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A pixel-based semi-empirical system for predicting vegetation diversity in boreal forest

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We have designed a system that predicts species richness in the mixed wood boreal forest of Canada. The system is based on a simple multivariate linear model that uses four landscape characteristics as independent variables: canopy species type, distance from the nearest ridgeline, time since the last fire and canopy stem density. The model is shown to provide statistically significant estimates of richness when using observed independent variables. We developed models for estimating the four landscape characteristics from geospatial data consisting of remotely sensed imagery and a digital elevation model. We ran the model at the stand scale and the pixel scale and found that stand scale predictions were be more accurate that pixel scale predictions. We produced a map of vegetation species richness for Prince Alberta National Park in central Saskatchewan Canada that is consistent with our expectations. We also estimated the uncertainty in the four landscape characteristic estimates and developed a methodology for propagating this uncertainty through the system to produce estimates of uncertainty in the pixel-based richness predictions. While the uncertainty is significant, the estimation and management of uncertainty in a mapping system of this type represents an innovation.
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Document Type: Research Article

Affiliations: 1: AXYS Environmental Consulting Ltd., 2045 Mills Road West, Sidney BC Canada V8L 3S8, Email: [email protected] 2: Department of Geomatics Engineering, University of Calgary, Calgary, AB, CANADA T2N 1N4, Email: [email protected]

Publication date: 2007-01-01

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