There is a growing need for operational biodiversity mapping methods to quantify and to assess the impact of climate change, habitat alteration, and human activity on ecosystem composition and function. Here, we present an original method for the estimation of α- and β-diversity of tropical forests based on high-fidelity imaging spectroscopy. We acquired imagery over high-diversity Amazonian tropical forest landscapes in Peru with the Carnegie Airborne Observatory and developed an unsupervised method to estimate the Shannon index (H′) and variations in species composition using Bray-Curtis dissimilarity (BC) and nonmetric multidimensional scaling (NMDS). An extensive field plot network was used for the validation of remotely sensed α- and β-diversity. Airborne maps of H′ were highly correlated with field α-diversity estimates (r = 0.86), and BC was estimated with demonstrable accuracy (r = 0.61–0.76). Our findings are the first direct and spatially explicit remotely sensed estimates of α- and β-diversity of humid tropical forests, paving the way for new applications using airborne and space-based imaging spectroscopy.