Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.landurbplan.2018.07.016 |
A Bayesian approach to mapping the uncertainties of global urban lands | |
Ouyang, Zutao1; Fan, Peilei1,2; Chen, Jiquan1,3; Lafortezza, Raffaele1,4; Messina, Joseph P.1,3; Giannico, Vincenzo4; John, Ranjeet1,3 | |
2019-07-01 | |
发表期刊 | LANDSCAPE AND URBAN PLANNING |
ISSN | 0169-2046 |
EISSN | 1872-6062 |
出版年 | 2019 |
卷号 | 187页码:210-218 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Italy |
英文摘要 | Global distribution of urban lands is one of the essential pieces of information necessary for urban planning. However, large disagreement exists among different products and the uncertainty remains difficult to quantify. We applied a Bayesian approach to map the uncertainties of global urban lands. We demonstrated the approach by producing a hybrid global urban land map that synthesized five different urban land maps in ca. 2000 at 1-km resolution. The resulting hybrid map is a posterior probability map with pixel values suggesting the probability of being urban land, which is validated by 30-m higher resolution references. We also quantified the minimum and maximum urban areas in 2000 for each country/continent based on subjective probability thresholds (i.e., 0.9 and 0.1) on our hybrid urban map. Globally, we estimated that the urban land area was between 377,000 and 533,000 km(2) in 2000. The credible interval of minimum/maximum urban area can help guide future studies in estimating urban areas. In addition to providing uncertainty information, the hybrid map also achieves higher accuracy than individual maps when it is converted into a binary urban/non-urban map using a probability threshold of 0.5. This new method has the ability to further integrate discrete site/location-based data, local, regional, and global urban land maps. As more data is sequentially integrated, the accuracy is expected to improve. Therefore, our hybrid map should not be regarded as a final product, but a new prior product for future synthesis and integration toward a "big data" solution. |
英文关键词 | Remote sensing Bayesian Urban MODIS Uncertainty Hybrid |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000467665900020 |
WOS关键词 | QUANTIFYING UNCERTAINTY ; COVER ; URBANIZATION ; DENSITY ; CLASSIFICATION ; FRAMEWORK ; MODELS ; SCALES ; MODIS ; MAP |
WOS类目 | Ecology ; Environmental Studies ; Geography ; Geography, Physical ; Regional & Urban Planning ; Urban Studies |
WOS研究方向 | Environmental Sciences & Ecology ; Geography ; Physical Geography ; Public Administration ; Urban Studies |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/184764 |
专题 | 资源环境科学 |
作者单位 | 1.Michigan State Univ, CGCEO, 1405 S Harrison Rd, E Lansing, MI 48823 USA; 2.Michigan State Univ, Sch Planning Design & Construct, E Lansing, MI 48823 USA; 3.Michigan State Univ, Dept Geog Environm & Spatial Sci, E Lansing, MI 48824 USA; 4.Univ Bari Aldo Moro, Dept Sci Agroambientali & Terr, Via Amendola 165-A, I-70126 Bari, Italy |
推荐引用方式 GB/T 7714 | Ouyang, Zutao,Fan, Peilei,Chen, Jiquan,et al. A Bayesian approach to mapping the uncertainties of global urban lands[J]. LANDSCAPE AND URBAN PLANNING,2019,187:210-218. |
APA | Ouyang, Zutao.,Fan, Peilei.,Chen, Jiquan.,Lafortezza, Raffaele.,Messina, Joseph P..,...&John, Ranjeet.(2019).A Bayesian approach to mapping the uncertainties of global urban lands.LANDSCAPE AND URBAN PLANNING,187,210-218. |
MLA | Ouyang, Zutao,et al."A Bayesian approach to mapping the uncertainties of global urban lands".LANDSCAPE AND URBAN PLANNING 187(2019):210-218. |
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