## 1 Introduction  
The variation between a city's temperature and that of its countryside area generally relies on the geometry of buildings (for example, their shape and orientation, as well as their construction and street size), in addition to particular built-up cover characteristics (i.e., heat, albedo, wetness, etc.) [1]. The geometry and distribution of streets in metropolis areas significantly affect the development of the surface temperature, leading to heat islands. A change in these attributes transforms the balanced radiation, heat capacity, and energy division into latent heat, as well as sensible heat [2]. Substantial amounts of impermeable surfaces within the urban environment are a result of rapid growth, which is actually regarded as an essential contributing element to UHIs. Reducing the quantity of impermeable surfaces within metropolitan areas is essential in order to diminish UHIs and preserve habitats [3]. Elevated temperatures aggravate power requirements within towns and cities, as they necessitate air-cooling to keep properties cool [4]. Additionally, the use of air cooling units throughout both domestic and industrial areas increases the UHI intensity [5]. Consequently, stress on electrical power availability increases on summer afternoons, which is the time of highest demand. The quality of the air, along with greenhouse gas emissions, should also be considered; emissions of greenhouse gases are boosted as a consequence of increasing power demand [6]. Such emissions negatively affect people's health and can contribute to atmospheric complications, such as acid rain. Additionally, CO\({}_{2}\) released from power plants that are run by fossil fuels contributes to global changes in the climate [7]. Land use transformation brought about by urbanization [8] is the major driving force behind UHIs [9]. A lower albedo, as well as the greater sealing level of urban areas, considerably affect the energy budget at the surface, bringing about greater heat than in non-urban locations [10]. The assessment of urban heat island intensity (UHII) relies on descriptions of pixels that represent urban and/or rural land [11]. Urban areas tend to be significantly impacted by anthropological behaviors, higher variability in impervious cover, and diverse ecosystems present in the adjacent countryside [12]. An understanding of how UHIs may respond, in terms of intensity and distribution, to these human activities was crucial to precisely estimate the imminent UHI effects over the study area. Typical data on these effects are principally significant for oases, as variations in LULC might occur as a reaction to human interventions and climatic changes. To solve UHI problems, an assessment of UHIs should be conducted, taking into account the spatiotemporal changes between all surface cover aspects. With the aim of quantifying alterations in LULC and UHIs, GIS utilization, along with remote sensing data, have become progressively more essential. Such technologies are usually affordable and enable monitoring of the degree of, and also the spatial and temporal differences in, UHIs [13]. The most popular indices used for estimating the spatiotemporal deviation in land surface temperatures (LSTs) are the \"normalized difference vegetation index (NDVI)\" and the \"normalized difference built-up index (NDBI)\", which can be determined via satellite data [14]. These two indices are usually used as measures of LULC dynamics [15]. Several researchers have explored the interactions between LULC and LST utilizing satellite images and GIS at the local, regional, and even global scales [16]. However, few research works have focused on arid and semiarid urban oases, where intense surface UHIs are predicted. Several studies have investigated and utilized numerous techniques to determine UHIs [13, 17, 18]. For example, [PERSON] et al. [19] applied spatial information to evaluate the highest surface UHI.