Landsat-8 and sentinel-2 based prediction of forest plantation c stock using spatially varying coefficient bayesian hierarchical models
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Remote Sensing
Abstract
This 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
Description
Keywords
Bayesian hierarchical modelling, Geostatistics, Eucalyptus grandis, Eucalyptus camaldulensis, Pinus patula
Citation
Chinembiri, 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.