Dynamics of Land Use /Land Cover Changes and Its Impact on Land Surface Temperature in the Keleghai River Basin, West Bengal, India: a Remote Sensing Approaches

International Journal of Scientific & Engineering Research Volume 12, Issue 2, February-2021 579 ISSN 2229-5518

Dynamics of Land Use /Land Cover changes and Its Impact on Land Surface Temperature in The Keleghai River Basin, West Bengal, India: A Remote Sensing Approaches. Mr. Goutam Kumar Das M.Phil. Student of Department of Earth Science, Vidyasagar University, Medinipur, W.B, India Abstract:

Land surface temperature is an important factor to play and change that estimate radiation budget and control the heat balance at the earth’s surface. Rapid population growth and changing the existing patterns of Land Use/Land Cover (LU/LC) globally, which is consequently increasing the land surface temperature (LST). The transformation of LU/LC due to rapid Built-up land expansion significantly affects the functions of Biodiversity and ecosystems as well as local and regional climates. This study evaluates the impact of LU/LC changes on LST for 2000, 2010 and 2020 in the keleghai River Basin using multi-temporal and multi-spectral Landsat-7 TM and Landsat-8 OLI satellite data sets. Our results indicated that, Built-up area and Brick kiln area were 1070.06 km2 and 46.18 km2 and decreased water bodies, agricultural land, agricultural fallow land and scrub land are 1382.13 km2, 4196.14 km2, 1529.95 km2 and 24.59 km2 respectively in the last 20 year in the study area. The distribution of changes in LST shows the Built-up area and Brick kiln area recorded the highest mean temperature and increased 26.72oC – 40.34oC and 29.28oC – 41.34oC respectively. The study demonstrates decreased in water bodies, scrub lands, agricultural lands, agricultural fallow lands and increased in the Built-up land and Brick kiln area which significantly increased Land Surface Temperature in the study area. Remote sensing and GIS techniques were found, one of the most suitable for rapid analysis of Built-up area and Brick kiln area expansion and to identify the Impact of the urbanization on Land Surface Temperature (LST).

Keywords: LU/LC, LST, Remote Sensing and GIS, Urbanization.

1. INTRODUCTION: surface to play an important role in the study and modeling for global climate change. Multi-spectral image of a remote

sensing system is an important source that widely available Rapidly growing urban population is important role to to produce and analysis land use and land cover changes. significantly change the LU/LC and impacts on the Land These multi-spectral data capable to provide up to surface temperature (Argueso et al., 2014, Bahi et al., 2016; temporally, synoptic view and repeatedly coverage of land Habitat, 2016). The changes in Land use/Land Cover is an IJSERsurface characteristics (Hegazy and Kallop, 2015, Zha et al., environmental and regulated process but cannot be 2003). Land Surface Temperature (LST) is completely stopped. Land use, including land transformation from one related to the physical process of surface temperature that to another type and land cover are modification of land provides information on temporal variation of earth surface through the land use management, which vastly alteration energy changes. it is importance factor to play for a large portion of the earth’s land surface to satisfy monitoring vegetation ,built-up area and climate change .so mankind indicated demand for natural resource today it is a serious environmental issue(Sahoo et (Rahman,2007). A popular issue in the research community al,2016).Remote sensing and GIS techniques are also relation to environmental changes and sustainable effective and powerful tools for analysis of Land Use /Land development considered of the relationship between land Cover changes and retrieval Land surface Temperature surface temperature and land use /land cover changes (Dewan and corner,2013.,Lilly,2009.,Scarano and sobrino (Muhammad Amir Siddique et al., 2019). Brick Kiln 2015;Zhon et al.,2011).The Land Surface Temperature and Industry is faster growing industrial sector in the both sides Vegetation Indices have been also collective in a scatter plot of Keleghai River. During Brick processing, the burning of to get the temperature–vegetation space which reveal the fossil fuel is required where the release of gasses and other chronological trajectory of pixcels ranging from low subsequences have potential harmful effects to the health as temperature high vegetation conditions to high well as on the Environment (Rahaman saidur, 2015). Land temperature vegetation conditions as an results of surface is combination of vegetation, water bodies, scrub urbanization method(Amiri et al,2009;Lim et al,2009;Weng land, agricultural lands, etc., so as a results land surface is et al,2004).A variety of studies and demonstrated the varied Land surface Temperature spatially and temporally. LU/LC changes and impacts on land surface Temperature Thermal band of multi-spectral satellite image can be using multi-dimensional remote sensing data (Ahmed utilized for retrieval and study terrestrial surface ,2011;celik et al.,2019;Dewan and temperature. Land surface temperature on the earth’s IJSER © 2021 http://www.ijser.org International Journal of Scientific & Engineering Research Volume 12, Issue 2, February-2021 580 ISSN 2229-5518

