Abstract

Evaluation of Terrain Datasets for LiDAR data thinning and DEM generation for watershed delineation applications

Celso Ferreira¹, Francisco Olivera², Dean Djokic³ ¹ PH.D. Student, Civil Engineering, Texas A&M University (email: celsoferreira@tamu.edu) ² Associate Professor, Civil Engineering, Texas A&M University ³ Environmental Systems Research Institute - ESRIEP51D-0582

Watershed delineation based on Digital Elevation Models (DEM) is currently standard practice in hydrologic studies. Efforts to develop high-resolution DEMs continue to take place, although the advantages of increasing the accuracy of the data are partially offset by the increased file size,

difficulty to handle them, slow screen rendering and increase computational needs. Among these efforts, those based on the use of Light Detection and Ranging (LiDAR) pose the problem that

interpolation techniques in commercially available GIS software packages (e.g., IDW, Spline, Kriging and TOPORASTER, among others) for developing DEMs from point elevations have difficulty

processing large amounts of data. Terrain Dataset is an alternative format for storing topographic data that intelligently decimates data points and creates simplified, yet equally accurate for practical purposes, DEMs or Triangular Irregular Networks (TIN). This study uses terrain datasets to evaluate

the impact that the thinning method (i.e., window size and z-value), pyramid level and the interpolation technique (linear or natural neighbor) used to create the DEMs have on the

watersheds delineated from them.

•Error Metric 3: 30 0

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error

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•How far are we?

•How much error?

•Are the areas the same?

•Error Metric 2:

•Error Metric 1: 10

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3. Terrain Datasets

1. Introduction

5. Preliminary Results

2. Case Studies

• Hillsborough County: 1.2 billion points

Data thinning: Z-value Data thinning: Window Size

6. Conclusions / Guidelines

7. Future Work

4. Watershed delineationHow to evaluate the best

watershed delineation?

• The use of DEM for watershed delineation with GIS is current standard practice in engineering fields.• Traditional interpolation methods have difficulty processing large datasets with high resolution.• Our goal is to evaluate the best settings to create DEMs from LiDAR data for watershed delineation using ESRI Terrain Datasets.

1. Too many points ?

2. Cells with no data ?

•DECIMATION• Window Size• Z-value

•INTERPOLATION• Linear• Natural Neighbors

Generating DEMs from huge data points? …Terrain Datasets• Designed to handle large point files.• Multi-resolution TIN-based surface build from measured points.• Stored as features in a geodatabase.• Ability to work with pyramid levels.• Includes hard and soft breaklines.

LiDAR DEM Watersheds Error EvaluationTerrain Datasets

Figure 1: Overall methodology : From LiDAR datasets to watershed delineation error evaluation.

• Williamson Creek: 24 million points

• Processing Time: Z-value is on average 8 times longer then window size and breakline inclusion does not affect the processing time.• Decimation Method: Window size is more consistent for larger pyramid levels and Z-value might generate outliers.• Interpolation method: Linear interpolation works better for Window size, and Natural neighbor interpolation is more consistent for z-value data thinning.• Guidelines: Include breaklines in all pyramids levels when creating terrain and use window size for watershed delineation.• Flat areas: Not recommended to use pyramids and Interpolation method can result in reasonable different watersheds.• Steeper terrain: Simplified pyramids can be used and interpolation method does not affect the results.

a) Processing time d) Decimation method (point delineation)

b) Interpolation method

c) Decimation method (batch delineation)

I) Error metric 1

II) Error metric 2

III) Error metric 3

Figure9: Processing time comparison for the Hillsborough dataset (~2.2 billion points) on a Intel® Core™ 2 Duo CPU E8500 @ 3.16 GHz, 2.00 GB of RAM Figure 12: Comparison of data thinning methods increasing pyramids level

for study watersheds: a) Hillsborough dataset was sensitive for increasing pyramids levels using both methods; and b) Austin dataset presented very low errors using both methods.

Figure 11: Comparison of 127 watersheds using batch delineation: a) Interpolation method; and b) Decimation method

Figure 10: Comparison of interpolation methods using full resolution. Figure shows difference in delineation from linear to natural neighbors.

Delineating watersheds from LiDAR data using terrain datasets

Raw 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 ?

Convert to DEMConvert to DEM

DEMDEM

Pyramid LevelInterpolation method

LiDA

RTE

RRAI

ND

EM

Flow analysesFlow analyses

Watershed PolygonWatershed Polygon

WAT

ERSH

ED• 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

• Lidar data:• 608 LAS Folders• Original Size: 60 GB• Total Points: 2,279,523,264• Mean per folder: 3,749,215• Average point spacing: 3 feet

1. Currently processing 3 additional LiDAR datasets from USGS CLICK (Colorado; Maryland and California).

2. Development of parametric/statistical analyses correlating terrain characteristics and best practices for delineating watershed from LiDAR data using terrain datasets

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

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

Figure 2: Hillsborough county (FL) LiDAR dataset and 3 study watersheds

Figure 3: Williamson creek (TX) LiDAR dataset and three study watersheds.

Figure 4: Example of decimation and interpolation areas using a Raster grid cell size of 5 feet and a LiDAR dataset of 3 feet.

Figure 5: Terrain datasets overview (source: adapted from the ESRI ArcGIS 9.3 Users Manual).

Figure 7: a) Full resolution data set; b) Thinning based on the cells defined by the gray lines; c) Thinning based on the cells defined by the blue lines and; d) Thinning based on the cells defined by the red lines. (adapted from the ESRI ArcGIS 9.3 Users Manual).

• Data thinning based on spatial parameters.• Partition the domain into equal areas (windows) with pre-define spatial dimensions.• One or two points are selected within each window size based on (mean, max, min, both).

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• Data thinning based on vertical accuracy.• Vertical tolerance based filter to remove points that are within a pre-established vertical range.• Assures a known vertical accuracy from the original data after data-thinning.

Figure 6: Data thinning using the z-value method increasing the vertical accuracy of the data set as a criterion to remove points

Figure 8: Examples of watershed delineation for the same point using different settings for generating DEM from LiDAR points

Figure 13: Comparison of data thinning methods increasing pyramids level for study watersheds: a) Hillsborough dataset presented a increasing error trend with the increase of pyramids levels; and b) Austin dataset presented again very low errors with exception of watershed 3.

Figure 14: Comparison of data thinning methods increasing pyramids level for study watersheds: a) Hillsborough dataset; and b) Austin dataset presented similar trend as in error metric 2.

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