Background: Identifying specific multitaxon biodiversity indicators in Mediterranean montane forests is pivotal to maximizing the efficiency of conservation policies.Methods: We examined the capacity of LiDAR data for monitoring the biodiversity of forest beetle and bird communities, analyzing the relationships between remote sensing-derived predictors, tree-related microhabitats (TreMs), and field-measured biodiversity. The study was conducted by measuring 47 sampling plots in pure and mixed stands of Fagus sylvatica and Abies alba in central Italy. Biodiversity analysis was performed by calculating both species richness of the species and types of saproxylic and epixylic TreMs and the relative abundance, using the Shannon index.Results: A total of 240 LiDAR metrics were calculated directly from the point cloud or at the pixel level using a rasterized canopy height model, together with RGB spectral statistics. The random forests model was used to predict species richness and the Shannon diversity index using the field plot measures as dependent variables and the LiDAR metrics as predictors for each taxon. The final models were used to produce the dependent variable wall-to-wall maps. RMSE% of the final models ranged between 8.2 (birds' Shannon index) and 49.7 (epixylic TreMs types' Shannon index). The Shannon index performed better than species richness, except for the epixylic TreMs, with a mean difference of -6.3%. On the other hand, the R2 was higher for the Richness (mean R2=0.25, against 0.2 for the Shannon index).Conclusions: Results show the LiDAR data importance in monitoring habitat features and their suitability for animal communities. Furthermore, certain specific forest area characteristics may represent the entire species community. These indications make it possible to design and optimize conservation strategies that allow management practices aimed at the particular ecological needs of certain taxons. The proposed method supports quantifying and monitoring the specific measures needed to implement better animal community conservation.
The forestry sector in Italy and throughout Europe is going through a critical period due to ongoing natural and anthropological processes, such as climate change and the abandonment of rural areas. These processes lead to a constant fragmentation of properties in small forest parcels, with direct impacts on management capacity. In this framework, new sustainable forest management methods are being tested and are shown to be good practices to oppose the decline of forest ecosystems. Their innovative aspects concern the introduction of a form of shared and circular economy, where management is built on the process, rather than on the product. Their technical activities are based on precision forestry systems and digitalization. The new approach takes into consideration the fact that the woods are an asset available to the whole community, in terms of benefits and protection. Forest Sharing® is an example of the application of shared forest management systems, due to which the owner user benefits from several services and opportunities, such as the advanced monitoring platform and the access to investment funds. After eighteen months of activity, the first results of the application of the new management systems can already be seen. Many aspects need further development, such as case studies concerning the enhancement due to forest certification and new recreational activities. Shared forest management systems have the potential to increase the level of knowledge and awareness of citizens about environmental and territorial issues.
Compaction and rutting on forest soils are consequences of harvesting operations. The traditional methods used to investigate these consequences are time consuming and unable to represent the entire longitudinal profile for a forest trail. New methods based on photogrammetry have been developed. The overall objective was to compare photogrammetry and traditional methods (e.g. cone penetrometer, manual rut depth measurements, bulk density and porosity) used for the evaluation of soil compaction and rutting (i.e. depth and rut volume) after multiple passes of a loaded forwarder using two different tyre pressure levels. The comparison of photogrammetric versus manually measured profiles resulted in R2 0.93. Both tyre inflation pressure and number of passes had effect on soil disturbance. The rut volumes on 100 m long trails after 60 passes were 8.48 and 5.74 m3 for tire pressures of 300 and 150 kPa, respectively. Increased rut volume correlated positively with increased soil compaction and decreased soil porosity. Structure-from-motion photogrammetry is an accurate method for informing the creation of high-resolution digital evolution models and for the morphological description of forest soil disturbance after forest logging. However, a problem with photogrammetry is object reflection (grass, logging residues and water) that in some cases influence the accuracy of the method.
Abstract. Strong winds may uproot and break trees and represent a major natural disturbance for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the effects of climate change. Despite the importance of such natural disturbances, there are currently no spatially explicit databases of wind-related impact at a pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND is comprised of more than 80 000 spatially delineated areas in Europe that were disturbed by wind in the period 2000–2018 and describes them in a harmonized and consistent geographical vector format. The database includes all major windstorms that occurred over the observational period (e.g. Gudrun, Kyrill, Klaus, Xynthia and Vaia) and represents approximately 30 % of the reported damaging wind events in Europe. Correlation analyses between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p value < 0.05). When recorded forest areas are rescaled based on their damage degree, correlation increases to 0.54. Wind-damaged growing stock volumes reported in national inventories (FORESTORM dataset) are generally higher than analogous metrics provided by FORWIND in combination with satellite-based biomass and country-scale statistics of growing stock volume. The potential of FORWIND is explored for a range of challenging topics and scientific fields, including scaling relations of wind damage, forest vulnerability modelling, remote sensing monitoring of forest disturbance, representation of uprooting and breakage of trees in large-scale land surface models, and hydrogeological risks following wind damage. Overall, FORWIND represents an essential and open-access spatial source that can be used to improve the understanding, detection and prediction of wind disturbances and the consequent impacts on forest ecosystems and the land–atmosphere system. Data sharing is encouraged in order to continuously update and improve FORWIND. The dataset is available at https://doi.org/10.6084/m9.figshare.9555008 (Forzieri et al., 2019).
