Study of Urban heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters

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1 Study of Urban heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters Aneesh Mathew a,*, Rishabh Chaudhary a, Neha Gupta a, a Research Scholar, Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur (Rajasthan) Sumit Khandelwal b, Nivedita Kaul b b Assistant Professor, Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur (Rajasthan) Abstract Rapid urbanization leads to an increase in land surface temperature (LST) which is governed by surface heat fluxes and is an important parameter in global climate change. The objectives of the present study are to analyze the spatial pattern of the night land surface temperature (LST) observed by remote sensing satellites and to analyze the effect of vegetation and urbanization over LST of Ahmedabad city, India. Normalized difference vegetation index (NDVI) and Enhanced vegetation index (EVI) have been used as an indicator for vegetation cover and Normalized difference built-up index () for level of urbanization. Landsat 5 Thematic Mapper (TM) and MODIS data were used for the retrieval of LST and the calculation of normalized difference built-up index (), normalized vegetation index (NDVI) and Enhanced vegetation index (EVI). From the present study, it has been observed that the areas with high LST were located principally in central built-up areas. As per the results of assessment of relationship between LST with NDVI, EVI and shows that LST had the negative correlation with NDVI and EVI and positive correlation with which means that green land can reduce the urban heat island (UHI) effect whereas built-up areas can strengthen the effect of UHI. It has been concluded that NDVI, EVI and can be used to investigate the risk of Urban Heat Island (UHI) and may help city planners better prepare for possible impacts of urban environmental change. Key Words Land Surface Temperature (LST), Urban Heat Island (UHI), NDVI, EVI, MODIS Introduction Urbanization results in global climate change in various ways and across multiple dimensions. Rapid urbanization has led to a significant increase in the number of urban population worldwide. Recent research reported that urban population is expected to reach 65 % by 2025 [1] and in 2008 more than half of the world s population were urban dwellers and 81% of the world population will live in urban areas by 2030 [2]. Natural landscape will transform to an impervious surface due to the significant growth of urban areas that will result in the reduction of green cover [3, 5] and, consequently, an increase in land surface temperature. The degree of Surface Urban Heat Island (SUHI) varies with different impervious surfaces, variation of vegetation cover and climatic conditions like season, wind and rainfall. Formation of the urban heat island (UHI) is one attribute of urban land transformation of the nature lands into impervious built-up lands may have significant impacts on the ecosystem, human health, hydrologic system, biodiversity and local climate. Land 126

2 surface temperatures can be used as the key parameter for representing Urban Heat Island (UHI) phenomenon. While surface temperatures can be used as both higher and more variable than concurrent air temperatures due to the complexity of the different land surface types in urban environments and variations in urban topography [6, 7]. The primary cause of the urban heat island is the increase of artificial environments which has led to the significant changes of land use and land cover, changes the energy budget at the land surface, produces a great amount of anthropogenic waste heat, and results in a series of changes in the urban environment [8]. These types of changes are usually significant in the urban heat island (UHI), which is the phenomenon where higher atmospheric and surface temperatures occur in urban areas than in the surrounding non-urban areas due to urbanization, particularly at night [9]. Land use and land cover is changed constantly by human activities from the past to the present and is one of the most visible reasons of environmental change [10, 11]. Remotely sensed data of land surface temperature, vegetation indices and other surface characteristics have been widely used to study UHI phenomenon [12-14]. The objectives of the present study are to construct spatial data of land surface temperature (LST), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and normalized difference built-up index () from satellite imagery and to analyze the relationship between thermal environment and spatial characteristics. 1. Study Area Ahmedabad, located on the bank of river Sabarmati, is the largest city in the Gujarat state of India. Ahmedabad is the fifth largest city and seventh largest metropolitan area of India with a population of 6,352,254 (Census of India 2011). Ahmedabad is situated between WikiMiniAtlas23 02 N E / N E / 23.03; coordinates. Climatic condition of the Ahmedabad is semi-arid and hot. The climate of the Ahmedabad is extremely dry during the summer season from March to mid-june, average recorded maximum temperature of summer is 42 o C and average minimum temperature is 28 o C, maximum average temperature during winter is 30 o C and minimum average temperature is 15 o C. Ahmedabad is situated on dry and sandy soil which absorbs heat during day time. This absorbed heat is than released during the night, thereby making the environment of the city hotter than its surrounding rural areas. This difference in temperature between urban and rural areas is called temperature gradient, making city environment too hostile and difficult to live. This environmental impact created by temperature gradient is called urban heat island (UHI) effect of the city. Thus during summers the temperature of the city go beyond its threshold level creating much warmer area in the city. As UHI phenomenon indicates warmer thermal climate of urban land, compared to non-urbanized area, the study area must include sufficient non-urbanized/sub-urban area outside the urban area for UHI studies. The urban area boundary of Ahmedabad city has been derived by extracting urban area polygon from the MODIS yearly land cover type image (MCD12Q1) of The urban area polygon from this imagery has been converted by using raster to polygon conversion tool in ArcGIS. The length and width of urban area polygon (hereafter referred as urban boundary) is approximately 12 km in North-South direction and 17 km in East-West direction. A buffer of 5 km has been added to the urban boundary avoiding the influence of Gandhinagar to mark the boundary of study area and the study has been carried out for area falling within this boundary. The study area sufficiently includes non-urbanized/sub-urban areas and satellite towns of the city (figure 1). The study area covers approximately 750 square kms. The raster pixel size of LST 127

