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Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm / Jaehoon Jung in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)
[article]
Titre : Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm Type de document : Article/Communication Auteurs : Jaehoon Jung, Auteur ; Sangpil Kim, Auteur ; Sungchul Hong, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 82 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] biomasse
[Termes IGN] classification barycentrique
[Termes IGN] Corée du sud
[Termes IGN] erreur aléatoire
[Termes IGN] erreur de positionnement
[Termes IGN] erreur systématique
[Termes IGN] estimation statistique
[Termes IGN] image Landsat-TM
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] puits de carboneRésumé : (Auteur) This paper suggested simulation approaches for quantifying and reducing the effects of National Forest Inventory (NFI) plot location error on aboveground forest biomass and carbon stock estimation using the k-Nearest Neighbor (kNN) algorithm. Additionally, the effects of plot location error in pre-GPS and GPS NFI plots were compared. Two South Korean cities, Sejong and Daejeon, were chosen to represent the study area, for which four Landsat TM images were collected together with two NFI datasets established in both the pre-GPS and GPS eras. The effects of plot location error were investigated in two ways: systematic error simulation, and random error simulation. Systematic error simulation was conducted to determine the effect of plot location error due to misregistration. All of the NFI plots were successively moved against the satellite image in 360° directions, and the systematic error patterns were analyzed on the basis of the changes of the Root Mean Square Error (RMSE) of kNN estimation. In the random error simulation, the inherent random location errors in NFI plots were quantified by Monte Carlo simulation. After removal of both the estimated systematic and random location errors from the NFI plots, the RMSE% were reduced by 11.7% and 17.7% for the two pre-GPS-era datasets, and by 5.5% and 8.0% for the two GPS-era datasets. The experimental results showed that the pre-GPS NFI plots were more subject to plot location error than were the GPS NFI plots. This study’s findings demonstrate a potential remedy for reducing NFI plot location errors which may improve the accuracy of carbon stock estimation in a practical manner, particularly in the case of pre-GPS NFI data. Numéro de notice : A2013-390 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.04.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.04.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32528
in ISPRS Journal of photogrammetry and remote sensing > vol 81 (July 2013) . - pp 82 - 92[article]Réservation
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