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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (3) : 148-156     DOI: 10.6046/gtzyyg.2019.03.19
Comparison of Landsat8 impervious surface extraction methods
Chang LIU1, Kang YANG1,2,3(), Liang CHENG1,2,3, Manchun LI1,2,3, Ziyan GUO1
1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023,China
2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China
3. Collaborative Innovation Center for the South Sea Studies, Nanjing 210023, China
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Impervious surface is an important land cover type. Extracting impervious surface from satellite images is crucial for land use and land cover change (LUCC) studies. Although several indexes have been proposed to detect impervious surface, there is a lack of systematic comparative analysis of these indexes. To address this problem, the authors estimated the performance of eight state-of-the-art impervious surface indexes using Landsat8 satellite images. The experimental results show that perpendicular impervious index (PII) performs best, yielding the highest detection accuracy of 89.6%. The accuracies of ratio resident-area index (RRI) and biophysical composition index (BCI) are slightly lower than the accuracy of PII, which are 87.5% and 87.4%, respectively. The accuracies of urban index (UI) and new built-up index (NBI) are 82.9% and 80.0%, respectively. Normalized difference impervious surface index (NDISI), normalized difference built-up index (NDBI), and index-based built-up index (IBI) fail to enhance the spectral characteristics of impervious surface from complex image background, thereby yielding the lowest accuracy (<75.0%). Importantly, the eight impervious surface indexes fail to distinguish the spectral characteristics of impervious surface from large bare land areas and the average detection accuracy is only 71.0%, hindering their applications in bare-land-rich areas.

Keywords impervious surface      remote sensing information extraction      impervious surface index      land cover      Landsat8     
:  TP79  
Corresponding Authors: Kang YANG     E-mail:
Issue Date: 30 August 2019
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Chang LIU
Manchun LI
Ziyan GUO
Cite this article:   
Chang LIU,Kang YANG,Liang CHENG, et al. Comparison of Landsat8 impervious surface extraction methods[J]. Remote Sensing for Land & Resources, 2019, 31(3): 148-156.
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Fig.1  Spectral characteristics of different land cover types in Landsat8 satellite image
名称 公 式 使用数据 实验区 是否进行大气校正
BCI[18] BCI=(TC1+TC3)/2-TC2(TC1+TC3)/2+TC2 Landsat7,
UI[19] UI=SWIR2-NIRSWIR2+NIR Landsat7 斯里兰卡科伦坡 未提及
NDBI[21] NDBI=SWIR1-NIRSWIR1+NIR Landsat5 无锡 未提及
NBI[22] NBI=RED×SWIR1NIR Landsat5/7 常州 未提及
PII[23] PII=mBLUE+nNIR+C Landsat8 武汉、北京
RRI[24] RRI=BLUENIR Landsat5 西安、咸阳
Tab.1  Summary of impervious surface indexes
Fig.2  Study areas distribution and Landsat8 satellite images
Fig.3  ROC curves of impervious surface indexes
不透水面指数 AUC 不透水面指数 AUC
实验区1 实验区2 实验区1 实验区2
PII 0.895 0.839 UI 0.850 0.812
BCI 0.893 0.837 IBI 0.787 0.769
RRI 0.885 0.835 NDISI 0.731 0.765
NDBI 0.777 0.760 NBI 0.849 0.812
Tab.2  AUC of impervious surface indexes in study areas 1 and 2
Fig.4  Optimal impervious surface extraction results of study area 1
Fig.5  Optimal impervious surface extraction results of study area 2
不透水面指数 实验区1 实验区2 不透水面指数 实验区1 实验区2
PII 89.6 81.2 9.5 77.3 77.1 22.7 UI 82.9 77.7 16.5 70.1 80.2 30.4
BCI 87.4 83.7 12.2 77.4 77.3 22.6 IBI 72.4 77.7 28.2 63.5 79.6 37.2
RRI 87.5 82.1 12.0 78.3 75.1 21.5 NDISI 74.6 65.2 24.4 69.4 72.8 30.8
NDBI 73.4 74.3 26.7 59.7 82.1 41.3 NBI 80.0 82.0 20.3 72.3 78.9 28.0
Tab.3  Accuracy evaluation of impervious surface indexes using the optimal threshold(%)
Tab.4  Example zoomed images showing the impervious surface extraction results
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