Developing Hyperspectral Vegetation Indices for Identifying Seagrass Species and Cover Classes

Seagrass habitats are characteristic features of shallow waters worldwide and provide a variety of ecosystem functions. To date, few studies have evaluated the efficiency of spectral vegetation indices (VIs) for characterizing aquatic plants. Here we evaluate the use of in situ hyperspectral data and hyperspectral VIs for distinguishing among seagrass species and levels of percentage submerged aquatic vegetation (%SAV) cover in a subtropical shallow water setting. Analysis procedures include (1) retrieving bottom reflectance, (2) calculating correlation matrices of VIs with %SAV cover and F value matrices from analysis of variance among species, (3) testing the difference of VIs between levels of %SAV cover and between species, and (4) discriminating levels of %SAV cover and species by using linear discriminant analysis (LDA) and classification and regression trees (CART) classifiers with selected VIs as input. The experimental results indicated that (1) the best VIs for discriminating the four levels of %SAV cover were simple ratio (SR) VI, normalized difference VI (NDVI), modified simple ratio VI, and NDVI x SR, whereas the best VIs for distinguishing the three seagrass species included the weighted difference VI, soil-adjusted VI (SAW), SAW x SR and transformed SAW; (2) the optimal central wavelengths for constructing the best VIs were 460, 500, 610, 640, 660, and 690 nm with spectral regions ranging from 3 to 20 nm at band width 3 nm, most of which were associated with absorption bands by photosynthetic and other accessory pigments in the visible spectral range. Compared with LDA, CART performed better in discriminating the four levels of %SAV cover and identifying the three seagrass species.