Terrain Datasets: How good are they?

Celso Ferreira¹, Francisco Olivera¹, Dean Djokic²¹Texas A & M University

² Environmental Systems Research Institute - ESRI

Outline

•Motivation

•Background

•Methodology

•Case studies

•Conclusions

•Guidelines

Motivation

•The use of DEM for watershed delineation with GIS is current standard practice in engineering fields.

•Traditional methods have difficulty to process large datasets with high resolution.

•Our goal is to evaluate the best settings to develop DEMs from LiDAR data for watershed delineation using ESRI terrain datasets.

LiDAR

DEM

Watersheds

Research overview

Error Evaluation

LiDAR to DEM

•Interpolation without breaklines• IDW• Spline• Kriging• Nearest Neighbor

•Interpolation with breaklines• TOPOGRID (ANUDEM)

•TIN• Point -> TIN -> Raster

Traditional Methods

Terrain Datasets

Source: Adapted from ESRI Users Manual

•Multi-resolution TIN-based surface build from measured points and stored as features in a geodatabase

•Design to handle large point files

•Ability to work with Pyramid levels

•Inclusion of hard and soft breaklines

Terrain Datasets

Too many points ?

Cells with no data ?

DECIMATION•Window Size•Z-value

INTERPOLATION•Linear•Natural Neighbors

LiDAR data with average point spacing 7.7 feet over a 7.7 feet grid cell size

Terrain DatasetsRaw Lidar Files/Folder

Import LAS/ASCII FilesImport LAS/ASCII Files

GEODATASEGEODATASE

TERRAINTERRAIN

Create TerrainCreate Terrain

Add Pyramids LevelsAdd Pyramids Levels

Add Feature ClassesAdd Feature Classes

Pyramid Type

Pyramids Levels / Scale

Include Breaklines

GP ToolGP Tool

Parameter

Legend

Convert to DEMConvert to DEM

DEMDEM

Pyramid LevelInterpolation method

LiDA

RTE

RRAI

ND

EM

Watersheds

Error Evaluation

•Error Metric 3:

3

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i i

w w

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error

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A

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How far are we?

How much error?

Are the areas the same?

•Error Metric 2:

•Error Metric 1:1

0

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w

A

A

Case Study 1

Williamson Creek, Austin TX

•Lidar data:• 116 LAS Folders• Original Size: 1 GB• Total Points: 24,478,766• Mean per folder: 422,047• Average point spacing: 7.7 feet

• Watershed processing:• Filled all sinks• Standard dendritic processing

1

2

3

Case Study 2

•Lidar data:• 608 LAS Folders• Original Size: 60 GB• Total Points: 2,279,523,264• Mean per folder: 3,749,215.894737• Average point spacing: 3 feet

• Watershed processing:• Sink pre-evaluation• Manual selection of real sinks (120)• Flow directions with sinks• Combined deranged/dendritic

processing

Hillsborought, Florida

1

2

3

ResultsProcessing Time

•Intel® Core™ 2 Duo CPU E8500 @ 3.16 GHz, 2.00 GB of RAM

•Hillsborough dataset: ~2.2 billion points

ResultsDecimation: Error Metric 1

10

w

w

A

A

ResultsDecimation: Error Metric 2

20

error

w

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ResultsDecimation: Error Metric 3

1

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i i

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ResultsInterpolation Method

Results127 Watersheds in Florida: Error Metric 1

Conclusions

•Processing Time• Z-value is on average 8 times longer then window size• Break-line inclusion does not interfere with the

processing time

•Decimation Method• Window size is more consistent for larger pyramid levels• Z-value might generate outliers

•Interpolation method• Linear works better for Window size• Natural neighbors is more consistent for z-value

Guidelines for watershed delineation•Include break lines in all pyramids levels when creating terrain

•Use window size for watershed delineation

• Flat areas:• Not recommended to use pyramids• Interpolation method can result in reasonable different

watersheds

• Steeper terrain:• Simplified pyramids can be used• Interpolation method don’t interfere on the results

Questions ?