Annual dynamics of impervious surfaces at city level of Pearl River Delta metropolitan

ABSTRACT

Human activities have transformed natural land surface to artificial constructions, which causes the urban regions increasingly expanding. Urban development can be reflected by the dynamic changes of impervious surfaces, which can be assessed using time-series satellite data. The Pearl River Delta (PRD) metropolitan has witnessed fast urban development over past decades. Even though many studied have focused on monitoring the urbanization of megacities, there is still a lack of comprehensive understanding about the long-term annual dynamics of urban impervious surfaces at city level in PRD. In this study, MOD09A1 (MODIS Surface Reflectance 8 Day L3 Global 500 m) products were chosen to assess the annual dynamics of impervious surface at city level in PRD. Temporal filtering was utilized to improve the accuracy of the impervious surface estimation. Moreover, a new index, normalized difference urban development index (NDUDI) was proposed to investigate relative development of impervious surface in PRD. Additionally, the economy factor, Gross Domestic Product (GDP) was introduced to reveal the correlation with the impervious surfaces. Results demonstrated that the overall accuracy for impervious surface extraction was about 91.00–94.00%. The impervious surfaces area in PRD dramatically increased from 6000 km² to 115,840 km² in the past 16 years. Guangzhou and Foshan have the fastest developing speed, while Dongguan, Shenzhen and Foshan sacrificed much more lands to the construction of PRD than other cities. Besides, areas of impervious surface per city show very good correlations with GDP in city of Guangzhou, Huizhou, Foshan, Zhongshan, Hong Kong and Zhuhai-Macau. The scatter plot between NDUDI and GDP data display different clustering pattern between different cities.

Acknowledgments

This study was jointly supported by National Natural Science Foundation of China (41401370), the Research Grants Council (RGC) General Research Fund (CUHK 14601515, CUHK 14635916 and CUHK 14605917) and the Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University (No. 2016LSDMIS02), and Zhuhai Joint Innovative Center for Climate-Environment-Ecosystem, Future Earth Research Institute, Beijing Normal University, Zhuhai, China. The authors would like to thank the editor and anonymous reviewers for their critical comments and suggestions to improve the original manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41401370];The Research Grants Council (RGC) General Research Fund [14601515,14605917,14635916];The Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University [2016LSDMIS02];