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We enjoy our long-term collaborations as well as inviting new visitors to share ideas, expertise and techniques.

Amanda Rickert

Amanda was a summer SULI student in 2016 who participated in a 10 week project-based internship in the Terrestrial Ecosystem Department at Brookhaven National Lab. By combining field work, remote sensing data (NASA G-LiHT http://gliht.gsfc.nasa.gov/), and statistical analysis, she explored the susceptibility of different trees to defoliation by gypsy moth caterpillars (Lymantria dispar dispar). Amanda found inspiration for her project by simply looking at trees around the lab, and noticing the extensive gypsy moth defoliation in 2016. Her study determined that similar defoliation statuses are “clustered” together, and taller trees, white oaks, and more isolated trees are more heavily defoliated. The results of her study will be used to help define management practices at BNL designed to protect trees from heavy defoliation, therefore mitigating the large scale impacts of defoliation on the Laboratory ecosystem as a whole. Amanda plans to pursue a graduate education in plant ecology.

Kristen Brewster

Kristen was a spring SULI student in 2016 who worked with TEST on a remote sensing project to map forest structure and composition around BNL using NASA Goddard's LiDAR, Hyperspectral & Thermal Imager remote sensing data (http://gliht.gsfc.nasa.gov/). This data was collected as part of a NASA-USFS collaboration focusing on mapping and quantifying insect pest disturbance across the eastern U.S. Kristen mapped the dominant vegetation cover within the BNL G-LiHT mosaic collected during the summer of 2015 at 1meter spatial resolution using the spectral and structural information provided by the G-LiHT package. She then validated her landcover maps generated using a range of approaches with a series of field plots she collected around BNL. The results of Kristen's study were used to guide further work to study vegetation structure and function at BNL and across the northeast using G-LiHT.

Wil Lieberman-Cribbin

Wil was a Spring SULI student in 2015 who worked with us on a project to link leaf biochemical traits to spectral signatures.  Wil stayed on with us as a research collaborator through the summer and joined us in Barrow, AK to help with NGEE-Arctic field work.  He left TEST to pursue a masters in Public Health at Mount Sinai.

Mary Alldred

Mary recently received her Ph.D. from the Department of Ecology & Evolution at Stony Brook University. Mary's thesis examined the effects of wetland plants on denitrification. She worked with us to do some gas exchange in the local salt marshes and analyzed carbon and nitrogen content in her samples.

Jin Wu

Jin visited as a Ph.D. candidate while working with Dr. Scott Saleska at the University of Arizona. He visited the TEST group to work with Dr. Shawn Serbin on linking leaf optical properties to plant functional properties. Specifically, Shawn and Jin focused on: (1) Exploring the universality of leaf level spectral-leaf trait relationships-- can we use relationships calibrated in temperate and boreal forests to predict traits in an Amazonian tropical rainforests; and (2) develop a remote sensing based approach to predict leaf age--which can not only enable detection of leaf aging (in terms of optical changes), but also expands our ability to track the life trajectory of leaf functional traits. The overall goal of this work was to build up a capacity to monitor key leaf traits based solely on remote sensing techniques, including leaf age demographics, leaf morphological/chemical properties, and leaf physiological properties.

Viridiana Silva-Pérez

Viri is a Ph.D. student working with John R. Evans at the Australian National University. The project “Improving photosynthetic efficiency and capacity in wheat for increased yield potential” is coordinated by CIMMYT as part of the International Wheat Yield Partnership. Viri has been measuring gas exchange, spectral and biochemical properties of wheat leaves in Mexico and Australia. At BNL she analyzed spectral properties of the leaves to predict the main parameters of leaf photosynthesis