Assessment of Land Surface Temperature (LST) using MODIS MOD11A2 Thermal Satellite Images Using Zero to Null Pixel Averaging Method for the Bengaluru Urban District

doi:10.21203/rs.3.rs-1932983/v1

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Assessment of Land Surface Temperature (LST) using MODIS MOD11A2 Thermal Satellite Images Using Zero to Null Pixel Averaging Method for the Bengaluru Urban District

https://doi.org/10.21203/rs.3.rs-1932983/v1

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In developing countries like India, rapid urbanisation is inevitable. Bengaluru urban district is one of the rapidly growing cities and newer settlement clusters are keep on growing to accommodate the migrating people from rural areas. One of the ill effects of urbanisation is an increase in impervious areas leading to higher land surface temperature (LST). Reported Studies have mentioned that Landsat thermal data has been utilised for the preparation of LST maps and usage of MODIS data is very limited. The present study attempted to prepare LST maps using 8-day average MODIS MOD11A2 images for the years 2001, 2006, 2011, 2016 and 2021. “Zero to Null” pixel average method was adopted to prepare the average LST maps yearwise. From the LST maps, it was found that the highest and lowest LSTs observed were 27.41°C and 35.95°C respectively for the years selected. Generated histograms for the LST indicate that the impervious areas have more pixels falling under the category from high to very high-class category.

Land Surface Temperature (LST) maps

Zero to Null Pixel Averaging method

MODIS MOD11A2 LST images

Histograms for LST

Cities are growing at a tremendous rate, people moving from villages to urban areas for better job opportunities, social security and comfortable living conditions. Urban areas include 50% of the world population and it is expected that this number will reach around sixty-six in the year 2050 [1]. According to United Nations, globally, the rapid increase in the number of megacities (greater than ten million) are expected to be 43 [2]. India and China alone have contributed to the population by around 2.75 billion. 40% of the total population of India and China constitutes people under the age of 24. This large segment of the population prefers to live in urban cities [3]. Bengaluru is a rapidly growing city and the urban expansion is never-ending process owing to social and economic advantages due to broader job opportunities [4]. One of the important ill effects of urbanisation is an increase in land surface temperature (LST) of urban areas. The climate at the regional level in cities depicts the connection between the urban land use such as roads, buildings and impervious areas. Hotter land surface temperature (LST) is a dominant position in urban areas compared to the underdeveloped rural surroundings that have more vegetated areas [5]. LST is the radiative surface temperature of the land resulting from solar radiation. LST determines the thermal radiance emission from the layer of land where solar energy interacts with the surface topographical features and heats the ground or vegetated canopy [6, 7]. The variant nature of LST for the urban landscape depends on the different land use/land cover features. Response to the different land use/land cover features differs, natural features have lower LST than man-made features [8]. Seasonality also has influenced the surface temperature of the urban fabric, temperature differences between the vegetated areas and urban fabric are greater during the summer than in winter [9]. Landsat thermal data have been utilised by most of the studies both in developed and developing countries, but the utilization of MODIS data for LST mapping is limited especially in India compared to other developed countries. In the present study, the attempt has been made to compare the land surface temperature for every 5 years 2001, 2006, 2011, 2016 and 2021, analyse its influence for the Bengaluru urban district using MODIS MOD11A2 thermal satellite images.

2.1 Study Area

The study area selected for the present study is Bengaluru urban district which is located at Latitude 12.9700° N, and longitude 77.6536° E. This district is in the southeast of the state. Administrative center of this district is Bangalore city and is the Capital City of Karnataka, the district is divided into Bengaluru North, Bengaluru South, Bengaluru East and Anekal. It is also known as “Garden City”, “City of Parks” and “Silicon Valley” of India. The study area covers 2196 Sq. kms is shown in Fig. 1.

2.2 Data used

Thermal remote sensing images from Moderate Resolution Imaging Spectroradiometer (MODIS) were used. Land Processes Distributed Active Archive Center (LPDAAC) distributes MODIS data products to users free of cost [10]. MOD11A2 Version-6 eight-day average land surface temperature (LST) images were downloaded from LPDAAC and used. MOD11A2 data has 1 km spatial resolution and is available in a 1200 x 1200 km grid. Each pixel value represents the average of eight days LST for that location. Both daytime and night-time LST were provided by MOD11A2. However, in the present study only daytime data were used for the LST analysis.

