Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (3): 638-648.doi: 10.12082/dqxxkx.2020.190047

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

LI Peilin1, LIU Xiaoping1,*(), HUANG Yinghuai1, ZHANG Honghui2   

  1. 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    2. Guangdong Guodi Planning Technology Company Limited, Guangzhou 510075, China
  • Received:2019-01-25 Revised:2019-07-22 Online:2020-03-25 Published:2020-05-18
  • Contact: LIU Xiaoping E-mail:liuxp3@mail.sysu.edu.cn
  • Supported by:
    National Key R&D Program of China(2017YFA0604404);National Natural Science Foundation of China(41671398);National Natural Science Foundation of China(41871318)

Abstract:

For assessing urbanization level and urban environment, the mapping of impervious surface has become a research hotspot. Compared with single-phase imagery, time series mapping can depict temporal trends, which is of great significance for monitoring urban expansion. Based on the Google Earth Engine platform, this paper calculated BCI and NDVI using Landsat TOA data from 2000 to 2017, and determined their thresholds by an adaptive iteration method to extract the initial impervious surface. Then, Temporal Consistency Check (TCC) was performed to make the time series of impervious surface more reasonable. Results show that: (1) Adding NDVI to both BCI and TCC improved the quality of impervious surface mapping. (2) The average accuracy of impervious surface mapping in this paper was 90.4%, and the average Kappa coefficient was 0.812. (3) The impervious surface area of Guangzhou downtown nearly doubled from 2000 to 2017 with a decreasing growth rate. (4)The newly developed impervious surface mainly concentrated on the relatively backward outskirts of Guangzhou downtown. (5) Elevation, road density, and shopping mart density were the main factors influencing the expansion of impervious surface.

Key words: Guangzhou Downtown, impervious surface, Google Earth Engine, Landsat, BCI, NDVI, Adaptive iterative method, temporal consistency check