Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China
文献类型：期刊论文
| 作者 | Zhou, Shuang2,3; Peng, Li1
|
| 刊名 | FRONTIERS IN PLANT SCIENCE
 |
| 出版日期 | 2021-11-04 |
| 卷号 | 12页码:16 |
| 关键词 | Bayesian belief networks alpine grassland degradation frequency ratio model NDVI risk assessment |
| ISSN号 | 1664-462X |
| DOI | 10.3389/fpls.2021.773759 |
| 英文摘要 | Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P < 0.05), 66.57% (P < 0.05), and 66.11% (P < 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P > 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration. |
| WOS关键词 | QUANTITATIVE ASSESSMENT ; ECOSYSTEM ; IMPACT ; WORLDS |
| 资助项目 | National Natural Science Foundation of China[42071222] ; National Natural Science Foundation of China[41930651] |
| WOS研究方向 | Plant Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000722322700001 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.imde.ac.cn/handle/131551/56329]  |
| 专题 | 成都山地灾害与环境研究所_山区发展研究中心 |
| 通讯作者 | Peng, Li |
| 作者单位 | 1.Sichuan Normal Univ, Coll Geog & Resources, Chengdu, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Mt Dev, Chengdu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Shuang,Peng, Li. Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China[J]. FRONTIERS IN PLANT SCIENCE,2021,12:16. |
| APA | Zhou, Shuang,&Peng, Li.(2021).Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China.FRONTIERS IN PLANT SCIENCE,12,16. |
| MLA | Zhou, Shuang,et al."Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China".FRONTIERS IN PLANT SCIENCE 12(2021):16. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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