The way plants absorb and scatter light depends on the chemical, structural, anatomical and morphological characteristics of their leaves. In addition, plant architecture, including growth form, branching structure and leaf angle distribution, influences light scattering and absorption. What does it mean that spectra of plants depend on all these characteristics? At the leaf level, we have found that plants that are more similar in their foliar traits and more closely related are also spectrally more similar. Moreover, more productive plants occupy a greater spectral space then less productive plants, but the positive effects of biodiversity on community productivity increase with spectral dissimilarity among plants (or spectral diversity). How can we utilize spectroscopy to study plant community composition and resource use? Can we decipher the spectral code of plants?

We have found evidence that the spectral similarity of plant communities scales with the similarity in plant community composition across many ecosystems. But how does community size influence this relationship? What is the "ideal" spatial resolution for remote sensing of plant diversity? What about understory plants? What about the diversity of other organisms? How can we integrate passive and active remote sensing methods, and environmental data to develop a remotely sensed biodiversity metric? 

Mapping plant traits and community composition across space and time allows investigating a wide range of questions. How are plant communities changing over time and across space? How effective are restoration projects? Are plants and plant communities under stress? We use imaging spectroscopy and lidar, statistical and physical models, for predicting chemical and structural plant characteristics at the community and individual plant level. We also develop methods for mapping plant community composition, and for the detection of plant species and disease. Research interests include investigating the effects of herbivores on plant communities and vice versa, and the prediction of below-ground functions, processes and diversity from above-ground vegetation characteristics that can be remotely sensed.