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Towards sustainable forestry: Using a spatial Bayesian belief network to quantify trade-offs among forest-related ecosystem services / Catherine Frizzle in Journal of Environmental Management, vol 301 ([01/01/2022])  

Public [article]  inJournal of Environmental Management > vol 301 [01/01/2022] . - n° 113817 Titre : Towards sustainable forestry: Using a spatial Bayesian belief network to quantify trade-offs among forest-related ecosystem services Type de document : Article/Communication Auteurs : Catherine Frizzle, Auteur ; Richard A. Fournier, Auteur ; Melanie Trudel, Auteur ; Joan E. Luther, Auteur Année de publication : 2022 Article en page(s) : n° 113817 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] gestion forestière durable [Termes IGN] réseau bayesien [Termes IGN] service écosystémique [Termes IGN] théorie de Dempster-Shafer [Vedettes matières IGN] Foresterie Résumé : (auteur) Assessing trade-offs among ecosystem services (ESs) that are provided by forests is necessary to support decision-making and to minimize negative effects of timber harvesting. In this study, we examined how spatial data, forest operational rules, ESs, and probabilistic statistics can be combined into a practical tool for trade-off analysis that could guide decision-making towards sustainable forestry. Our main goal was to analyze trade-offs among the wood provisioning ES and other forest ESs at the landscape level using a Bayesian belief network (BBN). We used LiDAR data to derive four ES layers as inputs to a spatial BBN: (i) wood provisioning; (ii) erosion regulating; (iii) climate regulating; and (iv) habitat supporting. We quantified operational constraints with four forest operational rules (FOR) that were defined in terms of: (i) potential harvest block size; (ii) distance between a small potential harvest block and a larger harvest block; (iii) gross merchantable volume (GMV); and (iv) distance to an existing resource road. Maps of the most probable trade-off classes between the wood provisioning ES and other ESs enabled us to identify areas where timber harvesting should be avoided or where timber harvesting should have a very low negative effect on other ESs. Even with our most restrictive management scenario, the total GMV that could be harvested met the annual allowable cut (AAC) volume required to meet sustainable forestry objectives. Through our study, we demonstrated that high-resolution spatial data could be used to quantify trade-offs among wood provisioning ES and other forest-related ESs and to simulate small changes in ES indicators within the BBN. We also demonstrated the potential to evaluate management scenarios to reduce trade-offs by considering FOR as inputs to the BBN. Maps of the most probable trade-off classes among two or three ESs under operational constraints provide key information to guide forest management decision-making towards sustainable forestry. Numéro de notice : A2022-338 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1016/j.jenvman.2021.113817 Date de publication en ligne : 01/10/2021 En ligne : https://doi.org/10.1016/j.jenvman.2021.113817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100709 [article]