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Unsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning / Jari Vauhkonen in Forestry, an international journal of forest research, vol 89 n° 4 (August 2016)  

Public [article]  inForestry, an international journal of forest research > vol 89 n° 4 (August 2016) . - pp 350 - 363 Titre : Unsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning Type de document : Article/Communication Auteurs : Jari Vauhkonen, Auteur ; Joni Imponen, Auteur Année de publication : 2016 Article en page(s) : pp 350 - 363 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Aves [Termes IGN] biodiversité végétale [Termes IGN] classification non dirigée [Termes IGN] couvert forestier [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] exploration de données géographiques [Termes IGN] gestion de la vie sauvage [Termes IGN] gestion forestière durable [Termes IGN] habitat d'espèce [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier étranger (données) [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) To account for ecological objectives in forest management planning, potential habitats need to be mapped, characterized and evaluated for utility in alternative management practices. Airborne laser scanning (ALS) is increasingly used to derive predictive maps of habitat quality. Unlike ecologically driven approaches that require spatially and temporally co-located training data of the specific species, we tested whether indicative information on the habitat potential could be obtained by means of an unsupervised classification of ALS data. Based on a literature review, altogether five ALS features quantifying vegetation height, cover and diversity were expected to capture the essential variation in the habitat requirements of western capercaillie (Tetrao urogallus L.) and hazel grouse (Tetrastes bonasia L.), which are the most important game birds occurring in the studied area. The features were extracted from sparse density, leaf-off ALS data at a resolution of 256 m2 and partitioned using an unsupervised k-means algorithm. By analysing the persistence of the cluster ensemble formed by the partitioning, altogether 158 plots in 16 structural classes were assigned for field measurements to determine which real-world forest phenomena affected the clustering. The clustering was found to stratify the area mainly in terms of size-related attributes such as timber volume and basal area. The understorey, shrub and herb layers had less correspondence with the clustering, indicating that an unsupervised classification is not directly suitable for habitat mapping. The result was improved using empirical threshold values for the ALS features determined according to the plots labelled as the most potential habitats in the field measurements. This semi-supervised classification of the data indicated 4 per cent of the total forest area as suitable for the specific species, which appears a reasonable estimate of the core area of the habitats considered. Overall, the partitioning formed aggregated, stand-like spatial patterns, even though the neighbourhoods of the individual 256 m2 cells were not considered at all. The result could be further refined by spatial optimization to produce indicative maps for forest management planning with ALS as the sole data source. Numéro de notice : A2016--155 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpw011 En ligne : https://doi.org/10.1093/forestry/cpw011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85780 [article]