Grand Challenge for Land Cover Dynamics

Classification maps generated by mixture modeling


Project Overview

Our National Science Foundation Grand Challenge Award focuses on employing high performance computing to address applications in remote sensing, specifically applications in land cover dynamics. Understanding land cover dynamics is one of the most important challenges in the study of global change. Many of these changes take place at very fine scales (less than 1 km cell size), and require the analysis of high resolution satellite images for accurate measurement. Databases of land cover dynamics are essential for global carbon models, biogeochemical cycling, hydrological modeling and ecosystem response modeling. Our research involves developing scalable and portable programs for a variety of image and map data processing applications, eventually integrated with new models for parallel I/O of large scale images and maps. The specific application area that we are focusing on, initially, is generating maps of the world's tropical rain forest during the past three decades. This is currently being done using manually labor intensive techniques, and is representative of a wide variety of remote sensing applications requiring nearly global data analysis.

Our research is conducted at three levels:

Science Level
At the science level we are developing new models for fundamental science problems such as atmospheric correction, determining ground reflectivity from imagery, mixture modeling, image feature extraction, image pixel and region classification, and spatial data structures.
Algorithms Level
At the algorithms level we design and develop portable and scalable parallel implementations of our algorithms and data structures, and test them - first in terms of their scientific merit, and then in terms of their parallel scalability and speed - on large image and map data sets.
High Performance Computing Level
At the high performance computing level we are developing new tools and techniques for supporting applications such as image processing and spatial data handling on parallel machines. Our research here focuses on object oriented parallel programming and on new models and techniques for parallel I/O of large image and map data structures.


Each of the areas of study for this project has it's own page. Click on the appropriate heading to learn more about that aspect of our research.


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