Consequences of global change for land cover, carbon cycles, and biodiversity loss involve complex interactions at fine scales, such as resource availability in forest understories, to regional land-cover, climate, and CO2. Global change research requires models developed through careful study of local phenomena that can be extended to landscape, regional, and global scales. Unfortunately, environmental scientists have been limited in their ability to determine how factors that operate at different scales impact landscapes.
The primary long-term goal of the research is to enhance the ability of biology and geoscience research programs to acquire, analyze, and distribute high-resolution GIS databases of important environmental attributes. In support of this goal the computer science team will develop new techniques to extract forest attributes in the form of GIS databases from remotely sensed data. The computer science team will build an aerial remote sensing platform and a suite of analysis tools for creating GIS databases of environmental attributes with sub-meter geo-registration and elevation accuracies.
The image acquisition, analysis and GIS tools developed by UMass and MHC provide the critical broad-scale, yet high-resolution, data needed to parameterize and validate models used to study global change. The products of these analyses will be integrated within a modeling framework at Duke University that includes extensive field data, application of new statistical computation methods, and development of a stand simulator. The combined effort will be used to determine how diversity is maintained in forest stands based on a comprehensive accounting of environmental impacts and uncertainties.
