Monitoring the invasion of Phragmites australis in coastal marshes of Louisiana, USA, using multi-source remote sensing data

Phragmites australis a native marshland species to the North American Atlantic Coast is presently expanding to new habitats at very high rates. To understand the causes and consequences of this invasion, monitoring programs, especially at the Gulf Coast, need to be established. The first step to this is to obtain a method for accurate mapping Phragmites distribution. In this study an object oriented classification approach that combines lidar and multispectral imagery is proposed. After segmentation of a dataset of three multispectral bands plus a lidar based digital surface model, two classification methods were explored: a class assignment (CA) and a nearest neighbor classification (NNC). CA requires more involvement and knowledge form the analyst, but the decisions to be made are better understood than in the NNC. Both methods performed similarly, and were able to map most of the Phragmites present in the study area. Results show that the use of multi-source data not only can produce accurate distribution maps for future monitoring, but also guide on present day surveys and even help in the interpretation of old data to map past conditions.