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
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.
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2019, Vol. 31 
), 程亮1,2,3, 李满春1,2,3, 郭紫燕1
