Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns
文献类型：期刊论文
| 作者 | Yang, Fengshuo3,4; Wang, Zhihua3; Yang, Xiaomei3,4,5; Liu, Yueming3,4; Liu, Bin3,4; Wang, Jun1,2; Kang, Junmei1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2019-11-02 |
| 卷号 | 11期号:22页码:19 |
| 关键词 | urban land product updating accuracy assessment GlobeLand30 Global Urban Footprint Global Human Settlement Layer Landsat Operational Land Imager Phased Array type L-band Synthetic Aperture Radar |
| DOI | 10.3390/rs11222664 |
| 通讯作者 | Wang, Zhihua(zhwang@lreis.ac.cn) |
| 英文摘要 | Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object's features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas. |
| WOS关键词 | OBJECT-BASED CLASSIFICATION ; BUILT-UP AREA ; SPATIAL-RESOLUTION ; COVER CHANGE ; SEGMENTATION ; SCALE ; PALSAR |
| 资助项目 | CAS Earth Big Data Science Project[XDA19060303] ; National Key Research and Development Program of China[2016YFB0501404] ; National Science Foundation of China[41671436] ; Innovation Project of LREIS[O88RAA01YA] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000502284300069 |
| 资助机构 | CAS Earth Big Data Science Project ; National Key Research and Development Program of China ; National Science Foundation of China ; Innovation Project of LREIS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/130897]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Zhihua |
| 作者单位 | 1.Changan Univ, Geol Engn, Xian 710054, Shaanxi, Peoples R China 2.Changan Univ, Inst Surveying & Mapping, Xian 710054, Shaanxi, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Fengshuo,Wang, Zhihua,Yang, Xiaomei,et al. Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns[J]. REMOTE SENSING,2019,11(22):19. |
| APA | Yang, Fengshuo.,Wang, Zhihua.,Yang, Xiaomei.,Liu, Yueming.,Liu, Bin.,...&Kang, Junmei.(2019).Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns.REMOTE SENSING,11(22),19. |
| MLA | Yang, Fengshuo,et al."Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns".REMOTE SENSING 11.22(2019):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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