An automatic method for impervious surface area extraction by fusing high-resolution night light and Landsat OLI images

Peng-Fei TANG; Ze-Lang MIAO; Cong LIN; Pei-Jun DU; Shan-Chuan GUO

doi:10.11972/j.issn.1001-9014.2020.01.017

[1] . Weng, Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment. 117, 34-49(2012).

[2] Z. Liu, C. He and Q. Zhang. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landscape and Urban Planning,. 106, 62-72(2012).

[3] C. L. and C. J. GibbonsC. L. and C. J. Gibbons. Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. Journal of the American Planning Association. 62(2), 243-258(1996).

[4] Q WengQ Weng. Modeling Urban Growth Effects on Surface Runoff with the Integration of Remote Sensing and GIS. Environmental Management. 28(6), 737-748(2014).

[5] J D Hurd and D L Civco. Temporal characterization of impervious surfaces for the State of Connecticut. (2004).

[6] S. E. Brun and L Band. E. Simulating runoff behavior in an urbanizing watershed. Environment and Urban Systems,. 24, 5-22(2000).

[7] L. Yang, C. Huang and C. G. Homer. An approach for mapping large-scale impervious surfaces: Synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Canadian Journal of Remote Sensing,. 29, 230-240(2003).

[8] F Yuan and M Bauer. E. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment,. 106, 375-386(2007).

[10] R. R. Gillies and J. B. Box. Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta — A satellite perspective. Remote Sensing of Environment,. 86, 411-422(2003).

[11] T CarlsonT Carlson. N. Analysis and prediction of surface run off in an urbanizing watershed using satellite imagery. Journal of the American Water Resources Association. 40(4), 1087-1098(2004).

[12] E. Boegh, R. N. Poulsen and M. Butts. Remote sensing based evapotranspiration and runoff modeling of agricultural, forest and urban flux sites in Denmark: From field to macro-scale. Journal of Hydrology. 3(4), 300-316(377).

[13] . Carlson T N and Arthur S T The impact of land use—land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global and Planetary Change. 25(1/2), 49-65(2000).

[14] Y. ZhaY. Zha. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing. 24(3), 583-594(2003).

[15] H Q XuH Q Xu. 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(2010).

[16] C S Deng C B and WuC S Deng C B and Wu. BCI: a biophysical composition index for remote sensing of urban environments. Remote Sensing of Environment. 127, 247-259(2012).

[17] C Li, J Wang and L Wang. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery. Remote sensing. 6(2), 964-983(2014).

[18] V. F. Rodriguez-GalianoV. F. Rodriguez-Galiano. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing. 67, 93-104(2012).

[19] S. and A. GunluS. and A. Gunlu. Comparison of Different Supervised Classification Algorithms for Land Use Classes. Kastamonu University Journal of Forestry Faculty. 16(2), 528-535(2016).

[20] P. SuttonP. Sutton. Census from Heaven: An estimate of the global human population using night-time satellite imagery. International Journal of Remote Sensing. 22(16), 3061-3076(2001).

[21] Y. XieY. Xie. Temporal variations of artificial nighttime lights and their implications for urbanization in the conterminous United States 2013–2017. Remote Sensing of Environment. 225, 160-174(2019).

[22] J. OuJ. Ou. Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection: A comparison with NPP-VIIRS nighttime light data. International Journal of Applied Earth Observation and Geoinformation. 81, 1-12(2019).

[23] Q. L. ZhangQ. L. Zhang. Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery. Remote Sensing. 7(9), 11887-11913(2015).

[25] X. MaX. Ma. Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas. Remote Sensing. 9(3), (2017).

[26] X. and P. LiX. and P. Li. A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis. ISPRS Journal of Photogrammetry and Remote Sensing. 135, 93-111(2018).

[28] M. DuM. Du. Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data. Remote Sensing. 10(12), (2018).

[29] D. C. ElvidgeD. C. Elvidge. Automatic Boat Identification System for VIIRS Low Light Imaging Data. Remote Sensing. 7(3), (2015).

[30] M. and N. A. DodgsonM. and N. A. Dodgson. Decolorize: Fast, contrast enhancing, color to grayscale conversion. Pattern Recognition. 40(11), 2891-2896(2007).

[31] C.D. Elvidge, K.E. Baugh and M. Zhizhin. Why VIIRS data are superior to DMSP for mapping nighttime lights. Proc. Asia Pac. Adv. Netw. 35(0), (2013).

[32] Q. ZhengQ. Zheng. A new source of multi-spectral high spatial resolution night-time light imagery—JL1-3B. Remote Sensing of Environment. 215, 300-312(2018).

[33] N OhtsuN Ohtsu. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems Man & Cybernetics. 9(1), 62-66(2007).

[34] G B Huang, Q Y Zhu and C K Siew. Extreme learning machine：a new learning scheme of feedforward neural networks. Budapest，Hungary: IEEE, 985-990(2004).

[35] Chee-Kheong SiewChee-Kheong Siew. Extreme learning machine: Theory and applications. Neurocomputing. 70(1), (2005).

[36] A Stumpf and N Kerle. Object-oriented mapping of landslides using Random Forests. Remote Sensing of Environment. 115(10), 2564-2577(2011).

[37] J J Rodriguez, L I Kuncheva and C J Alonso. Rotation forest: a new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10), 1619-1630(2006).

[38] P GongP Gong. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing. 34(7), 2607-2654(2013).