Detecting spatio-temporal dynamics of urban growth and sprawl through geospatial technique and spectral indicators on Habra-I and Habra-II blocks of North 24 Parganas, India

The worldwide fertility rate is becoming a most signicant context of anthropological condition. Rapid population pressure is one of increasing factors for the global land crisis and gradually affects the environment and boosting climatic vulnerability. But world population progressively increased and hammering the natural environmental condition. Urban heat island (UHI) is increased due to anthropogenic activities and urban expansion, which causes public health emergency. Space-based UHI identication methods are used to estimate the environmental degradation using Land surface temperature (LST) along with different spectral indicators derived from multi-temporal Landsat images. The Landsat imageries were used to calculated land use and land cover maps of 1990, 2000, 2010, and 2020 were used for Habra-I and Habra-II blocks of North 24 Parganas. A supervised classication technique was applied for LU/LC classication. Shannon’s entropy model has been used for detecting urban expansion over the last 30 years. Land use and land cover (LULC) changes are notied in this study region because of urban expansion. 17.81 Sq.km of Agricultural land and 17.99 Sq,km of thick vegetation have been decreased similarly 43.24 Sq.km of the built-up area increased. Central Business District (CBD) is more densely population rather than the peripheral part. In the last thirty years around 6.52 ° C temperatures have been increased in this area. The highest values of NDBI are 0.16 (1990) to 0.59 (2020) respectively. The highest values of NDVI are 0.808 (1990) to 0.459 (2020) respectively. That spectral indicator shows that vegetated area has been affected due to urban expansion. for To the images were projected into Geodetic (WGS) 1984 Universal Transverse Mercator (UTM) zone 45 N. pre-processed v14 and geo-referenced, atmospheric correction, radiometric correction and calculation raster calculation tool, UHI calculation and correlation analysis.


Introduction
The beginning of twentieth-century great scienti c inventions got a new engine like a steam engine, which boosted the process of industrialization. After the Second World War urbanization procedure has totally entered a new development phase where economic development has strongly clinched sub-urbanization. In the developing countries of urban expansion happen in the metropolitan region.
Mainly the rapid growth of the population due to rural to urban migration, industries, commercial progress and the housing use higher than the city to the periphery area. The rapid growth of the population results in uncontrolled expansion of settlement. Land is basically a very essential part of human life as well as the main part. In this current time, population expansion, progress in mortality rate (7.1 per thousand deaths), life expectancy rate (67.1 years in India) and sex ratio (940/ thousand) put huge pressure on land. According to the 2011 census of India to total 17.57 (2001-2011 census) corers population was increased. Due to this overloaded population increased problem, land use very much changed. In India 30 percent urbanization process was increase in last 50 years (2011 census). Rapid urbanization, industrialization and technologically build-up area created more land scarcity in worldwide. Many administrative authorities extended the urban area due to extreme population or people migrant another parts of some area. The overwhelming population has been migrated towards those areas because of health facility, transport accessibility and low land value rather than Kolkata.
The rapid growth of the urban areas has been created a huge amount of land cover change and triggered the expansion of peripheral parts of an area; this is known as urban sprawl (Fang et al. 2005;Sarvestani et al. 2011;Aithal and Ramachandra 2016). The results of urban sprawl are overwhelming outward expansion, low population density, poor quality of environment, rapid land use/land cover change, rural area decreased and reduced the vegetated land and agricultural land, random growth with increased land consumption (Sahani and Raghavaswamy 2018;Vani and Prasad 2020). Also, urban sprawl is generated by urban-related problems, like poor quality of drinking water, deprived economic condition, sanitation problem, food scarcity, and increased the health-related problem to the people who are living in the slums area and fringe region (Weber and Puissant 2003;Punia and Singh 2012). The urban sprawl typologies acknowledged are urban fringe, scattered growth, edge expansion, and ribbon development (Wu et al. 2016;Sahana et al. 2018;Fei and Zhao 2019). The urban sprawl is negatively affected the sustainable development of the city, like decreased the eco-environmental condition, increased the daily energy demand due to population pressure and industrialization, and disconnects workplace and residence and the lower housing affordability due to high land value (Chatterjee et al. 2016, Dadashpoor andSalarian 2020). The other signs of urban sprawl are the development of built-up land (Ji et al. 2006), high land value (Frenkel and Ashkenazi 2008), increase of private vehicles (Morollon et al. 2016), low quality of life (Alsharif and Pradhan 2014) and damage the natural land cover area (Sisodia et al. 2018). The Habra-I and Habra-II blocks of North 24 parganas were used for this study because of that area were more urbanized in some decades and urban development is hammering the environmental degradation. Urban expansion increased the heat stress related problem, vegetation degradation, air pollution and public health related problem. The two municipalities of the two blocks were shown huge amount of urban development and built-up expansion over some last decades. The peripheral parts of megacity Kolkata is developed the urban area but negative impact also shown like, land use and land cover change, land surface temperature increased, vegetation condition, losses of agricultural land and many others environmental condition.
The urban sprawl is also called the suburban sprawl, which is a rapid geographic extension of the cities or the towns. Due to population pressure and migration towards rural to urban, which is increased the urban sprawl and resulting in land degradation. People were migrated because of economic condition, transportation accessibility, and improve their health but the result in different and increased the health related problem and environmental degradation over the slum and fringe area. Sprawling measurement is a more impressive research topic for researchers and scientists to delineating urban planning (Inostroza et al. 2013;Dadras et al. 2015;Fertner et al. 2016;Ozturk 2017;Mosammam et al. 2017;Shukla and Jain 2019;Chettry and Surawar 2020). The urban sprawl is a study that measures the expansion of the built-up area over the urban location associated with concluding the trend, pattern, and extent not only for the urban sprawling phenomena but also for the connected with complications (Sudhira et al. 2004;Jat et al. 2008;Verzora and Gonzalez 2010). The study area was link to the state (West Bengal) capital Kolkata and other important cities and location for goods supply and industrial belts were improved gradually due to urban expansion and population demands. The main occupations were agriculture, shery and small scale business, which was build their socio-economic improvement (http://www.habramunicipality.com/). Due to urban expansion and infrastructural development of this study area West Bengal, Department if Urban Development and Municipal Affairs (http://www.wburbanservices.gov.in/) were adopted some strategies for development over the study area. Road connection, urban amenities centre, parks, hospital, hearth centres, housing for all schemes, gardens and many others essential aspect for overall development of the study area.
The land use and land cover change dynamics analysis in the eld of urban-related studies by the researchers is increasing by investigating the urban growth-related phenomenon (El-Kawy et al. 2010;Sandhya Kiran and Joshi 2013;Rawat and Kumar 2015;Patra et al. 2018;Shooshtari et al. 2020). Due to urban expansion and population pressure urban areas increased the heat and this reason has been increased the chance of Urban Heat Island (UHI). Land surface temperature study is mostly acceptable for urban temperature mapping and monitoring (Gazi & Mondal. 2018;Ramaiah et al 2020). Different satellite images (thermal band) and noti ed formulas were used to calculate the LST on this study region on the multitemporal time scale . Remote sensing and Geographic Information System (GIS) is a powerful technique for monitoring and measuring the land use and land cover change dynamic studies using high resolution of temporal satellite images which is low-cost e ciency than the uses of traditional methods (Bhatta et al. 2010;Belal and Mangham 2011;Kadihm et al. 2016;Srivastava et al. 2017;Mohan Rajan et al. 2020). Urban planners and stakeholders need modern and cost-effective methods and techniques for managing and monitoring urban growth studies using land use and land cover change analysis (Alpopi et al. 2011). Shannon's entropy is an advantageous procedure for studying the computing, mapping, assessing, and monitoring the physical development of urban built-up areas on the neighboring region of the core town (Verzosa and Gongzalez 2010;Dhali et al. 2019;Kumar et al. 2020).
The main focus of this study is (1) Land use and land cover change over 1990-2020, (2) Land surface temperature (LST) calculation of various time interval, (3) Spectral indicator analysis and estimated the relationship with LST and various spectral indicator derived from satellite imageries, (4) Urban sprawl calculation using Shannon's entropy model, (5) calculate the urban expansion over Habra I and Habra II block of North 24 Parganas district, West Bengal, India and (6) Urban Heat Island (UHI) study over the study area. Population pressure is triggering the urban expansion and develops the land also agricultural land and the vegetated area has been converted into the urban area in this study region. This study is to help out to understanding the trend and pattern of manifestation of urban development over the urban space. Also, this research is helpful for urban planners, developers, and stakeholders in the future to build sustainable development and healthy livelihood for the people of this area.

