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

Automated summary

Improving estimates of stand-level canopy cover through adjustments of tree crown area equations and modeling from climatic variables and standard forest measurements showed that simple crown width equations adjusted for tree social position had the lowest error. Random crown overlap applied well in drier forest types but struggled to match high line intercept cover levels in productive forest types. Statistical models had greater precision than simpler approaches, but comparable accuracy.