Italian National Forest Inventory Research Articles

Canopy Height Mapper: a Google Earth Engine application for predicting global canopy heights combining GEDI with multi-source data.

Spatially and temporally discontinuous canopy height footprints collected by NASA’s GEDI (Global Ecosystem Dynamics Investigation) mission are accessible on the Google Earth Engine (GEE) cloud computing platform. This study introduces an open-source, user-friendly, code-free GEE web application called Canopy Height Mapper (CH-GEE), available at https://ee-calvites1990.projects.earthengine.app/view/ch-gee, which automatically generates (10 m) high-resolution canopy height maps for a specific area by integrating GEDI with multi-source remote sensing data: Copernicus and topographical data from the GEE data catalogue. CH-GEE generates local-to-country scale calibrated canopy height maps worldwide using machine learning algorithms and leveraging the GEE platform's big data and cloud computing capabilities. CH-GEE allows customization of geographic area, algorithms and time windows for GEDI and predictors. Canopy heights generated by CH-GEE were validated using the Italian National Forest Inventory across 110,000 km2 at multiple scales (Country-based R-squared = 0.89, RMSE = 17%). CH-GEE's accuracy and scalability make it suitable for forest monitoring.

Mapping Forest Growing Stock and Its Current Annual Increment Using Random Forest and Remote Sensing Data in Northeast Italy

The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making processes. This article presents the methodology and the results of the wall-to-wall spatialization of the growing stock volume and the current annual increment measured in 273 plots of data of the Italian National Forest Inventory over an area of more than 3260 km2 in the Friuli Venezia Giulia region (Northeast Italy). To this aim, a random forest model was tested using as predictors 4 spectral indices from Sentinel-2, a high-resolution Canopy Height Model derived from LiDAR, and geo-morphological data. According to the Leave One Out cross-validation procedure, the model for the growing stock shows an R2 and an RMSE% of 0.67 and 41%, respectively. Instead, an R2 of 0.47 and an RMSE% of 57% were obtained for the current annual increment. The validation with an independent dataset further improved the models’ performances, yielding significantly higher R2 values of 0.84 and 0.83 for volume and for increment, respectively. Our results underline a relatively higher importance of LiDAR-derived metrics compared to other covariates in estimating both attributes, as they were even twice as important as vegetation indices for growing stock. Therefore, these metrics are promising for the development of a national LiDAR-based model.

Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy

Forest planning, forest management, and forest policy require updated, reliable, and harmonized spatial datasets. In Italy a national geographic Forest Information System (FIS) designed to store and facilitate the access and analysis of spatial datasets is still missing. Among the different information layers which are useful to start populating a FIS, two are essential for their multiple use in the assessment of forest resources: (i) forest mapping, and (ii) data from Airborne Laser Scanning (ALS). Both layers are not available wall-to-wall for Italy, though different local sources of information potentially useful for their implementation already exist. The objectives of this work were to: (i) review forest maps and ALS data availability in Italy; (ii) develop for the first time a high resolution forest mask of Italy which was validated against the official statistics of the Italian National Forest Inventory; (iii) develop the first mosaic of all the main ALS data available in Italy producing a consistent Canopy Height Model (CHM). An on-line geographic FIS with free access to both layers from (ii) and (iii) was developed for demonstration purposes. The total area of forest and other wooded lands computed from the forest mask was 102.608.82 km2 (34% of the Italian territory), i.e., 1.9% less than the NFI benchmark estimate. This map is currently the best wall-to-wall forest mask available for Italy. We showed that only the 63% of the Italian territory (the 60% of the forest area) is covered by ALS data. These results highlight the urgent need for a national strategy to complete the availability of forest data in Italy.

Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy

In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m3/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson’s correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m3/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m3/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales.

