Adding disturbance information from landsat and lidar to an advanced ecosystem model to investigate spatial and temporal disturbance and recovery dynamics on regional forest structure and carbon fluxes

Forest disturbance and recovery are critical mechanisms for transferring carbon between the land surface and the atmosphere, yet the role of forest disturbance within the terrestrial carbon cycle still remains uncertain and only recently have these events been accounted for within regional-scale and global carbon models. Adding ecological disturbance into biogeochemical models is noted as critical to estimating current and future carbon stocks and fluxes. The long history of optical remote sensing combined with emerging active remote sensing technologies, such as lidar, provide powerful tools to study forest disturbance and recovery. This research is utilizing Landsat time-series data run through the Vegetation Change Tracker (VCT), and the Ecosystem Demography model (ED) in conjunction with lidar, and regional forest inventories to study disturbance impacts on vegetation structure and carbon over spatial and temporal gradients. The research is conducted in three regions of the United States; the Northeast, Southeast and Western US, representing different dominant mechanisms of forest disturbance ranging from land conversion, clear-cuts, wind damage, fire and pest outbreaks.