coveR: An R package for processing Digital Cover Photography images to retrieve forest canopy attributes

Digital Cover Photography (DCP) is an increasingly popular tool for estimating canopy cover and leaf area index (LAI). However, existing solutions to process canopy images are predominantly tailored for fisheye photography, whereas open-access tools for DCP are lacking. We developed an R package ( coveR ) to support the whole processing of DCP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline, while also allowing simple implementation for batch-processing bunches of images. A case study is presented to demonstrate the reliability of canopy attributes derived from coveR in pure beech ( Fagus sylvatica L.) stands with variable canopy density and structure. Estimates of gap fraction and effective LAI from DCP were validated against reference measurements obtained from terrestrial laser scanning. By providing a simple, transparent, and flexible image processing procedure, coveR supported the use of DCP for routine measurements and monitoring of forest canopy attributes. This, combined with the implementability of DCP in many devices, including smartphones, micro-cameras, and remote trail cameras, can greatly expand the accessibility of the method also by non-experts.