Carbon stock prediction in managed forest ecosystems using Bayesian and frequentist geostatistical techniques and new generation remote sensing metrics

Loading...
Thumbnail Image

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