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Pixel-wise vs. object-based impervious surface analysis from remote sensing: correlations with land surface temperature and population density
Please always quote using this URN: urn:nbn:de:bvb:20-opus-197829
- 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 integratedImpervious 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.…
Author: | Chunzhu Wei, Thomas Blaschke |
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URN: | urn:nbn:de:bvb:20-opus-197829 |
Document Type: | Journal article |
Faculties: | Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) / Institut für Geographie und Geologie |
Language: | English |
Parent Title (English): | Urban Science |
ISSN: | 2413-8851 |
Year of Completion: | 2018 |
Volume: | 2 |
Issue: | 1 |
Pagenumber: | 2 |
Source: | Urban Science 2018, 2(1), 2; https://doi.org/10.3390/urbansci2010002 |
DOI: | https://doi.org/10.3390/urbansci2010002 |
Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 52 Astronomie / 526 Mathematische Geografie |
Tag: | impervious surface areas; land surface temperature; object-based image analysis; population density |
Release Date: | 2020/04/17 |
Date of first Publication: | 2018/01/02 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |