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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (4) : 227-234     DOI: 10.6046/gtzyyg.2019.04.29
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Extraction of urban built-up areas based on Sentinel-2Aand NPP-VIIRS nighttime light data
Zhili LIU1, Qibin ZHANG1, Depeng YUE1(), Yuguang HAO2, Kai SU1
1. College of Forestry, Beijing Forestry University, Beijing 100083, China
2. Experimental Center of Desert Forestry, Chinese Academy of Forestry, Bayannur 015200, China
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Abstract  

Recently, the utilization of nighttime light data and optical remote sensing images to extract urban built-up areas has become a research hotspot, and the vegetation adjusted nighttime light data (NTL) urban index (VANUI) is widely used. However, it may easily lead to confusion of buildings and water bodies at the edge of the city, and the spatial resolution is relatively low. Therefore, some improvements were made on this index in this paper, and the building adjusted NTL urban index was proposed. The means was used to extract urban built-up areas in Baotou City in this paper. Firstly, normalized difference build-up index (NDBI) was extracted from Sentinel-2A image data and it was combined with NTL to obtain building adjusted NTL urban index BANUI with the spatial resolution of 20 m, which has higher spatial resolution and more information about the building. Finally, the watershed segmentation algorithm was applied to the extraction of urban built-up area of Baotou City from BANUI, VANUI and NTL, and the results were comparatively studied. The extraction results show that the overall precision of the urban built-up area extracted by BANUI could reach 93.61%, the Kappa coefficient is 0.793 4, the user accuracy is 81.34%, and the producer accuracy is 85.34%. The extraction results are consistent with the distribution of actual urban built-up area, and the accuracy is high. The result is better than the area extracted by the other two kinds of data. This method could provide some reference for the study of the extraction of urban built-up area from NTL, and could also be used to monitor the development of urban planning.

Keywords nighttime light data      Sentinel-2A data      urban built-up area      BANUI      watershed segmentation     
:  TP79  
Corresponding Authors: Depeng YUE     E-mail: yuedepeng@126.com
Issue Date: 03 December 2019
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Zhili LIU
Qibin ZHANG
Depeng YUE
Yuguang HAO
Kai SU
Cite this article:   
Zhili LIU,Qibin ZHANG,Depeng YUE, et al. Extraction of urban built-up areas based on Sentinel-2Aand NPP-VIIRS nighttime light data[J]. Remote Sensing for Land & Resources, 2019, 31(4): 227-234.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.04.29     OR     https://www.gtzyyg.com/EN/Y2019/V31/I4/227
Fig.1  Image of study area
Fig.2  Technology roadmap
Fig.3  Mean brightness of each band in Sentinel-2A
Fig.4  Representative areas of NDBI11 and NDBI12
Fig.5  Probability distribution and normal distribution curves of different land types in NDBI
类别 植被 水体 未利用地
NDBI11建筑 2.06 1.64 0.03
NDBI12建筑 2.47 0.56 0.24
Tab.1  R between buildings and the other land types in NDBI11 and NDBI12
Fig.6  Values of VANUI and BANUI on the cross section
指数 像元亮度值范围 像元亮度均值 像元亮度值标准差 水体像元亮度均值 植被像元亮度均值 建筑像元亮度均值
VANUI 343.70 1.61 8.04 2.12 0.01 31.51
BANUI 389.01 2.06 9.74 1.77 0.01 38.97
Tab.2  Statistical data of VANUI and BANUI
Fig.7  Typical area
Fig.8  Probability distribution and normal distribution curves of different land types of VANUI and BANUI in the typical area
Fig.9  Extracted urban built-up areas from different data
Fig.10  Accuracy comparison of urban built-up areas extracted by different data sources
数据 TA/km2 TA.Diff/% LSI LSI.Diff/% AI/% AI.Diff/%
NPP-
VIIRS
1 149.01 24.12 3.73 28.17 91.68 -3.62
VANUI 1 079.00 16.55 3.39 16.49 98.35 3.39
BANUI 971.25 4.91 2.43 -16.49 97.66 2.66
验证数据 925.75 0 2.91 0 95.13 0
Tab.3  Comparison of landscape pattern indexes of urban built-up areas extracted by different data sources
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