Midlands State University Library

Uncertainty due to DEM error in landslide susceptibility mapping (Record no. 160650)

MARC details
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fixed length control field 03124nam a22002417a 4500
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control field ZW-GwMSU
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control field 20221128160927.0
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fixed length control field 221128b |||||||| |||| 00| 0 eng d
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Original cataloging agency MSU
Transcribing agency MSU
Description conventions rda
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Qin, Cheng-Zhi
Relator term author
245 10 - TITLE STATEMENT
Title Uncertainty due to DEM error in landslide susceptibility mapping
Statement of responsibility, etc. created by Cheng-Zhi Qin,Li-Li Bao,A-Xing Zhu ,Rong-Xun Wang &Xue-Mei Hu
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Beijing:
Name of producer, publisher, distributor, manufacturer Taylor and Francis
Date of production, publication, distribution, manufacture, or copyright notice 2013
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Content type term text
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Media type term unmediated
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Summary, etc. Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km2) and the other for model extrapolation (approximately 4600 km2). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.<br/><br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element DEM error
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element landslide susceptibility mapping
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element error propagation
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Uniform Resource Identifier https://doi.org/10.1080/13658816.2013.770515
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Journal Article
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    Library of Congress Classification     Main Library Main Library - Special Collections 14/10/2014 Vol 27 .No.7-8 pages 1364-1380   G70.2 INT 28/11/2022 SP17852 28/11/2022 Journal Article For Inhouse use only