【摘要】 不透水面是重要的地表覆盖类型,利用卫星遥感影像准确提取不透水面信息对于掌握地表覆盖变化具有重要意义。现有研究已提出了多种不透水面遥感信息提取指数,但目前尚缺乏对这些不透水面指数的系统对比分析。利用Landsat8卫星遥感影像,测试了目前8种主要不透水面指数的提取精度。结果表明,在现有不透水面指数中,垂直不透水层指数能够有效增强不透水面信息,不透水面提取精度最高(89. 6%),其次是比值居民地指数和生物物理组分指数(87. 5%和87. 4%),城市指数与新建筑指数提取精度再次之(82. 9%和80. 0%),归一化差值不透水面指数、归一化建筑指数和基于指数的建筑指数未能有效增强不透水面信息,提取精度较低(<75. 0%)。此外,这8种不透水面指数都未能有效解决不透水面与大片裸地光谱混淆的问题,在裸地广泛分布的区域难以准确提取不透水面,平均提取精度仅为71. 0%,影响了不透水面指数的大区域应用。更多还原

【Abstract】 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.更多还原