A1 Refereed original research article in a scientific journal
Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography
Authors: Claude Flener, Matti Vaaja, Anttoni Jaakkola, Anssi Krooks, Harri Kaartinen, Antero Kukko, Elina Kasvi, Hannu Hyyppä, Juha Hyyppä, Petteri Alho
Publisher: MDPI
Publication year: 2013
Journal: Remote Sensing
Journal acronym: Remote Sens
Number in series: 12
Volume: 5
Issue: 12
First page : 6382
Last page: 6407
Number of pages: 26
ISSN: 2072-4292
DOI: https://doi.org/10.3390/rs5126382
Abstract
Accurate terrain models are a crucial component of studies of river channel evolution. In this paper we describe a new methodology for creating high-resolution seamless digital terrain models (DTM) of river channels and their floodplains. We combine mobile laser scanning and low-altitude unmanned aerial vehicle (UAV) photography-based methods for creating both a digital bathymetric model of the inundated river channel and a DTM of a point bar of a meandering sub-arctic river. We evaluate mobile laser scanning and UAV-based photogrammetry point clouds against terrestrial laser scanning and combine these data with an optical bathymetric model to create a seamless DTM of two different measurement periods. Using this multi-temporal seamless data, we calculate a DTM of difference that allows a change detection of the meander bend over a one-year period.
Accurate terrain models are a crucial component of studies of river channel evolution. In this paper we describe a new methodology for creating high-resolution seamless digital terrain models (DTM) of river channels and their floodplains. We combine mobile laser scanning and low-altitude unmanned aerial vehicle (UAV) photography-based methods for creating both a digital bathymetric model of the inundated river channel and a DTM of a point bar of a meandering sub-arctic river. We evaluate mobile laser scanning and UAV-based photogrammetry point clouds against terrestrial laser scanning and combine these data with an optical bathymetric model to create a seamless DTM of two different measurement periods. Using this multi-temporal seamless data, we calculate a DTM of difference that allows a change detection of the meander bend over a one-year period.
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