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.authorChinembiri, Tsikai Solomon
dc.contributor.authorMutanga, Onisimo
dc.contributor.authorDube, Timothy
dc.date.accessioned2023-06-07T09:24:33Z
dc.date.available2023-06-07T09:24:33Z
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 constructed. The SVC models built for each of the two multispectral remote sensing data sets were assessed based on the goodness of fit criterion as well as the predictive performance using a 10-fold cross-validation technique.en_US
dc.identifier.citationChinembiri, T. S. et al. (2022). Landsat-8 and sentinel-2 based prediction of forest plantation c stock using spatially varying coefficient Bayesian hierarchical models. Remote Sensing, 14(22), 5676. https://doi.org/10.3390/rs14225676en_US
dc.identifier.issn2072-4292
dc.identifier.urihttps://doi.org/10.3390/rs14225676
dc.identifier.urihttp://hdl.handle.net/10566/9055
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectGeographyen_US
dc.subjectStatistics studiesen_US
dc.subjectBayesian hierarchical modellingen_US
dc.subjectEcosystemen_US
dc.subjectClimate changeen_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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