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, 286-296. doi: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.
相关数据集
1.全球逐年不透水面数据 (Global Artificial Impervious Area, GAIA)
相关论文: 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.
数据简介: GAIA数据是基于长时序的30米分辨率的Landsat系列光学遥感及其他辅助数据（VIRRS夜间灯光数据及Sentinel-1雷达数据），首先通过空间掩模和特征评价算法实现了对逐年不透水面的快速制图，再通过时序一致性检验算法对不透水面序列进行滤波与转化逻辑推理，保证不透水面序列在时空上的合理性。该数据集已更新至2021年。
数据链接: https://pan.baidu.com/s/1dt8SILPrLCJZBr1NlskrOA?pwd=7tgm
GEE数据链接: projects/ee-mzyuguojiang/assets/Urban/Product/GAIA/GAIA_1985_2022
存储格式: GeoTiff
空间分辨率: 30m
命名规则: GAIA_1985_2022_{longitude}_{latitude} (以左上角坐标命名)
像元值0到38, 0为非城市, 2为2021年新增城市, 以此类推, 38为1985年及以前的城市
2.全球城市边界数据(Global Urban Boundary, GUB)
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.
数据简介: 该数据集以全球不透水面数据(GAIA)数据为主要数据源，首先采用空间聚合、核密度估计的方法，填补城市内部；再采用膨胀和腐蚀的形态学处理方法，合并、去除零散城市斑块；最后识别城市边界目标，再移除内部空洞，得到城市矢量边界。该数据集已更新至2020年。
数据链接: https://pan.baidu.com/s/1iBKqLMyEqGnJAtDCLlLPCg?pwd=9s36
存储格式: Shapefile
