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E-grāmata: Terrigenous Mass Movements: Detection, Modelling, Early Warning and Mitigation Using Geoinformation Technology

  • Formāts: PDF+DRM
  • Izdošanas datums: 02-Apr-2012
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642254956
  • Formāts - PDF+DRM
  • Cena: 106,47 €*
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  • Formāts: PDF+DRM
  • Izdošanas datums: 02-Apr-2012
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642254956

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Mass movements of terrain, including cliff collapses and landslides, can be disastrous. This volume shows how the latest developments in geoinformation technology, including data modeling and remote sensing, can help to predict movement in advance.

Terrestrial mass movements (i.e. cliff collapses, soil creeps, mudflows, landslides etc.) are severe forms of natural disasters mostly occurring in mountainous terrain, which is subjected to specific geological, geomorphological and climatological conditions, as well as to human activities. It is a challenging task to accurately define the position, type and activity of mass movements for the purpose of creating inventory records and potential vulnerability maps. Remote sensing techniques, in combination with Geographic Information System tools, allow state-of-the-art investigation of the degree of potential mass movements and modeling surface processes for hazard and risk mapping. Similarly, through statistical prediction models, future mass-movement-prone areas can be identified and damages can to a certain extent be minimized. Issues of scale and selection of morphological attributes for the scientific analysis of mass movements call for new developments in data modeling and spatio-temporal GIS analysis. The book is a product of a cooperation between the editors and several contributing authors, addressing current issues and recent developments in GI technology and mass movements research. Its fundamental treatment of this technology includes data modeling, topography, geology, geomorphology, remote sensing, artificial neural networks, binomial regression, fuzzy logic, spatial statistics and analysis, and scientific visualization. Both theoretical and practical issues are addressed.
1 An Automated Approach for Detection of Shallow Landslides from LiDAR Derived DEM Using Geomorphological Indicators in a Tropical Forest
1(22)
Ulrich Mann
Biswajeet Pradhan
Nikolas Prechtel
Manfred F. Buchroithner
2 Landslide Susceptibility Mapping Using a Spatial Multi Criteria Evaluation Model at Haraz Watershed, Iran
23(28)
H. R. Pourghasemi
Biswajeet Pradhan
Candan Gokceoglu
K. Deylami Moezzi
3 Soft Computing Modeling in Landslide Susceptibility Assessment
51(40)
C. Gokceoglu
E. Sezer
4 Application and Verification of Fractal Approach to Landslide Susceptibility Mapping
91(18)
Changjiang Li
Tuhua Ma
Leling Sun
Wei Li
Aiping Zheng
5 Preliminary Slope Mass Movement Susceptibility Mapping Using DEM and LiDAR DEM
109(62)
M. Jaboyedoff
M. Choffet
M.-H. Derron
P. Horton
A. Loye
C. Longchamp
B. Mazotti
C. Michoud
A. Pedrazzini
6 Application of GIS and RS for Mapping Landslides at the Watershed Level
171(22)
Jiann-Yeou Rau
Kang-Tsung Chang
Chi-Chung Lau
Liang-Chien Chen
Yi-Chen Shao
Jin-King Liu
7 Ensemble-Based Landslide Susceptibility Maps in Jinbu Area, Korea
193(28)
Saro Lee
Hyun-Joo Oh
8 Geoinformatics and Mass Movements: A Study on Li-shan Landslide, Taiwan
221(18)
Keh-Jian Shou
9 Landslide Inventory, Hazard and Risk Assessment in India
239(44)
Cees J. van Westen
Pankaj Jaiswal
Saibal Ghosh
Tapas R. Martha
Sekhar L. Kuriakose
10 Vision-Based Terrestrial Surface Monitoring
283(66)
Gerhard Paar
Niko Benjamin Huber
Arnold Bauer
Michael Avian
Alexander Reiterer
11 LaSIRF: Landslide Safe Intelligent Route Finder for Mountainous Terrain in GIS Environment
349(20)
M. K. Arora
A. K. Saha
P. Gupta
R. P. Gupta
12 Identification of Potentially Dangerous Glacial Lakes in the Northern Tian Shan
369
Tobias Bolch
Juliane Peters
Alexandr Yegorov
Biswajeet Pradhan
Manfred Buchroithner
Victor Blagoveshchensky