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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (4) : 194-203     DOI: 10.6046/zrzyyg.2024161
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Analyzing impact of the Beijing-Guangzhou high-speed railway on cities along the Hebei section based on remote sensing monitoring
SU Boxiong1,2(), WU Mingquan1,2(), NIU Zheng1,2, CHEN Fang2,3, HUANG Wenjiang1,2
1. Aerospace Information Research Institute, Key Laboratory of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing 100094, China
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Abstract  

At present, the impacts of high-speed railways (HSRs) on cities along rail lines remain unclear. Previous analyses of these impacts based on remote sensing data focused primarily on qualitative assessment. Given this, this study investigated the Hebei section of the Beijing-Guangzhou HSR and introduced a remote sensing monitoring-based method that integrated qualitative and quantitative analyses for assessing the impacts of HSR on urban development. First, this study established a parameter index system used to characterize urban development changes. Then, multi-source and multi-scale remote sensing data were employed to monitor the spatiotemporal variations in these indices before and after the operation of the Beijing-Guangzhou HSR within this study area. Finally, four cities that were adjacent to the study area but lacked available HSRs were selected as a control group. Using the difference-in-differences (DID) model, this study quantified the impacts of HSRs on four cities along the Hebei section. The results indicate that the four cities along the Hebei section of the Beijing-Guangzhou HSR saw a rapid expansion in the construction land between 2005 and 2020, with an average annual expansion rate of 2.00%. The HSR construction exerted a significant impact on the direction of urban expansion, with the impact related to the spatial relationship between both. Compared to the four cities in the control group, the operation of the Beijing-Guangzhou HSR has slowed down the expansion rates of urban areas in the four cities along the line.
Keywords urban expansion      land use      night light      high-speed railway (HSR)     
ZTFLH:  TP79  
Issue Date: 03 September 2025
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Boxiong SU
Mingquan WU
Zheng NIU
Fang CHEN
Wenjiang HUANG
Cite this article:   
Boxiong SU,Mingquan WU,Zheng NIU, et al. Analyzing impact of the Beijing-Guangzhou high-speed railway on cities along the Hebei section based on remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2025, 37(4): 194-203.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024161     OR     https://www.gtzyyg.com/EN/Y2025/V37/I4/194
Fig.1  Location of the four cities in the Hebei section of the Beijing-Guangzhou high-speed railway and neighboring comparison cities
分组 所在地级市 城区范围 高速铁路通车时间 所属省份
实验组 保定市 满城区、徐水区、清苑区、莲池区、竞秀区 2012年12月26日 河北省
石家庄市 新华区、长安区、裕华区、桥西区、栾城区、鹿泉区、正定县、藁城区
邢台市 襄都区、任泽区、南和区、信都区
邯郸市 丛台区、肥乡区、邯山区、复兴区
对照组 濮阳市 华龙区、濮阳县 2022年6月20日 河南省
聊城市 东昌府区① 2023年12月8日
菏泽市 牡丹区、定陶区 2021年12月26日 山东省
济宁市 任城区 2021年12月26日
Tab.1  Scope of urban administrative districts of the eight cities in the study area
地级市 2005年 2009年 2013年 2017年 2020年
保定 √① 2011*② √ 2018* √
石家庄 √ √ √ 2018* √
邢台 Landsat③ 2008* √ √ √
邯郸 Landsat 2008* √ √ √
聊城 Landsat Landsat √ √ √
濮阳 Landsat 2010* 2014* √ √
菏泽 Landsat Landsat √ √ √
济宁 Landsat √ √ √ 2019*
Tab.2  Satellite imagery used
指标计算基础 指标参数 意义
土地利用数据 用地扩张速率 反映一定时期内各类型土地变化情况
