Urban impervious surface information extracting and its eco-environment effect analysis

Zhihong Gao; Naiming Wu

doi:10.1117/12.2205885

14 December 2015 Urban impervious surface information extracting and its eco-environment effect analysis

Zhihong Gao, Naiming Wu

Proceedings Volume 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 98150P (2015) https://doi.org/10.1117/12.2205885
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Urban impervious surface is not only a major indicator of urbanization but also a key factor for environmental quality evaluation. NDISI is applied for enhancing and extracting impervious surface information in this study. NDVI derived from measurements of the optical imagery reflectance of sunlight in the red and near-infrared wavelengths, can reveal the vegetation cover condition. LST plays an important role in energy exchange between the land surface and the atmosphere. The objective of this paper is to extract the urban impervious surface information and its eco-envionment effect. Experiment results indicate that NDISI is effective and easy-to-use. And the correlation coefficient among NDISI, NDVI and LST are -0.82, 0.95 and -0.7. Combined with the field survey meteorological site data, this study reveals that the urban impervious surface area has a strong positive exponential relationship with the LST. This suggests that the areas with high NDISI value will accelerate the increase in LST much more than the areas with low NDISI value. And urban impervious surface area is the most important factor contributing to the development of the UHI while vegetation cover (high NDVI area) can significantly reduce the LST.

Citation Download Citation

Zhihong Gao and Naiming Wu "Urban impervious surface information extracting and its eco-environment effect analysis", Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 98150P (14 December 2015); https://doi.org/10.1117/12.2205885

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KEYWORDS

Earth observing sensors

Landsat

Vegetation

Remote sensing

Associative arrays

Meteorology

Near infrared

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