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Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements / Mikko Kukkonen in Silva fennica, vol 55 n° 1 (January 2021)  

Public [article]  inSilva fennica > vol 55 n° 1 (January 2021) . - n° 10360 Titre : Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements Type de document : Article/Communication Auteurs : Mikko Kukkonen, Auteur ; Eetu Kotivuori, Auteur ; Matti Maltamo, Auteur ; Lauri Korhonen, Auteur ; Petteri Packalen, Auteur Année de publication : 2021 Article en page(s) : n° 10360 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classification barycentrique [Termes IGN] données de terrain [Termes IGN] données localisées 3D [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] image Sentinel-MSI [Termes IGN] inventaire forestier local [Termes IGN] modèle de simulation [Termes IGN] régression [Termes IGN] semis de points [Termes IGN] volume en bois Résumé : (auteur) Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available. Numéro de notice : A2021-738 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10360 En ligne : https://doi.org/10.14214/sf.10360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98703 [article]