Coupled topographic and vegetation patterns in coastal dunes: Remote sensing observations and ecomorphodynamic implications

Abstract

Vegetation plays a key role in stabilizing coastal dunes and barrier islands by mediating sand transport, deposition, and erosion. Dune topography, in turn, affects vegetation growth, by determining local environmental conditions. However, our understanding of vegetation and dune topography as coupled and spatially extensive dynamical systems is limited. Here we develop and use remote sensing analyses to quantitatively characterize coastal dune ecotopographic patterns by simultaneously identifying the spatial distribution of topographic elevation and vegetation biomass. Lidar‐derived leaf area index and hyperspectral‐derived normalized difference vegetation index patterns yield vegetation distributions at the whole‐system scale which are in agreement with each other and with field observations. Lidar‐derived concurrent quantifications of biomass and topography show that plants more favorably develop on the landward side of the foredune crest and that the foredune crestline marks the position of an ecotone, which is interpreted as the result of a sheltering effect sharply changing local environmental conditions. We conclude that the position of the foredune crestline is a chief ecomorphodynamic feature resulting from the two‐way interaction between vegetation and topography.

Investigating dune‐building feedback at the plant level: Insights from a multispecies field experiment

“…The weak negative correlation between A. breviligulata growth and the combination of distance from shore and elevation (when both factors are considered along with total change in elevation) is surprising because other authors have found A. breviligulata growth to be densest at some distance from the shoreline where sand burial is greatest, which tends to be around the peak of the foredune (Cheplick, 2005;Young et al, 2011;Yousefi Lalimi et al, 2017). The weak negative correlation between A. breviligulata growth and the combination of distance from shore and elevation (when both factors are considered along with total change in elevation) is surprising because other authors have found A. breviligulata growth to be densest at some distance from the shoreline where sand burial is greatest, which tends to be around the peak of the foredune (Cheplick, 2005;Young et al, 2011;Yousefi Lalimi et al, 2017).…”

“…The multiple linear regression analysis provides more insight into the combination of factors that influence plant growth for each species. The weak negative correlation between A. breviligulata growth and the combination of distance from shore and elevation (when both factors are considered along with total change in elevation) is surprising because other authors have found A. breviligulata growth to be densest at some distance from the shoreline where sand burial is greatest, which tends to be around the peak of the foredune (Cheplick, 2005;Young et al, 2011;Yousefi Lalimi et al, 2017). Our use of transplants, which necessarily had shallow root systems and therefore did not have access to groundwater when planted on the foredune, may at least partially explain why the transplants grew more slowly farther from shore.…”

Examining the utility of Simultaneous Localisation and Mapping (SLAM) LiDAR on a low-altitude unmanned aerial vehicle (UAV) across environments of various composition and dynamic complexity

“…With an mean RMSE of 0.050 ± 0.31m, the absolute accuracy of the Hovermap point clouds to GCP targets were again consistent with the 0.02-0.8m range reported in previous manned airborne LiDAR [19,89,117] and UAV SfM (0.17-0.3m) studies [41][42][43]124]. According to Le Mauff [118] these relatively small differences may be attributed to noise inherent to LiDAR data.…”

“…Over the past two decades, airborne LiDAR has been increasingly used and significant advances have been made in describing, and quantifying, the dynamic processes in both environmental and agricultural applications. Mills [18] and Lalimi et al [19], for example, have recently combined airborne LiDAR with hyperspectral imagery to map and classify vegetation patterns in coastal dunes. Similarly, Shfri et al [20], employed airborne LiDAR and optical remote sensing for plantation road and terrain planning for planting and Gao et al [21], used similar techniques to extract density and fractional cover of maize crops.…”

“…GSD traditionally associated with airborne LiDAR from manned aircraft [19,86,115,117]. The cost, however, is realised in the relatively limited spatial scales that can be achieved by UAVs compared to that of manned aircraft.…”