Abstract
As one of the most open and dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been urbanizing rapidly in recent decades. The surface water in the GBA also has been suffering from urbanization and intensified human activities. The study aimed to characterize the spatiotemporal patterns and assess the losses and gains of surface water caused by urbanization in the GBA via long time-series remote sensing data, which could support the progress towards sustainable development goals (SDGs) set by the United Nations, especially for measuring SDG 6.6.1 indicator. Firstly, utilizing 4750 continuous Landsat TM/ETM+/OLI images during 1986–2020 and the Google Earth Engine cloud platform, the multiple index water detection rule (MIWDR) was performed to extract surface water extent in the GBA. Secondly, we achieved surface water dynamic type classification based on annual water inundation frequency time-series in the GBA. Finally, the spatial distribution and temporal variation of urbanization-induced water losses and gains were analyzed through a land cover transfer matrix. Results showed that (1) the average minimal and maximal surface water extents of the GBA during 1986–2020 were 2017.62 km2 and 6129.55 km2, respectively. The maximal surface water extent fell rapidly from 7897.96 km2 in 2001 to 5087.46 km2 in 2020, with a loss speed of 155.41 km2 per year (R2 = 0.86). (2) The surface water areas of permanent and dynamic types were 1529.02 km2 and 2064.99 km2 during 2000–2020, accounting for 42.54% and 57.46% of all water-related areas, respectively. (3) The surface water extent occupied by impervious land surfaces showed a significant linear downward trend (R2 = 0.98, slope = 36.41 km2 per year), while the surface water restored from impervious land surfaces denoted a slight growing trend (R2 = 0.86, slope = 0.99 km2 per year). Our study monitored the long-term changes in the surface water of the GBA, which can provide valuable information for the sustainable development of the GBA urban agglomeration. In addition, the proposed framework can easily be implemented in other similar regions worldwide.
Highlights
Terrestrial surface water bodies, which include lakes, rivers, reservoirs, ponds and partially impounded farmland, are a critically important component of water resources for human life and terrestrial ecosystems [1]
Area.PrecipitaPrecipitation tion is a key component of climatic factors, which has a complicated relationship with is a key component of climatic factors, which has a complicated relationship with surface surface water extent changes
We characterized the spatial and temporal variations in surface water in the Greater Bay Area (GBA) and classified dynamic types of surface water by pixels to explore the features of surface water dynamics in detail
Summary
Terrestrial surface water bodies, which include lakes, rivers, reservoirs, ponds and partially impounded farmland, are a critically important component of water resources for human life and terrestrial ecosystems [1]. Surface water provides and supports diverse ecosystem services for human activities and natural habitats [2]. It is necessary to monitor surface water dynamics to inform water resources protection and regional sustainable development. Among the Sustainable Development Goals (SDGs) proposed by the United. Nations in 2015, SDG 6.6.1, which measures “the spatial extent of water-related ecosystem”, aims to track changes in different types of water-related ecosystems to support the recovery of already degraded water-related ecosystems and prevent surface water degradation [4,5]. SDG 6.6.1 is closely related to the monitoring of water-related ecosystems. As in the previous studies of Fitoka et al and Weise et al, spatial mapping methods of wetlands were developed and their research results were applied to the analysis of SDG 6.6.1 [6,7]
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