Biomass accounting for Sustainable Forest Management Plans

Measuring the amount of CO2 stored in forests is crucial for supporting the European Union's Forest Strategy for 2030, which is a key part of the European Green Deal aiming to achieve carbon neutrality by 2050 (Commission European 2019; European Commission 2021). In this context, evaluating forest biomass, or the amount of plant material that absorbs carbon, is essential for understanding how much carbon forests can store (Ruiz-Peinado et al. 2017; Peñuelas and Sardans 2021; Martes and Köhl 2022). n Mediterranean regions, forests are losing their ability to store carbon due to climate change (such as hotter, drier summers, irregular rainfall, and rising temperatures) and more frequent extreme events like wildfires or storms (Peñuelas and Sardans 2021; Mauri A., Girardello M., Strona G., Beck P. S. A., Forzieri G., Caudullo G. 2022). For example, climate change has reduced the carbon storage ability of unmanaged forests in Spain, mainly due to less water being available (Vayreda et al. 2012). Therefore, it’s important to have accurate regional-level estimates of forest biomass and carbon fluxes to support sustainable forest management practices that can help maintain carbon storage. At the European level, there are several maps that estimate forest biomass (Avitabile and Camia 2018), such as those from Thurner et al. (Thurner et al. 2014), Gallaun et al. (Gallaun et al. 2010), Kindermann et al. (Kindermann et al. 2008), Barredo et al. (Barredo et al. 2012), and others. However, these maps often have a high level of error, ranging from 29% to 40%, and the Joint Research Center (JRC) has considered them unreliable. In 2020, the JRC developed a new forest biomass map, JRC-BIO, which is based on National Forest Inventory (NFI) data and has a higher accuracy (Santoro et al. 2021). The European Space Agency (ESA) also created global maps showing above-ground biomass for the years 2010, 2017, and 2018 using satellite data (Santoro et al. 2021). Global or continental maps are often not detailed enough to provide accurate estimates at the national level. For this reason, countries usually rely on NFI data to estimate CO2 fluxes. While these data are helpful for large-scale strategies, they don’t provide the level of detail needed for managing forests on smaller scales (D’amico et al. 2021; Giannetti et al. 2022). To get more accurate and detailed estimates of forest variables, Enhanced Forest Inventories (EFIs) combine NFI data with remote sensing information (White et al. 2017). This method can provide more precise estimates of forest variables like growing stock volume (GSV), annual volume increments, and biomass. EFI data is useful for analyzing forests on different scales, from national to local, and can support forest management decisions through Forest Information Systems (FIS) and Decision Support Systems (DSS). The main difference between global maps and EFI maps is their spatial resolution. Maps created using EFIs have a much finer resolution (less than 30 meters), which is better suited for detailed forest management. In the context of the GO-SURF project, which aimed to develop a decision support system (DSS) for forest management in Tuscany Region, a 23x23-meter biomass map was created at the regional level using a modeling approach. This map was designed to enhance forest management decision-making by providing stakeholders with easy access to spatially explicit data. By integrating this map into the GO-SURF DSS platform (go-surf.app), users can query and visualize biomass information across the forested area. Biomass accounting for Sustainable Forest Management Plans Francesca Giannetti, UNIFI This allows for more informed, data-driven decisions regarding forest management practices, carbon storage, and sustainability efforts. The use of this map within the DSS enables forest managers, policymakers, and other stakeholders to make better decisions based on accurate, high-resolution biomass data, ultimately supporting sustainable forest management and contributing to climate change mitigation efforts. The decision support system ensures that relevant forest information is accessible, enabling the efficient and effective management of forest resources. The Corpo Forestale dello Stato (CFS), in collaboration with the Council for Agricultural Research and the Analysis of Agricultural Economics (CREA) of the Ministry of Agricultural, Food, and Forestry Policies, provides free online access to the raw data of the National Forest Inventory and Carbon Forest Reservoirs - INFC2005, which is the second Italian national forest inventory. The data is available along with the related metadata at the following address ⇒ http://www.inventarioforestale.org/. Upon registration, the INFC 2005 data regarding the plot measurements in Tuscany and neighboring regions (Liguria, Emilia Romagna, Umbria, Lazio) (Figure 2) were downloaded by the University of Florence and processed using custom R-CRAN codes to derive inventory variables