- freely available
ISPRS Int. J. Geo-Inf. 2019, 8(1), 31; https://doi.org/10.3390/ijgi8010031
2. Study Area and Data Acquisition
2.1. Study Area
2.2. Data Acquisition
3.1. Selection of Urban Impervious Surface Extraction Index in the Study Area
3.1.1. Calculation of the Three Indexes
3.1.2. Acquisition of Benchmark Urban Impervious Surface Data Based on Landsat-8
3.1.3. Practicability of the Three Indexes
3.2. Quantifying the Spatiotemporal Patterns of Urban IS
3.2.1. Determination of Quantitative Indicators
3.2.2. Classification of Regional Characteristics of the Study Area
4. Result and Discussion
4.1. Selecting the Best Index for Urban Impervious Surface Extraction
4.1.1. Comparative Analysis of the Results of Three Indexes of Urban Impervious Surface Extraction
4.1.2. Verification of the Accuracy of the Three Indexes
4.2. Spatial and Temporal Differentiation of Impervious Surfaces over 2003–2013
- 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.
Conflicts of Interest
- Arnold, C.L.; Gibbons, C.J. Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. J. Am. Plan. Assoc. 1996, 62, 243–258. [Google Scholar] [CrossRef]
- Weng, Q. Remote Sens. of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens. Environ. 2012, 117, 34–49. [Google Scholar] [CrossRef]
- Yang, J.; He, Y. Automated mapping of impervious surfaces in urban and suburban areas: Linear spectral unmixing of high spatial resolution imagery. Int. J. Appl. Earth Obs. Geoinf. 2017, 54, 53–64. [Google Scholar] [CrossRef]
- He, C.; Ma, Q.; Liu, Z.; Zhang, Q. Modeling the spatiotemporal dynamics of electric power consumption in Mainland China using saturation-corrected DMSP/OLS nighttime stable light data. Int. J. Digit. Earth 2013, 7, 993–1014. [Google Scholar] [CrossRef]
- Ma, Q.; He, C.; Wu, J.; Liu, Z.; Zhang, Q.; Sun, Z. Quantifying spatiotemporal patterns of urban impervious surfaces in China: An improved assessment using nighttime light data. Landsc. Urban Plan. 2014, 130, 36–49. [Google Scholar] [CrossRef]
- Lu, D.; Tian, H.; Zhou, G.; Ge, H. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data. Remote Sens. Environ. 2008, 112, 3668–3679. [Google Scholar] [CrossRef]
- Zhang, Q.; Schaaf, C.; Seto, K.C. The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity. Remote Sens. Environ. 2013, 129, 32–41. [Google Scholar] [CrossRef]
- Milesi, C.; Elvidge, C.D.; Nemani, R.R.; Running, S.W. Assessing the impact of urban land development on net primary productivity in the southeastern United States. Remote Sens. Environ. 2003, 86, 401–410. [Google Scholar] [CrossRef]
- Deng, C.; Wu, C. A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution. Remote Sens. Environ. 2013, 133, 62–70. [Google Scholar] [CrossRef]
- Xu, J.; Zhao, Y.; Zhong, K.; Zhang, F.; Liu, X.; Sun, C. Measuring spatio-temporal dynamics of impervious surface in Guangzhou, China, from 1988 to 2015, using time-series Landsat imagery. Sci. Total Environ. 2018, 627, 264–281. [Google Scholar] [CrossRef]
- Zhang, L.; Weng, Q.; Shao, Z. An evaluation of monthly impervious surface dynamics by fusing Landsat and MODIS time series in the Pearl River Delta, China, from 2000 to 2015. Remote Sens. Environ. 2017, 201, 99–114. [Google Scholar] [CrossRef]
- Shuster, W.D.; Bonta, J.; Thurston, H.; Warnemuende, E.; Smith, D.R. Impacts of impervious surface on watershed hydrology: A review. Urban Water J. 2005, 2, 263–275. [Google Scholar] [CrossRef]
- Pappas, E.A.; Smith, D.R.; Huang, C.; Shuster, W.D.; Bonta, J.V. Impervious surface impacts to runoff and sediment discharge under laboratory rainfall simulation. Catena 2008, 72, 146–152. [Google Scholar] [CrossRef]
- Weng, Q. Modeling Urban Growth Effects on Surface Runoff with the Integration of Remote Sensing and GIS. Environ. Manag. 2014, 28, 737–748. [Google Scholar] [CrossRef]
- Brabec, E.; Schulte, S.; Richards, P.L. Impervious surfaces and water quality: A review of current literature and its implications for watershed planning. J. Plan. Lit. 2002, 16, 499–514. [Google Scholar] [CrossRef]
- Ebrahimian, A.; Wilson, B.N.; Gulliver, J.S. Improved methods to estimate the effective impervious area in urban catchments using rainfall-runoff data. J. Hydrol. 2016, 536, 109–118. [Google Scholar] [CrossRef]
- Jartun, M.; Ottesen, R.T.; Steinnes, E.; Volden, T. Runoff of particle bound pollutants from urban impervious surfaces studied by analysis of sediments from stormwater traps. Sci. Total Environ. 2008, 396, 147–163. [Google Scholar] [CrossRef] [PubMed]
