Mapping Inundation Uncertainty Using LiDAR

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.