Estimation of Surface Roughness Using Terrestrial LiDAR Measurements

The major damage from a hurricane to buildings and people in the State of Florida is caused by high wind and storm surges. Buildings, trees, and terrains, and the spatial arrangement of these objects can greatly influence the turbulence structure of hurricane wind near the land surface. High-resolution measurements of the terrain and surface roughness determined by the arrangement of land surface objects are essential to understand and quantify the hurricane damage to build structures and to create new hurricane resistant products. In order to accurately estimate the surface roughness, large scale data on geometric shapes of buildings, trees, and terrains are needed.

Remote sensing imagery and measurements are useful data sources for effectively estimating the surface roughness for a large area. For example, the national land cover dataset (NLCD) derived from Landsat images by USGS with a 30 m spatial resolution covers the entire Florida State. The Florida Division of Emergency Management collected airborne light detection and ranging (LiDAR) data for the coastal area vulnerable to storm surge flooding in 2007. The Landsat imagery provides a large scale snapshot of land cover, but the critical information such as building and tree heights for estimating surface roughness is lacking from the NLCD. The LiDAR remote sensing improves the estimation of surface roughness by providing direct measurements of building and tree heights, but still, the stem sizes and species of trees and the structures such as windows on the walls cannot be derived directly from airborne LiDAR measurements. The surface roughness that is determined by the spatial arrangement of buildings, trees, and terrains has a great effect on the hurricane wind speed near the land surface, and as a result, greatly affect impacts of wind on built structures. The national land cover dataset (NLCD) derived from optical satellite imagery provides a large scale snapshot of land cover for estimating surface roughness, but the data set lacks critical vertical geometric data of buildings and trees. The terrestrial laser LiDAR can directly measure 3D shapes of vegetation and buildings, providing an important complement to existing remote sensing data for calculating surface roughness. We propose (1) to collect tree and building data at sample sites for various types of land cover in South Florida using the terrestrial LiDAR system, and (2) to develop the methods to extract buildings, trees, terrains from LiDAR measurements at these sites and to estimate surface roughness parameters. With a maximum range more than 1000 m, a scan rate more than 70 KHZ (70,000 measurements per second), and a precision of 1 cm, the terrestrial laser scanner can provide accurate and effective measurements of land surface objects for calculating surface roughness. Surface roughness models created from this research will be used to determine hurricane wind interaction with various types of communities, especially in urban settings. The deliverables of this project include:

  • Terrestrial LiDAR measurements for the selected areas in South Florida
  • Methods to extract the terrain, buildings, and trees from terrestrial LiDAR measurements
  • Estimated surface roughness at the selected areas

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