地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (11): 2055-2072.doi: 10.12082/dqxxkx.2021.200751

• 地理空间分析综合应用 • 上一篇    下一篇

1979—2019年武汉市重点水体多要素协同的时空演变特征

文超1(), 詹庆明1,*(), 樊智宇1, 湛德2, 赵皇2, 吴凯2   

  1. 1. 武汉大学城市设计学院,武汉 430072
    2. 中建三局绿投公司战略研究所,武汉 430074
  • 收稿日期:2020-12-10 修回日期:2021-02-16 出版日期:2021-11-25 发布日期:2022-01-25
  • 通讯作者: *詹庆明(1964- ), 男,福建永安人,博士,博士生导师,教授,主要从事数字城市研究。E-mail: qmzhan@whu.edu.cn
  • 作者简介:文超(1989- ), 男,河南郑州人,博士生,主要从事城市规划信息化应用研究。E-mail: 1150550945@qq.com
  • 基金资助:
    广东省重点领域研发计划项目(2020B0202010002);国家自然科学基金项目(52078389);中国工程科技发展战略湖北研究院咨询项目(HB2019B14)

Spatiotemporal Characteristics of Multi-factor Synergy in Urban Key Water Bodies of Wuhan from 1979 to 2019

WEN Chao1(), ZHAN Qingming1,*(), FAN Zhiyu1, ZHAN De2, ZHAO Huang2, WU Kai2   

  1. 1. School of Urban Design, Wuhan University, Wuhan 430072, China
    2. China Construction Third Bureau Green Industry Investment Company of Limited Liability, Wuhan 430074, China
  • Received:2020-12-10 Revised:2021-02-16 Online:2021-11-25 Published:2022-01-25
  • Contact: *ZHAN Qingming, E-mail: qmzhan@whu.edu.cn
  • Supported by:
    Key- Area Research and Development Program of Guangdong Province, China, No(2020B0202010002);National Natural Science Foundation of China, No.52078389(52078389);the Consultancy Project for the Hubei Branch of Chinese Academy of Engineering, No(HB2019B14)

摘要:

城市发展对水体多方面要素产生了巨大影响,尤其是对水资源丰富的城市,迫切需要开展相关监测研究。研究以武汉市为例,通过梳理国内外相关研究以及武汉水体保护政策,提出融合水体面积、水质、水体景观、滨水区生态环境4个方面要素的分析技术路线以更为全面的反映城市尺度的水体时空演变特征,具体的,利用随机森林模型基于1979—2019年遥感影像获取武汉67个重点水体信息,并以此获取水体面积和水体景观的变化特征;同时,通过梳理多年水质监测数据分析水质变化特征;另外,基于遥感生态指数(Remote Sensing Ecological Index, RSEI)分析滨水区生态环境变化;最后,采用多尺度地理加权回归模型(Multi-scale Geographically Weighted Regression, MGWR)对重点水体面积变化的影响因素研究,希冀为政府制定差异化的水体保护政策提供科学支撑,并为其他地区水体的多要素分析提供有益借鉴。结果表明:① 武汉市水体面积呈下降趋势,水体总面积、重点水体面积分别减少10.75%及13.12%,中心城区及郊区水体变化存在显著差异;② 水体景观呈退化趋势,周长面积分维数、平均斑块面积、聚合指数及结合指数分别减少了6.43%,79.35%,1.55%及10.94%;③ 重点水体水质总体呈恶化趋势,其中江河及水库多数常年为III类及以上水体,中心城区湖泊多为V类及以下水体,郊区湖泊多为IV类及V类水体;④ 中心城区及郊区滨水区遥感生态指数(RSEI)分析表明滨水区生态环境呈恢复态势,其中中心城区滨水区平均RSEI增长了14.29%;⑤ MGWR分析表明,自然气象因素中,相对湿度的增加对江夏区湖泊恢复影响更为显著,降水对水面较小的水体恢复影响更为显著;社会经济因素中,各行政区GDP的增加有助于水体恢复,对中心城区、黄陂及新洲的水体保护影响更为显著;滨水缓冲区内不透水面占比(IS)的增加导致大多数水体面积的减少,然而,对于少数重点修复水体,IS增加是受相关保护政策影响,IS增加有助于这些水体恢复。

关键词: 城市重点水体, 多要素融合, 时空演变, 影响因素, 随机森林, 多尺度地理加权回归

Abstract:

Urban development has a great impact on water bodies in many aspects, especially for cities rich in water resources. Thus, it is urgent to carry out relevant monitoring and research. This study takes Wuhan as study area. Based on relevant studies at home and abroad, as well as Wuhan water protection policies, this study put forward a technical route, integrating water area, water quality, water landscape, and waterfront ecology, to reflect the spatiotemporal characteristics of urban scale water more comprehensively. Specifically, the random forest model was used to obtain water information of 67 key water bodies from remote sensing images from 1979 to 2019. Based on the information, the changes of water area and water landscape were analyzed. The evolution characteristics of water quality and waterfront ecology were analyzed based on water quality monitoring data and Remote Sensing Ecological Index (RSEI), respectively. Then, the influencing factors of the changes in water body area were analyzed using the Multi-scale Geographical Weighted Regression model (MGWR). This study aims to provide scientific support for the government to formulate differentiated water protection policies, and provide useful reference for the multi-factor analysis of water bodies in other regions. The results showed that the total water areas of Wuhan and 67 key water bodies had decreased by 10.75% and 13.12%, respectively. There were significant differences in the changes of water bodies in the downtown area and the suburban area. The water landscape showed a degradation trend. The Perimeter Area Fractal Dimension (PAFRAC), Edge Density(ED), Mean Patch Area(MPA), Aggregation Index(AI), and Cohesion Index (COHESION) decreased by 6.43%, 79.35%, 1.55%, and 10.94%, respectively. The water quality of Wuhan was deteriorating. Most of the rivers and reservoirs are of class III or above all the year round. Most of the lakes in the downtown area are of class V or below. Most of the lakes in the suburbs are of class IV or V. Changes of RSEI of the waterfront in the downtown area and the suburb area showed that the eco-environment of the waterfront zone was recovering. The average RSEI of waterfront in the downtown area had increased by 14.29%. MGWR analysis showed that among the natural factors, the increase of relative humidity had more effects on the recovery of lakes in Jiangxia, while the increase of precipitation had a more significant impact on the recovery of lakes with smaller water area. Among the socioeconomic factors, the increase of GDP in each administrative region was helpful to water restoration, especially for water bodies in the downtown area, Huangpi, and Xinzhou. The increase of Impervious Surface (IS) proportion in waterfront area had led to the shrinkage of most water bodies. For a few key recovering water bodies, the growth of IS due to relevant protection policies had a positive effect on the recovery of these bodies.

Key words: key urban water bodies, multi factor fusion, spatio-temporal evolution, influencing factors, random forest model, Multi-scale Geographically Weighted Regression model