In the EU 2020 biodiversity strategy, maintaining and enhancing forest biodiversity is essential. Forest managers and technicians should include biodiversity monitoring as support for sustainible forest management and conservation issues, through the adoption of forest biodiversity indices. The present study investigates the potential of a new type of Structure from Motion (SfM) photogrammetry derived variables for modelling forest structure indicies, which do not require the availability of a digital terrain model (DTM) such as those obtainable from Airborne Laser Scanning (ALS) surveys. The DTM-independent variables were calculated using raw 3D UAV photogrammetric data for modeling eight forest structure indices which are commonly used for forest biodiversity monitoring, namely: basal area (G); quadratic mean diameter (DBH<inf>mean</inf>); the standard deviation of Diameter at Breast Height (DBH<inf>σ</inf>); DBH Gini coefficient (Gini); the standard deviation of tree heights (H<inf>σ</inf>); dominant tree height (H<inf>dom</inf>); Lorey's height (H<inf>l</inf>); and growing stock volume (V). The study included two mixed temperate forests areas with a different type of management, with one area, left unmanaged for the past 50 years while the other being actively managed. A total of 30 field sample plots were measured in the unmanaged forest, and 50 field plots were measured in the actively managed forest. The accuracy of UAV DTM-independent predictions was compared with a benchmark approach based on traditional explanatory variables calculated from ALS data. Finally, DTM-independent variables were used to produce wall-to-wall maps of the forest structure indices in the two test areas and to estimate the mean value and its uncertainty according to a model-assisted regression estimators. DTM-independent variables led to similar predictive accuracy in terms of root mean square error compared to ALS in both study areas for the eight structure indices (DTM-independent average RMSE<inf>%</inf> = 20.5 and ALS average RMSE<inf>%</inf> = 19.8). Moreover, we found that the model-assisted estimation, with both DTM-independet and ALS, obtained lower standar errors (SE) compared to the one obtained by model-based estimation using only field plots. Relative efficiency coefficient (RE) revealed that ALS-based estimates were, on average, more efficient (average RE ALS = 3.7) than DTM-independent, (average RE DTM-independent = 3.3). However, the RE for the DTM-independent models was consistently larger than the one from the ALS models for the DBH-related variables (i.e. G, DBH<inf>mean</inf>, and DBH<inf>σ</inf>) and for V. This highlights the potential of DTM-independent variables, which not only can be used virtually on any forests (i.e., no need of a DTM), but also can produce as precise estimates as those from ALS data for key forest structural variables and substantially improve the efficiency of forest inventories. © 2020 Elsevier Ltd

Epiphytic lichens represent one of the most suitable indicators of forest continuity and management, especially in the context of ancient and old-growth forests. Nevertheless, they have not yet been included among Sustainable Forest Management (SFM) indicators to which Pan-European forest policy and governance refer. In addition, currently adopted SFM indicators are mainly designed for high forests rather than coppice forests, despite the fact that today this management system covers more than 10% of the total European forests. In this study we investigated these two issues by examining epiphytic lichen diversity in three coppice forest stands, located in the two Italian regions of Tuscany and Sardinia. In particular, we addressed: i) the role of lichen diversity as SFM indicator and ii) its relationship with consolidated and new SFM indicators dealing with structural, health, biodiversity, protective and socioeconomic functions. Multivariate Factor Analysis and Generalised Linear Models were adopted for data analysis. We found that lichen diversity and the frequency of single sensitive species were mainly related to the biodiversity of plants and fungi (Criterion 4), the health and vitality of the forests (Criterion 2) and their protective functions (Criterion 5). Furthermore, our results show that the lichen species highlighted by the models may represent suitable indicators in long-term studies, especially in relation to complex and interconnected aspects of sustainable forest management. Although our findings represent a first contribute to this issue, more in-depth researches will be needed to clarify further aspects of the complex interactions among SFM indicators in the context of coppice forests. © 2020 Elsevier Ltd