Abstract. The objective of this research is to test Sentinel-1 SAR multitemporal data, supported by multispectral and SAR data at other wavelengths, for fine-scale mapping of above-ground biomass (AGB) at the provincial level in a Mediterranean forested landscape. The regression results indicate good accuracy of prediction (R 2 ¼ 0.7) using integrated sensors when an upper bound of 400 Mg ha −1 is used in modeling. Multitemporal SAR information was relevant, allowing the selection of optimal Sentinel-1 data, as broadleaf forests showed a different response in backscatter throughout the year. Similar accuracy in predictions was obtained when using SAR multifrequency data or joint SAR and optical data. Predictions based on SAR data were more conservative, and in line with those from an independent sample from the National Forest Inventory, than those based on joint data types. The potential of S1 data in predicting AGB can possibly be improved if models are developed per specific groups (deciduous or evergreen species) or forest types and using a larger range of ground data. Overall, this research shows the usefulness of Sentinel-1 data to map biomass at very high resolution for local study and at considerable carbon density. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Policies to mitigate climate change and biodiversity loss often assume that protecting carbon‐rich forests provides co‐benefits in terms of biodiversity, due to the spatial congruence of carbon stocks and biodiversity at biogeographic scales. However, it remains unclear whether this holds at the scales relevant for management, and particularly large knowledge gaps exist for temperate forests and for taxa other than trees. We built a comprehensive dataset of Central European temperate forest structure and multi‐taxonomic diversity (beetles, birds, bryophytes, fungi, lichens, and plants) across 352 plots. We used Boosted Regression Trees (BRTs) to assess the relationship between above‐ground live carbon stocks and (a) taxon‐specific richness, (b) a unified multidiversity index. We used Threshold Indicator Taxa ANalysis to explore individual species’ responses to changing above‐ground carbon stocks and to detect change‐points in species composition along the carbon‐stock gradient. Our results reveal an overall weak and highly variable relationship between richness and carbon stock at the stand scale, both for individual taxonomic groups and for multidiversity. Similarly, the proportion of win‐win and trade‐off species (i.e., species favored or disadvantaged by increasing carbon stock, respectively) varied substantially across taxa. Win‐win species gradually replaced trade‐off species with increasing carbon, without clear thresholds along the above‐ground carbon gradient, suggesting that community‐level surrogates (e.g., richness) might fail to detect critical changes in biodiversity. Collectively, our analyses highlight that leveraging co‐benefits between carbon and biodiversity in temperate forest may require stand‐scale management that prioritizes either biodiversity or carbon in order to maximize co‐benefits at broader scales. Importantly, this contrasts with tropical forests, where climate and biodiversity objectives can be integrated at the stand scale, thus highlighting the need for context‐specificity when managing for multiple objectives. Accounting for critical change‐points of target taxa can help to deal with this specificity, by defining a safe operating space to manipulate carbon while avoiding biodiversity losses.

No abstract

Global climate change is expected to result in more frequent and intense drought events, especially during the warm season. In such perspective, it is crucial to assess the forest stands vulnerability to extreme climatic events, such as drought, even for Mediterranean forest tree species, commonly considered resistant to dry spell. To test the capability of multitemporal imagery derived by Sentinel-2 (S2) in detecting the impacts of extreme drought events on forest health assessed as crown dieback, some forest stands in Tuscany (central Italy) were analyzed. Vegetation indices (VIs) and ancillary digital terrain model-derived data have been collected in 118 observational samples distributed along an ecological gradient. VIs detected a reduction of trees of photosynthetic activity in August 2017. S2 data have allowed the observation of the different response strategies of the tree species considered in this study to the extreme climatic event that occurred. The case study presented shows that S2 can be applied for monitoring climaterelated processes providing a synthetic overview of forest conditions at regional scale.

The ecological and economic relevance of sweet chestnut (Castanea sativa Mill.) has long been related to its widespread geographical distribution and multipurpose product potential. In Central Italy, chestnut management represents a paradigmatic example of the potential conflict between landowner targets and biodiversity conservation: options for preserving stand-scale biodiversity are not fully considered as current management is based on monospecific, even-aged coppice stands and clearcutting on wide areas. Relationships between silvicultural treatment and floristic diversity of chestnut coppices are here investigated focusing the attention on rotation length and on the role of thinning. Seven coppice stands were selected in such a way to be of similar size (about 10 ha) and to cover a wide range of ages and a different number of thinnings. Plot sampling was performed across the stands and their floristic diversity was compared and analyzed by means of indicators in order to assess statistical relationships between floristic data and stand structural attributes. The achieved results suggest alternative suitable options for managing chestnut coppice stands in order to enhance biodiversity while maintaining wood production.