JSM 2005 - Toronto

Abstract #302564

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 301
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #302564
Title: Predictive Mapping of Forest Composition and Structure with Direct Gradient Analysis and Nearest-Neighbor Imputation for Regional Policy Analysis and Ecological Research
Author(s): Janet L. Ohmann*+
Companies: U.S.D.A. Forest Service
Address: Pacific Northwest Research Station, Portland, OR, 97331,
Keywords: gradient analysis ; imputation ; gradient nearest neighbor ; predictive vegetation mapping ; biodiversity assessment ; forest ecology
Abstract:

Natural resource policy analysts and researchers require broad-scale information about forest vegetation that is detailed, spatially complete, and consistent across land ownerships and allocations. The Gradient Nearest Neighbor method for predictive vegetation mapping integrates vegetation data from regional grids of field plots, mapped environmental data, and Landsat imagery. Direct gradient analysis and nearest-neighbor imputation are used to ascribe detailed vegetation attributes to each pixel in a digital map. In the western US, species gradients were strongly associated with environmental variables, especially climate, whereas forest structure was best explained by Landsat data. The maps are being used in biodiversity assessment, forest planning, watershed analysis, assessments of fuel conditions and fire hazard, and to simulate future landscapes under alternative scenarios. The imputed maps provide analytical flexibility by representing vegetation as multiple continuous variables. Range of variability is maintained in the regional map, and covariance structure is maintained at the map unit level.

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Revised March 2005