PUBLICATION on UAVs potential for vegetation mapping

The potential of Unmanned Aerial Systems: A tool towards precision classification of hard-to-distinguish vegetation types?

Abstract

Detail plant species classification using very high spatial resolution data is a challenging task. Exploring the potential of imagery acquired by Unmanned Aerial Vehicle (UAV) to identify individual species of plants and assessing values of additional inputs such as height and thermal information into classification process are hot research topics. Our study uses a fusion of visible, multispectral and thermal imagery acquired through the low altitude aerial survey for detail classification of land cover and vegetation types. The study area is located in the central part of the Czech Republic and situated in an environmentally specific area - an arboretum of 2.45 ha. Visible (i.e. RGB), multispectral, and thermal sensors were mounted on a flying fixed-wing Unmanned Aerial System. The imagery was acquired at a very detailed scale with Ground Sampling Distance of 3-18 cm. Besides three mosaics (one from each sensor), normalized Digital Surface Models were built from visible and multispectral sensors. Eight classification models were created - each mosaic (visible/multispectral) was enriched with height data, thermal data, and combined height and thermal information. A classification into a three level system was performed through Geographic Object-based Image Analysis using Support Vector Machine algorithm. In general, Overall Accuracy grew with the amount of information entering the classification process. Accuracy reached 77 - 91 % depending on the level of generalization for the best model based on multispectral data and 67 - 80 % for data from the visible sensor. Both thermal data and height information improved the accuracy; however, the statistical evaluation did not reveal any significant difference between the contribution of height and thermal data. Results also indicate that increasing spectral resolution leads to a significantly better performance of the models than higher spatial resolution. UAVs equipped with a proper sensor provide a convenient technology for detail land cover classification even in areas with many similar plant species.