Carbon stock prediction in managed forest ecosystems using Bayesian and frequentist geostatistical techniques and new generation remote sensing metrics
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
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 aides from the Landsat-8 and Sentinel-2 multispectral remote
sensing platforms. The frequentist geostatistical approach’s reliance on the long-run frequency of
repeated experiments for constructing confidence intervals is not always practical or feasible, as
practitioners typically have access to a single dataset due to cost constraints on surveys and sampling.
We evaluated two approaches for C stock prediction using two new generation multispectral remote
sensing datasets because of the inherent uncertainty characterizing spatial prediction problems in
the unsampled locations, as well as differences in how the Bayesian and frequentist geostatistical
paradigms handle uncertainty.
Description
Keywords
Bayesian methodology, Geostatistics, Remote sensing, Ecosystem, Climate change, sub-Saharan Africa
Citation
Chinembiri, T. S. et al. (2023). Carbon stock prediction in managed forest ecosystems using Bayesian and frequentist geostatistical techniques and new generation remote sensing metrics. Remote Sensing, 15(6), 1649. https://doi.org/10.3390/rs15061649