Vincenzo Giannico

Modeling the influence of alternative forest management scenarios on wood production and carbon storage: A case study in the Mediterranean region.

Forest ecosystems are fundamental for the terrestrial biosphere as they deliver multiple essential ecosystem services (ES). In environmental management, understanding ES distribution and interactions and assessing the economic value of forest ES represent future challenges. In this study, we developed a spatially explicit method based on a multi-scale approach (MiMoSe-Multiscale Mapping of ecoSystem services) to assess the current and future potential of a given forest area to provide ES. To do this we modified and improved the InVEST model in order to adapt input data and simulations to the context of Mediterranean forest ecosystems. Specifically, we integrated a GIS-based model, scenario model, and economic valuation to investigate two ES (wood production and carbon sequestration) and their trade-offs in a test area located in Molise region (Central Italy). Spatial information and trade-off analyses were used to assess the influence of alternative forest management scenarios on investigated services. Scenario A was designed to describe the current Business as Usual approach. Two alternative scenarios were designed to describe management approaches oriented towards nature protection (scenario B) or wood production (scenario C) and compared to scenario A. Management scenarios were simulated at the scale of forest management units over a 20-year time period. Our results show that forest management influenced ES provision and associated benefits at the regional scale. In the test area, the Total Ecosystem Services Value of the investigated ES increases 85% in scenario B and decreases 82% in scenario C, when compared to scenario A. Our study contributes to the ongoing debate about trade-offs and synergies between carbon sequestration and wood production benefits associated with socio-ecological systems. The MiMoSe approach can be replicated in other contexts with similar characteristics, thus providing a useful basis for the projection of benefits from forest ecosystems over the future.

Estimating Stand Volume and Above-Ground Biomass of Urban Forests Using LiDAR

Assessing forest stand conditions in urban and peri-urban areas is essential to support ecosystem service planning and management, as most of the ecosystem services provided are a consequence of forest stand characteristics. However, collecting data for assessing forest stand conditions is time consuming and labor intensive. A plausible approach for addressing this issue is to establish a relationship between in situ measurements of stand characteristics and data from airborne laser scanning (LiDAR). In this study we assessed forest stand volume and above-ground biomass (AGB) in a broadleaved urban forest, using a combination of LiDAR-derived metrics, which takes the form of a forest allometric model. We tested various methods for extracting proxies of basal area (BA) and mean stand height (H) from the LiDAR point-cloud distribution and evaluated the performance of different models in estimating forest stand volume and AGB. The best predictors for both models were the scale parameters of the Weibull distribution of all returns (except the first) (proxy of BA) and the 95th percentile of the distribution of all first returns (proxy of H). The R2 were 0.81 (p < 0.01) for the stand volume model and 0.77 (p < 0.01) for the AGB model with a RMSE of 23.66 m3·ha−1 (23.3%) and 19.59 Mg·ha−1 (23.9%), respectively. We found that a combination of two LiDAR-derived variables (i.e., proxy of BA and proxy of H), which take the form of a forest allometric model, can be used to estimate stand volume and above-ground biomass in broadleaved urban forest areas. Our results can be compared to other studies conducted using LiDAR in broadleaved forests with similar methods.

Estimating belowground biomass and root/shoot ratio of Phillyrea latifolia L. in the Mediterranean forest landscapes

Key messageThis study developed and tested models to predict the belowground biomass and root/shoot ratio using aboveground field measures. The predictive power of such indirect measurement is useful for a rapid and reliable assessment of the biomass of the Mediterranean species.ContextForest biomass estimation has been simplified by the availability of indirect methodologies for automatic measuring. However, most of the information on forest root systems is largely unexplored due to the difficulty in estimating belowground biomass (BGB) at large scale. A plausible approach to investigating forest BGB is to establish a relationship between a number of dendrometric parameters related to the aboveground vegetation (AGB) (e.g., tree diameter and height) and the belowground component of the total biomass.AimsThis work presents findings for indirect measurements of BGB in the typical Mediterranean landscapes focusing on sclerophyllous vegetation, specifically Phillyrea latifolia L. The purpose of the present study is twofold: (a) to develop a model explaining the BGB distribution of P. latifolia based on field data for dendrometric parameters, and (b) to understand how the ratio between BGB and AGB varies according to stem diameter as a proxy of plant growth.MethodsA total of 50 P. latifolia plants were randomly selected in the study areas and considered for excavation. Individual plants were analyzed to determine AGB and BGB development. A number of models were developed and tested to predict the BGB and root/shoot ratio using aboveground field measures. Allometric equations were employed to predict the AGB and BGB and relative partitioning in the Phillyrea community.ResultsModels for P. latifolia AGB and BGB estimation that include crown diameter and stem height measures augment the models’ predictive power. When used alone, the predictive power of the root collar diameter appears to be overestimated, while its effect is stronger for a subset of observations with larger crown diameter and stem height. The root/shoot ratio values of plant species typically related to the Mediterranean context seem to be largely superior to the ratio values of trees and forests.ConclusionsThe model is ideally suited to incorporate indirectly measured tree height for a rapid and reliable assessment of the biomass of single Mediterranean species. Further research might include replication of the same studies in different geographic areas of the Mediterranean and in-depth analyses of AGB.

