Assessment of urban heat islands (UHI) of Noida City, India usingmulti-temporal satellite data

Sustainable Cities and Society 22 (2016) 19–28

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Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data Noyingbeni Kikon a , Prafull Singh a,∗ , Sudhir Kumar Singh b , Anjana Vyas c a b c

Amity Institute of Geo-Informatics and Remote Sensing, Amity University, Sector 125, Noida, India K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Allahabad 211002, India Faculty of Technology, CEPT University, Ahmedabad, India

a r t i c l e

i n f o

Article history: Received 21 October 2015 Accepted 4 January 2016 Available online 6 January 2016 Keywords: Thermal data Urban heat island Land surface temperature Noida

a b s t r a c t In the present research work an integrated use of Landsat thermal data sets of year 2000 and 2013, field data and meteorological observation were used to assess the temporal changes in rising trends of urban heat island (UHI) in Noida city, India. The temperature estimation was performed on the basis of grid level analysis and compared with the land cover pattern for validation of temperature with reference to urban land use/land cover. During 2000, the total built up area was 28.17 km2 which it further increased to 88.35 km2 during 2013. Over the period of thirteen years from 2000 to 2013 it was observed that the built up area has increased by 26.94% of the total area (203 km2 ). In order to study the relationship between UHI and land cover, statistical analysis was performed between temperature and Normalized Difference Builtup Index (NDBI), Normalized Difference Vegetation Index (NDVI), Albedo and Emissivity. The correlation between NDVI, Emissivity and temperature was negative but NDBI, Albedo and temperature showed a positive correlation. Results showed that the change in temperature was mainly due to increase in impervious areas. The results of this study will be useful to the urban planners and environmentalists in formulating local policies. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Over the last five decades, the fast growth of urban area and conversion of natural landscape into anthropogenic structure results in change of local atmosphere and elevated land surface temperature compared to the surrounding open areas. The temperature variability represents human-urban and rural contrast, which is due to deforestation and conversion natural land surface into impervious land due to the urbanization (Chakraborty, Kant, & Mitra, 2015). Un-planned and non-managed urbanization activities are generally having negative outcomes loss in green spaces, loss of water bodies and environmental degradation. Urban heat island (UHI) was described by Luke Howard on the onset of 1833 (Howard, 1833), described as the urbanized areas which are having higher temperature than the nearby rural areas and ever since this subject matter has received a lot of interest (Detwiller, 1970; Dousset & Gourmelon, 2003; Fukui, 1970; Johnson et al., 1993; Katsoulis &

∗ Corresponding author at: Amity Institute of Geo-Informatics and Remote Sensing, Amity University, Sector 125, 201303, India. E-mail addresses: [email protected], [email protected] (P. Singh). http://dx.doi.org/10.1016/j.scs.2016.01.005 2210-6707/© 2016 Elsevier Ltd. All rights reserved.

Theoharatos, 1985; Wang, Zheng, & Karl, 1990). UHI is a familiar effects, which is an exemplification of environmental degradation (Hove et al., 2015; Ramachandra & Aithal, 2013; Streuker, 2002) and leads to adverse impact on the human health, it is expected to exacerbate in the upcoming years. The variation in land use/land cover (LULC) and population ballooning also caused a substantial change in the spatiotemporal patterns of the UHI due to the loss of water bodies and vegetated areas (Ramachandra, Aithal, & Sowmyashree, 2015; Zhang et al., 2013). In comparison with the surrounding lands, the dense-built up areas exhibits higher land surface temperature (LST) (Mallick, 2014), results in urban warming; globally urban cities are warmer compared to surrounding rural areas (Oke, 1973), the day temperature variation between rural and urban regions varies from 3 ◦ C to 5 ◦ C whereas the night time difference is observable as high 12 ◦ C mainly due to slow release of heat from the urban surfaces. A study on UHI carried out for China stated that during the past 50 years UHI contributed to 0.2–0.33 ◦ C of the overall warming in China. The differences in the thermal properties of the radiating surfaces and a decrease in the rate of evapo-transpiration are the major reasons responsible for the formation of UHI (Streuker, 2002). The temperature in the mega cities of India which houses nearly 18 million people is expected to increase to 46 ◦ C. The Delhi-based

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Fig. 1. Map of study area, Noida, India.

