Remote Sensing of Earth Systems
Our Team works on a variety of projects related to the remote sensing of landscape-scale ecological processes in an effort to monitor and model global environmental change.
Our projects aim to:
Develop and evaluate surface albedo and reflectance anisotropy products from satellite sensors (MODIS, VIIRS, and Landsat). As satellites continuously record data of sunlight reflected off the earth, our algorithms process that data and correct it in order to better detect and monitor vegetation phenology (seasonal growth and decline), ephemeral snow, and land cover disturbance.
Integrate satellite data with models of the hydrologic biogeochemical cycles (RHESSys) in watersheds in order to predict the chemical fluxes from forests to coastal regions.
Employ 3D data collected by airborne, spaceborne, and terrestrial lidar scanners to monitor ecosystem disturbances and model vegetation structure. We are developing techniques to employ our own terrestrial lidar technology, developed in-house (the Compact Biomass Lidar), to answer ecological questions and to validate other remote sensing observations.
SPECTRALMASS: Structure and Productivity of the Environment with Coastal and Terrestrial Reflectance and Albedo from Lidar and Multi-Angle Satellite Systems
Professor Crystal Schaaf’s Lab
University of Massachusetts Boston
100 Morrissey Blvd.