Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes

Details

• Journal Title:
  Remote Sensing of Environment
• Personal Author:
  Buffington, Kevin J. ; Dugger, Bruce D. ; Thorne, Karen M. ; Takekawa, John Y.
• NOAA Program & Office:
  NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science)
• Description:
  Airborne light detection and ranging (lidar) is a valuable tool for collecting large amounts of elevation data across large areas; however, the limited ability to penetrate dense vegetation with lidar hinders its usefulness for measuring tidal marsh platforms. Methods to correct lidar elevation data are available, but a reliable method that requires limited field work and maintains spatial resolution is lacking. We present a novel method, the Lidar Elevation Adjustment with NDVI (LEAN), to correct lidar digital elevation models (DEMs) with vegetation indices from readily available multispectral airborne imagery (NAIP) and RTK-GPS surveys. Using 17 study sites along the Pacific coast of the U.S., we achieved an average root mean squared error (RMSE) of 0.072 m, with a 40–75% improvement in accuracy from the lidar bare earth DEM. Results from our method compared favorably with results from three other methods (minimum-bin gridding, mean error correction, and vegetation correction factors), and a power analysis applying our extensive RTK-GPS dataset showed that on average 118 points were necessary to calibrate a site-specific correction model for tidal marshes along the Pacific coast. By using available imagery and with minimal field surveys, we showed that lidar-derived DEMs can be adjusted for greater accuracy while maintaining high (1 m) resolution.
• Keywords:
  Sea level
• Source:
  Remote Sensing of Environment, 186: 616-625
• DOI:
  https://doi.org/10.1016/j.rse.2016.09.020
• Document Type:
  Journal Article
• Funding:
  Grant no. NA15NOS4780171
• Rights Information:
  Accepted Manuscript
• Compliance:
  CHORUS
• Main Document Checksum:
  urn:sha256:f805fe91a598713f7ac8a307948057ce57a68fc8318837c45ff27fc4d7208714
• Download URL:
  https://repository.library.noaa.gov/view/noaa/48453/noaa_48453_DS1.pdf
• File Type:
  [PDF - 1.30 MB ]