Corner,2013;Lilly,2009;aduako et al.,2016;Mallick et al ,2008.,pal and Ziaul,2017;Shatanawl and Abn Qdies;Tran et al.,2017).Land surface Temperature are essential environmental phenomena of the earth surface that is directly influence by LU/LC changes with implications for the study micro climatic changes and environmental impacts(Abreu-Harbich,Labaki and atzarakis,2014;Feng,Liu and Qu,2014;Sobrino;Jimenez-Munoz and Paolini,2004;Walaweder et al.,2014).However, this type of the study has not carried out for whole west Bengal. Keleghai river basin are covered in the Medinipur and Jhargram district in west Bengal to shows tremendous LU/LC changes and increase in land surface temperature that are one of the predominate problems due to the hues brick kiln industry development and urbanization in the last 20 years.Therfore,the present study investigates the effects of LU/LC changes on LST for the year of 2000,2010, and 2020 in the keleghai river basin using multi-spectral image of Landsat ETM+ and Landsat 8 OLI satellite Figure-1; Location of the Study area. datasets. 3. MATERIALS AND METHODOLOGY: 2. STUDY AREA: The present study includes with multi-temporal Landsat 7 Keleghai river basin in the Medinipur and Jhargram ETM+ (Enhance Thematic Mapper) and Landsat OLI district is developed large number of the brick kiln industry (Operational Land Imager) satellite image (with path /row in the both side of the river Bank (Figure-1).Keleghai river is 139/45) obtained from US Geological Survey (USGS) basin is located in north-western part of the east Medinipur website (https://earth explorer .usgs.gov) on the 22th,24th and southern part and western part of the Jhargram district and 15th of July 2000, 2010, and 2020 respectively).All the between 21°59'35.97"N to 22°24'6.854"N and satellite images contain 30 m / 30 m spatial resolution 87°49'54.683"E to 87°5'53.453"E .Topographically ,surface (Level 1 terrain corrected product) were projected to UTM elevation ranging from 4 m to 102 m and 60% of total area zone 45 North projection using WGS-84 datum. Each of the fall with the 60-80 m altitudeIJSER and 70% area falls under the moderate to moderately steep slope(5o – 20o) including soil Landsat images pre-processing was radiometric calibration, erosion and soil type are alluvial plain with clay and atmospheric correction and enhancing using histogram alluvial coastal loamy soil. However, climate, soil, slope, equalization techniques to performed a higher contrast of vegetation, Built-up land etc. are most importance factors the images. Supervised Images classification were prepared on influence Land Surface Temperature (LST). Area is by maximum likelihood techniques and change detection 1926.75 km2 and consists of 25 villages. It predominantly was carried out by matrix union function, retrieve Land has sub-tropical with monsoon climate. The average Surface Temperature (LST) and calculated Normalized temperature is between 20oC to 24oC with average annual Difference Vegetation Index (NDVI) using in ERDAS precipitation 116 mm and highest intensity precipitation imagine 2013 software and LST distribution mapping were June to September. Total amount of precipitation in the conducted in Arc Map 10.1 software. Validated Land four-monsoon month for more than half of the total amount Surface Temperature (LST) correlated between estimated precipitation during the year of the study area. LST from remotely sensed data and LST data produced from the Indian Metrological Department (IMD) for the year July 2000, 2010 and 2020 in the day time. The detailed information regarding the data given in table -1, after, supervised images classification accuracy check was performed by GPS survey and photographs are taken in collecting the ground truth data in 2020 and prepare accuracy assessment of classified images has been made

through kappa coefficient statistics to validate the post Landsat-7, ETM+ and Lanadast-8, OLI classification analysis. (2000, 2010, 2020)

Image Pre-processing

Geometric Correction

Radiometric Correction

Atmospheric Correction

Input Thermal Band (Band-6, 10) NDVI (NIR-R/NIR+R)

Vegetation Proportion Convert Thermal Band to

Spectral Radiance

Convert to Radiant Temperature

Temperature (Kelvin) Accuracy Validation Assessment LST in Degree Celsius of LST

LST-2000 LST-2010 LST-2020 LU/LC-2000

IJSER Changes

Impact of Land use /Land cover changes on Land surface temperature

Figure-2; Flowchart showing the methodology for mapping LST.

