Mapping impervious surface distribution in China using multi-source remotely sensed data

References

• Arnold, C. L., Jr, and C. J. Gibbons. 1996. “Impervious Surface Coverage: The Emergence of a Key Environmental Indicator.” Journal of the American Planning Association 62 (2): 243–258. doi:10.1080/01944369608975688.
   Web of Science ®Google Scholar
• Bai, X., R. J. Dawson, D. Ürge-Vorsatz, G. C. Delgado, A. S. Barau, S. Dhakal, D. Dodman, et al. 2018. “Six Research Priorities for Cities and Climate Change.” Nature 555 (7694): 19–21. doi:10.1038/d41586-018-02409-z.
   Web of Science ®Google Scholar
• Brabec, E., S. Schulte, and P. L. Richards. 2002. “Impervious Surfaces and Water Quality: A Review of Current Literature and Its Implications for Watershed Planning.” Journal of Planning Literature 16 (4): 499–514. doi:10.1177/088541202400903563.
   Web of Science ®Google Scholar
• Cao, S., D. Hu, W. Zhao, Y. Mo, C. Yu, and Y. Zhang. 2019. “Monitoring Changes in the Impervious Surfaces of Urban Functional Zones Using Multisource Remote Sensing Data: A Case Study of Tianjin, China.” GIScience & Remote Sensing 56 (7): 967–987. doi:10.1080/15481603.2019.1600110.
   Web of Science ®Google Scholar
• Chaudhuri, A. S., P. Singh, and S. C. Rai. 2017. “Assessment of Impervious Surface Growth in Urban Environment through Remote Sensing Estimates.” Environmental Earth Sciences 76 (15): 541. doi:10.1007/s12665-017-6877-1.
   Web of Science ®Google Scholar
• Che, X.-Z., and J.-C. Shang. 2004. “Strategic Environmental Assessment for Sustainable Development in Urbanization Process in China.” Chinese Geographical Science 14 (2): 148–152. doi:10.1007/s11769-004-0024-0.
   Web of Science ®Google Scholar
• Congalton, R. G., and K. Green. 2008. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd ed. Boca Raton, FL: CRC Press. doi:10.1201/9780429052729.
   Google Scholar
• Deng, C., and C. Wu. 2013. “A Spatially Adaptive Spectral Mixture Analysis for Mapping Subpixel Urban Impervious Surface Distribution.” Remote Sensing of Environment 133: 62–70. doi:10.1016/j.rse.2013.02.005.
   Web of Science ®Google Scholar
• Deng, C., and Z. Zhu. 2018. “Continuous Subpixel Monitoring of Urban Impervious Surface Using Landsat Time Series.” Remote Sensing of Environment 238: 110929. doi:10.1016/j.rse.2018.10.011.
   Web of Science ®Google Scholar
• Feng, Y., D. Lu, E. Moran, L. Dutra, M. Calvi, and M. de Oliveira. 2017. “Examining Spatial Distribution and Dynamic Change of Urban Land Covers in the Brazilian Amazon Using Multitemporal Multisensor High Spatial Resolution Satellite Imagery.” Remote Sensing 9 (4): 381. doi:10.3390/rs9040381.
   Web of Science ®Google Scholar
• Foody, G. M. 2002. “Status of Land Cover Classification Accuracy Assessment.” Remote Sensing of Environment 80 (1): 185–201. doi:10.1016/S0034-4257(01)00295-4.
   Web of Science ®Google Scholar
• Gong, P., X. Li, and W. Zhang. 2019. “40-Year (1978–2017) Human Settlement Changes in China Reflected by Impervious Surfaces from Satellite Remote Sensing.” Science Bulletin 64 (11): 756–763. doi:10.1016/j.scib.2019.04.024.
   PubMed Web of Science ®Google Scholar
• Guo, W., G. Li, W. Ni, Y. Zhang, and D. Lu. 2018. “Exploring Improvement of Impervious Surface Estimation at National Scale through Integration of Nighttime Light and Proba-v Data.” GIScience & Remote Sensing 55 (5): 699–717. doi:10.1080/15481603.2018.1436425.
