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  • Cited by 3
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    This chapter has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Li, Zhenlong Hodgson, Michael E. and Li, Wenwen 2018. A general-purpose framework for parallel processing of large-scale LiDAR data. International Journal of Digital Earth, Vol. 11, Issue. 1, p. 26.

    Robinson, Sarah E. Bohon, Wendy Kleber, Emily J. Arrowsmith, J Ramón and Crosby, Christopher J. 2017. Applications of high-resolution topography in Earth science education. Geosphere, Vol. 13, Issue. 6, p. 1887.

    Carter, William E. Glennie, Craig L. and Shrestha, Ramesh L. 2015. IAG 150 Years. Vol. 143, Issue. , p. 399.

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  • Print publication year: 2011
  • Online publication date: October 2011

16 - Online access and processing of LiDAR topography data

from Part V - Web services and scientific workflows
Summary

Introduction

Real-time sensor networks, space and airborne-based remote sensing, real-time geodesy and seismology, massive geospatial databases, and large computational models are all enabling new and exciting research on the forefront of the earth sciences. However, with these technologies comes a prodigious increase in the volume and complexity of scientific data that must be efficiently managed, archived, distributed, processed, and integrated in order for it to be of use to the scientific community. Data volume, processing expertise, or computing resource requirements may be a barrier to the scientific community's access to and effective use of these datasets. An emerging solution is a shared cyberinfrastructure that provides access to data, tools, and computing resources. A key objective of geoinformatics initiatives (e.g., Sinha, 2000) is to build such cyberinfrastructure for the geosciences through collaboration between earth scientists and computer scientists.

Airborne LiDAR (Light Distance And Ranging) data have emerged as one of the most powerful tools available for documenting the Earth's topography and its masking vegetation at high resolution (defined here as pixel dimensions less than 2 meters). LiDAR-derived digital elevation models (DEMs) are typically of a resolution more than an order of magnitude better than the best-available 10-meter DEMs. The ability to use these data to construct 2.5-D and 3-D models of the Earth's topography and vegetation is rapidly making them an indispensable tool for earth science research (e.g., Carter et al., 2001).

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