Determining Effective Meter-Scale Image Data and Spectral Vegetation Indices for Tropical Forest Tree Species Differentiation

Abstract

We evaluate several high-resolution remote sensing data products derived from WorldView-3 satellite imagery to determine which product or product grouping would be most applicable for identifying and mapping tropical tree species. The study site, La Selva Biological Station in Costa Rica, provides the infrastructure to facilitate collection of proper ground-truth data for image validation. An objective statistical analysis demonstrated that the WorldView-3 imagery, after applying a series of spectral and illumination image corrections, was able to accurately identify selected tree species. Specifically, this study defines the image bands and image-derived spectral vegetation indices that are the most effective for tree species discrimination. We show that corrected absolute reflectivity values from the green, red, red edge, and near-infrared are good differentiators of tropical tree species crowns. We also evaluate 14 possible multiband vegetation indices and show that two new indices developed here, using WorldView-3 image bands, have the highest discriminatory power for tropical tree species. A combination of both individual band information and vegetation indices would significantly improve image-based classification of tropical forests.