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Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data / Fardin Moradi in Annals of forest research, vol 65 n° 1 (January - June 2022)  

Public [article]  inAnnals of forest research > vol 65 n° 1 (January - June 2022) . - pp 165 - 182 Titre : Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data Type de document : Article/Communication Auteurs : Fardin Moradi, Auteur ; Seyed Mohammad Moein Sadeghi, Auteur ; Hadi Beygi Heidarlou, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 165 - 182 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] allométrie [Termes IGN] biomasse aérienne [Termes IGN] classification barycentrique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] forêt méditerranéenne [Termes IGN] image proche infrarouge [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] Iran [Termes IGN] Quercus brantii [Termes IGN] taillis Résumé : (auteur) Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m2). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: MultiLayer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold cross-validation was used to verify the effectiveness of models. Examination of the Pearson’s correlation coefficient between AGB and the extracted ‎values showed that IPVI and NDVI vegetation indices had the highest correlation with AGB (r = 0.897). The results indicated that the MLPNN algorithm was the best ML option (RMSE = 1.71 t ha-1; MAE = 1.37 t ha-1; relative RMSE = 24.75%; R2 = 0.87) in estimating the AGB, providing new insights on the capability of remotely sensed-based AGB modeling of sparse Mediterranean forest ecosystems in an area with limited number of field sample plots. Numéro de notice : A2022-876 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15287/afr.2022.2390 Date de publication en ligne : 29/06/2022 En ligne : https://doi.org/10.15287/afr.2022.2390 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102180 [article]