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Symbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography / Somnath Paramanik in Applied Geography, vol 139 (February 2022)  

Public [article]  inApplied Geography > vol 139 (February 2022) . - n° 102649 Titre : Symbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography Type de document : Article/Communication Auteurs : Somnath Paramanik, Auteur ; Mukunda Dev Behera, Auteur ; J. Dash, Auteur Année de publication : 2022 Article en page(s) : n° 102649 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] allométrie [Termes IGN] canopée [Termes IGN] densité de la végétation [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] image hémisphérique [Termes IGN] Leaf Area Index [Termes IGN] mangrove [Termes IGN] régression Résumé : (auteur) The leaf area index (LAI) serves as a proxy to understand the dynamics of plant productivity, energy balance, and gas exchange. Cost-effective and accurate estimation of LAI is essential for under-assessed carbon-rich tropical forests, e.g., mangroves. Here, we developed allometric equations to estimate LAI using a combination of non-destructive, optical measurements through digital hemispherical photographs (DHP), and genetic programming-based Symbolic Regression (SR). We used three structural variables: diameter at breast height (DBH), tree density (TD), and canopy height (Ht) for a mangrove forest in the BhitarKanika Wildlife Sanctuary (BWS), located along the Eastern coast of India. Triplet combination using SR provided the best equation (R2 = 0.51) than any singlet or duplet combination of the variables, and even it was better than Partial Least Square (PLS) based regression (R2 = 0.42). To the best of our knowledge, the current study is the maiden attempt to develop an allometric model to estimate LAI for a mangrove ecosystem in India. In-situ measurements of structural variables such as DBH, Ht, and TD can be used for LAI estimates, as shown here. LAI estimates using cost-effective methods would greatly enhance our understanding of the spatial and temporal dynamics of mangrove ecosystems. Numéro de notice : A2022-456 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.apgeog.2022.102649 Date de publication en ligne : 29/01/2022 En ligne : https://doi.org/10.1016/j.apgeog.2022.102649 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101239 [article]