This is a repost from the now defunct Whatever-Weather.com website.
I have written in the past about how Geographic Information Systems (GIS) and Meteorology relate to each other. I want to extend that blog post to a form of remote sensing which overlaps with GIS.
LiDAR is Light Detection and Ranging, basically a repeating laser and receiver. How you use it in meteorology does depend on which way you aim it. Traditionally it is pointed up into the sky for air quality studies. The laser reflects off particles in the atmosphere to measure the amount of light scatter and particulate mass in the atmosphere.
The other way is to point the laser towards the ground from an elevated platform like an airplane or helicopter or a ground tripod setup. If you use it in this way you overlap Meteorology with civil engineering/survey applications and GIS/Remote Sensing applications. When you combine disciplines in this way you get applications of the technology which lead to stronger products for public safety and commerce.
When you collect LiDAR data from any platform, you get a highly detailed height return off the surface. If it is an aerial platform (plane or helicopter) you get returns from above looking down. Depending on how you process the data, you can use it in many ways. One way is to get a highly detailed Digital Elevation Model (DEM), which is highly useful for flooding applications and precision agriculture applications. Some studies have also been able to obtain snow pack estimates by comparing flights in the winter to the summer model and compute height differences.
Another product from aerial LiDAR area is 3D building reconstruction. There are two forms of 3D building reconstruction on the market. One is the simple model where you have a building footprint model just extended in the vertical (Z) with a generic flat top based on a estimated or known building height (obtained via survey or some photogrammetry programs). This form is good for numerical modeling of urban flooding and heat island effects, estimating urban population with energy demand, and large scale 3D visualization (Shan, Toth 2009).
There are also highly detailed 3D building models you can extract from LiDAR data. These models are better than the simple models for hurricane wind damage models and detailed urban landscape modeling. These models take longer to render and do not always work with many mainstream GIS software packages on the market. The detailed models are still under software development while algorithms improve and work to overcome some limitations in obtaining data such as building shadowing from trees and building size with data resolution.
I am planning more detailed blog posts on each part of LiDAR I have mentioned. The more cross-discipline knowledge and technology we share the safer we are from natural disasters and the more efficient commerce can be.
(c) 2012 Charles Schoeneberger