such as Wood Stock, Biomass, and Basal Area. For the calculation of wood stock (m3/ha) and biomass (kg/ha), the equations from the national forest inventory (Tabacchi et al., 2011) were used. The wood stock (m3/ha) and biomass (kg/ha) of each tree were estimated using species-specific allometric models developed within the INFC framework, which use tree diameter at 1.30 meters and total tree height as independent variables (Tabacchi et al., 2011). The wood stock and biomass per hectare for each plot were estimated by aggregating the volume of all the trees measured in the plot (Figure 1). The uncertainty in the predictions of the 8 allometric models for estimating inventory variables in the plots for subsequent mapping operations is considered negligible and is ignored (McRoberts et al., 2016a, b). Area-Based Approach for Estimating Growing Stock Volume The area-based approach (ABA) is a popular method for estimating forest stand characteristics, such as growing stock volume, by integrating LiDAR/Satellite data with field plot measurements. This technique leverages the advantages of both data sources: the broad spatial coverage and predictive variables obtained from remote sensing, combined with the precise and detailed information provided by field plots. By using ABA, the number of field plots needed for survey work can be reduced. Field Plot Data Collection: National Forest Inventory 2015 Figure 1: National Forest Inventory plots in the Tuscany region. Extraction of Predictor Variables from Sentinel-2, Digital Terrain Model and climate data Various predictor variables were extracted from the remote sensing data, which included spectral band values, vegetation indices from Sentinel-2 satellite images, and climate and orography data. Statistical Modeling The field plot data were then linked to the extracted predictor variables using regression models or machine learning algorithms. Growing Stock Volume Mapping After the model was calibrated, it was applied to the predictor variable dataset to produce spatially explicit maps of standing timber volume (Figure 2). These maps provide continuous estimates of growing stock volume throughout the entire forested area under study. Within the GO-SURF operational group. “Random Forest” machine learning techniques for modeling complex relationships. These models were applied to predict growing stock volume across the entire study area, with a spatial resolution of 23 m × 23 m. Figure 2: Map of Growing Stock Volume, resolution 23x23 m (m3/ha) in the GO-SURF Decisional Support System Map of Biomass The map biomass was obtained using the optimized procedure developed by (Giannetti et al. 2022) which is based on identifying the best set of independent variables and parameters for the predictive model of Growing Stock Volume (Figure 2) and then transform the GSV in Biomass using species specific coefficients (Figure 3). In detail, to convert the growing stock volume (GSV) into biomass, two key pieces of information are required: (i) the spatial distribution of forest types, and (ii) biomass expansion factors (BEFs) and wood basic densities (WBDs) specific to each forest type. CLC IV Forest Types Nomenclature Systems BEF (Volume of Aboveground Biomass / Volume of Growing Stock) WBD (Dry Weight t / Fresh Volume of Aboveground Biomass m³) 3.1.1.1. Forest dominated by holm oak and/or cork oak 1.45 0.72 3.1.1.2. Forest dominated by deciduous oak (Turkey oak, downy oak, farnetto oak, and/or English oak) 1.39 0.65 3.1.1.3. Mixed forests with a prevalence of mesophilic and mesothermophilous broad-leaved trees (mapleash, cute black-ash) 1.28 0.66 3.1.1.4. Chestnut forests 1.33 0.49 3.1.1.5. Beech forests 1.36 0.61 3.1.1.6. Forests dominated by hygrophilous species (forests with a prevalence of willows, poplars, and/or alders, etc.) 1.39 0.41 3.1.2.1. Forests dominated by Mediterranean pines (stone pine, pine maritime) and cypress 1.53 0.53 3.1.2.2. Forests dominated by mountain and Mediterranean pines (black pine and larch, Scots pine, Bosnian pine) 1.33 0.47 3.1.2.3. Forests dominated by silver fir and/or spruce 1.34 0.38 3.1.2.5. Forests dominated by larch and/or stone pine 1.37 0.43 3.1.3.1. Mixed Forests with a prevalence of broadleaved trees 1.53 0.53 3.1.3.2. Mixed Forests with a prevalence of conifers 1.37 0.43 Since a comprehensive forest type map for the entire national territory of Tuscany is not available,we opted to use the CORINE Land Cover (CLC) dataset at the IV level (Table 1) for wich based on Federici et al ,(Federici et al. 2008) we calculate the BEF and the WBD . Table 1 - This table provides the forest types, their corresponding Biomass Expansion Factors (BEF), and the Wood Basic Densities (WBD) used for biomass calculations.  