Enhanced Access to High-Resolution LiDAR Topography through Cyberinfrastructure-Based Data Distribution and Processing
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1 Enhanced Access to High-Resolution LiDAR Topography through Cyberinfrastructure-Based Data Distribution and Processing Christopher J. Crosby, J Ramón Arrowsmith Jeffrey Connor, Gilead Wurman Efrat Jaeger-Frank, Vishu Nandigam, Ashraf Memon, Chaitan Baru Han S. Kim AGU session IN41C: Turning Floods of Data into Oceans of Knowledge December 14, 2006
2 LiDAR (LIght Detection And Ranging) a.k.a ALSM (Airborne Laser Swath Mapping) Airborne pulsed laser scanning system + differential GPS + inertial measurement unit (IMU) > 30,000 points/second Ground sampled at multiple points/sq. meter ~ 15 cm vertical accuracy ~$300 - $500 per sq. km acquisition cost
3 Canopy Height (ft)
4 Survey Complete LiDAR Workflow Interpolate / Grid USGS Coastal & Marine Point Cloud x, y, z, Analyze / Do Science
5 The Flood of Data LiDAR coverage of the southern San Andreas and San Jacinto faults - ~ 36 billion LiDAR returns - Very high-res., supports cm DEMs
6 Multi-institution collaboration between IT and Earth Science researchers Funded by NSF large ITR program GEON Cyberinfrastructure provides: Authenticated access to data and Web services - Scientific workflow environment Registration of data sets and tools, with metadata - Data and map integration capability Search for data, tools, and services, using ontologies- Visualization and GIS mapping GEON was designed as an equal collaboration between Information Technology (IT) and Geoscience researchers, with the goal of developing an enabling IT platform to facilitate the next generation of Geoscience research. Distributed Network GEON / ASU node Agassiz :
7 The Vision: Utilize cyberinfrastructure developed by GEON to offer online data distribution, DEM generation, and analysis of large LiDAR datasets. Conceptual GEON LiDAR Workflow Completely internetbased workflow: Point cloud to visualization Utilize modular web services to complete a variety of processing and analysis tasks. Offer users control of processing and analysis parameters.
8 Current GLW features: - Interactive polygon data selection via WMS map - Attribute selection (ground returns, vegetation returns) - Rapid estimate of points within selection area - Point cloud download - DEM generation via two pathways: - local binning algorithm (see G53C-0921) - Spline interpolation algorithm (GRASS GIS web service) - User defined grid resolution and processing parameters - Products produced in multiple, user-defined, file formats - User provided job title and description - Dynamically generated and customized metadata file - notification
9 Pt. Density IDW DEM G53C-0921 An efficient implementation of a local binning algorithm for digital elevation model generation Kim et al.
10 Implementation Overview: The GLW utilizes advanced spatial databases (IBM DB2), GRASS Open Source GIS, custom DEM generation code, Kepler Workflow manager and web service technology to distribute, interpolate, and analyze LiDAR data. GRASS GIS & Custom binning algorithm
11 Current GLW Status Datasets online: 1. Northern San Andreas Fault 2. West Rainier Seismic Zone Source: 3. Eastern California Shear Zone (Mike Oskin PI) 4. Full B4 Dataset (Southern SAF and SJF) Total of ~38 billion LiDAR returns available via GLW
12 A cyberinfrastructure-based model for managing the flood of community LiDAR datasets
13 Implications GLW is a proof of concept for utilization of cyberinfrastructure to democratize access to the next generation of community geoscience datasets and processing tools. Generic and scaleable GEON architecture applicable to numerous (geo) science datasets. GLW proposed as distribution pathway for forthcoming GeoEarthscope LiDAR datasets: 5 more B4 style data acquisitions in next 2 years. Discussions underway with USGS EROS Data Center to expose their LiDAR archive (CLICK) via the GLW.
14 GEON LiDAR Workflow Access Instructions: More information on the GLW: Efrat Jaeger-Frank, Christopher J. Crosby, Ashraf Memon, Viswanath Nandigam, J. Ramon Arrowsmith, Jeffery Conner, Ilkay Altintas, Chaitan Baru, A Three Tier Architecture for LiDAR Interpolation and Analysis, Lecture Notes in Computer Science, Volume 3993, Apr 2006, Pages , DOI: / _123. Crosby, C.J. et al., A geoinformatics-based approach to LiDAR data distribution and processing in preparation. Abstracts, posters & presentations:
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