Classification Accuracy for Stratification with Remotely Sensed Data
Tools are developed that help specify the classification accuracy required from remotely sensed data. These tools are applied during the planning stage of a sample survey that will use poststratification, prestratification with proportional allocation, or double sampling for stratification. Accuracy standards are developed in terms of an “error matrix,” which is familiar to remote sensing specialists. In addition, guidance is provided to determine when new remotely sensed classifications are needed to maintain acceptable levels of statistical precision with stratification. FOR. SCI. 49(3):402–408.
Keywords: Forest inventory and monitoring; environmental management; forest; forest management; forest resources; forest statistics; forestry; forestry research; forestry science; natural resource management; natural resources
Document Type: Miscellaneous
Affiliations: 1: Project Leader Rocky Mountain Research Station, USDA Forest Service, 2150 Centre Ave, Bldg. A, Suite 350, Fort Collins, CO, 80526-1891, Phone: (970) 295-5973; Fax: (970) 295-5959 firstname.lastname@example.org 2: Project Scientist Rocky Mountain Research Station, USDA Forest Service, 2150 Centre Ave, Bldg. A, Suite 350, Fort Collins, CO, 80526-1891, Phone: (970) 295-5966 email@example.com
Publication date: 2003-06-01
- 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
Also published by SAF:
Journal of Forestry
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