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Accurate cost-effective stratification of forest vegetation and timber inventory is the primary goal of a Forest Classification and Inventory System (FOCIS) developed at the University of California, Santa Barbara, and the Jet Propulsion Laboratory, Pasadena. Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine-processing techniques to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. FOCIS was developed in northern California's Klamath National Forest (KNF), where the rugged terrain and diverse ecological conditions provided an excellent area for testing Landsat-based inventory techniques. The FOCIS procedure was further refined in the Eldorado National Forest (ENF), where the portability and flexibility of FOCIS was verified. Using FOCIS as a basis for stratified sampling, the softwood timber volume of the western portion of the Klamath (944 833 acres; 422 340 ha) was estimated at 3.83 x 109 ft3 (1.08 x 108 m3), with a standard error of 4.8 percent based on 89 sample plots. For the Eldorado, the softwood timber volume was estimated at 1.88 x 109 ft3 ( 0.53 x 108 m3) for an area of 342 818 acres (138 738 ha) with a standard error of 4.0 percent, based on 56 sample plots. These results illustrate the power of FOCIS methods to produce timely accurate large-area inventories with comparable accuracies and reduced costs when compared to conventional timber inventory methods.