Marc Linderman

Marc Linderman conducts research on monitoring and modeling the dynamics of agro-ecosystems.  His research focuses on the interactions between dynamic human and natural systems.  He uses remote sensing and spatio-temporally explicit models to study the scaling of these processes to spatial and temporal scales typically prohibitive for studies based on field observations only. His work extends from field-level soil carbon monitoring to global analyses of vegetation dynamics. His lab has explored the use of aerial hyperspectral imagery to improve soil carbon estimates, the detection of floodplain invasive species, and quantifying water quality. Linderman, also examines the interactions between human activities and natural systems at landscape to regional-level scales with particular interest in studying the impacts of human activities on the spatio-temporal dynamics of natural systems through the integration of remotely sensed data and spatially explicit models. His lab is currently exploring deep learning approaches to satellite image classification, improved modeling of agro-ecosystem carbon dynamics, and machine learning for feature detection.