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
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.
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.
Armstrong, M. P., & Marciano R. (1993). Parallel spatial interpolation. In Autocarto-Conference.
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
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.
Deilami, K., & Hashim, M. (2011). Very high resolution optical satellites for DEM generation: A review. European Journal of Scientific Research, 49(4), 542–554.
Ding, Y., & Densham, P. J. (1996). Spatial strategies for parallel spatial modelling. International Journal of Geographical Information Systems, 10(6), 669–698.
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.
Foster, I. (1995). Designing and building parallel programs (Vol. 78). Boston: Addison Wesley Publishing Company.
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.
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.
HPC. (2016). Windows HPC Cluster Manager. https://technet.microsoft.com/en-us/library/ff919397.aspx.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
McKean, J., & Roering, J. (2004). Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology, 57(3), 331–351.
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.
Mitas, L., & Mitasova, H. (1999). Spatial interpolation. Geographical Information Systems: Principles, Techniques, Management and Applications, 1, 481–492.
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.
Naoum, S., & Tsanis, I. K. (2003). Hydroinformatics in evapotranspiration estimation. Environmental Modelling and Software, 18(3), 261–271.
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.
Rauber, T., & Rünger, G. (2013). Parallel programming: For multicore and cluster systems, Springer Science & Business Media.
Shepard, W. E. (2000). A parallel approach to searching for nearest neighbors with minimal interprocess communication. uga.
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.
Tang, W., & Wang, S. (2009). HPABM: A hierarchical parallel simulation framework for spatially-explicit agent-based models. Transactions in GIS, 13(3), 315–333.
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.
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.
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.
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.
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.
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.
Wilkinson, B., & Allen, M. (1999). Parallel programming: Techniques and applications using networked workstations and parallel computers. Prentice-Hall.
Wise, S. (2000). Assessing the quality for hydrological applications of digital elevation models derived from contours. Hydrological Processes, 14(11–12), 1909–1929.
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.
Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data, McGraw-Hill Osborne Media.
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.
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”.
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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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