Active governance of agro-pastoral, forest and protected areas mitigates wildfire impacts in Italy.

Wildfire regimes affected by global change have been the cause of major concern in recent years. Both direct prevention (e.g., fuel management planning) and land governance strategies (e.g., agroforestry development) can have an indirect regulatory effect on wildfires. Herein, we tested the hypothesis that active land planning and management in Italy have mitigated wildfire impacts in terms of loss of ecosystem services and forest cover, and burned wildland-urban interface, from 2007 to 2017. At the national scale, we assessed the effect size of major potential fire drivers such as climate, weather, flammability, socio-economic descriptors, land use changes, and proxies for land governance (e.g., European funds for rural development, investments in sustainable forest management, agro-pastoral activities), including potential interactions, on fire-related impacts via Random Forest modelling and Generalized Additive Mixed Model. Agro-forest districts (i.e., aggregations of neighbouring municipalities with homogeneous forest and agricultural characteristics) were used as spatial units of analysis. Our results confirm that territories with more active land governance show lower wildfire impacts, even under severe flammability and climatic conditions. This study supports current regional, national, and European strategies towards "fire resistant and resilient landscapes" by fostering agro-forestry, rural development, and nature conservation integrated policies.

An open and georeferenced dataset of forest structural attributes and microhabitats in central and southern Apennines (Italy).

Forests cover 30% of the Earth's landmass, host 80% of the biodiversity on land, and represent one of the main sinks of carbon. Studying forest ecosystems and dynamics is more crucial than ever now that the climate is changing. On the other hand, forest structural attributes and microhabitats data acquisition is challenging, and require huge efforts. Here we provide a georeferenced dataset of living trees, deadwood, and microhabitats referring to 199 plots (13 m radius), collected between 2012 and 2018, and located over six Apennine mountainous forest types across Italy. The dataset we provide promotes collaboration among researchers and improves the possibilities to analyze the evolution of forest ecosystems.

A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020.

Forests absorb 30% of human emissions associated with fossil fuel burning. For this reason, forest disturbances monitoring is needed for assessing greenhouse gas balance. However, in several countries, the information regarding the spatio-temporal distribution of forest disturbances is missing. Remote sensing data and the new Sentinel-2 satellite missions, in particular, represent a game-changer in this topic. Here we provide a spatially explicit dataset (10-meters resolution) of Italian forest disturbances and magnitude from 2017 to 2020 constructed using Sentinel-2 level-1C imagery and exploiting the Google Earth Engine GEE implementation of the 3I3D algorithm. For each year between 2017 and 2020, we provide three datasets: (i) a magnitude of the change map (between 0 and 255), (ii) a categorical map of forest disturbances, and (iii) a categorical map obtained by stratification of the previous maps that can be used to estimate the areas of several different forest disturbances. The data we provide represent the state-of-the-art for Mediterranean ecosystems in terms of omission and commission errors, they support greenhouse gas balance, forest sustainability assessment, and decision-makers forest managing, they help forest companies to monitor forest harvestings activity over space and time, and, supported by reference data, can be used to obtain the national estimates of forest harvestings and disturbances that Italy is called upon to provide.

Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy.

Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests' capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985-2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems.

The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination.

Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups.