Regionalized Linear Models for River Depth Retrieval Using 3-Band Multispectral Imagery and Green LIDAR Data

Abstract

Bathymetry is of vital importance in river studies but obtaining full-scale riverbed maps often requires considerable resources. Remote sensing imagery can be used for efficient depth mapping in both space and time. Multispectral image depth retrieval requires imagery with a certain level of quality and local in-situ depth observations for the calculation and verification of models. To assess the potential of providing extensive depth maps in rivers lacking local bathymetry, we tested the application of three platform-specific, regionalized linear models for depth retrieval across four Norwegian rivers. We used imagery from satellite platforms Worldview-2 and Sentinel-2, along with local aerial images to calculate the intercept and slope vectors. Bathymetric input was provided using green Light Detection and Ranging (LIDAR) data augmented by sonar measurements. By averaging platform-specific intercept and slope values, we calculated regionalized linear models and tested model performance in each of the four rivers. While the performance of the basic regional models was comparable to local river-specific models, regional models were improved by including the estimated average depth and a brightness variable. Our results show that regionalized linear models for depth retrieval can potentially be applied for extensive spatial and temporal mapping of bathymetry in water bodies where local in-situ depth measurements are lacking.

Integrating Depth Measurements From Gaging Stations With Image Archives for Spectrally Based Remote Sensing of River Bathymetry

“…A longstanding and important goal in remote sensing of rivers is to establish depth retrieval models that are highly general and broadly portable, capable of accurately estimating water depths for long river segments or even entire watersheds (Hugue et al., 2016; Legleiter & Fosness, 2019; Legleiter & Harrison, 2019; Sundt et al., 2021). However, this investigation leads us to believe that this objective is likely to remain elusive.…”

Automated fluvial hydromorphology mapping from airborne remote sensing

“…Our HMU classification system used class divisions that were only based on surface patterns and gradient, so only provided four broad HMU types. However, extraction of depth via remote sensing is possible, using bathymetric LiDAR (Hugue et al, 2016; Puig‐Mengual et al, 2021; Sundt et al, 2022), or analysis of image spectra (Legleiter & Harrison, 2019; Sundt et al, 2021), or Structure from Motion applied to UAV imagery (Dietrich, 2017). Velocity may be determined using large‐scale particle image velocimetry, applied to UAV imagery (Detert & Weitbrecht, 2015).…”

Automated fluvial hydromorphology mapping from airborne remote sensing

“…Our HMU classification system used class divisions that were only based on surface patterns and gradient, so only provided four broad HMU types. However, extraction of depth via remote sensing is possible, using bathymetric LiDAR (Hugue et al 2016, Puig-Mengual et al 2021, or analysis of image spectra (Legleiter & Harrison 2019, Sundt et al 2021, or Structure from Motion applied to UAV imagery (Dietrich 2017). Velocity may be determined using large-scale particle image velocimetry, applied to UAV imagery (Detert & Weitbrecht 2015).…”