Energy and Resource Institute (TERI) carried out a primary survey which showed that the temperature in the mega-cities of India, i.e., Delhi and Mumbai had risen by 2 ◦ C to 3 ◦ C in just about 15 years. Nesarikar-Patki1 and Raykar-Alange (2012) study of Pune, India from 1999 to 2006 to see the impact of the changing land use pattern in the trend of LST. As a result it was observed that the built up area has increased by 32.68% which resulted to a decline of the area of agricultural land by 10%, vegetative land by 10% and barren land by 21.91% and as a consequence an increase in the LST was observed with rise in temperature from 1 ◦ C to 4 ◦ C. A case study for the Delhi was undertaken to evaluate and compare the UHI hotspots based on Remote Sensing observations and in situ measurements (Mohan et al., 2012). The areas occupied by dense built up infrastructures and commercial centers were found to have higher temperatures and the intensity of UHI was seen to be higher in magnitude during both the afternoon and midnight hours. On comparing this field campaign results with the MODIS-Terra data

of the LST, they found that the UHI hotspots were comparable only during the night hours. The aim of the study was to provide information about the major land use factors which is contributing to the rise in LST. This study also assesses the impact of built-up growth in Noida on its surface temperature using remote sensing and GIS techniques. 2. Study area and data 2.1. Study area New Okhla Industrial Development Authority (NOIDA) is located at 28◦ .57 N 77◦ .32 E, lies in northern India in Gautam Buddha Nagar District of state Uttar Pradesh, India. It is bound on the west and south-west by the Yamuna River, on the north and north-west by the city of Delhi, on the north-east by the cities of Delhiand Ghaziabad, and on the north-east, east and south-east by

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Table 1 Data used and their source. Data used

Data acquisition date

Data source

LANDSAT ETM LANDSAT 8

1st May 2000 29th May 2013

http://earthexplorer. usgs.gov/

the Hindon River. Noida is spread over an area of 203 km2 , and has a population of around 0.64 million. Noida has hot and humid climate for most of the year. The weather remains hot during summers, i.e., from March to June, and temperature ranges from maximum of 48 ◦ C to minimum of 28 ◦ C. Monsoon season prevails during midJune to mid-September with an average rainfall of 93.2 cm (36.7 in.), but sometimes frequent heavy rain causes flood. Temperatures fall down to as low as 3 to 4 ◦ C at the peak of winters. Noida also has fog and smog in winters (http://noida.trade/cityClimatesection). Due to a rapid industrialization and urbanization and infrastructure development in Delhi and Noida, develops ecological imbalance due to exploitation and overuse of environmental resources which have adverse effect as UHI (Fig. 1). 2.2. Data used 2.2.1. Satellite data and other auxiliary data The details of satellite images are given in Table 1 and other auxiliary data as Survey of India Toposheets and MOSDAC data has been used in the study. 2.2.2. Preprocessing Satellite data pre-processing was carried out using ENVI 4.7 software. Each Landsat ETM and Landsat 8 data consisted of independent distinct band images which was first layer stacked and combined into a multi-band image. These images have a spatial resolution of 30 m per pixel. In this study the band 6 (thermal infrared band) of ETM and band 10 (thermal infrared band) of Landsat 8 was used to retrieve the LST by converting the Digital number (DNs) into radiances. The bands within solar reflectance spectral range were used for extracting the vegetation and built up indexes. After pre-processing, the images of the study area were used for the analysis of UHI study. Further, processing has been carried out on Arc GIS 10.2.1 software. Statistical analysis was carried out using SPSS software.

