Figure 6 - uploaded by Vincent Lecours
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A measure of multiscale roughness developed by Lindsay et al. (2019) and applied to a DSM of a mudflat. The analysis reports the scale at which maximum rugosity levels are quantified and the magnitude of that rugosity. This analysis highlighted individual oysters on top of the mudflat that were not visible solely from the UAS imagery and DSM.

A measure of multiscale roughness developed by Lindsay et al. (2019) and applied to a DSM of a mudflat. The analysis reports the scale at which maximum rugosity levels are quantified and the magnitude of that rugosity. This analysis highlighted individual oysters on top of the mudflat that were not visible solely from the UAS imagery and DSM.

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Florida depends on the oceans, yet its waters have not been extensively mapped to the highest standards. While there is a need for marine spatial data for a wide range of applications and issues, there is also a need to develop data acquisition, processing , and analytical workflows and to integrate different surveying instruments that can capture...

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... work combines UAS-derived multispectral imagery (up to ten spectral bands), structure-frommotion (SfM) photogrammetry digital surface and terrain models (DSMs and DTMs), and lidar data. So far, we have focused on object-based image analysis for identifying and delineating habitat types (e.g., ) and on understanding the topographic structure of the different habitat types using geomorphometry (e.g., Figures 6 and 7). Our current semi-automated classification efforts reach accuracies in the low 90%, and the integration of topographic and bathymetric information is likely to improve these even more. ...