Elsevier

Ecological Indicators

Volume 119, December 2020, 106847
Ecological Indicators

Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 years

https://doi.org/10.1016/j.ecolind.2020.106847Get rights and content

Highlights

  • Day/Night remote sensing scheme was used to monitor coastal zone eco-environment.

  • The feasibility of large-scale RSEI was explored based on medium-resolution imagery.

  • Reveals the ecological changes in coastal zone at different urbanization intensities.

Abstract

With the rapid development of urbanization and population migration, since the 20th century, the natural and eco-environment of coastal areas have been under tremendous pressure due to the strong interference of human response. To objectively evaluate the coastal eco-environment condition and explore the impact from the urbanization process, this paper, by integrating daytime remote sensing and nighttime remote sensing, carried out a quantitative assessment of the coastal zone of China in 2000–2019 based on Remote Sensing Ecological Index (RSEI) and Comprehensive Nighttime Light Index (CNLI) respectively. The results showed that: 1) the overall eco-environmental conditions in China's coastal zone have shown a trend of improvement, but regional differences still exist; 2) during the study period, the urbanization process of cities continued to advance, especially in seaside cities and prefecture-level cities in Jiangsu and Shandong, which were much higher than the average growth rate; 3) the Coupling Coordination Degree (CCD) between the urbanization and eco-environment in coastal cities is constantly increasing, but the main contribution of environmental improvement comes from non-urbanized areas, and the eco-environment pressure in urbanized areas is still not optimistic. As a large-scale, long-term series of eco-environment and urbanization process change analysis, this study can provide theoretical support for mesoscale development planning, eco-environment condition monitoring and environmental protection policies from decision-makers.

Introduction

As a special geographical area connecting the marine and land systems, the coastal zone is not only the most active natural area on the earth's surface, with the most concentrated human activities and the most developed economy, but also the area with the most superior resources and environmental conditions (Crossland et al., 2005). Currently, nearly 60% of the world's population lives in coastal areas, and environmental changes in coastal areas are closely related to human survival and development (Chen and Wang, 2003, Fan et al., 2010). However, since the 20th century, with the development of coastal economy and urbanization, the large-scale population continues to concentrate in coastal areas, the regional environment is increasingly disturbed by human activities, and the natural and eco-environment are facing great pressure (Weston et al., 2009, Zhai et al., 2019, Zhang and Zhu, 1997). Facing increasing ecological disturbances, effective models are increasingly needed to detect the temporal and spatial changes of ecological conditions to ensure the sustainable development of the coastal zone.

The rapid development of remote sensing technology provides data sources and technical support for regional eco-environment monitoring and evaluation, which can effectively reflect the ecological status at different scales (Ivits et al., 2009, Caccamo et al., 2011, Willis, 2015, De Araujo Barbosa et al., 2015). In particular, the frequent application of remote sensing based ecological assessment makes it an important means to monitor the coastal environment, and plays an important role in the global coastal ecological assessment research (Arnous and Green, 2011, Sirirwardane et al., 2015, Wang et al., 2018). In general, remote sensing-based coastal ecological research mainly focuses on landuse/cover change (Zoran et al., 2010, Bui et al., 2014, Vatseva, 2015), landscape pattern analysis (Feng and Han, 2011; (Hepcan, 2013, Li et al., 2015, Chu et al., 2015, Vorovencii, 2015, Xu et al., 2015), coastline change detection (Lo and Gunasiri, 2014, Guneroglu, 2015, Wang et al., 2016, Chen et al., 2016), vegetation change monitoring (Bird et al., 2004, Stanley et al., 2005, Alatorre et al., 2011, Shibly and Takewaka, 2013, Anwar and Takewaka, 2014, Aslan et al., 2016) and habitat change identification based on indicator system (Qin et al., 2008, Gong et al., 2011, Ning et al., 2016).

In fact, in an ecosystem, the interaction between each environmental indicator affects the whole ecosystem, and they are inseparable (Xu, 2013). However, most of these remote sensing-based coastal ecological environment assessments are based on a single index, such as vegetation index to describe vegetation change (Maselli, 2004, Gillespie et al., 2018) and water body index to represent coastline change (Wicaksono and Wicaksono, 2019). Due to the insufficient ability of these assessment models to reveal the comprehensive ecological status of the region, the detection of ecological change based on the aggregate remote sensing index is still a challenge (Behling et al., 2015). Gradually, some indicators or models that aggregate multiple indicators to monitor ecological conditions have been proposed, such as forest disturbance index (DI) which combines three components of tasseled cap transformation, MODIS global disturbance index (MGDI) which combines the enhanced vegetation index (EVI) and land surface temperature, Ecological Niche Modeling (ENM) which combines the red index, EVI and normalized difference water index (NDWI).