Fig. 2. NASA webpage for atmospheric correction. Source: atmcorr.gsfc.nasa.gov/

(a) For Landsat ETM CVR1 =

(2) where CVR1 is the cell value as radiance, QCAL = Digital Number, LMIN = spectral radiance scales to QCALMIN, LMAX = spectral radiance scales to QCALMAX, QCALMIN = the minimum quantized calibrated pixel value (typically 1) and QCALMAX = the maximum quantized calibrated pixel value (typically 255). (b) For Landsat 8 L = MLQCal + AL

3.1. Mono-window algorithm for the retrieval of LST

Ts = {a(1 − C − D) + [b(1 − C − D) + C + D]Ti − D ∗ Ta}/C where a = −67.355351, b = 0.4558606, C = εi *  i , [1 + (1 − εi ) *  i ), εi = emissivity and  i = transmissivity.

(1)

(3)

where L = TOA spectral radiance (Watts/(m2 × srad × ␮m)), ML = band-specific multiplicative rescaling factor from the metadata (RADIANCE MULT BAND x, where x is the band number), AL = Band-specific additive rescaling factor from the metadata (RADIANCE ADD BAND x, where x is the band number), QCal = quantized and calibrated standard product pixel values (DN). These useful values can all be obtained from the meta-data file of the satellite image data.

3. Methodology used

Mono-window algorithm proposed by Qin, Karnieli, and Berliner (2001), for the retrieval of LST from Landsat TM 6 data have been used in the study (Liu & Zhang, 2011). This algorithm necessitates three main parameters – emissivity, transmittance and mean atmospheric temperature. Band 6 of Landsat ETM and band 10 of Landsat 8 records the radiation with spectral range from 10.40 to 12.50 ␮m for Landsat ETM and 10.60 to 11.19 ␮m for Landsat 8. The following expression is given below as Eq. (1):

(LMAX − LMIN ) (QCALMAX − QCALMIN) ∗ (QCAL − QCALMIN) + LMIN

3.1.2. Calculation of brightness temperature Once the radiance values have been calculated using the DNs of the thermal bands, the inverse of the Plank function is applied to derive the temperature values (Wang et al., 1990) expressed as Eq. (4). T=

ln

K

K2

1 ×ε CVR1



+1

(4)

D = (1 −  i )

where T = degrees (in K), CVR1 = cell value as radiance. K1 and K2 values can be obtained from the meta-data file.

3.1.1. Conversion of digital numbers to radiance In order to convert the DN data of band 6 of Landsat ETM and band 10 of Landsat 8 into spectral radiance Eqs. (2) and (3) can be written in band math of ENVI 4.7 as:

3.1.3. Calculation of atmospheric transmittance The atmospheric transmittance for Landsat ETM and Landsat 8 data was calculated using the “NASA webpage for atmospheric correction” module (Fig. 2).

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Table 2 Emissivity estimation using NDVI.

where NIR = Band 4 (For Landsat ETM) and Band 5 (For Landsat 8) and R = Band 3 (For Landsat ETM) and Band 4 (For Landsat 8).

NDVI

Land surface emissivity

NDVI < −0.185 −0.185 ≤ NDVI < 0.157 0.157 ≤ NDVI ≤ 0.727 NDVI > 0.727

0.995 0.970 1.0094 + 0.047ln(NDVI) 0.990

3.1.4. Calculation of land surface emissivity Land surface emissivity estimation can be done using NDVI. The following equation can be applied when the values of NDVI ranges from 0.157 to 0.727. Van de Griend and Owe (1993) proposed this method (Eq. (5)). i = 1.0094 + 0.0047 ln(NDVI)

(5)

Zhang, Wang, and Li (2006) proposed another complete land surface emissivity estimation method and the following equations can be used for calculating emissivity using NDVI (Table 2). 3.2. Retrieval of land surface parameters 3.2.1. Derivation of NDVI NDVI from Landsat ETM and Landsat 8 is calculated from reflectance measurements in the red and near infrared (NIR) portion of the spectrum (Liu & Weng, 2011). The NDVI expressed as in Eq. (6): NDVI =

NIR − R NIR + R

(6)

3.2.2. Derivation of NDBI NDBI from Landsat ETM and Landsat 8 is calculated from reflectance measurements in the red and mid infrared (MIR) portion of the spectrum (Liu & Weng, 2011). The NDBI expressed as in Eq. (7): NDBI =

MIR − R MIR + R

(7)

where MIR = Band 5 (for Landsat ETM) and Band 6 (for Landsat 8) and R = Band 3 (for Landsat ETM) and Band 4 (for Landsat 8).