Study Area
Population pressure, urbanization, and industrial works have been increased and damage the natural environment like; land use and land cover change increased land surface temperature (LST), decreased urban greening, and affected air quality. This study area is located in the West Bengal district, the state of India. The North 24 Parganas district is a part of West Bengal state, which is situated in the southern part of this state. The Sundarban tiger reserve is located in the southern parts of the North 24 Parganas district. The study area is situated into two blocks of North 24 Parganas, named Habra block I and II. The Kolkata and Howrah municipality is the nearest urban area, due to transportation accessibility, health facility, urban amenities, and overwhelming population pressure, this area is converted into urban agglomeration. Two municipalities are included in this study area, named Habra and Ashokenagar Kalyangarh Municipality. The total study area was around 251 sq.km and Habra and Ashokenagar Kalyangarh Municipality are sharing their boundary in eastern parts (Fig. 1).
The total study area is situated into 88° 32' -88 ° 48 ' east longitude to 22 ° 46' -22 ° 56' North latitude with an average elevation is 13 m (42 feet). Habra municipality is situated within Habra Police Station which is a part of Barasat Sub-Division. The municipality was established in the year of 1979 and initially, nineteen wards have divided this municipality. Habra-I and Habra-II blocks of North 24 Parganas are covered 148 Sq.km and 133 Sq.km respectively. At the established time population of Habra municipality was approximately 72500 people and currently, this municipality has a 21.82 sq.km of total area and around 147221 population (Census of India, 2011)( http://www.habramunicipality.com/). Ashokenagar Kalyangarh Municipality is also a densely populated with 20.50 sq.km area and 23 wards. The population of this municipality is 121592, as per Census of India (https://censusindia.gov.in/). The population pressure is increased the urbanization process and affected the natural environment. Increased land surface temperature, vegetation degradation, LULC change and increased the urban heat island. The urbanization trends and pattern analysis are helpful for future urban planning with the suitable modi cation. This study is also helpful for urban planner and decision maker for future sustainable development of those two municipalities.

Materials And Methods
The urban expansion of this study area affecting the land use and land cover change. This study is focused on the urban expansion scenario, identifying the changing pattern, quantifying and interpreting overall land use/land cover change and build-up growth based on the Landsat Thematic Mapper (TM), Landsat Operational Land Imager (OLI) and eld observation over the study area (Fig. 2).

Data Source
In the data for study area, land use and land cover change established using Landsat image. Different year image were using to identify the actual change scenario for earth surface (https://earthexplorer.usgs.gov/) with the least clouds and snow cover < 10%. 199, 2000, 2010 and 2020 year images were taken for land use/land cover change detection and detect the urban sprawl over the area. For the well understanding the urban sprawl of this area 2020 satellite image was deliberately included in this area despite a difference in the time interval. The selected satellite imageries information was shown in the Table 1. The Population data, administrative boundary and total urban agglomeration area of this study area were derived from Census of India (https://censusindia.gov.in/). Supervised classi cation and maximum likelihood algorithm is used to classi ed the satellite images and delineation of the urban sprawl over the study area.

Image processing phase
The satellite images were pre-processed to detect the built-up expansion of the study area for the year of 1990, 2000, 2010 and 2020. With the minimal alteration of the geometric accuracy the satellite imageries were collected from different dates. To development the satellite images quality; data were enhanced using histogram equalization (Meshesha et al. 2016). Recti cation is necessary for eliminate the image distortion. To eliminate the image distortion images were projected into World Geodetic System (WGS) 1984 Universal Transverse Mercator (UTM) zone 45 N. Data are pre-processed in ERDAS IMAGINE v14 and geo-referenced, atmospheric correction, radiometric correction and nally subsisting Region of Interest (ROI). The ArcGIS v10.5was used for map layout and data preparation, LST calculation using raster calculation tool, UHI calculation and correlation analysis.

