A spatially explicit approach for stand-scale economic valuation of current and future potential of cork forests with respect to ecosystem production is developed and presented. The approach, which relies in large part on the mensura-tion of stand top height and number of trees as main drivers, has been tested on the pure cork forests of Sardinia (Italy). The test was conducted to assess the effects of alternative silvicultural options on cork and fodder production, carbon sequestration, and water yield. Under current conditions, the surveyed pure cork oak forest stands in Sardinia are characterized, on average, by an annual economic production of 93 euro ha^-1 yr^-1 as concerns cork, 37 euro ha^-1 yr^-1 as concerns carbon sequestration and 261 euro ha^-1 yr^-1 as concerns water yield. The value of cork production on an 11-year cycle equals 1023 euro ha^-1 on average. The total economic production values among the tested silvicultural alternatives have proven to be characterized by relatively small differences, due to the trade-offs among the considered goods and services. Therefore, at least under conditions similar to those surveyed, managers may safely rely on different stand density options, without any relevant detrimental effect on total economic production. The tested spatial visualization of the economic values of goods and services production can be useful in supporting forest management planning, e.g., to identify priority areas in order to maximize ecosystem production for local communities. The approach proposed here and tested to this end proves to be readily applicable to other cork contexts with similar characteristics under Mediterranean conditions. © SISEF.

A key topic in landscape ecology and vegetation science is the quantitative analysis of changes in forest cover over time, through the use of geomatics monitoring tools. Ecologists and landscape researchers are pointing out that a full understanding of ecosystems and landscapes should be based on the analysis of their functioning over long time series. Under this perspective, a long-term historical reconstruction of forest cover is essential. This study has aimed at examining the long-term dynamics of forest landscapes in Italy, over the last century, using recent remote-sensing based map (2012) and an accurate historical map (1936). A forest-non forest approach has been followed by the computation of a variety of landscape metrics using two analysis tools, with the final objective of quantifying changes in forest cover patterns and in the composition of specific landscape elements. Results show that forest landscape structure has significantly changed across Italy, resulting in a general trend of decreasing fragmentation and patchiness, mainly through enlargement of existing forest patches, which have also assumed a more geometrically regular shape. In relative terms, the greatest expansion of forest areas has occurred mainly in lowland districts characterised by the highest level of human pressure in the country. © 2017 Società Botanica Italiana.

Key message: Trees outside forests (TOF) have crucial ecological and social-economic roles in rural and urban contexts around the world. We demonstrate that a large-scale estimation strategy, based on a two-phase inventory approach, effectively supports the assessment of TOF’s diversity and related climate change mitigation potential. Context: Although trees outside forest (TOF) affect the ecological quality and contribute to increase the social and economic developments at various scales, lack of data and difficulties to harmonize the known information currently limit their integration into national and global forest inventories. Aims: This study aims to develop and test a large-scale estimation framework to assess ecological diversity and above-ground carbon stock of TOF. Methods: This study adopts a two-phase inventory approach. Results: In the surveyed territory (Molise region, Central Italy), all the attributes considered (tree abundance, basal area, wood volume, above-ground carbon stock) are concentrated in a few dominant species. Furthermore, carbon stock in TOF above-ground biomass is non-negligible (on average: 28.6 t ha⁻¹). Compared with the low field sampling effort (0.08% out of 52,796 TOF elements), resulting uncertainty of the estimators are more than satisfactory, especially those regarding the diversity index estimators (relative standard errors < 10%). Conclusion: The proposed approach can be suitably applied on vast territories to support landscape planning and maximize ecosystem services balance from TOF. © 2018, INRA and Springer-Verlag France SAS, part of Springer Nature.

The development of laser scanning technologies has gradually modified methods for forest mensuration and inventory. The main objective of this study is to assess the potential of integrating ALS and TLS data in a complex mixed Mediterranean forest for assessing a set of five single-tree attributes: tree position (TP), stem diameter at breast height (DBH), tree height (TH), crown base height (CBH) and crown projection area radii (CPAR). Four different point clouds were used: from ZEB1, a hand-held mobile laser scanner (HMLS), and from FARO® FOCUS 3D, a static terrestrial laser scanner (TLS), both alone or in combination with ALS. The precision of single-tree predictions, in terms of bias and root mean square error, was evaluated against data recorded manually in the field with traditional instruments. We found that: (i) TLS and HMLS have excellent comparable performances for the estimation of TP, DBH and CPAR; (ii) TH was correctly assessed by TLS, while the accuracy by HMLS was lower; (iii) CBH was the most difficult attribute to be reliably assessed and (iv) the integration with ALS increased the performance of the assessment of TH and CPAR with both HMLS and TLS. © 2018, © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Spatially-explicit information on forest composition provides valuable information to fulfil scientific, ecological and management objectives and to monitor multiple changes in forest ecosystems. The recently developed Sentinel-2 (S2) satellite imagery holds great potential for improving the classification of forest types at medium-large scales due to the concurrent availability of multispectral bands with high spatial resolution and quick revisit time. In this study, we tested the ability of S2 for forest type mapping in a Mediterranean environment. Three operational S2 images covering different phenological periods (winter, spring, summer) were processed and analyzed. Ten 10 m and 20 m bands available from S2 and four vegetation indices (VIs) were used to evaluate the ability of S2 to discriminate forest categories (conifer, broadleaved and mixed forests) and four forest types (beech forests; mixed spruce-fir forests; chestnut forests; mixed oak forests). We found that a single S2 image acquired in summer cannot discriminate neither the considered forest categories nor the forest types and therefore multitemporal images collected at different phenological periods are required. The best configuration yielded an accuracy > 83% in all considered forest types. We conclude that S2 can represent an effective option for repeated forest monitoring and mapping. © 2018 Centro di Ricerca per la Selvicoltura Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.

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 (R2=0.7) using integrated sensors when an upper bound of 400Mg 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.