Analysis of Lidar Elevation Data for Improved Identification and Delineation of Lands Vulnerable to Sea-Level Rise

被引:4
|
作者
Gesch, Dean B. [1 ]
机构
[1] US Geol Survey, Earth Resources Observat & Sci Ctr, Sioux Falls, SD 57198 USA
关键词
accuracy assessment; digital elevation model; geospatial data; hazards; maps; uncertainty; COVER;
D O I
10.2112/SI53-006.1
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The importance of sea-level rise in shaping coastal landscapes is well recognized within the earth science community, but as with many natural hazards, communicating the risks associated with sea-level rise remains a challenge. Topography is a key parameter that influences many of the processes involved in coastal change, and thus, up-to-date, high-resolution, high-accuracy elevation data are required to model the coastal environment. Maps of areas Subject to potential inundation have great utility to planners and managers concerned with the effects of sea-level rise. However, most of the maps produced to date are simplistic representations derived from older, coarse elevation data. In the last several years, vast amounts of high quality elevation data derived from lidar have become available. Because of their high vertical accuracy and spatial resolution, these lidar data are an excellent source of up-to-date information from which to improve identification and delineation of vulnerable lands. Four elevation datasets of varying resolution and accuracy were processed to demonstrate that the improved quality of lidar data leads to more precise delineation of coastal lands Vulnerable to inundation. A key component of the comparison was to calculate and account for the vertical uncertainty of the elevation datasets. This comparison shows that lidar allows for a Much more detailed delineation of the potential inundation zone when compared to other types of elevation models. It also shows how the certainty of the delineation of lands vulnerable to a given sea-level rise scenario is much improved when derived from higher resolution lidar data.
引用
收藏
页码:49 / 58
页数:10
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