LULC classi cation and accuracy assessment
Land cover is the natural earth surface features which is develop by naturally, like vegetation coverage, water bodies, agricultural land, etc. the land use and land cover change analysis is more important to identifying the urban expansion along with the urban sprawling over the study area using different time periods (Ramachandra et al. 2013;Bharath et al. 2017). The urbanization processes, population pressure and industrialization were hammering the natural land cover area and affecting the vegetation cover, agricultural land, etc. The supervised classi cation approach with the maximum likelihood algorithm was used to identifying the land use/land cover change analysis using different year Landsat images to conduct multitemporal LULC classi cation ). The area were classi ed into ve classes like, built-up area, vegetation cover, agricultural land, water body and open spaces over this study area.
The post-classi cation stage is used to increase the LULC classi cation accuracy (Cheruto et al. 2016) Using different years satellite images, medium-spatial resolution of Landsat data (TM and OLI) mixed pixels are a common problem (Lu and Weng 2005), mainly the urban areas are heterogeneous mixtures including a residential area, roads, railway, soil, trees, water, and grassland (Jensen et al. 2007). Remote sensing software (ERDAS IMAGINE v14 and ArcGIS v10.5) is used for the accuracy assessment. Accuracy assessment is a technique, which represents the correspondence between the aspect of earth surface and results of the classi cation. It is very important to detect the perfect accuracy by the proper classi cation of different year satellite imageries to analyse the land-use changes (Owojori and Xie 2005). The accuracy assessment of the land use and land cover maps were checked using the ground truth data and the results were interpreted using overall accuracy (OA) and kappa coe cient (k i ) (Table 3) Eq. 2 Where, n ij is representing the diagonal elements in the error matrix, k is total number of classes in the LULC classi cation, n is total number of samples in the error matrix and K i representing the kappa coe cient.

Land use and Land Cover Change
A post-classi cation change detection technique was calculated in ERDAS Imagine. Many researchers were used to detect the actual change of the urban environmental area or natural location and rate of change (Hardin et al. 2007). The change detection was calculated in pixel by pixel basis and a new thematic layer was created using ve class maps, containing different combination of 'from-to' change classes (Hassan et al. 2016) (Table 2).

Shannon's entropy model
Land use and land cover (LULC) dynamic study is more essential for anthropological effect assessment and climate change impact. But LULC is not su cient to estimate the change analysis, trend, and pattern of physical growth of and urban areas. Therefore, intensity and level of change in urban areas are most essential for physical growth analysis of the urban areas. Shannon's entropy model was used for this study for generating the spatial concentration and dispersion in an urban environment (Tewolde and Cabral 2011). Many researchers have been used this method to measure the urban sprawl with help of the Geographic Information System (GIS) (Verzosa and Gongzalez 2010;Dhali et al. 2019;Kumar et al. 2020). Shannon's entropy model is also specifying the degree of urban expansion by investigating whether the land development is dispersed or compact (Lata et al. 2001). The literature review is also indicating that Shannon's entropy model is a mostly used technique to identifying urban expansion.
Shannon's entropy (H) model is used to detecting the degree of spatial absorption or diffusion of a geographical variable (Xi) between different zones (Thomas 1981). The entropy model has calculated this following formula (Eq. 3). Where n denote the number of zones for urban sprawling study.
Shannon's entropy model is used for urban sprawl study and this method is used by various aspect like buffer zone wise (Yeh and Li et al. 2001;Sudhira et al. 2004;Sun et al. 2007;Mosammam et al. 2017;Dhali et al. 2019), Direction-wise techniques (Alsharif et al. 2015;Ozturk 2017) and ward-wise (Jat et al. 2008;Punia and Singh 2012). In this study, direction based Shannon's entropy model has been used to estimate the urban expansion over two municipality of Habra-I and Habra-II block, West Bengal, India. The CBD location is identi ed and then eight direction are establish for calculating the urban sprawling area that is N: North, NE: North-east, E: East, SE: Southeast,S: South, SW: South-west, W: West and NW: Northwest).

