Hierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabwe

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dc.contributor.authorChinembiri, Tsikai S.
dc.contributor.authorMutanga, Onisimo
dc.contributor.authorDube, Timothy
dc.date.accessioned2023-03-16T09:43:31Z
dc.date.available2023-03-16T09:43:31Z
dc.date.issued2022
dc.description.abstractWe develop and present a novel Bayesian hierarchical geostatistical model for the prediction of plantation forest carbon stock (C stock) in the eastern highlands of Zimbabwe using multispectral Landsat-8 and Sentinel-2 remotely sensed data. Specifically, we adopt a Bayesian hierarchical methodology encompassing a model based inferential framework making use of efficient Markov Chain Monte Carlo (MCMC) techniques for assessing model input parameters. Our proposed hierarchical modelling framework evaluates the influence of two but related covariate information sources in C stock prediction in order to build sustainable capacity on carbon reporting and monitoring. The perceived improvements in the spectral and spatial properties of Landsat-8 and Sentinel-2 data and their potential to predict C stock with shorter uncertainty bounds is tested in the developed hierarchical Bayesian modelsen_US
dc.identifier.citationChinembiri, T.S., Mutanga, O., Dube, T., 2023. Hierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabwe. Ecological Informatics 73, 101934. https://doi.org/10.1016/j.ecoinf.2022.101934en_US
dc.identifier.issn15749541
dc.identifier.urihttps://doi.org/10.1016/j.ecoinf.2022.101934
dc.identifier.urihttp://hdl.handle.net/10566/8605
dc.language.isoenen_US
dc.publisherEcological Informaticsen_US
dc.subjectGeostatisticsen_US
dc.subjectBayesian inferenceen_US
dc.subjectHierarchical modellingen_US
dc.subjectMarkov chain monte carloen_US
dc.subjectClimate changeen_US
dc.titleHierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabween_US
dc.typeArticleen_US

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