scholarly journals A Report on GIS Based Analysis of Landslides in Myagdi District

2021 ◽  
Vol 16 (1) ◽  
pp. 69-76
Author(s):  
Sumit Thapa
Keyword(s):  
Remote Sensing ◽  
Disaster Preparedness ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Landslide Event ◽  
Simple Step ◽  
Preliminary Study ◽  
New Form ◽  
Precautionary Measures

Lanslide have become a routine event during monsoon in Nepal which accompanies a huge social, physical and economical loss. As the number of landslide event is in increasing order each year but their proper study is still limited, this assessment is an example of simple step in landslide study. Also, the developmental activities disturb the topology and hence increase or bring new form of landslide in the region. This report is mainly a preliminary study of existing landslide in Myagdi district which is generally carried out using QGIS software and remote sensing data available from http://earthexplorer.usgs.gov and recent Google satellite image. From the analysis carried out by using the inbuilt features in QGIS, relationship between various terrain, hydrological and anthropogenic parameters with landslide was driven. Based on this approach a simple precautionary measures in development activities, disaster preparedness and mitigation activities can be carried out.

A PRELIMINARY STUDY ON THE RE_INITIALIZATION/RE_PARAMETERIZATION OF A CROP MODEL BASED ON REMOTE SENSING DATA

2005 ◽  
Vol 29 (6) ◽  
pp. 918-926
Author(s):  
MA Yu-Ping ◽  
◽  
WANG Shi-Li ◽  
ZHANG Li ◽  
HOU Ying-Yu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Sensing Data ◽  
Model Based ◽  
Preliminary Study

scholarly journals Aplikasi Penghitungan Gross Primary Production dari Data Penginderaan Jauh

2016 ◽  
pp. 31
Author(s):  
Komang Gede Kurniadi ◽  
I Putu Agung Bayupati ◽  
I Dewa Nyoman Nurweda Putra
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Automatic Data ◽  
Sensing Data ◽  
Metadata File ◽  
Production Calculation

Calculation of Gross Primary Production that utilize remote sensing data is can be done on commercial remote sensing software by manual method. The commercial remote sensing software does not provides a specific feature that allow the user to do the Gross Primary Production calculation. This research is aimed to to build a remote sensing software that can be specifically used to do the Gross Primary Production calculation for Denpasar area. This software accepts remote sensing data as an input, such as satellite image from Landsat 8 OLI and TIRS and metadata file. The formulas and supporting data that required on the Gross Primary Production calculation are implemented on software in order to make an automatic image processing software. There also some additional feature on this software such as automatic data parsing from metadata file, cropping, masking and zoom that could help user to do the Gross Primary Production calculation. The developed software is able to produce information such as Gross Primary Production  value that depicted by a figure with color segmentation, area of the segments and mean, minimum and maximum value of the Gross Primary Production.  

scholarly journals RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 565-568
Author(s):  
N. Fu ◽  
L. Sun ◽  
H. Z. Yang ◽  
J. Ma ◽  
B. Q. Liao
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Management System ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Database Management System ◽  
Sensor Data ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Effective Organization

Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.

scholarly journals Engineering-Geological Analysis of a Subaerial Landslide in Taan Fiord, Alaska

2021 ◽  
Vol 13 (21) ◽  
pp. 4258
Author(s):  
Xiaoru Dai ◽  
Barbara Schneider-Muntau ◽  
Wolfgang Fellin ◽  
Andrea Franco ◽  
Bernhard Gems
Keyword(s):  
Remote Sensing ◽  
Slope Stability ◽  
Large Scale ◽  
Limit Equilibrium ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Stability Analyses ◽  
Subaerial Landslide ◽  
Landslide Event

On 17 October 2015, a large-scale subaerial landslide occurred in Taan Fiord, Alaska, which released about 50 Mm3 of rock. This entered the water body and triggered a tsunami with a runup of up to 193 m. This paper aims to simulate the possible formation of a weak layer in this mountainous slope until collapse, and to analyze the possible triggering factors of this landslide event from a geotechnical engineering perspective so that a deeper understanding of this large landslide event can be gained. We analyzed different remote-sensing datasets to characterize the evolution of the coastal landslide process. Based on the acquired remote-sensing data, Digital Elevation Models were derived, on which we employed a 2D limit equilibrium method in this study to calculate the safety factor and compare the location of the associated sliding surface with the most probable actual location at which this landslide occurred. The calculation results reflect the development process of this slope collapse. In this case study, past earthquakes, rainfall before this landslide event, and glacial melting at the toe may have influenced the stability of this slope. The glacial retreat is likely to be the most significant direct triggering factor for this slope failure. This research work illustrates the applicability of multi-temporal remote sensing data of slope morphology to constrain preliminary slope stability analyses, aiming to investigate large-scale landslide processes. This interdisciplinary approach confirms the effectiveness of the combination of aerial data acquisition and traditional slope stability analyses. This case study also demonstrates the significance of a climate change for landslide hazard assessment, and that the interaction of natural hazards in terms of multi-hazards cannot be ignored.