Buffer zone formation
The Central Business District (CBD) area has been detected from the eld visit. The CBD of two municipalities is the Rail Gate 2 area, which is the joint road of Highway 112 (Barasat-Petrapole Road) and Jirat road. Barasat-Petrapole Road and Jirat road are then named Jessore road. To measure the urban expansion six buffer zones are created from the CBD area. The buffer zones are 1 km, 2 km, 3 km, 4 km, 5km, and 6.5 km from the CBD area. Built-up densities are calculated from each buffer zones to estimate the urban expansion of each year from 1990-2020.  Ca et al (2008), to estimated brightness temperature a two-stop process are followed from Landsat TM images. The equation is that.
1. Conversion of the digital number (DN) to spectral radiance (L) (USGS 2001) is calculated by Eq. 5.
Where L is that the spectral radiance, Lmin is that the 1.238 (Spectral radiance of the DN value 1), Lmax is that the 15.6000 (Spectral radiance of DN value 255), and DN is that the digital number.
2. The spectral radiation was need to conversion of kelvin. To estimate the spectral radiance to temperature in kelvin the noti ed formula was used for Landsat 5 Tm data. Conversion of spectral radiance to temperature in kelvin (USGS 2001)

Validation of the result
To validate the results of this study, Google Earth and eld visit data are used to identifying the various land use land cover classes.
Different year's satellite imageries are classi ed in ArcGIS v10.5 and ERDAS IMAGINE v14 using supervised classi cation method with maximum likelihood algorithm. A eld visit is helping out for the road, educational institute, administrative, markets, commercial place, and many other urban amenities. These urban amenities have triggered the attraction of the population and boosting the process of urbanization over the study area. The Google Earth and eld visit can help to identify the appropriate results and validation of this study.

Land Use and Land Cover Map
Red, Green and blue colour band combination of the four different year satellite imageries were used for classi cation using standard colour combination. Landsat 5 TM and Landsat 8 OLI/TIRS all bands were combine in erdas imagine software in 'Layer Staking' toolbar. Population pressure has been increased the food and residential area demand over the world and this population pressure was increased the urban expansion and infrastructural development, which was negatively impact over the society. After the urban expansion of this area, in between the central part of this study area observed huge land use change. Basically there was handling of population pressure, Build-up area was rapidly increased and vegetation area was decreased similarly. The Land use and Land cover change dynamics was calculated in the years of 1990 to 2020 (Fig. 3). After expansion of urbanization, transportation system is very good for local people. Land value was increased and living sustainably upgrading day by day.  (Table 4). In the difference of area where thick vegetation has been increased in the year of 17.99 Sq.km rather than the grassland area is decreased in 6.15 Sq.km (2000), 5.59

Land Use and Land Cover Change Dynamics
Sq.km(2010), and 4.81 Sq.km(2020) respectively (Table 4-5). Vegetation area is decreased due to urbanization, basically residential needs. Land transformation is more effected on this area, Habra and Ashokenagar Kalyangarh municipalities are more built-up areas. Agriculture is the main occupation of this location but a huge amount of land transformation is affected by this natural phenomenon. This case is the reason for primary economic activities are converted into secondary and tertiary economic activities because of residential land,  Table 4). Those results show that the agricultural land mostly transforms to other land classes of Habra-I and Habra-II blocks of North 24 Parganas, West Bengal, India. In the initial phase of this study, 1990 map vegetation area was shows that the huge amount but after that the vegetated area has been decreased due to urban expansion, population pressure, industrial works and many others anthropogenic activities. The healthy vegetation increased the oxygen level in atmosphere and control the urban heat island related problem over the study area. Vegetation area also decreased the soil erosion, increased soil moisture and many others think. But after urban expansion and population pressure the Habra area was located huge vegetation de ciency and vegetated land was converted into agricultural land and urban built-up land.
Other land classes like built-up areas are increased due to population pressure, migration to another place, low land values, and near the business city Kolkata. In the year 1990, the built-up land of to block is 10.22 Sq.km but after that urbanization is targeted the built-up land and increased gradually, which is 18.54 Sq.km (2000)

