基于GEE平台的广州市主城区不透水面时间序列提取

doi:10.12082/dqxxkx.2020.190047

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (3): 638-648.doi: 10.12082/dqxxkx.2020.190047

• “土地利用模拟”专栏 • 上一篇下一篇

基于GEE平台的广州市主城区不透水面时间序列提取

李培林¹, 刘小平^1,^*(), 黄应淮¹, 张鸿辉²

^1. 中山大学地理科学与规划学院,广州 510275
^2. 广东国地规划科技股份有限公司,广州 510075

收稿日期:2019-01-25 修回日期:2019-07-22 出版日期:2020-03-25 发布日期:2020-05-18
通讯作者: 刘小平 E-mail:liuxp3@mail.sysu.edu.cn
作者简介:李培林（1996— ）,男,广东广州人,硕士生,研究方向为土地利用变化监测。E-mail:liplin6@mail2.sysu.edu.cn
基金资助:
国家重点研发计划项目(2017YFA0604404);国家自然科学基金项目(41671398);国家自然科学基金项目(41871318)

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

LI Peilin¹, LIU Xiaoping^1,^*(), HUANG Yinghuai¹, ZHANG Honghui²

^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)

摘要/Abstract

摘要：

不透水面作为城市化水平以及城市环境的重要评价指标,其提取已经是当下的研究热点。与单时相影像相比,时间序列制图能够获取其准确的变化趋势,对于监测城市的快速发展具有重要意义。本文以广州市主城区为研究区,以Google Earth Engine平台为基础,利用2000—2017年的Landsat TOA影像计算BCI和NDVI,并通过自适应迭代法确定它们的阈值,从而提取初始的不透水面,然后进行时间一致性检验,使不透水面时间序列更加合理。研究结果表明：①BCI与NDVI的结合以及时间一致性检验能够提高不透水面的提取质量;②本文中不透水面提取的平均总体精度为90.4%,平均Kappa系数为0.812;③在2000—2017年广州市主城区不透水面面积增加近一倍,但增速有所放缓。④新增的不透水面主要集中在原本相对落后的主城区外围;⑤高程、道路密度和购物场所密度等是影响广州市主城区不透水面扩张的主要因素。

关键词: 广州市主城区, 不透水面, Google Earth Engine, Landsat, BCI, NDVI, 自适应迭代法, 时间一致性检验

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

李培林, 刘小平, 黄应淮, 张鸿辉. 基于GEE平台的广州市主城区不透水面时间序列提取[J]. 地球信息科学学报, 2020, 22(3): 638-648.DOI:10.12082/dqxxkx.2020.190047

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

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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

基于GEE平台的广州市主城区不透水面时间序列提取

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

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