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Improving Finnish multi-source national forest inventory by 3D aerial imaging / Sakari Tuominen in Silva fennica, vol 51 n° 4 (2017)  

Public [article]  inSilva fennica > vol 51 n° 4 (2017) Titre : Improving Finnish multi-source national forest inventory by 3D aerial imaging Type de document : Article/Communication Auteurs : Sakari Tuominen, Auteur ; Timo P Pitkänen, Auteur ; Andras Balazs, Auteur ; et al., Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique [Termes IGN] corrélation automatique de points homologues [Termes IGN] distribution spatiale [Termes IGN] Finlande [Termes IGN] image aérienne [Termes IGN] image satellite [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] photogrammétrie numérique [Termes IGN] placette d'échantillonnage [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Optical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km2. Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials. On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower short-range correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential. Numéro de notice : A2017-646 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article En ligne : https://doi.org/10.14214/sf.7743 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87003 [article]