Suppr超能文献

基于地理探测器的中国青藏高原东南部滑坡与环境因子的空间关联性分析。

Analysis of the spatial association of geographical detector-based landslides and environmental factors in the southeastern Tibetan Plateau, China.

机构信息

State Key laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, China Academy of Sciences, Beijing, China.

University of the Chinese Academy of Sciences, Beijing, China.

出版信息

PLoS One. 2021 May 20;16(5):e0251776. doi: 10.1371/journal.pone.0251776. eCollection 2021.

Abstract

Steep canyons surrounded by high mountains resulting from large-scale landslides characterize the study area located in the southeastern part of the Tibetan Plateau. A total of 1766 large landslides were identified based on integrated remote sensing interpretations utilizing multisource satellite images and topographic data that were dominated by 3 major regional categories, namely, rockslides, rock falls, and flow-like landslides. The geographical detector method was applied to quantitatively unveil the spatial association between the landslides and 12 environmental factors through computation of the q values based on spatially stratified heterogeneity. Meanwhile, a certainty factor (CF) model was used for comparison. The results indicate that the q values of the 12 influencing factors vary obviously, and the dominant factors are also different for the 3 types of landslides, with annual mean precipitation (AMP) being the dominant factor for rockslide distribution, elevation being the dominant factor for rock fall distribution and lithology being the dominant factor for flow-like distribution. Integrating the results of the factor detector and ecological detector, the AMP, annual mean temperature (AMT), elevation, river density, fault distance and lithology have a stronger influence on the spatial distribution of landslides than other factors. Furthermore, the factor interactions can significantly enhance their interpretability of landslides, and the top 3 dominant interactions were revealed. Based on statistics of landslide discrepancies with respect to diverse stratification of each factor, the high-risk zones were identified for 3 types of landslides, and the results were contrasted with the CF model. In conclusion, our method provides an objective framework for landslide prevention and mitigation through quantitative, spatial and statistical analyses in regions with complex terrain.

摘要

高山环绕的陡峭峡谷是青藏高原东南部研究区的特征,这些峡谷是由大规模山体滑坡造成的。通过综合利用多源卫星图像和地形数据的遥感解译,共识别出 1766 个大型滑坡,主要分为 3 大类,即岩崩、岩屑流和土石流。应用地理探测器方法,通过基于空间分层异质性的 q 值计算,定量揭示滑坡与 12 个环境因子之间的空间关联。同时,还使用确定性因子 (CF) 模型进行比较。结果表明,12 个影响因子的 q 值差异明显,3 种滑坡类型的主导因子也不同,年平均降水量 (AMP) 是岩崩分布的主导因子,海拔是岩屑流分布的主导因子,岩性是土石流分布的主导因子。综合因子探测器和生态探测器的结果,AMP、年平均气温 (AMT)、海拔、河流密度、断层距离和岩性对滑坡空间分布的影响比其他因素更强。此外,因子相互作用可以显著增强它们对滑坡的解释能力,揭示了前 3 个主要的相互作用。根据各因子不同分层的滑坡差异统计,确定了 3 种类型滑坡的高风险区,并与 CF 模型进行了对比。总之,该方法通过对复杂地形地区的定量、空间和统计分析,为滑坡的预防和治理提供了客观的框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1d/8136732/df49bfd8d1c1/pone.0251776.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验