Adaptive Cluster Sampling for Forest Inventories
Abstract:Adaptive cluster sampling is shown to be a viable alternative for sampling forests when there are rare characteristics of the forest trees which are of interest and occur on clustered trees. The ideas of recent work in Thompson (1990) have been extended to the case in which the initial sample is selected with unequal probabilities. An example is given in which the initial sample of trees is selected with probability proportional to tree basal area. If a characteristic of interest is observed on a sample tree, additional trees within a fixed distance of the sample tree are also included in the sample. For. Sci. 39(4):655-669.
Document Type: Journal Article
Affiliations: Mathematical Statistician, Institute for Quantitative Studies, Southern Forest Experiment Station, USDA Forest Service, Room T-10210, 701 Loyola Avenue, New Orleans, LA 70113
Publication date: 1993-11-01
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- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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