Wall-to-Wall Forest Biomass Map was obtained by the GSV following Federici et al (Federici et al. 2008) BIO = GSV · BEF · WDB where GSV is the pixel value of growing stock volume (m3 ha−1), BEF is the biomass expansion factor of each CLC forest type, and WBD is the wood basic density of each CLC forest type. The obtained biomass map has pixels of 23 m × 23 m reporting the forest biomass in t ha−1 Figure 3. Figure 3 – Biomass Map 23x23 m (t/ha) in the GO-SURF Decisional Support System Discussion and Conclusion In conclusion, the 23x23-meter biomass map developed within the GO-SURF project represents a significant advancement in forest management at the regional level. By utilizing a modeling approach, this high-resolution map offers valuable, spatially explicit data on biomass distribution across forested areas. The integration of this map into the GO-SURF decision support system (DSS) enhances the ability of stakeholders to access and analyze forest data efficiently. This enables more informed decision-making regarding forest management strategies, carbon sequestration efforts, and overall sustainability. The map's incorporation into the DSS platform allows forest managers, policymakers, and other stakeholders to interact with the data in a userfriendly manner, promoting evidence-based decisions that contribute to sustainable forest practices and climate change mitigation. Additionally, the high spatial resolution of the map ensures that users can make more precise decisions at a localized level, addressing variability within different forest types and conditions. Overall, this biomass map serves as a powerful tool for improving forest management practices, fostering better-informed decisions, and supporting the long-term sustainability of forest ecosystems, while also contributing to broader environmental goals such as carbon neutrality and biodiversity conservation. References Avitabile V, Camia A (2018) An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots. For Ecol Manage 409:489–498. https://doi.org/10.1016/j.foreco.2017.11.047 Barredo JI, San Miguel J, Caudullo G, Busetto L (2012) A European map of living biomass and carbon stock. Executive report. 12. https://doi.org/10.2788/780 Commission European (2019) COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE EUROPEAN COUNCIL, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS The European Green Deal COM/2019/640 final D’amico G, Vangi E, Francini S, et al (2021) Are we ready for a national forest information system? State of the art of forest maps and airborne laser scanning data availability in Italy. IForest 14:144– 154. https://doi.org/10.3832/ifor3648-014 European Commission (2021) New EU Forest Strategy for 2030. Eur Comm 5–24 Federici S, Vitullo M, Tulipano S, et al (2008) An approach to estimate carbon stocks change in forest carbon pools under the UNFCCC: The Italian case. IForest 1:86–95. https://doi.org/10.3832/ifor0457-0010086 Gallaun H, Zanchi G, Nabuurs GJ, et al (2010) EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements. For Ecol Manage 260:252–261. https://doi.org/10.1016/j.foreco.2009.10.011 Giannetti F, Chirici G, Vangi E, et al (2022) Wall-to-Wall Mapping of Forest Biomass and Wood Volume Increment in Italy. I: Kindermann GE, McCallum I, Fritz S, Obersteiner M (2008) A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fenn 42:387–396. https://doi.org/10.14214/sf.244 Martes L, Köhl M (2022) Improving the Contribution of Forests to Carbon Neutrality under Different Policies—A Case Study from the Hamburg Metropolitan Area. Sustain 14:. https://doi.org/10.3390/su14042088 Mauri A., Girardello M., Strona G., Beck P. S. A., Forzieri G., Caudullo G. MF& CA (2022) EU-Trees4F, a dataset on the future distribution of European tree species. Peñuelas J, Sardans J (2021) Global change and forest disturbances in the mediterranean basin: Breakthroughs, knowledge gaps, and recommendations. Forests 12:1–27. https://doi.org/10.3390/f12050603 FOREST4EU Project FOREST4EU Project [email protected] forest4eu.eu Contacts Funded by the European Union (Grant n. 101086216). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or REA. Neither the European Union nor the granting authority can be held responsible for them. partners: Further information [email protected] Information from Operational’s Group database Ruiz-Peinado R, Bravo-Oviedo A, López-Senespleda E, et al (2017) Forest management and carbon sequestration in the Mediterranean region: A review. For Syst 26:1–25. https://doi.org/10.5424/fs/2017262-11205 Santoro M, Cartus O, Carvalhais N, et al (2021) The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst Sci Data 13:3927–3950. https://doi.org/10.5194/essd-13-3927-2021 Thurner M, Beer C, Santoro M, et al (2014) Carbon stock and density of northern boreal and temperate forests. Glob Ecol Biogeogr 23:297–310. https://doi.org/10.1111/geb.12125 Vayreda J, Martinez-Vilalta J, Gracia M, Retana J (2012) Recent climate changes interact with stand structure and management to determine changes in tree carbon stocks in Spanish forests. Glob Chang Biol 18:1028–1041. https://doi.org/10.1111/j.1365-2486.2011.02606.x White JC, Tompalski P, Vastaranta M, et al (2017) A model development and application guide for generating an enhanced forest inventory using airborne laser scanning data and an area-based approach