Mapping Understory Invasive Plants in Urban Forests with Spectral and Temporal Unmixing of Landsat Imagery

Abstract

Successful eradication and management of invasive plants require frequent and accurate maps. Detection of invasive plants is difficult at moderate resolution because target species are often located in the forest understory among other vegetation types, and so produce mixed spectral signatures. Spectral unmixing approaches can help to decompose these spectral mixtures; however, they are typically applied to only one or a few images, and thus neglect phenological variability that may improve invasive species discrimination. We compared two approaches to multiple endmember spectral mixture analysis for detecting Ligustrum sinense in the southeastern United States: the use of temporal signatures of endmembers from full and select-date normalized difference vegetation index time series, and conventional spectral unmixing using a single image date. Our results suggest that using temporal signatures from all available imagery may be a good choice, with minimal impact to achievable accuracy, if a priori information on phenological differences between endmembers is unavailable, or imagery for periods of high phenological difference are unavailable.