Circa 2014 African land-cover maps compatible with FROM-GLC and GLC2000 classification schemes based on multi-seasonal Landsat data
文献类型：期刊论文
| 作者 | Feng, Duole1; Zhao, Yuanyuan1; Yu, Le1; Li, Congcong1; Wang, Jie1; Clinton, Nicholas1; Bai, Yuqi1; Belward, Alan1; Zhu, Zhiliang1; Gong, Peng1 |
| 刊名 | International Journal of Remote Sensing
 |
| 出版日期 | 2016 |
| 卷号 | 37期号:19页码:4648-4664 |
| 通讯作者 | Gong, Peng (penggong@mail.tsinghua.edu.cn) |
| 英文摘要 | A new African land-cover data set has been developed using multi-seasonal Landsat Operational Land Imager (OLI) imagery mainly acquired around 2014, supplemented by Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+). Each path/row location was covered by five images, including one in the growing season of vegetation and the others in four meteorological seasons (i.e. spring, summer, autumn, and winter), choosing the image with the least cloud coverage. The data set has two classification schemes, i.e. Finer Resolution Observation and Monitoring – Global Land Cover (FROM-GLC) and Global Land Cover 2000 (GLC2000), providing greater flexibility in product comparisons and applications. Random forest was used as the classifier in this project. Overall accuracies were 71% and 67% for the maps in the FROM-GLC classification scheme at level 1 and level 2, respectively, and 70% for the map in the GLC2000 classification scheme. The newly developed African land-cover map achieved a greater improvement in accuracy compared to previous products in the FROM-GLC project. Multi-seasonal imagery helped increase the mapping accuracy by better differentiating vegetation types with similar spectral features in one specific season and identifying vegetation with a shorter growing season. Night light data with 1 km resolution was used to identify the potential area of impervious surfaces to avoid overestimating the distribution of impervious surfaces without decreasing the spatial resolution. Stacking multi-seasonal mapping results could adequately minimize the disturbance of cloud and shade. © 2016 Informa UK Limited, trading as Taylor & Francis Group. |
| 收录类别 | EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20164202901798 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39670]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1. Ministry of Education Key Laboratory for Earth System Modelling, Centre for Earth System Science, Tsinghua University, Beijing, China 2. Joint Centre for Global Change Studies, Beijing, China 3. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing Normal University, Beijing, China 4. European Commission, Joint Research Centre, Institute for Environment and Sustainability, Land Resource Management Unit, Ispra 5.Varese, Italy 6. US Geological Survey, Reston 7.VA, United States |
| 推荐引用方式 GB/T 7714 | Feng, Duole,Zhao, Yuanyuan,Yu, Le,et al. Circa 2014 African land-cover maps compatible with FROM-GLC and GLC2000 classification schemes based on multi-seasonal Landsat data[J]. International Journal of Remote Sensing,2016,37(19):4648-4664. |
| APA | Feng, Duole.,Zhao, Yuanyuan.,Yu, Le.,Li, Congcong.,Wang, Jie.,...&Gong, Peng.(2016).Circa 2014 African land-cover maps compatible with FROM-GLC and GLC2000 classification schemes based on multi-seasonal Landsat data.International Journal of Remote Sensing,37(19),4648-4664. |
| MLA | Feng, Duole,et al."Circa 2014 African land-cover maps compatible with FROM-GLC and GLC2000 classification schemes based on multi-seasonal Landsat data".International Journal of Remote Sensing 37.19(2016):4648-4664. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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