Landsat-8 and sentinel-2 based prediction of forest plantation c stock using spatially varying coefficient bayesian hierarchical models

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dc.contributor.authorDube, Timothy
dc.contributor.authorChinembiri, Tsikai Solomon
dc.contributor.authorMutanga, Onisimo
dc.date.accessioned2023-06-15T09:04:19Z
dc.date.available2023-06-15T09:04:19Z
dc.date.issued2022
dc.description.abstractThis study sought to establish the performance of Spatially Varying Coefficient (SVC) Bayesian Hierarchical models using Landsat-8, and Sentinel-2 derived auxiliary information in predicting plantation forest carbon (C) stock in the eastern highlands of Zimbabwe. The development and implementation of Zimbabwe’s land reform program undertaken in the year 2000 and the subsequent redistribution and resizing of large-scale land holdings are hypothesized to have created heterogeneity in aboveground forest biomass in plantation ecosystems. The Bayesian hierarchical framework, accommodating residual spatial dependence and non-stationarity of model predictors, was evaluated. Firstly, SVC models utilizing Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), derived from Landsat-8 and Sentinel-2 data and 191 sampled C stock observations were constructeden_US
dc.identifier.citationChinembiri, T.S., Mutanga, O. and Dube, T., 2022. Landsat-8 and Sentinel-2 Based Prediction of Forest Plantation C Stock Using Spatially Varying Coefficient Bayesian Hierarchical Models. Remote Sensing, 14(22), p.5676.en_US
dc.identifier.urihttps://doi.org/10.3390/rs14225676
dc.identifier.urihttp://hdl.handle.net/10566/9088
dc.language.isoenen_US
dc.publisherRemote Sensingen_US
dc.subjectBayesian hierarchical modellingen_US
dc.subjectGeostatisticsen_US
dc.subjectEucalyptus grandisen_US
dc.subjectEucalyptus camaldulensisen_US
dc.subjectPinus patulaen_US
dc.titleLandsat-8 and sentinel-2 based prediction of forest plantation c stock using spatially varying coefficient bayesian hierarchical modelsen_US
dc.typeArticleen_US

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