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Reliable measures of canopy cover are important in the management of public and private forests. However, direct sampling of canopy cover is both labor- and time-intensive. More efficient methods for estimating percent canopy cover could be empirically derived relationships between
more readily measured stand attributes and canopy cover or, alternatively, the use of aerial photos. In this study, we compared field-based measures of percent canopy cover with estimates from aerial photography, with equations of individual tree crown width and crown overlap used in the US
Forest Service Forest Vegetation Simulator (FVS) equations and with models we developed from standard stand-level forest mensuration estimates. Standard inventory estimates of cover using 1:40,000 scale aerial photos were poorly correlated with field-measured cover, especially in wet hardwood
(r = 0.60) and dry hardwood (r = 0.51) stands. FVS equations underestimated cover by 17% on average at high cover levels (>70%) in wet conifer and wet hardwood stands. We also developed predictive models of canopy cover for three forest groups sampled on 884 plots by the Forest
Inventory and Analysis program in western Oregon: wet conifer, wet hardwood, and dry hardwood. Predictions by the models were within 15% of measured cover for >82% of the observations. Compared with previous studies modeling canopy cover, our best predictive models included species-specific
stocking equations, whereas species-invariant basal area was not an important predictor for most forest types. Accuracies of these new predictive models may be adequate for some purposes, reducing the need for direct measures of canopy cover in the field. FOR. SCI. 58(2):154‐167.
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.