影响城市生态风险的因素探讨——以印度特大城市区为例

IF 8.5 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Geoscience frontiers Pub Date : 2023-01-01 DOI:10.1016/j.gsf.2022.101488
Manob Das, Arijit Das, Ashis Mandal
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引用次数: 9

摘要

生态风险评估是适应和缓解发展中国家城市生态恶化的关键方法。在发展中国家,由于城市的快速扩张,植被、水体和湿地等生态景观非常脆弱。因此,城市ER(UER)评估及其驱动因素对于指导生态保护和恢复至关重要。本研究旨在探讨印度加尔各答特大城市地区UER的时空格局以及城市空间形态对UER的影响。这项研究开发了一个UER指数,并使用了整个城市中心的空间回归模型。从2000年到2020年,ER已在城市和电网规模(2公里×2公里和5公里×5公里)进行了评估。结果表明,ER在过去20年中大幅增加。ER非常高和非常高的城市中心大幅增加,即从2000年的21.95%增加到2020年的31.70%。加尔各答及其周边城市中心大多具有极高的ER特征。ER受空间变量(如土地利用和景观格局)的影响。然而,遥感参数与ER的相关性较弱。空间滞后模型(SLM)(R2=0.686)比空间误差模型(SEM)(R2=88661)和普通线性回归模型(OLS)(R20.8641)更适合。因此,研究结果可以完善城市生态资源的研究和综合框架,为城市生态系统规划和恢复提供科学依据。除此之外,它还将保证城市生态系统的可持续利用。
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Exploring the factors affecting urban ecological risk: A case from an Indian mega metropolitan region

Assessment of ecological risk (ER) is a key approach to adapting and mitigating ecological deterioration in cities of developing countries. In developing countries, the ecological landscapes such as vegetation cover, water bodies, and wetlands are highly vulnerable due to rapid urban expansion. Therefore, urban ER (UER) assessment and its drivers are crucial to guide ecological protection as well as restoration. This study aims to explore the spatiotemporal pattern of UER and the impact of urban spatial form on UER in the Kolkata Megacity Region (KMR), India. This study developed a UER index and used spatial regression models across the urban centres. The ER has been assessed at city scale as well as grid-scale (2 km × 2 km and 5 km × 5 km) from 2000 to 2020. The results showed that ER has substantially increased over the last 20 years. The urban centres with very high and high ER substantially increased, i.e. from 21.95% in 2000 to 31.70% in 2020. Kolkata and its surrounding urban centres were mostly characterized by very high and high ER. ER was influenced by spatial variables (such as land use and landscapes pattern). However, remote sensing parameters were weakly related to ER. The spatial lag model (SLM) (R2 = 0.8686) was found to be better fit model than spatial error model (SEM) (R2 = 0.8661) and ordinary linear regression model (OLS) (R2 = 0.8641). Thus, the findings of the study can improve research and a comprehensive framework for urban ecological resources and provide a scientific basis for urban ecosystem planning and restoration. In addition to this, it will guarantee the sustainable utilization of urban ecosystems.

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来源期刊
Geoscience frontiers
Geoscience frontiers Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
17.80
自引率
3.40%
发文量
147
审稿时长
35 days
期刊介绍: Geoscience Frontiers (GSF) is the Journal of China University of Geosciences (Beijing) and Peking University. It publishes peer-reviewed research articles and reviews in interdisciplinary fields of Earth and Planetary Sciences. GSF covers various research areas including petrology and geochemistry, lithospheric architecture and mantle dynamics, global tectonics, economic geology and fuel exploration, geophysics, stratigraphy and paleontology, environmental and engineering geology, astrogeology, and the nexus of resources-energy-emissions-climate under Sustainable Development Goals. The journal aims to bridge innovative, provocative, and challenging concepts and models in these fields, providing insights on correlations and evolution.
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