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| Reference : Diagnosing structure and composition typologies in uneven-aged broad-leaved forests: a c... |
| 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  [Université de Liège - ULg > Forêts, Nature et Paysage > Gestion des ressources forestières et des milieux naturels >] | |
| Brostaux, Yves [Université de Liège - ULg > Sciences agronomiques > Statistique, Inform. et Mathém. appliquée à la bioingénierie >] | |
| Claessens, Hugues [Université de Liège - ULg > Forêts, Nature et Paysage > Gestion des ressources forestières et des milieux naturels >] | |
| Lejeune, Philippe [Université de Liège - ULg > Forêts, Nature et Paysage > Gestion des ressources forestières et des milieux naturels >] | |
| Sep-2012 | |
| 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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