Models of tree crown radius were developed for several conifer species of California. Typical forest inventory variables (DBH, height, height-to-crown base, crown class, basal area per hectare, and trees per hectare) were considered as independent variables in model development. Models were fitted using both ordinary and weighted least squares methods. It was found that for the species studied, an ordinary least squares linear regression with DBH as the only independent variable was appropriate. For some species studied, the addition of other independent variables provided minor improvements over the model with only DBH. These models of crown radius could be summed to give an estimation of canopy cover. Using crown mapped data, it was possible to test and calibrate these models to predict non-overlapping canopy cover. Linear and non-linear models were considered for calibration. A non-linear model with an upper asymptote seemed to be the best calibration. These models enable an efficient and unbiased method of estimation of canopy cover as an alternative to photointerpretation estimation of cover.
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