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The Cerro Grande/Los Alamos wildfire devastated approximately 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos. The need to monitor the continuing impact of the fire on the local environment has led to the application of a number of advanced remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multispectral imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before and after the wildfire. Using an existing land cover classification based on a Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, along with clouds and cloud shadows. The details of our evolved classification are compared to the manually produced land-cover classification.

Nondestructive wideband rf beam diagnostics, designed for use in the IUCF Cooler storage ring, are being used in the cyclotron beam lines. Wall gap monitors monitor the beam microscopic time structure, macroscopic time structure, and phase with respect to the main synthesizer; electrostatic pickup electrodes monitor the beam position and intensity. The signal processing and rf-inter-ference-elimination techniques which allow these monitors to operate with beam intensities as low as 10 nA are described. The performance limitations for rf pickups in low energy, low intensity accelerators, and possible methods for using the powerful synchronous detection techniques with nonbunched beams are discussed.