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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 ...
Landsat-8 and sentinel-2 based prediction of forest plantation c stock using spatially varying coefficient Bayesian hierarchical models
(MDPI, 2022)
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) ...