3 product is m and LST image of the study area has 804 pixels. Figure 1 shows the Google Earth image of the study area and urban area of Ahmedabad city. Figure 1: Google Earth image of the Ahmedabad study area 2. Data and Methodology Eight day, 1 km MYD11A2 and one day, 1 km MYD11A1 land surface temperature and emissivity product [8] and 16 day 1km MYD13Q1, vegetation indices product of MODIS Aqua of overlapping dates have been used for the present study. The study has been undertaken for the year The land surface temperature and emissivity product is available with quality flag which was checked to include only the good quality pixels in the analysis. Landsat Thematic Mapper (TM) images from 21 September 2009 have been selected for the present study for their good imaging quality. Table 1: Remote sensing data used for the present work Vegetation Indices MYD13Q1 MODIS Aqua 16 Day m Landsat - TM m The Landsat images have been geometrically rectified to the UTM projection system (datum WGS 84, Zone 43). The ground control points have been carefully selected in order to make sure that the 128 Remote Sensing Product Short Name Sensor Land Cover Type MCD12Q1 MODIS Land Surface Temperature and Emissivity Land Surface Temperature and Emissivity Platform Combined Aqua & Terra Temporal Resolution Yearly Spatial Resolution m MYD11A1 MODIS Aqua Daily m MYD11A2 MODIS Aqua 8 Day m

4 RMS errors lies below pixels. A second-order polynomial and the nearest neighbour resampling method have been employed for implementing the georectification. The digital numbers of the TM images have been converted to the ex-atmospheric reflectance using the methods provided by Chander and Markham and the Landsat 7 Science Data Users Hand book [15, 16]. The downloaded MODIS data is in HDF-EOS format and in Sinusoidal Projection System. The Earth gridded tile area of each MODIS image covers approximately 1100 km x 1100 km. Preprocessing of downloaded MODIS images has been done using MODIS Re-projection Tools (MRT). MRT is used for sub setting of the data to smaller area. The data has also simultaneously been reprojected from Sinusoidal projection to UTM Zone 43N projection system with WGS84 datum and has been reformatted from HDF-EOS to GeoTIFF format. The MODIS product MYD13Q1 gives vegetation indices at approximately m ground resolution, whereas MYD11A2 product for Night LST has a resolution of approximately m. In order to compare EVI with LST it is necessary to have both of them have same resolution so that EVI layers were aggregated to the same resolution as of LST layer and they were also snapped to LST image. Derivation of NDVI and from Landsat 5 TM The normalized difference vegetation index (NDVI) was used to identify vegetation cover in the study area. The NDVI is a measure of the amount of vegetation at the surface [17, 18]. The value of NDVI varied between -1 and +1. NDVI can be calculated by the following equation [19]. NDVI = NIR RED / NIR +RED Where RED = Red Reflectance; NIR = Near Infrared Reflectance. The normalized difference built-up index () was used to extract built-up areas. The is sensitive to the built-up areas and it can be determined by the following equation [20]. = MIR NIR / MIR + NIR Where NIR = Near Infrared Reflectance; MIR = Mid Infrared Reflectance. 3. Results and Discussions Figure 2: LST images of the study area for 2009, 264 th Day & 2009, Days (September) In the present study, MODIS eight days (2009, ( days)) and one day (2009, 264 th day) night LST data have been used for the retrieval of surface temperatures of the study area and have been shown in figure 2. It has been observed that urban area shows higher LST pixels compared to rural areas and the LST pattern has been found to be well distributed in the high density commercial, residential and industrial areas of Ahmedabad city which shows the clear picture of urban heat island 129

