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Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system / Minh Hai Pham in Plos one, vol 15 n° 5 (May 2020)  

Public [article]  inPlos one > vol 15 n° 5 (May 2020) . - n° 0233110 Titre : Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system Type de document : Article/Communication Auteurs : Minh Hai Pham, Auteur ; Thi Hoai Do, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0233110 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] biomasse forestière [Termes IGN] changement d'occupation du sol [Termes IGN] image Sentinel-SAR [Termes IGN] image SPOT 6 [Termes IGN] Inférence floue [Termes IGN] mangrove [Termes IGN] Viet Nam [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Background : Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB). Methods : This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Results : The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation. Conclusion : From the experiments, such hybrid integration can be recommended for use as an alternative solution for biomass estimation. In a broader context, the fast growth of metaheuristic search algorithms has created new scientifically sound solutions for better analysis of forest cover. Numéro de notice : A2020-833 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article DOI : https://doi.org/10.1371/journal.pone.0233110 Date de publication en ligne : 21/05/2020 En ligne : https://doi.org/10.1371/journal.pone.0233110 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97667 [article]