Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data
Previous studies measured gravel bed surfaces by terrestrial laser scanning (TLS) and close-range photogrammetry suggested the presence of at least two different scales of spatial variation in gravel bed surfaces. This study investigated the spatial variation of airborne laser scanning (ALS) point c...
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doaj-9165489af4164b9c9b6d0f1f8a366dbb2021-09-05T20:50:49ZengDe GruyterOpen Geosciences2391-54472018-10-0110160761710.1515/geo-2018-0048geo-2018-0048Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning dataHuang Guo-Hao0Atkinson Peter M.1Wang Chi-Kuei2Geographic Information System Research Center, Feng Chia University, Taichung407, TaiwanFaculty of Science and Technology, Lancaster University, Bailrigg, Lancaster LA1 4YR, United KingdomDepartment of Geomatics, National Cheng Kung University, Tainan701, TaiwanPrevious studies measured gravel bed surfaces by terrestrial laser scanning (TLS) and close-range photogrammetry suggested the presence of at least two different scales of spatial variation in gravel bed surfaces. This study investigated the spatial variation of airborne laser scanning (ALS) point clouds acquired in gravel bed. Due to the large footprint of ALS systems, a smoother surface is expected, but there exists some uncertainty over the precise scale of ALS measurement (hereafter referred to as the spatial support). As a result, we applied the regularization method, which is a variogram upscaling approach, to investigate the true support of ALS data. The regularization results suggested that the gravel bed surface described by the ALS is much smoother than expected in terms of the ALS reported measurement scale. Moreover, we applied the factorial kriging (FK) method, which allows mapping of different scales of variation present in the data separately (different from ordinary kriging which produces a single map), to obtain the river bed topography at each scale of spatial variation. We found that the short-range and long-range FK maps of the TLS-derived DSMs were able to highlight the edges of gravels and clusters of gravels, respectively. The long-range FK maps of the ALS data shows a pattern of gravel-bed clusters and aggregations of gravels. However, the short-range FK maps of the ALS data produced noisy maps, due to the smoothing effect. This analysis, thus, shows clearly that ALS data may be insufficient for geomorphological and hydraulic engineering applications that require the resolution of individual gravels.https://doi.org/10.1515/geo-2018-0048laser scanningvariogramupscalingfactorial kriginggeomorphology |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huang Guo-Hao Atkinson Peter M. Wang Chi-Kuei |
spellingShingle |
Huang Guo-Hao Atkinson Peter M. Wang Chi-Kuei Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data Open Geosciences laser scanning variogram upscaling factorial kriging geomorphology |
author_facet |
Huang Guo-Hao Atkinson Peter M. Wang Chi-Kuei |
author_sort |
Huang Guo-Hao |
title |
Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data |
title_short |
Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data |
title_full |
Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data |
title_fullStr |
Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data |
title_full_unstemmed |
Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data |
title_sort |
quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data |
publisher |
De Gruyter |
series |
Open Geosciences |
issn |
2391-5447 |
publishDate |
2018-10-01 |
description |
Previous studies measured gravel bed surfaces by terrestrial laser scanning (TLS) and close-range photogrammetry suggested the presence of at least two different scales of spatial variation in gravel bed surfaces. This study investigated the spatial variation of airborne laser scanning (ALS) point clouds acquired in gravel bed. Due to the large footprint of ALS systems, a smoother surface is expected, but there exists some uncertainty over the precise scale of ALS measurement (hereafter referred to as the spatial support). As a result, we applied the regularization method, which is a variogram upscaling approach, to investigate the true support of ALS data. The regularization results suggested that the gravel bed surface described by the ALS is much smoother than expected in terms of the ALS reported measurement scale. Moreover, we applied the factorial kriging (FK) method, which allows mapping of different scales of variation present in the data separately (different from ordinary kriging which produces a single map), to obtain the river bed topography at each scale of spatial variation. We found that the short-range and long-range FK maps of the TLS-derived DSMs were able to highlight the edges of gravels and clusters of gravels, respectively. The long-range FK maps of the ALS data shows a pattern of gravel-bed clusters and aggregations of gravels. However, the short-range FK maps of the ALS data produced noisy maps, due to the smoothing effect. This analysis, thus, shows clearly that ALS data may be insufficient for geomorphological and hydraulic engineering applications that require the resolution of individual gravels. |
topic |
laser scanning variogram upscaling factorial kriging geomorphology |
url |
https://doi.org/10.1515/geo-2018-0048 |
work_keys_str_mv |
AT huangguohao quantifyingthescalesofspatialvariationingravelbedsusingterrestrialandairbornelaserscanningdata AT atkinsonpeterm quantifyingthescalesofspatialvariationingravelbedsusingterrestrialandairbornelaserscanningdata AT wangchikuei quantifyingthescalesofspatialvariationingravelbedsusingterrestrialandairbornelaserscanningdata |
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