Andrew Hudak got his Ph.D. in 1999 from the University of Colorado. From 1999-2001, he began working for the U.S. Forest Service as a postdoctoral Research Ecologist with the Pacific Northwest Research Station. Since 2001, he has worked as a Research Forester with the Rocky Mountain Research Station. He currently studies biophysical relationships between field and remotely sensed data, including estimating aboveground biomass carbon across the western U.S. from airborne lidar, Landsat time series, and other environmental data; predicting fuel/carbon loads from 3D point cloud metrics at multiple scales; and relating fuel consumption to energy flux and fire effects.

Dr. Bentley is an Associate Professor in the Department of Biology at Sonoma State University. Her research, often led by graduate and undergraduate students, employs innovative approaches to advance the field of remote sensing of 3D forest structure. Studies are motivated by emerging needs to manage fire fuels and timber, to improve carbon accounting, and to ultimately aid in wildfire prediction and prevention. Work in her lab also focuses on integrating research with education in novel ways (i.e. virtual reality) to increase scientific literacy and public engagement around the topic of wildfire while promoting inclusion, accessibility and equity.

Francisco Mauro is research associate at University of Valladolid. Francisco obtained his PhD at Technical University of Madrid, worked for 7 years at Oregon State University and now develops his research at University of Valladolid in Soria. Francisco Mauro is interested in applications of small area estimation techniques to forest inventories and in producing remote sensing products that can satisfy needs of information or land managers.

Cam Brown is the Operations Manager of Forsite’s Resource, Management and Technology group based in Salmon Arm, British Columbia, Canada. He holds a BSF from the University of British Columbia and a Masters in Forest Engineering from Oregon State University. Cam is an RPF in both British Columbia and Saskatchewan and has worked in the Canadian and US forest sectors for over 30 years – mostly in consulting roles. Cam’s work is focused on strategic planning, resource economics, forest inventory, forest carbon, and sustainability assessments.

Noah is a PhD student at the Warnell School of Forestry and Natural Resources, University of Georgia, housed in the Plantation Management Research Cooperative (PMRC). He attended Cornell University (B.S.) and Oklahoma State University (M.S.). His research interests are silviculture, forest biology, biometrics, and remote sensing. Though he grew up a dairy farmer in Upstate New York, he has spent the last eleven years in the forestry realm due to not having to milk cows at 4am.

Steven is a research associate at Colorado State University where he completed his master’s degree and has also recently begun his PhD. His work has spanned an array of remote sensing applications from quantifying forest disturbance with lidar and photogrammetry to using remotely sensed evapotranspiration to estimate ground water pumping in croplands. His primary interest is in testing new remote sensing approaches for monitoring of semi-arid woodlands like the pinyon-juniper woodlands of the western US and savannas of South Africa. Being a typical Coloradoan, Steven loves all things outdoors. Anyone interesting in talking about remote sensing while enjoying an afternoon session of climbing, mountain biking, or hiking in New Zealand is welcome to reach out.

Andrew Hudak got his Ph.D. in 1999 from the University of Colorado. From 1999-2001, he began working for the U.S. Forest Service as a postdoctoral Research Ecologist with the Pacific Northwest Research Station. Since 2001, he has worked as a Research Forester with the Rocky Mountain Research Station. He currently studies biophysical relationships between field and remotely sensed data, including estimating aboveground biomass carbon across the western U.S. from airborne lidar, Landsat time series, and other environmental data; predicting fuel/carbon loads from 3D point cloud metrics at multiple scales; and relating fuel consumption to energy flux and fire effects.