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Hierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabwe
(Ecological Informatics, 2022)
We 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 ...
Carbon stock prediction in managed forest ecosystems using Bayesian and frequentist geostatistical techniques and new generation remote sensing metrics
(MDPI, 2023)
The study compares the performance of a hierarchical Bayesian geostatistical methodology
with a frequentist geostatistical approach, specifically, Kriging with External Drift (KED), for
predicting C stock using prediction ...