Mapping and assessment of forest ecosystems and their services - Applications and guidance for decision making in the framework of MAES

The aim of this report is to illustrate by means of a series of case studies the implementation of mapping and assessment of forest ecosystem services in different contexts and geographical levels. Methodological aspects, data issues, approaches, limitations, gaps and further steps for improvement are analysed for providing good practices and decision making guidance. The EU initiative on Mapping and Assessment of Ecosystems and their Services (MAES), with the support of all Member States, contributes to improve the knowledge on ecosytem services. MAES is one of the building-block initiatives supporting the EU Biodiversity Strategy to 2000.

Modeling fire ignition patterns in Mediterranean urban interfaces

The rapid growth of built-up areas and infrastructure in the Mediterranean environment has resulted in the expansion of urban interfaces where fire can ignite and spread. Within this context, there is a need to understand spatial patterns of ignition distribution and the relative importance of influencing drivers. In response to this need we developed an analysis of fire ignition patterns using human and biophysical explanatory variables by firstly developing two different linear models to assess patterns of fire ignition points in terms of occurrence (presence/absence) and frequency (number of ignition points per area and secondly applying statistical tests to both models to evaluate the most important human and/or biophysical drivers influencing these patterns. The probability of ignition point occurrence and frequency were mapped using the predicted values of the two models in the Apulia region (southern Italy). Our findings revealed that dependent variables (fire ignition occurrence points and frequency) are negatively correlated with population density, but positively correlated for presence of urban areas with a significantly higher likelihood of ignition in cultivated (crop)land, forest, shrubland, grassland, and other natural spaces. The probability of ignition increased with elevation and slope. The maps show that the probability of ignition occurrence is relevant along the coast in the northern and southern parts of the region, especially in urban interfaces with a strong presence of shrubland and Mediterranean maquis. Ignition point frequency was predicted along the coast, particularly in the south and in some densely urbanized inland areas. By adopting the models, forest managers and decision makers may avail of the knowledge gained to design and promote sustainable fire management strategies in the Apulia region.

Co-creating urban green infrastructure connecting people and nature: A guiding framework and approach

Urban green infrastructure (UGI) and nature-based solutions are increasingly recognized as instruments to address urban sustainability challenges, yet rely on a good understanding of complex social-ecological system (SES) to function adequately. Adaptive co-management (ACM), engaging a broad variety of stakeholders in collaborative learning, is an effective strategy to improve the resilience of a SES. However, ACM studies have been criticized for neglecting the urban context, while also offering little clarity on process objectives and outcomes. To address these knowledge gaps, while also drawing attention to the important issue of socially inclusive UGI development, we present a guiding framework and approach to encourage the ACM of UGI featuring two main components. Firstly, a Learning Alliance (LA) serves as an instrument for collaborative learning and experimentation across different scales. To facilitate upscaling, we propose to establish a complementary Urban Learning Lab (ULL) to facilitate a regular exchange between the LA and legitimate peripheral networks and stakeholders in the city region. Secondly, a stepwise approach to SES analysis serves to engage a representative group of stakeholders in the LAs and ULLs, and support the processes of setting LA objectives and monitoring of adaptive capacity. We illustrate our approach to the ACM of UGI with a case study of LivadaLAB in Ljubljana, Slovenia. Applying the framework and approach, we demonstrate increased adaptive capacity of the SES around UGI as indicated by: 1) improved overall stakeholder salience, in particular for previously disempowered actor groups, 2) increased number and strength of connections between stakeholders, and 3) the consideration of a broader range of sustainable development objectives by stakeholders in their daily practice.