37 Most of Nepal's population lives in rural areas, with less than half are living in urban areas (Bhattarai & Conway, 2021). Nepal is rapidly urbanizing, and demographics are changing rapidly due to income inequalities that have forced the mass out-migration of the rural population (Bhattarai & Conway, 2021). Urbanization refers to the growth and expansion of urban centers due to various factors, including a general shift of people from rural areas to cities (Chapagain, 2018; Hulley, 2012; Welford & Yarbrough, 2021). Nepal has experienced rapid urbanization in recent years (Rimal, Zhang, Keshtkar, Sun, & Rijal, 2018) and is one of the world's top ten fastest-urbanizing countries (Heiling, 2012). Nepal's annual urbanization rate is projected to increase to 1.9 % by 2050 (Bakrania, 2015). Urban expansion is occurring mainly around metropolitan cities, district headquarters (the then administrative centers), inner Terai valleys, and the markets and towns located at highway junctures along the Banko Janakari, Vol 32 No. 2, 2022 Pp 37‒51https://doi.org/10.3126/banko.v32i2.50895 Assessment of Urban Heat Islands (UHIs) Using Satellite- Derived Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST) in Three Metropolitan Cities of Nepal Urban Heat Islands (UHIs) are urban areas that are relatively warmer than nearby rural areas due to the presence of infrastructures, such as buildings, roads, and associated development. This study explored the UHIs in Nepal's three largest metropolitan cities, i.e., Pokhara, Bharatpur, and Nepalgunj. Using freely available data, we explored LST dynamics between 2000 and 2019 and how changes in NDVI affect LST and their relationship with UHI. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day product (MOD11A2) to evaluate LST and the MODIS-derived NDVI 16-day product (MOD13Q1) to quantify land surface characteristics. Using a simple linear regression technique, we explored the relationship between LST and NDVI. The results indicated that LSTs for the urban areas are consistently greater than LSTs for the nearby rural areas, and an inverse relation between LST and NDVI was obtained. The results from Pokhara and Bharatpur showed that increasing LST resulting from declining NDVI is responsible for UHIs. However, the results from Nepalgunj suggested that factors other than NDVI are responsible for variation in LST. These results indicate a need for systematic mapping, planning, and managing open and green areas in large cities. This research also highlights the scope of applying UHI conceptual models to rapidly developing urban areas in different locations of Nepal for better planning and management of open spaces. Keywords: Land surface temperature, metropolitan cities, MODIS NDVI, Nepal, Urban Heat Islands S. Kandel 1*, B. Gyawali 1, J. Sandifer 1, S. Shrestha 1, and S. Upadhaya 2,3 Received: 9, September 2022 Revised: 16, November 2022 Accepted: 14, December 2022 Published: 31, December 2022 1 College of Agriculture, Community, and the Sciences, Kentucky State University, Frankfort, KY, 40601 *Email : smritikandel@kysu.edu 2 Bioeconomy Institute, Iowa State University, Ames, IA 50011 3 Himalayan Conservation and Research Institute, Dolpa, Nepal https://orcid.org/0000-0001-5133-0833 https://orcid.org/0000-0003-3929-3536 https://orcid.org/0000-0002-8284-2775 https://orcid.org/0000-0002-8173-9397 Banko Janakari, Vol 32 No. 2 38 Kandel et al. east-west highway, which tend to be the primary urban centers (Acharya, 2018; Rimal et al., 2020). Urban centers are the centers of economic activities that attract businesses and workers that tend to be associated with enhanced productivity. Internal migration is the most significant contributor to urban growth and is increasing over time (Acharya, 2018; KC, 2020). Cities that provide a variety of options for employment and education are drawing more and more people, especially those seeking work in the manufacturing, construction, and service industries. A significant portion of the national economy comes from the urban sector (Bakrania, 2015). With regard to Nepal, urban regions provide around one-third of the country's GDP (GDP). Increased population growth is one of the primary reasons for the gradual transition from rural to urban centers (Bakrania, 2015). The population of Nepal was 26.5 million in 2011, then projected to be 29.1 million in 2022 (Central Bureau of Statistics,2022). Due to both political and economic factors, such as rural poverty and better employment possibilities in cities, Nepal's urban population has grown rapidly during the past