scholarly journals Driving Factors and Risk Assessment of Rainstorm Waterlogging in Urban Agglomeration Areas: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area, China

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 770
Author(s):  
Fan Liu ◽  
Xiaoding Liu ◽  
Tao Xu ◽  
Guang Yang ◽  
Yaolong Zhao
Keyword(s):  
Risk Assessment ◽  
Spatial Distribution ◽  
Hong Kong ◽  
Central Area ◽  
Impervious Surface ◽  
Driving Factors ◽  
Urban Agglomeration ◽  
Driving Mechanism ◽  
Bay Area

Understanding the driving factors and assessing the risk of rainstorm waterlogging are crucial in the sustainable development of urban agglomerations. Few studies have focused on rainstorm waterlogging at the scale of urban agglomeration areas. We used the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China as a case study. Kernel density estimation (KDE) and spatial autocorrelation analysis were applied to study the spatial distribution characteristics of rainstorm waterlogging spots during 2013–2017. A geographical detector (GD) and geographically weighted regression (GWR) were used to discuss the driving mechanism of rainstorm waterlogging by considering eight driving factors: impervious surface ratio (ISR), mean shape index of impervious surface (Shape_MN), aggregation index of impervious surface (AI), fractional vegetation cover (FVC), elevation, slope, river density, and river distance. The risk of rainstorm waterlogging was assessed using GWR based on principal component analysis (PCA). The results show that the spatial distribution of rainstorm waterlogging in the GBA has the characteristics of multicenter clustering. Land cover characteristic factors are the most important factors influencing rainstorm waterlogging in the GBA and most of the cities within the GBA. The rainstorm waterlogging density increases when ISR, Shape_MN, and AI increase, while it decreases when FVC, elevation, slope, and river distance increase. There is no obvious change rule between rainstorm waterlogging and river density. All of the driving factors enhance the impacts on rainstorm waterlogging through their interactions. The relationships between rainstorm waterlogging and the driving factors have obvious spatial differences because of the differences in the dominant factors affecting rainstorm waterlogging in different spatial positions. Furthermore, the result of the risk assessment of rainstorm waterlogging indicates that the southwest area of Guangzhou and the central area of Shenzhen have the highest risks of rainstorm waterlogging in GBA. These results may provide references for rainstorm waterlogging mitigation through urban renewal planning in urban agglomeration areas.

scholarly journals Impervious Surface Expansion: A Key Indicator for Environment and Urban Agglomeration—A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area by Using Landsat Data

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Fan Liu ◽  
Yaolong Zhao ◽  
Rizwan Muhammad ◽  
Xiaoding Liu ◽  
Mingqiang Chen
Keyword(s):  
Hong Kong ◽  
Diversity Index ◽  
Impervious Surface ◽  
Shape Index ◽  
Urban Agglomeration ◽  
Spatial Process ◽  
Landsat Data ◽  
Urbanization Process ◽  
Bay Area ◽  
Key Indicator

Impervious surface (IS) is a key indicator to measure the urbanization process and ecological environment. Many studies have observed an urbanization process based on IS at the city scale. Understanding the changes in the IS over a period at a regional level offers an alternative and effective approach to characterize and quantify the spatial process of urban agglomeration. This study focuses on the urban agglomeration of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) by utilizing the sensor-based Landsat data during 1987-2017 and investigates the spatiotemporal distribution of IS expansion at both regional and city scales. The modified linear spectral mixture analysis (MLSMA) method is used to extract the IS of the GBA. Then, the IS mapping accuracies were assessed after comparison with high-resolution historical data. The spatiotemporal and directional changes of IS surfaces for GBA are analyzed by using Gravity Center (GC) and Standard Deviational Ellipse (SDE). Finally, Shannon’s Diversity Index (SHDI) is used to analyze the overall characteristics of landscape level, and the Patch Density (PD) and Landscape Shape Index (LSI) are used to describe the characteristics of different classes of the IS. The results show that the IS of the whole region experienced rapid and massive expansion during the past 30 years and exhibited a distinct characteristic along the Pearl River Delta (PRD) and the coastline. Furthermore, the IS area increased rapidly in the PRD, while it is relatively stable in Hong Kong and Macao. We believe that the findings of this study can help policy makers to better understand and maintain the sustainable development of the GBA.