   Web of Science ®Google Scholar
• Guo, W., D. Lu, and W. Kuang. 2017. “Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data.” Remote Sensing 9 (4): 375. doi:10.3390/rs9040375.
   Web of Science ®Google Scholar
• Guo, W., D. Lu, Y. Wu, and J. Zhang. 2015. “Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data.” Remote Sensing 7 (9): 12459–12477. doi:10.3390/rs70912459.
   Web of Science ®Google Scholar
• Hao, P., Z. Niu, Y. Zhan, Y. Wu, L. Wang, and Y. Liu. 2016. “Spatiotemporal Changes of Urban Impervious Surface Area and Land Surface Temperature in Beijing from 1990 to 2014.” GIScience & Remote Sensing 53 (1): 63–84. doi:10.1080/15481603.2015.1095471.
   Web of Science ®Google Scholar
• Imhoff, M. L., W. T. Lawrence, C. D. Elvidge, T. Paul, E. Levine, M. V. Privalsky, and V. Brown. 1997a. “Using Nighttime DMSP/OLS Images of City Lights to Estimate the Impact of Urban Land Use on Soil Resources in the United States.” Remote Sensing of Environment 59 (1): 105–117. doi:10.1016/S0034-4257(96)00110-1.
   Web of Science ®Google Scholar
• Imhoff, M. L., W. T. Lawrence, D. C. Stutzer, and C. D. Elvidge. 1997b. “A Technique for Using Composite DMSP/OLS “City Lights” Satellite Data to Map Urban Area.” Remote Sensing of Environment 61 (3): 361–370. doi:10.1016/S0034-4257(97)00046-1.
   Web of Science ®Google Scholar
• Kaufmann, R. K., K. C. Seto, A. Schneider, Z. Liu, L. Zhou, and W. Wang. 2007. “Climate Response to Rapid Urban Growth: Evidence of a Human-induced Precipitation Deficit.” Journal of Climate 20 (10): 2299–2306. doi:10.1175/JCLI4109.1.
   Web of Science ®Google Scholar
• Kuang, W. 2019. “Mapping Global Impervious Surface Area and Green Space within Urban Environments.” Science China Earth Sciences 1–16. doi:10.1007/s11430-018-9342-3.
   Web of Science ®Google Scholar
• Kuang, W., W. Chi, D. Lu, and Y. Dou. 2014. “A Comparative Analysis of Megacity Expansions in China and the US: Patterns, Rates and Driving Forces.” Landscape and Urban Planning 132: 121–135. doi:10.1016/j.landurbplan.2014.08.015.
   Web of Science ®Google Scholar
• Kuang, W., Y. Dou, C. Zhang, W. Chi, A. Liu, Y. Liu, R. Zhang, and J. Liu. 2015. “Quantifying the Heat Flux Regulation of Metropolitan Land Use/land Cover Components by Coupling Remote Sensing Modeling with in Situ Measurement.” Journal of Geophysical Research: Atmospheres 120 (1): 113–130. doi:10.1002/2014JD022249.
   Web of Science ®Google Scholar
• Kuang, W., A. Liu, Y. Dou, G. Li, and D. Lu. 2019. “Examining the Impacts of Urbanization on Surface Radiation Using Landsat Imagery.” GIScience & Remote Sensing 56 (3): 462–484. doi:10.1080/15481603.2018.1508931.
   Web of Science ®Google Scholar
• Kuang, W., J. Liu, Z. Zhang, D. Lu, and B. Xiang. 2013. “Spatiotemporal Dynamics of Impervious Surface Areas across China during the Early 21st Century.” Chinese Science Bulletin 58 (14): 1691–1701. doi:10.1007/s11434-012-5568-2.
   Web of Science ®Google Scholar
• Kuang, W., T. Yang, A. Liu, C. Zhang, D. Lu, and W. Chi. 2017. “An EcoCity Model for Regulating Urban Land Cover Structure and Thermal Environment: Taking Beijing as an Example.” Science China Earth Sciences 60 (6): 1098–1109. doi:10.1007/s11430-016-9032-9.
