Killer Data - LiDAR

By Simon Greener

What is the next "killer data"? Judging from the recent explosion of portals delivering satellite and aerial imagery to any curious Web surfer, one might say that sort of imagery will take us to the next phase.

Satellite imagery, no matter the resolution, will not cover all our needs.There is, to my current thinking, a non-satellite based technology that has the potential to provide up-to-date, accurate data where previously we had to use statistical sampling and costly field survey and ground-truth programs to capture data: LiDAR.(Light Detection And Ranging, defined here.) I believe that LiDAR technology has the potential to be one of the next big killer data sources.Let me explain using two examples.

Much of the GIS data in Australia is inaccurate, old and so very out-of-date.Much of the data was captured for topographic mapping purposes and dates back over 30 years.In remote, heavily forested areas, geodetic control was poor and photogrammetrists struggled to identify spot-heights and stream networks through heavy canopy.

Foresters have a need for accurate digital elevation data (particularly for cable harvesting, a method of removing trees rather like a cable car at a ski resort - you put stations at key points and then run cables between them to pick up the logs once the trees are cut down, and landscape visualization, etc.) and drainage networks (to determine stream side reserves for statutory compliance when harvesting, to determine water catchments, etc).They also need accurate road networks (for coupe-to-mill logistics, road design and cost control).But the main killer data that foresters, specializing in native forestry, have needed for years are data that would help them accurately measure the standing volume of timber in a forest, using as much automation as possible.

Satellite imagery has been tracked for years as a potential source for this but, at least in Australian native forestry, these data were not of much use.In Tasmania, current techniques involve manual photogrammetry - to delineate stands of trees of similar height and density - coupled with unbiased statistical sampling, field survey and predictive computer modeling.

A recent LiDAR trial in Tasmania showed just how much LiDAR is threatening to be killer data.I saw a forester process the results of a trial of around 60,000ha of forest using the open source System for Automated Geoscientific Analyses (SAGA) and the powerful Manifold GIS ($245).First, he took the LiDAR first return (the "first thing" the laser altimeter hits, which could be a top of a tree, for example) and ground points (the actual ground, lower than a tree top), then he Kriged (a statistical process that fills in between the laser points to create a continuous surface) both as separate surfaces, subtracted them and then ran a simple tree crown algorithm to find individual trees.He then went on to demonstrate how he could predict the best trees for thinning, find the old-growth trees, etc.This was awesome stuff! He also took the Kriged ground surface and produced an accurate drainage network from it.From the ground surface a set of nested catchments could also be produced.

This fellow was an inventory forester in charge of the unbiased statistical sampling measurement program.He was so excited by what he had been able to do that it started me thinking that LiDAR just might be the next "killer data" data source!

Sure, LiDAR still measures the world by sampling, but its power is that its sampling can be tuned to the objects being measured, which combined with carefully designed post-processing, enables us to get very close to measuring everything! With LiDAR, my forester friend came close to an estimate of actual trees and their properties (height and density) in a forest whereas previously he was left to extrapolate an estimate from statistical samples.

Roads and Other Infrastructure Data
A digital elevation model (DEM) lets us get at drainage, a powerful piece of infrastructure data that can be extracted from LiDAR.What else is there?

We could identify all the roads in the sample area.We could see the pavement area and, with suitable hyperspectral scanners, probably measure road surface and condition as well.But could we extract accurate 3D road centerlines from the LiDAR data just as we can extract drainage lines from the DEM?

At the SSI conference in Melbourne I found that Merrick now has an Australian connection via Merrick Mars LiDAR of Brisbane, Queensland, Australia. At the stand I had some time to "chew the fat" with Gary Outlaw, Merrick's US-based VP for business development, and had a look at their MARS software.The conversation confirmed what I suspected - it is possible to extract a 3D road centerline from the same LiDAR data my forester friend was extracting products specific to forestry.

Having a 3D, accurate road centerline dataset would be of immense value to the state of Tasmania.Transportation logistics could be computed from real road slope distances rather than the planimetric distances currently being used.Transportation models could take into account the hilly nature of Tasmania, and model actual truck behavior (e.g., slow speeds while ascending a hill or descending under airbrakes).To these data we could then add statutory road speeds, traffic lighting, one-way attribution, etc., to get a truly useful dataset that would drive economic efficiency.

Data as Foundational Infrastructure
So, let's return to where this reflection on data started.Put bluntly, the community wants better, up-to-date, data.Google is showing us the power of data and, as Nixon points out, the power of the value-add! The current out-of-date data infrastructure is simply not capable of being the powerful, accurate foundation on which a modern economy can innovate and value-add.

Government departments, the custodians of the existing topographic data, have retreated from significant and active data capture programs due, in part, to "economic rationalism." With little money to capture newer data, these departments spend what little resources they have managing out-of-date topographic and thematic data.So, in Tasmania we are still editing a drainage dataset that, outside of the urban areas, is woefully inaccurate and incomplete.This is also true for digital elevation data; and even extends to our road network.Even where there is an active capture program in place for roads, the turn around time from edit to publication is slow.

LiDAR has the potential to be "killer data" because it can rid us of the costs involved in trying to sample a complex reality and trying improve the impossibly inaccurate.LiDAR can help us capture new and accurate data using automated methods that require relatively little in the way of costly field-capture programs or other manual methodologies. It can liberate operational expenditure from trying to make a silk purse (good data) out of a sow's ear (bad data), to concentrating on value-adding the large amounts of data that will become available to us via Google (and other mapping portal efforts) and LiDAR.Because, as technology improves and pricing drops (LiDAR), or data use grows exponentially via new uses (Google), we will be swamped with data riches that require us to "think outside the square" of our own industry.

The great thing about the Google and map portal phenomenon is that it is forcing the geospatial community to look at itself long and hard.To use LiDAR and newer data sources effectively, we have to start thinking about the value-add and not the technology per se.To this end, Nixon's talk which I discussed in my article last week was timely and is the response of one geospatial industrial visionary.

What will yours be?

Ed.Note: This article follows on another article written by Greener about data.

Published Thursday, October 13th, 2005

Written by Simon Greener

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