地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (5): 1161-1174.doi: 10.12082/dqxxkx.2020.190711

• “空间综合人文学与社会科学”专辑 • 上一篇    下一篇

基于DMSP-OLS与NPP-VIIRS整合数据的中国三大城市群城市空间扩展时空格局

董鹤松1, 李仁杰1,2,3,*(), 李建明1, 李帅1   

  1. 1.河北师范大学资源与环境科学学院,石家庄 050024
    2.河北省环境演变与生态建设实验室,石家庄 050024
    3.河北省环境变化遥感识别技术创新中心,石家庄 050024
  • 收稿日期:2019-11-23 修回日期:2019-12-06 出版日期:2020-05-25 发布日期:2020-07-25
  • 通讯作者: 李仁杰 E-mail:lrjgis@hebtu.edu.cn
  • 作者简介:董鹤松(1995— ),男,河北承德人,硕士生,主要从事地理时空数据挖掘与分析研究。E-mail:dhs_gis@163.com
  • 基金资助:
    国家自然科学基金项目(41471127);河北师范大学在读研究生创新能力培养资助项目(CXZZSS2019074)

Study on Urban Spatiotemporal Expansion Pattern of Three First-class Urban Agglomerations in China Derived from Integrated DMSP-OLS and NPP-VIIRS Nighttime Light Data

DONG Hesong1, LI Renjie1,2,3,*(), LI Jianming1, LI Shuai1   

  1. 1. School of Resources and Environment Science, Hebei Normal University, Shijiazhuang 050024, China
    2. Hebei Key Laboratory of Environmental Change and Ecological Construction, Shijiazhuang 050024, China
    3. Hebei Innovation Center of Remote Sensing Technology for Environmental Change, Shijiazhuang 050024, China
  • Received:2019-11-23 Revised:2019-12-06 Online:2020-05-25 Published:2020-07-25
  • Contact: LI Renjie E-mail:lrjgis@hebtu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41471127);Graduate Student Innovation Ability Cultivation Support Project of Hebei Normal University(CXZZSS2019074)

摘要:

通过拟合最优幂函数模型,将NPP-VIIRS影像模拟为DMSP-OLS影像,构建了京津冀、长江三角洲(简称长三角)和珠江三角洲(简称珠三角)三大城市群1992—2017年长时间序列夜间灯光影像集。参考城市建成区统计数据确定夜间灯光最佳阈值提取城市范围,有效剥离统计数据中包含的经济活力不足的城市空间,识别出不属于统计范围的低等级、高活力城镇区,创新了数据应用视角。研究表明:① 县级城镇和市级以上城市对三大城市群城市范围的贡献度不同。京津冀腹地广阔,县级城镇是区域经济活力的重要组成部分,整体上贡献度最大;1990s初期长三角部分县级城镇经济活力较强,大量撤县设市后县级城镇数量减少,逐渐在2005年后低于京津冀;珠三角受到社会经济发展条件和行政单元划分的影响,县级城镇对城市范围的贡献在3个城市群中始终最小。② 三大城市群城市扩展非均衡性特征存在差异。京津冀城市扩展为京、津主导下的“双核”模式,非均衡性显著,尚未形成完善的城市规模体系;长三角和珠三角城市集聚特征明显,均衡性更强。重心迁移的路径、方向和距离反映各城市群不同的扩展强度和作用模式。③ 城市空间扩展格局整体均呈现热点区不断扩大、冷点区不断缩小的特征。其中京津冀热点和冷点区相对稳定,热点区向心集聚作用较强;长三角和珠三角空间格局变化较大,区域核心城市带动作用较强。

关键词: 夜间灯光数据, 数据整合, 三大城市群, 建成区, 时空格局, 扩展强度, 重心指数, 热点分析

Abstract:

This study took Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta as study areas. Based on correlation analysis of overlapping years in 2012 and 2013, a power function model was used to transform NPP-VIIRS NTL data(2012—2017) to simulate DMSP-OLS NTL data. We generated a temporally consistent NTL dataset of study areas from 1992 to 2017 (consisting of 1992—2013 DMSP-OLS NTL data and 2014—2017 NPP-VIIRS NTL data). By referring to the statistical data of urban built-up area, the optimal threshold of nighttime light was determined to extract urban scope. Based on this method, urban scope with insufficient economic vitality contained in the statistical data was effectively stripped off, while those low-grade,high-vitality urban areas that did not belong to the statistical scope were identified, which innovated the perspective of data application. Results show that: ① The contribution of county-level towns and cities of the municipal level to the economic vitality in three first-class urban agglomerations was different. The Beijing-Tianjin-Hebei had a vast hinterland, and county-level towns were an important part of the regional economic vitality. As a whole, they had the largest contribution in three first-class urban agglomerations. In the early 1990s, some county-level towns in the Yangtze River Delta had strong economic vitality. After 2005, it was lower than the Beijing-Tianjin-Hebei with the process of transforming county into urban district. Affected by the socioeconomic development and the division of administrative units, the contribution of county-level towns to the urban scope in the Pearl River Delta was always the smallest in three urban agglomerations. ② There were differences in the imbalanced characteristics of urban spatial expansion in three first-class urban agglomerations. The urban spatial expansion of the Beijing-Tianjin-Hebei was led by Beijing and Tianjin, which has formed a "dual-core" model. It has not yet formed a sophisticated urban system. The Yangtze River Delta and the Pearl River Delta had obvious urban agglomeration characteristics and stronger equilibrium. The path, direction, and distance of center of gravity migration reflected the different expansion intensity and behavioral model in three first-class urban agglomerations; ③ On the whole, urban spatial expansion pattern presented the characteristics of continuously expanding hot-spot regions and shrinking cold-spot regions. Specifically, hot-spot regions and cold-spot regions were relatively stable in the Beijing-Tianjin-Hebei, and the hot-spot regions had a strong centripetal agglomeration effect. The spatial patterns of the Yangtze River Delta and the Pearl River Delta has changed greatly and the regional core cities had a stronger driving role.

Key words: Nighttime light data, data integration, three first-class urban agglomerations, built-up area, spatiotemporal pattern, intensity of spatial expansion, center of gravity index, hotspot analysis