Pixel-Wise vs. Object-Based Impervious Surface Analysis from Remote Sensing: Correlations with Land Surface Temperature and Population Density

Chunzhu Wei,Thomas Blaschke

doi:10.3390/urbansci2010002

Chunzhu Wei, Thomas Blaschke

Open Access

https://doi.org/10.3390/urbansci2010002

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Journal: Urban Science	Publication Date: Jan 2, 2018
Citations: 15	License type: CC BY 4.0

Affiliation: University of Würzburg, University of Salzburg

Abstract

Impervious surface areas (ISA) are heavily influenced by urban structure and related structural features. We examined the effects of object-based impervious surface spatial pattern analysis on land surface temperature and population density in Guangzhou, China, in comparison to classic per-pixel analyses. An object-based support vector machine (SVM) and a linear spectral mixture analysis (LSMA) were integrated to estimate ISA fraction using images from the Chinese HJ-1B satellite for 2009 to 2011. The results revealed that the integrated object-based SVM-LSMA algorithm outperformed the traditional pixel-wise LSMA algorithm in classifying ISA fraction. More specifically, the object-based ISA spatial patterns extracted were more suitable than pixel-wise patterns for urban heat island (UHI) studies, in which the UHI areas (landscape surface temperature >37 °C) generally feature high ISA fraction values (ISA fraction >50%). In addition, the object-based spatial patterns enable us to quantify the relationship of ISA with population density (correlation coefficient >0.2 in general), with global human settlement density (correlation coefficient >0.2), and with night-time light map (correlation coefficient >0.4), and, whereas pixel-wise ISA did not yield significant correlations. These results indicate that object-based spatial patterns have a high potential for UHI detection and urbanization monitoring. Planning measures that aim to reduce the urbanization impacts and UHI intensities can be better supported.

Highlights

Impervious surface coverage is increasingly being acknowledged to go hand in hand with urban sprawl while influencing ground runoff, jeopardizing water quality, destroying ecosystem functions and causing the urban heat island (UHI) effect [1,2,3]
All of them coincided with the model test standard of 0.2, support vector machine (SVM)-linear spectral mixture analysis (LSMA) obviously outperformed LSMA
While the object-based SVM model in this research dealt with the mixed-pixel problems that exist in traditional pixel-wise LSMA methods to some extent, especially to distinguish roads, building, and parking lots from the vegetation areas and small green spaces from high density residential areas

Summary

Introduction

Impervious surface coverage is increasingly being acknowledged to go hand in hand with urban sprawl while influencing ground runoff, jeopardizing water quality, destroying ecosystem functions and causing the urban heat island (UHI) effect [1,2,3]. The medium (10–100 m) spatial resolution satellite images are the most popular data that have been successfully applied in many previous impervious surface analyses [7,8,9]. Review of impervious surface mapping studies based on medium resolution images has revealed that the linear spectral mixture analysis (LSMA) is one of the effective approaches for estimating. With the advantages of extracting sub-pixel information and dealing with the spectral mixture problem effectively, different optimized algorithms based on LSMA model have been developed to improve the pixel-wise ISA estimation [12,13].

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