How do urban morphological blocks shape spatial patterns of land surface temperature over different seasons? A multifactorial driving analysis of Beijing, China

Abstract The present study examines the efficiency of discrete and continuous approaches to measuring urban heterogeneity effects on land surface temperature (LST). In the discrete approach, landscape metrics have been widely applied to quantifying the relationship between land surface temperature and urban spatial patterns and have received acceptable verification from landscape ecologists but some studies have shown their inaccurate results. The objective of the study is to compare landscape metrics and alternative approaches to measuring urban heterogeneity effects on LST. We compared landscape metrics results with nine texture-based measures, and two local spatial autocorrelation indices (local Moran’s I and Gi statistics) applied to NDVI and BAI indices as a proxy of the spatial patterns of Tehran vegetation and built-up classes. The statistical results showed that urban landscape heterogeneity had significant impacts on the LST variations, and there was a compatibility between landscape metrics and alternative measures results. Overall results showed that the less-fragmented, the more complex, larger, and the higher number of patches, the lower LST. The most significant relationship was between patch density (PD) and LST (r= -0.71). Higher values of PD have mostly been interpreted to show higher fragmentation, but other landscape metrics and alternative measures declined this conclusion. Our study demonstrated that PD was not a reliable metric and presented no information about the spatial distribution of landscape elements. This study confirms alternative measures for overcoming landscape metrics shortcomings in estimating the effects of landscape heterogeneity on LST variations and gives land managers and urban planners new insights into the urban design.

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Diurnal Land Surface Temperature Characteristics of Local Climate Zones: A Case Study in Beijing, China

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8898456 ◽

2019 ◽

Author(s):

Jinling Quan

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Climate Zones ◽

Local Climate ◽

Temperature Characteristics ◽

Local Climate Zones ◽

Beijing China

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Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China

Remote Sensing ◽

10.3390/rs12183006 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3006

Author(s):

Chaobin Yang ◽

Fengqin Yan ◽

Xuelei Lei ◽

Xiuli Ding ◽

Yue Zheng ◽

...

Keyword(s):

Surface Temperature ◽

Snow Cover ◽

Spatial Patterns ◽

Land Surface Temperature ◽

Land Surface ◽

Thermal Environment ◽

Driving Factors ◽

Seasonal Effects ◽

Landsat 8 ◽

Crop Species

Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in northeast China, as an example, to investigate the seasonal effects of different dominant driving factors on the spatial patterns of LST. Twelve Landsat 8 images were used to retrieve monthly LST, to characterize the urban thermal environment, and spectral mixture analysis was employed to estimate the effect of the driving factors, and correlation and linear regression analyses were used to explore their relationships. Results indicate that, (1) the spatial pattern of LST has dramatic monthly and seasonal changes. August has the highest mean LST of 38.11 °C, whereas December has the lowest (−19.12 °C). The ranking of SUHI intensity is as follows: summer (4.89 °C) > winter with snow cover (1.94 °C) > spring (1.16 °C) > autumn (0.89 °C) > winter without snow cover (−1.24 °C). (2) The effects of driving factors also have seasonal variations. The proportion of impervious surface area (ISA) in summer (49.01%) is slightly lower than those in spring (56.64%) and autumn (50.85%). Almost half of the area is covered with snow (43.48%) in winter. (3) The dominant factors are quite different for different seasons. LST possesses a positive relationship with ISA for all seasons and has the highest Pearson coefficient for summer (r = 0.89). For winter, the effect of vegetation on LST is not obvious, and snow becomes the dominant driving factor. Despite its small area proportion, water has the strongest cooling effect from spring to autumn, and has a warming effect in winter. (4) Human activities, such as agricultural burning, harvest, and different choices of crop species, could also affect the spatial patterns of LST.

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