Ensembles of multiple spectral water indices for improving surface water classification
文献类型：期刊论文
| 作者 | Wen, Zhaofei1,3 ; Zhang, Ce2,4; Shao, Guofan1; Wu, Shengjun3; Atkinson, Peter M.4
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| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
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| 出版日期 | 2021-04-01 |
| 卷号 | 96页码:13 |
| 关键词 | Water index Threshold Integrated decision making Mixed pixels MNDWI |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2020.102278 |
| 通讯作者 | Wen, Zhaofei(wenzhaofei@cigit.ac.cn) ; Zhang, Ce(c.zhang9@lancaster.ac.uk) |
| 英文摘要 | Mapping surface water distribution and its dynamics over various environments with robust methods is essential for managing water resources and supporting water-related policy design. Thresholding Single Water Index image (TSWI) with threshold is a common way of using water index (WI) for mapping water for it is easy to use and could obtain acceptable accuracies in many applications. As more and more WIs are available and each has its distinct merits, the real-world application of TSWI, however, often face two practical concerns: (1) selection of an appropriate WI and (2) determination of an appropriate threshold for a given WI. These two issues are problematic for many users who rely either on trial-and-error procedures that are time-consuming or on their personal preferences that are somewhat subjective. To better deal with these two practical concerns, an alternative way of using WIs is suggested here by transforming the current paradigm into a simple but robust ensemble approach called Collaborative Decision-making with Water Indices (CDWI). A total of 145 subsite images (900 x 900 m) from 22 Landsat-8 OLI scenes that covering various water-land environments around the world were used to assess the performance of TSWI and the CDWI. Five benchmark WIs were adopted in five TSWI methods and CDWI method: Normalized Difference Water Index (NDWI), the Modified NDWI (MNDWI), the Automated Water Extraction Indices without considering (AWEI0) and with considering (AWEI1) shadows, and the state-of-the-art 2015 water index (WI2015). Two aspects of performance were analyzed: comparing their accuracies (indicated by both F1-scores and Youden's Index) over various environments and comparing their accuracy sensitivities to threshold. The results demonstrate that CDWI produced higher accuracies than the other five TSWI methods for most application cases. Particularly, more cases (indicated by percentage) produced higher F1-scores by CDWI than the other five TSWI methods, i.e. 67% (CDWI) vs. 15% (TSWINDWI), 54% (CDWI) vs. 22% (TSWIMNDWI), 42% (CDWI) vs. 12% (TSWIAWEI0), 57% (CDWI) vs. 17% (TSWIAWEI1), and 34% (CDWI) vs. 12% (TSWIWI2015). Moreover, the F1-score of the CDWI is less sensitive to the change of thresholds compared with that of the five TSWI methods. These important benefits of CDWI make it a robust approach for mapping water. The uncertainty of CDWI method was thoroughly discussed and a general guidance (or look-up-table) for determining parameters of CDWI method was also suggested. The underlying framework of CDWI could be readily generalizable and applicable to other satellite images, such as Landsat TM/ETM+, MODIS, and Sentinel-2 images. |
| 资助项目 | Open Fund of the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University[18R07] ; National Natural Science Foundation of China[41501096] ; National Natural Science Foundation of China[51779241] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000608483100003 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/12766]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Wen, Zhaofei; Zhang, Ce |
| 作者单位 | 1.Purdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47906 USA 2.UK Ctr Ecol & Hydrol, Lib Ave, Lancaster LA1 4AP, England 3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Key Lab Reservoir Aquat Environm, Chongqing 400714, Peoples R China 4.Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England |
| 推荐引用方式 GB/T 7714 | Wen, Zhaofei,Zhang, Ce,Shao, Guofan,et al. Ensembles of multiple spectral water indices for improving surface water classification[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2021,96:13. |
| APA | Wen, Zhaofei,Zhang, Ce,Shao, Guofan,Wu, Shengjun,&Atkinson, Peter M..(2021).Ensembles of multiple spectral water indices for improving surface water classification.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,96,13. |
| MLA | Wen, Zhaofei,et al."Ensembles of multiple spectral water indices for improving surface water classification".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 96(2021):13. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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