3.2.3. Derivation of Albedo Albedo from Landsat ETM and Landsat 8 is calculated from the reflectance measurements (Coakley, 2003; Liang, 2000) expressed by Eq. (8) as: Formula: ˛=

0.356˛1 + 0.130˛3 + 0.373˛4 + 0.085˛5 + 0.072˛7 − 0.0018 1.016 (8)

where ˛i = Band number 1, 3, 4, 5 and 7 (for Landsat ETM) and Band number 2, 4, 5, 6 and 7 (for Landsat 8).

Fig. 3. LULC map of Noida of May 2000 and May 2013.

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Fig. 4. NDVI map of Noida of May 2000 and May 2013.

Table 3 Noida temporal LULC. Date

Built up Vegetation Cultivation and others Water bodies

Table 4 Noida mean NDVI (temporal change). 1st May 2000

29th May 2013

Date

Minimum NDVI

Maximum NDVI

Mean NDVI

1st May 2000 29th May 2013

−0.21 −0.07

0.39 0.62

0.04 0.06

Sq km

%

Sq km

%

28.17 29.33 150.21 7.49

13.09 13.62 69.79 3.48

88.35 54.56 69.29 3.06

41.03 25.34 32.18 1.42

cultivated lands and other open lands were replaced by buildings, roads, pavements and other infrastructures which also resulted in the increase of urban vegetation.

4. Results 4.2. Spatio-temporal analysis of NDVI The spatial distribution of LST, NDVI and LULC within the study area is shown in figure below. LST is carried out on the basis of these LST parameters. 4.1. Spatio-temporal analysis of LULC Maximum Likelihood Classifier, a statistical decision in which the pixels are allotted based on the class of highest probability, results was obtained as LULC types, i.e., built-up, vegetation, water bodies and cultivation and others (Fig. 3, Table 3). In Noida, the percentage of Built up area has increased rapidly from 2000 to 2013. During 2000, the total built up area was 28.17 km2 which it further increased to 88.35 km2 during 2013. Over the period of thirteen years from 2000 to 2013 it was observed that the built up area has increased by 26.94% of the total area (203 km2 ). The changes in the land cover category also showed some positive land use analysis in which the wastelands are getting reduced as it is getting replaced by vegetative area which is showing an increasing trend over the years. Vegetative land was found to be 29.33 km2 in 2000 which increased to 54.56 km2 during 2013. Thus, most of the increase in the urban area resulted from the conversion of agricultural land to other land use classes in which

Fig. 4 shows the spatial distribution of NDVI from Landsat image for the years 2000 and 2013 in the city of Noida. The minimum and maximum NDVI values of 2000 are in the range between −0.21 and 0.39 and during 2013, the range was between −0.07 and 0.62. The city was showing an overall increase in the trend of vegetation over the years. It was observed that with the increase in urbanization, the urban plantations are also increasing due to which the NDVI is showing an increasing trend over the years. According to the India State of Forest Report 2011, brought out by the Union Ministry of Environment and Forests, over the decade Delhi’s green cover has doubled up from 151 km2 in 2001 to 296.2 km2 in 2011. Around 367 km2 of land officially classified as forest was lost countrywide between the years 2009 and 2011. According to the report, Delhi lists a remarkable 20% of their area under forest cover despite the other major cities in India having less than 15% of forest cover. It is claimed by the city’s forest department that the number has increased at least by 2% now and is set to keep increasing over the years. The area having value more than zero represents green areas with increasing value of NDVI showing more greener areas whereas values below zero or near to zero represents non-vegetated features such as barren lands and water (Fig. 5, Table 4).

