Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes
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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.
| 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 |
| Google Analytic Metrics | Metrics page |