ten years. Other driving factors of urban population growth include social, physical, developmental, high net migration, and administrative reclassification (Rijal, Rimal, Stork, & Sharma, 2020; Rimal et al., 2020, 2018). Climate and physiographic features make the area attractive for urban development, along with good government plans and policies (Rijal et al., 2020). For instance, in Nepal, urbanization primarily occurred at the periphery of the East-West, postal highway, and connecting road networks to the mid-hills (Rimal et al., 2020). As a result, the areas accessible from the mid-hill districts were rapidly expanded. Rapid urbanization poses multiple challenges, such as hazards and uncontrolled growth of built-up areas (i.e., areas populated with residential structures and other anthropogenic facilities that meet the need of the increased population) that require urgent policy attention. Unplanned urban development leads to rapid and uncontrolled sprawl, loss of open spaces, increased vulnerability to disasters such as earthquakes, and decreased human suitability (Aksha, Juran, & Resler, 2018; Pandey, 2013). Urbanization significantly changes the local landscape by replacing open vegetated areas with buildings, roads, and associated infrastructures. Such a shift in landscape from rural to urban leads to the phenomenon called "urban heat island (UHI)." UHIs are urban or metropolitan areas that experience significantly warmer temperatures than their nearby rural areas (Cao, Li, Zhang, & Chen, 2008; Kim, 1992; W. Li, Cao, Lang, & Wu, 2017; Y. Wang & Akbari, 2017). Satellite-based remote sensing technology and a GIS-based approach can dynamically and comprehensively detect change in the urban thermal environment. Land surface temperature (LST) is an important parameter in analyzing UHIs (Cao et al., 2008; Qiao et al., 2020; Shi, Xiang, & Zhang, 2019). LST and normalized difference vegetation index (NDVI) are widely used to explore the relationship between landscape change patterns and UHI characteristics. For instance, C. Li et al. (2014) found a negative correlation between NDVI and UHI in Shanghai, China. Studies have used LST based on various remote sensing data such as NOAA AVHRR, Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Thermal Infrared (TIR) data, Moderate Resolution Imaging Spectroradiometer (MODIS) derived data (Azevedo, Chapman, & Muller, 2016; Grover & Singh, 2015; Mishra, Sandifer, & Gyawali, 2019; Sridhar, Sathyanathan, & Sree Shivani, 2020; R. Wang, Gao, & Peng, 2020) to study UHI. This study used MODIS-derived LST and NDVI data to understand the impact of vegetation changes on UHI. A major advantage of using MODIS imagery is the availability of products made on varying temporal schedules, ranging from raw images to highly processed products (Masuoka, Fleig, Wolfe, & Patt, 1998). In addition, several previous studies have successfully used MODIS data to study the relationship between LST and NDVI (Mishra et al., 2019; Teodoro, Duarte, Barradas, Mateus, & Neto, 2018; R. Wang et al., 2020; Yuan et al., 2017). MODIS-derived LST and Landsat image-derived LST were used to study UHI phenomena in Banko Janakari, Vol 32 No. 2 39 Kandel et al. Kathmandu Metropolitan City, Nepal (Mishra et al., 2019; Sarif, Rimal, & Stork, 2020). Similarly, Landsat satellite imagery-derived NDVI were used to assess increased urbanization in other metropolitan cities, including Pokhara, Bharatpur, and Nepalgunj (Rai, Yili, Paudel, Khanal, & Acharya, 2020; Rimal et al., 2020). However, the study of UHI phenomena is limited in these metropolitan cities. UHIs raise the demand for energy consumption, such as air conditioning, and contribute to compromised human health and environmental stress. However, studies on LST, UHIs, and their relationship with other possible factors are limited in Nepal. Understanding the distribution of land surface temperature is important because it affects many aspects of life. Therefore, in this study, we explored LST dynamics between 2000 and 2019 and how changes in NDVI affect LST and their relationship with UHI in the three major Nepalese cities. Materials and methods Study area In this study, we focused on three metropolitan cities in Nepal, namely Pokhara, Bharatpur, and Nepalgunj. Indicators of physical and human geography, as well as growth in terms of population size, vary between these three cities. Pokhara is the second-largest and the most rapidly growing city located in the mid-hills of western Nepal, with an annual population growth rate of five percent (Acharya, 2018). The East- West