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Parallel Generation of Very High Resolution Digital Elevation Models: High-Performance Computing for Big Spatial Data Analysis

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Big Data in Engineering Applications

Part of the book series: Studies in Big Data ((SBD,volume 44))

Abstract

Very high resolution digital elevation models (DEM) provide the opportunity to represent the micro-level detail of topographic surfaces, thus increasing the accuracy of the applications that are depending on the topographic data. The analyses of micro-level topographic surfaces are particularly important for a series of geospatially related engineering applications. However, the generation of very high resolution DEM using, for example, LiDAR data is often extremely computationally demanding because of the large volume of data involved. Thus, we use a high-performance and parallel computing approach to resolve this big data-related computational challenge facing the generation of very high resolution DEMs from LiDAR data. This parallel computing approach allows us to generate a fine-resolution DEM from LiDAR data efficiently. We applied this parallel computing approach to derive the DEM in our study area, a bottomland hardwood wetland located in the USDA Forest Service Santee Experimental Forest. Our study demonstrated the feasibility and acceleration performance of the parallel interpolation approach for tackling the big data challenge associated with the generation of very high resolution DEM.

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References

  1. Amoah, J. K. O., Amatya, D., & Nnaji, S. (2013). Quantifying watershed surface depression storage: determination and application in a hydrologic model. Hydrological Processes, 27(17), 2401–2413.

    Article  Google Scholar 

  2. Anderson, C. J, & Lockaby, B. G. (2011). Forested wetland communities as indicators of tidal influence along the Apalachicola River, Florida, USA. Wetlands, 31(5), 895.

    Article  Google Scholar 

  3. Armstrong, M. P., & Marciano R. (1993). Parallel spatial interpolation. In Autocarto-Conference.

    Google Scholar 

  4. Brubaker, K. M., Myers, W. L., Drohan, P. J., Miller, D. A., & Boyer, E. W. (2013). The use of LiDAR terrain data in characterizing surface roughness and microtopography. Applied and Environmental Soil Science, 13. https://doi.org/10.1155/2013/891534

  5. Cramer, B. E., & Armstrong, M. P. (1999). An evaluation of domain decomposition strategies for parallel spatial interpolation of surfaces. Geographical Analysis, 31(2), 148–168.

    Article  Google Scholar 

  6. Deilami, K., & Hashim, M. (2011). Very high resolution optical satellites for DEM generation: A review. European Journal of Scientific Research, 49(4), 542–554.

    Google Scholar 

  7. Ding, Y., & Densham, P. J. (1996). Spatial strategies for parallel spatial modelling. International Journal of Geographical Information Systems, 10(6), 669–698.

    Article  Google Scholar 

  8. Emerson, C. H., Welty, C., & Traver, R. G. (2005). Watershed-scale evaluation of a system of storm water detention basins. Journal of Hydrologic Engineering, 10(3), 237–242.

    Article  Google Scholar 

  9. Foster, I. (1995). Designing and building parallel programs (Vol. 78). Boston: Addison Wesley Publishing Company.

    Google Scholar 

  10. Griffin, L. F., Knight, J. M., & Dale, P. E. R. (2010). Identifying mosquito habitat microtopography in an Australian mangrove forest using LiDAR derived elevation data. Wetlands, 30(5), 929–937. https://doi.org/10.1007/s13157-010-0089-8.

    Article  Google Scholar 

  11. Guan, X., & Huayi, W. (2010). Leveraging the power of multi-core platforms for large-scale geospatial data processing: Exemplified by generating DEM from massive LiDAR point clouds. Computers and Geosciences, 36(10), 1276–1282.

    Article  Google Scholar 

  12. HPC. (2016). Windows HPC Cluster Manager. https://technet.microsoft.com/en-us/library/ff919397.aspx.

  13. Hickey, R., Smith, A., & Jankowski, P. (1994). Slope length calculations from a DEM within ARC/INFO GRID. Computers, Environment and Urban Systems, 18(5), 365–380.

    Article  Google Scholar 

  14. Hohl, A., Delmelle, E. M., & Tang, W. (2015). Spatiotemporal domain decomposition for massive parallel computation of space-time kernel density. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(4), 7.