   Web of Science ®Google Scholar
• Lawrence, W. T., M. L. Imhoff, N. Kerle, and D. Stutzer. 2002. “Quantifying Urban Land Use and Impact on Soils in Egypt Using Diurnal Satellite Imagery of the Earth Surface.” International Journal of Remote Sensing 23 (19): 3921–3937. doi:10.1080/01431160110115951.
   Web of Science ®Google Scholar
• Li, G., D. Lu, E. Moran, and S. Hetrick. 2013. “Mapping Impervious Surface Area in the Brazilian Amazon Using Landsat Imagery.” GIScience & Remote Sensing 50 (2): 172–183. doi:10.1080/15481603.2013.780452.
   PubMed Web of Science ®Google Scholar
• Li, K., and Y. Chen. 2018. “A Genetic Algorithm-based Urban Cluster Automatic Threshold Method by Combining VIIRS DNB, NDVI, and NDBI to Monitor Urbanization.” Remote Sensing 10 (2): 277. doi:10.3390/rs10020277.
   Web of Science ®Google Scholar
• Li, L., D. Lu, and W. Kuang. 2016. “Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolis.” Remote Sensing 8 (3): 265. doi:10.3390/rs8030265.
   Web of Science ®Google Scholar
• Li, W., and C. Wu. 2016. “A Geostatistical Temporal Mixture Analysis Approach to Address Endmember Variability for Estimating Regional Impervious Surface Distributions.” GIScience & Remote Sensing 53 (1): 102–121. doi:10.1080/15481603.2015.1118975.
   Web of Science ®Google Scholar
• Li, X., L. Zhao, D. Li, and H. Xu. 2018. “Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery.” Sensors 18 (11): 3665. doi:10.3390/s18113665.
   PubMed Web of Science ®Google Scholar
• Liu, C., Q. Zhang, H. Luo, S. Qi, S. Tao, H. Xu, and Y. Yao. 2019. “An Efficient Approach to Capture Continuous Impervious Surface Dynamics Using Spatial-temporal Rules and Dense Landsat Time Series Stacks.” Remote Sensing of Environment 229: 114–132. doi:10.1016/j.rse.2019.04.025.
   Web of Science ®Google Scholar
• Liu, X., G. Hu, B. Ai, X. Li, and Q. Shi. 2015. “A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area.” Remote Sensing 7 (12): 17168–17189. doi:10.3390/rs71215863.
   Web of Science ®Google Scholar
• Liu, X., G. Hu, Y. Chen, X. Li, X. Xu, S. Li, F. Pei, and S. Wang. 2018. “High-resolution Multi-temporal Mapping of Global Urban Land Using Landsat Images Based on the Google Earth Engine Platform.” Remote Sensing of Environment 209: 227–239. doi:10.1016/j.rse.2018.02.055.
   Web of Science ®Google Scholar
• Lu, D., S. Hetrick, and E. Moran. 2011. “Impervious Surface Mapping with Quickbird Imagery.” International Journal of Remote Sensing 32 (9): 2519–2533. doi:10.1080/01431161003698393.
   PubMed Web of Science ®Google Scholar
• Lu, D., G. Li, W. Kuang, and E. Moran. 2014. “Methods to Extract Impervious Surface Areas from Satellite Images.” International Journal of Digital Earth 7 (2): 93–112. doi:10.1080/17538947.2013.866173.
   Web of Science ®Google Scholar
• Lu, D., L. Li, G. Li, P. Fan, Z. Ouyang, and E. Moran. 2018. “Examining Spatial Patterns of Urban Distribution and Impacts of Physical Conditions on Urbanization in Coastal and Inland Metropoles.” Remote Sensing 10 (7): 1101. doi:10.3390/rs10071101.
   Web of Science ®Google Scholar
• Lu, D., H. Tian, G. Zhou, and H. Ge. 2008. “Regional Mapping of Human Settlements in Southeastern China with Multisensor Remotely Sensed Data.” Remote Sensing of Environment 112 (9): 3668–3679. doi:10.1016/j.rse.2008.05.009.