route passes through the urban areas of Bharatpur and Nepalgunj, where populations are dispersed, and agricultural lands have been exploited for residential uses, fragmenting the area. The existing urban agglomerations were spatially expanded along the peripheries of major cities and highways (Rimal et al., 2020). For example, Pokhara is densely populated and is growing towards Prithivi Highway and Pokhara- Baglung Highway. The characteristics of all three cities are provided in table 1. And figure 1 shows the geographic location of Pokhara, Nepalgunj, and Bharatpur. Table 1: Characteristics of three study cities, Pokhara, Bharatpur, and Nepalgunj District Pokhara Bharatpur Nepalgunj Kaski Chitwan Banke Area (km2) 464.24 433 85.94 Latitude 280 12'30''N 27° 41′ 0″ N 28° 3′ 0″ N Longitude 830 59' 20''E 84° 26′ 0″ E 81° 37′ 0″ E Elevation (m) 1740 208 150 Annual Precipitation (mm/year) 3350 2407 1447.8 Average Annual Temperature (oC) 13.5 23.2 30.2 Population Density (per km2) 868 503 1592 Banko Janakari, Vol 32 No. 2 40 Kandel et al. Figure 1: Location (top panel) and land use land cover (bottom panels) of three study cities, Bharatpur, Pokhara, and Nepalgunj Data acquisition We downloaded MOD11A2 and MOD13Q1 data from EARTHDATA Search (Table 2). MOD13Q1 represents the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices, which is generated every 16 days at 250-meter spatial resolution. The Land Surface Temperature and Emissivity (LST&E) product of MOD11A2 Version 6 offers an average 8-day per-pixel LST&E with a 1 km spatial resolution. Banko Janakari, Vol 32 No. 2 41 Kandel et al. Table 2: Description of MODIS data used for this study Data Type Date of acquisition Spatial Resolution Source MOD11A2 Start: 2000-5-15 End: 2019-5-18 1000 m 250 m https://search.earthdata.nasa. gov/ MOD13Q1 Start: 2000-5-8 End: 2019-5-23 Normalized Difference Vegetation Index (NDVI) Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between the near-infrared band (which reflects vegetation strongly) and the red band (which absorbs vegetation) (Viana, Oliveira, Oliveira, & Rocha, 2019). Its values range from -1 to +1, where values near +1 indicate dense vegetation, low positive values represent grassland, 0 indicates barren areas, and negative values represent water or impervious surfaces. It has been reported that impervious surfaces are warmer than green areas (Kuang et al., 2015); therefore, there is likely a negative relationship between NDVI and LST. ….(1) Land Surface Temperature (LST) One of the MODIS land products is Land- surface temperature (LST). The MOD11A2 Version 6 product offers an average Land Surface Temperature and Emissivity (LST&E) of 8 days per pixel. Under clear skies, the accuracy criteria for MODIS LST is 1°K at 1 km of spatial resolution. A simple average of all the relevant MOD11A1 LST pixels gathered throughout that 8-day period is used to calculate each pixel value in the MOD11A2 dataset. The 8-day compositing duration was selected since the Terra and Aqua platforms' ground track repeat periods are exactly twice that length of time. Emitted spectral radiance L at wavelength λ from a surface at thermodynamic temperature Ts is given by multiplying the Planck function by spectral emissivity e(λ) (K. Wang et al., 2007). L(λ, T) = e(λ) B(λ, Ts) ………………(2). Data preparation and analysis MOD13Q1 and MOD11A2 data were pre- processed and analyzed using Esri geospatial software ArcMap version 10.5. The raster images representing LST were rescaled by multiplying with a scale factor of 0.02, while the raster representing NDVI was multiplied by a scale factor of 0.0001. Rescaling was done to reduce the uncertainties of satellite data and receive the actual data value. Data were projected into the WGS1984 UTM Zone 44N coordinate system. Since the downloaded LST was in degrees Kelvin, we converted it into degrees Celsius. Finally, LST images were resampled to match the spatial resolution of the NDVI image. About one hundred random points each were created inside the boundaries of Pokhara, Bharatpur, and Nepalgunj (Figure 2). LST and NDVI values of the random pixels were extracted and recorded in the attribute table of the random point feature class. This attribute table with a hundred LST and NDVI values was exported for further statistical analysis in R software Version 1.2.5001 (R Core Team, 2020). Simple linear regression (alpha level = 0.05) was used to assess the relationship between LST and NDVI for the years between 2000 and 2019. This analysis was applied to all sampled pixels separately for Bharatpur, Pokhara, and Nepalgunj