SPATIO-TEMPORAL ANALYSIS OF SURFACE URBAN HEAT ISLAND BASED ON LANDSAT ETM+ AND OLI/TIRS IMAGERY IN THE CITY OF KOŠICE, SLOVAKIA

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development.

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Investigating spatio-temporal surface urban heat island growth over Jaipur city using geospatial techniques

Sustainable Cities and Society ◽

10.1016/j.scs.2018.04.018 ◽

2018 ◽

Vol 40 ◽

pp. 484-500 ◽

Cited By ~ 6

Author(s):

Aneesh Mathew ◽

Sumit Khandelwal ◽

Nivedita Kaul

Keyword(s):

Urban Heat Island ◽

Heat Island ◽

Island Growth ◽

Geospatial Techniques ◽

Urban Heat ◽

Surface Urban Heat Island ◽

Spatio Temporal ◽

Jaipur City

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Development of a Long-Term Dataset of China Surface Urban Heat Island for Policy Making: Spatio-Temporal Characteristics

10.1109/igarss47720.2021.9554127 ◽

2021 ◽

Author(s):

Lu Niu ◽

Zhong Peng ◽

Ronglin Tang ◽

Zhengfeng Zhang

Keyword(s):

Urban Heat Island ◽

Policy Making ◽

Heat Island ◽

Temporal Characteristics ◽

Urban Heat ◽

Surface Urban Heat Island ◽

Spatio Temporal

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Assessment of Changes in Land Use/Land Cover and Land Surface Temperatures and Their Impact on Surface Urban Heat Island Phenomena in the Kathmandu Valley (1988–2018)

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9120726 ◽

2020 ◽

Vol 9 (12) ◽

pp. 726

Author(s):

Md. Omar Sarif ◽

Bhagawat Rimal ◽

Nigel E. Stork

Keyword(s):

Urban Heat Island ◽

Land Surface ◽

Agricultural Land ◽

Kathmandu Valley ◽

Heat Island ◽

City Center ◽

Urban Heat ◽

Surface Urban Heat Island ◽

The City ◽

Normal Difference

More than half of the world’s populations now live in rapidly expanding urban and its surrounding areas. The consequences for Land Use/Land Cover (LULC) dynamics and Surface Urban Heat Island (SUHI) phenomena are poorly understood for many new cities. We explore this issue and their inter-relationship in the Kathmandu Valley, an area of roughly 694 km2, at decadal intervals using April (summer) Landsat images of 1988, 1998, 2008, and 2018. LULC assessment was made using the Support Vector Machine algorithm. In the Kathmandu Valley, most land is either natural vegetation or agricultural land but in the study period there was a rapid expansion of impervious surfaces in urban areas. Impervious surfaces (IL) grew by 113.44 km2 (16.34% of total area), natural vegetation (VL) by 6.07 km2 (0.87% of total area), resulting in the loss of 118.29 km2 area from agricultural land (17.03% of total area) during 1988–2018. At the same time, the average land surface temperature (LST) increased by nearly 5–7 °C in the city and nearly 3–5 °C at the city boundary. For different LULC classes, the highest mean LST increase during 1988–2018 was 7.11 °C for IL with the lowest being 3.18 °C for VL although there were some fluctuations during this time period. While open land only occupies a small proportion of the landscape, it usually had higher mean LST than all other LULC classes. There was a negative relationship both between LST and Normal Difference Vegetation Index (NDVI) and LST and Normal Difference Moisture Index (NDMI), respectively, and a positive relationship between LST and Normal Difference Built-up Index (NDBI). The result of an urban–rural gradient analysis showed there was sharp decrease of mean LST from the city center outwards to about 15 kms because the NDVI also sharply increased, especially in 2008 and 2018, which clearly shows a surface urban heat island effect. Further from the city center, around 20–25 kms, mean LST increased due to increased agriculture activity. The population of Kathmandu Valley was 2.88 million in 2016 and if the growth trend continues then it is predicted to reach 3.85 million by 2035. Consequently, to avoid the critical effects of increasing SUHI in Kathmandu it is essential to improve urban planning including the implementation of green city technologies.

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