Assessing the Urban Land Cover Complexity

Authored by: Hongsheng Zhang , Hui Lin , Yuanzhi Zhang , Qihao Weng

Remote Sensing of Impervious Surfaces

Print publication date:  September  2015
Online publication date:  September  2015

Print ISBN: 9781482254839
eBook ISBN: 9781482254860
Adobe ISBN:

10.1201/b18836-6

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Abstract

Urban land use/land cover (LULC) classification is important for monitoring urbanization and its impacts on the environment (Lu and Weng 2006). However, the accuracy needs to be improved and it is still a challenge due to the diversity of LULC types (Lu et al. 2010; Lu and Weng 2006). Various satellite data has been applied to classify LULC using coarse, medium, and high-resolution images (Myint et al. 2011). However, there are some open problems that need to be addressed in order to improve the classification accuracy (Lu et al. 2004; Lu and Weng 2006). First, bare soils or sands were reported to be often confused with bright impervious surfaces (e.g., cool roofs and new concrete roads), while shade and water were often confused with dark impervious surfaces (e.g., asphalt and old concrete roads). These confusions are caused by the similar spectral reflectance of different materials (Lu and Weng 2006). Second, clouds and their shadows are considered a difficult issue to deal with in subtropical humid regions, where cloudy and rainy weather occurs throughout the entire year. Both these problems lower the accuracy of the LULC classification in subtropical humid urban areas. To deal with these problems, SAR remote sensing data is employed and combined with optical images to provide complementary information to help differentiate similar spectral reflectance of different LULC types and help identify the LULC information in cloudy areas. However, before combining the optical and SAR remote sensing data, the spectral confusion between various land covers should be investigated to better understand the situation in tropical and subtropical urban areas. This chapter aims to investigate the urban land cover complexity using optical remote sensing data to determine the confusion between different land covers in four tropical and subtropical cities.

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