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In the managed beech forests of Central Italy (Molise), the diversity of saproxylic species is potentially under threat by intensive management. To evaluate the impact of forestry on the biodiversity of these ecosystems, we analyzed the relationship between abundance of saproxylic beetles and (i) forest stand exposure and elevation, (ii) deadwood availability (coarse woody debris – CWD – and stumps), (iii) abundance of microhabitats. Four sampling sectors with different altitudes and exposure were identified in a 400-ha study area in the Appenine mountains. Fifteen circular plots (13 m radius) were established in each sector where deadwood and microhabitats were surveyed and saproxylic beetles sampled. We fitted joint species distribution models to quantify the relationship between forest attributes and saproxylic species’ abundance, including the interactions with their family and trophic category. Overall, 2334 specimens belonging to 64 species of saproxylic beetles were collected. Both abundance and species richness were higher in the sectors with high elevation (respectively, 55% and 44%) and South exposure (respectively, 28% and 44%). Average deadwood volumes were low (stumps: 7.6 m³ ha-1; CWD: 0.3 m³ ha-1; snags: 0.4 m³ ha-1), and insect galleries were the most abundant microhabitat (380 records over a total of 434). The most important variables affecting abundance were stump characteristics (model deviance = 81.2), elevation (deviance = 64.7), and CWD characteristics (deviance = 58.0). Our results show that topographical variables and forest structure jointly affect the abundance patterns of saproxylic beetle communities in managed beech forests. These ecological interactions imply that management has different impacts on the saproxylic communities in different topographic conditions. To acknowledge this complexity we advocate for a landscape-level forest management supporting the local beetle diversity maintaining a mosaic of semi-natural forest characteristics in different topographic contexts. The ecological value of the forest landscape will be further enhanced by the application of closer-to-nature management interventions based on deadwood retention, microhabitat creation and tree retention, in line with the guidelines of the new EU Forest Strategy for 2030. [ABSTRACT FROM AUTHOR]

The study of afforestation is crucial to monitor land transformations and represents a central topic in sustainable development procedures, in terms of climate change, ecosystem services monitoring, and planning policies activities. Although surveying afforestation is important, the assessment of the growing forests is difficult, since land cover has different durations depending on the species. In this context, remote sensing can be a valid instrument to evaluate the afforestation process. Nevertheless, while a vast literature on forest disturbance exists, only a few studies focus on afforestation and almost none directly exploits remote sensing data. This study aims to automatically classify non-forest, afforestation, and forest areas using remote sensing data. To this purpose, we constructed a reference dataset of 61 polygons that suffered a change from non-forest to forest in the period 1988-2020. The reference data were constructed with the Land Use Inventory of Italy and through photointerpretation of orthophotos (1988-2012, spatial resolution 50 × 50 cm) and very high-resolution images (2012-2020, spatial resolution 30 × 30 cm). Using Landsat Best Available Pixel composites time-series (1984-2020) we calculated 52 temporal predictors: four temporal metrics (median, standard deviation, Pearson's correlation coefficient R, and slope) calculated for 13 different bands (the six Landsat spectral bands, three Spectral Vegetation Indices, and four Tasseled Cap Indices). To verify the possibility of distinguishing afforestation from non-forest and forest, given the differences between them can be minimal, we tested four different models aiming at classifying the following categories: (i) non-forest/afforestation, (ii) afforestation/forest, (iii) non-forest/forest and (iv) non-forest/afforestation/forest. Temporal predictors were used with random forest which was calibrated using random search, validated using k-fold Cross-Validation Overall Accuracy (OAcv), and further using out-ofbag independent data (OAoob). Results illustrate that the distinction of afforestation/forest reaches the largest OAcv (87%), followed by non-forest/forest (83%), non-forest/afforestation (75%) and non-forest/afforestation/forest (72%). The different OA values confirm that the difference in photosynthetic activity between forest and afforestation can be analysed through remote sensing to distinguish them. Although remote sensing data are currently not exploited to monitor afforestation areas our results suggest it may be a valid support for country-level monitoring and reporting. [ABSTRACT FROM AUTHOR]

Forests produce a wide range of ecosystem services, including the traditional wood production. Sustainable forest management approaches are used to locally calibrate wood harvesting on the basis of local conditions and should not adversely affect other ecosystem services. To assess forest harvesting sustainability and impacts it is essential to know their spatial distribution. At the present date accurate statistics on wood harvesting are not available in Italy. In this context, remote sensing and automatic mapping algorithms constitute an important tool for providing spatially explicit information to quantify wood harvesting and thus supporting more sustainable forest management approaches. In this work we tested an automatic mapping algorithm (3I3D) using multitemporal Sentinel-2 imagery to produce a map of forest disturbance produced by different types of wood harvesting in the province of Arezzo for the year 2018. Thanks to a photo-interpretation work of high resolution PlanetScope imagery and field data collected by the Carabinieri Forestali, we were able to calculate commission (2%) and omission (18%) errors of the automatic 3I3D map. The results of this work motivate the introduction of remote sensing tools as a support for monitoring and quantification of forest wood harvesting. [ABSTRACT FROM AUTHOR]

Forest planning, forest management, and forest policy require updated, reliable, and harmonized spatial datasets. In Italy a national geographic Forest Information System (FIS) designed to store and facilitate the access and analysis of spatial datasets is still missing. Among the different information layers which are useful to start populating a FIS, two are essential for their multiple use in the assessment of forest resources: (i) forest mapping, and (ii) data from Airborne Laser Scanning (ALS). Both layers are not available wall-to-wall for Italy, though different local sources of information potentially useful for their implementation already exist. The objectives of this work were to: (i) review forest maps and ALS data availability in Italy; (ii) develop for the first time a high resolution forest mask of Italy which was validated against the official statistics of the Italian National Forest Inventory; (iii) develop the first mosaic of all the main ALS data available in Italy producing a consistent Canopy Height Model (CHM). An on-line geographic FIS with free access to both layers from (ii) and (iii) was developed for demonstration purposes. The total area of forest and other wooded lands computed from the forest mask was 102,608.82 km2 (34% of the Italian territory), i.e., 1.9% less than the NFI benchmark estimate. This map is currently the best wall-to-wall forest mask available for Italy. We showed that only the 63% of the Italian territory (the 60% of the forest area) is covered by ALS data. These results highlight the urgent need for a national strategy to complete the availability of forest data in Italy. [ABSTRACT FROM AUTHOR]

Is forest harvesting increasing in Europe? There is scientific debate about methodological approach and data regarding clearcut increment in Europe but, besides the discussion, there is a general agreement about the need to collect reliable scientifically robust remote sensing data for sound forest policymaking. [ABSTRACT FROM AUTHOR]