Xiaocong Xu

Mr. Xiaocong Xu


Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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27 April 2017 in Annals of the American Association of Geographers
Verified
Journal Article
Closed access
XLXia Li
Xia Li
Guangzhao ChenGuangzhao Chen
Guangzhao Chen
Xiaoping LiuXiaoping Liu
Xiaoping Liu
XLXun Liang
Xun Liang
Shaojian WangShaojian Wang
Shaojian Wang
XXXiaocong Xu
Xiaocong Xu
Global land-use and land-cover change (LUCC) data are crucial for modeling a wide range of environmental conditions. So far, access to high-resolution LUCC products at a global scale for public use is difficult because of data and technical issues. This article presents a Future Land-Use Simulation (FLUS) system to simulate global LUCC in relation to human–environment interactions, which is built and verified by using remote sensing data. IMAGE has been widely used in environmental studies despite its relatively coarse spatial resolution of 30 arc-min, which is about 55 km at the equator. Recently, an improved model has been developed to simulate global LUCC with a 5-min resolution (about 10 km at the equator). We found that even the 10-km resolution, however, still produced major distortions in land-use patterns, leading urban land areas to be underestimated by 19.77 percent at the global scale and global land change relating to urban growth to be underestimated by 60 to 97 percent, compared with the 1-km resolution model proposed through this article. These distortions occurred because a large percentage of small areas of urban land was merged into other land-use classes. During land-use change simulation, a majority of small urban clusters were also lost using the IMAGE product. Responding to these deficiencies, the 1-km FLUS product developed in this study is able to provide the spatial detail necessary to identify spatial heterogeneous land-use patterns at a global scale. We argue that this new global land-use product has strong potential in radically reducing uncertainty in global environmental modeling.