Estimation of forest canopy structure and understory light using spherical panorama images from smartphone photography

Abstract Accurate estimates of forest canopy structure are central for a wide range of ecological studies. Hemispherical photography (HP) is a popular tool to estimate canopy attributes. However, traditional HP methods require expensive equipment, are sensitive to exposure settings, and produce limited resolution which dramatically affects the accuracy of gap fraction estimates. As an alternative, hemispherical images can be extracted from spherical panoramas produced by many smartphone camera applications. I compared hemispherical photos captured with a digital single lens reflex camera and 180° lens to those extracted from smartphone spherical panoramas (SSP). The SSP HP method leverages built-in features of current generation smartphones to produce sharper images of higher resolution, resulting in more definition of fine canopy structure. Canopy openness and global site factor from SSP HP are highly correlated with traditional methods (R2 > 0.9), while leaf area index estimates are lower, especially in more closed canopies where traditional methods fail to capture fine gaps.