Table-1; Satellite image data sets. Spatial resolution Spectral region Time of Sensors Spectral Resolution Path/row (m.) (µm) acquisition IJSER © 2021 http://www.ijser.org International Journal of Scientific & Engineering Research Volume 12, Issue 2, February-2021 582 ISSN 2229-5518

Band 1 Blue 30 0.45 – 0.52 Band 2 Green 30 0.52 – 0.60 Band 3 Red 30 0.63 – 0.69 Landsat 7-ETM+ Band 4 NIR 30 0.77 – 0.90 07/22/2000 (Sensor) Band 5 SWIR1 30 1.55 – 1.75 07/24/2010

Band 7 SWIR2 30 2.09 – 2.35 Band 8 Pan 15 0.52 – 0.90 Band 6 TIR 30/60 10.40 – 12.50 Band 1 Coastal 30 0.43 – 0.45 Band 2 Blue 30 0.45 – 0.51 139/39 Band 3 Green 30 0.53 – 0.59 Band 4 Red 30 0.64 – 0.67 Landsat 8-OLI Band 5 NIR 30 0.85 – 0.88 (Sensor) Band 6 SWIR1 30 1.57 – 1.65 07/15/2020

Band 7 SWIR2 30 2.11 – 2.29

Band 8 Pan 15 0.50 – 0.68 Band 9 Cirrus 30 1.36 – 1.38 Band 10 TIRS1 100 10.6 – 11.19 Band 11 TIRS2 100 11.5 – 12.51

and Landsat-8 OLI like NIR is the infrared band 5 and red 3.1. LAND USE /LAND COVER CLASSIFICATION: radiance band 4 distribution of NDVI in the linear way. The NDVI values are represented, ranging in value from -1 to Land use /Land Cover categorized into seven types-water +1. In this study, NDVI map is generated in ERDAS bodies, Vegetation, Scrub lands, Built-up lands, Imagine 2013 software.

Agricultural Lands, Agricultural fellow Lands and Brick Kiln Fields. This LU/LC categorization to use supervised 3.3. DERIVATION OF LAND SURFACE classification method. For this classification, data collected TEMPERATURE: from the field survey as well as also helped local IJSER knowledge. The determined training areas are used to Digital Number (DN) of the thermal Bands (Band-6 in classify satellite image complying maximum likelihood Landsat 7 ETM+ and Band 10 TIRS of Landsat-8 OLI) was supervised classification algorithm. The accuracy of the used to estimate the land surface temperature (LST). In the classification process is usually assessed by comparing the primarily, spectral radiance (Lλ) of the Landsat-7 ETM+ classification results with mention data from obtaining field and Landsat-8 OLI thermal bands from the calculated by visits, Google earth image or high spatial resolution image. the using Equation-1 and equation-2 and finally, Lλ were used to derive the LST in degree Celsius using the 3.2. NDVI CLASSIFICATION: Equation-3.

NDVI (Normalized Difference Vegetation Index) is a numerical indicator that uses visible and Near-infrared bands of the electromagnetic spectrum (kayet Narayan et al., 2016). NDVI has been used to validate a different object. It’s measured the greenness of the environment and Where, Lλ is spectral radiance, Lmin is the 1.238(spectral amount of vegetation. The (NDVI) is computed from the radiance of DN values), Lmax is 15.600 (spectral radiance of following equation- DN values 255) and DN is the Digital Number.

NDVI= (NIR-RED/NIR+RED) Lλ (Landsat-8 OLI) = ML × DN × AL………………. (2)

Where, NIR is the near infrared radiance from band 4 and Where, ML= (0.00033342) is a multiplicative rescaling factor RED is red radiating from the band 3 of Landsat-7 ETM+ (Band-specific) and values of AL is 0.1 which is a additive IJSER © 2021 http://www.ijser.org International Journal of Scientific & Engineering Research Volume 12, Issue 2, February-2021 583 ISSN 2229-5518 rescaling factor (Band Specific) (Kamran et al.,2015; (1238.38 km2), Brick kiln area (50.65 km2) and vegetation Sholihah and shibata, 2019). The values for Landsat (9416.83 km2) from in the year 2020 and in 2010, increased ETM+,Lmax and Lmin collected from the satellite metadata. Built-up lands 168.32 km2 (0.87%) to 845.87 km2 (4.38%), The wave Length of Emitted Radiance λ is the 11.5 µm. Brick kiln lands 4.46 km2 (0.02%) to 6.01 km2 (0.03%), and vegetation 7655.16 km2 (39.67%) to 9554.53 km2 (49.52%) Tb=k2/Ln(k1/Lλ) +1………………………...…. (3) respectively. The water bodies, agricultural and agricultural fallow lands, Scrub lands was the predominate land use Where, K1 is the calibration constant 1 for Landsat ETM+ type that was significantly decreased from 11467.37 (607.76 wm-2 sr-1 m-1) and Landsat OLI (666.09 wm-2 sr-1 km2(59%) to 8589.35 km2(44.52%) in the year 2000 to m-1). k2 is calibration constant 2 for Landsat ETM+ (607.76 2020.The maximum water bodies, agricultural lands, scrub k) and Landsat OLI (1282.71 k). Lλ is the spectral radiance lands initially covered to built-up lands. of 6 TIR band for ETM+ and 10 TIR band for OLI. Tb is Radiant temperature. 4.2. LAND USE / LAND COVER (LU/LC) TRANSITION MATRIX: 3.4. NDVI CALCULATION: Table-3; demonstrate, a transition matrix was used to Land surface Emissivity (LSE) is calculated based on derived statistics and location of major LU/LC changes the NDVI values. It used the NDVI threshold method during 2000-2020.A matrix function embedded within (NDVITHM) by applying the following formula ERDAS IMAGINE union matrix was used to obtain the (Equation-4). transition matrix for three-time spans (2000-2010,2010-2020 LSE=1.0094+0.047 × Ln (NDVI)……………… (4) and 2000-2020) and relatively change between different LU/LC were calculated based on the derived statistics. The NDVI values range from the 0.157 to 0.77 when the Maximum area of water bodies (599.39km2) occupied from NDVI values out of the range (0.157-0.727), the the other LU/LC classes and minimum area of brick kiln corresponding input LSE constant values are used. (0.43 km2) occupied to others Land use classes, but water bodies and Brick kiln area was not change 356.06 km2 and Conversion of Kelvin to Celsius (semcuzael et al, 1995) is 0.17 km2 respectively for the year 2000-2010.In 2010-2020, obtained by equation-5 the maximum area of water bodies (478.15 km2) and Tb=Tb-273.15…………………….……. (5) minimum area of brick kiln (5.91 km2) occupied to others IJSERland but water bodies (462.00 km2) and Brick kiln area (0.31 4. RESULTS AND DISCUSSION: km2) was not change. In respectively, for the year 2000- 2 4.1. LAND USE/LAND COVER: 2020, highest land area water bodies (596.56 km ) and lowest area of brick kiln (6.02 km2) are occupied from the The LU/LC (2000,2010 and 2020) (Figure-3) was calculated others land out of total area 343.58 km2 and 0.20 km2.So, all from classified image (table-2).LU/LC maps demonstrate a LU/LC classes maximum to medium area occupied from rapid increase in built-up land (5.55%), Brick kiln fields others lands except Built-up and Brick kiln area throughout (0.24%) and vegetation (9.13%) significantly decrease water the all year. bodies (-7.16%), Scrub Lands (-0.13%), Agricultural lands (- 4.3. Accuracy Assessment of LU/LC 3.86%) and Agricultural fellow lands (-3.76%) from in the classified Maps: year 2000 to 2020.Moderate increase and decrease in brick Accuracy Assessment for supervised classification methods kiln field (0.24%) and scrub lands (-0.13%) are also notable. is established trough the error matrix which contains In 2000, the area under water bodies and agricultural fellow information about actual class and predicted class carried lands was 3573.48 km2 and 154.14 km2, which significantly out by a classification system (Lucas and van der wel 1994; reduced to 2191.35 km2 and 807.76 km2 in the year 2020 Hay 1988; Stehman and czaplewski 1998; van densen 1996; respectively. In 2020, the built-up lands (168.32 km2), Yuan1997). The user accuracy of the classification (LU/LC) vegetation (7655.18 km2) and Brick kiln fields (4.46 km2) and Kappa coefficient were determined by using ARC map which significantly increased 1238.38 km2, 2191.35 km2 and 10.1 software. Table-3 demonstrates the overall accuracy, 50.65 km2 respectively in 2020.In 2020, total Built-up lands kappa coefficient of LU/LC classification. The overall massively increased which replaced by the area agricultural classification accuracy was 98%, 91% and 90% for the year lands and Vegetation. Rapidly increased Built-up lands IJSER © 2021 http://www.ijser.org International Journal of Scientific & Engineering Research Volume 12, Issue 2, February-2021 584 ISSN 2229-5518