between 2000 and 2019. We considered NDVI as the independent variable and LST as the dependent variable. Y=mx + c ………. (3) where y is the dependent variable, x is the independent variable, m is the coefficient, and c is the constant. Banko Janakari, Vol 32 No. 2 42 Kandel et al. Figure 2: Spatial distribution of one hundred random points were created inside the boundaries of Pokhara, Bharatpur, and Nepalgunj Results Change in LST and UHI between 2000 and 2019 From the LST images,we can see UHI effects from past years. It was observed that higher temperatures exist in urban areas, and the distribution of higher LST values has increased over time from 2000 to 2019 (Figures 3, 4, 5). Along with expanding higher temperature zones to the city centers, nearby rural areas also faced increased temperatures. The variation in land surface temperature is observed in the central part of Pokhara, as shown in Figure 3, with more developed infrastructures such as houses and roads, where higher temperature exists between 32oC–33oC. Also, variation in LST distribution from 2000 to 2019 is observed in areas surrounding the core city, where the temperature seems to increase. Banko Janakari, Vol 32 No. 2 43 Kandel et al. In Bharatpur, a higher variation in the distribution of land surface temperature is observed from 2000 to 2019, as shown in Figure 4. During 2000, higher temperatures existed at inner terai, which is located in the southern part of Bharatpur. The distribution of higher temperatures expanded toward the northern part. This is due to the city's expansion in Narayangadh, which is the central hub for trade, business, and transportation for Kathmandu, Pokhara, Hetauda, and western Nepal. Moreover, this urban expansion has influenced suburban temperature in the northernmost part, where high elevation exists, and the southeastern part, which is a forested area and part of Chitwan National Park. The UHI phenomenon mostly influences sub-urban areas. Figure 3: Land surface temperature maps of Pokhara Metropolitan City for the year 2000 (upper panel) and 2019 (lower panel) Figure 4: Land surface temperature maps of Bharatpur Metropolitan City for the year 2000 (upper panel) and 2019 (lower panel) Figure 5: Land surface temperature maps of Nepalgunj Metropolitan City for the year 2000 (upper panel) and 2019 (lower panel) Banko Janakari, Vol 32 No. 2 44 Kandel et al. Similarly, variation in land surface temperature was observed in Nepalgunj sub-metropolitan city from 2000 to 2019, as shown in Figure 5. Higher temperature exists in the central and eastern parts of Nepalgunj. This might be due to heat captured by the impervious surface at the core city area. Nepalgunj is the hottest place in Nepal, which lies at a low elevation, where the highest temperature ever recorded was 45oC during the hot summer months. The lowest temperature of Nepalgunj has risen from 36oCin 2000 to 37oC in 2019. Change in NDVI between 2000 and 2019 Our results showed that NDVI value significantly changed between the years 2000 and 2019 in all three cities. Figures 6, 7, and 8 show the range of NDVI values between the years 2000 and 2019 for Pokhara, Bharatpur, and Nepalgunj, respectively. The green indicates healthy vegetation, while the red represents no vegetation or impervious surface. NDVI value is less in the central part of Pokhara in 2019 than in 2000 (Figure 6). This indicates less vegetation and a more impervious surface, as represented by the red color. Pokhara’s built-up area has grown specifically along the highways and Phewa lakeside. This can result in increased LST. In the Narayanghad area of Bhatarpur metropolitan city, NDVI values in 2019 are lower and negative compared to NDVI values in 2000. This represents the urbanization of Narayanghad over time. Additionally, the vegetation toward the northern part of Bharatpur decreased gradually from 2000 to 2019. Those are the areas with higher elevation, where development activities like the construction of ungravelled roads occurred, which leave the land barren and also prone to erosion. Barren lands produce higher radiance, giving rise to LST values. Lower NDVI values toward the southern part of Bharatpur during 2000 might be due to drought or less vegetation on the ground. In the case of Nepalgunj sub-metropolitan city, vegetative land has decreased gradually, and areas with lower NDVI values increased from 2000 to 2019. NDVI values indicate the degree of vegetation greenness (Lo & Quattrochi, 2003). Figure 6: Maps showing NDVI for the years 2000 and 2019 for Pokhara Metropolitan City Figure 7: Maps showing NDVI for the years 2000 and 2019 for Bharatpur Metropolitan City Banko Janakari, Vol 32 No. 2 45 Kandel et al. Figure 8: Maps showing NDVI for the years 2000 and 2019 for Nepalgunj Sub-metropolitan City Relationship between LST and NDVI Figures 9, 10, and 11 show the relation between temperature and vegetation for the years 2000 and 2019. It is known that an increase in vegetation correlates to decreases in UHI intensity. This shows an inverse relationship between temperature and NDVI (Sridhar et al., 2020). Figure 9: Scatterplots showing the relation between NDVI and LST for the years 2000 and 2019 in Pokhara Figure 10: Scatterplots showing the relation between NDVI and LST for the years 2000 and 2019 for Bharatpur Banko Janakari, Vol 32 No. 2 46 Kandel et al. Figure 11: Scatter plots showing the relation between NDVI and LST for the years 2000 and 2019 for Nepalgunj Discussion This research investigated the relationship between LST and NDVI concerning UHI in three metropolitan cities viz, Pokhara, Bharatpur, and Nepalgunj of Nepal, which has been facing rapid urbanization in recent years. Our study showed increased LST and decreased vegetation as represented by NDVI in these major urban centers, which resulted in UHI phenomena. Furthermore, LST has been found to be strongly determined by vegetation health (Kant, Bharath, Mallick, Atzberger, & Kerle, 2009; Yue, Xu, Tan, & Xu, 2007). In this study, the high value of LST was found in areas with lower vegetation intensity. Generally, negative NDVI depicts water bodies, positive NDVI depicts bare ground and built-up areas, and NDVI greater than 0.2 depicts vegetation, with higher NDVI denoting better vegetation (Guha & Govil, 2020; Guha, Govil, & Mukherjee, 2017). Several studies have found that LST negatively correlates with NDVI (Grover & Singh, 2015; Guha & Govil, 2020; Mishra et al., 2019). The dependency between the LST and NDVI for Pokhara, Bharatpur, and Nepalgunj is represented by regression analysis (Figures 9, 10, and 11.). In the case of Pokhara, 40% of the relationship is explained by NDVI, which indicates that the lower vegetation index is responsible for varied LST patterns (Figure 9). About 37% of the relationship between LST and NDVI is reflected in Bharatpur ( Figure 10), and only 18% of the relationship is explained by NDVI in Nepalgunj (Figure 11). This reflects that there are other possible factors for high temperature in Nepalgunj, which can be attributed to the humid subtropical climate, the presence of grasslands and shrubs other than the dense forest, and its location in the Terai plains near the border of India. The distribution of water bodies, the amount of impermeable concrete, asphalt, and metal used, as well as the roughness of the surface are a few of the many variables that determine the development and intensity of LST and UHI (Grover & Singh, 2015; Kuang et al., 2015). Furthermore, although the spatial relationship between the change in LST and NDVI is statistically significant, it is not necessarily spatially coincident (Mishra et al., 2019). People choose to move to urban areas for many different reasons, for example, better access to educational and health institutions, social services, better employment opportunities, and other facilities to improve their lifestyles (Dussault & Franceschini, 2006). Urban regions in Nepal have traditionally seen a noticeable rise in the quantity and size of small urban communities (Bhattarai & Conway, 2021). The main drivers of urban expansion across the nation have been population increase and internal migration. (Ishtiaque, Shrestha, & Chhetri, 2017; Poudel, 2013; Rai et al., 2020). The urban expansion causes a corresponding decrease in cultivated lands. The built-up area in Pokhara has increased mainly along the Phewa lakesides and highways (Pokhara to Kathmandu and Baglung highways) due to its location between mountainous and terai regions, which is an important east-west stage point in the trans-Himalayan trade route (Rai et al., 2020; Rimal et al., 2020, 2018). Moreover, Pokhara is a major tourist destination in Nepal that has surged urbanization (Rimal et al., 2018). Banko Janakari, Vol 32 No. 2 47 Kandel et al. Along with the rapid expansion of places from mid-hill districts such as Pokhara, extensive areas of agricultural land have been converted to urban in the cities of terai districts such as Bharatpur and Nepalgunj (Rimal et al., 2020). Urban areas near major population centers and thoroughfares, particularly those along the East-West and North- South highways and the Indian border, experienced the highest growth (Portnov, Adhikari, & Schwartz, 2007; Rimal et al., 2020). In the western terai of Nepal, including Bharatpur and Nepalgunj, the land was fragmented due to scattered settlements, and agricultural lands were plotted for residential purposes. The trend of the rapid growth of urban areas and associated losses of cultivated land is projected to continue to 2026 and 2036 (Rimal et al., 2020). Furthermore, the trend of urbanization was found to be higher in Bharatpur, with increased built-up area by 500% as compared to Pokhara, where built-up areas increased by 300% during the past 28 years, which is primarily due to migration from the mountain and hill regions into the low- lying plains (Rai et al., 2020). Since most cultivated areas in Nepal are frequently close to urban areas, farmers and urban dwellers compete for these lands. As a result, there is a high probability of transition of cultivated lands to built-up lands, as reported by various studies(Adhikari, Shrestha, Singh, Upadhaya, & Stapp, 2016; Ishtiaque et al., 2017; Rai et al., 2020; Rimal et al., 2020, 2018). The increase in new urban and semi-urban areas may raise the area’s temperature, leading to UHIs. It may also potentially influence local weather conditions, such as the presence of fog, humidity levels, and wind patterns, posing health hazards to the urban people and ecosystem. Conclusions LST and NDVI variations were observed during the study period between 2000 and 2019. LST images indicate a significant increase in LST from urban centers to nearby rural areas. This indicates that the temperature of the core city centers is likely to be associated with the temperature of the surrounding semi-urban areas if the increasing trend of LST continues. The increasing patterns of LST and UHI suggest that these metropolitan cities have experienced an unsystematic conversion of open and agricultural lands into urban areas. Understanding the increase in LST and its relation to NDVI change assists in developing strategies for controlling the UHI phenomenon. For instance, retaining vegetation or planting trees in open or barren lands and regulating land use change practices assist in reducing LST. The urban planners and government agencies should continue to compile and periodically monitor LST, NDVI, and UHIs indicators for the sustainable development of these cities. Additionally, it is now possible to model LST variation and analyze UHI at higher resolutions and in greater detail, thanks to the availability of free spatial data and the decline in processing costs. So, there is a need to conduct more studies on deriving LST, NDVI, and UHI phenomena in different cities located in different regions of Nepal to understand the pattern of UHI all over the country. Although this study used readily available data and robust methodology selecting three different locations of Nepal as a study area, it still has a few limitations. For example, in this study, we did not consider several probable drivers of UHI, including local population growth, policy, and topography, due to a lack of consistent data for associating with spatially-explicit land-cover change. Therefore, future studies on land-change scenarios, demographic factors, and municipal border expansion should mainly be considered for studying UHI expansion patterns in Nepal. Author Contributions Buddhi Gyawali and Smriti Kandel conceptualized the topic; Methodology, Jeremy Sandifer and Smriti Kandel; Analysis, Sandesh Shrestha and Smriti Kandel; Writing-original Draft Preparation, Smriti Kandel; Writing-review & editing, Buddhi Gyawali, Jeremy Sandifer, Suraj Upadhaya and Sandesh Shrestha; All authors have read and agreed to the published version of the manuscript. Banko Janakari, Vol 32 No. 2 48 Kandel et al. Conflict of Interest The authors declare no conflict of interest. References Acharya, K. R. (2018). Urban Planning and Economic Development of Nepal. Urban Planning and Economic Development in Nepal, 32(2), 107–118. https://doi. org/10.3126/tuj.v32i2.24706 Adhikari, S., Shrestha, S. M., Singh, R., Upadhaya, S., & Stapp, J. R. (2016). Land use change at sub-watershed level. Hydrology Current Research, 7(3), 3–7. https://doi. org/10.4172/2157-7587.1000256 Aksha, S. K., Juran, L., & Resler, L. M. (2018). Spatial and temporal analysis of natural hazard mortality in Nepal. Environmental Hazards, 17(2), 163–179. https://doi.org/1 0.1080/17477891.2017.1398630 Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night- time urban heat Island in Birmingham, UK: A comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2). https://doi.org/10.3390/rs8020153 Bakrania, S. (2015). Urbanisation and urban growth in Nepal. GSDRC Helpdesk Research Report, 1–24. Retrieved from www.gsdrc.org Bhattarai, K., & Conway, D. (2021). Contemporary Environmental Problems in Nepal: Geographic Prespectives. In Advances in Asian Human-Environmental Research. Cao, L., Li, P., Zhang, L., & Chen, T. (2008). Remote sensing image-based analysis of the relationship between urban heat island and vegetation fraction. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 37. Chapagain, D. (2018). Present situation of urbanization in Nepal. International Journal of Humanities, Social Sciences and Education, 5 (12), 170–175. https:// doi.org/10.20431/2349-0381.0512017 Dussault, G., & Franceschini, M. C. (2006). Not enough there, too many here: Understanding geographical imbalances in the distribution of the health workforce. Human Resources for Health, 4, 1–16. https://doi.org/10.1186/1478-4491-4-12 Grover, A., & Singh, R. B. (2015). Analysis of urban heat island (Uhi) in relation to normalized difference vegetation index (ndvi): A comparative study of delhi and mumbai. Environments - MDPI, 2(2), 125–138. https://doi.org/10.3390/ environments2020125 Guha, S., & Govil, H. (2020). An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability, 23 (2), 1944–1963. https://doi.org/10.1007/ s10668-020-00657-6 Guha, S., Govil, H., & Mukherjee, S. (2017). Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India. Journal of Applied Remote Sensing, 11 (03), 1. https://doi.org/10.1117/1.jrs.11.036020 Heiling, G. K. (2012). World urbanization prospects: the 2011 revision. New York. Hulley, M. E. (2012). The urban heat island effect: Causes and potential solutions. Metropolitan Sustainability, 79–98. https:// doi.org/10.1533/9780857096463.1.79 Ishtiaque, A., Shrestha, M., & Chhetri, N. (2017). Rapid urban growth in the kathmandu valley, nepal: Monitoring land use land cover dynamics of a himalayan city with landsat imageries. Environments - MDPI, 4(4), 1–16. https://doi.org/10.3390/ environments4040072 Banko Janakari, Vol 32 No. 2 49 Kandel et al. Kant, Y., Bharath, B. D., Mallick, J., Atzberger, C., & Kerle, N. (2009). Satellite-based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi, india. Journal of the Indian Society of Remote Sensing, 37 (2), 201–214. https://doi.org/10.1007/ s12524-009-0030-x KC, S. (2020). Internal Migration in Nepal BT - Internal Migration in the Countries of Asia: A Cross-national Comparison (M. Bell, A. Bernard, E. Charles-Edwards, & Y. Zhu, Eds.). https://doi.org/10.1007/978- 3-030-44010-7_13 Kim, H. H. (1992). Urban heat island. International Journal of Remote Sensing, 13 (12), 2319–2336. https://doi. org/10.1080/01431169208904271 Kuang, W., Liu, Y., Dou, Y., Chi, W., Chen, G., Gao, C., … Zhang, R. (2015). What are hot and what are not in an urban landscape: quantifying and explaining the land surface temperature pattern in Beijing, China. Landscape Ecology, 30(2), 357–373. https://doi.org/10.1007/s10980-014-0128- 6 Li, C., Shen, D., Dong, J. shan, Yin, J. yuan, Zhao, J. juan, & Xue, D. (2014). Monitoring of urban heat island in Shanghai, China, from 1981 to 2010 with satellite data. Arabian Journal of Geosciences, 7 (10), 3961– 3971. https://doi.org/10.1007/s12517-013- 1053-8 Li, W., Cao, Q., Lang, K., & Wu, J. (2017). Linking potential heat source and sink to urban heat island: Heterogeneous effects of landscape pattern on land surface temperature. Science of the Total Environment, 586, 457–465. https://doi. org/10.1016/j.scitotenv.2017.01.191 Lo, C. P., & Quattrochi, D. A. (2003). Land-use and land cover change, urban heat island phenomenon, and health implications: A remote sensing approach. Photogrammetric Engineering & Remote Sensing, 69(9), 1053–1063. Masuoka, E., Fleig, A., Wolfe, R. E., & Patt, F. (1998). Key characteristics of MODIS data products. IEEE Transactions on Geoscience and Remote Sensing, 36 (4), 1313–1323. https://doi.org/10.1109/36.701081 Mishra, B., Sandifer, J., & Gyawali, B. R. (2019). Urban heat island in Kathmandu, Nepal: Evaluating relationship between NDVI and LST from 2000 to 2008. International Journal of Environment, 8 (2), 55–69. Pandey, G. K. (2013). Evolution of Municipalities in Nepal: Challenges and Planning. Tribhuvan University Journal, 28(1–2), 61–74. https://doi.org/10.3126/tuj.v28i1- 2.26216 Portnov, B. A., Adhikari, M., & Schwartz, M. (2007). Urban growth in Nepal: Does location matter? Urban Studies, 44(5–6), 915–937. https://doi. org/10.1080/00420980701258118 Poudel, K. P. (2013). National development plan and urbanization in Nepal. 