Exploring improvement of impervious surface estimation at national scale through integration of nighttime light and Proba-V data

GIScience and Remote Sensing

Volumn 55, Issue 5, 2018, Pages 699-717

  Guo, Wei ^a,b   Li, Guiying ^b   Ni, Wenjian ^a   Zhang, Yuhuan ^c   Lu, Dengsheng ^b,d  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b ZHEJIANG A F UNIVERSITY   (China)

^c NANJING INSTITUTE OF ENVIRONMENTAL SCIENCES   (China)

^d MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

impervious surface area; Landsat; modified impervious surface index; nighttime light data; Proba V NDVI

Indexed keywords

DATA QUALITY; LAND SURFACE; LANDSAT; MAPPING; MODIS; NDVI; SPATIAL RESOLUTION;

PROBA;

EID: 85041826897     PISSN: 15481603     EISSN: None     Source Type: Journal    
DOI: 10.1080/15481603.2018.1436425     Document Type: Article

References (36)

1. Bai, X., P., Shi, and Y., Liu. 2014. “Realizing China’s Urban Dream.” Nature 509 (7499): 158–160. doi:10.1038/509158a
2. Cao, X., J., Chen, H., Imura, and O., Higashi. 2009. “A SVM-based Method to Extract Urban Areas from DMSP-OLS and SPOT VGT Data.” Remote Sensing of Environment 113 (10): 2205–2209. doi:10.1016/j.rse.2009.06.001
3. Carlson, T. N., and T. S., Arthur. 2000. “The Impact of Land Use — Land Cover Changes Due to Urbanization on Surface Microclimate and Hydrology: A Satellite Perspective.” Global and Planetary Change 25 (1): 49–65. doi:10.1016/S0921-8181(00)00021-7
4. Carroll, M. L., J. R., Townshend, C. M., DiMiceli, P., Noojipady, and R. A., Sohlberg. 2009. “A New Global Raster Water Mask at 250 M Resolution.” International Journal of Digital Earth 2 (4): 291–308. doi:10.1080/17538940902951401
5. Deng, C., and W., Changshan. 2013. “The Use of Single-Date MODIS Imagery for Estimating Large-Scale Urban Impervious Surface Fraction with Spectral Mixture Analysis and Machine Learning Techniques.” ISPRS Journal of Photogrammetry and Remote Sensing 86: 100–110. doi:10.1016/j.isprsjprs.2013.09.010
6. Elvidge, C., D., Benjamin, T., Tuttle, P. C., Sutton, K. E., Baugh, A. T., Howard, C., Milesi, B., Bhaduri, and R., Nemani. 2007. “Global Distribution and Density of Constructed Impervious Surfaces.” Sensors (Basel) 7 (9). doi:10.3390/s7091962
7. Guo, W., L., Dengsheng, and W., Kuang. 2017. “Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data.” Remote Sensing 9: 4. doi:10.3390/rs9040375
8. Guo, W., L., Dengsheng, W., Yanlan, 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
9. Hao, R., Y., Deyong, Y., Sun, Q., Cao, Y., Liu, and Y., Liu. 2015. “Integrating Multiple Source Data to Enhance Variation and Weaken the Blooming Effect of DMSP-OLS Light.” Remote Sensing 7 (2): 1422–1440. doi:10.3390/rs70201422
10. Imhoff, M. L., P., Zhang, R. E., Wolfe, and L., Bounoua. 2010. “Remote Sensing of the Urban Heat Island Effect across Biomes in the Continental USA.” Remote Sensing of Environment 114 (3): 504–513. doi:10.1016/j.rse.2009.10.008
11. Jennings, D. B., and T. S., Jarnagin. 2002. “Changes in Anthropogenic Impervious Surfaces, Precipitation and Daily Streamflow Discharge: A Historical Perspective in a Mid-Atlantic Subwatershed.” Landscape Ecology 17 (5): 471–489. doi:10.1023/A:1021211114125
12. Kaufmann, R., 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
13. Kuang, W., J., Liu, Z., Zhang, L., Dengsheng, and B., Xiang. 2012. “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
14. Li, G., L., Dengsheng, 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
15. Li, L., and L., Dengsheng. 2016. “Mapping Population Density Distribution at Multiple Scales in Zhejiang Province Using Landsat Thematic Mapper and Census Data.” International Journal Remote Sensing 37 (18): 4243–4260. doi:10.1080/01431161.2016.1212422
