On the use of remotely sensed data to constrain process modeling and ecological forecasting

Abstract:

The ability to seamlessly integrate information on vegetation structure, function, and dynamics across a continuum of scales, from the field to satellite observations, greatly enhances our ability to understand how terrestrial vegetation-atmosphere interactions change over time and in response
to disturbances and global change. For example, ecosystem process models require detailed information on the state (e.g. structure, leaf area index), surface properties (e.g. albedo), and dynamics (e.g. phenology, succession) of ecosystems in order to properly simulate the fluxes of carbon (C), water, and energy from the land to the atmosphere as well as address the vulnerability of ecosystems to environmental, pest and pathogen, and other anthropogenic perturbations. Other activities such as species distribution and environmental niche modeling (SDENM) require not only presence/absence information but also detailed spatial and temporal datasets, including climate and remotely sensed observations, to accurately project species ranges under current and often future climatic scenarios. Despite the many challenges of adequately initializing and parameterizing models, the last several decades have shown a substantial increase in the amount of available data useful for improving ecological predictions. Specifically remote sensing data provides an important data constraint for projecting species and successional changes as well as vegetation dynamics and the fluxes of C, water and energy and the storage of C in ecosystems, principally as a synoptic observational dataset for capturing short- to long-term plant-climate interactions. In this talk we will highlight the current and potential uses of various remotely sensed data sources in constraining process modeling and SDENM activities. We will pay particular attention to the uses of remote sensing as a direct constraint on ecological forecasts or a key observational dataset used to capture plant-climate interactions