2000, 2010 and 2020 respectively (Table-3). Overall accuracy range of 18.54oC to 34.01oC during 2000, 22.38oC to 36.83oC is higher in 2000 and the value of kappa coefficient was during 2010 and 32.07oC to 41.34oC during the 2020.out of above 0.85 for the entire image. The value of kappa the total area (19295.325 km2), maximum area 30.52% coefficient is higher than 0.75 the degree of accuracy is (5889.744 km2) represents temperature from 25.61oC to categorized as very well (Conalton and Green,2008; 28.32oC in 2000 and 44.94 % (8671.50 km2) maximum area foody,2002; Pontius Jr and milloues ,2011; story and are covered in between temperature area 25.61 to 28.32oC conganlton,1986). The validate LU/LC classifications, a in 2010 and in 2020, 43.97% (8483.56 km2) followed comparison was done between the sampling points (400 temperature from 34.02oC to 36.83oC. The built-up area and points) and their corresponding point on the Google images brick kiln field are sensitive to high concentration of the in the same time period. The validation for all periods is LST. From 2000 to 2020, the maximum temperature was more than 90%, so that, it can be concluded accuracy was faced 34.86oC to 41.34oC were area covered 1.95% (376.96 good to estimate in the overall accuracy assessment and km2) in the year 2020. These spatial growths of the average Kappa statistics and validation. temperature approximate 7.33oC LST have increased. The South eastern part of the study area exhibits lowing in the 4.4. DISTRIBUTION AND CHANGES OF LST: temperature due to the higher concentrate of vegetation Spatial pattern and areal distribution of LST in the three and agricultural lands, water bodies, whereas North phases-2000, 2010, and 2020, indicate in the figure-4; and western part and river side’s exhibits the higher in LST due Table-5; clear reddish hue shows higher temperature and to the rapid expansion of built-up lands and Brick kiln greenish colour shows low LST in the all maps. The spatial fields and decline water bodies as well as vegetation cover. pattern of LST concentration and temporal shift LST and changes in the LU/LC classes. LST is confined within the Table-2; Distribution of LU/LC. (2000 (2010 (2000 Change Change Change - - - 2000 2010 2010) 2010 2020 2020) 2020) Class Name

IJSERkm2 % km2 % % km2 % km2 % % % Water bodies 3573.48 18.52 2204.56 11.43 7.09 2204.56 11.43 2191.35 11.36 -0.07 -7.16 Vegetation 7655.18 39.67 9554.53 49.52 -9.84 9554.53 49.52 9416.83 48.80 -0.71 9.13 Scrub Lands 2145.63 11.12 703.03 3.64 7.48 703.03 3.64 2121.04 10.99 7.35 -0.13 Built-up Lands 168.32 0.87 845.87 4.38 -3.51 845.87 4.38 1238.38 6.42 2.03 5.55 Agricultural Lands 4214.12 21.84 4839.75 25.08 -3.24 4839.75 25.08 3469.20 17.98 -7.10 -3.86 Agricultural Fallow Lands 1534.14 7.95 1141.45 5.92 2.04 1141.45 5.92 807.76 4.19 -1.73 -3.76 Brick kiln fields 4.46 0.02 6.01 0.03 -0.01 6.01 0.03 50.65 0.26 0.23 0.24 Total 19295.20 100 19295.20 100 19295.20 100 19295.20 100

IJSER

Figure-3; LU/LC map of Keleghai River Basin (2000, 2010 and 2020).

Table-3; Distribution of LU/LC Changes matrix. LU/LC of 2010

Agricultural Vegeta Water Scrub Built-up Agricultural Brick kin Fallow Grand Total tions bodies Lands Lands Crops Land fields LU/LC Lands classes k km % km2 % km2 % km2 % km2 % km2 % m % km2 %

LU/LC of 2000 2 2

Vegetat 6.51 4.68 2.14 5.97 0.24 0.11

ions 50.47 23.12 50.08 14.20 26.37 12.08 13.02 11.33 109.33 218.31

Water 3.38 0.35 9.37 0.02 0.00

bodies 16.84 37.27 12.13 24.04 89.51 49.57 160.89 356.06 115.92 229.68 955.45

Scrub 0.60 0.86 6.37 9.07 0.00 0.00 8.63 0.00 0.00 3.65

Lands 23.96 34.08 30.73 43.72 12.28 70.30

Built- up 4.11 4.86 4.41 5.21 4.19 4.95 0.01 0.01 4.39 Lands 24.93 29.47 33.88 40.05 13.06 15.44 84.59

Agricul

tural .00 8.52 1.90 0.39 6.94 0.02 0

Crops 21.27 41.78 41.23 21.10 33.58 25.11 102.96 202.18 102.10 483.98 Land

Agricul

tural 2.46 2.15 4.48 3.92 0.00 0.00 5.92

Fallow 15.19 13.31 57.57 50.44 15.06 13.19 19.39 16.98 114.14 Lands

Brick

kin 0.15 0.12 0.07 0.20 0.08 0.01 2.15 0.17 0.60 0.03 fields 24.96 19.30 11.97 13.60 27.82

