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Validating, Scaling and
Parameterizing a Forest Regrowth Model for the
Amazon Region Using Aircraft and Spaceborne Sensors
and GIS
Principal Investigators: William Salas, Diogenes
Alves, Mark Ducey, Daniel Zari, and Jiaguo Qi
This project is in response to the NASA-Carbon Cycle
Science and LBA-Ecology Program request for remote
sensing research and the development of methods and
datasets for exploring the response of ecosystems to
disturbance at the regional scale. We propose a
four-step, incremental approach directed toward the
spatially explicit modeling and mapping of forest
regrowth potential for the Amazon region. Each of
the four steps will make a significant contribution
to current understanding of the response of
ecosystems to disturbance at the regional scale.
Developing an ability to predict forest regrowth
potential has considerable implications for our
understanding of carbon dynamics in a future
characterized by increased conversion of old-growth
Amazonian forests and the subsequent abandonment of
many areas originally cleared for agricultural
activities. A central focus of our approach is the
development of remote sensing approaches for
quantifying vegetation recovery and changes in
biomass following disturbance, determination of the
optimal scale for these approaches, and testing of
disturbance-specific parameters that may influence
rates of forest regrowth in Amazonia.
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The Center for Global Change & Earth Observations, Michigan
State University
218 Manly Miles Building, 1405 S. Harrison Road, East
Lansing, Michigan 48823, Phone: (517) 432-7774 |
