Assessing Surface Water Losses and Gains under Rapid Urbanization for SDG 6.6.1 Using Long-Term Landsat Imagery in the Guangdong-Hong Kong-Macao Greater Bay Area, China

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.

Assessing and Characterizing Carbon Storage in Wetlands of the Guangdong–Hong Kong–Macau Greater Bay Area, China, During 1995–2020

“…Surrounding the Pearl River Delta, with of approximately 86.17 million people, the GBA is the largest and the richest economic region in South China. Additionally, the GBA has a regional GDP of approximately USD 1,668.8 billion at the end of 2020, which was equivalent to 12% of the GDP for all of mainland China [38]. The Chinese central government released 'Outline Development Plan for the Guangdong-Hong Kong-Macau Greater Bay Area' in February 2019 to plan the development of the GBA.…”

“…The yearly continuous annual surface water time series from 1995 to 2020 in the GBA used in this study were already derived by employing a simple and fast water extraction method, the multiple index water detection rule (MIWDR) [38], developed by Deng et al [26]. This water extraction method utilizes four indices-Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index(EVI), Modified Normalized Difference Water Index (MNDWI) and Automated Water Extraction Index (AWEI) [38]- [41] to construct the following rule: (AWEInsh -AWEIsh > -0.1) and (MNDWI > NDVI or MNDWI >EVI) to retrieve surface water datasets in each year during the study period. The time series surface waterbody datasets used in this study are derived from existed datasets generated in another study [38], where detailed water extraction process and information about remote sensing datasets utilized could be found.…”

“…This water extraction method utilizes four indices-Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index(EVI), Modified Normalized Difference Water Index (MNDWI) and Automated Water Extraction Index (AWEI) [38]- [41] to construct the following rule: (AWEInsh -AWEIsh > -0.1) and (MNDWI > NDVI or MNDWI >EVI) to retrieve surface water datasets in each year during the study period. The time series surface waterbody datasets used in this study are derived from existed datasets generated in another study [38], where detailed water extraction process and information about remote sensing datasets utilized could be found. Moreover, the accuracy of the surface water datasets in the GBA is relatively high, with an overall accuracy of 93.42% [38].…”

“…The time series surface waterbody datasets used in this study are derived from existed datasets generated in another study [38], where detailed water extraction process and information about remote sensing datasets utilized could be found. Moreover, the accuracy of the surface water datasets in the GBA is relatively high, with an overall accuracy of 93.42% [38]. The spatial resolution of this surface water datasets is 30 m. 2) Carbon storage assessment for wetland ecosystems by the InVEST model: In this study, we employed the InVEST model to estimate the carbon storage in wetlands.…”

“…The WIF index indicates the frequency of water body occurrence in a given pixel during a certain period of time. The WIF could be calculated as follows [38], [45]:…”