LiDAR Data Management and Exploitation Joe Bob Penor

Staff Scientist

GIS Product Specialist: Geospatial Operations

Overview

How is LiDAR data useful in Disaster Management

Acquiring Point Data – Traditional Airborne Capture

– Points from Pixel correlation

– Pro’s and Con’s of each collection

Managing and Deliver your LiDAR Data

LiDAR Data Exploitation

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Disaster Happens

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How is LiDAR useful

Prevention and mitigation

Search and Rescue

Insurance assessment

Restoration

At Risk Analysis (3D Visualization)

5 3/14/2012 GeoMedia 3D

Potential Slide Areas Historical Flood Zones

Potential Water Extents

Capture

What if Pre-date LiDAR does not exist?

Pixel correlation

The result….

…a very dense color encoded point cloud.

Aerial vs. Pixel Correlation

Aerial LiDAR Pixel Correlation

Typical high point density

(points/m2)

25

400

Typical point accuracy

(sigma x, y in m)

0.15

0.025

Typical point accuracy

(sigma z in m)

0.050

0.075

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There is a little problem…

Distributed Processing

One Step further…Processing as a Service

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Other Considerations

• What point density is required?

• What accuracy is required?

• What types of surfaces are more important (e.g., vertical versus horizontal)?

• How quickly must the data be collected (e.g., hours versus days)?

• When can the data be collected (e.g., which season)?

• Is data collection at night an advantage (e.g., lower security risk, or avoiding air traffic near

major airports, less ‘clutter’)?

• What details are required (e.g., courtyards, roofs, tops of bridges, under bridges)?

• What type of vegetation cover exists and do you need to penetrate the vegetation?

• For Comparison, historical imagery may exist but LiDAR may not

Manage LiDAR in ERDAS APOLLO

Discover

Catalog

Edit Metadata

Visualize

Execute WPS models

CZS - Clip-zip-Ship

Export

Manage LiDAR in ERDAS APOLLO

ERDAS IMAGINE and LPS LiDAR Capabilities

Terrain Analysis and visualization (slope, Aspect, shading, viewshed, intervisibility)

Import / export, Merge, Split, Thin

Generate contours

Use in Ortho generation

Create LAS in eATE

Edit in Terrain Editor

3D visualization

Classification cue in ERDAS IMAGINE Objective

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Terrain Analysis and

visualization (slope, aspect,

shading, viewshed, etc.)

Import / export

Generate true vector

contours

Statistical Analysis

Advanced Interpolation,

including Kriging

Downhill flow and flow

concentration analysis

Classification

GeoMedia LiDAR Capabilities

Identifying change in Lidar Data

Point Cloud Tools

Conclusions

Point Clouds can be a useful tool in disaster management

– Prevention and mitigation

– Search and Rescue

– Insurance assessment

– Restoration

There are alternative methods to collect LiDAR data with pro’s and con’s

– Traditional airborne survey

– Pixel correlation from stereo imagery

Once you have LiDAR you need effective management and exploitation tools

LiDAR and Dam Analysis

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Thank you

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