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Fig. 5. Bar graph showing mean NDVI for Noida (temporal change) of May 2000 and May 2013.

Fig. 7. Bar graph showing mean LST for Noida (temporal change) of May 2000 and May 2013.

4.3. Spatio-temporal analysis of LST

Table 5 Noida mean LST (temporal change) Noida 2000.

Due to the spatial variations in land cover, the soil representative meteorological conditions from the limited number of climate stations cannot always be obtained. In such cases the remote sensing data helps in procuring the consistent and frequent observation of land surface on both micro as well as macro scale (Southworth, 2004). The LST is calculated with the radiance value of thermal band from Landsat ETM and Landsat 8 data. Fig. 6 shows the land surface temperature maps of Noida (Fig. 7, Table 5). It was observed that during 2000, the temperature ranged between 32.46 ◦ C and 47.83 ◦ C having a mean LST of 40.14 ◦ C. The overall mean temperature showed an increasing trend during May 2013 with a mean LST of 40.95 ◦ C and the temperature ranging between 33.89 ◦ C and 48.01 ◦ C. As Noida becoming one of the fastest developing cities in Delhi/NCR, urbanization is also

Date

Minimum LST

Maximum LST

Mean LST (◦ C)

1st May 2000 29th May 2013

32.46 33.89

47.83 48.01

40.14 40.95

increasing rapidly in which the natural land surface are getting replaced by roadways, buildings and other constructions which is contributing to rise in temperature thus increasing the urban heat island effect in a number of ways. In the built-up regions, the radiations are getting trapped because to the various building materials used nowadays and as observed closely from 2000 onwards it was seen that in the areas where built-up has increased, LST is also reportedly found to be increased. But in some regions during 2013, low LST is also reported. This is because of the increase in the green cover and the moisture trapping properties of the vegetation due to

Fig. 6. LST map of Noida of May 2000 and May 2013.

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Table 6i Impact of LST on land use change of Noida of May 2000. Noida 2000 Grid number

0 1 2 3 4

Built up

Cultivation and others

Vegetation

Water bodies

LST

Mean LST ◦

Sq km

%

Sq km

%

Sq km

%

Sq km

%

◦

0.19 2.74 0.31 0.83 1.41

2.32 1.71 1.97 5.27 8.86

7.01 15.08 14.71 14.12 11.91

82.08 94.02 92.44 88.73 74.83

0.95 0.64 0.85 0.91 2.58

11.12 3.99 5.38 5.72 16.22

0.38 0.04 0.03 0.04 0.01

4.46 0.26 0.19 0.26 0.07

45–47 42–44 45–47 44–45 44–45

46 43 46 44.5 44.5

Water bodies

LST

Mean LST ◦

C

C

Table 6ii Impact of LST on land use change of Noida of May 2013. Noida 2013 Grid number