Grand 100 0.45

Total 16.83 355.19 765.52 214.56 421.41 153.41 1927.37 IJSER LU/LC of 2020 Vegeta Water Scrub Built-up Agricultural Agricultural Brick kin Grand tions bodies Lands Lands Crops Land Fallow Lands fields Total LU/LC k k classes km km % m % m % % km2 % km2 % km2 % km2 % 2 2 2 2

Vegetat

8.09 4.89 0.12 0.05

ions 73.98 33.92 41.73 19.13 17.65 10.66 48.30 22.15 25.67 11.77 11.32 218.11

Water 2.95 5.50 5.82 0.12 0.01

bodies 11.46 49.14 27.77 51.72 25.11 54.70 48.78 107.74 462.00 236.10 940.15 LU/LC of 2010

Scrub 4.71 6.69 8.07 2.79 5.20 0.02 0.01

Lands 13.61 54.63 19.34 75.08 25.97 15.02 15.00 157.93 289.08

Built- up 8.17 0.06 0.14 5.13 6.42 0.02 0.03 2.27 Lands 13.78 31.47 18.67 11.72 10.19 23.29 14.67 43.77

Agricul

tural .29 6.90 1.97 1.02 0 6.80 1.95 9.62 2.75 0.00 0.00

Crops 67.06 90.72 25.97 18.13 234.28 349.34 Land Agricul

tural 1.52 1.88 2.73 3.38 1.98 2.45 2.30 2.85 0.01 0.01 4.19

Fallow 49.49 61.31 22.69 28.11 80.72 Lands

Brick

kin 31 2.67 0.86 1.65 0.11 1.84 0.35 5.65 0.27 4.28 0. 4.93 6.22 0.32 fields 42.89 13.86 26.56

Grand 100 0.60

Total 70.23 84.47 220.19 954.47 483.43 113.99 1927.37

LU/LC of 2020 Vegeta Water Scrub Built-up Agricultural Agricultural Fallow Brick kin Grand tions bodies Lands Lands Crops Land Lands fields Total LU/LC k classes km km % km2 % m % % km2 % km2 % km2 % km2 % 2 2 2

Vegetat 9.51 5.35 2.45 9.81 4.50 0.16 0.07

ions 37.38 17.14 50.97 20.74 33.48 15.35 11.32 111.17 218.11

Water 49 9.70 9.17 0.97 6. 0.07 0.01

bodies 27.07 36.55 91.19 19.20 61.06 48.78 254.53 IJSER343.58 180.54 940.14

Scrub 6.80 0.18 0.06 0.00 0.00

Lands 19.66 94.98 32.86 40.39 13.97 99.85 34.54 34.02 11.77 15.00 289.08

Built-

LU/LC of 2000 up 0.73 1.68 1.81 4.14 1.26 2.88 0.33 0.75 0.02 0.03 2.27 Lands 12.93 29.54 26.69 60.97 43.77

Agricul

tural 7.80 0.26 0.07 0.00 0.00

Crops 27.26 45.87 50.23 14.38 74.24 21.25 37.11 10.62 18.13 160.25 349.34 Land Agricul

tural 4.15 5.15 9.49 0.03 0.03 0.00 0.00 4.19

Fallow 26.41 32.72 11.76 30.07 37.26 10.56 13.09 80.72 Lands

Brick

kin 54 0.96 1.61 1. 0.01 0.18 1.53 0.37 5.89 0.20 3.14 6.22 0.32 fields 15.51 25.89 24.72 24.66

Grand 100 0.45

Total 16.81 356.89 764.68 214.31 420.98 153.26 1927.37

Table-4; Accuracy Assessment of LU/LC classified Maps.

User Accuracy (%) Producer Accuracy (%)

Year Accuracy Lands Lands Brick kiln Brick kiln Classification Vegetation Vegetation Settlements Settlements Agricultural Agricultural Agricultural Agricultural Scrubs Land Scrubs Land Fallow Land Fallow Land Kappa Statistics Water Bodies Water Bodies

2000 95 100 100 94 100 100 100 100 100 100 100 88 100 100 98.04 0.976 2010 90 100 100 94 100 100 100 100 100 100 90 83 90 100 91.36 0.898 2020 100 85 94 80 90 100 100 100 91 94 95 83 100 100 90.48 0.878 IJSER

Figure-4; LST map of Keleghai River Basin (2000, 2010 and 2020).

