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
MDPI
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. 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.
Description
Keywords
Geography, Statistics studies, Bayesian hierarchical modelling, Ecosystem, Climate change
Citation
Chinembiri, 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/rs14225676