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

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dc.contributor.authorChinembiri, Tsikai Solomon
dc.contributor.authorMutanga, Onisimo
dc.contributor.authorDube, Timothy
dc.date.accessioned2023-04-20T10:05:24Z
dc.date.available2023-04-20T10:05:24Z
dc.date.issued2023
dc.description.abstractThe 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.en_US
dc.identifier.citationChinembiri, 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/rs15061649en_US
dc.identifier.issn2072-4292
dc.identifier.urihttps://doi.org/10.3390/rs15061649
dc.identifier.urihttp://hdl.handle.net/10566/8843
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectBayesian methodologyen_US
dc.subjectGeostatisticsen_US
dc.subjectRemote sensingen_US
dc.subjectEcosystemen_US
dc.subjectClimate changeen_US
dc.subjectsub-Saharan Africaen_US
dc.titleCarbon stock prediction in managed forest ecosystems using Bayesian and frequentist geostatistical techniques and new generation remote sensing metricsen_US
dc.typeArticleen_US

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