Perpendicular Impervious Index for Remote Sensing of Multiple Impervious Surface Extraction in Cities

被引：0

作者：

Tian Y. ^{[1
]}

Xu Y. ^{[1
]}

Yang X. ^{[1
]}

机构：

[1] College of Information Engineering, China University of Geosciences(Wuhan), Wuhan

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2017年 / 46卷 / 04期

关键词：

Impervious layers line; Impervious surface extraction; Perpendicular impervious index (PII); Soil line;

D O I：

10.11947/j.AGCS.2017.20160304

中图分类号：

学科分类号：

摘要：

Focusing on the issues of impervious layers' heterogeneity and confusion with soil, a method is presented-perpendicular impervious index (PII), which considers blue and near infrared bands selected based on spectral characteristics of ground objects in LandSat-8 images. The PII is established in a linear form, and its reference line is calculated based on the angle bisector of impervious layer line and soil line. Impervious surfaces are extracted using PII on LandSat-8 images of Beijing and Wuhan, which is compared with the normalized difference building index (NDBI), ratio resident-area index (RRI) and biophysical composition index (BCI) in the same areas. The conclusions are as follow: ① PII is superior than other indexes in separating impervious layers from bare soil in both the Wuhan and Beijing, the extracting accuracy is 96.05% and 96.76%, respectively; ② PII is also effective in different environments, where the impervious layer shows various spectrums. Due to the linear combination form, PII can adjust its coefficients depending on spectra of ground objects in different study areas, which gets higher accuracy in extracting impervious layers than other indexes, especially in regions containing more bare soil. © 2017, Surveying and Mapping Press. All right reserved.

引用

页码：468 / 477

页数：9

共 29 条

[1]

Zhang Z., Wang X., Zhao X., Et al., A 2010 Update of National Land Use/Cover Database of China at 1:100 000 Scale Using Medium Spatial Resolution Satellite Images, Remote Sensing of Environment, 149, pp. 142-154, (2014)

[2]

Zhou D., Zhao S., Liu S., Et al., Surface Urban Heat Island in China's 32 Major Cities: Spatial Patterns and Drivers, Remote Sensing of Environment, 152, pp. 51-61, (2014)

[3]

Weng Q., Remote Sensing of Impervious Surfaces in the Urban Areas: Requirements, Methods, and Trends, Remote Sensing of Environment, 117, pp. 34-49, (2012)

[4]

Masek J.G., Lindsay F.E., Goward S.N., Dynamics of Urban Growth in the Washington DC Metropolitan Area, 1973-1996, from Landsat Observations, International Journal of Remote Sensing, 21, 18, pp. 3473-3486, (2000)

[5]

Deng C., Wu C., A Spatially Adaptive Spectral Mixture Analysis for Mapping Subpixel Urban Impervious Surface Distribution, Remote Sensing of Environment, 133, pp. 62-70, (2013)

[6]

Wu C., Murray A.T., Estimating Impervious Surface Distribution by Spectral Mixture Analysis, Remote Sensing of Environment, 84, 4, pp. 493-505, (2003)

[7]

Small C., Lu J.W.T., Estimation and Vicarious Validation of Urban Vegetation Abundance by Spectral Mixture Analysis, Remote Sensing of Environment, 100, 4, pp. 441-456, (2006)

[8]

Zha Y., Gao J., Ni S., Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery, International Journal of Remote Sensing, 24, 3, pp. 583-594, (2003)

[9]

Deng C., Wu C., BCI: A Biophysical Composition Index for Remote Sensing of Urban Environments, Remote Sensing of Environment, 127, pp. 247-259, (2012)

[10]

Xu H., A New Remote Sensing Index for Fastly Extracting Impervious Surface Information, Geomatics and Information Science of Wuhan University, 33, 11, pp. 1150-1153, (2008)

← 1 2 3 →