This paper aims to provide general considerations, in the form of a scientific review, with reference to selected experiences of ALS applications under alpine, temperate and Mediterranean environments in Italy as case studies. In Italy, the use of ALS data have been mainly focused on the stratification of forest stands and the estimation of their timber volume and biomass at local scale. Potential for ALS data exploitation concerns their integration in forest inventories on large territories, their usage for silvicultural systems detection and their use for the estimation of fuel load in forest and pre-forest stands. Multitemporal ALS may even be suitable to support the assessment of current annual volume increment and the harvesting rates.
Awareness of exhaustible forest resources is not recent in human history; rather, it dates back to the late Middle Ages, when it became clear that some kind of planning was needed to utilise forest resources and to do so, assessment was necessary. Postponed in time, enlarged to a national scale and based on statistical sampling, compared to the inventory methods adopted at that time, modern NFIs are assigned to produce sound information necessary to support forest policies. Forest areas and composition, ownership, growing stock and increment, as well as management, silviculture and structural characters are among the variables assessed by NFIs. This chapter provides statistics on those variables. For areas, estimates are shown for Total wooded area, Forest, Other wooded land, and their distribution among inventory categories and forest types, which describe species composition. In addition, the chapter also addresses distribution by altitude classes. For stands characters, areas are shown by crown coverage, development stage and age class. Lastly, inventory statistics are given on the presence and amount of small trees and shrubs.
LiDAR-based techniques to estimate forest variables at the stand level require accurate calibration through ground truth data. One purpose of this study was to verify whether angle count samples can be used as suitable ground truth to calibrate LiDAR-based models for timber volume estimation. Volume data were acquired on the ground for 79 plots in the Latemar forest (province of Bolzano, Italian Alps). A simple linear regression model, using the sum of all of the tree canopy heights in the plot as the explanatory variable, was adopted. As angle count samples have no fixed area, three different methods to approximate their size were compared. The angle count sample area can be properly approximated by visual assessment of the tree size classes and by callipering the largest tree in the plot. The results show that angle count sampling can be an efficient solution to calibrate LiDAR-based models: they produced fair estimates at the plot level (relative root mean square error (RMSE), 26.6%) that were better than fixed-radius plot estimates with full callipering (RMSE, 29.7%). Estimate uncertainty at increasingly large forest stand areas was also calculated by means of a simulation procedure. It showed that low uncertainty (standard error of estimate = approximately 2%) could be reached at a forest compartment level (19 ha on average). Résumé :Les techniques qui font appel au LiDAR pour estimer les variables forestières à l'échelle du peuplement requièrent un étalonnage précis à partir de points de contrôle sur le terrain. Un premier objectif de cette étude était de vérifier si un échantillonnage par placettes à rayon variable pouvait être utilisé comme points de contrôle appropriés pour calibrer les modèles basés sur des données LiDAR en vue d'estimer le volume de bois. Les données de volume ont été acquises sur le terrain dans 79 parcelles de la forêt de Latemar (province de Bolzano dans les Alpes italiennes). Le modèle de régression linéaire simple qui a été adopté utilisait la somme de la hauteur du houppier de tous les arbres dans la parcelle comme variable explicative. Puisque les échantillons provenant de placettes à rayon variable n'ont pas une superficie fixe, trois méthodes différentes d'approximation de leur dimension ont été comparées. La surface de la placette à rayon variable peut être correctement approximée par une évaluation visuelle des classes de taille d'arbres et par la mesure au pied à coulisse du diamètre du plus gros arbre dans la parcelle. Les résultats montrent que l'échantillonnage par placette à rayon variable peut être une solution efficace pour étalonner les modèles basés sur des données LiDAR : ils ont produit des estimations justes à l'échelle de la parcelle (EQM relatif de 26,6 %), meilleures que celles qui ont été obtenues avec des placettes à rayon fixe où la totalité des tiges étaient mesurées au moyen du pied à coulisse (EQM relatif de 29,7 %). Une estimation de l'incertitude a également été calculée au moyen d'une procédure de simulation pour des surfaces de peuplements forestiers...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.