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Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)  

Public [article]  inAnnals of forest research > vol 62 n° 1 (January - June 2019) . - pp 109 - 122 Titre : Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest Type de document : Article/Communication Auteurs : Aline Bernarda Debastiani, Auteur ; Carlos Roberto Sanquetta, Auteur ; Ana Paula Dalla Corte, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 109 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] Amazonie [Termes IGN] apprentissage automatique [Termes IGN] arbre aléatoire [Termes IGN] bande C [Termes IGN] biomasse aérienne [Termes IGN] Brésil [Termes IGN] forêt tropicale [Termes IGN] fusion d'images [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] réseau neuronal convolutif Résumé : (auteur) The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (AGB) in a high-biomass tropical ecosystem. This study was carried out in Jamari National Forest, located in the Brazilian Amazon. The response variable was AGB (Mg/ha) estimated from airborne laser surveys. The following treatments were considered as model predictors: 1) Sentinel-1 Sigma 0 at VV and VH polarizations; 2) (1) plus Sentinel-1 textural metrics; 3) (2) plus Sentinel-2 bands and derived vegetation indices (LAI, RVI, SAVI, NDVI).Our modeling design estimated the relative importance of SAR vs. optical variables in explaining AGB. The modeling was performed with twelve machine-learning algorithms including, neural network and regression tree. The addition of texture and optical data provided a noticeable improvement (3%) over models with SAR backscatter only. The best model performance was achieved with the Random Tree algorithm. Our results demonstrate the potential of freely-available SAR data and machine learning for mapping AGB in tropical ecosystems. Numéro de notice : A2019-335 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15287/afr.2018.1267 Date de publication en ligne : 30/07/2019 En ligne : http://dx.doi.org/10.15287%2Fafr.2018.1267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93349 [article]