用地扩张强度 反映一段时期内各类型土地平均每年的变化情况
建设用地重心迁
移距离		 反映城市建设用地重心迁移情况
夜间灯光数据 城区扩张方向性
指数		 反映城区向不同方向扩张的集中程度
Tab.3  Remote sensing indicator system for the impact of high-speed rail opening on urban development
Fig.2  Results of urban area extraction using different statistical values as thresholds
年份 高分卫星影像/景 准确率/% Kappa系数
2005年 2 92.103 0.854
2009年 6 91.296 0.892
2013年 7 97.182 0.964
2017年 8 97.202 0.973
2020年 8 97.940 0.962
Tab.4  Land classification accuracy in the study area
Fig.3  Classification results of land use types in Shijiazhuang urban area
年份 建设用地 林草用地 耕地 水体 裸地
2005年 554.89 228.67 1 497.07 23.34 4.08
2009年 599.21 237.74 1 429.67 25.20 3.61
2013年 609.36 354.15 1 298.96 30.00 2.08
2018年 835.41 258.75 1 164.59 33.21 2.51
2020年 774.83 387.03 1 093.33 36.10 4.44
Tab.5  Changes in the land area of various types in Shijiazhuang urban area(km2)
城市 类型 V/(km2·a-1) R/(%·a-1)
T1 T2 T3 T4 T1 T2 T3 T4
建设用地 11.080 2.536 45.210 -30.291 0.020 0 0.004 2 0.074 2 -0.036 3
林草用地 2.267 29.103 -19.081 64.140 0.009 9 0.122 4 -0.053 9 0.247 9
石家庄 耕地 -16.851 -32.677 -26.875 -35.631 -0.011 3 -0.022 9 -0.020 7 -0.030 6
水体 0.464 1.200 0.642 1.446 0.019 9 0.047 6 0.021 4 0.043 5
裸地 -0.118 -0.383 0.086 0.967 -0.028 9 -0.106 2 0.041 3 0.386 0
建设用地 6.970 18.337 18.428 -5.385 0.016 7 0.040 0 0.037 2 -0.009 2
林草用地 14.282 8.452 -75.213 209.186 0.044 0 0.020 6 -0.176 2 4.117 0
保定 耕地 -21.887 -25.867 -11.311 -33.231 -0.015 0 -0.019 5 -0.008 9 -0.027 3
水体 0.960 -0.854 -1.006 1.501 0.119 8 -0.062 0 -0.083 4 0.213 5
裸地 -0.029 -0.187 0.022 0.496 -0.017 0 -0.122 1 0.018 6 0.391 5
建设用地 6.362 16.189 1.129 5.064 0.022 5 0.053 7 0.003 0 0.013 1
林草用地 71.906 5.923 25.032 4.877 0.075 4 0.005 1 0.020 9 0.003 8
邢台 耕地 -39.563 -22.341 -31.744 -4.909 -0.030 7 -0.019 1 -0.030 0 -0.005 3
水体 1.068 0.694 3.035 -3.139 0.123 9 0.058 7 0.198 4 -0.114 4
裸地 -4.033 -0.240 2.546 -1.815 -0.216 6 -0.036 7 0.478 4 -0.117 0
建设用地 2.534 7.840 7.112 6.661 0.007 4 0.029 5 0.023 3 0.020 0
林草用地 20.297 7.419 8.745 14.973 8.039 9 0.118 2 0.087 6 0.111 0
邯郸 耕地 -24.705 -15.070 -17.215 -22.359 -0.017 8 -0.015 6 -0.019 3 -0.027 2
水体 0.049 0.286 1.140 0.720 0.006 0 0.046 2 0.149 6 0.059 1
裸地 0.209 -0.064 0.017 0.050 0.356 9 -0.059 8 0.023 1 0.061 0
Tab.6  Expansion rate and intensity of urban areas in the four cities along the high-speed railway
Fig.4-1  Expansion of construction land in urban areas of the four cities along the high-speed railway
Fig.4-2  Expansion of construction land in urban areas of the four cities along the high-speed railway
Fig.5  Transfer route of construction land focus in the four cities along the high-speed railway
Fig.6  Distribution of expansion areas in various directions in urban areas of the eight cities in the study area
组别 城市 方向性指数 平均值
实验组 保定 3.91 6.12
石家庄 13.37
邢台 2.64
邯郸 4.54
对照组 聊城 2.70 3.68
菏泽 3.03
济宁 4.18
濮阳 4.80
Tab.7  The directional index of expansion in urban areas of the eight cities in the study area
变量类型 名称 单位 含义
因变量 城区面积 km2 反映城市城区面积
自变量 时间 — 京广高铁开通前后5个年份
控制变量 GDP 亿元 代表城市经济水平
人口数 人 代表城市规模
第二产业产值 亿元 弥补夜间灯光无法探测的工业区经济活动
Tab.8  Variable selection of DID model
时间 组别 系数 标准误差 t p
事件发生前 对照组(Control) 0.408 — — —
实验组(Treated) 0.571 — — —
Diff(T-C) 0.162 0.102 1.586 0.125
事件发生后 对照组(Control) 0.096 — — —
实验组(Treated) -0.020 — — —
Diff(T-C) -0.116 0.076 -1.515 0.142
Diff(T-C)(未加入控制变量) -0.132 0.073 -1.814 0.080*①
Diff-in-Diff -0.278 0.113 -2.457 0.022**
Tab.9  Regression results of the impact of the opening of the Beijing-Guangzhou high-speed railway on urban areas of the four cities along the Hebei section
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