Complexity of Spatial and Categorical Scale in Landcover Characterization: A Statistical and Computational Framework

Land cover characterization is essential for modeling, monitoring, and prediction of the terrestrial ecosystem and climate. Such characterizations are produced based largely on information extracted from satellite remote sensing measurements. However, much of the global and regional land cover consists of heterogeneous mixtures of different land cover types at varying spatial scales, while standard methods for remote sensing land cover characterization operate at a single scale and use a fixed set of categories. This research will develop an integrated statistical and computational framework that will produce land cover characterizations from remote sensing data that allow generalization in spatial resolution and categorical scale simultaneously. The result of this land cover characterization process will no longer be a land cover map, as a map implies a single spatial scale, but rather a land cover database, which can be queried in both traditional manners and ways currently unavailable. In essence, users will be free to select a range of spatial and categorical scales most appropriate for their needs. The key elements involved in the development of this framework include new contributions to the fields of geography and remote sensing, statistics, and computer science. At the foundation is a new class of multi-scale statistical models , called mixlets, from which there will result a new paradigm for representation and visualization of land cover categorization. In turn, these advances will be integrated with new developments in spatial database representations and spatial query systems.

Land cover characterizations, critical for many studies involving terrestrial ecosystems and climate, and the human impacts on the natural environment, are needed at many different spatial scales and by a variety of user communities. This project will develop a comprehensive framework and prototype system for producing such characterizations in a manner that adapts automatically to multiple scales of information using remote sensing and GIS data. Our research has direct impact in the fields of ecology, biology, geography, forestry and environmental sciences dealing with multiscale patterns and processes. More broadly, the development and availability of these tools will contribute significantly to the improved understanding within the scientific community, and ultimately in the community at large, of the complexity of land cover.

This award is jointly supported by the Division of Mathematical Sciences and the Directorate for Social, Behavioral, and Economic Sciences as part of the Mathematical Sciences Priority Area.