Mapping Impervious Surface Dynamics of Guangzhou Downtown based on Google Earth Engine

Peilin LI; Xiaoping LIU; Yinghuai HUANG; Honghui ZHANG

doi:10.12082/dqxxkx.2020.190047

[1] Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends[J]. Remote Sensing of Environment, 117, 34-49(2012).

[2] Impervious surface coverage: The emergence of a key environmental indicator[J]. Journal of the American Planning Association, 62, 243-258(1996).

[3] The importance of imperviousness[J]. Watershed protection techniques, 1, 100-101(1994).

[4] et alExtending satellite remote sensing to local scales: Land and water resource monitoring using high-resolution imagery[J]. Remote Sensing of Environment, 88, 144-156(2003).

[5] 徐涵秋, 王美雅. 地表不透水面信息遥感的主要方法分析[J]. 遥感学报, 2016,20(5):1270-1289. [ Xu HQ, Wang MY. Remote sensing-based retrieval of ground impervious surfaces[J]. Journal of Remote Sensing, 2016,20(5):1270-1289. ] [ Xu H Q, Wang M Y. Remote sensing-based retrieval of ground impervious surfaces[J]. Journal of Remote Sensing, 2016,20(5):1270-1289. ]

[6] et alSub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil[J]. Remote Sensing of Environment, 106, 253-267(2007).

[7] Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI)[J]. Photogrammetric Engineering & Remote Sensing, 76, 557-565(2010).

[8] 向超, 朱翔, 胡德勇, 等. 近20年京津唐地区不透水面变化的遥感监测[J]. 地球信息科学学报. 2018,20(5):684-693. [ XiangC, ZhuX, Hu DY, et al. Monitoring of the impervious surface with multi-resource remote sensing images in Beijing-Tianjin-Tangshan urban agglomeration in the past two decades[J]. Journal of Geo-information Science, 2018,20(5):684-693. ] [ Xiang C, Zhu X, Hu D Y, et al. Monitoring of the impervious surface with multi-resource remote sensing images in Beijing-Tianjin-Tangshan urban agglomeration in the past two decades[J]. Journal of Geo-information Science, 2018,20(5):684-693. ]

[9] Estimating and mapping impervious surface area by regression analysis of Landsat imagery[M]. Remote sensing of impervious surfaces, 31-48(2007).

[10] Automatic fuzzy object-based analysis of VHSR images for urban objects extraction[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 171-184(2013).

[11] Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery[J]. Remote Sensing of Environment, 93, 480-492(2004).

[12] BCI: A biophysical composition index for remote sensing of urban environments[J]. Remote Sensing of Environment, 127, 247-259(2012).

[13] et alIntegrating CART algorithm and multi-source remote sensing data to estimate sub-pixel impervious surface coverage: A case study from Beijing Municipality, China[J]. Chinese Geographical Science, 27, 614-625(2017).

[14] Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 298-306(2011).

[15] 王燕燕, 于海洋. 基于倾斜摄影综合SVM与模糊规则的城市不透水面信息提取[J]. 地理与地理信息科学, 2018,34(6):20-26. [ Wang YY, Yu HY. Urban impervious surface extraction by integrating SVM and fuzzy rules based on oblique photography[J]. Geography and Geo-Information Science, 2018,34(6):20-26. ] [ Wang Y Y, Yu H Y. Urban impervious surface extraction by integrating SVM and fuzzy rules based on oblique photography[J]. Geography and Geo-Information Science, 2018,34(6):20-26. ]

[16] 左家旗, 王泽根, 边金虎, 等. 地表不透水面比例遥感反演研究综述[J]. 国土资源遥感, 2019,31(3):1-9. [ Zuo JQ, Wang ZG, Bian JHet al. Research review of remote sensing for ground impervious surface percentage retrieval[J]. Remote Sensing for Land & Resources, 2019,31(3):1-9. ] [ Zuo J Q, Wang Z G, Bian J H et al. Research review of remote sensing for ground impervious surface percentage retrieval[J]. Remote Sensing for Land & Resources, 2019,31(3):1-9. ]

[17] 广州市统计局. 广州统计年鉴-2018[M]. 北京: 中国统计出版社, 2018. [ Guangzhou Statistics Bureau. Guangzhou statistical yearbook-2018[M]. Beijing: China Statistics Press, 2018. ] [ Guangzhou Statistics Bureau. Guangzhou statistical yearbook-2018[M]. Beijing: China Statistics Press, 2018. ]

[18] Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors[J]. Remote Sensing of Environment., 113, 893-903(2009).

