Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019)

Abstract

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

“…Since LULC change is most common in developing countries in the central parts of individual cities, investigating land cover change and the accompanying environmental impacts in developing countries is critical [10]. Hassan et al [11] analyzed land cover change and its influence on the surface urban heat island intensity (SUHII) in seven Asian megacities-namely, Kabul, Dhaka, Thimphu, Delhi, Kathmandu, Colombo, and Karachi. The LULC in each city was classified using a machine learning classifier and Landsat data and was linked to changes in the thermal environment [11].…”

“…Hassan et al [11] analyzed land cover change and its influence on the surface urban heat island intensity (SUHII) in seven Asian megacities-namely, Kabul, Dhaka, Thimphu, Delhi, Kathmandu, Colombo, and Karachi. The LULC in each city was classified using a machine learning classifier and Landsat data and was linked to changes in the thermal environment [11]. Kikon et al [12] assessed temporal changes in rising trends of UHI in Noida City, India, using Landsat data and grid-level analysis, and compared them with the LULC change pattern.…”

“…The changes in SUHI in Karachi and its relationship with rapid urbanization and LULC were analyzed based on day and nighttime LST data from MODIS and Landsat images in 2009 and 2017 [28]. Hassan et al [11] conducted a comparative study to examine the changes in the thermal environment in seven South Asian megacities from 2000 to 2019 using yearly average LST from MODIS data with a 1-km spatial resolution. A comparative study was conducted to detect the urbanization patterns of four rapidly developing lowland South Asian cities, including Karachi, in 2019 [29].…”

A Multi-Criteria Evaluation of the Urban Ecological Environment in Shanghai Based on Remote Sensing

“…. , y (m) } be a vector sequence composed of m samples, and let {z (1) , z (2) , ..., z (m) } be a real number sequence formed by the projection of y (i) onto a, i = 1, 2, . .…”

“…Currently, the urban ecological environment faces a series of problems, such as urban heat island effect, deforestation, and land degradation [1]. These problems have a connection with human health and may even induce life-threatening diseases [2]. The public has an urgent need to know the conditions of the urban ecological environment.…”