OpenTopography Blog

Information and discussion related to high-resolution LiDAR topography for the Earth sciences

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Posts from February 2010

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Filtering vegetation from Terrestrial Laser Scanning data using the Point2Grid tool

Posted on Fri, February 26, 2010 by C. Crosby in Software • TLS

Max Wilkinson, a PhD student in the Dept. of Earth Sciences at Durham University in the UK, recently sent us a link to a video of a seminar he gave on methods of vegetation filtration from Terrestiral Laser Scanning (TLS) data using the Points2Grid tool developed by the OpenTopography team (originally developed with GEON funding).  Points2Grid is a Windows application that implements the exact same DEM generation algorithm that is used in the OpenTopography point cloud processing system.  It was developed to allow users to get the same DEM generation capabilities as are available in OpenTopography for non-hosted data. 

Dealing with vegetation in TLS scanner data can be a significant problem, and Max shows in the video how he is able to use the MIN surface feature in P2G to remove much of the vegetation from the scan.  Points2Grid is not technically designed to filter vegetation, but the minimum surface function can act as a low budget filter.  P2G is also handy for reducing the complexity of the point cloud data down to something more manageable for analysis.  In this case, Max has much higher shot densities than he needs to construct a surface from the data, and P2G helps to reduce the amount of data he is working with:

From the TLS perspective, I’m finding P2G is tremendously useful for distilling large pointsets down to a size whereby the computing time required to test various interpolation methods becomes bearable! I had previously spent many hours waiting to see the output of an interpolation based on millions of points, only to find the parameters I used were not quite right. With the reduced computing time I’m now able to run more interpolations in various programs with a range of different parameters to see the results. I’m now using arcGIS for interpolations of TLS data, rather than just goCAD. For example, I’ve just finished a flow routing analysis of footwall drainage in arcGIS. A big thank you to the team for making P2G available!

Screen capture of the video:

You can download the video from Max’s page here: Vegetation Removal.avi (124 MB download). 

I had difficulties getting the file to play in Quicktime on a Mac, but the video played perfectly in open source (free) VLC player.  You may also need this ffdshow codec provided by Max.

The GEON Points2Grid tool can be downloaded via the lidar.asu.edu site.  I recommend reading the P2G Instructions / Help doc and also looking at the this page on how the search radius parameter in P2G works.

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Cyber-GIS Opportunities for High-Resolution Topography Data Access, Processing, and Analysis

Posted on Mon, February 22, 2010 by C. Crosby in Meetings • OpenTopography Updates

Earlier this month, I had the privilege of participating in the National Science Foundation TeraGrid Workshop on Cyber-GIS in Washington, DC.  The workshop was sponsored by the National Science Foundation (NSF) TeraGrid Science Gateway program and the Office of Cyberinfrastructure with the goal of “underpin fundamental issues of Cyber-GIS for enhancing cyberinfrastructure while advancing the next-generation GIS with synergistic high-performance, distributed, and collaborative capabilities.”

Each participant in the workshop was required to submit a position paper that highlighted an issue or opportunity in Cyber-GIS.  My paper, “Cyber-GIS Opportunities for High-Resolution Topography Data Access, Processing, and Analysis”, highlights activities OpenTopography is currently engaged in, and also points to opportunities and challenges we are pursuing.  You can download a PDF of the position paper, or read it below.

Cyber-GIS Opportunities for High-Resolution Topography Data Access, Processing, and Analysis

Christopher Crosby
San Diego Supercomputer Center, University of California, San Diego, CA

High-resolution topography data acquired with lidar (light detection and ranging) technology are revolutionizing the way we study the geomorphic, biologic and anthropogenic processes acting along the Earth’s surface (e.g. Carter et al., 2007).  These data, acquired from either an airborne platform or a tripod-mounted scanner, are emerging as a fundamental tool for research on a variety of topics ranging from earthquake hazards to urban modeling.  Lidar topography data are powerful because they represent processes and features at a scale not previously possible yet essential for their appropriate representation.  These data sets also have significant implications for earth science education and outreach because when visualized, they provide an accurate digital representation of landforms, natural hazards and processes, and the built environment. 

However, along with the potential of lidar topography comes an increase in the volume and complexity of data that must be efficiently managed, archived, distributed, processed and integrated in order for them to be of use to the community.  A single lidar data acquisition may generate terabytes of data in the form of point clouds, digital elevation models (DEMs), and derivative products.  This massive volume of data is often difficult to manage and poses significant distribution challenges when trying to allow access to the data for a large scientific user community. Furthermore, the data sets can be technically challenging to work with and may require specific software and computing resources that are not readily available to many users.