    Article  Google Scholar 

  15. Hohl, A., Delmelle, E., Tang, W., & Casas, I. (2016). Accelerating the discovery of space-time patterns of infectious diseases using parallel computing. Spatial and Spatio-Temporal Epidemiology, 19, 10–20.

    Article  Google Scholar 

  16. Huang, C.-H., & Bradford, J. M. (1992). Applications of a laser scanner to quantify soil microtopography. Soil Science Society of America Journal, 56(1), 14–21.

    Article  Google Scholar 

  17. Huang, Q., & Yang, C. (2011). Optimizing grid computing configuration and scheduling for geospatial analysis: An example with interpolating DEM. Computers and Geosciences, 37(2), 165–176.

    Article  Google Scholar 

  18. Jensen, R. P., Bosscher, P. J., Plesha, M. E., & Edil, T. B. (1999). DEM simulation of granular media—structure interface: Effects of surface roughness and particle shape. International Journal for Numerical and Analytical Methods in Geomechanics, 23(6), 531–547.

    Article  MATH  Google Scholar 

  19. Knight, J. M., Dale, P. E. R., Spencer, J., & Griffin, L. (2009). Exploring LiDAR data for mapping the micro-topography and tidal hydro-dynamics of mangrove systems: An example from southeast Queensland, Australia. Estuarine, Coastal and Shelf Science, 85(4), 593–600.

    Article  Google Scholar 

  20. Komiyama, A., Santiean, T., Higo, M., Patanaponpaiboon, P., Kongsangchai, J., & Ogino, K. (1996). Microtopography, soil hardness and survival of mangrove (Rhizophora apiculata BL.) seedlings planted in an abandoned tin-mining area. Forest Ecology and Management, 81(1), 243–248.

    Article  Google Scholar 

  21. Lassueur, T., Joost, S., & Randin, C. F. (2006). Very high resolution digital elevation models: Do they improve models of plant species distribution? Ecological Modelling, 198(1), 139–153.

    Article  Google Scholar 

  22. Li, Z., Hodgson, M. E., & Li, W. (2016). A general-purpose framework for parallel processing of large-scale LiDAR data. International Journal of Digital Earth, 1–22.

    Google Scholar 

  23. McKean, J., & Roering, J. (2004). Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology, 57(3), 331–351.

    Article  Google Scholar 

  24. Milne, L., Lindner, D., Bayer, M., Husmeier, D., McGuire, G., Marshall, D. F., et al. (2008). TOPALi v2: A rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops. Bioinformatics, 25(1), 126–127.

    Article  Google Scholar 

  25. Mitas, L., & Mitasova, H. (1999). Spatial interpolation. Geographical Information Systems: Principles, Techniques, Management and Applications, 1, 481–492.

    MATH  Google Scholar 

  26. Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30.

    Article  Google Scholar 

  27. Naoum, S., & Tsanis, I. K. (2003). Hydroinformatics in evapotranspiration estimation. Environmental Modelling and Software, 18(3), 261–271.

    Article  Google Scholar 

  28. Prasannakumar, V., Vijith, H., & Geetha, N. (2013). Terrain evaluation through the assessment of geomorphometric parameters using DEM and GIS: Case study of two major sub-watersheds in Attapady, South India. Arabian Journal of Geosciences, 6(4), 1141–1151.

    Article  Google Scholar 

  29. Rauber, T., & Rünger, G. (2013). Parallel programming: For multicore and cluster systems, Springer Science & Business Media.

    Chapter  MATH  Google Scholar 

  30. Shepard, W. E. (2000). A parallel approach to searching for nearest neighbors with minimal interprocess communication. uga.

    Google Scholar 

  31. Tang, W., & Feng, W. (2017). Parallel map projection of vector-based big spatial data: Coupling cloud computing with graphics processing units. Computers, Environment and Urban Systems, 61, 187–197.

    Article  Google Scholar 

  32. Tang, W., & Wang, S. (2009). HPABM: A hierarchical parallel simulation framework for spatially-explicit agent-based models. Transactions in GIS, 13(3), 315–333.

