Please use this identifier to cite or link to this item:
Mapping Inundation Uncertainty Using LiDAR
|Cooper Hannah r.pdf||Version for non-UH users. Copying/Printing is not permitted||4.45 MB||Adobe PDF||View/Open|
|Cooper Hannah uh.pdf||Version for UH users||4.46 MB||Adobe PDF||View/Open|
|Title:||Mapping Inundation Uncertainty Using LiDAR|
|Authors:||Cooper, Hannah M.|
|Contributors:||Chen, Qi (advisor)|
Geography and Environment (department)
mapping inundation uncertainty
|Date Issued:||May 2013|
|Publisher:||[Honolulu] : [University of Hawaii at Manoa], [May 2013]|
|Abstract:||Decision-makers, faced with the problem of adapting to sea-level rise (SLR), utilize elevation data to identify assets vulnerable to inundation. This thesis reviews techniques and challenges stemming from the use of Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) in support of SLR decision-making. The practice of mapping SLR vulnerability is based on the assumption that LiDAR errors follow a Gaussian distribution with zero bias, which is intermittently violated. In order to address this challenge when data do not follow a Gaussian distribution, we present a Monte Carlo approach and incorporate uncertainty in future SLR estimates into the vulnerability mapping. Excluding uncertainty in SLR estimates is found to underestimate potential land and building monetary loss by 21%, and overestimate wetland land cover classifications by 74% at the high probability threshold (inundated areas above 80% rank). Additional uncertainties in SLR vulnerability mapping may be integrated using this approach.|
|Description:||MA University of Hawaii at Manoa 2013|
Includes bibliographical references (leaves 74–83).
|Pages/Duration:||viii, 83 leaves|
|Rights:||All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.|
|Appears in Collections:||
M.A. - Geography|
Please email firstname.lastname@example.org if you need this content in ADA-compliant format.
Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.