- Goetz, S.; Fiske, G. Linking the diversity and abundance of stream biota to landscapes in the mid-Atlantic USA. Remote Sens. Environ. 2008, 112, 4075–4085. [Google Scholar] [CrossRef]
- Xian, G.; Homer, C. Updating the 2001 National Land Cover Database Impervious Surface Products to 2006 using Landsat Imagery Change Detection Methods. Remote Sens. Environ. 2010, 114, 1676–1686. [Google Scholar] [CrossRef]
- Yao, R.; Wang, L.; Gui, X.; Zheng, Y.; Zhang, H.; Huang, X. Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin. Remote Sens. 2017, 9, 540. [Google Scholar] [CrossRef]
- Yuan, F.; Bauer, M.E. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens. Environ. 2007, 106, 375–386. [Google Scholar] [CrossRef]
- Imhoff, M.L.; Zhang, P.; Wolfe, R.E.; Bounoua, L. Remote Sens. of the urban heat island effect across biomes in the continental USA. Remote Sens. Environ. 2010, 114, 504–513. [Google Scholar] [CrossRef]
- Pickett, S.T.; Cadenasso, M.L.; Grove, J.M.; Groffman, P.M.; Band, L.E.; Boone, C.G.; Burch, W.R.; Grimmond, C.S.; Hom, J.; Jenkins, J.C.; et al. Beyond urban legends: An emerging framework of urban ecology, as illustrated by the baltimore ecosystem study. Am. Inst. Biol. Biosci. 2008, 58, 139–150. [Google Scholar] [CrossRef]
- Zhou, Y.; Smith, S.J.; Elvidge, C.D.; Zhao, K.; Thomson, A.; Imhoff, M. A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sens. Environ. 2014, 147, 173–185. [Google Scholar] [CrossRef]
- Zhang, L.; Weng, Q. Annual dynamics of impervious surface in the Pearl River Delta, China, from 1988 to 2013, using time series Landsat imagery. ISPRS J. Photogramm. Remote Sens. 2016, 113, 86–96. [Google Scholar] [CrossRef]
- Guo, W.; Lu, D.; Wu, Y.; Zhang, J. Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data. Remote Sens. 2015, 7, 12459–12477. [Google Scholar] [CrossRef][Green Version]
- Wu, C. Quantifying high-resolution impervious surfaces using spectral mixture analysis. Int. J. Remote Sens. 2009, 30, 2915–2932. [Google Scholar] [CrossRef]
- Ridd, M.K. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through Remote Sensing: Comparative anatomy for cities. Int. J. Remote Sens. 1995, 16, 2165–2185. [Google Scholar] [CrossRef]
- Wu, C.; Murray, A.T. Estimating impervious surface distribution by spectral mixture analysis. Remote Sens. Environ. 2003, 84, 493–505. [Google Scholar] [CrossRef]
- Wu, C. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sens. Environ. 2004, 93, 480–492. [Google Scholar] [CrossRef]
- Fan, F.; Fan, W.; Weng, Q. Improving urban impervious surface mapping by linear spectral mixture analysis and using spectral indexes. Can. J. Remote Sens. 2015, 41, 577–586. [Google Scholar] [CrossRef]
- Chen, F.; Wang, K.; Van de Voorde, T.; Tang, T.F. Mapping urban land cover from high spatial resolution hyperspectral data: An approach based on simultaneously unmixing similar pixels with jointly sparse spectral mixture analysis. Remote Sens. Environ. 2017, 196, 324–342. [Google Scholar] [CrossRef]
- Zhao, N.; Zhou, Y.; Samson, E.L. Correcting incompatible dn values and geometric errors in nighttime lights time-series images. IEEE Trans. Geosci. Remote Sens. 2015, 53, 2039–2049. [Google Scholar] [CrossRef]
- Pok, S.; Matsushita, B.; Fukushima, T. An easily implemented method to estimate impervious surface area on a large scale from MODIS time-series and improved DMSP-OLS nighttime light data. ISPRS J. Photogramm. Remote Sens. 2017, 133, 104–115. [Google Scholar] [CrossRef]
- Zhang, X.; Li, P. A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis. ISPRS J. Photogramm. Remote Sens. 2018, 135, 93–111. [Google Scholar] [CrossRef]
- Shao, Z.; Liu, C. The Integrated Use of DMSP-OLS Nighttime Light and MODIS Data for Monitoring Large-Scale Impervious Surface Dynamics: A Case Study in the Yangtze River Delta. Remote Sens. 2014, 6, 9359–9378. [Google Scholar] [CrossRef][Green Version]
- Xie, Y.; Weng, Q. Spatiotemporally enhancing time-series DMSP/OLS nighttime light imagery for assessing large-scale urban dynamics. ISPRS J. Photogramm. Remote Sens. 2017, 128, 1–15. [Google Scholar] [CrossRef]
- Xie, Y.; Weng, Q. Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries. Energy 2016, 100, 177–189. [Google Scholar] [CrossRef]
- Chen, J.; Jönsson, P.; Tamura, M.; Gu, Z.; Matsushita, B.; Eklundh, L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 2004, 91, 332–344. [Google Scholar] [CrossRef]