    Article  Google Scholar 

  33. Tang, W., Feng, W., Zheng, M., & Shi, J. (2017). Land cover classification of fine-resolution remote sensing data. In Reference module in earth systems and environmental sciences. Elsevier.

    Chapter  Google Scholar 

  34. Tomczak, M. (1998). Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW)-cross-validation/jackknife approach. Journal of Geographic Information and Decision Analysis, 2(2), 18–30.

    Google Scholar 

  35. Trettin, C. C., Czwartacki, B. J., Allan, C. J., & Amatya, D. M. (2016). Linking freshwater tidal hydrology to carbon cycling in bottomland hardwood wetlands. In Stringer, C. E., Krauss, K. W., Latimer, J. S. (Eds.), Headwaters to estuaries: Advances in watershed science and management-proceedings of the fifth interagency conference on research in the watersheds (p. 302). March 2–5, 2015, North Charleston, South Carolina. e-General Technical Report SRS-211. Asheville, NC: US Department of Agriculture Forest Service, Southern Research Station.

    Google Scholar 

  36. Wang, S., & Armstrong, M. P. (2003). A quadtree approach to domain decomposition for spatial interpolation in grid computing environments. Parallel Computing, 29(10), 1481–1504.

    Article  Google Scholar 

  37. Wang, S., & Armstrong, M. P. (2009). A theoretical approach to the use of cyberinfrastructure in geographical analysis. International Journal of Geographical Information Science, 23(2), 169–193.

    Article  Google Scholar 

  38. Werner, M. G. F. (2001). Impact of grid size in GIS based flood extent mapping using a 1D flow model. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7–8), 517–522.

    Article  Google Scholar 

  39. Wilkinson, B., & Allen, M. (1999). Parallel programming: Techniques and applications using networked workstations and parallel computers. Prentice-Hall.

    Google Scholar 

  40. Wise, S. (2000). Assessing the quality for hydrological applications of digital elevation models derived from contours. Hydrological Processes, 14(11–12), 1909–1929.

    Article  Google Scholar 

  41. Wu, S., Li, J., & Huang, G. H. (2008). A study on DEM-derived primary topographic attributes for hydrologic applications: Sensitivity to elevation data resolution. Applied Geography, 28(3), 210–223.

    Article  Google Scholar 

  42. Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data, McGraw-Hill Osborne Media.

    Google Scholar 

  43. Zimmerman, D., Pavlik, C., Ruggles, A., & Armstrong, M. P. (1999). An experimental comparison of ordinary and universal kriging and inverse distance weighting. Mathematical Geology, 31(4), 375–390.

    Article  Google Scholar 

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Acknowledgements

We thank support from US NSF XSEDE Supercomputing Resource Allocation (SES170007), and USDA Forest Service grant “Development and Operation of a Web GIS-enabled Data Management System for the Santee Experiment Forest”.

Author information

Authors and Affiliations

  1. Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 28223, Charlotte, USA

    Minrui Zheng, Wenwu Tang, Yu Lan, Meijuan Jia & Craig Allan

  2. Center for Applied GIScience, University of North Carolina at Charlotte, 28223, Charlotte, USA

    Minrui Zheng, Wenwu Tang, Yu Lan & Meijuan Jia

  3. U.S. Forest Service, Center for Forested Wetlands Research, 29434, Cordesville, USA

    Carl Trettin

  4. School of Resources and Environmental Science, Wuhan University, Wuhan, China

    Xiang Zhao

Authors
  1. Minrui Zheng
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  2. Wenwu Tang
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  3. Yu Lan
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  4. Xiang Zhao
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  5. Meijuan Jia
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  6. Craig Allan
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  7. Carl Trettin
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Corresponding author

Correspondence to Wenwu Tang .

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Editors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

  3. University of Southern Queensland, Springfield, Queensland, Australia

    Ravinesh Deo

  4. Polytechnic University of Milan, Milan, Italy

    Stavros Ntalampiras

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Zheng, M. et al. (2018). Parallel Generation of Very High Resolution Digital Elevation Models: High-Performance Computing for Big Spatial Data Analysis. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_2

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  • DOI: https://doi.org/10.1007/978-981-10-8476-8_2

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  • Print ISBN: 978-981-10-8475-1

  • Online ISBN: 978-981-10-8476-8

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