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LiDARHub: a free and open source software platform for web-based management, visualization and analysis of LiDAR data

Geosciences Journal volume 19, pages 741–749 (2015)Cite this article

Abstract

LiDAR is an active remote sensing technique with a unique capability to capture three-dimensional information of the earth’s surface even in heavily vegetated areas, and it is proven to be useful in many research applications. Although it is becoming the remote sensing platform of choice for planning and natural resource agencies that require three-dimensional information, the enormous data that are generated and the lack of available software analysis packages make LiDAR still unavailable to a typical user of spatial data. LiDARHub is a free and open source platform for web-based management, visualization and analysis of LiDAR data that enables development of online tools for LiDAR data processing in a web browser. The framework provides a foundation to develop online tools for LiDAR data processing and tools can be shared. The framework is also flexible so that the developed tools can be easily ported to High Performance Computing (HPC) environments that speed up the computationally extensive LiDAR data processing. Two example LiDARHub tools are presented as case studies to demonstrate potential software development scenarios. The developed tools provide easy to use user interface and hide complex computation so that users can take advantage of the LiDAR technology with only a web browser. The LiDARHub allows not only the sharing of large volume of LiDAR data but also developing online LiDAR processing platform for a large audience.

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References

  • Ahokas, E., Kaartinen, H., and Hyyppä, J., 2003, A quality assessment of airborne laser scanner data. International Archives of Photogrammetry. Remote Sensing and Spatial Information Sciences, 34, 1–7.

    Google Scholar 

  • American Society for Photogrammetry & Remote Sensing, 2009, LAS Specification Version 1.3-R11. http://asprs.org/a/society/committees/standards/LAS_1_3_r11.pdf.

  • Anderson, H.E., McGaughey, R.J., and Reutebuch, S.E., 2005, Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94, 441–449.

    Article  Google Scholar 

  • Anderson, J.E., Plourde, L.C., Martin, M.E., Braswell, B.H., Smith, M.L., Dubayah, R.O., Hofton, M.A., and Blair, J.B., 2008, Integrating waveform LIDAR with hyperspectral imagery for inventory of a northern temperate forest. Remote Sensing of Environment, 112, 1856–1870.

    Article  Google Scholar 

  • Bradbury, R.B., Hill, R.A., Mason, D.C., Hinsley, S.A., Wilson, J.D., Balzter, H., Anderson, G.Q.A., Whittingham, M.J., Davenport, I.J., and Bellamy, P.E., 2005, Modeling relationship between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments. International Journal of Avian Science, 147, 443–452.

    Google Scholar 

  • Dubayah, R.O., Sheldon, S.L., Clark, D.B., Hofton, M.A., Blair, J.B., Hurtt, G.C., and Chazdon, R.L., 2010, Estimation of tropical forest height and biomass dynamics using LIDAR remote sensing at La Selva, Costa Rica. Journal of Geophysical Research, 115, G00E09.

    Article  Google Scholar 

  • Farid, A., Goodrich, D.C., Bryant, R., and Sorooshian, S., 2008, Using airborne LiDAR to predict Leaf Area Index in cottonwood trees and refine riparian water-use estimates. Journal of Arid Environments, 72, 1–15.

    Article  Google Scholar 

  • Forlani, G., Nardinocchi, C., Scaioni, M., and Zingaretti, P., 2006, Complete classification of raw LiDAR data and 3D reconstruction of buildings. Pattern Analysis & Applications, 8, 357–374.

    Article  Google Scholar 

  • Haklay, M. and Weber, P., 2008, Openstreetmap: User-generated street maps. Pervasive Computing IEEE, 7, 12–18.

    Article  Google Scholar 

  • Jung, J., Pekin, B.K., and Pijanowski, B.C., 2013, Mapping open space in an old-growth, secondary-growth, and selectively-logged tropical rainforest using discrete return LiDAR. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 2453–2461.

    Article  Google Scholar 

  • Jung, J. and Crawford, M.M., 2012, Extraction of features from LiDAR waveform data for characterizing forest structure. IEEE Geoscience and Remote Sensing Letters, 9, 492–496.

    Article  Google Scholar 

  • Jung, J., Crawford, M., and Lee, S., 2009, Complexity estimation based work load balancing for a parallel LiDAR waveform decomposition algorithm. Korean Journal of Remote Sensing, 25, 547–557.

    Google Scholar 

  • Kraus, K. and Pfeifer, N., 2001, Advanced DTM generation from LiDAR data. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34, 23–30.