- Schneider, A.; Friedl, M.A.; Potere, D. Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’. Remote Sens. Environ. 2010, 114, 1733–1746. [Google Scholar] [CrossRef]
- Schneider, A. Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sens. Environ. 2012, 124, 689–704. [Google Scholar] [CrossRef]
- Elvidge, C.D.; Tuttle, B.T.; Sutton, P.S.; Baugh, K.E.; Ara, T. Howard; Milesi, C.; Bhaduri, B.L.; Nemani, R. Global distribution and density of constructed impervious surfaces. Sensors 2007, 7, 1962–1979. [Google Scholar] [CrossRef] [PubMed]
- Zhuo, L.; Zheng, J.; Zhang, X.; Li, J.; Liu, L. An improved method of night-time light saturation reduction based on EVI. Int. J. Remote Sens. 2015, 36, 4114–4130. [Google Scholar] [CrossRef]
- Bai, X.; Chen, J.; Shi, P. Landscape urbanization and economic growth in China: Positive feedbacks and sustainability dilemmas. Environ. Sci. Technol. 2012, 46, 132–139. [Google Scholar] [CrossRef] [PubMed]
- Tan, M.; Li, X.; Xie, H.; Lu, C. Urban land expansion and arable land loss in China—a case study of Beijing–Tianjin–Hebei region. Land Use Policy 2005, 22, 187–196. [Google Scholar] [CrossRef]
- Xu, J.; Zhao, Y.; Zhong, K.; Ruan, H.; Liu, X. Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China. Water 2016, 8, 550. [Google Scholar] [CrossRef]
- Zhou, T.; Pan, J.; Zhang, P.; Wei, S.; Han, T. Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region. Sensors 2017, 17, 1210. [Google Scholar] [CrossRef]
- Yuan, W.; Liu, S.; Yu, G.; Bonnefond, J.-M.; Chen, J.; Davis, K.; Desai, A.R.; Goldstein, A.H.; Gianelle, D.; Rossi, F.; et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens. Environ. 2010, 114, 1416–1431. [Google Scholar] [CrossRef][Green Version]
- Pan, Y.; Li, L.; Zhang, J.; Liang, S.; Zhu, X.; Sulla-Menashe, D. Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sens. Environ. 2012, 119, 232–242. [Google Scholar] [CrossRef]
- Wu, J. Urban sustainability: An inevitable goal of landscape research. Landsc. Ecol. 2010, 25, 1–4. [Google Scholar] [CrossRef]
- Wang, S.; Li, Q.; Fang, C.; Zhou, C. The relationship between economic growth, energy consumption, and CO2 emissions: Empirical evidence from China. Sci. Total Environ. 2016, 542 Pt A, 360–371. [Google Scholar] [CrossRef]
- Wu, J.; Xiang, W.N.; Zhao, J. Urban ecology in China: Historical developments and future directions. Landsc. Urban Plan. 2014, 125, 222–233. [Google Scholar] [CrossRef]
- Weng, Q.; Lu, D. A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. Int. J. Appl. Earth Obs. Geoinf. 2008, 10, 68–83. [Google Scholar] [CrossRef]
|Type of Data||Data Position and Time||Data Characterization|
|2003, 2008, and 2013.||Stable light annual image composites product spatial resolution: 1 km.|
|Data of whole China.|
Annual data from 2003, 2008, and 2013 (h21v03, h22v03–h22v04, h23v03–h23v05, h24v03–h21v06, h25v03–h21v06, h26v03–h21v06, h27v04–h21v06, h28v04–h21v07, h29v05–h21v06, h30v06).
The total number of scenes: 644 × 3.
|MODIS 16-day-composited Normalized Difference Vegetation Index (NDVI) and enhanced vegetation index (EVI) series data level-3 products.|
Annual data from different periods within one year (23 cycle of one year, 28 views of one cycle, 3 years).
Spatial resolution: 1 km.
|Center path/row:||Data time||Six 30 m resolution multispectral bands and one 15 m resolution panchromatic band.|
Two 100 m resolution infrared bands were not used, due to their low resolution.
For the cloud-influenced clipped images, the errors due to the influence of clouds in the extraction result were removed.
The result was resampled to 1 km.
|116/40:||4 November 2013|
|122/32:||17 November 2013|
|113/23:||29 November 2013|
|114/30:||3 June 2013|
|103/36:||23 July 2013|
|87/45:||28 August 2013|
|109/35:||13 September 2013|
|118/32:||11 August 2013|
|MOD44W||Global land water mask.|
Data from 2003, 2008, and 2013
|Global land water mask was used to eliminate the effects of water on the classification results.|
Spatial resolution: 250 m.
The result was resampled to 1 km.
|Google earth image||Data from Google Earth in 2013||Some data for eight regions including Beijing, Shanghai, Guangzhou, Nanjing, Wuhan, Xi’an, Urumqi, and Lanzhou.|
The result was resampled to 5 m.
|Urban Impervious Surface||Non-Urban Impervious Surface||Water||Overall Accuracy (%)||Kappa|
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