12th International Asian Urbanization Conference on Urban Dynamics, Environment and Health: Challenges for the Twenty First Century. Department of Geography, Banaras Hindu University, India, (December 2013), 1–16. Qiao, Z., Liu, L., Qin, Y., Xu, X., Wang, B., & Liu, Z. (2020). The impact of urban renewal on land surface temperature changes: A case study in the main city of Guangzhou, China. Remote Sensing, 12 (5), 1–15. https://doi.org/10.3390/rs12050794 R Core Team. (2020). R: A language and environment for statistical computing. Retrieved from https://www.r-project.org/ Rai, R., Yili, Z., Paudel, B., Khanal, N. R., & Acharya, B. K. (2020). Satellite image- based monitoring of urban land use Banko Janakari, Vol 32 No. 2 50 Kandel et al. change and assessing the driving factors in Pokhara and Bharatpur Metropolitan Cities, Gandaki Basin, Nepal. Journal of Resources and Ecology, 11 (1), 87. https://doi.org/10.5814/j. issn.1674- 764x.2020.01.009 Rijal, S., Rimal, B., Stork, N., & Sharma, H. P. (2020). Quantifying the drivers of urban expansion in Nepal. Environmental Monitoring and Assessment, 192(10). https:// doi.org/10.1007/s10661-020-08544-3 Rimal, B., Sloan, S., Keshtkar, H., Sharma, R., Rijal, S., & Shrestha, U. B. (2020). Patterns of historical and future urban expansion in Nepal. Remote Sensing, 12 (4), 1–22. https://doi.org/10.3390/rs12040628 Rimal, B., Zhang, L., Keshtkar, H., Sun, X., & Rijal, S. (2018). Quantifying the spatiotemporal pattern of urban expansion and hazard and risk area identification in the Kaski District of Nepal. Land, 7 (1). https://doi.org/10.3390/land7010037 Sarif, M. O., Rimal, B., & Stork, N. E. (2020). 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, 9 (12). https://doi. org/10.3390/ijgi9120726 Shi, Y., Xiang, Y., & Zhang, Y. (2019). Urban design factors influencing surface urban heat island in the high-density city of guangzhou based on the local climate zone. Sensors (Switzerland), 19 (16). https://doi. org/10.3390/s19163459 Sridhar, M. B., Sathyanathan, R., & Sree Shivani, N. (2020). Spatial and temporal structure of urban heat island in Ludhiana city. IOP Conference Series: Materials Science and Engineering, 912 (6). https://doi. org/10.1088/1757-899X/912/6/062072 Teodoro, A. C., Duarte, L., Barradas, P., Mateus, T. L., & Neto, Z. (2018). NDVI and LST extraction of MODIS data under a GIS open source application - Rickettsia study case in Angola. Free and Open Source Software for Geospatial (FOSS4G) Con- ference Proceedings, 18. Retrieved from https://scholarworks.umass.edu/foss4g/ vol18/iss1/7?utm_source=scholarworks. umass.edu%2Ffoss4g%2Fvol18%2Fis- s1%2F7&utm_medium=PDF&utm_cam- paign=PDFCoverPages Viana, C. M., Oliveira, S., Oliveira, S. C., & Rocha, J. (2019). Land use/land cover change detection and urban sprawl analysis. In Spatial Modeling in GIS and R for Earth and Environmental Sciences. https://doi. org/10.1016/b978-0-12-815226-3.00029-6 Wang, K., Wan, Z., Wang, P., Sparrow, M., Liu, J., & Haginoya, S. (2007). Evaluation and improvement of the MODIS land surface temperature/emissivity products using ground-based measurements at a semi-desert site on the western Tibetan Plateau. International Journal of Remote Sensing, 28(11), 2549–2565. https://doi. org/10.1080/01431160600702665 Wang, R., Gao, W., & Peng, W. (2020). Downscale MODIS land surface temperature based on three different models to analyze surface urban heat island: A case study of Hangzhou. Remote Sensing, 12 (13). https://doi.org/10.3390/rs12132134 Wang, Y., & Akbari, H. (2017). Urban heat island and mitigation solutions evaluation in cold climates: a case of Montreal. In Adv. En- viron. Res. Retrieved from https://www. researchgate.net/profile/Yupeng_Wang7/ publication/314874350_URBAN_HEAT_ ISLAND_AND_MITIGATION_SOLU- TIONS_EVALUATION_IN_COLD_CLI- MATES_A_CASE_OF_MONTREAL/ links/58c6d3624585150ab4207471/ URBAN-HEAT-ISLAND-AND-MIT- IGATION-SOLUTIONS-EVALUA- TION-IN-COLD- Banko Janakari, Vol 32 No. 2 51 Kandel et al. Welford, M. R., & Yarbrough, R. A. (2021). Human environment interactions an introduction. Yuan, X., Wang, W., Cui, J., Meng, F., Kurban, A., & De Maeyer, P. (2017). Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Scientific Reports, 7 (1), 3–10. https:// doi.org/10.1038/s41598-017-03432-2 Yue, W., Xu, J., Tan, W., & Xu, L. (2007). The relationship between land surface temperature and NDVI with remote sensing: Application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, 28 (15), 3205–3226. https://doi. org/10.1080/01431160500306906