Dynamic Changes, Spatiotemporal Differences, and Ecological Effects of Impervious Surfaces in the Yellow River Basin, 1986–2020

Abstract

Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries.

Impervious surface extraction and spatiotemporal analysis in the main urban area of Zhengzhou

“…Impervious surfaces mainly refer to artificial surfaces such as cement and asphalt pavements, roofs and other hardened surfaces 1 . The rapid expansion of impervious surfaces has been shown to bring substantial impact to urban heat island effect and surface runoff 2 . Densification of impervious surfaces can seriously lead to the decrease of infiltration rates and the increase of evaporation rates and surface runoff 3 .…”

Analysis of changes in the spatiotemporal characteristics of impervious surfaces and their influencing factors in the Central Plains Urban Agglomeration of China from 2000 to 2018

“…Many scholars quantitatively analyze the characteristics of impervious surface changes using methods such as the ISA ratio, area growth amount and growth rate, urban expansion intensity index, etc. [ [18] , [19] , [20] , [21] , [22] , [23] , [24] ]. For example, Jian et al [ 25 ] quantified the dynamic changes of impervious surfaces in Beijing by calculating the percentage of total ISA in 2001 and 2009.…”

Impervious Surface Mapping Based on Remote Sensing and an Optimized Coupled Model: The Dianchi Basin as an Example

“…Traditional research methodologies typically take into account a limited number of features present in remote sensing images, thereby failing to comprehensively capture the distinctions between IS and other land types. There are studies that attempt to leverage a diverse set of features in the process of IS identification, but they often overlook the significance of feature selection [25,26], leading to feature redundancy. In this research, it is hypothesized that the classification accuracy may be influenced by both the category and the number of combined features, and distinct categories of features might be optimally suited to varying machine learning algorithms.…”