Projects such as the National Science Foundation-funded OpenTopography Facility (http://www.opentopography.org) (e.g. Crosby et al., 2009) are successfully leveraging emerging cyberinfrastructure technologies such as portal-based data access, service oriented architectures, high-performance parallel database systems (Nandigam et al., 2010), and optimized processing algorithms to improve internet-based access to these massive geospatial data sets.  The OpenTopography system provides free and on-demand access to tens of billions of lidar point cloud measurements as well as processing tools that permit users to generate custom digital elevation models on-the-fly.  OpenTopography’s growing user community of several thousand scientists, educators, students, government agency staff, and private sector users illustrate that cyberinfrastructure-based geospatial data access systems can have a significant impact by democratizing access to these massive data sets.

OpenTopography’s success is an illustration of the potential opportunities that exist through the application of cyberinfrastructure resources to geospatial data management and processing. However, the OpenTopography effort has only just scratched the surface of how routine data management and processing tasks could be enhanced with access to cloud or grid-based resources.  As any regular user of high-resolution topography appreciates, many of the existing geographic information system (GIS) algorithms currently available for processing, analysis, and visualization point cloud and DEM data fail, or perform very slowly, when applied to lidar data.  Taking a Cyber-GIS approach to lidar topography processing and analysis would allow users to carry out computationally intensive LiDAR data processing without having appropriate hardware locally.  Resources such as Hadoop (http://hadoop.apache.org/)-based processing in the cloud, the TeraGrid (http://www.teragrid.org/), or Condor pools (http://www.cs.wisc.edu/condor/) could allow users to “outsource” their geospatial data processing to computing resources better equipped to handling significant data volumes.

However, to effectively utilize high-performance grid or cloud resources will require that the user community develop a new “toolkit” of algorithms and tools that are optimized to perform in these environments.  This new toolkit should exist in the open source domain and consist of libraries that allow users to construct customized processing workflows that run in a distributed environment. Examples of necessary algorithms include those for high-performance gridding of lidar point cloud data (e.g. Kim et al., 2006), algorithms for hydrologic processing of DEMs (e.g. Wallis et al., 2009) including calculations of slope, slope-aspect, stream profiles, catchment areas, and topographic roughness and curvature, geomorphic change detection analysis, feature extraction (including vegetation classification and structural analysis, and building footprint extraction), as well as tools for the processing and analysis of full waveform lidar data.

REFERENCES:
Carter, W. E., R. Shrestha and K.C. Slatton, 2007, Geodetic Laser Scanning, Physics Today, Vol. 60, Number 12, pp 41-47.

Crosby, C.J., Nandigam, V., Arrowsmith, R., Baru, C., 2009, Enhancing Access to High-Resolution Lidar Topography – From Point Clouds To Google Earth, Geological Society of America Abstracts with Programs, Vol. 41, No. 7, p. 384

Kim, H., Arrowsmith, J R., Crosby, C.J., Jaeger-Frank, E., Nandigam, V., Memon, A., Conner, J., Badden, S.B., Baru, C., An Efficient Implementation of a Local Binning Algorithm for Digital Elevation Model Generation of LiDAR/ALSM Dataset, Eos Trans. AGU, 87(52), Fall Meet. Suppl., Abstract G53C-0921, 2006.

Nandigam, V., Baru, C., Crosby, C.J., Database Design for High-Resolution LIDAR Topography Data in preparation, 2010 International Conference on Scientific and Statistical Database Management

Wallis, C., Watson, D., Tarboton, D., Wallace, R., 2009, Parallel Flow-Direction and Contributing Area Calculation for Hydrology Analysis in Digital Elevation Models, Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA 2009, Las Vegas, Nevada, USA

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Haiti LiDAR imagery in Google Earth

Posted on Thu, February 11, 2010 by C. Crosby in 2010 Haiti EQ • Data • Google Earth

As discussed in previous blog posts (here and here), LiDAR data have been collected over parts of Haiti following the January 12th earthquake.  The data collected by the Center for Imaging Science at Rochester Institute of Technology (RIT), Kucera International, and ImageCat, Inc., has recently become available via an FTP site maintained by the USGS that is hosting geospatial data acquired in response to the Haiti earthquake.  These data were collected during a campaign between January 21st and the 27th.

