2016
DOI: 10.1016/j.ecoleng.2016.05.049
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Dynamics of surface elevation and microtopography in different zones of a coastal Phragmites wetland

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Cited by 22 publications
(12 citation statements)
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“…, in an effort to quantify marsh surface breakup (Karstens et al, 2016;Moser et al, 2007). As an additional metric of microheterogeneity, we quantified the number of holes, defined as locations with an elevation difference between that exceeded 20 mm.…”
Section: Elevation Trendsmentioning
confidence: 99%
“…, in an effort to quantify marsh surface breakup (Karstens et al, 2016;Moser et al, 2007). As an additional metric of microheterogeneity, we quantified the number of holes, defined as locations with an elevation difference between that exceeded 20 mm.…”
Section: Elevation Trendsmentioning
confidence: 99%
“…They showed that the observed differences could be explained by wind disturbance in lentic sites and by the water regime disturbance in lotic habitats. However, P. australis is an "ecosystem engineer species" par excellence, and its dense stands play an essential role as a wind and wave breaker (Takeda and Kurihara 1988;Vymazal 2011;Karstens et al, 2016). Therefore, the same high level of reeds coverage may have buffered different abiotic factors and led to similar levels of functional diversity across habitats.…”
Section: Discussionmentioning
confidence: 99%
“…Known as a high-precision and high-resolution mapping tool, the terrestrial LiDAR system provides a rapid and practical approach to map coastal micro-topography, which has a considerable influence on the hydrology, the habitat variability including vegetation patterns, and the functioning of ecosystems [27][28][29]. However, high uncertainty under dense vegetation remains a significant challenge due to the inability of laser signals to penetrate tall and dense vegetation [30]. Coveney and Fotheringham (2011) [31] explored the terrestrial laser scan error in the presence of dense ground vegetation and clarified the component contributions to elevation error deriving from vegetation occlusion, scan coregistration errors, point-cloud geo-referencing errors and target position definition.…”
Section: Introductionmentioning
confidence: 99%