ASSESSMENT OF LST VARIATION IN KATHMANDU, NEPAL

Rapid Urbanization replaces the natural vegetation with impervious surfaces (Xu et al. 2013)). The uneven distribution of natural vegetation, open spaces, water bodies, and impervious surface disturbs the natural interaction between surface and atmosphere. The increase in impervious surface alters the energy radiation and increases the land surface temperature (LST) (EPA, 2008). The green vegetation increases the evapotranspiration and photosynthesis, and decreases the temperature (Sharifi & Lehmann 2014).

Kathmandu, the capital city of Nepal is urbanising rapidly (Chitrakar et al. 2016). The urbanization rate peaked during civil unrest which was observed from 1996 to 2006 (Thapa 2017). Besides, economic opportunities in the city and population growth in the fringe area supported the urban expansion (Thapa & Murayama 2010). The current urban development trend is in critical stage, which has caused unprecedented stress on environment (Thapa & Murayama 2012) and has resulted increase in LST of Kathmandu valley.

The extent of urban growth from 1978 to 2000 was 450 percent in Kathmandu (Haack & Rafter 2006).

The study is carried out at both spatial and temporal scale. This study assesses the land surface temperature (LST) of Kathmandu, Nepal in the winter season (November to March), in six different years,1995, 2000, 2005, 2010, 2015, and 2019. The specific objectives are: ·     

•  To compare the LST of Kathmandu from 1995 to 2019 at five years interval.

• To assess the spatial variation of LST from core urban to the fringe.

Kathmandu valley (Figure 1), is one of the most populated metropolitan city of Nepal (Thapa & Murayama 2010). The valley is located between latitudes 27°32’13” and 27°49’10” north and longitudes 85°11’31” and 85°31’38” east (Mohanty 2011). Kathmandu experiences pre-monsoon, monsoon, post-monsoon, and winter seasons (Aryal et al. 2008). The annual maximum and minimum temperature is between 29.7°C in May and 2°C in January (Thapa et al. 2008).

The Landsat TM and Landsat 8 with less than 10% cloud cover were downloaded from the site of the United States Geological Survey ( https://earthexplorer.usgs.gov/ ). The thermal band was retrieved from Landsat C1 level 1 data, and Near-infrared and Red band were downloaded from Landsat C1 level 2 data. The shapefile of Kathmandu was referenced from (Thapa & Murayama 2010). 

12 Landsat images from the month December, January, February and March were retrieved for the LST assessment. Landsat images of summer for the year 2010, 2015 and 2019 were covered by cloud in the study area. So, only the winter data were taken to maintain the consistency. The details of the data is given in Table 1.

ArcGIS 10.5 software was used to calculate the LST. Raster Calculator (Spatial Analyst), Cell Statistics (Spatial Analyst), Clip (Data Management), Multiple Ring Buffer (Analysis) tools were used in analysis. Multiple ring buffer was done at 500 m interval from the centre of the city. Raster calculator tool was used to calculate number of intermediate outputs viz: radiance, atmospheric brightness, proportional vegetation, emissivity and LST. 

Top of Atmospheric Radiance (TOA): For calculating the TOA, thermal band 6 of Landsat TM and thermal band 10 of Landsat 8 were taken. The equation used was, TOA (L) = ML * Qcal + AL, where, ML is the band-specific multiplicative rescaling factor, 𝑄cal is the Band 10 or Band 6 image, 𝐴𝐿 is the band-specific additive re-scaling factor (Table 2).

Conversion of Radiance (TOA) to Brightness Temperature (BT): The equation used for calculation of BT was, BT = (K2 / (ln (K1 / L) + 1)) − 273.15, where 𝐾1 and 𝐾2 are the band-specific thermal conversion constants from the metadata (Table 2).

Normalized Difference Vegetation Index (NDVI): Landsat visible (Red) bands (Band 4 from Landsat TM and Band 5 from Landsat 8) and near-infrared (NIR) bands (Band 3 from Landsat TM and Band 4 from Landsat 8) were used for calculating NDVI. The equation used was, NDVI=(NIR-Red)/(NIR+Red).

Proportion of Vegetation (Pv): The Pv was calculated using the equation, Pv = Square ((NDVI – NDVImin) / (NDVImax – NDVImin)), 1 was taken for NDVImax and -1 was taken for NDVImin.

Land Surface Emissivity (ε): For calculation of land surface emissivity, the equation used was, ε= 0.004 * Pv + 0.986, where 0.986 is for correcting the value of the equation.