16. 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.” 7 (2 12): 17168–17189. doi:10.3390/rs71215863
17. Liu, X., Y., Yang, W., Jing, L., Yao, X., Yue, and X., Zhao. 2017. “A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL.” Remote Sensing 9 (8). doi:10.3390/rs9080777
18. Lu, D., E., Moran, and S., Hetrick. 2011. “Detection of Impervious Surface Change with Multitemporal Landsat Images in an Urban–Rural Frontier.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 298–306. doi:10.1016/j.isprsjprs.2010.10.010
19. Lu, D., H., Tian, G., Zhou, and G., Hongli. 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
20. Lu, D., L., Guiying, 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
21. Ma, Q., H., Chunyang, W., Jianguo, Z., Liu, Q., Zhang, and Z., Sun. 2014a. “Quantifying Spatiotemporal Patterns of Urban Impervious Surfaces in China: An Improved Assessment Using Nighttime Light Data.” Landscape and Urban Planning 130: 36–49. doi:10.1016/j.landurbplan.2014.06.009
22. Ma, T., C., Zhou, T., Pei, S., Haynie, and J., Fan. 2014b. “Responses of Suomi-NPP VIIRS-derived Nighttime Lights to Socioeconomic Activity in China’s Cities.” Remote Sensing Letters 5 (2): 165–174. doi:10.1080/2150704x.2014.890758
23. Ma, T., Y., Zhou, Y., Wang, C., Zhou, S., Haynie, and X., Tao. 2014c. “Diverse Relationships between Suomi-NPP VIIRS Night-Time Light and Multi-Scale Socioeconomic Activity.” Remote Sensing Letters 5 (7): 652–661. doi:10.1080/2150704x.2014.953263
24. Pandey, B. P., K., Joshi, and K. C., Seto. 2013. “Monitoring Urbanization Dynamics in India Using DMSP/OLS Night Time Lights and SPOT-VGT Data.” International Journal of Applied Earth Observation and Geoinformation 23: 49–61. doi:10.1016/j.jag.2012.11.005
25. 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
26. Shao, Z., and C., Liu. 2014. “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 Sensing 6 (10). doi:10.3390/rs6109359
27. Shi, K., C., Huang, Y., Bailang, B., Yin, Y., Huang, and W., Jianping. 2014b. “Evaluation of NPP-VIIRS Night-Time Light Composite Data for Extracting Built-Up Urban Areas.” Remote Sensing Letters 5 (4): 358–366. doi:10.1080/2150704X.2014.905728
28. Shi, K., Y., Bailang, Y., Huang, H., Yingjie, B., Yin, Z., Chen, L., Chen, and W., Jianping. 2014a. “Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data.” Remote Sensing 6 (2): 1705–1724. doi:10.3390/rs6021705
29. Srinivasan, V., K. C., Seto, R., Emerson, and S. M., Gorelick. 2013. “The Impact of Urbanization on Water Vulnerability: A Coupled Human–Environment System Approach for Chennai, India.” Global Environmental Change 23 (1): 229–239. doi:10.1016/j.gloenvcha.2012.10.002
30. Wang, J., W., Zhifeng, W., Changshan, Z., Cao, W., Fan, and P., Tarolli. 2017. “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. doi:10.1080/15481603.2017.1417690
31. Weng, Q., 2012. “Remote Sensing of Impervious Surfaces in the Urban Areas: Requirements, Methods, and Trends.” Remote Sensing of Environment 117: 34–49. doi:10.1016/j.rse.2011.02.030
32. Weng, Q., L., Dengsheng, and J., Schubring. 2004. “Estimation of Land Surface Temperature–Vegetation Abundance Relationship for Urban Heat Island Studies.” Remote Sensing of Environment 89 (4): 467–483. doi:10.1016/j.rse.2003.11.005
33. 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
34. Xie, Y., and Q., Weng. 2016. “Updating Urban Extents with Nighttime Light Imagery by Using an Object-Based Thresholding Method.” Remote Sensing of Environment 187 (Supplement C): 1–13. doi:10.1016/j.rse.2016.10.002
35. 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
36. Zhuo, L., J., Zheng, X., Zhang, L., Jun, 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