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We examine the current status of greenhouse gas inventories of the sector Land Use, Land-Use Change and Forestry (LULUCF), in European countries, with specific focus on the utilization of National Forest Inventory (NFI) programs. LULUCF inventory is an integral part of the reporting obligations under the United Nations Framework Convention on Climate Change (UNFCCC) and its Kyoto Protocol. The analysis is based on two questionnaires prepared by the COST Action E43 âHarmonisation of National Forest Inventories in Europeâ, which were answered by greenhouse gas reporting experts in European countries. The following major conclusions can be drawn from the analysis: 1) definitions used to obtain carbon pool change estimates vary widely among countries and are not directly comparable 2) NFIs play a key role for LULUCF greenhouse gas estimation and reporting under UNFCCC, and provide the fundamental data needed for the estimation of carbon stock changes covering not only living biomass, but increasingly also deadwood, litter and soil compartments. The study highlights the effects of adopting different definitions for two major reporting processes, namely UNFCCC and FAO, and exemplifies the effect of different tree diameter thresholds on carbon stock change estimates for Finland. The results demonstrate that more effort is needed to harmonize forest inventory estimates for the purpose of making the estimates of forest carbon pool changes comparable. This effort should lead to a better utilization of the data from the European NFI programs and improve the European greenhouse gas reporting.

Accurate measurement of forest growing stock is a prerequisite for implementing climate-smart forestry strategies. This study deals with the use of airborne laser scanning data to assess carbon stock at the tree level. It aims to demonstrate that the combined use of two unsupervised techniques will improve the accuracy of estimation supporting sustainable forest management. Based on the heterogeneity of tree height and point cloud density, we classified 31 forest stands into four complexity categories. The point cloud of each stand was further divided into three horizontal layers, improving the accuracy of tree detection at tree level for which we calculated volume and carbon stock. The average accuracy of tree detection was 0.48. The accuracy was higher for forest stands with lower tree density and higher frequency of large trees, as well as a dense point cloud (0.65). The prediction of carbon stock was higher with a bias ranging from -0.3% to 1.5% and a root mean square error ranging from 0.14% to 1.48%. Key words: tree detection, airborne laser scanning (ALS), forest structure, carbon stock, climate-smart forestry. La mesure precise du capital forestier en croissance est un prerequis a l'implantation de strategies forestieres intelligentes face au climat. Cette etude porte sur l'utilisation de donnees de balayage laser aeroporte pour evaluer le stock de carbone a l'echelle de l'arbre. Elle vise a demontrer que l'utilisation combinee de deux techniques non supervisees va ameliorer la precision de l'estimation sur laquelle s'appuie un amenagement forestier durable. Sur la base de l'heterogeneite de la hauteur des arbres et de la densite du nuage de points, nous avons classe 31 peuplements forestiers dans quatre categories de complexite. Le nuage de points de chaque peuplement a par la suite ete divise en trois couches horizontales, ce qui ameliore la precision de la detection de chacun des arbres pour lesquels nous avons calcule le volume et le stock de carbone. La precision moyenne de la detection des arbres etait de 0,48. La precision etait plus elevee pour les peuplements forestiers qui avaient une plus faible densite et une frequence plus elevee de gros arbres, ainsi qu'un nuage de points dense (0,65). La prediction du stock de carbone etait plus elevee avec un biais allant de -0,3 a 1,5 % et un ecart moyen quadratique (EMQ) entre 0,14 et 1,48 %. [Traduit par la Redaction] Mots-cles: detection des arbres, balayage laser aeroporte (BLA), structure de la foret, stock de carbone, foresterie intelligente face au climat., 1. Introduction In Europe, forests cover about 35% of the total land area (SoEF 2020) and play a significant role in climate change mitigation thanks to their capacity to remove [...]