0 1 2 3 4

Built up

Cultivation and others

Vegetation

Sq km

%

Sq km

%

Sq km

%

Sq km

%

◦

2.45 8.32 7.25 5.82 7.94

29.03 33.01 42.81 60.28 63.08

4.21 2.37 4.64 5.57 0.65

49.74 53.04 49.54 25.68 11.78

1.31 5.67 4.01 4.17 7.30

15.74 14.38 7.64 13.87 24.81

0.48 0.04 0.01 0.01 0.01

5.73 0.02 0 0.15 0.31

45–47 45–47 45–47 45–47 43–45

which the LST appears to be low. Vegetation has a high emissivity due to which the LST is low. NDVI plays a vital role in determination of the vegetation pixels and provides useful information as to understand the condition of the urban areas. Open lands are also reportedly found to have high temperatures. Water bodies exhibit minimum temperatures. 4.4. Grid level analysis of LST with LULC Grid level analysis was carried out to estimate the land surface temperature for Noida urban area and the area was divided into 2/2 km2 grid using the Arc GIS zonal statistical tool. The main objective of performing this analysis was to find out and correlate the major land use or land cover category which is responsible for the rising of land surface temperature. Grid level analysis of LST was performed by calculating the mean of land surface temperature within the area of 2/2 km2 grid. The results observed from these analysis and their variations are shown in Fig. 8 (i and ii) and Tables 6i and 6ii. During the years from May 2000 to May 2013, it was observed that out of the four land-use category, i.e., built-up, cultivation and others, vegetation and water bodies, grids having the major category of built-up greatly contributed to the rise in temperature. Grids having majority of built up near water bodies were found to have lower temperatures. Least temperature was observed in case of grids having majority of vegetation class and water bodies. Further, analysis has been carried out by selecting grids which was showing a major temperature deviation. The selected grids are numbered in the map in Fig. 8 (i and ii). It was observed that built up has a direct impact on the rising temperature. Fig. 9 (i and ii) showed some examples of images taken from Google Earth Historical Imagery of Grid numbers 3 and 4 which shows how urbanization has increased over the years. 4.4.1. Grid number 3 (Noida) As Noida becoming a fast developing city, it can be seen from the image that over the period of year’s urbanization has rapidly taken place in which the agricultural lands are getting replaced by pavements, highways, buildings and other infrastructures. The percentage of built up area in this grid increased tremendously with the percentage of built-up being 31.65% in May 2000 which increased to 60.28% during May 2013. Similarly, with the changing pattern in

C

C

46 46 46 46 42

land use especially with the increase in the built up area the LST was also showing an increasing trend. The trend of land use change and temperature variation for the years 2000 and 2013 within this grid number 3 can be seen in Tables 6i and 6ii. 4.4.2. Grid number 4 (Noida) This grid is showing one of the most important highways of Noida which is the Greater Noida Expressway. This expressway also connects to Yamuna expressway which is a new and shorter route to Taj Mahal in Agra, one of the important tourist destinations in India. This expressway connects many universities, workplaces, residential townships and independent settlements. Over the period years, it can be witnessed from the image on how development has taken place in this region. Built-up is replacing the agricultural lands and as a consequence the temperature was also found to increase. The trend of land use change and temperature variation for the years 2000, and 2013 within this grid number 4 can be seen in Tables 6i and 6ii. 4.5. Pearson’s correlation Correlation analysis between the LST and various indices, i.e. NDVI, NDBI, Emissivity and Albedo was done for finding out the relationships. The analysis showed that there is a strong positive correlation of LST with NDBI which indicated a direct relation of LST with NDBI. In other words, as NDBI increases the LST is also increasing. Similarly, a weak positive correlation was seen between LST and Albedo which showed that where there is high albedo, the temperature is also high (Table 7). The results obtained through the correlation between LST and NDVI showed a negative correlation in which the areas with high NDVI values was found to have a lower temperature as compared to the areas with low values of NDVI. This is because plants are good absorbers as vegetation and moisture trapping soils utilize a relatively large proportion of the absorbed radiation in the evapotranspiration process and release water vapor that contributes to cool the air in their vicinity due to which the heat gets trapped and hence the emissivity in those regions are found to be high. Emissivity was found to be strongly negatively correlated because di-electric properties of a feature greatly impacts its ability to absorb or radiate heat. For example, all areas where water bodies were found to exist in both the study regions, lower temperature

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Fig. 8. (i) Map of grid wise LST of Noida of May 2000 and May 2013. Note: The meaning of Grid as 0, 1, 2, 3, and 4 are the numbering of grids as these grids are sum of the grids where major land use change and temperature change is observed both in positive and negative way and its tabulations of the grid wise major land use changes of these numbered grids are shown is Tables 6i and 6ii. Only some particular grids are selected for validation point of view. (ii) Map of grid wise major LULC category of Noida of May 2000 and May 2013.

