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城市制图

长时序全球城市动态制图

该研究方向主要是基于长时序的遥感影像时序数据,基于Google Earth Engine (GEE)云计算平台,通过设计时序分析算法来提取长时序、高时空分辨率的城市用地制图产品及相关的衍生数据集(例如城市高度及土地利用)。通过分析长时序的城市时空演变数据,可以进一步分析城市增长的驱动要素,从而为开展城市增长模拟及未来的发展情景提供重要的技术支撑。


相关论文:

Li, X.C., Gong, P.*, Zhou, Y.Y.*, Wang, J., Bai, Y.Q., Chen, B., Hu, T.Y., Xiao, Y.X., Xu, B., Yang, J., Liu, X.P., Cai, W.J., Huang, H.B., Wu, T.H., Wang, X., Lin, P., Li, X., Chen, J., He, C.Y., Li, X., Yu, L., Clinton, N., & Zhu, Z.L. 2020. Mapping global urban boundaries from the global artificial impervious area (GAIA) data. Environmental Research Letters, 15, 094044. doi: 10.1088/1748-9326/ab9be3.

Liu, X.P., Huang, Y.H., Xu, X.C., Li, X.C., Li, X.*, Ciasi, P., Gong, K., Ziegler, A.D., Chen, A.P., Gong, P., Chen, J., Hu, G.H., Chen, Y.M., Wang, S.J., Wu, Q.S., Huang, K.N., Estes, L., & Zeng, Z.Z.* 2020. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability, doi: 10.1038/s41893-020-0521-x.

Li, X.C., Zhou, Y.Y.*, Gong, P., Seto, K.C., & Clinton, N. 2020. Developing a method to estimate building height from Sentinel-1 data. Remote Sensing of Environment, 240, 111705. doi: 10.1016/j.res.2020.111705.

Li, X.C., Zhou, Y.Y.*, Zhu, Z.Y., & Cao, W.T. 2020. A national dataset of 30-m annual urban extent dynamics (1985–2015) in the conterminous United States. Earth System Science Data, 12, 357-371. doi: https://doi.org/10.5194/essd-12-357-2020.

Gong, P.*, Li, X.C., Wang, J.*, Bai, Y., Chen, B., Hu, T.Y., Liu, X.P., Xu, B., Yang, J., Zhang, W., & Zhou, Y.Y. 2020. Annual maps of global artificial impervious areas (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, 111510. doi: 10.1016/j.rse.2019.111510.

Gong, P.*, Chen, B., Li, X.C., Liu, H., Wang, J., Bai, Y.Q., Chen, J.M., Chen, X., Feng, S.L., Huang, H.B., Huang, X.C., Jie, Y.W., Kang, Y.D., Lei, G.B., Li, A.N, Li, X.T., Li, X., Li, Y.C., Li, Z.L., Li, Z.D., Liu, C., Liu, C.X., Liu, M.C., Liu, S.G., Mao, W.L., Miao., C.H., Ni, H., Suen, H.P., Sun, B., Sun, F.D., Sun, J., Sun, L., Tian. T., Tong, X.H., Tseng, Y.S., Tu, Y., Wang, H., Wang, L., Wang, X., Wang, Z.M., Wu, T.H., Yang, J., Yue, W.Z., Zeng, H.D., Zhang, K., Zhang, N., Zhang, T., Zhang, Y., Zhao, F., Zheng, Y.C., Zhou, Q.M., Clinton, N., Zhu, Z.L., & Xu, B*. 2020. Mapping essential urban land use categories in China (EULUC-China): Preliminary results for 2018. Science Bulletin, 65, 182-187. doi: 10.1016/j-sclb.2019.12.007.

Gong, P.*, Li, X.C., & Zhang, W. 2019. 40-year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Science Bulletin, 64, 756-763. doi: 10.1016/j.scib.2019.04.024. 

Li, X.C., Zhou, Y.Y.*, Zhu, Z.Y., Liang, L., Yu, B.L, & Cao, W.T. 2018. Mapping annual urban dynamics (1985-2015) using time series of Landsat data. Remote Sensing of Environment, 216, 674-683. doi: 10.1016/j.rse.2018.07.030.

Hu, T.Y., Yang, J.*, Li, X.C., & Gong, P. 2016. Mapping urban land use by using Landsat images and open social data. Remote Sensing, 8(2), 151. doi:10.3390/rs8020151.

Li, X.C. & Gong, P.* 2016. An “exclusion-inclusion” framework for extracting human settlements in rapidly developing regions of China from Landsat images. Remote Sensing of Environment, 188, 286-296doi:10.1016/j.rse.2016.08.029

Li, X.C., Gong, P.* & Lu, Liang. 2015. A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sensing of Environment, 166, 78-90. doi: 10.1016/j.rse.2015.06.007.

Li, X.C., Liu, X.P.* & Yu, L. 2014. Aggregative model-based classifier ensemble for improving land-use/cover classification of Landsat TM Images. International Journal of Remote Sensing, 35(4), 1481-1495. doi: 10.1080/01431161.2013.878061