Experimental analysis of sand grain size mapping using UAV remote sensing

ABSTRACTTo map sand grain size distribution using Unmanned Aerial Vehicle (UAV) digital cameras, we conducted experiments for six sand classes with the UAV images acquired at six different heights. We used the maximum likelihood classification and the Grey Level Co-occurrence Matrixtexture indices on the images and evaluated classification accuracy under each flight height/ image resolution. The image at the spatial resolution of 1.23 mm can effectively separate all six classes. The image of lower resolutions were only effective for coarser classes with an overall accuracy of 83.92% or better: the 2.48 mm pixel size was good for the four classes coarser than medium sand; 3.78 mm was good for the three classes coarser than coarse sand; 4.89 and 5.42 mm were good for the two coarsest classes of sand and gravel. This research is the first comprehensive study to test different UAV survey quality control parameters for sand classification, which could serve as a guidance for UAV survey parameter selection such...