Paper No. 354-3
Presentation Time: 9:00 AM-6:30 PM
USING REMOTELY ACQUIRED IMAGES TO MAP VEGETATION DENSITIES IN OPEN DUNE ENVIRONMENTS: A CASE STUDY FROM COASTAL DUNES ALONG THE SOUTHEASTERN SHORE OF LAKE MICHIGAN
A patchy distribution of vegetation is characteristic of open dune grasslands. Because patches devoid of vegetation have the potential to develop into blowouts and migrating dunes, this patchiness has potential implications for dune stability and mobility. We are developing a technique to map the degree of vegetation patchiness (i.e., percent of area covered by vegetation) in open dune environments using images acquired during drone flights and analyzing them using an artificial neural network. Our test site is a 1000 m x 400 m open dune area at Saugatuck Harbor Natural Area on the southeastern coast of Lake Michigan. High resolution (1 cm2 per pixel) images were obtained for the red, green, and near-infrared spectral bands during a small unmanned aircraft system (a.k.a., drone) flight. Ninety-five training quadrats, each 360 cm2 in area, were selected and marked before the flight. True color (red, green, and blue) images were taken overhead of each training quadrat from a distance of 1.5 m, and variations in hue, saturation, and value in these images were used to determine the percent of area covered by live vegetation, dead vegetation, and bare sand. The results of this analysis are being used to train a neural network to determine the percent coverage by live vegetation, dead vegetation, and bare sand from the drone images. After the neural network training is complete, the goal is to apply the neural network to all of the drone image data to create a high resolution map showing the percent coverage for the entire open dune area at Saugatuck Harbor Natural Area. Ultimately this tool will allow us to remotely monitor vegetation density in large dune complexes for both scientific and management purposes.