It is worth noting that a recent study on the remote sensing-based ecological index (RSEI) has brought new hope for the realization of long-span and large-scale ecological assessment (Xu, 2013). RSEI is completely based on remote sensing images and can integrate multiple index factors to objectively and quickly evaluate regional ecological quality. It selects four indicators, such as greenness, humidity, heat, and dryness, which can be directly felt by the human body, comprehensively reflects the regional ecological environment, and integrates various indicators through principal component transformation (PCA). RSEI overcomes the shortcomings of a single indicator, makes the aggregation of sub-indicators more reasonable, and has been successfully applied to many regions to provide support for the analysis, modeling and prediction of regional ecological characteristics (Zhang et al., 2015, Zhang et al., 2018, Song and Xue, 2016, Hu and Xu, 2018, Yue et al., 2019).

The continuous urbanization process has caused great changes in the natural ecological environment in China's coastal areas since the 1980s. Under the premise of ensuring the sustainable development of the coastal zone, it is of great significance to explore the interaction between the eco-environment and human activities in China's coastal areas in the past few decades. Therefore, based on day-time and nighttime remote sensing images, the interaction between eco-environment and urbanization process in China’s coastal areas are explored in this paper. The specific objectives of this study were: 1) monitoring the long-term dynamics of eco-environment in the coastal zone of China under the background of rapid urbanization processes; 2) evaluating the spatiotemporal differences in the intensity of human activity and the level of urbanization in the coastal zone over the last 20 years; 3) assessing the coupling coordination level between the eco-environment and the urbanization level in coastal area, and recognizing the impact of continuous urbanization on local environmental.

Section snippets

Study area

Generally, the coastal zone is a belt area that extends to the land and sea sides of the coastline, involving land areas and near-shore waters (He, 2017). In fact, the coastal zone is commonly defined according to management purposes and research needs, leading to a diversity of coastal zone extent definitions. For example, the United Nations Millennium Ecosystem Assessment project defines coastal zones as lowlands extending 100 km from the coast to the mainland, while scholars usually conduct

Construction of RSEI

The RSEI consists of four components: Greenness, Heat, Wetness and Dryness, which is often used for ecological assessment (Li et al., 2017, Yuan and Bauer, 2007, Ivits et al., 2009).

Although existing RSEI-based studies use NDVI as the green component, considering the NDVI's saturation problem in high vegetation coverage area, EVI is used as the green component. Since the MOD13A1 V6 image set contains the EVI layer, there is no need to calculate the EVI according to the formula (Index DataBase,

Coastal zone RSEI and its changes

Fig. 2 shows the distribution of the RSEI index in 2000, 2005, 2010, 2015, and 2019. The results indicate that the RSEI in China's coastal regions showed a significant upward trend from 2000 to 2019. The RSEI mean-value of the coastal zone in 2000 was the lowest, only 0.492, while the RSEI mean-value of 2019 increased by about 17% from 2000 to 0.575. The increase in RSEI over the past 2 decades indicates that the ecological environment in China's coastal zones has improved to some extent. For

Improvement of coastal ecological environment

Through the statistics of the average RSEI of the provinces and cities in the past 2 decades, it is an indisputable fact that the overall eco-environment of the coastal zone of China is gradually improving. During the study period, the average RSEI value has increased by 17%, and the area with the ecological status of “Good” and “Natural” has increased by nearly 200% and 1500%, respectively. The negative effects that urbanization and the expansion of impervious surfaces will have on regional

Conclusion

Remote sensing technology is of great value to the detection of eco-environment, and has been widely used in recent years. However, long-term large-scale analysis and assessment of ecological-urbanization interactions remain a challenge due to the limitations of data and analytical models. Therefore, this study proposes a detection scheme that combines daytime remote sensing and nighttime remote sensing, which can effectively characterize the regional eco-environment conditions, the intensity

CRediT authorship contribution statement

Zihao Zheng: Conceptualization, Methodology, Software, Validation, Investigation, Writing - original draft. Zhifeng Wu: Supervision, Project administration, Funding acquisition. Yingbiao Chen: Writing - review & editing. Zhiwei Yang: Software, Validation. Francesco Marinello: Conceptualization, Writing - review & editing, Supervision, Project administration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was financially supported by the National Natural Science Foundation of China (No. 41671430), Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (No. GML2019ZD0301) and NSFC-Guangdong Joint Fund (No. U1901219).

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