Reference : Diagnosing structure and composition typologies in uneven-aged broad-leaved forests: ...
Scientific congresses and symposiums : Poster
Life sciences : Environmental sciences & ecology
http://hdl.handle.net/2268/130931
Diagnosing structure and composition typologies in uneven-aged broad-leaved forests: a comparison of classification methods
English
Bonnet, Stéphanie mailto [Université de Liège - ULg > Forêts, Nature et Paysage > Gestion des ressources forestières et des milieux naturels >]
Brostaux, Yves mailto [Université de Liège - ULg > Sciences agronomiques > Statistique, Inform. et Mathém. appliquée à la bioingénierie >]
Claessens, Hugues mailto [Université de Liège - ULg > Forêts, Nature et Paysage > Gestion des ressources forestières et des milieux naturels >]
Lejeune, Philippe mailto [Université de Liège - ULg > Forêts, Nature et Paysage > Gestion des ressources forestières et des milieux naturels >]
Sep-2012
Yes
International
Silvilaser 2012
du 17 septembre 2012 au 19 septembre 2012
[en] Forest types ; LiDAR ; Classification
[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.
http://hdl.handle.net/2268/130931

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