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Fig. 9. (i) Google Earth Historical Imagery for Grid 3 (a) Noida 2000 and (b) Noida 2013 are showing the level of urbanization in the area. (ii) Google Earth Historical Imagery for Grid 4 (a) Noida 2000 and (b) Noida 2013 are showing the level of urbanization in the area.

were reported with its emissivity being the highest of about 0.993–0.998. On the contrary in Urban areas as as well as in open lands due to building material property and soil/sand di-electric constant, comparatively higher temperatures were reported. Table 7 Correlation table of Noida.

5. Future implications for reducing the effect of urban heat island

Correlations LST 1

LST

Pearson correlation Sig. (2-tailed) N

NDVI

Pearson correlation Sig. (2-tailed) N

−547 .083 10

NDBI

Pearson correlation Sig. (2-tailed) N

.812 .004 10

Emissivity

Pearson correlation Sig. (2-tailed) N

−.574 .083 10

Albedo

Pearson correlation Sig. (2-tailed) N

.572 .4 10

10

The unbalanced temperature rise has adverse effects both on the human population of the city and the ecosystem of the surroundings. As urbanization is never ending process it is becoming mandatory to take necessary steps to create a balance between the environment and human settlements. So actions should be taken to minimize the urban heat island phenomenon. First and foremost steps to reduce the effects are to increase the vegetation cover in the city which would help in stimulating the rate of evapotranspiration. Planting trees around the settlements will help in shading the urban surfaces which reduces the temperature of roofs and walls. It leads to considerable decreases in energy usage for air conditioning. Secondly, other UHI reduction strategy is to increase surface reflectivity (i.e., high albedo) for reducing the radiation absorption properties of urban surfaces. So Building materials with high emissivity property should be used as it will store less heat and the roofs

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tops, pavements etc. should be painted in light color like white as it will absorb less amount of solar radiation and keep the temperature low. Roof top Gardens or “Green roofs” which uses live vegetation on roofs are gaining popularity in order to reduce heat accumulation and helps in extending the lifespan of roofing materials as compared to traditional rooftops, reducing air pollutants and greenhouse gases, and insulation of buildings. Appropriate planning such as planting of trees and vegetation cover in urban areas, creation of green space such as parks will help in cooling of the atmosphere. Usage of renewable resources of energy like solar and wind should be promoted and fuels having low carbon emissions must be implemented. Carbon Credits or cap-and-trade markets are useful and an abrupt solutions for reducing Green House Gas (GHGs) emissions in the atmosphere. It is helpful in lowering the costs of renewable and low carbon technologies. 6. Conclusion Temporal analysis was performed for the year 2000 and 2013 to study the trend of LST. LST was retrieved using mono-window algorithm using the Landsat ETM and Landsat 8 data. From the results obtained it was found that escalating trend of LST was observed in major parts of Noida city where built up area has increased. Through the correlation analysis, the relationship of LST with NDVI, NDBI, Albedo and Emissivity it was noticed that LST and NDBI share a positive relationship because in built up areas there is no type of restrictions such as sun radiations do not directly get in contact with the surface and hence the emissivity is comparatively lower. Albedo was also found to have a direct relationship with LST as the higher the albedo, the LST increases. A significant negative relationship was observed between LST and NDVI in which it was found that the areas where vegetation exists, the UHI effect was weak. Emissivity was also found to have a strong negative correlation with LST because di-electric properties of a feature greatly impact its ability to absorb or radiate heat and hence it was observed that the areas where emissivity was found to be high, low LST was reported. Grid level analysis was also carried out to see which land use category had a major influence on the effect of LST. It was evident that built-up is one of the major land use category which is contributing to the formation of UHI. Acknowledgments The corresponding author expresses his gratefulness to the Vice Chancellor and Director, Amity Institute of Geoinformatics and Remote Sensing, Noida, for providing facility and constant encouragement for carried out this research work. References Chakraborty, S. D., Kant, Y., & Mitra, D. (2015). Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data. Journal of Environmental Management., 148, 143–152.

