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Auteur Syed Adnan |
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A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions / Syed Adnan in Forest ecology and management, vol 433 (15 February 2019)
[article]
Titre : A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions Type de document : Article/Communication Auteurs : Syed Adnan, Auteur ; Matti Maltamo, Auteur ; David A. Coomes, Auteur ; Antonio Garcia-Abril, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 111 - 121 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] classification barycentrique
[Termes IGN] classification et arbre de régression
[Termes IGN] coefficient de Gini
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] dynamique de la végétation
[Termes IGN] écorégion
[Termes IGN] hétérogénéité environnementale
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Pinophyta
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter , Gini coefficient , basal area larger than mean and density of stems –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, and were the most important variables in the identification of FSTs. Lower, medium and high values of and characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using and . Then we used similar structural predictors derived from ALS – maximum height (), L-coefficient of variation (), L-skewness (), and percentage of penetration (), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions. Numéro de notice : A2019-007 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2018.10.057 Date de publication en ligne : 03/11/2018 En ligne : https://doi.org/10.1016/j.foreco.2018.10.057 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91600
in Forest ecology and management > vol 433 (15 February 2019) . - pp 111 - 121[article]