5 effect. The mean LST values of the study area for 2009, days and 2009, 264 th day is observed.659 and.451, respectively and UHI intensities (difference between the maximum and minimum temperature of the study area) values for 2009, days and 2009, 264 th day are 3.62K and 4.1K respectively. Statistical results of land surface temperature values for the two periods of the study area are shown in table 2. Table 2: Statistical Results of LST (K), NDVI, EVI and UHI Parameters Maximum Minimum Mean Standard Deviation LST One Day(K) LST Eight Days(K) NDVI Aggregated NDVI EVI Aggregated EVI Aggregated In the present study, Landsat TM and MODIS data have been used for the derivation of NDVI and EVI, respectively. It has been found that higher values of NDVI and EVI have been observed in agricultural fields in rural areas and comparatively lower values in built-up areas inside the city having less vegetation coverage. Sabarmathi River and lakes in Ahmedabad city have lower values of NDVI and EVI as shown in figures 3 and 4. The statistical results of NDVI and EVI of the study area are shown in table 2. Figure 3: Landsat TM Derived NDVI and Aggregated NDVI images of the study area for 2009, 264 th Day (September) Figure 4: MODIS Derived EVI and Aggregated EVI images of the study area for 2009, Days (September) 130

6 LST(K) MEAN LST(K) International Journal of Computer & Mathematical Sciences In the present study, Landsat TM data has been used for the calculation of. It has been found that higher values of is observed in high density built-up areas which includes commercial, residential and industrial areas within the city and lower values in agricultural fields and water bodies as shown in figure 5. The statistical results of of the study area are shown in table 2. Figure 5: Landsat TM Derived and Aggregated images of the study area for 2009, 264 th Day (September) The scatterplots of LST vs NDVI and LST vs EVI for (2009, 264 th day) and (2009, ( days)) are shown in figure 6 and 7 respectively. Regression analysis has been applied to find a relationship between LST with NDVI and EVI respectively. It has been observed that there is a negative correlation of LST with both NDVI and EVI, which interprets that green spaces can reduce the effect of UHI. Thus vegetation cover is an important parameter which the variations in surface temperature of the study area due to the difference in behaviour of land use/land cover types. Variations in surface temperature is very less in dense vegetated areas. The trend lines of scatter plots show negative relationship between LST and vegetation indices for both periods. From figure 6 and 7, it can be concluded that the relationship of mean LST with EVI and NDVI is linear and shows the negative relationship between mean LST and vegetation indices for both periods. The figures also show the coefficient of correlation (R 2 ) for the linear relationship. 301 LST vs NDVI.5 MEAN LST vs NDVI 30.5 y = x +.47 R² = NDVI - - NDVI Figure 6: LST vs NDVI and Mean LST vs NDVI scatterplots of the study area for 2009, 264 th Day (September).0 131

