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Developing a Regional Scale Landslid...

Chakrapani Lekha, Vishnu,

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Developing a Regional Scale Landslide Early Warning System in a Data-Sparse Region Using Remote Sensing, Geostatistics, and Google Earth Engine /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Developing a Regional Scale Landslide Early Warning System in a Data-Sparse Region Using Remote Sensing, Geostatistics, and Google Earth Engine // Vishnu Chakrapani Lekha.
Author:	Chakrapani Lekha, Vishnu,
Description:	1 electronic resource (198 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Geological engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30421766
ISBN:	9798379549541

Developing a Regional Scale Landslide Early Warning System in a Data-Sparse Region Using Remote Sensing, Geostatistics, and Google Earth Engine /
Chakrapani Lekha, Vishnu,

Developing a Regional Scale Landslide Early Warning System in a Data-Sparse Region Using Remote Sensing, Geostatistics, and Google Earth Engine /Vishnu Chakrapani Lekha. - 1 electronic resource (198 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

The Landslide Early Warning System (LEWS) is a non-structural approach to mitigate landslide risk by alerting vulnerable communities at an early stage. This study aimed to develop a regional LEWS for rain-induced shallow landslides in Idukki, a mountainous district in India with sparse rainfall data. The landslide model consists of a rainfall component and a slope stability component. Satellite precipitation data can be used in data-sparse regions, but they must be calibrated because they tend to underestimate rainfall. To improve the accuracy of satellite data, this study used a geostatistics-based multi-criteria approach to identify optimal locations to install new rain gauges, thus enhancing the rain gauge network's monitoring capability. A rainfall threshold was developed for Idukki, accounting for intra-seasonal variations in rainfall patterns and extreme rainfall events. The slope stability component of the model is limited by the lack of high-resolution soil properties, which are time-consuming and impractical to acquire using conventional methods. To overcome this limitation, this research proposed developing empirical relationships between sub-surface resistivity and soil properties, providing a regional-scale high-resolution soil property dataset for slope susceptibility assessment. Finally, a cloud-based LEWS was developed using Google Earth Engine, combining the rainfall threshold and high-resolution slope stability models, with the advantage of readily available near real-time data, processing power, user accessibility, and the opportunity for future updates.

English

ISBN: 9798379549541Subjects--Topical Terms:

2122713
Geological engineering.
Subjects--Index Terms:

Early warning

Developing a Regional Scale Landslide Early Warning System in a Data-Sparse Region Using Remote Sensing, Geostatistics, and Google Earth Engine /
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245 1 0 $a Developing a Regional Scale Landslide Early Warning System in a Data-Sparse Region Using Remote Sensing, Geostatistics, and Google Earth Engine / $c Vishnu Chakrapani Lekha.
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520 $a The Landslide Early Warning System (LEWS) is a non-structural approach to mitigate landslide risk by alerting vulnerable communities at an early stage. This study aimed to develop a regional LEWS for rain-induced shallow landslides in Idukki, a mountainous district in India with sparse rainfall data. The landslide model consists of a rainfall component and a slope stability component. Satellite precipitation data can be used in data-sparse regions, but they must be calibrated because they tend to underestimate rainfall. To improve the accuracy of satellite data, this study used a geostatistics-based multi-criteria approach to identify optimal locations to install new rain gauges, thus enhancing the rain gauge network's monitoring capability. A rainfall threshold was developed for Idukki, accounting for intra-seasonal variations in rainfall patterns and extreme rainfall events. The slope stability component of the model is limited by the lack of high-resolution soil properties, which are time-consuming and impractical to acquire using conventional methods. To overcome this limitation, this research proposed developing empirical relationships between sub-surface resistivity and soil properties, providing a regional-scale high-resolution soil property dataset for slope susceptibility assessment. Finally, a cloud-based LEWS was developed using Google Earth Engine, combining the rainfall threshold and high-resolution slope stability models, with the advantage of readily available near real-time data, processing power, user accessibility, and the opportunity for future updates.
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650 4 $a Geological engineering. $3 2122713
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Physical geography. $3 516662
650 4 $a Environmental geology. $3 535218
653 $a Early warning
653 $a Geostatistics
653 $a Google earth engine
653 $a Landslides
653 $a Remote sensing
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690 $a 0368
690 $a 0388
690 $a 0407
710 2 $a Michigan Technological University. $b Geological/Mining Engineering & Sciences. $e degree granting institution. $3 3829673
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773 0 $t Dissertations Abstracts International $g 84-11B.
790 $a 0129
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