Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest
文献类型：期刊论文
| 作者 | Shi, Tiezhu2,3; Hu, Xianjun1; Guo, Long4; Su, Fenzheng5; Tu, Wei2,3; Hu, Zhongwen2,3; Liu, Huizeng2,3; Yang, Chao2,3; Wang, Jingzhe2,3; Zhang, Jie2,3 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2021-10-20 |
| 卷号 | 792页码:8 |
| 关键词 | Digital soil mapping Remote sensing data Social sensing data Geodetector Urban functional type |
| ISSN号 | 0048-9697 |
| DOI | 10.1016/j.scitotenv.2021.148455 |
| 通讯作者 | Tu, Wei(tuwei@szu.edu.cn) |
| 英文摘要 | This study aimed to map the spatial patterns of Zn in urban topsoil by using multisource geospatial data and machine learning method. Geological map, digital elevation models, and Landsat images were used to extract data related to geology, relief, and land use types and a vegetation index. Urban functional types were derived from the fusion of Systeme Probatoire d'Observation de la Terre 5 images, points of interest, and real-time Tencent user data. A geodetector was adopted to select key environmental covariates. Random forest (RF) and geographically weighted regression (GWR) were employed to model and map Zn concentrations in urban topsoil. The results showed that urban functional type, geology, NDVI, elevation, slope, and aspect were key environmental covariates. Compared with land use types, urban functional types could better reflect the spatial variation in Zn. The RF and GWR models were established using the key environmental covariates, with leave-one-out cross-validated R values of 0.68 and 0.58 and root mean square errors of 0.51 and 0.57, respectively. The results indicated that digital mapping of Zn in urban topsoil by using multisource geospatial data and RF was feasible. RF might be more suitable to fit the stochastic characteristics of Zn in urban topsoils than GWR, which considers deterministic trends in modeling. (c) 2021 Elsevier B.V. All rights reserved. |
| WOS关键词 | HEAVY-METALS ; SPATIAL-DISTRIBUTION ; SOIL ; POLLUTION ; CONTAMINATION ; MULTIVARIATE ; PATTERNS ; RISK ; IDENTIFICATION ; LANDSCAPE |
| 资助项目 | National Natural Science Foundation of China[41890854] ; National Natural Science Foundation of China[41701476] ; National Natural Science Foundation of China[4170010438] ; Natural Science Funding of Shenzhen University[2019060] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000689491000002 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Funding of Shenzhen University |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/164898]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Tu, Wei |
| 作者单位 | 1.Naval Univ Engn, Sch Elect Engn, Wuhan 430070, Peoples R China 2.Shenzhen Univ, MNR Key Lab Geo Environm Monitoring Great Bay, Shenzhen Key Lab Spatial Smart Sensing & Serv, Guangdong Key Lab Urban Informat & Guangdong, Shenzhen 518060, Guangdong, Peoples R China 3.Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen 518060, Peoples R China 4.Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Tiezhu,Hu, Xianjun,Guo, Long,et al. Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2021,792:8. |
| APA | Shi, Tiezhu.,Hu, Xianjun.,Guo, Long.,Su, Fenzheng.,Tu, Wei.,...&Wu, Guofeng.(2021).Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest.SCIENCE OF THE TOTAL ENVIRONMENT,792,8. |
| MLA | Shi, Tiezhu,et al."Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest".SCIENCE OF THE TOTAL ENVIRONMENT 792(2021):8. |
入库方式： OAI收割
来源：地理科学与资源研究所
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