Geographic Information Systems (GIS) are a key component of environmental research. Increasing amounts of imagery are becoming available, including access to higher resolution data. This includes NASA’s remotely sensed MODIS data, and high resolution digital elevation models (DEMs). Many opportunities exist for geospatial analysis using new data and computing technology, unfortunately, a lack of formal IT training often prevents researchers from taking advantage of this. This presentation looks at a collaboration between the National Computational Infrastructure (NCI) and the Fenner School of Environment and Society (FSES) to process a backlog of MODIS data. In a high performance computing environment, Python is used extensively for various GIS/data applications, including processing remotely sensed data, terrain analysis, and bioregional classification.