Analyzing land surface temperature trends using non-parametric approach: A case of Delhi, India

被引：27

作者：

Panwar, Manoj ^{[1
,2
]}

Agarwal, Avlokita ^{[2
]}

Devadas, Veruval ^{[2
]}

机构：

[1] DCR Univ Sci & Technol, Dept Architecture, Murthal 131039, India

[2] Indian Inst Technol, Dept Architecture, Roorkee 247667, Uttar Pradesh, India

来源：

URBAN CLIMATE | 2018年 / 24卷

关键词：

Urban Heat Island (UHI); LST; Trend Robustness; MODIS; Mann Kendall and Theil-Sen slope; URBAN HEAT-ISLAND; CHINA;

D O I：

10.1016/j.uclim.2018.01.003

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Urban Heat Island (UHI) creates backwash effect in the system. The existing studies consider averaging of Land Surface Temperature (LST) over the area of a land-use/land-cover for analysis of UHI, whereas it is not necessary that all pixels observe similar behavior. Besides this, averaging of LST over space obstructs the spatial continuity. The analysis of the behavior of LST over a pixel is important. The pixel-wise temperature trend analysis will further open up the layers of intensities in a particular land-use/land-cover, and is more meaningful. The LST images have been used for trend analysis in different studies but the robustness has never been analyzed. In this research, the robustness of the LST trend is analyzed pixel-wise by using non-parametric Mann-Kendall to detect trends in monthly LST, and Theil-Sen slope estimator for analyzing the extent of significance of this trend in Delhi. MODIS data from 2001 to 2015 has been used. The results highlighted a mix of positive and negative trends in monthly LST. February (nighttime) and September (daytime) trends are most important due to high robustness at all significance levels. This research concludes that there is a positive temperature trend throughout the study period especially in night time in Delhi.

引用

页码：19 / 25

页数：7

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