The study on the drive mechanism and prediction of the impervious surface expansion with index of entropy

2020 ◽  
Vol 10 (3) ◽  
pp. 185
Author(s):  
Guilin Wang ◽  
Jia Xiang
2018 ◽  
Vol 10 (10) ◽  
pp. 3761 ◽  
Author(s):  
Huafei Yu ◽  
Yaolong Zhao ◽  
Yingchun Fu ◽  
Le Li

Urban rainstorm waterlogging has become a typical “city disease” in China. It can result in a huge loss of social economy and personal property, accordingly hindering the sustainable development of a city. Impervious surface expansion, especially the irregular spatial pattern of impervious surfaces, derived from rapid urbanization processes has been proven to be one of the main influential factors behind urban waterlogging. Therefore, optimizing the spatial pattern of impervious surfaces through urban renewal is an effective channel through which to attenuate urban waterlogging risk for developed urban areas. However, the most important step for the optimization of the spatial pattern of impervious surfaces is to understand the mechanism of the impact of urbanization processes, especially the spatiotemporal pattern of impervious surfaces, on urban waterlogging. This research aims to elucidate the mechanism of urbanization’s impact on waterlogging by analysing the spatiotemporal characteristics and variance of urban waterlogging affected by urban impervious surfaces in a case study of Guangzhou in China. First, the study area was divided into runoff plots by means of the hydrologic analysis method, based on which the analysis of spatiotemporal variance was carried out. Then, due to the heterogeneity of urban impervious surface effects on waterlogging, a geographically weighted regression (GWR) model was utilized to assess the spatiotemporal variance of the impact of impervious surface expansion on urban rainstorm waterlogging during the period from the 1990s to the 2010s. The results reveal that urban rainstorm waterlogging significantly expanded in a dense and circular layer surrounding the city centre, similar to the impervious surface expansion affected by urbanization policies. Taking the urban runoff plot as the research unit, GWR has achieved a good modelling effect for urban storm waterlogging. The results show that the impervious surfaces in the runoff plots of the southeastern part of Yuexiu, the southern part of Tianhe and the western part of Haizhu, which have experienced major urban engineering construction, have the strongest correlation with urban rainstorm waterlogging. However, for different runoff plots, the impact of impervious surfaces on urban waterlogging is quite different, as there exist other influence factors in the various runoff plots, although the impervious surface is one of the main factors. This result means that urban renewal strategy to optimize the spatial pattern of impervious surfaces for urban rainstorm waterlogging prevention and control should be different for different runoff plots. The results of the GWR model analysis can provide useful information for urban renewal strategy-making.


2019 ◽  
Vol 11 (6) ◽  
pp. 640 ◽  
Author(s):  
Beibei Wang ◽  
Zhenjie Chen ◽  
A-Xing Zhu ◽  
Yuzhu Hao ◽  
Changqing Xu

As urbanization has profound effects on global environmental changes, quick and accurate monitoring of the dynamic changes in impervious surfaces is of great significance for environmental protection. The increased spatiotemporal resolution of imagery makes it possible to construct time series to obtain long-time-period and high-accuracy information about impervious surface expansion. In this study, a three-step monitoring method based on time series trajectory segmentation was developed to extract impervious surface expansion using Landsat time series and was applied to the Xinbei District, Changzhou, China, from 2005 to 2017. Firstly, the original time series was segmented and fitted to remove the noise caused by clouds, shadows, and interannual differences, leaving only the trend information. Secondly, the time series trajectory features of impervious surface expansion were described using three phases and four types with nine parameters by analyzing the trajectory characteristics. Thirdly, a multi-level classification method was used to determine the scope of impervious surface expansion, and the expansion time was superimposed to obtain a spatiotemporal distribution map. The proposed method yielded an overall accuracy of 90.58% and a Kappa coefficient of 0.90, demonstrating that Landsat time series remote sensing images could be used effectively in this approach to monitor the spatiotemporal expansion of impervious surfaces.


2021 ◽  
Vol 13 (14) ◽  
pp. 7901
Author(s):  
Jifeng Du ◽  
Mengxiao Yu ◽  
Junhua Yan

Empirical evidence shows that the expansion of impervious surface threatens soil organic carbon (SOC) sequestration in urbanized areas. However, the understanding of deep soil excavation due to the vertical expansion of impervious surface remains limited. According to the average soil excavation depth, we divided impervious surface into pavement (IS20), low-rise building (IS100) and high-rise building (IS300). Based on remote-sensing images and published SOC density data, we estimated the SOC storage and its response to the impervious surface expansion in the 0–300 cm soil depth in Guangzhou city, China. The results showed that the total SOC storage of the study area was 8.31 Tg, of which the top 100 cm layer contributed 44%. The impervious surface expansion to date (539.87 km2) resulted in 4.16 Tg SOC loss, of which the IS20, IS100 and IS300 contributed 26%, 58% and 16%, respectively. The excavation-induced SOC loss (kg/m2) of IS300 was 1.8 times that of IS100. However, at the residential scale, renovating an IS100 plot into an IS300 plot can substantially reduce SOC loss compared with farmland urbanization. The gains of organic carbon accumulation in more greenspace coverage may be offset by the loss in deep soil excavation for the construction of underground parking lots, suggesting a need to control the exploitation intensity of underground space and promote residential greening.


2021 ◽  
Vol 33 (5) ◽  
pp. 1574-1583
Author(s):  
Sun Yanwei ◽  
◽  
Xu Youpeng ◽  
Gao Bin ◽  
Wang Qiang ◽  
...  

1986 ◽  
Vol 47 (C4) ◽  
pp. C4-289-C4-303
Author(s):  
R. LACEY ◽  
N. N. AJITANAND ◽  
J. M. ALEXANDER ◽  
D.M. DE CASTRO RIZZO ◽  
G. F. PEASLEE ◽  
...  

2019 ◽  
Vol 7 ◽  
pp. 132-139 ◽  
Author(s):  
A.D. Bardovsky ◽  
◽  
A.A. Gerasimova ◽  
Keyword(s):  

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