Table-5; Distribution and changes of LST Area (2000) Area (2010) Area (2020) Temperature in oC km2 % km2 % km2 % 18.54-22.37 4455.9 23.09315858 - - - - 22.38-25.60 4183.263 21.68018937 6304.952 32.67627 - - 25.61-28.32 5889.744 30.52420211 8671.498 44.94122 - - 28.33-32.06 3281.661 17.00754457 2592.801 13.43754 - - 32.07-34.01 1484.757 7.694905372 1263.159 6.546494 4121.357 21.35949 34.02-36.83 - - 462.789 2.398467 8483.56 43.96721 36.84-38.34 - - - - 4159.02 21.55469 38.35-39.85 - - - - 2154.302 11.16496 39.86-41.34 - - - - 376.96 1.953647 Total 19295.325 100 19295.2 100 19295.2 100

o o 4.5. VALIDATION OF ESTIMATED LST: 2010 and 32.07 C-41.34 C in 2020 respectively. The mean surface temperatures of the study area were 24.25oC, The Land Surface Temperature (LST) is obtaining from the 25.21oC and 35.47oC in 2000, 2010 and 2020 respectively thermal band of landsat-7(TM) and Landsat-8(OLI) images. (table-7). From the following box plot defines maximum The LST retrieving through the simplest techniques it and minimum with the nature of the land surface overrun with certain limitation- clear sky is major require temperature by define quantiles (1st Quantile-23.48,2nd to obtain accurate reading for the retrieval LST using Quanlite-28.16 and 3rd Quantile-32.84) in the year 2000 - satellite remote sensing techniques and another, all surface 2020 (figure-5). In the year 2010, 1st Quantile, 2nd quantile materials don’t have unique, emissivity value in a specific and 3rd quantile are more than in year 2000.In 2020, all area(Chen et al,2006;Dear et al.,2019;Neteler,2010).To quantile is higher than the 2000 and 2010. validate the estimated LST from remotely sensed data, the maximum and minimum LST data produced from the Indian metrological Department(IMD) for the year July 4.6. CHANGES OF LST FOR EACH LU/LC 2000,2010 and 2020 in the IJSER day time. Table-6 demonstrate, CLASSES: the deviation between estimated LST from remotely sensed data and recorded LST of IMD.The deviation have been Table-8, year wise minimum and maximum temperature calculated based on LST by the IMD.Negative deviation in for each LU/LC categories. Land Surface Temperature LST indicates estimation is higher than the recorded changed over the time depending upon the different temperature of IMD and positive deviation value indicates activities of the different Land cover and Land use lower recorded LST of remotely sensed data. The highest categories of the study area. According to the table-8; deviation was noted in minimum and maximum Maximum Land Surface Temperature of Built-up lands > temperature for the year 2010 (2.18) and 2000(-1.30) 28oC in 2000, > 34oC in 2010 and > 40oC in 2020 respectively. respectively, lowest deviation was minimum and Also, highest temperature is record of the Built-up lands maximum temperature for the year -0.7 and -1.43 in (26.72oC to 40.64oC) and Brick kiln fields (29.28oC to 2020.Finally, on the basis of IMD estimation, highest 41.34oC) in the year 2000 to 2020 and lowest temperature maximum deviation was found for 2010 (8.88%). Difference records is mainly found vegetation and water bodies LST for both remotely sensed estimated and IMD recorded 19.19oC to 32.07oC and 18.49oC to 35.68oC respectively in the LST in 7.33oC and 7.93oC respectively in the last 20years. year 2000 to 2020.Following the maximum temperature was Considering, all the limitation of remote sensing derived increased for the year 2010 to 2020, built-up lands and Brick LST estimation, the deviation between estimated and kiln fields was recorded 35.89oC – 40.34oC and 36.83oC – recorded LST is acceptable and can be used further analysis 41.34oC respectively. in future research.

Land surface Temperature (LST) of the keleghai River basin was calculated with Maximum and Minimum land surface temperature 18.54oC-34.01oC in 2000, 22.38oC-36.83oC in IJSER © 2021 http://www.ijser.org International Journal of Scientific & Engineering Research Volume 12, Issue 2, February-2021 590 ISSN 2229-5518

4.7. RELATION OF LAND USE AND LAND COVER their impact on the LST.In the future, urban planner and CHANGES ON LST: researcher could be focused on the issue of public health as well as environmental health and infrastructure associated In this study, Remote Sensing Techniques were used to with rapid Urbanization. estimate Land Surface Temperature (LST) and Land CKNOWLEDGEMENT Use/Land Cover (LU/LC) changes and study their 6. A : relationship effects between 2000-2020.Our findings shows I am thanks to earth science department of Vidyasagar that Built-up land and Brick kiln area is a primary factor University, West Bengal, India for the continuous support affecting LST, which in turn produces an abnormal during the work. I also thank my guide assistant professor radiation flux.LU/LC has relative impact on the LST, Dr. Dipanwita k. Dutta, department of remote sensing & especially Built-up and Brick kiln area. Active management GIS, to constructive and suggestions to improve the of LU/LC and its understanding are essential in the context structure and presentation of the manuscript. of anthropogenic climate change and global warming

(Turner, Lambin & Reenberg, 2007; wang, Zhan & Guo, 2016). In the figure-6; negative relationship between the vegetation and LST in the year 2000, 21.45oC and 34.39oC in 2020, were positive relation Built-up land, Brick kiln area and Temperature in the year 2000, 22.70oC and 26.39oC to

36.36oC and 37.80oC in 2020 (Table-9). Highly positive relation Temperature in the year 2000 with 2010 and 2020 is 0.63 and 0.66 respectively, but very low positive relation in the year 2000 within the year 2010 is 0.29.

