Validation of high-precision effects of a movable riverbed simulation using unmanned aerial vehicles and structure from motion
For advanced and strategic management of gravel riverbed restoration and stopping of woodland overgrowth, movable riverbed simulations and vegetation dynamic models that can describe a ‘detailed riverbed materials’ transport’ influencing the vegetation dynamics and estimate the creation area of a gr...
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EDP Sciences
2018-01-01
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Series: | E3S Web of Conferences |
Online Access: | https://doi.org/10.1051/e3sconf/20184002055 |
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doaj-197307800d754448bb71d590519fed2e2021-02-02T03:18:52ZengEDP SciencesE3S Web of Conferences2267-12422018-01-01400205510.1051/e3sconf/20184002055e3sconf_riverflow2018_02055Validation of high-precision effects of a movable riverbed simulation using unmanned aerial vehicles and structure from motionDenda MasatoshiFor advanced and strategic management of gravel riverbed restoration and stopping of woodland overgrowth, movable riverbed simulations and vegetation dynamic models that can describe a ‘detailed riverbed materials’ transport’ influencing the vegetation dynamics and estimate the creation area of a gravel riverbed are required. As first steps we verified the hypothesis whether high-accuracy observation data using UAV and SfM improve the accuracy of water flow condition and movable riverbed simulation that can describe the riverbed materials’ transport in detail. In the results, High-precision river morphology using UAVs offers the ability to improve the movable riverbed simulation. And these progresses indicated possibilities of UAV and SfM to develop vegetation dynamics model considering the riverbed materials’ transport.https://doi.org/10.1051/e3sconf/20184002055 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Denda Masatoshi |
spellingShingle |
Denda Masatoshi Validation of high-precision effects of a movable riverbed simulation using unmanned aerial vehicles and structure from motion E3S Web of Conferences |
author_facet |
Denda Masatoshi |
author_sort |
Denda Masatoshi |
title |
Validation of high-precision effects of a movable riverbed simulation using unmanned aerial vehicles and structure from motion |
title_short |
Validation of high-precision effects of a movable riverbed simulation using unmanned aerial vehicles and structure from motion |
title_full |
Validation of high-precision effects of a movable riverbed simulation using unmanned aerial vehicles and structure from motion |
title_fullStr |
Validation of high-precision effects of a movable riverbed simulation using unmanned aerial vehicles and structure from motion |
title_full_unstemmed |
Validation of high-precision effects of a movable riverbed simulation using unmanned aerial vehicles and structure from motion |
title_sort |
validation of high-precision effects of a movable riverbed simulation using unmanned aerial vehicles and structure from motion |
publisher |
EDP Sciences |
series |
E3S Web of Conferences |
issn |
2267-1242 |
publishDate |
2018-01-01 |
description |
For advanced and strategic management of gravel riverbed restoration and stopping of woodland overgrowth, movable riverbed simulations and vegetation dynamic models that can describe a ‘detailed riverbed materials’ transport’ influencing the vegetation dynamics and estimate the creation area of a gravel riverbed are required. As first steps we verified the hypothesis whether high-accuracy observation data using UAV and SfM improve the accuracy of water flow condition and movable riverbed simulation that can describe the riverbed materials’ transport in detail. In the results, High-precision river morphology using UAVs offers the ability to improve the movable riverbed simulation. And these progresses indicated possibilities of UAV and SfM to develop vegetation dynamics model considering the riverbed materials’ transport. |
url |
https://doi.org/10.1051/e3sconf/20184002055 |
work_keys_str_mv |
AT dendamasatoshi validationofhighprecisioneffectsofamovableriverbedsimulationusingunmannedaerialvehiclesandstructurefrommotion |
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1724308127511216128 |