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Estimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 1 (January 2016) . - pp 41 - 49 Titre : Estimation of forest biomass using multivariate relevance vector regression Type de document : Article/Communication Auteurs : Alireza Sharifi, Auteur ; Jalal Amini, Auteur ; Ryutaro Tateishi, Auteur Année de publication : 2016 Article en page(s) : pp 41 - 49 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] biomasse forestière [Termes IGN] estimation statistique [Termes IGN] forêt [Termes IGN] image ALOS-PALSAR [Termes IGN] Iran [Termes IGN] Perceptron multicouche [Termes IGN] régression multiple [Termes IGN] séparateur à vaste marge [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) The objective of this study is to develop a method based on multivariate relevance vector regression (MVRVR) as a kernelbased Bayesian model for the estimation of above-ground biomass (AGB) in the Hyrcanian forests of Iran. Field AGB data from the Hyrcanian forests and multi-temporal PALSAR backscatter values are used for training and testing the methods. The results of the MVRVR method are then compared with other methods: multivariate linear regression (MLR), multilayer perceptron neural network (MLPNN), and support vector regression (SVR). The MLR model showed lower values of R2 than the three other approaches. Although the SVR model was found to be more accurate than MLPNN, it had the lowest saturation point of 224.75 Mg/ha. The use of MVRVR model significantly improves the estimation of AGB (R2 = 0.90; RMSE = 32.05 Mg/ha), and the model showed a superior performance in estimating AGB with the highest saturation point (297.81 Mg/ha). Numéro de notice : A2016-053 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.14358/PERS.83.1.41 En ligne : https://doi.org/10.14358/PERS.83.1.41 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79654 [article]