Search

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 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]