![]() At the site level, mapped predictions maintained the covariance structure among multiple response variables. At the regional level, mapped predictions represented the range of variability in the sample data, and predicted area by vegetation type closely matched sample-based estimates. In the Oregon coastal province, species gradients were most strongly associated with regional climate and geographic location, whereas variation in forest structure was best explained by Landsat TM variables. The gradient nearest neighbor method integrates vegeta - tion measurements from regional grids of field plots, mapped environmental data, and Landsat Thematic Mapper (TM) imagery. We present a method for predictive vegetation mapping that applies direct gradient analysis and nearest-neighbor imputation to ascribe detailed ground at - tributes of vegetation to each pixel in a digital landscape map. Spatially explicit information on the species composition and structure of forest vegetation is needed at broad spatial scales for natural resource policy analysis and ecological research. Results from this study could be applied to other large boreal regions with modifications, and the techniques developed here could be further tested. Results from this study indicate that an automated approach, which combines a priori information specific to a region, greatly enhances the value of the final result. Traditionally, satellite-based approaches to vegetation classification over large areas rely upon one main input dataset, and the use of trained algorithms for classification. This spatial vegetation model is part of a regionalization system for IIASA’s Geographic Information System (GIS)-based landscape ecosystem model for full terrestrial greenhouse gas accounting. Rates of CO2-absorption (Net Primary Production ― NPP). The resulting spatially-based description consists of vegetation units, which are homogenous in vegetation composition and stand conditions and therefore in above-ground carbon content (living biomass) and ![]() Hierarchical decision rules were developed specifically for this boreal region to indicate vegetation distribution and relied mainly on satellite derived datasets such as land cover, digital elevation models, Vegetation Continuous Fields, and a disturbance dataset, as well as a soil database. This study dealt with the development of spatial methods for generating a land cover database over the Siberia-II study region in Central Siberia, for the purpose of full terrestrial carbon accounting.
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