The changes in Mulberry-based fish ponds in the Guangdong-Hong Kong-Macao Greater Bay Area over the past 40 years

2021 ◽  
Author(s):  
Wenxin Zhang ◽  
Zihao Cheng ◽  
Xianfeng Liu ◽  
Gangte Lin ◽  
Junan He ◽  
...  
Keyword(s):  
Hong Kong ◽  
Water Environment ◽  
Fish Ponds ◽  
Visual Interpretation ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Industrial Transfer ◽  
Intensity Index ◽  
Bay Area ◽  
Water Intensity

<p>Mulberry-based fish ponds are representative traditional eco-agriculture in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Investigations about the changes in such ponds and their relevant water environment under the background of rapid urbanization can provide a reference for the protection and development of these ponds. Using the Landsat images obtained after 1986, this study employed supervised classification and visual interpretation approaches and water intensity index as well as calculating synthesized index to identify the spatial patterns of changes in Mulberry-based fish ponds in the GBA. The results indicated that the year of 2013 was the inflection point of fish pond changes, which can also be proved by calculating synthesized index. The causes to the changes in fish ponds were further explored from four aspects: land use change, industrial transfer, government guidance and financial motives.</p>

scholarly journals Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong–Hong Kong–Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2215 ◽  
Author(s):  
Chao Yang ◽  
Qingquan Li ◽  
Tianhong Zhao ◽  
Huizeng Liu ◽  
Wenxiu Gao ◽  
...  
Keyword(s):  
Hong Kong ◽  
Urban Area ◽  
Urban Areas ◽  
Rural Landscape ◽  
The Other ◽  
Elastic Coefficient ◽  
Landsat Images ◽  
The Sustainable Development ◽  
Bay Area ◽  
Time Periods

The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the major bay areas in the world. However, the spatiotemporal characteristics and rationalities of urban expansions within this region over a relatively long period of time are not well-understood. This study explored the spatiotemporal evolution of 11 cities within the GBA in 1987–2017 by integrating remote sensing, landscape analysis, and geographic information system (GIS) techniques, and further evaluated the rationalities of their expansion using the urban area population elastic coefficient (UPEC) and the urban area gross domestic product (GDP) elastic coefficient (UGEC). The results showed the following: (1) Guangzhou, Shenzhen, Foshan, Dongguan, Zhongshan, and Zhuhai experienced unprecedented urbanization compared with the other cities, and from 1987 to 2017, their urban areas expanded by 10.12, 11.48, 14.21, 24.90, 37.07, and 30.15 times, respectively; (2) several expansion patterns were observed in the 11 cities, including a mononuclear polygon radiation pattern (Guangzhou and Foshan), a double-nucleated polygon pattern (Macau and Zhongshan), and a multi-nuclear urbanization pattern (Shenzhen, Hong Kong, Dongguan, Jiangmen, Huizhou, Zhaoqing, and Zhuhai); (3) with regard to the proportion of area, the edge-expansion and outlying growth types were the predominant types for all 11 cities, and the infilling growth type was the one of the important types during 2007–2017 for Shenzhen, Hong Kong, Dongguan, Zhongshan, and Foshan; (4) the expansion of most cities took on an urban-to-rural landscape gradient, especially for Guangzhou, Shenzhen, Foshan, Zhongshan, Dongguan, and Zhuhai; and (5) the rationalities of expansion in several time periods were rational for Guangzhou (1997–2007), Hong Kong (2007–2017), Foshan (1987–2007), Huizhou (1987–1997), and Dongguan (1997–2007), and the rationalities of expansion in the other cities and time periods were found to be irrational. These findings may help policy- and decision-makers to maintain the sustainable development of the Guangdong–Hong Kong–Macau Greater Bay Area.

scholarly journals Dynamic simulation of coastal wetlands for Guangdong-Hong Kong-Macao Greater Bay area based on multi-temporal Landsat images and FLUS model

2021 ◽  
Vol 125 ◽  
pp. 107559
Author(s):  
Hongjiang Guo ◽  
Yanpeng Cai ◽  
Zhifeng Yang ◽  
Zhenchang Zhu ◽  
Yiran Ouyang
Keyword(s):  
Hong Kong ◽  
Dynamic Simulation ◽  
Coastal Wetlands ◽  
Landsat Images ◽  
Bay Area ◽  
Multi Temporal

Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China

2021 ◽  
Vol 31 (1) ◽  
pp. 93-108
Author(s):  
Chao Yang ◽  
Huizeng Liu ◽  
Qingquan Li ◽  
Aihong Cui ◽  
Rongling Xia ◽  
...  
Keyword(s):  
Hong Kong ◽  
Urban Land ◽  
Forest Loss ◽  
Rapid Urbanization ◽  
Bay Area

scholarly journals A Quantile Approach for Retrieving the “Core Urban-Suburban-Rural” (USR) Structure Based on Nighttime Light

2020 ◽  
Vol 12 (24) ◽  
pp. 4179
Author(s):  
Yaohuan Huang ◽  
Chengbin Wu ◽  
Mingxing Chen ◽  
Jie Yang ◽  
Hongyan Ren
Keyword(s):  
Vegetation Index ◽  
Regional Scale ◽  
Metropolitan Region ◽  
Average Increase ◽  
Visual Interpretation ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
The Core ◽  
Impervious Area ◽  
Nighttime Light

Accurate and timely information on the “core urban-suburban-rural” (USR) spatial structure in a metropolitan region is significant for both the scientific and policy-making communities. However, USR is usually considered as a single land use type, such as an impervious area, rather than three combined subcategories in remote-sensing image retrieval, especially for suburban areas, which obscures the details of the urbanization process. In this paper, we propose a quantile approach to retrieve the structure of USR based on stable nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and apply it in the Beijing-Tianjin-Hebei (JJJ) of China from 1995 to 2013. The key parameters of the NTL threshold, which is the maximum change point of the NTL intensity at the USR boundary, used to retrieve the three subcategories of USR are automatically defined based on the quantile approach with three iterations. Then, the overall accuracy and consistency of the retrieval results are evaluated using the corresponding visual interpretation map from Landsat images with a 30 m resolution. Moreover, the influence of parameter uncertainty is compared by introducing the human settlement index (HSI). According to the time-series analysis of USR retrieval in this study, the JJJ experienced rapid urbanization from 1995 to 2013, with the core urban area expanding by 7098 km2 (average increase of 2.7 times), the suburban area expanding by 12,690 km2 (average increase of 2.8 times), and the rural area increasing by 4986 km2 (average increase of 0.38 times). The USR results retrieved based on the approach agree well with the validation of the visual interpretation map, with an overall accuracy (OA) of 0.904 and a kappa coefficient (KC) of 0.650 at the city level. The USR result with the HSI as the input shows that NTL is more suitable for USR structure retrieval as the NTL shows less uncertainty compared with other parameters such as the vegetation index (VI). This study proposes an improved quantile approach for USR mapping from NTL images on a regional scale, which will provide a useful method for urbanization dynamics analysis.

scholarly journals Impacts of Climate Change and Land Use/Cover Change on Regional Hydrological Processes: Case of the Guangdong-Hong Kong-Macao Greater Bay Area

2022 ◽  
Vol 9 ◽  
Author(s):  
Xueling Tan ◽  
Suning Liu ◽  
Yong Tian ◽  
Zhaoqiang Zhou ◽  
Yao Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Hong Kong ◽  
Baseline Period ◽  
Assessment Tools ◽  
Hydrological Processes ◽  
Efficiency Coefficient ◽  
Rapid Urbanization ◽  
Bay Area ◽  
Impacts Of Climate Change

Climate change and land use/cover change (LUCC) have been widely recognized as the main driving forces that can affect regional hydrological processes, and quantitative assessment of their impacts is of great importance for the sustainable development of regional ecosystems, land use planning and water resources management. This study investigates the impacts of climate change and LUCC on variables such as streamflow (SF), soil moisture (SM) and evapotranspiration (ET) in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) by using Soil and Water Assessment Tools (SWAT) model under different scenarios during 1979–2018. The results show that the simulation performances were overall good, with Nash-Sutcliffe Efficiency Coefficient (NSE) and coefficient of determination (R2) greater than 0.80 for the monthly-scale SF calibration and validation. According to the results of trend and change point tests of meteorological series, the baseline period (1979–1997) and the interference period (1998–2018) were determined. Interestingly, other land use types were basically converted to urban land, leading to a rapid urbanization in the GBA. Compared with the SF values of the eight estuaries of the Pearl River Basin in the baseline period, both climate change and LUCC has led to the decrease in the SF values in the interference period, and the combined effect of climate change and LUCC was slightly greater than their individual effect. Overall, climate change and LUCC both have important impacts on regional hydrological processes in the GBA.