An individual-tree linear mixed-effects model for predicting the basal area increment of major forest species in Southern Europe

Aims of the study. Assessment of growth is essential to support sustainability of forest management and forest policies. The objective of the study was to develop a species-specific model to predict the annual increment of tree basal area through variables recorded by forest surveys, to assess forest growth directly or in the context of more complex forest growth and yield simulation models.Area of the study. Italy.Material and methods. Data on 34638 trees of 31 different forest species collected in 5162 plots of the Italian National Forest Inventory were used; the data were recorded between 2004 and 2006. To account for the hierarchical structure of the data due to trees nested within plots, a two-level mixed-effects modelling approach was used.Main results. The final result is an individual-tree linear mixed-effects model with species as dummy variables. Tree size is the main predictor, but the model also integrates geographical and topographic predictors and includes competition. The model fitting is good (McFadden’s Pseudo-R2 0.536), and the variance of the random effect at the plot level is significant (intra-class correlation coefficient 0.512). Compared to the ordinary least squares regression, the mixed-effects model allowed reducing the mean absolute error of estimates in the plots by 64.5% in average.Research highlights. A single tree-level model for predicting the basal area increment of different species was developed using forest inventory data. The data used for the modelling cover 31 species and a great variety of growing conditions, and the model seems suitable to be applied in the wider context of Southern Europe. Keywords: Tree growth; forest growth modelling; forest inventory; hierarchical data structure; Italy.Abbreviations used: BA - basal area; BAI – five-year periodic basal area increment; BALT - basal area of trees larger than the subject tree; BASPratio - ratio of subject tree species basal area to stand basal area; BASTratio - ratio of subject tree basal area to stand basal area; CRATIO - crown ratio; DBH – diameter at breast height ; DBH0– diameter at breast height corresponding to five years before the survey year; DBHt– diameter at breast height measured in the survey year; DI5 - five-year, inside bark, DBH increment; HDOM - dominant height; LULUCF - Land Use, Land Use Changes and Forestry; ME - mean error; MAE - mean absolute error; MPD - mean percent deviation; MPSE - mean percent standard error; NFI(s) - National Forest Inventory/ies; OLS - ordinary least squares regression; RMSE - root mean squared error; UNFCCC - United Nation Framework Convention on Climate Change.

Probabilistic Provenance Detection and Management Pathways for Pseudotsuga menziesii (Mirb.) Franco in Italy Using Climatic Analogues.

The introduction of Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] in Europe has been one of the most important and extensive silvicultural experiments since the 1850s. This success was mainly supported by the species’ wide genome and phenotypic plasticity even if the genetic origin of seeds used for plantations is nowadays often unknown. This is especially true for all the stands planted before the IUFRO experimentation in the 1960s. In this paper, a methodology to estimate the Douglas-fir provenances currently growing in Italy is proposed. The raw data from the last Italian National Forest Inventory were combined with literature information to obtain the current spatial distribution of the species in the country representing its successful introduction. Afterwards, a random forest classification model was run using downscaled climatic data as predictors and the classification scheme adopted in previous research studies in the Pacific North West of America. The analysis highlighted good matching between the native and the introduction range in Italy. Coastal provenances from British Columbia and the dry coast of Washington were detected as the most likely seed sources, covering 63.4% and 33.8% of the current distribution of the species in the country, respectively. Interior provenances and those from the dry coast of Oregon were also represented but limited to very few cases. The extension of the model on future scenarios predicted a gradual shift in suitable provenances with the dry coast of Oregon in the mid-term (2050s) and afterwards California (2080s) being highlighted as possible new seed sources. However, only further analysis with genetic markers and molecular methods will be able to confirm the proposed scenarios. A validation of the genotypes currently available in Italy will be mandatory as well as their regeneration processes (i.e., adaptation), which may also diverge from those occurring in the native range due to a different environmental pressure. This new information will also add important knowledge, allowing a refinement of the proposed modeling framework for a better support for forest managers.

Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management

Surface biomass characterization plays a key role in wildfire management. It allows classifying vegetation fuels flammability for fire risk analysis, to define silvicultural prescriptions for fire hazard reduction, to plan prescribed burning, or to model fire behavior and its effects, such as greenhouse gas and pollutant emissions. To facilitate fuel classification and analysis of potential fire behavior and effects in Italy, we harmonized 634 measurements of surface wildland fuels from Alpine, temperate and Mediterranean environments. The dataset provides quantitative data for duff, fine dead fuels and downed woody material, live grasses and shrubs fuel components. Surface fuel data were harmonized by subdividing loads (Mg ha-1) to standard size classes for dead (0-6, 6-25 and 25-75 mm) and live (0-6, 6-25 mm) fuels, collecting percent cover and depth/height (cm) of the various fuel components, and classifying observations into 19 fuelbed categories. To ensure comparability with existing vegetation classification systems, we classified each observation according to the European Fuel Map, the Corine Land Cover classes (level IV), the European Forest Types, and the forest categories of the Italian National Forest Inventory. The dataset and a photo description of each fuelbed category are available as Supplementary material. This dataset is the first step to develop several products at the national scale such as: (i) fuel type classification and mapping; (ii) carbon stock and wildfire emission estimates; (iii) calibration of fuel models for the simulation of fire behavior and effects.

Predicting Diameter at Breast Height from Stump Measurements of Removed Trees to Estimate Cuttings, Illegal Loggings and Natural Disturbances

Predicting diameter at breast height (DBH) of trees from stump information may be necessary to reconstruct silvicultural practices, to assess harvested timber and wood, or to estimate forest products’ losses caused by illegal cuttings or natural disasters (disturbances). A model to predict DBH of felled trees was developed by the first Italian National Forest Inventory in 1985 (IFNI85). The model distinguished between the two broad groups of conifers and broadleaves and used stump diameter as the sole quantitative variable. Using an original dataset containing data from about 1200 trees of sixteen species recorded throughout Italy, we assessed the performance of that model. To improve the prediction of the DBH of removed trees over large areas and for multiple species, we developed new models using the same dataset. Performance of the new models was tested through indices computed on cross-validated data obtained through the leave-one-out method. A new model that performs better than the old one was finally selected. Compared to the old NFI model, the selected model improved DBH prediction for fourteen species up to 31.28%. This study proved that species specification and stump height are variables needed to improve the models’ performance and suggested that data collection should be continued to get enhanced models, accurate for different ecological and stand conditions.

Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data

Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.

Linkage of forest productivity to tree diversity under two different bioclimatic regimes in Italy

We analyzed the Italian National Forest Inventory data set to evaluate the interdependence of forest productivity, tree species richness (used to indicate biodiversity), climate, and soil factors. We tested the hypotheses that the relationship between biodiversity and forest productivity is positive and significant for all forests in Italy and whether the relationship is the same for forests growing in the temperate and Mediterranean bioclimatic domains (regions) of Italy. We used generalized additive models to explore the univariate response curves for the data and then performed structural equation modeling (SEM) and multi-group SEM analyses to evaluate the relationship between biodiversity and productivity. We found that the SEM model for the entire dataset explained about 60% of the variation in forest productivity. In addition, the variation associated with species richness was greater than variation due to climatic factors and the variation in climate factors was greater than the variation in soil factors (all relative to their contributions to productivity). The multi-group SEM showed a more predominant effect of biodiversity and climate on productivity in Mediterranean compared to temperate forests. In both cases, we observed a moderate effect of soil (factors) on forest productivity. Our results support the hypothesis that increasing tree diversity in forests could help reduce the effects of climate warming and enhance ecosystem productivity in the Mediterranean region.

Reviewing climatic traits for the main forest tree species in Italy

Abstract: The future dynamics of forest species and ecosystems depend on the effects of climate change and are related to forest management strategies. The expected impacts of climate change are linked to forest growth and productivity. An increase in the length of the growing season and greater productivity are likely as well as shifts in average climatic values and more variable frequencies, intensities, durations and timings of extreme events. The main aim of this work is to assess and describe the climatic requirements for Italian forest tree species. We used 7.272 field observations from Italian National Forest Inventory plots and average annual temperatures and precipitation as interpolated from raster maps with 1 km spatial resolution. On this basis we evaluated the current observed distributions of the 19 most important tree species in Italy with respect to potential climatic limits based on expert knowledge and the available literature. We found that only 46% of the observations fall within the potential joint temperature and precipitation limits as defined by expert knowledge. For precipitation alone, 70% of observations were within the potential limits, and for temperature alone, 80% of observations were within the potential limits. Similarity between current observed and potential limits differ from species-to-species with broadleaves in general more frequently distributed within the potential climatic limits than conifers. We found that ecological requirements and potential information should be revised for some species, particularly for the Pinus genus and more frequently for precipitation. The results of the study are particularly relevant given the threat of climate change effects for Italian forests which are broadly acknowledged to be a biodiversity hotspot. Further investigations should be aimed at modelling the effects of climate changes on Italian forests as a basis for development of mitigation and adaptation forest management strategies.

Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery

Abstract: Forest biomass is a renewable energy source, more climate-friendly than fossil fuels and widely available in Europe. The wood energy chain has been suggested as a means to re-activate forest management and improve the value of forest stands in marginalized rural areas. However, wall-to-wall estimates of forest biomass, needed to design the location and size of power and heat biomass plants in any given territory, are notoriously difficult to obtain. This paper tests an algorithm to predict forest biomass using publicly available Landsat satellite imagery in the Liguria region, northern Italy. We used regional forest inventory data to train and validate an artificial neural network (ANN) classifier that uses remotely-sensed information such as three principal components of Landsat-5 TM spectral bands, the Enhanced Vegetation Index (EVI), and topography, to retrieve aboveground live tree volume. Percent root mean square error was -9% and -23% for conifers and broadleaves respectively in the calibration dataset, and -27% and -24% in the validation dataset. The reconstructed volume map was updated to present day using current volume increment rates reported by the Italian National Forest Inventory. A wall-to-wall map of forest biomass from harvest residues was finally produced based on species-specific wood density, biomass expansion factors, volume logged for timber assortments, forest accessibility, and topography. Predicted aboveground forest volume ranged from 81 to 391 m3 ha-1. In forests available for wood supply (70% of the total), planned volume removals averaged 25.4 m3 ha-1, or 18.7% of the average standing stock across. Biomass available for bioenergy supply was 1.295.921 million Mg dry matter or 8.95 Mg ha-1. This analysis workflow can be replicated in all mountain regions with a predominant broadleaved coppice component.

Applying quantitative structure models to plot-based terrestrial laser data to assess dendrometric parameters in dense mixed forests

Aim of study: To assess terrestrial laser scanning (TLS) accuracy in estimating biometrical forest parameters at plot-based level in order to replace manual survey for forest inventory purposes.Area of study: Monte Morello, Tuscany region, ItalyMaterial and methods: In 14 plots (10 m radius) in dense Mediterranean mixed conifer forests, diameter at breast height (DBH) and height were measured in Summer 2016. Tree volume was computed using the second Italian National Forest Inventory (INFC II) equations. TLS data were acquired in the same plots and quantitative structure models (QSMs) were applied to TLS data to compute dendrometric parameters. Tree parameters measured in field survey, i.e. DBH, height, and computed volume, were compared to those resulting from TLS data processing. The effect of distance from the plot boundary in the accuracy of DBH, height and volume estimation from TLS data was tested.Main results: TLS-derived DBH showed a good correlation with the traditional forest inventory data (R2=0.98, RRMSE=7.81%), while tree height was less correlated with the traditional forest inventory data (R2=0.60, RRMSE=16.99%). Poor agreement was observed when comparing the volume from TLS data with volume estimated from the INFC II prediction equations.Research highlights: The study demonstrated that the application of QSM to plot-based terrestrial laser data generates errors in plots with high density of coniferous trees. A buffer zone of 5 m would help reduce the error of 35% and 42% respectively in height estimation for all trees and in volume estimation for broadleaved trees.

Soil nitrogen explanatory factors across a range of forest ecosystems and climatic conditions in Italy

Abstract N is known to be the most limiting element for vegetation growth in temperate and boreal forests. The expected increases in global temperature are predicted to accelerate N mineralization, therefore incrementing N availability in the soil and affecting the soil C cycle as well. While there is an abundance of C data collected to fulfill the requirements for national GHG accounting, more limited information is available for soil N accumulation and storage in relation to forest categories and altitudinal gradients. The data collected by the second Italian National Forest Inventory, spanning a wide range of temperature and precipitation values (10° latitudinal range), represented a unique opportunity to calculate N content and C/N ratio of the different soil layers to a depth of 30 cm. Boosted Regression Tree (BRT) models were applied to investigate the main determinants of soil N distribution and C/N ratio. Forest category was shown to be the main explanatory factor of soil N variability in seven out of eight models, both for forest floor and mineral soil layers. Moreover latitude explained a larger share of variability than single climate variables. BRT models explained, on average, the 49% of the data variability, with the remaining fraction likely due to soil-related variables that were unaccounted for. Accurate estimations of N pools and their determinants in a climate change perspective are consequently required to predict the potential impact of their degradation on forest soil N pools.

