Journal of Geo-information Science >
Mapping Impervious Surface Dynamics of Guangzhou Downtown based on Google Earth Engine
Received date: 2019-01-25
Request revised date: 2019-07-22
Online published: 2020-05-18
Supported by
National Key R&D Program of China(2017YFA0604404)
National Natural Science Foundation of China(41671398)
National Natural Science Foundation of China(41871318)
Copyright
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.
LI Peilin , LIU Xiaoping , HUANG Yinghuai , ZHANG Honghui . Mapping Impervious Surface Dynamics of Guangzhou Downtown based on Google Earth Engine[J]. Journal of Geo-information Science, 2020 , 22(3) : 638 -648 . DOI: 10.12082/dqxxkx.2020.190047
图5 2000—2017年广州市主城区不透水面提取结果Fig. 5 Mapping results of the impervious surface in Guangzhou downtown from 2000 to 2017 |
表1 各影响因子对不透水面扩张的作用Tab. 1 Effect of various factors on the impervious surface expansion |
类别 | 自变量 | q值 |
---|---|---|
自然环境因子 | 高程 | 0.2432 |
坡度 | 0.1602 | |
到水体的距离 | 0.0044 | |
交通因子 | 道路密度 | 0.1918 |
到地铁站的距离 | 0.0601 | |
到公交站的距离 | 0.1591 | |
服务设施因子 | 购物场所密度 | 0.1785 |
餐饮场所密度 | 0.1371 | |
医疗机构密度 | 0.1165 | |
学校密度 | 0.1282 |
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