Spectral mixture analysis of AVIRIS-NG data for grouping plant functional types

Abstract

In a recent scenario, the prediction of environmental changes through plant functional types can aid in simplification of ecosystem processes. The present study attempts to identify and map plant functional types (PFTs) in the AVIRIS-NG campaign site, namely Shoolpaneshwar Wildlife Sanctuary (site id 67), using AVIRIS-NG data combined with spectral mixture analysis that accounts for endmember variability. Due to the occurrence of heterogeneous vegetation in the selected AVIRIS-NG site, the measured spectral signal for every pixel of surface reflectance data will be the outcome of fractions in which various plant functional types and the soil background exist. The interest of the present research lies in these fractions; hence spectral mixture analysis was applied. Ground truthing was carried out simultaneous to AVIRIS-NG flight pass. Six plant functional traits, namely Diameter at breast height (DBH), Height, biomass, leaf chlorophyll content (CC), Fraction of Photosynthetic Active Radiation (FPAR), and Leaf Area Index (LAI), were measured for twenty-three tree species found in the study site which were further used for plant functional grouping by applying k-means clustering algorithm. Trait-based cluster analysis classified the tree species into four plant functional types. Endmember selection from AVIRIS-NG image for these plant functional types was done using a manual field observation-based approach, which was used as input in spectral mixture analysis. The final product of the analysis is a set of fractional abundance images for each plant functional type. Considerable accuracy was obtained on validating the fractional abundance image by in situ data. The study highlighted the potential of a spectral mixture analysis classifier in identifying and mapping different plant functional types using AVIRIS-NG data when performed using an appropriate number of end members.