Hierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabwe

Chinembiri, Tsikai Solomon; Mutanga, Onisimo; Dube, Timothy

doi:10.1016/j.ecoinf.2022.101934

Cited by 6 publications

(8 citation statements)

References 78 publications

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“…As such, the Sentinel-2 model seems to underpredict more of the C stock at unsampled locations compared to that of its Landsat-8-based C stock predictive model counterpart. The slight underprediction by Sentinel-2 can partly be attributed to the finer spatial and spectral resolutions of the sensor within the visible and near-infrared segments of the electromagnetic spectrum (EMS) [16,69,70]. Enhancements in the spectral and spatial resoultion of Sentinel-2 confirms the much shorter 95% Credible Interval Widths (CIWs) displayed by the Sentinel-2-based C stock predictive model in Figure 4d (0.40-1.78 MgCha −1 ) than those of the Landsat-8-based predictive model in Figure 4b (2.0-4.7 MgCha −1 ).…”

Section: Stock and Medium Resolution Sensor-derived Vegetation Indicesmentioning

confidence: 99%

“…As such, the Sentinel-2 model seems to underpredict more of the C stock at unsampled locations compared to that of its Landsat-8-based C stock predictive model counterpart. The slight underprediction by Sentinel-2 can partly be attributed to the finer spatial and spectral resolutions of the sensor within the visible and near-infrared segments of the electromagnetic spectrum (EMS) [16,69,70] Furthermore, Takagi et al [73] employed LiDAR for the prediction of forest biomass in Hokkaido, Japan, and determined an RMSE biomass prediction of 19.1 MgCha −1 . The differences between the prediction accuracy results reported in the literature and our study can also be justified by the differences in forest density, since the erstwhile studies were carried out in subtropical rainforest biomes.…”

Section: Model Validation and Diagnosticsmentioning

confidence: 99%

“…The tendency of underprediction in Sentinel-2-derived C stock models can partly be attributed to the finer spatial resolutions of the sensor within the visible and near-infrared segments of the electromagnetic spectrum (EMS) [16,69,70]. As highlighted in [78], there are notable improvements in the radiometric resolution of the Landsat-8 OLI sensor from 8 bits to 12 bits.…”

Section: Frequentist Geostatistical Predictive Model Evaluationmentioning

confidence: 99%

“…Important research in the field of biomass estimation using the Bayesian techniques include [13][14][15][16]. Recent studies assessing AGB distribution in temperate European ecosystems established accuracies of 17.52 Mg/ha by Babcock et al [14], and 1.16 MgCha −1 and 2.69 MgCha −1 accuracies were found for Sentinel-2-based and Landsat-8-based C stock predictive models, respectively [16].…”

Section: Comparative Bayesian and Frequentist C Stock Predictive Mode...mentioning

confidence: 99%

“…Some studies in the literature dwell at one of the two approaches as the principal methodology of inference. Notable work in the realm of biomass estimation using the Bayesian techniques include [13][14][15][16]. Recent studies assessing AGB distribution established accuracies of 17.52 Mg/ha [14] and 1.16 MgCha −1 and 2.69 MgCha −1 for Sentinel-2-based and Landsat-8-based Carbon (C) stock predictive models, respectively [16].…”

Section: Introductionmentioning

confidence: 99%

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Carbon Stock Prediction in Managed Forest Ecosystems Using Bayesian and Frequentist Geostatistical Techniques and New Generation Remote Sensing Metrics

2023

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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. Information on C stock spectral prediction in the form of NDVI, SAVI, and EVI derived from multispectral remote sensing platforms, Landsat-8 and Sentinel-2, was used to build Bayesian and frequentist-based C stock predictive models in the sampled plantation forest ecosystem. Sentinel-2-based C stock predictive models outperform their Landsat-8 counterparts using both the Bayesian and frequentist inference approaches. However, the Bayesian-based Sentinel-2 C stock predictive model (

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Section: Stock and Medium Resolution Sensor-derived Vegetation Indicesmentioning

confidence: 99%

“…As such, the Sentinel-2 model seems to underpredict more of the C stock at unsampled locations compared to that of its Landsat-8-based C stock predictive model counterpart. The slight underprediction by Sentinel-2 can partly be attributed to the finer spatial and spectral resolutions of the sensor within the visible and near-infrared segments of the electromagnetic spectrum (EMS) [16,69,70] Furthermore, Takagi et al [73] employed LiDAR for the prediction of forest biomass in Hokkaido, Japan, and determined an RMSE biomass prediction of 19.1 MgCha −1 . The differences between the prediction accuracy results reported in the literature and our study can also be justified by the differences in forest density, since the erstwhile studies were carried out in subtropical rainforest biomes.…”

Section: Model Validation and Diagnosticsmentioning

confidence: 99%