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

Remote Sensing of Environment
By: , and 

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Abstract

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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes
Series title Remote Sensing of Environment
DOI 10.1016/j.rse.2016.09.020
Volume 186
Year Published 2016
Language English
Publisher Elsevier
Contributing office(s) Western Ecological Research Center
Description 10 p.
First page 616
Last page 625