Can airborne laser scanning assist in mapping and monitoring natural forests?

• We use airborne laser scanning (ALS) to assess naturalness of boreal forests in Norway.
• We use metrics describing both vertical and horizontal spatial structures.
• ALS metrics provide a robust classification of near-natural and managed old forests.
• With local calibration, ALS is useful for mapping of near-natural forest.

Old-growth boreal forests display high within-stand structural variation, with large variation in tree sizes and age structure, a heterogeneous horizontal spatial pattern with gaps of different sizes and multi-layered canopies. Old managed forests, on the other hand, are characterized by more homogeneous structural patterns. In many parts of Europe, no true ‘virgin forests’ remains. Still, in many places forests previously influenced by selective logging have been abandoned, and is now developing towards a more natural forest structure. Such near-natural forest areas can be of special importance for forest biodiversity and related ecosystem services, and it is therefore important to map and monitor such forests. Recently, airborne laser scanning (ALS) has been applied to provide ecologically relevant information on forest structure. The advantage of ALS is that it can characterize three-dimensional forest structure in fine detail across broad areas, replacing more labour-intensive field-based measurements.In this study, we use ALS to compare the structural characteristics of 89 near-natural and 280 managed old forest stands in a 17,000 ha boreal forest study site in southeastern Norway. For the study area, both maps of logging history separating old near-natural and old managed forests, and high-resolution ALS data was available.Our aim was to investigate if ALS data could be used to separate vertical and horizontal forest structure in old near natural versus old managed forests. We also wanted to compare different methods for classification, and to describe the potential for use of ALS in classification of forest naturalness for future mapping and monitoring purposes.We found that ALS data had a strong potential in separating stands of old near-natural forests from old managed stands. The most important metrics for classification were those reflecting canopy height, canopy density, variation in canopy height, number of trees per area and gap patterns. This fits well with ecological knowledge of relevant structural differences between forests of different degree of naturalness. Both vertical and horizontal metrics provided useful input to the classification, but the horizontal metrics performed slightly better on accuracy and were easier to interpret. Although the method has a good potential for wider use in boreal forest landscapes, the need for local reference data for calibration as well as the importance of ALS pulse density for precision of classification should be further investigated.Large-scale spatial information on remaining source areas of natural and near-natural forests will be essential in order to manage forests in a way that safeguards biodiversity and ecosystem services. We suggest that classification of forest naturalness based on ALS data can be a helpful tool in future forest mapping and monitoring.