1. Lsevlet: web-based processing tool for LiDAR data J Ramon Arrowsmith December 14, 2004

2. PART 1: LiDAR data handling and processing - the challenges Huge datasets: –8.79 million pts –Files getting larger with higher pulse rate instruments How do we grid and distribute these data? –ArcGIS can’t handle it easily –Expensive commercial software not an option for most data consumers Hector Mine Earthquake ALSM coverage (Mojave Desert)

4. ALSM data schematic workflow with partial implementation -possible scenario for NCALM data handling system? -incorporation of greater flexibility into system as user community grows -Crosby and Arrowsmith, 2004 watershed analysis (?)

5. GRASS as a processing tool for LiDAR GRASS: Open source GIS –Interpolation commands designed for large data sets –Splines use local pt density to segment data into rectangular areas for interpolation –Can control spline tension and smoothness Modular configuration could be easily implemented with in the GEON work flow –EX: User uploads point data to remote site where GRASS interpolation module runs on super computer and returns user a raster file. Ultimately a gridding utility for all large, computationally intensive data - gravity

10. What’s next? Build projection library Add functionality - TIN, Kriging, “Oskin”-type local plane fits, etc. More performance testing Comparison of interpolation algorithms for different landscapes Migrate to SDSC HP cluster for improved performance (?) Ties to NCALM and Earthscope Web-based front end for data distribution http://calm.geo.berkeley.edu/ncalm/ims/NapaDwnld/viewer.jsp

11. A quick primer on… From geongrid.org: “Our mission is to prototype cyberinfrastructure for the Earth Sciences, based on close collaboration among IT and Earth Science researchers.” “We have deployed a distributed system consisting of “nodes” that host datasets and/or tools and applications programs, called the GEONgrid.” “…Such an environment enables new modes of science and can transform the day-to-day conduct of science.”

12. A quick primer on…

13. Data node:  CPU Xeon 2.8 Ghz MHz Hyperthreaded. 2GB Ram  2TB of Storage Agassiz (Point of Presence Node):  CPU Xeon 2.8 Ghz MHz Hyperthreaded. 2GB Ram roughly  550GB of Storage Compute-0-* (* = 1-4):  CPU Xeon 2.8 Ghz) MHz Hyperthreaded. 2GB Ram  roughly 35GB of Storage Linux platform GEON computing resources