[en] Structure and composition of forest stands are crucial factors for forest planning and
biodiversity management. In Belgium, typologies of structure and composition exist to support
planning in uneven-aged broadleaved forests (typically dominated by oak and beech). The
principle of these typologies is to classify irregular stands with the percentage of small, medium,
large, and very large trees (regarding dbh), and the percentage of basal area of oak and beech.
This paper investigates the potential of LiDAR data processed with classification methods (k-nn,
K-Means, CART, etc.) to allocate a forest structure and composition type. For this purpose
several supervised and unsupervised classification methods are compared, as well as the impact
of leaf-on (summer) and leaf-off (winter) data to discriminate the forest types.
Disciplines :
Environmental sciences & ecology
Author, co-author :
Bonnet, Stéphanie ; Université de Liège - ULiège > Forêts, Nature et Paysage > Gestion des ressources forestières et des milieux naturels
Brostaux, Yves ; Université de Liège - ULiège > Sciences agronomiques > Statistique, Inform. et Mathém. appliquée à la bioingénierie
Claessens, Hugues ; Université de Liège - ULiège > Forêts, Nature et Paysage > Gestion des ressources forestières et des milieux naturels
Lejeune, Philippe ; Université de Liège - ULiège > Forêts, Nature et Paysage > Gestion des ressources forestières et des milieux naturels
Language :
English
Title :
Diagnosing structure and composition typologies in uneven-aged broad-leaved forests: a comparison of classification methods
