Impervious surfaces (ISs) are defined as human-made land covers where water cannot infiltrate the soil, including the rooftops of buildings, roads, driveways, sidewalks, airports, parking, and so on [1
]. Urban IS is often used as a quantitative indicator to study the relationship between urban development, population, and economy; to determine the potential for urban development [4
]; to provide a basis for relevant policy planning; and to promote the optimal allocation of urban energy and resources [4
]. Urban IS has been used frequently in studies of urban environments and ecology, with an increased focus on environmental issues being given [6
]. The impervious surfaces in a city prevent precipitation from quickly infiltrating into the soil, thereby affecting the supply of groundwater and the circulation of water in the area [12
]. ISs increase surface runoff, which increases the frequency of floods and the surface runoff, correspondingly [13
]. Urban toxic and hazardous substances are flushed into rivers, resulting in non-point source pollution of water areas such as rivers and lakes. As the flow of water spreads, the affected areas continue to expand [15
]. This inevitably affects water quality and hydrology in the area, causing negative ecological and other effects [18
]. Urban ISs have the ability to absorb solar radiation on the earth’s surface, and the absorbed energy is released by the means of long-wave radiation, changing the latent heat flux of the city and increasing the urban heat island effect [20
]. The expansion reduces biodiversity, and affects the rational use of energy and resources of a region [18
]. Therefore, accurately quantifying impervious surface area and understanding the details of urban changes are conducive to the study of urban environments and ecosystems. This quantification allows us to explore temporal and spatial changes for the allocation of resources and energy, formulating policies, protecting the environment, and maintaining the sustainable development of urban areas.
Due to its coherence and speed, remote sensing has been used to rapidly and accurately study urbanization and its change across a variety of temporal and spatial scales [17
]. Numerous methods have been developed to estimate impervious surface area [2
]. These methods include sub-pixel and per-pixel classifications based on medium-and high-resolution images of individual cities or small-scale areas [9
], such as Spectral Mixture Analysis (SMA), the spatially adaptive SMA (SASMA) technique, and the normalized spectral mixture analysis (NSMA) method, based on the vegetation-impervious surface-soil (V-I-S) model [28
]. These studies were mostly based on the ISs of individual cities or small-scale areas. However, urbanization is not only a challenge for individual cities or on small scales, but it is also a regional and global problem. Although medium–high-resolution remote sensing images have high spatial resolution, the spatial extent of each image is limited. Therefore, medium- and high-resolution remote sensing images have some limitations in extracting large-scale urban impervious areas [5
]. The wide coverage of low- and medium-resolution remote sensing images is an advantage when extracting large-scale urban impervious areas. Due to the Defense Meteorological Satellite Program (DMSP)’s operational line-scan system (OLS), which produces nighttime light (NTL) data as coarse spatial resolution images, they have larger spatial coverage and a shorter satellite return visit period. These data capture stable artificial light luminosity on the earth’s surface, and provide long-term data records [7
] that are closely related to human activities, such as urban settlements, population density, economic activity, energy use, and carbon emissions [5
]. NTL data have been widely used for regional or global large-scale urban impervious surface mapping and dynamics [36
]. However, these data suffer from saturation due to the limited range of the dynamic digital number (from 0 to 63), especially in urban centers, and there is a common mixed pixel problem in coarse- or medium-resolution imagery [5
]. Therefore, the IS result derived from NTL data has uncertainty, and the diversity of cities is not accurately displayed. Many researchers have attempted to solve this problem [6
]. The most effective method was the joint use of a vegetation index combined with NTL data [25
]. For example, Lu et al. [6
] proposed a human settlement index (HSI) in 2008, and Zhang et al. [7
] developed a vegetation-adjusted NTL urban index (VANUI) in 2013. These indexes are able to extract the urban impervious area, and these indexes have also been widely applied. However, this index does not perform well in cities that have experienced rapid urbanization. In addition, the DMSP-OLS data still suffer from the blooming effect [7
]. Zhuo et al. [43
] developed the enhanced vegetation index (EVI)-adjusted NTL index (EANTLI) combining DMSP-OLS NTL with Moderate Resolution Imaging Spectroradiometer (MODIS) EVI in order to further reduce the performance of the saturation effect of DMSP-OLS, especially in the core of the city.
