Journal article

Understanding land surface temperature differences of local climate zones based on airborne remote sensing data

12 Apr 2018
Description

The local climate zones (LCZ) scheme has attracted the interest of climate researchers as it enables the standardized study of urban heat islands by combining thermal and physical parameters of built and natural structures. Most recent work on LCZ has concentrated on understanding air temperature differences, adapting the scheme to different contexts and improving satellite-based classification methods. However, studies using very high-resolution imagery, including 3-D descriptors and analyzing their land surface temperature (LST) variability are scarcer. Since a correct delineation of LCZ implies significant temperature differences among classes, the aims of this study are:

  1. to test a GIS-based method for the classification of LCZ based on cut-off values valid to the Australian context; and
  2. to examine the quality of classifications by analyzing the LST variability among LCZ using high-resolution airborne remote sensing data.

Results show that diurnal and nocturnal mean LSTs significantly differ among most LCZs. Welch's ANOVA and subsequent post hoc tests for pairwise comparisons also demonstrate that these differences prevail for 71.8% of zones during the daytime and 73.6% of zones at night. Overall, LCZs A, 8, and 3 are the most distinguishable zones during the daytime and LCZs D, G, 1, 4, and 5 are well differentiated at night. In contrast, LCZs 1 and 4 are the least distinguishable at daytime and LCZs 10, A, and E are not well differentiated at night. The present study has successfully validated the present airborne-based classification method which is contingent on further accuracy assessment and improvements.

Publication Details
Identifiers: 
DOI: 
10.1109/JSTARS.2018.2815004
Volume: 
11
Issue: 
8
Pagination: 
2724-2730
Access Rights Type: 
Paid subscription required
Language: 
English
License Type: 
All Rights Reserved
Peer Reviewed: 
Yes
Published year only: 
2018
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