2018
DOI: 10.1111/jvs.12629
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Applying a hydrogeomorphic channel classification to understand spatial patterns in riparian vegetation

Abstract: Questions Hydrogeomorphic channel classifications are widely employed to understand natural phenomena in Earth sciences, but are rarely used in riparian vegetation studies. However, when these types of classifications correspond to physical process domains (discrete landscape units with consistent abiotic attributes), they may be useful in distinguishing habitat and vegetation types. We assessed the ecological significance of a hydrogeomorphic stream channel classification by addressing the following questions… Show more

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Cited by 6 publications
(3 citation statements)
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References 68 publications
(114 reference statements)
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“…Our results are in alignment with other studies of riparian vegetation influence on stream channel geomorphic characteristics (Hupp, 1992; Shaw et al, 2018; Tabacchi et al, 1998), as well as shading and stream water temperature regime (Sweeney & Newbold, 2014). Temperature variations and organic content, two parameters with strong correlations to our observed community variations, fluctuate predictably along the riparian gradient due to changes in canopy cover and channel geomorphology.…”
Section: Discussionsupporting
confidence: 92%
“…Our results are in alignment with other studies of riparian vegetation influence on stream channel geomorphic characteristics (Hupp, 1992; Shaw et al, 2018; Tabacchi et al, 1998), as well as shading and stream water temperature regime (Sweeney & Newbold, 2014). Temperature variations and organic content, two parameters with strong correlations to our observed community variations, fluctuate predictably along the riparian gradient due to changes in canopy cover and channel geomorphology.…”
Section: Discussionsupporting
confidence: 92%
“…The use of remote sensing technology has become the main approach for vegetation ecological resource surveys and environmental monitoring due to the corresponding real-time, repeatability, and wide-coverage advantages [2][3][4]. With the development of remote sensing technology, visible light, multispectral, hyperspectral, and other sensors have been widely used in the remote sensing of vegetation [5,6], and more hyperspectral and high-resolution information has been obtained than ever before, greatly improving the accuracy of image classification [7,8].…”
Section: Introductionmentioning
confidence: 99%
“…Following this classification all the sampling sites correspond to medium to high reach gradient (measured values between 2% and 7%). In all the studied places, the channel has low to medium width/depth ratio and low sinuosity (Table ; Rosgen, ; Shaw, Cooper, & Sutfin, ). The sampled river has mean monthly flows varying between 5 to 30 m 3 /s.…”
Section: Methodsmentioning
confidence: 94%