Land transformation
Land transformation is the main reason for land use and land cover change. The overall area is decreased open space, thick vegetation, and agricultural land over the thirty years. Gradually built-up land, water bodies, and grassland are increased. Built-up land is increased due to urbanization and population pressure. One open space is located beside the Habra railway station, which name is 'Habra Math' and many others distributed in many parts of this study area. Many urban amenities like, market, bank, ATM, post o ce, shopping mall, playground, and restaurants were increased the urbanization in this location. The CBD is located near Habra railway station, which is joint The land transformation is very high due to urban expansion over Habra-I and Habra-II blocks of North 24 Parganas. Built-up area is increased around 8.32 Sq.km (1990-2000), 13 Sq.km (2000) 21.92 Sq.km (2010) and total 43.24 Sq.km (1990. The two municipalities' areas are more urbanized. Gradually thick vegetation and agricultural land have been decreased due to urban expansion. The thick vegetation is decreased in the year between 1990-2000, which is 3.68 Sq.km but after that 4.02 Sq.km (2000), 10.29 Sq.km (2010 and overall 17.99 Sq.km of area decreased . Agricultural land is decreased which is noti ed that 5.25 Sq.km (1990-2000), 6.17 Sq.km (2000), 6.39 Sq.km (2010 and overall 17.81 Sq.km area of agricultural land has been decreased between 1990-2020 (Table 5). Overall 6.19 Sq.km of grassland are decreased and 4.68 Sq.km open space are decreased due to urban expansion ( Fig. 5a-d) The maps show that urban expansion was affected the natural environment and many parts of this study area have been converted the agricultural land, thick vegetation, and open space into built-up land due to land demand and population pressure. The built-up land development is used for residential and commercial purposes have been a major impact on urbanization over the study area for thirty years. From this study, urban expansion is the crucial factor for land use land cover transformation. So, LULC planning and management are the major factors for the sustainable livelihood of this area.

Analysis of urban growth using Shannon's entropy model
The Shannon Entropy model is used to identifying the urban growth is compact or discrete one. The built-up area is more affecting part of this location due to urban expansion. The eight quadrants are used to identify the built-up growing over the land use due to high rate of urbanization over the last thirty years ( Table 6). The built-up rate is high in North-west (NW); West (W); North-east (NE) and South-East (SE) parts in the year of 1990 (Fig. 6).

Buffer zone wise urban area calculation
This study is to identifying the built-up expansion over the Habra and Ashokenagar Kalyangarh municipality using six buffer zone and also identifying the LULC change, LST change, Spectral indicator (NDVI and NDBI) change over Habra-I and Habra-II blocks of North 24 Parganas from 1990-2020. The main focus is that built-up expansion over two municipalities. The CBD areas more densely because the residential and commercial areas are a huge amount identi ed. Like Flats, Market, Shopping Malls, Bank, Park, and many other urban amenities. Also, the road networks are created densely over the CBD area. The 'Habra Railway Market' is a more populated area rather than the peripheral and core part of this study area. The second buffer zone 2 has been named as a core area because the area is the quintessence of central business district (CBD) is left and the actuality of housing area can similarly be seen here (Table 7). Buffer zone 3 is also created because the residential area can be located and the name of this area is the outer core area (Fig. 7). The 4th zone of the buffer is named an urban fringe area because this zone is mixed up with the urban-rural culture. 5th and 6th buffer also created and the names of those buffer zones are Thresholds urban limit area and Periphery area (Fig. 7). The agricultural land is also proved that this area has rural activities like crop production, livestock farming, etc. also rural-urban interaction can be identi ed clearly.
The results show that the proportion of the built-up area is mostly in CBD which has noticed that 10. 88% (1990), 19.33% (2000), 42.04% (2010), and 79.52% (2020). Also the core areas have 4.25% (1990), 5.24% (2000), 36.18% (2010) and 49.24% (2020) respectively (Fig. 7). Table 7 shows that the year and the percentage-wise built-up area in different named buffer zones. The peripheral part of this area shows that the negative relationship between built-up area and distance from CBD location due to transportation accessibility, urban amenities, etc. the built-up land density is lower from CBD area to periphery parts of this study location. The homogeneous land structure has increased the chance of more built-up in the CBD area. The periphery part of this study area has been lower opportunities for urbanization and higher diversi ed land use structure like agricultural land, thick vegetation, etc. The results show that the CBD area more densely urbanized rather than other parts of this area.

