Coniferous Forest Classification and Inventory Using Landsat and Digital Terrain Data

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.