scholarly journals Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2757 ◽  
Author(s):  
Kongming Li ◽  
Mingming Feng ◽  
Asim Biswas ◽  
Haohai Su ◽  
Yalin Niu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Satellite Remote Sensing ◽  
Gansu Province ◽  
Driving Factors ◽  
Driving Mechanism ◽  
Second Stage ◽  
Arid And Semiarid ◽  
Land Use And Cover

Land use and cover change (LUCC) is an important issue affecting the global environment, climate change, and sustainable development. Detecting and predicting LUCC, a dynamic process, and its driving factors will help in formulating effective land use and planning policy suitable for local conditions, thus supporting local socioeconomic development and global environmental protection. In this study, taking Gansu Province as a case study example, we explored the LUCC pattern and its driving mechanism from 1980 to 2018, and predicted land use and cover in 2030 using the integrated LCM (Logistic-Cellular Automata-Markov chain) model and data from satellite remote sensing. The results suggest that the LUCC pattern was more reasonable in the second stage (2005 to 2018) compared with that in the first stage (1980 to 2005). This was because a large area of green lands was protected by ecological engineering in the second stage. From 1980 to 2018, in general, natural factors were the main force influencing changes in land use and cover in Gansu, while the effects of socioeconomic factors were not significant because of the slow development of economy. Landscape indices analysis indicated that predicted land use and cover in 2030 under the ecological protection scenario would be more favorable than under the historical trend scenario. Besides, results from the present study suggested that LUCC in arid and semiarid area could be well detected by the LCM model. This study would hopefully provide theoretical instructions for future land use planning and management, as well as a new methodology reference for LUCC analysis in arid and semiarid regions.

scholarly journals Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017

2020 ◽  
Vol 12 (16) ◽  
pp. 2615
Author(s):  
Jie Zhang ◽  
Le Yu ◽  
Xuecao Li ◽  
Chenchen Zhang ◽  
Tiezhu Shi ◽  
...  
Keyword(s):  
Growth Rate ◽  
Hong Kong ◽  
Urban Areas ◽  
Urban Expansion ◽  
Driving Factors ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Spatial And Temporal Scales ◽  
Urban Dynamics ◽  
Bay Area

The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the largest bay areas in the world. However, the spatiotemporal characteristics and driving mechanisms of urban expansions in this region are poorly understood. Here we used the annual remote sensing images, Geographic Information System (GIS) techniques, and geographical detector method to characterize the spatiotemporal patterns of urban expansion in the GBA and investigate their driving factors during 1986–2017 on regional and city scales. The results showed that: the GBA experienced an unprecedented urban expansion over the past 32 years. The total urban area expanded from 652.74 km2 to 8137.09 km2 from 1986 to 2017 (approximately 13 times). The annual growth rate during 1986–2017 was 8.20% and the annual growth rate from 1986 to 1990 was the highest (16.89%). Guangzhou, Foshan, Dongguan, and Shenzhen experienced the highest urban expansion rate, with the annual increase of urban areas in 51.51, 45.54, 36.76, and 23.26 km2 y−1, respectively, during 1986–2017. Gross Domestic Product (GDP), income, road length, and population were the most important driving factors of the urban expansions in the GBA. We also found the driving factors of the urban expansions varied with spatial and temporal scales, suggesting the general understanding from the regional level may not reveal detailed urban dynamics. Detailed urban management and planning policies should be made considering the spatial and internal heterogeneity. These findings can enhance the comprehensive understanding of this bay area and help policymakers to promote sustainable development in the future.

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