2.3 Methods

For the years, 2001, 2006, 2011, 2016 and 2021 8-day average MODIS MOD11A2 LST images were downloaded. To calculate the average LST for each year mentioned, in ArcGIS 10.8 software, the map algebra tool has been used. “Zero to Null” pixel averaging method has been used in the study. To avoid the wrong summation while calculating average pixelwise, for the zero pixels, the first step is to eliminate the zero pixels to null pixels. Zero pixels values are converted into null pixels using the command “SetNull” using the map algebra tool for all the images selected so that zero pixels will not be considered in LST average calculation. Finally, using the map algebra function average LST maps were prepared. Then using the “attribute table command”, an attribute table was created. In the table, a new field “Temp” has been created and converted the raw DN values into Degree celsius. To convert the raw DN values to LST in degrees Celsius, the following expression (Eq. 4.1) was executed using the field calculator. The equation was obtained from MODIS datasheet and Senanayake [11]. The mean LST map in degrees Celsius would then be clipped to the study area.

T (^oC) = DN * 0.02–273.15 (4.1)

Where, T - Land surface temperature in degree celsius, DN - Digital number and 0.02 is a scale factor as per MOD11A2 data sheet.

The land surface temperature maps prepared for the years 2001, 2006, 2011, 2016 and 2021 are shown in Fig. 2. From the LST maps, it was found that the highest and lowest LSTs observed were 27.41°C and 35.95°C respectively. Hence the LST was divided into five classes starting from less than or equal to 28°C to 36°C with a range of 2°C interval for each class, i.e., less than 28°C (Very Low), 28.01-30°C (Low), 30.01-32°C (Medium), 32.01-34°C (High), 34.01-36°C (Very High). As seen in Fig. 2, wherever the open lands and vegetated areas have shown lower LST when compared to impervious areas. In all the selected years, overall, it could be seen that the western part of the district has LST in the class high to very high. One interesting phenomenon that could be seen in Fig. 2 is in the year 2016 the LST was very high in the western parts of the district conversely in the year 2021, LST is falling under the Medium category. The main reason behind the drastic change in LST is due to the lockdown, as it restricted the vehicular movements to substantial extent. This phenomenon might have resulted in the reduction of air pollution to a minimum level which influences LST reduction. Similarly, in the south-eastern parts of the district, few pixels of green color could be seen and that is the open area and water bodies that exhibit lower LST. Thus, we can conclude that in the areas where forest cover, vegetation, water bodies, wetlands and estuaries are there, the LST was minimum. On the other side, in areas where the considerable built-up area is there, LST was found to be very high.

The histograms of the selected years are shown in Fig. 3, it can be seen from the histograms that for the years 2001, 2006, 2011 and 2016 most of the pixels are falling in the medium to very high-class category. It indicates that most of the settlement areas have higher LST. But for the year 2021, the maximum number of pixels is falling under the low to Medium class category. Lockdowns made a significant effect on the LST, due to the reduction of vehicle movements which resulted in reducing the air pollution due to which LST might have come down to the maximum extent. Minimum, mean and maximum LST found were shown in Fig. 4. It can be seen from Fig. 4, the year 2016 was hotter compared to other years. Urbanisation was rapid in the past decade i.e., from 2011 to 2020 and the huge increase in settlements is the major reason behind the higher LST.

Urbanisation is inevitable in a developing country like India. Important ill effect of urbanisation is increase in impervious areas leads to increase in land surface temperature (LST). In the present study, MODIS MOD11A2 thermal images has been used to prepare the land surface temperature maps for the years 2001, 2006, 2011, 2016 and 2021. Results clearly indicate that settlements area having higher LST and areas having open lands and vegetation, lower LST. LST maps prepared for the various years in this study which will be helpful for urban planners and policy makers to continuous monitoring of city where the settlements clusters being developed and mitigation measures can be adopted.

Data Availability: All data generated or analysed during this study are included in this published article [and its supplementary information files].

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No competing interests reported.

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Assessment of Land Surface Temperature (LST) using MODIS MOD11A2 Thermal Satellite Images Using Zero to Null Pixel Averaging Method for the Bengaluru Urban District

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Abstract

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1. Introduction

2. Materials And Methods

2.1 Study Area

2.2 Data used

2.3 Methods

3. Results And Discussion

4. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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