Wavelet-Compressed Representation of Landscapes for Hydrologic and Geomorphologic Applications

Chandana Gangodagamage, Efi Foufoula-Georgiou, Steven P. Brumby, Rick Chartrand, Alexander Koltunov, Desheng Liu, Michael Cai, Susan L. Ustin

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

The availability of high-resolution digital elevation data (submeter resolution) from LiDAR has increased dramatically over the past few years. As a result, the efficient storage and transmission of those large data sets and their use for geomorphic feature extraction and hydrologic/environmental modeling are becoming a scientific challenge. This letter explores the use of multiresolution wavelet analysis for compression of LiDAR digital elevation data sets. The compression takes advantage of the fact that, in most landscapes, neighboring pixels are correlated and thus contain some redundant information. The space-frequency localization of the wavelet filters allows one to preserve detailed high-resolution features where needed while representing the rest of the landscape at lower resolution. We explore a lossy compression methodology based on biorthogonal wavelets and demonstrate that, by keeping only approximately 10% of the original information (data compression ratio ∼94%), the reconstructed landscapes retain most of the information of relevance to geomorphologic applications, such as the ability to accurately extract channel networks for environmental flux routing, as well as to identify geomorphic process transition from the curvature-slope and slope-distance relationships.

Original languageEnglish (US)
Article number7403883
Pages (from-to)480-484
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume13
Issue number4
DOIs
StatePublished - Apr 2016

Keywords

  • Biorthogonal modulation
  • Digital Elevation models (DEMs)
  • LiDAR
  • biorthogonal wavelets
  • channel networks
  • data compression
  • high spatial resolution data
  • hydrology
  • image resolution
  • lossy compression
  • wavelet transforms
  • wavelets

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  • Cite this

    Gangodagamage, C., Foufoula-Georgiou, E., Brumby, S. P., Chartrand, R., Koltunov, A., Liu, D., Cai, M., & Ustin, S. L. (2016). Wavelet-Compressed Representation of Landscapes for Hydrologic and Geomorphologic Applications. IEEE Geoscience and Remote Sensing Letters, 13(4), 480-484. [7403883]. https://doi.org/10.1109/LGRS.2015.2513011