A multi-source data approach to carbon stock prediction using bayesian hierarchical geostatistical models in plantation forest ecosystems
| dc.contributor.author | Dube, Timothy | |
| dc.contributor.author | Chinembiri, Tsikai Solomon | |
| dc.contributor.author | Mutanga, Onisimo | |
| dc.date.accessioned | 2025-10-29T11:09:29Z | |
| dc.date.available | 2025-10-29T11:09:29Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | 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. | |
| dc.identifier.citation | Chinembiri, T.S., Mutanga, O. and Dube, T., 2024. A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems. GIScience & Remote Sensing, 61(1), p.2303868. | |
| dc.identifier.uri | https://doi.org/10.1080/15481603.2024.2303868 | |
| dc.identifier.uri | https://hdl.handle.net/10566/21309 | |
| dc.language.iso | en | |
| dc.publisher | Taylor and Francis Ltd. | |
| dc.subject | Bayesian | |
| dc.subject | Geostatistics | |
| dc.subject | Hierarchical | |
| dc.subject | Prediction | |
| dc.subject | Remote sensing | |
| dc.title | A multi-source data approach to carbon stock prediction using bayesian hierarchical geostatistical models in plantation forest ecosystems | |
| dc.type | Article |