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Auteur Eetu Kotivuori |
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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)
[article]
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 boisRésumé : (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
in Silva fennica > vol 55 n° 1 (January 2021) . - n° 10360[article]Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland / Eetu Kotivuori in Silva fennica, vol 50 n° 4 (2016)
[article]
Titre : Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland Type de document : Article/Communication Auteurs : Eetu Kotivuori, Auteur ; Lauri Korhonen, Auteur ; Petteri Packalen, Auteur Année de publication : 2016 Article en page(s) : 280 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] régression
[Termes IGN] volume en bois
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) The aim of this study was to examine how well stem volume, above-ground biomass and dominant height can be predicted using nationwide airborne laser scanning (ALS) based regression models. The study material consisted of nine practical ALS inventory projects taken from different parts of Finland. We used field sample plots and airborne laser scanning data to create nationwide and regional models for each response variable. The final models had one or two ALS predictors, which were chosen based on the root mean square error (RMSE), and cross-validated. Finally, we tested how much predictions would improve if the nationwide models were calibrated with a small number of regional sample plots. Although forest structures differ among different parts of Finland, the nationwide volume and biomass models performed quite well (leave-inventory-area-out RMSE 22.3% to 33.8%, mean difference [MD] –13.8% to 18.7%) compared with regional models (leave-plot-out RMSE 20.2% to 26.8%). However, the nationwide dominant height model (RMSE 5.4% to 7.7%, MD –2.0% to 2.8%, with the exception of the Tornio region – RMSE 11.4%, MD –9.1%) performed nearly as well as the regional models (RMSE 5.2% to 6.7%). The results show that the nationwide volume and biomass models provided different means than real means at regional level, because forest structure and ALS device have a considerable effect on the predictions. Large MDs appeared especially in northern Finland. Local calibration decreased the MD and RMSE of volume and biomass models. However, the nationwide dominant height model did not benefit much from calibration. Numéro de notice : A2016--113 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.14214/sf.1567 En ligne : https://doi.org/10.14214/sf.1567 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84766
in Silva fennica > vol 50 n° 4 (2016) . - 280 p.[article]