基于Google Earth Engine的长三角城市群生态环境变化与城市化特征分析

作为城市发展的最高空间组织形式,城市群在高速城市化进程中将不可避免的对地区生态环境产生胁迫影响。如何平衡生态环境和城市化发展已经成为了值得关注的问题。以长三角城市群为例,基于Google Earth Engine云平台,通过整合日间光学遥感和夜间灯光遥感数据对生态环境状况、城市化强度以及二者在发展过程中的耦合协调特征开展了长时间序列的对比分析。结果表明：1）长三角城市群在过去近20年间的遥感生态环境指数（Remote Sensing-based Ecological Index,RSEI）稳定上升,生态环境呈现出好转的态势,但地区间的差异依旧显著;2）综合灯光指数（Comprehensive Nighttime Light Index,CNLI）能够较为准确的刻画城市群的城市化水平,研究期间内长三角城市群形成了"层次分明"、"由东向西"逐步推进的多层次的城市化格局,其中高强度城市化城市集中在上海市及其周边的无锡、苏州和嘉兴,低强度城市化城市则分布在城市群西部;3）基于CNLI和RSEI指数构建的耦合协调距离模型能够有效的识别出城市群内部城市化与生态环境的耦合协调程度,并根据象限特征将城市群城市划分为良好协调类型、初级协调类型、城市化滞后型和生态环境滞后型城市。

Analyzing the ecological environment and urbanization characteristics of the Yangtze River Delta Urban Agglomeration based on Google Earth Engine

As the highest spatial organization of urban development, urban agglomerations will inevitably have a coercive impact on the regional ecological environment during the rapid urbanization process. How to balance the ecological environment and the development of urbanization has become a problem worthy of attention. Taking the Yangtze River Delta Urban Agglomeration (YRDUA) as an example, based on the Google Earth Engine platform, this paper analyzes the ecological environment, urbanization intensity and the coupling and coordination characteristics of the two in the development process by integrating the daytime optical remote sensing and nighttime light remote sensing data. The results show that: 1) in the past 20 years, the Remote Sensing-based Ecological Index (RSEI) of the YRDUA has been rising steadily, and the ecological environment has shown a trend of improvement, but the regional differences are still significant. 2) The Comprehensive Nighttime Light Index (CNLI) can accurately depict the urbanization level of the urban agglomeration. During the research period, the YRDUA has formed a multi-level urbanization pattern from east to west, in which the high-intensity urbanization cities are concentrated in Shanghai and its surrounding area, while the low-intensity urbanization cities are distributed in the west of the urban agglomeration. 3) The Coupling Coordination Distance Model (CCDM) based on CNLI and RSEI can effectively identify the coupling coordination degree of urbanization and ecological environment in urban agglomerations, and divide cities into good coordination type, primary coordination type, urbanization lag type and ecological environment lag type according to quadrant characteristics.