USING DRONE-ACQUIRED, HIGH-RESOLUTION, MULTISPECTRAL IMAGING TO PREDICT AND MONITOR COASTAL DUNE MOBILITY: AN EXAMPLE FROM THE SOUTHEASTERN SHORE OF LAKE MICHIGAN

Dune mobility helps preserve ecological diversity in coastal dune complexes but can also threaten human infrastructure. Aerial photography has been used to monitor regional changes in mobility using the amount and kind of vegetation as a proxy of dune mobility. We are developing techniques to use drone-acquired remote sensing data to study dune mobility on a local scale. The 70 ha dune preserve at Saugatuck Harbor Natural Area (SHNA) is our test area. We use multispectral (red, green, near-infrared) data with a resolution of 0.75 cm² per pixel to examine vegetation density and RGB images to make topographic maps. NDVI values (NIR – Red) / (NIR + Red) are used to characterize each pixel as predominantly vegetated or predominantly bare. From this information we calculate proportions of vegetation in larger areas which are combined with topography in a mathematical model that attempts to predict sensitivity to aeolian deflation. The model contains five indices. Two indices depend on vegetation density: the extent of vegetation surrounding a patch of ground and the extent of vegetation upwind from that patch. The wind shadow index is based on the assumption that a dune creates a barrier inhibiting aeolian deflation in the lee of the dune. The elevation index takes into account the effect of topographic acceleration in enhancing the ability of wind to transport sand. The slope orientation index is based on the orientation of the slope with respect to wind direction. For a given wind direction maps showing the relative values of each of these indices are calculated and combined, using the R statistical software (http://www.R-project.org/), to create a map of relative deflation potential. Separate maps are created for all 16 directions of the compass rose. Data from a nearby weather station are used to calculate the relative sand drift potential in each direction. The deflation potentials are combined in proportion to the sand drift potential in each direction to create a map of overall sensitivity to aeolian deflation in the study area. To date, spectral data has been collected in July 2017, and July 2018. Spectral data will be acquired each July over the next several years in order to monitor changes in vegetation cover. These changes will be used to test and refine the model used to create maps of sensitivity to aeolian deflation.