Forest biodiversity has been declining across the globe due to anthropogenic activities. Losses of biodiversity have led to reduced forest health and ecosystem services. Therefore, it has become necessary to monitor changes in biodiversity over wide geographic areas. Remote sensing has the potential to monitor biodiversity changes, but the accuracy in which it can be estimated is under debate. In this study, we tested 1) the relationship among distinct metrics of biodiversity, 2) the role topographic measures have on determining biodiversity, and 3) the ability of hyperspectral remote sensing to estimate biodiversity in temperate forests of the Northeastern United States. We characterized biodiversity according to four different metrics: species, functional, structural, and phylogenetic diversity. All four metrics were quantified using species inventory data as well as Light Detecting and Ranging (LiDAR) to calculate additional indices of structural diversity. A digital elevation model was used to obtain measures of slope, aspect, and other topographic indices such as topographic wetness. Hyperspectral imagery was used to obtain reflectance, entropy, and several vegetation indices. In our analyses, species, functional, and phylogenetic diversity were shown to be moderately correlated suggesting similarities between the metrics while correlations between structural diversity and the other metrics were weak. The calculated topographic indices also showed weak correlations with the biodiversity metrics suggesting that topography does not influence measures of biodiversity at the plot level. Depending on the biodiversity metric, relationships between the hyperspectral analyses and biodiversity were weak to moderately strong. These findings suggest that hyperspectral imagery holds some potential for estimating multiple metrics of biodiversity.