7 LST(K) MEAN LST(K) LST(K) MEAN LST(K) International Journal of Computer & Mathematical Sciences LST vs EVI MEAN LST vs EVI y = x +.77 R² = EVI EVI Figure 7: LST vs EVI and Mean LST vs EVI scatterplots of the study area for 2009, Days (September) The scatterplots between LST vs relationship for (2009, 264 th day) and (2009, ( days)) are shown in figure 8 and 9 respectively. The same regression analysis which has been used in above two relations is applied to find the relationship between LST and of the study area for the two periods. LST v/s scatterplots show an irregular and compacted pattern and a rising trend can be observed. The analysis in previous case showed negative trend but here it has been found showing a positive correlation between LST and which means that built-up areas can strengthen the effect of UHI. Whereas the higher vegetation covers in rural areas show relatively lower values of. The built up areas like roads, buildings, industries etc. have more imperviousness than the rural areas. Thus it can be deduced that has a direct relationship with the imperviousness of the surface. The trendlines of scatterplots show linear and positive correlation between LST and. Figures 8 and 9 show a linear and positive relationship between mean LST and along with the coefficient of correlation (R 2 ) for the linear relationship. The R 2 value for 2009, 264 th Day (September) is 4 and for 2009, Days (September) is 96, shows that one day analysis gives better result i.e. higher the R 2 value better is the result. The reason behind the better result for one day is over eight day period is that eight day period gives the average effect of land surface temperature over the study area. LST vs MEAN LST vs y = x +.27 R² =

8 NDVI LST(K) MEAN LST(K) International Journal of Computer & Mathematical Sciences Figure 8: LST vs and Mean LST vs scatterplots of the study area for 2009, 264 th Day (September) LST vs MEAN LST vs y = x +.47 R² = Figure 9: LST vs and Mean LST vs scatterplots of the study area for 2009, Days (September) The figures 10 and 11, shows a strong negative relationship between vegetation indices and, which means that higher the vegetation cover, lower will be the value and thus weakens the UHI effect. The figures also show the 3-D scatter plots relationship of LST vs. vs. NDVI and LST vs. vs. NDVI, respectively. NDVI vs LST vs vs NDVI LST(K) NDVI Figure 10: NDVI vs and Mean LST vs vs NDVI scatterplots of the study area for 2009, 264 th Day(September) 133

9 EVI International Journal of Computer & Mathematical Sciences EVI vs LST vs vs EVI LST(K) 0.8 EVI Figure 11: EVI vs and Mean LST vs vs EVI scatterplots of the study area for 2009, Days (September) 4. Conclusion In the present study, Landsat TM and MODIS data have been used for the derivation of four parameters i.e. LST, NDVI, EVI and for Ahmedabad city. The present paper investigated individual relationships of LST with the three parameters NDVI, EVI and. It has been concluded that most of the hotter pixels are located in the built up areas of Ahmedabad city whereas rural areas have lower LST pixels because of the presence of dense vegetation cover. The built-up areas and human activities show high Land Surface Temperature compared to green vegetation cover areas. There is a negative correlation of LST with NDVI and EVI which means that green spaces can reduce the effect of UHI and positive correlation between LST and which means that built-up areas can strengthen the effect of UHI. That is urban areas show smaller NDVI and EVI values and higher values compared to rural areas and there is a strong negative relationship between NDVI and EVI with in the study area. From the results, it has been concluded that rapid urbanization and urban sprawling contributes the major changes in the Land Surface Temperature. Therefore built-up areas like residential, commercial and industrial mainly account for UHI effect. References [1] I Douglas. The case for urban ecology. Urban Nat. Mag. 1992; 1, [2]UNFPA (2007). The state of world population 2007: Unleashing the potential of urban growth. United Nations Population Fund, United Nations Publications 1 pp. [3] HA Choi, WK Lee and WH Byun. Determining the effect of green spaces on urban heat distribution using satellite imagery. Asian J. Atmos. Environ. 2012; 6, [4] OR García-Cueto, AT Martínez and GB Morales. Urbanization effects upon the air temperature in Mexicali, B.C., México. Atmósfera 2009; 22, [5] KS Kumar, PU Bhaskar and K Padmakumari. Estimation of land surface temperature to study urban heat island effect using landsat ETM+image. Int. J. Eng. Sci. Tech. 2012; 4, [6] Nichol, J. (1996). High-resolution surface temperature patterns related to urban morphology in a tropical city: a satellite-based study. Journal of Applied Meteorology, 35(1), [7] Streutker, D. R. (2002). A remote sensing study of urban heat island of Houston, Texas. International Journal of Remote Sensing, 23(13),

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