Celso Ferreira¹, Francisco Olivera², Dean Djokic³ ¹ PH.D. Student, Civil Engineering, Texas A&M University ( ² Associate Professor, Civil Engineering, Texas A&M University ³ Environmental Systems Research Institute - ESRI EP51D-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: Error Metric 2: Error Metric 1: 3. Terrain Datasets 1. Introduction 5. Preliminary Results 2. Case Studies 6. Conclusions / Guidelines 7. Future Work 4. Watershed delineation How 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. Window Size Z-value 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. Figure 1: Overall methodology : From LiDAR datasets to watershed delineation error evaluation. 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 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 Files GEODATASE TERRAIN Create Terrain Add Pyramids Levels Add Feature Classes Pyramid Type Pyramids Levels / Scale Include Breaklines ? Convert to DEM DEM Pyramid Level Interpolation method Flow analyses Watershed Polygon 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). 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. a)b) a) b) c)