    Google Scholar 

  • Krishnan, S., Viswanath, N.C., Crosby, M.P., Cowart, C., Baru, C., and Arrowsmith, R., 2011, OpenTopography: a services oriented architecture for community access to LIDAR topography. Proceedings of the 2nd International Conference on Computing for Geospatial Research and Applications, Washington D.C., May 23–25.

    Book  Google Scholar 

  • Lefsky, M.A., Cohen, W.B., Parker, G.G., and Harding, D.J., 2002, LiDAR remote sensing for ecosystem studies. BioScience, 52, 19–30.

    Article  Google Scholar 

  • Lesak, A.A., Radeloff, V.C., Hawbaker, T.J., Pidgeon, A.M., Gobakken, T., and Contrucci, K., 2011, Modeling forest songbird species richness using LiDAR-derived measures of forest structure. Remote Sensing of Environment, 115, 2823–2835.

    Article  Google Scholar 

  • Means, J.E., Acker, S.A., Harding, D.J., Blair, J.B., Lefsky, M.A., Cohen, W.B., Harmon, M.E., and McKee, W.A., 1999, Use of large footprint scanning airborne LiDAR to estimate forest stand characteristics in the western cascades of Oregon. Remote Sensing of Environments, 67, 298–308.

    Article  Google Scholar 

  • Miller, K.W., Voas, J., and Costello, T., 2010, Free and open source software. IT Professional, 12, 14–16.

    Article  Google Scholar 

  • Muss, J.D., Mladenoff, D.J., and Townsend, P.A., 2011, A pseudowaveform technique to assess forest structure using discrete LIDAR data. Remote Sensing of Environment, 115, 824–835.

    Article  Google Scholar 

  • Næsset, E., 2011, Estimating above-ground biomass in young forests with airborne laser scanning. International Journal of Remote Sensing, 32, 473–501.

    Article  Google Scholar 

  • Neuenschwander, A., Crawford, M., and Weed, C., 2000, Extraction of digital elevation models for airborne laser terrain mapping data. Proceedings of the International Geoscience and Remote Sensing Symposium, Honolulu, July 24–28, p. 2305–2307.

    Google Scholar 

  • Pekin, B.K., Jung J., Villanueva-Rivera L.J., Pijanowski B.C., and Ahumada J.A., 2012, Modeling acoustic diversity using soundscape recordings and LIDAR-derived metrics of vertical forest structure in a neotropical rainforest. Landscape Ecology, 27, 1513–1522.

    Article  Google Scholar 

  • Pijanowski, B.C., Villanueva-Rivera, L.J., Dumyahn, S.L., Farina, A., Krause, B.L., Napoletano, B.M., Gage, S.H., and Pieretti, N., 2011, Soundscape Ecology: The science of sound in the landscape. BioScience, 61, 203–216.

    Article  Google Scholar 

  • Pijanowski, B.C., Farina, A., Gage, S.H., Dumyahn, S.L., and Krause, B., 2011, What is soundscape ecology? Landscape Ecology, 26, 1213–1232.

    Article  Google Scholar 

  • Pijanowski, B.C., Tayyebi, A., Doucette, J., Pekin, B., Braun, D., and Plourde, J., 2014, A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing environment. Environmental Modelling and Software, 51, 250–268.

    Article  Google Scholar 

  • Stoker, J.M., Greenlee, S.K., Gesch, D.B., and Menig, J.C., 2006, CLICK: The new USGS center for LiDAR information coordination and knowledge. Photogrammetric Engineering and Remote Sensing, 72, 613–616.

    Google Scholar 

  • Sullivan, J., 2011, Chicago Wilderness: Atlas of Biodiversity. Chicago Region Biodiversity Council, Chicago, 74 p.

    Google Scholar 

  • Weed, C.A., Crawford, M.M., Neuenschwander, A.L., and Gutierrez, R., 2002, Classification of LiDAR data using a lower envelope follower and gradient-based operator. Proceedings of the International Geoscience and Remote Sensing Symposium, Toronto, June 24–28, p. 1384–1386.

    Google Scholar 

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Authors and Affiliations

  1. School of Science and Engineering, Texas A&M University — Corpus Christi, Corpus Christi, TX, 78412, USA

    Jinha Jung

  2. Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, 47907, USA

    Bryan C. Pijanowski

Authors
  1. Jinha Jung
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  2. Bryan C. Pijanowski
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Correspondence to Jinha Jung.

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Jung, J., Pijanowski, B.C. LiDARHub: a free and open source software platform for web-based management, visualization and analysis of LiDAR data. Geosci J 19, 741–749 (2015). https://doi.org/10.1007/s12303-015-0003-8

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  • DOI: https://doi.org/10.1007/s12303-015-0003-8

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