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Modern technologies for landslide mo...

Scaioni, Marco.

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Modern technologies for landslide monitoring and prediction

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modern technologies for landslide monitoring and prediction/ edited by Marco Scaioni.
其他作者:	Scaioni, Marco.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2015.,
面頁冊數:	xii, 249 p. :ill., digital ;24 cm.
內容註:	Close-Range Photogrammetric Techniques for Deformation Measurement: Applications to Landslides -- A Fixed Terrestrial Photogrammetric System for Landslide Monitoring -- A New Approach Based on Terrestrial Remote Sensing Techniques for Rock Fall Hazard Assessment -- Multi-Temporal Terrestrial Laser Scanning Survey of a Landslide -- Micro-Scale Landslide Displacements Detection Using Bayesian Methods Applied to GNSS Data -- Analysis of Microseismic Activity within Unstable Rock Slopes -- The State of the Art of SPH Modelling for Flow-Slide Propagation -- Predictability of A Physically-based Model for Rainfall-induced Shallow Landslides: Model Development and Case Studies -- Monitoring Landslide Activities in the Three Gorges Area with Multi-Frequency Satellite SAR Datasets -- Radar Technologies for Landslide Detection, Monitoring, Early Warning and Emergency Management -- A new Approach to Satellite Time Series Co-registration for Landslide Monitoring.
Contained By:	Springer eBooks
標題:	Landslide hazard analysis. -
電子資源:	http://dx.doi.org/10.1007/978-3-662-45931-7
ISBN:	9783662459317 (electronic bk.)

Modern technologies for landslide monitoring and prediction
Modern technologies for landslide monitoring and prediction[electronic resource] /edited by Marco Scaioni. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - xii, 249 p. :ill., digital ;24 cm. - Springer natural hazards. - Springer natural hazards..

Close-Range Photogrammetric Techniques for Deformation Measurement: Applications to Landslides -- A Fixed Terrestrial Photogrammetric System for Landslide Monitoring -- A New Approach Based on Terrestrial Remote Sensing Techniques for Rock Fall Hazard Assessment -- Multi-Temporal Terrestrial Laser Scanning Survey of a Landslide -- Micro-Scale Landslide Displacements Detection Using Bayesian Methods Applied to GNSS Data -- Analysis of Microseismic Activity within Unstable Rock Slopes -- The State of the Art of SPH Modelling for Flow-Slide Propagation -- Predictability of A Physically-based Model for Rainfall-induced Shallow Landslides: Model Development and Case Studies -- Monitoring Landslide Activities in the Three Gorges Area with Multi-Frequency Satellite SAR Datasets -- Radar Technologies for Landslide Detection, Monitoring, Early Warning and Emergency Management -- A new Approach to Satellite Time Series Co-registration for Landslide Monitoring.

Modern Technologies for Landslide Investigation and Prediction presents eleven contributed chapters from Chinese and Italian authors, as a follow-up of a bilateral workshop held in Shanghai on September 2013. Chapters are organized in three main parts: ground-based monitoring techniques (photogrammetry, terrestrial laser scanning, ground-based InSAR, infrared thermography, and GNSS networks), geophysical (passive seismic sensor networks) and geotechnical methods (SPH and SLIDE), and satellite remote-sensing techniques (InSAR and optical images). Authors of these contributes are internationally-recognized experts in their respective research fields. Marco Scaioni works in the college of Surveying and Geo-Informatics at Tongji University, Shanghai (P.R. China). His research fields are mainly Close-range Photogrammetry, Terrestrial Laser Scanning, and other ground-based sensors for metrological and deformation monitoring applications to structural engineering and geosciences. In the period 2012-2016 he is chairman of the Working Group V/3 in the International Society for Photogrammetry and Remote Sensing, focusing on ‘Terrestrial 3D Imaging and Sensors’.

ISBN: 9783662459317 (electronic bk.)

Standard No.: 10.1007/978-3-662-45931-7doiSubjects--Topical Terms:

596837
Landslide hazard analysis.

LC Class. No.: QE599.2

Dewey Class. No.: 363.349

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