The North American Forest Dynamics (NAFD) project is completing nationwide processing of historic Landsat data to provide annual, wall-to-wall analysis of US disturbance history over nearly the last three decades. Because understanding the causes of disturbance (e.g., harvest, fire, stress) is important to quantifying carbon dynamics, work was conducted to attribute causal agents to the nationwide change maps. This case study describes the production of disturbance agent maps at 30-m resolution across 434 Landsat path/rows covering the conterminous US. Geoprocessing was based entirely on open source software implemented at the NASA Advanced Supercomputing facility. Several classes of predictor variables were developed and tested for their contribution to classification models. Predictors included the geometric attributes of disturbance patches, spectral indices, topographic metrics, and vegetation types. New techniques based on shape-restricted splines were developed to classify patterns of spectral signature across Landsat time series, comprising another class of predictor variables. Geospatial Data Abstraction Library (GDAL) and the R statistical software were used extensively in all phases of data preparation, model development, prediction, and post-processing. Parallel processing on the Pleiades supercomputer accommodated CPU-intensive tasks on large data volumes. Here we present our methods and resultant 30-m resolution maps of forest disturbance and causes for the conterminous US, 1985 _ 2011. We also discuss the computing approach and performance, along with some enhancements and additions to open source geospatial packages that have resulted.