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Characterizing fractional vegetation cover and land surface temperature based on sub-pixel fractional impervious surfaces from Landsat TM/ETM+

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journal contribution
posted on 2015-06-01, 15:06 authored by Youshui Zhang, Angela Harris, Heiko Balzter
Estimating the distribution of impervious surfaces and vegetation is important for analyzing urban landscapes and their thermal environment. The application of a crisp classification of land cover types to analyze urban landscape patterns and land surface temperature (LST) in land cover types to analyze urban landscape patterns and land surface temperature (LST) in detail presents a challenge, mainly due to the complex characteristics of urban landscapes. In this paper, sub-pixel percentage impervious surface area (ISA) and fractional vegetation cover (FVC) were extracted from bi-temporal TM/ETM+ data by linear spectral mixture analysis (LSMA). Their accuracy was assessed with proportional area estimates of impervious surface and vegetation extracted from high resolution data. A range approach was used to classify percentage ISA into different categories by setting thresholds of fractional values and these were compared for their LST patterns. For each ISA category, FVC, LST and percentage ISA were used to quantify the urban thermal characteristics of different developed areas in the city of Fuzhou, China. Urban LST scenarios in different seasons and ISA categories were simulated to analyze the seasonal variations and the impact of urban landscape pattern changes on the thermal environment. The results show that FVC and LST based on percentage ISA can be used to quantitatively analyse the process of urban expansion and its impacts on the spatial-temporal distribution patterns of the urban thermal environment. This analysis can support urban planning by providing knowledge on the climate adaptation potential of specific urban spatial patterns.

History

Citation

International Journal of Remote Sensing, 36:16,4213-4232 (2015)

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Geography/GIS and Remote Sensing

Version

  • AM (Accepted Manuscript)

Published in

International Journal of Remote Sensing

Publisher

Taylor and Francis

issn

0143-1161

eissn

1366-5901

Acceptance date

2015-07-23

Copyright date

2015

Available date

2016-08-25

Publisher version

http://www.tandfonline.com/doi/abs/10.1080/01431161.2015.1079344

Language

en

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