   Web of Science ®Google Scholar
• Lu, D., and Q. Weng. 2006. “Use of Impervious Surface in Urban Land-use Classification.” Remote Sensing of Environment 102 (1–2): 146–160. doi:10.1016/j.rse.2006.02.010.
   Web of Science ®Google Scholar
• Lu, D., and Q. Weng. 2009. “Extraction of Urban Impervious Surfaces from an IKONOS Image.” International Journal of Remote Sensing 30 (5): 1297–1311. doi:10.1080/01431160802508985.
   Web of Science ®Google Scholar
• Lyu, R., J. Zhang, M. Xu, and J. Li. 2018. “Impacts of Urbanization on Ecosystem Services and Their Temporal Relations: A Case Study in Northern Ningxia, China.” Land Use Policy 77: 163–173. doi:10.1016/j.landusepol.2018.05.022.
   Web of Science ®Google Scholar
• Ma, T., Y. Zhou, C. Zhou, S. Haynie, T. Pei, and T. Xu. 2015. “Night-time Light Derived Estimation of Spatio-temporal Characteristics of Urbanization Dynamics Using DMSP/OLS Satellite Data.” Remote Sensing of Environment 158: 453–464. doi:10.1016/j.rse.2014.11.022.
   Web of Science ®Google Scholar
• Peng, J., H. Shen, W. Wu, Y. Liu, and Y. Wang. 2016. “Net Primary Productivity (NPP) Dynamics and Associated Urbanization Driving Forces in Metropolitan Areas: A Case Study in Beijing City, China.” Landscape Ecology 31 (5): 1077–1092. doi:10.1007/s10980-015-0319-9.
   Web of Science ®Google Scholar
• Piyoosh, A. K., and S. K. Ghosh. 2017. “Semi-automatic Mapping of Anthropogenic Impervious Surfaces in an Urban/suburban Area Using Landsat 8 Satellite Data.” GIScience & Remote Sensing 54 (4): 471–494. doi:10.1080/15481603.2017.1282414.
   Web of Science ®Google Scholar
• Roberts, D. A., D. A. Quattrochi, G. C. Hulley, S. J. Hook, and R. O. Green. 2012. “Synergies between VSWIR and TIR Data for the Urban Environment: An Evaluation of the Potential for the Hyperspectral Infrared Imager (Hyspiri) Decadal Survey Mission.” Remote Sensing of Environment 117: 83–101. doi:10.1016/j.rse.2011.07.021.
   Web of Science ®Google Scholar
• Schneider, A., C. Chang, and K. Paulsen. 2015. “The Changing Spatial Form of Cities in Western China.” Landscape and Urban Planning 135: 40–61. doi:10.1016/j.landurbplan.2014.11.005.
   Web of Science ®Google Scholar
• Schneider, A., M. A. Friedl, and D. Potere. 2010. “Mapping Global Urban Areas Using MODIS 500-m Data: New Methods and Datasets Based on ‘Urban Ecoregions.’.” Remote Sensing of Environment 114 (8): 1733–1746. doi:10.1016/j.rse.2010.03.003.
   Web of Science ®Google Scholar
• Shao, Z., H. Fu, D. Li, O. Altan, and T. Cheng. 2019. “Remote Sensing Monitoring of Multi-scale Watersheds Impermeability for Urban Hydrological Evaluation.” Remote Sensing of Environment 232: 111338. doi:10.1016/j.rse.2019.111338.
   Web of Science ®Google Scholar
• Sun, Z., R. Xu, W. Du, L. Wang, and D. Lu. 2019. “High-resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine.” Remote Sensing 11 (7): 752. doi:10.3390/rs11070752.
   Web of Science ®Google Scholar
• Sutton, P. C., S. J. Anderson, C. D. Elvidge, B. T. Tuttle, and T. Ghosh. 2009. “Paving the Planet: Impervious Surface as Proxy Measure of the Human Ecological Footprint.” Progress in Physical Geography 33 (4): 510–527. doi:10.1177/0309133309346649.
   Web of Science ®Google Scholar
• Wang, J., Z. Wu, C. Wu, Z. Cao, W. Fan, and P. Tarolli. 2018. “Improving Impervious Surface Estimation: An Integrated Method of Classification and Regression Trees (CART) and Linear Spectral Mixture Analysis (LSMA) Based on Error Analysis.” GIScience & Remote Sensing 55 (4): 583–603. doi:10.1080/15481603.2017.1417690.
   Web of Science ®Google Scholar
• Wang, P., C. Huang, and E. Brown de Colstoun. 2017. “Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data.” Remote Sensing 9 (4): 366. doi:10.3390/rs9040366.
   Web of Science ®Google Scholar
• Wu, C., and A. T. Murray. 2003. “Estimating Impervious Surface Distribution by Spectral Mixture Analysis.” Remote Sensing of Environment 84 (4): 493–505. doi:10.1016/S0034-4257(02)00136-0.
   Web of Science ®Google Scholar
• Wu, J. 2014. “Urban Ecology and Sustainability: The State-of-the-science and Future Directions.” Landscape and Urban Planning 125: 209–221. doi:10.1016/j.landurbplan.2014.01.018.
   Web of Science ®Google Scholar
• Xian, G., and C. Homer. 2010. “Updating the 2001 National Land Cover Database Impervious Surface Products to 2006 Using Landsat Imagery Change Detection Methods.” Remote Sensing of Environment 114 (8): 1676–1686. doi:10.1016/j.rse.2010.02.018.
   Web of Science ®Google Scholar
• Xu, H. 2006. “Modification of normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery.” International Journal of Remote Sensing 27 (14): 3025–3033. doi:10.1080/01431160600589179.
   Web of Science ®Google Scholar
• Xu, H. 2010. “Analysis of Impervious Surface and Its Impact on Urban Heat Environment Using the Normalized Difference Impervious Surface Index (NDISI).” Photogrammetric Engineering and Remote Sensing 76 (5): 557–565. doi:10.14358/PERS.76.5.557.
   Web of Science ®Google Scholar
• Yin, C., M. Yuan, Y. Lu, Y. Huang, and Y. Liu. 2018. “Effects of Urban Form on the Urban Heat Island Effect Based on Spatial Regression Model.” Science of the Total Environment 634: 696–704. doi:10.1016/j.scitotenv.2018.03.350.
   PubMed Web of Science ®Google Scholar
• Zhang, C., Y. Chen, and D. Lu. 2015. “Detecting Fractional Land-cover Change in Arid and Semiarid Urban Landscapes with Multitemporal Landsat Thematic Mapper Imagery.” GIScience & Remote Sensing 52 (6): 700–722. doi:10.1080/15481603.2015.1071965.
   Web of Science ®Google Scholar
• Zhang, Q., C. Schaaf, and K. C. Seto. 2013. “The Vegetation Adjusted NTL Urban Index: A New Approach to Reduce Saturation and Increase Variation in Nighttime Luminosity.” Remote Sensing of Environment 129: 32–41. doi:10.1016/j.rse.2012.10.022.
   Web of Science ®Google Scholar
• Zhao, P., and M. Zhang. 2018. “The Impact of Urbanisation on Energy Consumption: A 30-year Review in China.” Urban Climate 24: 940–953. doi:10.1016/j.uclim.2017.11.005.
   Web of Science ®Google Scholar
• Zhuo, L., Q. Shi, H. Tao, J. Zheng, and Q. Li. 2018. “An Improved Temporal Mixture Analysis Unmixing Method for Estimating Impervious Surface Area Based on MODIS and DMSP-OLS Data.” ISPRS Journal of Photogrammetry and Remote Sensing 142: 64–77. doi:10.1016/j.isprsjprs.2018.05.016.
   Web of Science ®Google Scholar
• Zhuo, L., J. Zheng, X. Zhang, J. Li, and L. Liu. 2015. “An Improved Method of Night-time Light Saturation Reduction Based on EVI.” International Journal of Remote Sensing 36 (16): 4114–4130. doi:10.1080/01431161.2015.1073861.
   Web of Science ®Google Scholar