[19] et alGoogle Earth Engine: Planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment., 202, 18-27(2017).

[20] et alCharacterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: A 12-year case study in Beijing, China[J]. Remote Sensing of Environment, 204, 826-837(2018).

[21] Annual dynamics of impervious surface in the Pearl River Delta, China, from 1988 to 2013, using time series Landsat imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 113, 86-96(2016).

[22] et alExtracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information[J]. Journal of Geographical Sciences., 26, 325-338(2016).

[23] 徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005,9(5):589-595. [ Xu HQ, A Study on information extraction of water body with the Modified Normalized Difference Water Index (MNDWI)[J]. Journal of Remote Sensing, 2005,9(5):585-595. ] [ Xu H Q, A Study on information extraction of water body with the Modified Normalized Difference Water Index (MNDWI)[J]. Journal of Remote Sensing, 2005,9(5):585-595. ]

[24] 赵英时等. 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2013: 12, 502. [ Zhao YSet al. Principles and methods of remote sensing application analysis[M]. Beijing: Science Press, 2013: 12, 502. ] [ Zhao Y S et al. Principles and methods of remote sensing application analysis[M]. Beijing: Science Press, 2013:12,502. ]

[25] 李晶, 徐涵秋, 李霞, 等. 水土流失区马尾松林植被提取的土壤调节指数分析[J]. 地球信息科学学报, 2015,17(9):1128-1134. [ LiJ, Xu HQ, LiXet al. Vegetation information extraction of pinus massoniana forest in soil erosion areas using soil-adjusted vegetation index[J]. Journal of Geo-information Science, 2015,17(9):1128-1134. ] [ Li J, Xu H Q, Li X et al. Vegetation information extraction of pinus massoniana forest in soil erosion areas using soil-adjusted vegetation index[J]. Journal of Geo-information Science, 2015,17(9):1128-1134. ]

[26] 贾祎琳, 张文, 孟令奎. 面向GF-1影像的NDWI分割阈值选取方法研究[J]. 国土资源遥感. 2019,31(1):95-100. [ Jia WL, ZhangW, Meng LK. A study of selection method of NDWI segmentation threshold for GF-1 image[J]. Remote Sensing For Land & Resources, 2019,31(1):95-100. ] [ Jia W L, Zhang W, Meng L K. A study of selection method of NDWI segmentation threshold for GF-1 image[J]. Remote Sensing For Land & Resources, 2019,31(1):95-100. ]

[27] 徐蓉, 张增祥, 赵春哲. 湖泊水体遥感提取方法比较研究[J]. 遥感信息, 2015,30(1):111-118. [ XuR, Zhang ZX, Zhao CZ. Different models used in extraction of lake water body based on MODIS data[J]. Remote Sensing Information, 2015,30(1):111-118. ] [ Xu R, Zhang Z X, Zhao C Z. Different models used in extraction of lake water body based on MODIS data[J]. Remote Sensing Information, 2015,30(1):111-118. ]

[28] A 30-year (1984-2013) record of annual urban dynamics of Beijing City derived from Landsat data[J]. Remote Sensing of Environment., 166, 78-90(2015).

[29] Comparison of sampling procedures and data analysis for a land-use and land-cover map[J]. Photogrammetric Engineering and Remote Sensing, 47, 343-351(1981).

[30] et alGeographical detectors based health risk assessment and its application in the neural tube defects study of the Heshun Region, China[J]. International Journal of Geographical Information Science, 24, 107-127(2010).

[31] 40-year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing[J]. Science Bulletin., 64, 756-763(2019).

[32] 刘纪远, 匡文慧, 张增祥, 等. 20世纪80年代末以来中国土地利用变化的基本特征与空间格局[J]. 地理学报, 2014,69(1):3-14. [ Liu JY, Kuang WH, Zhang ZX, et al. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s[J]. Journal of Geographical Sciences, 2014,69(1):3-14. ] [ Liu J Y, Kuang W H, Zhang Z X, et al. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s[J]. Journal of Geographical Sciences, 2014,69(1):3-14. ]

[33] An “exclusion-inclusion” framework for extracting human settlements in rapidly developing regions of China from Landsat images[J]. Remote Sensing of Environment, 186, 286-296(2016).