The application of remote sensing techniques in mapping, classifying and monitoring
land cover, land use and vegetation are popular among the researchers and scientists for several
decades. It became more productive and economical in recent years with the advancement of
information technology in a sophisticated and revolutionary manner. Currently, remote sensing is
a widely used effective technique that provides spatial and temporal information about vegetation
and invasive species in wetlands. The first objective of this study was to assess the effectiveness
of the data obtained via Unmanned Ariel Vehicles (UAV) to identify invasive Phragmites
australis in the Old Woman Creek (OWC) in Ohio. Secondly, the study aimed to determine the
most suitable algorithm to distinguish between Phragmites australis and other vegetation types
using pixel based and object based classification methods and a combination of feature layers
derived from the UAV images.
Pixel based classification found to be performing better than object based classification.
Pixel based Neural Network (NN) was identified as the best classifier to map Phragmites in
OWC with the least error of omission of 1.59% and the overall accuracy of 94.80% based on the
Sequoia image acquired in August that was stacked with Canopy Height Model (CHM) from
August and NDVI, which was derived using UAV data acquired in October (NDVIOct). The study
emphasizes the necessity of a suitable sampling method and the use of optimum parameters of
non-parametric classifiers. The study provides future directions for data acquisition to map
Phrgamites at early and mid-summer to find data to eradicate Phragmites effectively in OWC
estuary.