A stand-level model derived from National Forest Inventory data to predict periodic annual volume increment of forests in Italy

ABSTRACTA model was developed for predicting the periodic annual volume increment (PAI) of forests using variables commonly recorded through field surveys or the remote sensing. The model was developed using the Italian National Forest Inventory (INFC2005) data, publicly available at www.inventarioforestale.org. Data from 5707 plots were split into two groups. The first was used for fitting the model; the second was used for cross validation. Model reliability for applications at the local, in the Alpine and Mediterranean regions, and at the country level was tested. A sensitivity analysis was carried out to investigate the effects of entering inaccurate values of the number of trees per hectare, one of the predictors of the final model, that may occur in case of biased estimates from the remote sensing. During model calibration, the highest proportion of increment variation was captured using forest category (FC) as dummy variable and, in this respect, this study supports the classification of forests on ecological basis as a stratification criterion in environmental sampling. The model explained 72% of PAI and it predicted annual increment at plot level with no statistical difference to the observed value in any FC, at the country level.

TaCAL: un sistema di tariffe del volume legnoso e delle fitomasse delle specie forestali della Calabria

Abstract: In the frame of the ALFORLAB project, a harmonised set of tree volume (and biomass) single entry tables (“tariffs”) was developed for the main 20 forest species of Calabria (Southern Italy), to outline the “coordinates” of volume expression for inventory purposes at both regional and forest planning level. The system, called TaCAL, was based on the double entry volume and biomass functions of the Italian National forest Inventory - INFC 2005 -, and was built studying the height/dbh relationships on 2300 trees measured during the INFC campaign in Calabria, on over 300 sampling plots randomly distributed. For each of the 20 species, or group of similar species, the tariff system is articulated into 9 h/d relationship curves, which are in turn related to corresponding single entry tables of tree volume (stem and branches with dbh above 5 cm), and respective biomass tables. According to INFC 2005 stereometric equations, TaCAL system is suitable for both high forests and coppices. TaCAL provides a uniform volume and biomass estimation system for all main forest species of Calabria as well as, under certain conditions, a unique criterion of expression of the site fertility. TaCAL tariff system has already been integrated into the construction of forecasting models of timber volume and biomass based on angle count samplings, at forest management scale. TaCAL has also provided the basis for ground truth volume estimates to develop forecast models through remote sensing LiDAR data. It must be emphasized that TaCAL represents the first case in Italy in which single tree data by INFC field surveys were used to carry out a comprehensive dendrometric system at a regional level.

I dati dell’Inventario Forestale Nazionale Italiano sono on-line e disponibili in modalità open-access

Abstract: Raw data from the Italian National Forest Inventory are on-line and publicly available. The National Forest Service in cooperation with the Forest Monitoring and Management Research Unit of the Council for Agricultural Research and Economics (CREA) released the raw data from the National Inventory of Forests and forest Carbon pools - INFC2005 project, the second Italian national forest inventory. Data are available together with metadata information at http://www.inventarioforestale.org/. Users, after registration, can download data from 230.874 living tree stems, 16.472 dead tree stems, 31.083 stumps, from a total of 7.272 field plots (for 1.384 of them additional data on fine woody debris and soil carbon pools are also available).

