide, which is debris flow, debris fall, mud flow, deep-seated landslide and shallow landslide. Finally, the result of landslide inventory map will be integrated into the developed metamodel for presentation to the users. This landslide inventory map is used as a non-structural mitigation step in one of disaster management phases that is suitable to prepare and use in mitigating the landslide hazard impact.</p>

Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas

2021 ◽  
Vol 32 (5) ◽  
pp. 1079-1091 ◽  
Author(s):  
Chen Guo ◽  
Qiang Xu ◽  
Xiujun Dong ◽  
Weile Li ◽  
Kuanyao Zhao ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Mountainous Areas ◽  
Airborne Lidar Data

scholarly journals Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2292 ◽  
Author(s):  
Wen Liu ◽  
Fumio Yamazaki ◽  
Yoshihisa Maruyama
Keyword(s):  
Image Interpretation ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Digital Terrain ◽  
Terrain Models ◽  
Short Period ◽  
Vertical Displacements ◽  
Good Potential ◽  
Collapsed Buildings ◽  
Airborne Lidar Data

A series of earthquakes hit Kumamoto Prefecture, Japan, continuously over a period of two days in April 2016. The earthquakes caused many landslides and numerous surface ruptures. In this study, two sets of the pre- and post-event airborne Lidar data were applied to detect landslides along the Futagawa fault. First, the horizontal displacements caused by the crustal displacements were removed by a subpixel registration. Then, the vertical displacements were calculated by averaging the vertical differences in 100-m grids. The erosions and depositions in the corrected vertical differences were extracted using the thresholding method. Slope information was applied to remove the vertical differences caused by collapsed buildings. Then, the linked depositions were identified from the erosions according to the aspect information. Finally, the erosion and its linked deposition were identified as a landslide. The results were verified using truth data from field surveys and image interpretation. Both the pair of digital surface models acquired over a short period and the pair of digital terrain models acquired over a 10-year period showed good potential for detecting 70% of landslides.

scholarly journals An Operational Forecasting System for Flash Floods in Mountainous Areas in Taiwan

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2100 ◽  
Author(s):  
Yung-Ming Chen ◽  
Che-Hsin Liu ◽  
Hung-Ju Shih ◽  
Chih-Hsin Chang ◽  
Wei-Bo Chen ◽  
...  
Keyword(s):  
Flash Flood ◽  
Flash Floods ◽  
Water Levels ◽  
Airborne Lidar ◽  
Mountainous Areas ◽  
Flood Prediction ◽  
Mountainous Regions ◽  
Operational Forecasting ◽  
Forecasting System ◽  
Elevation Model

Flash floods are different from common floods because they occur rapidly over short time scales, and they are considered to be one of the most devastating natural hazards worldwide. Mountainous areas with high population densities are particularly threatened by flash floods because steep slopes generate high flow velocities. Therefore, there is a great need to develop an operational forecasting system (OFS) for better flash flood prediction and warning in mountainous regions. This study developed an OFS through the integration of meteorological, hydrological, and hydrodynamic models. Airborne light detection and ranging (LiDAR) data were used to generate a digital elevation model (DEM). The OFS employs high-density and high-accuracy airborne LiDAR DEM data to simulate rapid water level rises and flooding as the result of intense rainfall within relatively small watersheds. The water levels and flood extent derived from the OFS are in agreement with the measured and surveyed data. The OFS has been adopted by the National Science and Technology Center for Disaster Reduction (NCDR) for forecasting flash floods every six hours in a mountainous floodplain in Taiwan. The 1D and 2D visualization of the OFS is performed via the National Center for Atmospheric Research Command Language (NCL).

Observation of biological macromolecules using various electron microscopic methods

1978 ◽  
Vol 36 (2) ◽  
pp. 170-171
Author(s):  
Mitsuo Ohtsuki ◽  
Michael Sogard
Keyword(s):  
Electron Microscope ◽  
Phase Contrast ◽  
Image Interpretation ◽  
Density Lipoprotein ◽  
Biological Macromolecules ◽  
Contrast Effects ◽  
Biological Structure ◽  
Electron Microscopic ◽  
Structural Investigations ◽  
Staining Techniques

Structural investigations of biological macromolecules commonly employ CTEM with negative staining techniques. Difficulties in valid image interpretation arise, however, due to problems such as variability in thickness and degree of penetration of the staining agent, noise from the supporting film, and artifacts from defocus phase contrast effects. In order to determine the effects of these variables on biological structure, as seen by the electron microscope, negative stained macromolecules of high density lipoprotein-3 (HDL3) from human serum were analyzed with both CTEM and STEM, and results were then compared with CTEM micrographs of freeze-etched HDL3. In addition, we altered the structure of this molecule by digesting away its phospholipid component with phospholipase A2 and look for consistent changes in structure.

Simulated Dark-Field Electron Micrographs of Point Defects and Organometallics

1975 ◽  
Vol 33 ◽  
pp. 200-201
Author(s):  
William Krakow
Keyword(s):  
Electron Micrographs ◽  
Point Defects ◽  
Structure Determination ◽  
Atomic Structure ◽  
Image Interpretation ◽  
Dark Field ◽  
Field Electron ◽  
Dark Field Microscopy ◽  
Dark Field Electron

Tilted beam dark-field microscopy has been applied to atomic structure determination in perfect crystals, several synthesized molecules with heavy atcm markers and in the study of displaced atoms in crystals. Interpretation of this information in terms of atom positions and atom correlations is not straightforward. Therefore, calculated dark-field images can be an invaluable aid in image interpretation.

A method for direct measurement of specimen noise in HREM images

1993 ◽  
Vol 51 ◽  
pp. 458-459
Author(s):  
Sidnei Paciornik ◽  
Roar Kilaas ◽  
Ulrich Dahmen ◽  
Michael Adrian O'Keefe
Keyword(s):  
Random Noise ◽  
Image Interpretation ◽  
Thin Foil ◽  
High Resolution Electron Microscopy ◽  
Black Dot ◽  
Atomic Column ◽  
Amorphous Oxide ◽  
Resolution Electron ◽  
Imaging Conditions ◽  
Image Simulations

High resolution electron microscopy (HREM) is a primary tool for studying the atomic structure of defects in crystals. However, the quantitative analysis of defect structures is often seriously limited by specimen noise due to contamination or oxide layers on the surfaces of a thin foil.For simple monatomic structures such as fcc or bcc metals observed in directions where the crystal projects into well-separated atomic columns, HREM image interpretation is relatively simple: under weak phase object, Scherzer imaging conditions, each atomic column is imaged as a black dot. Variations in intensity and position of individual image dots can be due to variations in composition or location of atomic columns. Unfortunately, both types of variation may also arise from random noise superimposed on the periodic image due to an amorphous oxide or contamination film on the surfaces of the thin foil. For example, image simulations have shown that a layer of amorphous oxide (random noise) on the surfaces of a thin foil of perfect crystalline Si can lead to significant shifts in image intensities and centroid positions for individual atomic columns.

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