scholarly journals An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiao Xie ◽  
Xiran Zhou ◽  
Jingzhong Li ◽  
Weijiang Dai
Keyword(s):  
Remote Sensing ◽  
Object Recognition ◽  
Probabilistic Models ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Low Level ◽  
Level Data ◽  
Major Disadvantage ◽  
High Level

Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human’s semantics on the object to be recognized are required more explorations. Moreover, interpretability of feature extraction becomes a major disadvantage of the state-of-the-art CNN. Especially for the complex urban objects, which varies in geometrical shapes, functional structures, environmental contexts, etc, due to the heterogeneity between low-level data features and high-level semantics, the features derived from remote sensing data alone are limited to facilitate an accurate recognition. In this paper, we present an ontology-based methodology framework for enabling object recognition through rules extracted from the high-level semantics, rather than unexplainable features extracted from a CNN. Firstly, we semantically organize the descriptions and definitions of the object as semantics (RDF-triple rules) through our developed domain ontology. Secondly, we exploit semantic web rule language to propose an encoder model for decomposing the RDF-triple rules based on a multilayer strategy. Then, we map the low-level data features, which are defined from optical satellite image and LiDAR height, to the decomposed parts of RDF-triple rules. Eventually, we apply a probabilistic belief network (PBN) to probabilistically represent the relationships between low-level data features and high-level semantics, as well as a modified TanH function is used to optimize the recognition result. The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning.

scholarly journals The use of Landsat 8 remote sensing data for assessing the temperature conditions of the Larus ridibundus L. habitat

2020 ◽  
Vol 9 (4) ◽  
pp. 184-191
Author(s):  
Sergey Arkadyevich Shurakov ◽  
Aleksey Nikolaevich Chashchin
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Close Correlation ◽  
Primary Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Temperature Conditions

This paper discusses the possibilities of using Landsat 8 remote sensing data for assessing the temperature conditions of aquatic landscapes when studying the abundance and density of gulls. The study of the ornithological situation was carried out on the territory of the Perm international airport of the Perm Region, where the black-headed gull is an unfavorable factor in the safety of passenger aircraft flights. Within the boundaries of the region, 5 reservoirs were identified. A method for calculating the surface temperature from a multispectral satellite image of the Landsat 8 series is described in detail with the presentation of primary data sources, atmospheric parameters and obtaining raster coverage with a resolution of 30 meters per pixel. The tool used for the calculation is the Land Surface Temperature module of the QGIS software. The paper presents maps of temperature within the area of conducted ornithological surveys and the density of gulls. The densities of birds for individual bodies of water are calculated using the Spatial Analyst module of the ArcGIS program with the kernel density tool. According to the research results, a close correlation was established between the attractiveness of reservoirs for gulls and water temperature. The correlation coefficients were 0,83 and 0,71, respectively, with the abundance and density of gulls.

scholarly journals Assessment of impact of mass movements on the upper Tayyah valley's bridge along Shear escarpment highway, Asir region (Saudi Arabia) using remote sensing data and field investigation

2015 ◽  
Vol 3 (1) ◽  
pp. 497-533 ◽  
Author(s):  
A. M. Youssef ◽  
M. Al-Kathery ◽  
B. Pradhan
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Negative Impact ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Shuttle Radar Topography Mission ◽  
Field Investigation ◽  
Running Water ◽  
Sensing Data ◽  
Heavy Trucks

Abstract. Escarpment highways, roads and mountainous areas in Saudi Arabia are facing landslide hazards that are frequently occurring from time to time causing considerable damage to these areas. Shear escarpment highway is located in the north of the Abha city. It is the most important escarpment highway in the area, where all the light and heavy trucks and vehicle used it as the only corridor that connects the coastal areas in the western part of the Saudi Arabia with the Asir and Najran Regions. More than 10 000 heavy trucks and vehicles use this highway every day. In the upper portion of Tayyah valley of Shear escarpment highway, there are several landslide and erosion potential zones that affect the bridges between tunnel 7 and 8 along the Shear escarpment Highway. In this study, different types of landslides and erosion problems were considered to access their impacts on the upper Tayyah valley's bridge along Shear escarpment highway using remote sensing data and field investigation. These landslides and erosion problems have a negative impact on this section of the highway. Results indicate that the areas above the highway and bridge level between bridge 7 and 8 have different landslides including planar, circular, rockfall failures and debris flows. In addition, running water through the gullies cause different erosional (scour) features between and surrounding the bridge piles and culverts. A detailed landslides and erosion features map was created based on intensive field investigation (geological, geomorphological, and structural analysis), and interpretation of Landsat image 15 m and high resolution satellite image (QuickBird 0.61 m), shuttle radar topography mission (SRTM 90 m), geological and topographic maps. The landslides and erosion problems could exhibit serious problems that affect the stability of the bridge. Different mitigation and remediation strategies have been suggested to these critical sites to minimize and/or avoid these problems in the future.

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