Discriminating the papyrus vegetation (Cyperus papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP

Techniques for discriminating swamp wetland species are critical for the rapid assessment and proactive management of wetlands. In this study, we tested whether the random forest (RF) algorithm could discriminate between papyrus swamp and its co-existent species (Phragmites australis, Echinochloa pyramidalis and Thelypteris interrupta) using in situ canopy reflectance spectra. Canopy spectral measurements were taken from the species using analytical spectral devices but later resampled to Hyperspectral Mapper (HYMAP) resolution. The RF algorithm and a simple forward variable selection (FVS) technique were used to identify key wavelengths for discriminating papyrus swamp and its co-existence species. The method yielded 10 wavelengths located in the visible and short-wave infrared portions of the electromagnetic spectrum with a lowest out-of-bag (OOB) estimate error rate of 9.5% and.632+ bootstrap error of 8.95%. The use of RF as a classification algorithm resulted in overall accuracy of 90.50% and a kappa value of 0.87, with individual class accuracies ranging from 93.73% to 100%. Additionally, the results from this study indicate that the RF algorithm produces better classification results than conventional classification trees (CTs) when using all HYMAP wavelengths (n = 126) and when using wavelengths selected by the FVS technique.