Estimating standing biomass of exotic macrophytes using sUAS

Abstract

With the advent of sUAS, research scientists and plant managers are capable of obtaining unique, fast, and low-cost quantitative data, which delivers many repeatable survey options. Benefits of autonomous sUAS platforms include minimal training, reduced human safety concerns, and creation of graphic outputs which may be readily viewed by any stakeholder who was not actively involved in the survey or management activity. Research conducted in the Wellington Region, New Zealand was used to evaluate consumer-grade sUAS technologies to map and estimate standing biomass of Manchurian Wild Rice (MWR), an exotic semi-aquatic grass which promotes flooding, and displacement of native flora and fauna. The goal of this research was to improve the speed and resolution of current survey strategies used to assess MWR among a lowland pasture site using unmanned systems and photogrammetry techniques. Image collection and data processing was conducted in a manner to provide a theoretic biomass estimation of remaining MWR following seasonal growth and herbicide applications. Post-processing methods and theories discussed attempt to identify and quantify MWR biomass using supervised imaging analysis, plant height modeling, and biomass collected in situ. The use of unmanned systems to map, monitor, and manage MWR is encouraged for future applications.