scholarly journals Variations of the Precipitation Extremes over the Guangdong-Hong Kong-Macao Greater Bay Area in China

2021 ◽  
Author(s):  
Xianru Li ◽  
Zhigang Wei ◽  
Huan Wang ◽  
Li Ma ◽  
Shitong Guo
Keyword(s):  
Hong Kong ◽  
Change Point ◽  
Abrupt Change ◽  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Change Points ◽  
China Meteorological Administration ◽  
Annual Variations ◽  
Intensity Index ◽  
Bay Area

Abstract By using the gridded 0.25°×0.25° observation dataset of CN05.1 provided by the China Meteorological Administration, this study investigates the variations of the nine precipitation extreme indices over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China in the period from 1961 to 2018. Based on trends and inter-annual variations, the nine kinds of extreme precipitation are classified into four categories: the category 1 is the very wet days (R95P), the extremely wet days (R99P), the maximum 1-day precipitation amount (RX1day) and the maximum 5-day precipitation amount (RX5day). The category 2 is the number of heavy precipitation days (R10day), the number of very heavy precipitation days (R20day) and the simple daily intensity index (SDII). The category 3 and 4 is the consecutive wet days (CWDday) and the consecutive dry days (CDDday), respectively. For the extreme precipitations in the category 1, the abrupt change point from less to more values occurs in 1991 in summer. Three abrupt change points, from less to more in 1972 and 2009, and from more to less in 1994 occur in spring. For the extreme precipitations in the category 2, the abrupt change point from less to more values occurs in 1993 in summer. Three abrupt change points, from less to more in 1965 and 2010, and from more to less in 1990 occur in spring. Annually and seasonally, the abrupt changes occur in early 2010s for CWDday which has clearly been more and for CDDday which has clearly been less. In addition, CWDday occurs abrupt change points from less to more in 1966 and from more to less in1983 in spring. The variations of these extreme precipitations have significant periodic oscillations of 3–5 years, quasi-8 years or 8–14 years. During 1961–1994, 1995–2009 and 2010–2018 three stages, the changes of the annual and most seasonal R95P, R99P, R10day, R20day and SDII are consistent with those of precipitation. The values in the latter stage are increasing compared with those in the former stage. The changes of RX1day, RX5day, CWDday and CDDday have their own characteristics.

scholarly journals Patterns of the Network of Cross-Border University Research Collaboration in the Guangdong-Hong Kong-Macau Greater Bay Area

2020 ◽  
Vol 12 (17) ◽  
pp. 6846
Author(s):  
Jinyuan Ma ◽  
Fan Jiang ◽  
Liujian Gu ◽  
Xiang Zheng ◽  
Xiao Lin ◽  
...  
Keyword(s):  
Social Sciences ◽  
Hong Kong ◽  
Research Collaboration ◽  
Citation Impact ◽  
High Quality ◽  
High Quality Research ◽  
Cross Border ◽  
Bay Area ◽  
Research Areas ◽  
Industrial Distribution

This study analyzes the patterns of university co-authorship networks in the Guangdong-Hong Kong-Macau Greater Bay Area. It also examines the quality and subject distribution of co-authored articles within these networks. Social network analysis is used to outline the structure and evolution of the networks that have produced co-authored articles at universities in the Greater Bay Area from 2014 to 2018, at both regional and institutional levels. Field-weighted citation impact (FWCI) is used to analyze the quality and citation impact of co-authored articles in different subject fields. The findings of the study reveal that university co-authorship networks in the Greater Bay Area are still dispersed, and their disciplinary development is unbalanced. The study also finds that, while the research areas covered by high-quality co-authored articles fit the strategic needs of technological innovation and industrial distribution in the Greater Bay Area, high-quality research collaboration in the humanities and social sciences is insufficient.

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