Sea-level rise vulnerability mapping for adaptation decisions using LiDAR DEMs

• Published in: Progress in physical geography Vol. 37; no. 6; pp. 745 - 766
• Main Authors: Cooper, Hannah M., Fletcher, Charles H., Chen, Qi, Barbee, Matthew M.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.12.2013; Sage Publications; Sage Publications Ltd
• Subjects: Bgi / Prodig; Coastal geography; Marine; Sea level; Tidal waves; World. Multiregional geography; vulnerability mapping; sea-level rise; LiDAR; adaptation; error; uncertainty; DEM; Uncertainty; Methodology; Spatial analysis; Data processing; Vulnerability; Sea level; Inundation; Remote sensing; Thematic mapping; Coastal environment; Decision making process; Geographical information system; Adaptation
• ISSN: 0309-1333; 1477-0296
• DOI: 10.1177/0309133313496835

Global sea-level rise (SLR) is projected to accelerate over the next century, with research indicating that global mean sea level may rise 18–48 cm by 2050, and 50–140 cm by 2100. Decision-makers, faced with the problem of adapting to SLR, utilize elevation data to identify assets that are vulnerable to inundation. This paper reviews techniques and challenges stemming from the use of Light Detection and Ranging (LiDAR) digital elevation models (DEMs) in support of SLR decision-making. A significant shortcoming in the methodology is the lack of comprehensive standards for estimating LiDAR error, which causes inconsistent and sometimes misleading calculations of uncertainty. Workers typically aim to reduce uncertainty by analyzing the difference between LiDAR error and the target SLR chosen for decision-making. The practice of mapping vulnerability to SLR is based on the assumption that LiDAR errors follow a normal distribution with zero bias, which is intermittently violated. Approaches to correcting discrepancies between vertical reference systems for land and tidal datums may incorporate tidal benchmarks and a vertical datum transformation tool provided by the National Ocean Service (VDatum). Mapping a minimum statistically significant SLR increment of 32 cm is difficult to achieve based on current LiDAR and VDatum errors. LiDAR DEMs derived from ‘ground’ returns are essential, yet LiDAR providers may not remove returns over vegetated areas successfully. LiDAR DEMs integrated into a GIS can be used to identify areas that are vulnerable to direct marine inundation and groundwater inundation (reduced drainage coupled with higher water tables). Spatial analysis can identify potentially vulnerable ecosystems as well as developed assets. A standardized mapping uncertainty needs to be developed given that SLR vulnerability mapping requires absolute precision for use as a decision-making tool.