Classifying common wetland plants using hyperspectral data to identify optimal spectral bands for species mapping using a small unmanned aerial systems — A case study

Plant species monitoring in wetland ecosystems is crucial for preservation of water quality and many other ecological functions. Difficulties associated with conducting field work (i.e., hazardous terrain) can affect the ability of resource managers to correctly identify wetland plant species in a timely fashion. Thus, in wetland sites where access can be difficult, differentiation of plant species from aerial imagery can be of use. In this work, we studied laboratory produced hyperspectral data to classify various wetland plants thereby determining optimal multispectral bands that can distinguish these species. We then used an unmanned aerial systems (UAS) mountable 5-band multispectral sensor to classify 11 different wetland plant species to determine effectiveness of the multispectral bands chosen by the preliminary hyperspectral analysis. Classification experiments on the hyperspectral data produced the overall accuracies of 94-97% with 20% training samples. Experiments on the UAS collected multispectral data produced overall accuracy of 75% when considering four classes and 58% overall accuracy when considering 11 classes.