5. CONCLUSION:

Base on the Landsat Satellite image of 2000, 2010 and 2020 we examined the trends of LU/LC changes and fluctuation of LST in Keleghai river Basin, W.B, India. It was found that there was an increased in the Built-up area and Brick kiln IJSER2 area from 2000 to 2020 about 1070.06 km and 46.18 km2 respectively, where are 1382.13 km2 area lost vegetation’s is rapidly increasing trend in the keleghai river basin area, while in the area occupied the vegetation less. In the last 20- year highest LST was increased by 25.81oC and 43.97% area was faced 21.75oC -24.01oC in the year 2020.Rapid growth of

Built-up lands and Brick kiln area in the key during process of LU/LC changes and subsequent rise of LST.A massive increased in LST can damage the human health and component of the Ecosystem (Kafy Abdulla-Ali et al.,2019). Local Government and Environmental planner should be reawakened to accomplish the desired ecological development concerning environmental resource planning and Management. The present study to provide useful implication for landscape planning that, needed for Landscape connectivity between green environment and

Table-6; Validation of Estimated LST 2000 2010 2020 Years Minimum Maximum Minimum Maximum Minimum Maximum

Remotely sensed 18.54 34.01 22.38 36.83 32.07 41.34 Estimated LST (o C)

Indian Metrological Department (IMD) 20.31 32.71 24.56 38.06 30.64 40.64 Recorded LST (o C)

Deviation (o C) 1.77 -1.30 2.18 1.23 -1.43 -0.7

% of Deviation (o C) 8.71% -3.97% 8.88% 3.23% -4.67% -1.72%

IJSER

Figure-5; Year wise Relationship of Land Surface Temperature of Keleghai River Basin. Table-7; Year Wise Distribution of Minimum & Maximum temperature. Years Minimum Maximum Range Mean SD Quantiles 2000 18.54 34.01 15.47 24.25 2.69 0%=18.47 2010 22.38 36.83 14.45 25.21 1.79 25%=23.48 (1st) 50%=28.16 (2nd) 2020 32.07 41.34 9.27 35.47 0.87 75%=32.84 (3rd) 100%=41.34

Table-8; LST for each LU/LC. 2000 2010 2020

Minimu Maximum Mean Minimu Maximum Mean Minimu Maximum Mean Class Name SD SD SD m temp. in Temp. m temp. in Temp. m temp. in Temp.

(oC) (oC) (oC) (oC) (oC) (oC)

23.98 24.04 40.63 1.53 1.6 0.9 Vegetation 19.19 33.12 20.2 25.07 32.07 34.35

22.69 22.48 40.67 2.56 1.93 0.58 Water bodies 18.49 33.83 22.38 27.07 35.08 37.05

26.91 29.12 40.89 1.88 0.56 2.3 Scrub Lands 2.16 34.01 21.82 30.83 38.32 41.10

22.22 40.36 22.7 0.83 0.62 0.8 Built-up Lands 20.11 26.72 25.88 35.89 36.88 40.34

26.02 22.01 4078 Agricultural 1.66 1.04 0.51 20.58 33.43 25.20 31.85 35.59 39.39

Lands 25.13 23.82 40.96 Agricultural 1.94 2.47 0.59 19.77 33.48 22.38 33.96 34.13 39.29

Fallow Lands 26. 24.49 1.09 0.85 41.8 Brick kiln 1.2 23.44 29.28 39 26.91 36.83 39.50 41.34

fields

Table-9; Distribution of LU/LC. Mean Temperature(oC) Mean Temperature(oC) Mean Temperature(oC) Class Name (2000) (2010) (2020) Vegetation 21.45 22.48 34.39 Water bodies IJSER23.98 24.04 36.30 Scrub Lands 26.91 29.12 36.01 Built-up Lands 22.7 22.22 36.36 Agricultural Lands 26.02 22.01 35.31 Agricultural Fallow Lands 25.13 23.82 36.96 Brick kiln fields 26.39 24.49 37.80

Figure-6; Year wise Relationship of Land Surface Temperature and LU/LC of Keleghai River Basin.

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