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Classification of Forest Resources with Landsat Data

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Abstract:

Landsat data were classified with guided clustering techniques to inventory and map conifer species and general land cover on the McCloud Ranger District of the Shasta-Trinity National Forest in northeastern California. Four conifer timber types were differentiated. Two stocking and two size classes were also determined for each conifer type. Eighty-two percent of the ranger district is forested. Of the total forestland, 53 percent was identified as mixed conifer, 31 percent as ponderosa pine, 12 percent as mixed fir, and 4 percent as lodgepole pine. Accuracy of the classification was 83 percent when timber types, size, and stocking were being discriminated, and 88 percent when timber types were being identified without detailed breakdown to size and stocking. USDA Forest Service ground plot data were used to calculate timber volume and other stand values within Landsat categories. From these results, it was determined that Landsat analysis could provide timber type classifications for forest management planning.

Document Type: Journal Article

Affiliations: Forester, McCloud Ranger District, Shasta-Trinity National Forest

Publication date: May 1, 1983

More about this publication?
  • The Journal of Forestry is the most widely circulated scholarly forestry journal in the world. In print since 1902, the Journal has received several national awards for excellence. The mission of the Journal of Forestry is to advance the profession of forestry by keeping forest management professionals informed about significant developments and ideas in the many facets of forestry: economics, education and communication, entomology and pathology, fire, forest ecology, geospatial technologies, history, international forestry, measurements, policy, recreation, silviculture, social sciences, soils and hydrology, urban and community forestry, utilization and engineering, and wildlife management. The Journal is published bimonthly: January, March, May, July, September, and November.
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