Spatial Stochastic Modeling of Growth and Forest Resource Evaluation
Possibilities of spatial interpolations by the unbiased and minimum variance technique of kriging for mapping forest inventories have been examined. Observations recorded on the height of Dharek (Melia azedarach Linn.) planted in a sodic soil at 3 x 6 m spacing were used as test data. Coefficient of variation (CV) in the tree height was 28%, 30%, and 27% after 1, 2, and 3 years of growth as compared to 6% 1 month after the sapling establishment. The heterogeneity was modeled as intrinsic random fields of geostatistics after removing the deterministic component nonparametrically. Tree height varied both as a function of direction in the field (anisotropy) as well as lag distance. Depending on the age, 70 to 77% of the heterogeneity was structured in the NW-SE direction. Since modeling could be validated very precisely, interpolation (kriging) of the values at locations between the sampled sites for the inventory mapping has been recommended. Error variance of the kriging estimator (jackknifing) was 9% of the general mean. Suitability of this technique for forest resource evaluation from the samples taken on the ground or on the remotely sensed devices has been indicated. For Sci. 35(3):663-676.
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Document Type: Journal Article
Affiliations: Scientist, Indian Agricultural Statistical Research Institute, New Delhi-110012, India
Publication date: 1989-09-01
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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.
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