**1. Introduction**

In the context of climate change and global warming, it is important to monitor the signatures of urban heat islands (UHIs) and to understand their impacts on ecosystems and human health. UHI arises from the phenomenon of relatively higher temperature in the urban center over its surrounding rural environment. The phenomenon behind UHI has been studied for a long time. It was first described by Luke Howard in the 1810s [1]. From 1964 to 1968, Bornstein [2] used a helicopter to study the UHI of New York City, and determined the effect of UHI in both vertical and horizontal directions. The results display a maximum intensity of UHI near the ground surface, and a decrease to zero at a height of 300 m [2]. Ackerman [3] studied the diurnal and seasonal variations of UHI in Chicago, recording an increase—averaging 1.85 ◦C—in temperature inside the city most of the time. Since the 1990s, 3D models have been developed to examine the effects of UHI in Tokyo, Japan, using satellite and land survey data [4]. In Nagoya, Japan, seasonal changes pertaining to UHI were analyzed using Landsat and ASTER images taken during the day, as well as at night, which were modeled to determine whether or not the heat fluxes are natural or artificial [5]. In Washington, the surface temperature of the city center was found to be higher than the surrounding vegetative areas by up to 10 ◦C [6]. These studies indicated that vegetation cover plays a key role in minimizing the UHI effect. The minimization of the UHI effect tends to be beneficial to the community because it may result in the enhancement of dangerous natural phenomena, in addition to its impacts on ecosystems and human health. For example, it has been reported that the UHI effect may alter the precipitation [7], characteristics of cloud-to-ground lightning activities through increased aerosols [8,9] and their enhancement [10,11] in response to urbanization, and modify the environmental and regional climate by reshaping the boundary layer and land–sea circulation [12].

Many other studies also demonstrated correlations between UHI and land-use composition in a city. Weng et al. [13] reported a correlation between the surface temperature and vegetation in Indianapolis, USA. Chen et al. [14] examined the relationship between UHI and land-use change, in certain cities of the Guangdong Province in Southeast China, using Landsat images from 1990 to 2000. A similar study on the relationship between UHI, land-use change, and population density was also conducted in Nagpur, India [15]. Other studies relevant to UHI effects were also conducted [16–20]. These studies provide similar conclusions that urban temperatures are highly correlated with land-use composition (water, vegetation, built-up, among others) in the cities. Due to easy access and wall-to-wall continuous coverage, LST derived from thermal infrared remote sensors are one of the most commonly used indicators for surface UHI (SUHI) analysis [21–24]. In this study, the SUHI is defined as the magnitude of the temperature differentials between any two land-use types, a more general way than that which is typically adopted in the literature. From the physical point of view, LST and air temperature are different entities, while strong correlations were found between them by many researchers in the literature [21,25,26].

More recently, Deilami et al. [27] provided a systematic and overarching review of different spatiotemporal factors that affect the UHI effect. It is indicated that the UHI effect can be considered as a critical factor contributing to heat-related mortalities, and unpredictable climatic changes. Lai et al. [28] were concerned with the quality control of the satellite data for investigating the SUHI. They used eighty-six major cities across mainland China, and analyzed SUHI intensity (SUHII) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. Their findings suggested the need to be extremely cautious when using LST product-based SUHIIs to interpret SUHIs. Li et al. [29] presented a new method to quantify the SUHI. They were concerned with the effective evaluation of potential heat risk. A new approach was proposed to quantify the SUHII by using the relationship between MODIS LST and impervious surface areas (ISA). The calculated SUHII shows high values in summer and during the day than in winter and at night. Despite a great effort being devoted to study the UHI and SUHI in the major cities of the world, fewer investigations have been conducted to solve the associated problems for developing countries such as Southeast Asian countries, among which Vietnam implemented a Master Plan 2030 for its capital, Hanoi, in 2011. How will the Master Plan

impact Hanoi's overall environmental conditions? Nam et al. [30] evaluated the influence of UHI under the Hanoi Master Plan 2030 on the energy consumption for space cooling in residential buildings. They found that the increments in built-up areas were larger than those in existing built-up areas, and that the cooling load in an apartment is approximately half of that in a detached house, which itself approximately half of that in a row house. It was also observed that although sensible cooling loads increased with the increase in outdoor temperature, the latent cooling loads decreased due to the decrease in absolute humidity and the increase in air temperature. Trihamdani et al. [31] assessed the UHI effects in the city under the present land use conditions, as well as those conditions proposed by the Hanoi Master Plan 2030 through numerical simulation, using Weather Research and Forecasting (WRF). They found that the peak air temperature in the built-up areas (approximately 1 ◦C higher at the maximum) was not significantly modified, but high temperature areas, with temperatures of 40–41 ◦C, would expand widely over the new built-up areas. They also stressed that the number of hotspots increased further when the strategic green spaces in the master plan were not taken into account.

Based on the literature survey, state-of-the-art of correlations between UHI or SUHI and land-use composition are being widely analyzed in the recent years. In contrast, the study of UHI or SUHI with respect to Hanoi City Master Plan 2030 is rather limited; for example, about spatial energy consumption [30], assessment of UHI effects based on WRF simulations [31]. Then, what would be the most updated status of land use change with respect to the city Master Plan? What would be the impact on the thermal signatures? What possible measures can the city take to mitigate UHI? Therefore, the objectives of this research are to (i) assess the land use changes in Hanoi, (ii) assess the quantitative relationships between the composition of the main land-use types and SUHI in Hanoi, (iii) analyze the effects of land-use composition on SUHI on an extremely hot day, (iv) derive a regression model for the prediction of SUHI, and (v) suggest the measures applicable for minimizing the SUHI impacts on human health, due to increased urban temperature.
