Near-infrared reflectance of vegetation (NIRv)

I am actively involved in efforts improve our understanding of photosynthesis at the canopy and global scale using new measurements of the near-infrared reflectance of vegetation (NIRv). My work combines recent theoretical advances in how light interacts with plant canopies and ecological theory to design new approaches for estimating plant light capture and whole-canopy photosynthetic capacity. Ongoing efforts to formalize the relationship between NIR reflectance and photosynthesis present an avenue to dramatically improve global models of photosynthesis.

An ecological approach to forest carbon offsets

Local, national, and international governments have begun adopting policies and economic instruments to protect and enhance forest carbon sinks, ranging from for trans-national payments to avoid deforestation to the development of forest carbon offset projects around the world. While ambitious in their goals, many forest carbon offset protocols do not yet rely on the best available scientific information about forest ecology and, especially, the permanence risks to carbon stocks from climate-driven disturbances such as drought, insects, and fire. Initial work involves comparing more traditional siviculture models (FVS) against more sophisticated ecosystem demography (ED2) models, with the ultimate goal of developing robust and modular tools that allow policy makers and landowners to properly account for future uncertainties in forest recruitment, growth, and mortality.

Characterizing vegetation from geostationary satellites

Historically, most vegetation remote sensing has relied on satellites that follow the pattern of the sun (sun-synchronous), allowing for weekly to daily observation of the entire globe. Weather satellites, however, hold a near-constant position over the Earth (geostationary), allowing observation of fixed points through time. I am involved in several projects to turn these data into meaningful measurements of plant function and, in time, to use geostationary data to drive next-generation ecosystem models.