Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm hit the Northeast regions of Italy with wind gusts exceeding 200 km h⁻¹. The forests in these regions have been seriously damaged. Over 490 Municipalities in six administrative Regions in Northern Italy registered forest damages caused by VAIA, that destroyed or intensely damaged forest stands spread over an area of 67,000 km². The present work tested the use of two continuous change detection algorithms, i.e., the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) and the continuous change detection and classification (CCDC) to map and estimate forest windstorm damage area using a normalized burned ration (NBR) time series calculated on three years Sentinel-2 (S2) images collection (i.e., January 2017–October 2019). We analyzed the accuracy of the maps and the damaged forest area using a probability-based stratified estimation within 12 months after the storm with an independent validation dataset. The results showed that close to the storm (i.e., 1 to 6 months November 2018–March 2019) it is not possible to obtain accurate results independently of the algorithm used, while accurate results were observed between 7 and 12 months from the storm (i.e., May 2019–October 2019) in terms of Standard Error (SE), percentage SE (SE%), overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and g_mean for both BEAST and CCDC (SE < 3725.3 ha, SE% < 9.69, OA > 89.7, PA and UA > 0.87, g_mean > 0.83).

Forests provide a range of ecosystem services essential for human wellbeing. In a changing climate, forest management is expected to play a fundamental role by preserving the functioning of forest ecosystems and enhancing the adaptive processes. Understanding and quantifying the future forest coverage in view of climate changes is therefore crucial in order to develop appropriate forest management strategies. However, the potential impacts of climate change on forest ecosystems remain largely unknown due to the uncertainties lying behind the future prediction of models. To fill this knowledge gap, here we aim to provide an uncertainty assessment of the potential impact of climate change on the forest coverage in Italy using species distribution modelling technique. The spatial distribution of 19 forest tree species in the country was extracted from the last national forest inventory and modelled using nine Species Distribution Models algorithms, six different Global Circulation Models (GCMs), and one Regional Climate Models (RCMs) for 2050s under an intermediate forcing scenario (RCP 4.5). The single species predictions were then compared and used to build a future forest cover map for the country. Overall, no sensible variation in the spatial distribution of the total forested area was predicted with compensatory effects in forest coverage of different tree species, whose magnitude and patters appear largely modulated by the driving climate models. The analyses reported an unchanged amount of total land suitability to forest growth in mountain areas while smaller values were predicted for valleys and floodplains than high-elevation areas. Pure woods were predicted as the most influenced when compared with mixed stands which are characterized by a greater species richness and, therefore, a supposed higher level of biodiversity and resilience to climate change threatens. Pure softwood stands along the Apennines chain in central Italy (e.g., Pinus, Abies) were more sensitive than hardwoods (e.g., Fagus, Quercus) and generally characterized by pure and even-aged planted forests, much further away from their natural structure where admixture with other tree species is more likely. In this context a sustainable forest management strategy may reduce the potential impact of climate change on forest ecosystems. Silvicultural practices should be aimed at increasing the species richness and favoring hardwoods currently growing as dominating species under conifers canopy, stimulating the natural regeneration, gene flow, and supporting (spatial) migration processes.

The future dynamics of forest species and ecosystems depend on the effects of climate change and are related to forest management strategies. The expected impacts of climate change are linked to forest growth and productivity. An increase in the length of the growing season and greater productivity are likely as well as shifts in average climatic values and more variable frequencies, intensities, durations and timings of extreme events. The main aim of this work is to assess and describe the climatic requirements for Italian forest tree species. We used 7.272 field observations from Italian National Forest Inventory plots and average annual temperatures and precipitation as interpolated from raster maps with 1 km spatial resolution. On this basis we evaluated the current observed distributions of the 19 most important tree species in Italy with respect to potential climatic limits based on expert knowledge and the available literature. We found that only 46% of the observations fall within the potential joint temperature and precipitation limits as defined by expert knowledge. For precipitation alone, 70% of observations were within the potential limits, and for temperature alone, 80% of observations were within the potential limits. Similarity between current observed and potential limits differ from species-to-species with broadleaves in general more frequently distributed within the potential climatic limits than conifers. We found that ecological requirements and potential information should be revised for some species, particularly for the Pinus genus and more frequently for precipitation. The results of the study are particularly relevant given the threat of climate change effects for Italian forests which are broadly acknowledged to be a biodiversity hotspot. Further investigations should be aimed at modelling the effects of climate changes on Italian forests as a basis for development of mitigation and adaptation forest management strategies.

Large-scale forest monitoring benefits greatly from change detection analysis based on remote sensing data because it enables characterizing forest dynamics of disturbance and recovery by detecting both gradual and abrupt changes on Earth’s surface. In this study, two of the main disturbances occurring in Mediterranean forests, harvesting operations and forest fires, were analyzed through the analysis of Landsat Times Series images in a case study in Central Italy (Tuscany region). Disturbances were characterized based on their distinct temporal behaviors before and after the event: a period of 20 years (1999–2018) was used to extract and analyze at pixel level spectral trajectories for each disturbance and produce descriptive temporal trends of the phenomena. Recovery metrics were used to characterize both short- (5 years) and long-term aspects of recovery for harvested and burned areas. Spectral, recovery, and trend analysis metrics were then used with the Random Forest classifier to differentiate between the two disturbance classes and to investigate their potential as predictors. Among spectral bands, the Landsat SWIR 1 band proved the best to detect areas interested by harvesting, while forest fires were better detected by the SWIR 2 band; among spectral indices, the NBR scored as the best for both classes. On average, harvested areas recovered faster in both short- and long-term aspects and showed less variability in the magnitude of the disturbance event and recovery rate over time. This tendency is confirmed by the results of the classifier, which obtained an overall accuracy of 98.6%, and identified the mean of the post-disturbance values of the trend as the best predictor to differentiate between disturbances.

In the last decade, a progressive and continuous increase in devastating forest fires has been observed. The changes in wildfire characteristics show higher levels of fire danger, longer fire seasons, and intense and rapidly spreading “mega-fires”. In this context, the Interreg IT-FR Maritime (cross-border Interreg Italy-France Maritime 2014–2020 programme) MED-Star project (Strategies and measures for fire risk mitigation in the Mediterranean area), co-financed by the European Agricultural Fund for Rural Development (EAFRD), aims to strengthen capacities in fire forecasting, prevention, and suppression. In order to evaluate wildfire risk, a better understanding of distribution and characteristics of different forest fuels is crucial. Within the MED-Star project, we carried out a field survey to collect information about fuel loads on accessible areas and investigated a geostatistical approach to extend forest fuel distribution quantification to the rest of the territories. The field survey campaign acquired more than 12.000 observations on fuel loads recorded by FuelGeoData App, a mobile device application, which was developed in the project by vanzotech srl (Rimini, Italy) and is based on the photoload sampling method designed by Keane in 2007. The recorded fuel loads were modelled using information related to stands height and Sentinel missions’ remotely sensed data. The stands height was obtained as a crown height model (CHM) produced by NASA as part of the GEDI (Global Ecosystem Dynamics Investigation) project. Other remote sensed information was derived by Sentinel 1 and Sentinel 2 cloud free images recorded in July 2021 (same period of the field survey). Our geostatistical approach was based on the random forest algorithm using the 70% of observations to train the model and the 30% to test model accuracy. The model results showed a general prediction accuracy higher than other previous studies in most of the fuel types investigated, highlighting how the number of observations and, as a consequence, the training sample size influence the predictive potential of the model.