In order to make these data easier for all users to access, I downloaded and processed the filtered (bare earth) and unfiltered DEM data into hillshade images (315 degree illumination angle, 1 meter resolution) that can be viewed in Google Earth.  The approach used was similar to what I’ve done for all of the EarthScope LiDAR imagery available via KML (more info is available in this AGU abstract).  The result is roughly ~1.5 GB of hillshade imagery for Haiti hosted on OpenTopography servers that can be browsed seamlessly in Google Earth.  Download the KML file using the button below and open in Google Earth to get started:

NOTES:

• The LiDAR topography data set from which these images were derived was provided by the Center for Imaging Science at Rochester Institute of Technology (RIT) and Kucera International, respectively, under contract to ImageCat, Inc. The Haiti campaign was funded by the World Bank and the Global Facility for Disaster Recovery and Recovery (GFDRR) and have made all data available in the public domain. More information about these data can be found at the RIT Information Products Laboratory for Emergency Response (IPLER) 2010 Haiti Earthquake page.
• The extent of the LiDAR data is shown by the cyan colored outlines.  The images will load once the user has zoomed into an area of interest.  The imagery becomes progressively higher resolution as you zoom in.
• All of the imagery is accessed via “Network Link” to servers in San Diego, thus a strong and consistent internet connection is required.
• The transparency of the LiDAR hillshades can be adjusted using the slider bar at the bottom of the PLACES menu in the left hand navigation bar.

EXAMPLES:

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ILMF meeting will highlight Haiti LiDAR

Posted on Tue, February 09, 2010 by C. Crosby in 2010 Haiti EQ • Data • News

The International LiDAR Mapping Forum (ILMF), a LiDAR industry conference in Denver next month, has just announced in a press release the addition of two presentations related to LiDAR data collected over Haiti (see this post and this post for previous discussion of Haiti LiDAR).

One presentation will be by Ken Hudnut of the USGS, who will discuss the application of post-earthquake LiDAR to evaluation of the ground rupture - or in this case the lack of rupture - associated with the event:

Imagery of the region damaged by the M 7 Haiti earthquake, including high-resolution photography and airborne LiDAR, has revealed a variety of ground failure that resulted from shaking. Surprisingly, the Enriquillo Fault seems to have not ruptured at the ground surface, so the negative result obtained from imagery has significant implications. The USGS issued a statement, based on imagery analysis, that because it is clear that the rupture of the Enriquillo Fault was clearly farther west than Port-au-Prince, and because rupture was buried deep on the fault, there is a significant risk of not only regular aftershocks, but also the threat of a subsequent large event that could occur even closer to Port-au-Prince. The probability of one or more subsequent earthquakes of M 7 or greater increased by about 3% for the 30 days following 21 January 2010. Although this is a low probability, it would be a potentially very high impact event. High-resolution imagery was crucial to this assessment.

Ken is a friend of OpenTopography and was a co-instructor at our Southern California Earthquake Center-sponsored short course on application of LiDAR data to studying active faults this past December.

The second ILMF presentation will be by representatives of Kucera International Inc. who, in collaboration with the Rochester Institute of Technology (RIT) and ImageCat, with funding from the World Bank, performed a high resolution aerial LiDAR and multispectral survey of primary earthquake damaged areas and fault zones:

Kucera’s presentation will review the performance of the aerial survey, the expedited processing and distribution of the aerial data, and potential future refinement and applications of the data.

I’ll be attending the ILMF meeting and I look forward to both of these presentations.  The Haiti earthquake is an important event in terms of being a model for rapid collection of LiDAR following a large earthquake, and I look forward to hearing about the lesson’s learned by both the science users of the data, and the acquisition and processing team.

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Article highlights application of Yakima EarthScope LiDAR to landslide research

Posted on Tue, February 09, 2010 by C. Crosby in News

The Ellensburg, WA Daily Record News published an article today entitled CWU student Tom Winter studies area’s slide history that discusses research being conducted by a Central Washington University graduate student who is mapping landslides in the Yakima River Canyon near Ellensburg:

Winter, 25, has the goal of gaining a master’s degree in resource management from Central by June and producing a slide hazards map for the canyon’s 20 miles.

He’s not only looking for old landslides, but debris flows when heavy rains have washed rocks and earth down the canyon sides and scoured out deep gullies.

The article references Winter’s use of LiDAR data to enhance his mapping activities:

Winter also uses current topography maps, stomping up and down the canyon and seeing with his own eyes, aerial photos and something called LiDAR — an optical remote sensing technology that uses laser pulses to detect in high resolution very small changes in range, shapes and elevation.

“With LiDAR things just jump out at you that you might not notice because it’s so large,” Winter said.

Given that I’m not aware of other LiDAR data in the Yakima area, I assume that he is using the recently released EarthScope Yakima LiDAR data accessible via OpenTopography for this work.  This is nice example of how providing online access to these powerful data allows them to be widely utilized in a variety of applications. 

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