Land Surface Temperature (LST): The LST was computed using the equation, LST = (BT / (1 + (𝜆* BT /𝜌) * Ln(ε))), where 𝜆 (0.00001145 for Landsat TM and 0.000010895 for Landsat 8) is the wavelength of emitted radiance, 𝜌 (0.01439) is the constant value.

Automation tool Iterate Raster in model builder was used while calculating Radiance (Figure 3), Atmospheric Brightness (Figure 4), Proportion Vegetation (Figure 6), Emissivity (Figure 7) and Clipping (Figure 8) the LST of Kathmandu. Python (Figure 5) was used for calculating the normalized difference vegetation index (NDVI). The steps followed to compute LST are presented in flow chart (figure 2).

• Automation in model builder was executed to calculate Radiance from Thermal band of Landsat TM and Landsat 8.

• Automation in model builder was carried out for computing Brightness Temperature from Radiance.

• Python was used to calculate NDVI from Landsat TM and Landsat 8. Looping was done separately for Landsat TM and Landsat 8.

• In model builder automation was done to compute the Proportional vegetation from NDVI.

• Automation was done in the model builder to calculate Emissivity from Proportional Vegetation.

• For clipping the LST of Kathmandu, automation was executed in model builder.

In 1995, Kathmandu observed 30.5 °C as maximum temperature and 4°C as minimum temperature as shown in Figure 9. The majority of city area observed temperature between 17°C to 18°C. Few locations like Airport and city centre had witnessed the maximum temperature.

The year 2000 is observed to be warm. For this year, minimum temperature is observed to be 3°C and maximum temperature is 31°C. The majority of city area had experienced the temperature between 21°C to 22°C (Figure 10). Airport, city centre and settlements area had highest LST.

In 2005, maximum temperature had peaked to 31.5°C and minimum temperature had lowered to 4°C. Inside the city area, majority of the area had temperature between 18 °C to 20 °C. The temperature around the airport and city centre were higher during this year (Figure 11).

In 2010, the maximum and minimum temperature were 25°C and 5°C, respectively. The majority of the area had observed the temperature between 19°C to 20°C. City centre, the airport and settlements area had higher temperature (Figure 12).

The year 2015 is observed to be a warmer year. The minimum and maximum temperature were 9°C and 28 °C, respectively. In majority of the city area, temperature was observed between 18°C to 20°C (Figure 13).

The maximum and minimum temperature of 2019 are 27°C and 0°C, respectively. The majority of city area had observed the temperature between 20.5°C to 21.5°C. The core of the city, airport area and settlements area inside the city observed highest temperature in 2019 (Figure 14).

The core city observed highest temperature in all the year. In 1995, the fringe area had less hot-spots however, the hot-spots increased in 2000, 2005, 2010 and 2015. The surrounding hills have observed relatively lesser temperature than the urban area in all the study year (Figure 15).

The assessment of land surface temperature of Kathmandu showed the temperature variation from 1995 to 2019. In the year 1995, the majority of the city observed the temperature between 17 °C to 18°C whereas in 2019 the temperature is observed between 20.5°C to 21.5°C. The analysis showed that the average temperature has been increasing continually from the year 1995 to 2019. The study of Mishra et al. (2019) revealed that temperature in Kathmandu increased by 0°C to 2°C between 2000 and 2018. (Wei et al. 2019) observed annual increment in the mean annual air surface temperature is from between 1970 to 2010 is 0.05°C. Similarly, the hot-spots in the capital are increasing every year. In 1995, few hot-spots were spotted but after 1995 the hot-spots inside the city increased dramatically, increasing temperature in semi-urban areas (Mishra et al. 2019).

After the onset of civil unrest, the city observed substantial migration from rural areas (ICIMOD et al. 2007). Ishtiaque et al. (2017) revealed that the conversion of land was increased by 120% between 1989 to 1999. The haphazard settlement in the fringe area expanded the city area. This expansion of the city modified the natural surface and increased the impervious neighbourhoods. In addition to this, density of city increased decreasing the green open patches which ultimately contributed to increase the temperature in the fringe.

Topographically Kathmandu is a valley surrounded by high hills. The temperature in the hills is observed lower than the city because most of the hills are covered by vegetations. Despite this, few places observed high LST because of shadow and cloud cover Landsat images.

The LULC of Kathmandu changed rapidly in last few decades. Haphazard urbanization due to the heavy influx of people from the rural area made it more severe. The shrinking of green open space contributed the urban heat island. The temperature of Kathmandu is in increasing from the year 1995 to 2019. The increase in temperature will have negative impacts in environment and in the economic sector. Hence to cope this problem, unplanned urbanization should be stopped, and vegetation patches should be re-established. Proper planning should be done in new developing cities. Concerned authority needs to plan and implement the strategies to stop migration from rural areas. 

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