Predicting canopy cover of diverse forest types from individual tree measurements

Quantifying tree canopy cover is fundamental to applications in forestry and ecology, but estimates vary substantially depending on type of field measurement, imagery, or active sensing used. Our objective was improve stand-level from standard inventory measurements, using representative data collected across diverse forest plant association groups Oregon, USA. Canopy measured with line intercept sampling 1706 plots compared calculations individually tallied trees. We investigated adjustments crown area equations, overlap factors, modeling climatic variables measurements estimate cover. Estimates based simple width equations adjusted for social position caps maximum cover, had the lowest error (RMSE = 14% cover) approaches all vegetation types. Random applied unadjusted only performed well drier types unable match high levels (>90%) often found productive Although statistical models somewhat greater precision than simpler crown-width summation 12%), accuracy comparable. The flexibility could make them more useful beyond our study area.