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Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory / Jonas Bohlin in Silva fennica, vol 51 n° 2 (2017)  

Public [article]  inSilva fennica > vol 51 n° 2 (2017) Titre : Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory Type de document : Article/Communication Auteurs : Jonas Bohlin, Auteur ; Inka Bohlin, Auteur ; Jonas Jonzén, Auteur ; Mats Nilsson, Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Betula (genre) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image aérienne [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] régression linéaire [Termes IGN] semis de points [Termes IGN] Suède [Termes IGN] surface terrière [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid- and south Sweden. Regression models were developed and applied to 12.5 m × 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image- and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping. Numéro de notice : A2017-648 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.14214/sf.2021 En ligne : https://doi.org/10.14214/sf.2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87007 [article]

Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory / Jonas Bohlin in Silva fennica, vol 51 n° 2 (2017)  

Public [article]  inSilva fennica > vol 51 n° 2 (2017) . - 18 p. Titre : Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory Type de document : Article/Communication Auteurs : Jonas Bohlin, Auteur ; Inka Bohlin, Auteur ; Jonas Jonzén, Auteur ; Mats Nilsson, Auteur Année de publication : 2017 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] appariement d'images [Termes IGN] carte forestière [Termes IGN] données dendrométriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] image aérienne [Termes IGN] inventaire forestier étranger (données) [Termes IGN] photogrammétrie numérique [Termes IGN] régression [Termes IGN] station forestière [Termes IGN] Suède [Termes IGN] surface terrière Résumé : (auteur) Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid- and south Sweden. Regression models were developed and applied to 12.5 m × 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image- and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping. Numéro de notice : A2017-185 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.2021 En ligne : https://doi.org/10.14214/sf.2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84764 [article]