Accurate evapotranspiration (ET) estimation is crucial for sustainable water management in the diverse and water-scarce Mediterranean region. This study compares three prominent models (Simulator of Terrestrial Ecohydrological Processes and Systems (STEPS), Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), and Two-Source Energy Balance (TSEB)) with established global datasets (Moderate Resolution Imaging Spectroradiometer 8-day global terrestrial product (MOD16A2), Global Land Evaporation Amsterdam Model (GLEAM), and TerraClimate) at multiple spatial and temporal scales and validates model outcomes with eddy covariance based ground measurements. Insufficient ground-based observations limit comprehensive model validation in the eastern Mediterranean part (Turkey and Balkans). The results reveal significant discrepancies among models and datasets, highlighting the challenges of capturing ET variability in this complex region. Differences are attributed to variations in ecosystem type, energy balance calculations, and water availability constraints. Ground validation shows that STEPS performs well in some French and Italian forests and crops sites but struggles with seasonal ET patterns in some locations. SCOPE mostly overestimates ET due to detailed radiation flux calculations and lacks accurate water limitation representation. TSEB faces challenges in capturing ET variations across different ecosystems at a coarser 10 km resolution. No single model and global dataset accurately represent ET across the entire region. Model performance varies by region and ecosystem. As GLEAM and TSEB excel in semi-arid Savannahs, STEPS and SCOPE are better in grasslands, croplands, and forests in few locations (5 out of 18 sites) which indicates these models need calibration for other locations and ecosystem types. Thus, a region-specific model calibration and validation, sensitive to extremely humid and arid conditions can improve ET estimation across the diverse Mediterranean region. [ABSTRACT FROM AUTHOR]

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA's Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%). [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning (TLS) technology characterizes standing trees with millimetric precision. An important step to accurately quantify tree volume and above-ground biomass using TLS point clouds is the discrimination between timber and leaf components. This study evaluates the performance of machine learning (ML)-derived models aimed at discriminating timber and leaf TLS point clouds, focusing on eight Mediterranean tree species datasets. The results show the best accuracies for random forests, gradient boosting machine, stacked ensemble model, and deep learning models with an average F1 score equal to 0.92. The top-performing ML-derived models showed well-balanced average precision and recall rates, ranging from 0.86 to 0.91 and 0.92 to 0.96 for precision and recall, respectively. Our findings show that Italian maple, European beech, hazel, and small-leaf lime tree species have more accurate F1 scores, with the best average F1 score of 0.96. The factors influencing the timber–leaf discrimination include phenotypic factors, such as bark surface (i.e., roughness and smoothness), technical issues (i.e., noise points and misclassification of points), and secondary factors (i.e., bark defects, lianas, and microhabitats). The top-performing ML-derived models report a time computation ranging from 8 to 37 s for processing 2 million points. Future studies are encouraged to calibrate, configure, and validate the potential of top-performing ML-derived models on other tree species and at the plot level. [ABSTRACT FROM AUTHOR]

Knowledge of forest biomass is an essential parameter for managing the forest in a sustainable way, as forest biomass data availability and reliability are necessary for forestry and forest planning, but also for the carbon market as well as to support the local economy in the mountain and inner areas. However, the accurate quantification of the above-ground biomass (AGB) is still a challenge both at the local and global levels. The use of remote sensing techniques with Unmanned Aerial Vehicle (UAV) platforms can be an excellent trade-off between resolution, scale, and frequency data of AGB estimation. In this study, we evaluated the combined use of RGB images from UAV, LiDAR data and ground truth data to estimate AGB in a forested watershed in Southern Italy. A low-cost AGB estimation method was adopted using a commercial fixed-wing drone equipped with an RGB camera, combined with the canopy information derived by LiDAR and validated by field data. Two modelling methods (stepwise regression, SR and random forest, RF) were used to estimate forest AGB. The output was an accurate maps of AGB for each model. The RF model showed better accuracy than the Steplm model, and the R2 increased from 0.81 to 0.86, and the RMSE and MAE values were decreased from 45.5 to 31.7 Mg ha-1 and from 34.2 to 22.1 Mg ha-1 respectively. We demonstrated that by increasing the computing efficiency through a machine learning algorithm, readily available images can be used to obtain satisfactory results, as proven by the accuracy of the Random forest above biomass estimation model. [ABSTRACT FROM AUTHOR]

Abstract: We investigated the relationship between time since death and morphological characteristics of Abies alba Mill. (European silver fir) and Fagus sylvatica L. (European beech) stumps in two forest stands [...]