European beech (Fagus sylvatica L.) forests have a long history of coppicing, but the majority of formerly managed coppices are currently under conversion to high forest. The long time required to achieve conversion requires a long-term perspective to fully understand the implication of the applied conversion practices. In this study, we showed results from a long-term (1992–2014) casestudy comparing two management options (natural evolution and periodic thinning) in a beech coppice in conversion to high forest. Leaf area index, litter production, radiation transmittance and growth efficiency taken as relevant stand descriptors, were estimated using both direct and indirect optical methods. Overall, results indicated that beech coppice showed positive and prompt responses to active conversion practices based on periodic medium-heavy thinning. A growth efficiency index showed that tree growth increased as the cutting intensity increased. Results from the case study supported the effectiveness of active conversion management from an economic (timber harvesting) and ecological (higher growth efficiency) point of view. © 2016, Finnish Society of Forest Science. All rights reserved.

Proximal sensing methods using digital photography have gained wide acceptance for describing and quantifying canopy properties. Digital hemispherical photography (DHP) is the most widely used photographic technique for canopy description. However, the main drawbacks of DHP have been the tedious and time-consuming image processing required and the sensitivity of the results to the image analysis methods. Recently, an alternative approach using vertical photography has been proposed, namely, digital cover photography (DCP). The method captures detailed vertical canopy gaps and performs canopy analysis by dividing gap fractions into large between-crown gaps and small withincrown gaps. Although DCP is a rapid, simple and readily available method, the processing steps involved in gap fraction analysis have a large subjective component by default. In this contribution, we propose an alternative simple, more objective and easily implemented procedure to perform gap fraction analysis of DCP images. We compared the performance of the two image analysis methods in dense deciduous forests. Leaf area index (LAI) estimates from the two image analysis methods were compared with reference LAI measurements obtained through the use of litter traps to measure leaf fall. Both methods provided accurate estimates of the total gap fraction and, thus, accurate estimates of the LAI. The new proposed procedure is recommended for dense canopies because the subjective classification of large gaps is most error-prone in stands with dense canopy cover. © SISEF.