The above research mostly focused on the extraction method of urban impervious surfaces in a small scale area, or on the extraction method of urban IS in large-scale areas based on a single period image. Few have explored the temporal and spatial variations in urban impervious area on a large scale. We are not aware of how urban development varies in time and across different regions. However, researching the temporal and spatial variability in urban development will help us to further understand the state of urban development and its impact on the ecological environment. Therefore, studying the spatial–temporal differentiation of urban impervious surfaces is required.
Since the 1980s, the Chinese economy has developed rapidly [44
], so we chose China as the research area. The mid- to low-resolution NTL data, MODIS NDVIA, EVI, and the Normalized Difference Water Index (NDWI) data from 2013 were used to calculate the above-mentioned three indexes (HIS, VANUI, and EANTLI). These are the three most common methods for extracting impervious surfaces, both domestically and in foreign countries. We used these three indexes to extract the IS of China in 2013. We used the high-resolution Landsat-8 urban impervious area data as the benchmark data to verify the accuracy of the urban impervious surface extracted by the three indexes. Through visual comparison and accuracy verification, we selected the index that was most suitable for extracting the urban IS, and to explore the spatial and temporal differentiation of urban impervious surfaces in the study area from 2003 to 2013. We wanted to explore the differences in urbanization in different regions by discussing the laws of urbanization and urban development in China in the 10-year period of 2003 to 2013. The results provide new ideas and methods for urbanization research, and they provide a basis for decision-making for the allocation of resources and energy for urban planning and regional development in the future.
By calculating the values of three urban impervious area indexes—Human Settlements Index (HSI), vegetation-adjusted NTL urban index (VANUI), and the EVI-adjusted NTL index (EANTLI)—using 2013 NTL data and MODIS-NDVI and EVI data, the IS area was extracted. Landsat-8 data were used as the benchmark to verify the IS accuracy extracted by the three indexes. We used the index with the best accuracy to extract the urban impervious surface area of the study area from 2003 to 2013, and explored the temporal and spatial variation in urban impervious surfaces using the urban IS percentage (UISP) index, YOYG, and UISEr. We obtained the following conclusions:
The three indexes can extract the impervious surface of the city, and the total classification accuracy was at least 85% for the three indexes. EANTLI had a higher classification accuracy than the VANUI and HIS indexes, with an overall accuracy of 95.41% and a kappa coefficient of 0.91. Therefore, EANTLI has a better recognition accuracy in extracting the urban impervious area in China.
China’s urban impervious area was 70,179.06 km2, accounting for 0.73% of the country’s land area. Compared with 2008 and 2003, this value was an increase of 0.42% and 0.52%, respectively. The growth rate of the impervious areas in Chinese cities from 2008 to 2013 was higher than that from 2003 to 2008.
On a spatial scale, impervious surface distribution is extremely uneven in different areas of China. The urban IS percentage (UISP) performance was characterized by a decreasing trend from NWC, SWC, MRYLR, NEC, MRYTR, SCC, and NCC, to ECC in 2013. UISEr demonstrated the considerable imbalance in different areas of China from 2003 to 2013. The expansion of the impervious area in the MRYTR and MRYLR areas during this decade was more obvious.
In short, the urban impervious surface of the whole study area presented an increasing trend from 2003 to 2013. However, the urban impervious surface coverage percentage differed considerably in different regions. This means that there is a large difference in urbanization in different regions. The urban IS expansion index (UISEr) was considerably different in different regions from 2003 to 2013. Combined with research on urban expansion and ecological environment changes, we should pay more attention to the regions where the impervious surface area is rapidly increasing, because these areas might have a greater impact on changes in the original ecological environment.