Analysis and Prediction of Landslide using Drone Image and GIS Techniques- Case Study Aranayaka Area

2020 ◽  
Vol 9 (1) ◽  
pp. 3466-3472
Author(s):  
T.D.C. Pushpakumara ◽  
◽  
P.P.G.P. Madushanka ◽  
Keyword(s):  
Image Processing ◽  
Satellite Image ◽  
Slope Angle ◽  
Economic Activities ◽  
Building Research ◽  
Landslide Hazards ◽  
Potential Map ◽  
The World ◽  
Satellite Image Processing

Occurrence of Landslides has become a major impact considering the damage a landslide can do in a quick time. Among the natural hazards the country is exposed up to now, severe landslides are the upcoming major issue since it can affect to the lives of people too. In this case study from Aranayaka area, a method is developed to analyses and identify landslide prone areas. The methodology of the research includes collecting terrain data, building a model using geographic information system (GIS), satellite image processing, preparation of a landslide susceptibility potential map and giving recommendations on the landslide hazards. Among the factors that influence a landslide such as drainage, bedrock condition, slope angle range, land forms and etc. (published by National Building Research Organization), the foremost controllable and mostly varying factor. In this research, landslide prone areas are identified using the land use data. Identification of landslide hazards plays an important role in disaster management and risk controlling since a severe landslide can affect several aspects such as human lives, agricultural aspects, economic activities and transportation. This paper includes the background of the entire research that has developed up to current situation. This method can be used around the world as well as in the country.

scholarly journals A WEB-BASED VIRTUAL LABORATORY FOR SATELLITE IMAGE PROCESSING AND ANALYSIS

2018 ◽  
Vol XLII-5 ◽  
pp. 9-13
Author(s):  
K. M. Buddhiraju ◽  
L. N. Eeti ◽  
K. K. Tiwari
Keyword(s):  
Image Processing ◽  
System Architecture ◽  
Satellite Images ◽  
Satellite Image ◽  
Virtual Laboratory ◽  
Web Based ◽  
Continuous Increase ◽  
Image Processing And Analysis ◽  
The World ◽  
Satellite Image Processing

<p><strong>Abstract.</strong> With continuous increase in the utilization of satellite images in various engineering and science fields, it is imperative to equip students with additional educational aid in subject of satellite image processing and analysis. In this paper a web-based virtual laboratory, which is accessible via internet to anyone around the world with no cost or constraints, is presented. Features of the laboratory has been discussed in addition to details regarding system architecture and its implementation. Virtual laboratory is tested by students, whose responses are also presented in this paper. Future development of this laboratory is outlined in the end.</p>

scholarly journals Optical Remote Sensing

2021 ◽  
pp. 315-344
Author(s):  
Man Sing Wong ◽  
Xiaolin Zhu ◽  
Sawaid Abbas ◽  
Coco Yin Tung Kwok ◽  
Meilian Wang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Information Extraction ◽  
Urban Areas ◽  
Satellite Image ◽  
Background Information ◽  
Optical Remote Sensing ◽  
Basic Operation ◽  
City Development ◽  
Satellite Image Processing

AbstractApplications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.

scholarly journals Analysis of Changes in Area and Density of Mangroves through Landsat 8 Satellite Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 16-22
Author(s):  
Nadya Fiqi Nurcahyani
Keyword(s):  
Image Processing ◽  
Satellite Imagery ◽  
Good Accuracy ◽  
Social Values ◽  
Satellite Image ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Android Application ◽  
Mangrove Area ◽  
Satellite Image Processing

Mangrove forests have high ecological, economic and social values ??which function to maintain shoreline stability, protect beaches and riverbanks, filter and remediate waste, and to withstand floods and waves. The facts show that mangrove damage is everywhere, even the intensity of damage and its area tends to increase significantly. Many roles of mangroves require proper management to maintain the existence of mangroves. One way to determine the area of ??mangroves is by processing Landsat 8 satellite imagery. The stages of mangrove identification are carried out by using 564 RGB band merger, then separating the mangrove and non-mangrove objects. Next step is to analyze the density of mangroves using NDVI formula. To maximize monitoring of mangrove area, an android application was created that provides information on the area and density of mangroves at several locations, namely Clungup, Bangsong Teluk Asmara and Cengkrong from 2015 to 2018.The results showed that Landsat 8 satellite imagery can be used to identify changes in the area of ??mangrove forests with good accuracy, namely in the Clungup area of ??90% and Cengkrong of 86.67%. From processing results, the mangrove area in the Clungup area has also decreased from 2015 to 2017 but has increased in 2018 so that the application provides recommendations for embroidering mangroves in 2016 to 2017 and mangrove recommendations are maintained in 2018. As for Bangsong Teluk area Asmara and Cengkrong have increased the area of ??mangroves every year so that the application provides recommendations to be maintained from 2016 to 2018.

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