Climate Change Data Portal
| DOI | 10.1080/15481603.2024.2303868 |
| A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems | |
| 发表日期 | 2024 |
| ISSN | 1548-1603 |
| EISSN | 1943-7226 |
| 起始页码 | 61 |
| 结束页码 | 1 |
| 卷号 | 61期号:1 |
| 英文摘要 | 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 ($1 \le MgCh{a<^>{ - 1}} \le 135)$1 <= MgCha-1 <= 135) and the Sentinel-2 ($1 \le MgCh{a<^>{ - 1}} \le 130)$1 <= MgCha-1 <= 130)-based C stock models than C stock predictions published in related studies. Distance to settlements ($DIST$DIST) and $NDVI$NDVI 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 ($1 \le MgCh{a<^>{ - 1}} \le4)$1 <= MgCha-1 <= 4) 95% credible interval whilst the Landsat-8-based C stock counterpart gave a 2.16 MgCha-1 RMSE with a ($2 \le MgCh{a<^>{ - 1}} \le4)$2 <= MgCha-1 <= 4) 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. |
| 英文关键词 | Bayesian; hierarchical; geostatistics; MCMC; prediction; remote sensing |
| 语种 | 英语 |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| WOS类目 | Geography, Physical ; Remote Sensing |
| WOS记录号 | WOS:001143148400001 |
| 来源期刊 | GISCIENCE & REMOTE SENSING
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/289131 |
| 作者单位 | University of Kwazulu Natal; University of the Western Cape |
| 推荐引用方式 GB/T 7714 | . A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems[J],2024,61(1). |
| APA | (2024).A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems.GISCIENCE & REMOTE SENSING,61(1). |
| MLA | "A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems".GISCIENCE & REMOTE SENSING 61.1(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