C Stocks in Forest Floor and Mineral Soil of Two Mediterranean Beech Forests

This study focuses on two Mediterranean beech forests located in northern and southern Italy and therefore subjected to different environmental conditions. The research goal was to understand C storage in the forest floor and mineral soil and the major determinants. Relative to the northern forest (NF), the southern forest (SF) was found to produce higher amounts of litterfall (4.3 vs. 2.5 Mg·ha−1) and to store less C in the forest floor (~8 vs. ~12 Mg·ha−1) but more C in the mineral soil (~148 vs. ~72 Mg·ha−1). Newly-shed litter of NF had lower P (0.4 vs. 0.6 mg·g−1) but higher N concentration (13 vs. 10 mg·g−1) than SF. Despite its lower Mn concentration (0.06 vs. 0.18 mg·g−1), SF litter produces a Mn-richer humus (0.32 vs. 0.16 mg·g−1) that is less stable. The data suggest that decomposition in the NF forest floor is limited by the shorter growing season (178 days vs. 238 days) and the higher N concentrations in newly shed litter and forest floor. Differences in C stock in the mineral soil reflect differences in ecosystem productivity and long-term organic-matter accumulation. The vertical gradient of soluble and microbial fractions in the soil profile of SF was consistent with a faster turnover of organic matter in the forest floor and greater C accumulation in mineral soil relative to NF. With reference to regional-scale estimates from Italian National Forest Inventory data, the C stock in the mineral soil and the basal area of Italian beech forests were found to be significantly related, whereas C stock in the forest floor and C stock in the mineral soil were not.

Evaluating the potential of an individual-tree sampling strategy for dendroecological investigations using the Italian National Forest Inventory data

European National Forest Inventories (NFIs) are well established in most of the European countries. In some NFIs, core samples are taken to estimate volume increments at annual resolution. However, the potential of the NFI for dendroecological investigations has not been evaluated so far. Therefore, the aim of this study was to test the potential of dendrochronological data collected during the Italian NFI in 2006. Silver fir was selected as a test species. Core samples were taken from eighty-one trees including one sample per inventory plot (IP). The 81 IPs were distributed across the Italian Alps and the northern Apennine thus well representing the site conditions of the study area. The effects of the detrending method and sample size on detecting middle- to long-term growth trends and high-frequency climate signals were tested. Further, cluster analysis was applied to find dissimilarities among tree-ring width (TRW) series.Results suggest the detrending method to be the most important factor for detecting growth trends, but not for identification of high-frequency climate signals. Sample size essentially influences the final mean chronology, but it does not bring new information when larger than 30 series. Two groups of trees were recognised with distinct middle-term TRW patterns, although being the same in terms of climate sensitivity. Results are mostly in line with recent findings of other authors, which suggest the potential of the NFI data for dendroecological investigations.

A national-scale, stand-level model to predict total above-ground tree biomass from growing stock volume

Interest in the accurate measurement of forest biomass has increased remarkably in the decades following the agreements made at the United Nations Framework Convention on Climate Change, particularly because of forest biomass’ role in carbon storage. Information on forest biomass over large areas is generally obtained from National Forest Inventory (NFI) statistics; however, these data are usually estimates of stem volumes and only rarely estimate tree biomass through direct measurements. Conversion from stem volumes to biomass is commonly achieved through factors known as biomass expansion factors (BEFs). BEFs may also expand the estimation of tree components and, in such cases, are also referred to as conversion and expansion factors (BCEFs). Experience has proven that biomass factors vary with stand characteristics such as composition, age, volume and others. For this reason, models that predict such factors as a function of stand variables have been developed. In Italy, the total above-ground tree biomass (AGB) of forests was estimated by the second Italian NFI through direct measurements and, on the basis of the stock estimates of tree volume and biomass, BCEFs representative of Italian broad-spectrum forest conditions (e.g., composition, density, silviculture treatment and others) for reference year 2005 were published. This paper presents a stand-level model developed to predict AGB density (Mgha−1) from growing stock volume (GSV) density (m3ha−1) by forest category. The model was calibrated using NFI plot level data. The fitted model was tested to estimate total AGB from aggregated data, which was represented as the mean GSV per hectare at the plot level, and two broader aggregation scales using the mean GSV per hectare of forest category in each administrative region and at the national level. The accuracy of the estimates generally decreased with the level of data aggregation, but the upscaling exercises only overestimated the national overall AGB value by a maximum of 2.00%, an acceptable difference given the broad datum used as the independent variable. The upscaling exercises proved that the model is also suitable for predicting AGB by forest category, particularly given that the estimated values always fell within the 95% confidence interval of the NFI reference estimates.