气候变化领域集成服务门户(CCPortal): A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems

CCPortal

DOI	10.1080/15481603.2024.2303868
	A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems
	Chinembiri, Tsikai S.; Mutanga, Onisimo; Dube, Timothy
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/289132
作者单位	University of Kwazulu Natal; University of the Western Cape
推荐引用方式 GB/T 7714	Chinembiri, Tsikai S.,Mutanga, Onisimo,Dube, Timothy. A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems[J],2024,61(1).
APA	Chinembiri, Tsikai S.,Mutanga, Onisimo,&Dube, Timothy.(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	Chinembiri, Tsikai S.,et al."A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems".GISCIENCE & REMOTE SENSING 61.1(2024).