Browsing by Author "Chinembiri, Tsikai Solomon"

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    A multi-source data approach to carbon stock prediction using bayesian hierarchical geostatistical models in plantation forest ecosystems
    (Taylor and Francis Ltd., 2024) Dube, Timothy; Chinembiri, Tsikai Solomon; Mutanga, Onisimo
    Modeling of environmental phenomena is usually confounded by the influence of multiple factors existing at different time and spatial scales. Bayesian modeling is presumed to be the best approach for modeling such complex systems. Using a Bayesian hierarchical inferential framework, we employed a multi-source data approach (i.e. remote sensing derived anthropogenic, climatic and topographic set of variables) to model Carbon (C) stock in a managed plantation forest ecosystem in Zimbabwe’s Eastern Highlands. We therefore investigated how two related multi-data sources of new generation remote sensing derived ancillary information influence C stock prediction required for building sustainable capacity in C monitoring and reporting. Two mainstream models constructed from Landsat-8 and Sentinel-2 derived vegetation indices coupled with climatic and topographic covariates were used to predict C stocks using forest inventory data collected using spatial coverage sampling. A multi-source data driven approach to C stock prediction yielded slightly lower predictions for both the Landsat-8 ((Formula presented.) and the Sentinel-2 ((Formula presented.) -based C stock models than C stock predictions published in related studies. Distance to settlements ((Formula presented.)) and (Formula presented.) are significant predictors of C stock with the Sentinel-2-based C stock model outperforming its Landsat-8 model variant in terms of prediction accuracy. The Sentinel-2-based C stock model resulted in a 1.17 MgCha−1 Root Mean Square Error (RMSE) with a ((Formula presented.) 95% credible interval whilst the Landsat-8-based C stock counterpart gave a 2.16 MgCha−1 RMSE with a ((Formula presented.) associated 95% credible interval. Despite a multi-source data prediction approach to the modeling of C stock in a managed plantation forest ecosystem set-up, the issues of scale still play a major role in modeling spatial variability of natural resource variables. Both climatic and topographic derived ancillary data are not significant predictors of C stock under the present modeling conditions. Accurate and precise accounting of C stock for climate change mitigation and action can best be done at landscape scales rather than local scale as the scale of variation for climate-change-related variables vary at larger spatial scales than the ones utilized in the present study.
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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) Chinembiri, Tsikai Solomon; Mutanga, Onisimo; Dube, Timothy
    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.
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    Landsat-8 and sentinel-2 based prediction of forest plantation c stock using spatially varying coefficient bayesian hierarchical models
    (Remote Sensing, 2022) Dube, Timothy; Chinembiri, Tsikai Solomon; Mutanga, Onisimo
    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) stock in the eastern highlands of Zimbabwe. The development and implementation of Zimbabwe’s land reform program undertaken in the year 2000 and the subsequent redistribution and resizing of large-scale land holdings are hypothesized to have created heterogeneity in aboveground forest biomass in plantation ecosystems. The Bayesian hierarchical framework, accommodating residual spatial dependence and non-stationarity of model predictors, was evaluated. Firstly, SVC models utilizing Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), derived from Landsat-8 and Sentinel-2 data and 191 sampled C stock observations were constructed
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    Landsat-8 and sentinel-2 based prediction of forest plantation c stock using spatially varying coefficient Bayesian hierarchical models
    (MDPI, 2022) Chinembiri, Tsikai Solomon; Mutanga, Onisimo; Dube, Timothy
    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) stock in the eastern highlands of Zimbabwe. The development and implementation of Zimbabwe’s land reform program undertaken in the year 2000 and the subsequent redistribution and resizing of large-scale land holdings are hypothesized to have created heterogeneity in aboveground forest biomass in plantation ecosystems. The Bayesian hierarchical framework, accommodating residual spatial dependence and non-stationarity of model predictors, was evaluated. Firstly, SVC models utilizing Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), derived from Landsat-8 and Sentinel-2 data and 191 sampled C stock observations were constructed. The SVC models built for each of the two multispectral remote sensing data sets were assessed based on the goodness of fit criterion as well as the predictive performance using a 10-fold cross-validation technique.