Coakley, J. A. (2003). Reflectance and Albedo, surface. Corvallis: Oregon State University. http://www.curry.eas.gatech.edu/Courses/6140/ency/Chapter9/ Ency Atmos/Reflectance Albedo Surface.pdf Detwiller, J. (1970). Deep soil temperature trends and urban effects at Paris. Journal of Applied Meteorology, 9, 178–180. Dousset, B., & Gourmelon, F. (2003). Satellite multi-sensor data analysis of urban surface temperatures and land cover. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 43–54. Fukui, E. (1970). The recent rise of temperature in Japan. In Japanese Progress in Climatology. pp. 46–65. Tokyo, Japan: Tokyo University of Education. Hove, V., Jacobs, J., Heusinkveld, B. G., Elbers, J. A., Driel, V., & Holtslag, M. (2015). Temporal and spatial variability of urban heat island and thermal comfort within the Rotterdam agglomeration. Building and Environment, 83, 91–103. Howard, L. (1833). . pp. 1818–1820. The climate of London (Vol. 2) London, UK: London Harvey and Dorton. Johnson, G. L., Davis, M., Karl, T. R., McNab, A. L., Gallo, K. P., Tarpley, J. D., et al. (1993). Estimating urban temperature bias using polar-orbiting satellite data. Journal of Applied Meteorology, 33, 358–369. Katsoulis, B. D., & Theoharatos, G. A. (1985). Indications of the urban heat island in Athens. Greece, 24, 1296–1302. Liang, S. (2000). Narrowband to broadband conversions of land surface Albedo I algorithms. Remote Sensing of Environment, 76, 213–238. Liu, H., & Weng, Q. (2011). Enhancing temporal resolution of satellite imagery for public health studies: A case study of West Nile Virus outbreak in Los Angeles in 2007. Remote Sensing of Environment, 117, 57–71. Liu, L., & Zhang, Y. (2011). Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing Journal, 3, 1535–1552. Mallick, J. (2014). Land characterization analysis of surface temperature of semi-arid mountainous city Abha. Saudi Arabia Using Remote Sensing and GIS, 6, 664–676. Mohan, M., Kikegawa, Y., Gurjar, B., Bhati, S., Kandya, A., & Ogawa, K. (2012). Urban heat island assessment for a tropical urban air shed in India. Atmospheric and Climate Sciences, 2, 127–138. Nesarikar-Patki1, P., & Raykar-Alange, P. (2012). Study of influence of land cover on urban heat islands in Pune using remote sensing. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMSE), 3, 39–43. Oke, T. R. (1973). City size and the urban heat island. Atmospheric Environment, 7, 769–779. Qin, Z., Karnieli.A., & Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Internal Journal of Remote Sensing, 22, 3719– 3746. Ramachandra, V., & Aithal, H. (2013). Urbanization and Sprawl in the Tier 2 city: Metrics, dynamics and modelling using spatio-temporal data. International Journal of Remote Sensing, 3, 65–74. Ramachandra, V., Aithal, H., & Sowmyashree, M. V. (2015). Monitoring urbanization and its implications in a mega city from space: Spatiotemporal patterns and its indicators. Journal of Environmental Management, 148, 67–81. Southworth, J. (2004). An assessment of Landsat TM Band 6 thermal data for analyzing land cover in tropical dry forest regions. International Journal of Remote Sensing, 25, 689–706. Streuker, D. R. (2002). A remote sensing study of the urban heat island of Houston, Texas. International Journal of Remote Sensing, 23(13), 2595–2608. Van de Griend, A. A., & Owe, M. (1993). On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of Remote Sensing, 14, 1119–1131. Wang, W., Zheng, Z., & Karl, T. R. (1990). Urban heat islands in China. Geophysical Research Letters, 17, 2377–2380. Zhang, H., Qi, Z., Ye, X., Cai, Y., Ma, W., & Chen, M. (2013). Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Applied Geography, 44, 121–133. Zhang, J., Wang, Y., & Li, Y. (2006). A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM Band 6. Computers and Geosciences, 32, 1796–1805.