LST variation
Land surface temperature is increased due to urbanization, population pressure, and industrial work. Habra-I and Habra-II blocks of the North 24 Parganas district are the peripheral areas of the Kolkata metropolitan area. High accessibility and business area joint the peripheral part. Habra railway station and highway 112 is directly joining the Kolkata area and Netaji Subhas International Airport area. Those reasons are increased the built-up area also land value is the important factor because in past days the land values are low rather than present days. Those reasons are increased the LST of this study location. In the years 1990, the high-temperature value is 23.52 ° C and the low temperature is 17.78 ° C. Similarly highest temperatures are 25.55 ° C, 27.19 ° C, and 30.04 ° C in the years of 2000, 2010, and 2020 respectively. The lowest temperature values are 18.07 ° C, 19.87 ° C, and 22.01 ° C in the years 2000, 2010, and 2020 accordingly (Fig. 8). In between thirty years around 6.52 ° C temperatures were increased due to anthropogenic activities.

Spectral Indicators
Two spectral indicators have been used in this study for identifying the correlation with land surface temperature (LST). Normalized Difference Vegetation Index (NDVI) is used for monitoring the vegetated area and degradation over 1990-2020 on this study area. The central part of Habra is more vegetated in the year 1990 but after that urban expansion is damaged the vegetated area. Also, NDVI maps are used for monitoring the vegetation health over Habra-I and Habra-II blocks of North 24 Parganas (Fig. 9). The highest value of NDVI is 0.808 but after thirty years the values decreased to 0.459 due to urban growth. Normalized Difference Built-up Index (NDBI) maps are used for monitoring the built-up expansion over this study area. The central part of this area like around the Habra railway station area is densely populated due to high accessibility and urban amenities. The highest value of NDBI is 0.16 in the year 1990 and the highest value of NDBI is 0.59 in the year 2020 respectively (Fig. 10). Those NDBI maps show the perfect variation of urban expansion.

Correlation analysis between LST and spectral indicators
Land surface temperature is the dominating factor for the climate change scenario. In the urban area, due to anthropogenic activities like urbanization, transportation development, and industrialization have increased the LST over an urban location. The urban area has more LST values rather than a rural area. This study location also has both types of land areas. accordingly (Fig. 11). These results indicate that vegetated areas decreased and built-up areas increased due to anthropogenic activates and need proper planning for environmental conservation.

Conclusion
Land use and land cover is the most dominating factor for our environment. This study is to identify the LULC dynamics using satellite remote sensing data over the two municipalities (Habra and Ashokenagar Kalyangarh Municipality) of Habra-I and Habra-II block, West Bengal. The spatial extension of the urban area is also identifying using Shannon's entropy model. Agricultural land and Vegetated area have been degraded due to the rapid growth of built-up areas along with urbanization and transportation accessibility. The built-up area has been extended during the periods of 1990-2000, 2000-2021, and 2010-2020, also  The Landsat Tm and OLI/TIRS satellite imageries were used for this study. Urban expansion is the most affecting parts of our environment and increased the environmental degradation over the study area. The urban heat island (UHI) and land surface temperature (LST) were used for this study. Habra-I and Habra-II blocks of North 4 Parganas district were used for this study to identifying the urban sprawl and LULC change detection over the study area. Due to 30 meter resolution, minor level investigation is not properly identifying but the general study has been improved for planning and urban development purpose. The urbanization process has been extended due to population pressure and the development of income. Urbanization is not only the built-up area; it's also the commercial, industrial, and many urban amenities area. The overwhelming population is the main worry of those areas because of the unplanned development of the urban areas.
The nearest metropolitan area Kolkata, the business hub is increased the income source and develop a healthy livelihood. Habra-I and Habra-II people were travelled towards Kolkata due to their income source. This research is carried out on the urban growth and condition of natural environmental variables over Habra-I and Habra-II blocks of West Bengal state, India. The outcome of this research may support the urban planner and local administrators to realize the past and present trends of urbanization in this area and prepare a proper planning strategy for the future. Urban planners and local administrators can change the improvement of the unplanned development of urbanization and reduce the effect of natural environmental variables.

Figure 2
Methodology of entire study.

Figure 3
Land use and land cover map of different years (1990-2020).

Figure 7
Division of buffer zones for measuring built-up density.

Figure 8
Land surface temperature (LST) maps of different years interval.

Figure 11
Correlation-ship with LST and different spectral indicators (NDVI and NDBI)of different time intervals .