TY - JOUR
T1 - Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data
AU - Huang, Guo Hao
AU - Atkinson, Peter M.
AU - Wang, Chi Kuei
N1 - Funding Information:
Acknowledgement: This work was supported by the Ministry of Science and Technology, Taiwan, under Grant no. NSC 96-2628-E-006-084-MY3, NSC 99-2628-E-006-160, NSC 100-2628-E-006-015, NSC 101-2628-E-006-009, and NSC 102-2119-M-006-003. The reviews from the anonymous referees improve this manuscript significantly.
Publisher Copyright:
© 2018 G.-H. Huang et al. published by De Gruyte.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
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U2 - 10.1515/geo-2018-0048
DO - 10.1515/geo-2018-0048
M3 - Article
AN - SCOPUS:85056130651
SN - 2391-5447
VL - 10
SP - 607
EP - 617
JO - Open Geosciences
JF - Open Geosciences
IS - 1
ER -