Terrain Datasets: How good are they? Celso Ferreira¹, Francisco Olivera¹, Dean Djokic² ¹Texas A & M University ² Environmental Systems Research Institute - ESRI

Motivation Background Methodology Case studies Conclusions Guidelines

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.

Interpolation without breaklines IDW Spline Kriging Nearest Neighbor Interpolation with breaklines TOPOGRID (ANUDEM) TIN Point -> TIN -> Raster

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

Window Size Z-value Linear Natural Neighbors LiDAR data with average point spacing 7.7 feet over a 7.7 feet grid cell size

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 GP Tool Parameter Legend Convert to DEM DEM Pyramid Level Interpolation method

Error Metric 3: Error Metric 2: Error Metric 1:

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

Lidar data: 608 LAS Folders Original Size: 60 GB Total Points: 2,279,523,264 Mean per folder: 3,749, Average point spacing: 3 feet Watershed processing: Sink pre-evaluation Manual selection of real sinks (120) Flow directions with sinks Combined deranged/dendritic processing

Intel® Core™ 2 Duo CPU 3.16 GHz, 2.00 GB of RAM Hillsborough dataset: ~2.2 billion points

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

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