Impact of Land-Use/Land-Cover Change on Land Surface Temperature Using Satellite Data: A Case Study of Rajarhat Block, North 24-Parganas District, West Bengal

Increase in land surface temperature (LST) of growing urban areas in the current global warming scenario is a cause of concern for city planners. This study discusses the impact of land-use/land-cover (LULC) change on LST of the area in and around Rajarhat block, North 24-Parganas District, West Bengal, covering an area of 165 km2. Multi-spectral and multi-temporal satellite data from Landsat 5 TM (1990), Landsat 8 OLI (2016) and Sentinel 2A (2016) are used for the LULC mapping, and thermal infrared data from Landsat 5 TM and Landsat 8 TIRS (2016) are used for estimating the LST of 1990 and 2016. Results show that land-use pattern in November has changed in Rajarhat from 1990 to 2016: 13 km2 of vegetation cover lost due to urbanization; 9.3 km2 of open land converted to agricultural land and open fields/parks; 1.4 km2 of aquaculture ponds converted to tree cover/scrublands and 1.45 km2 of lakes/ponds filled up. Loss of vegetation (scrubland and tree) cover resulted in LST rise by about 1.5 °C. Aquaculture ponds have the ability to resist the rise in LST since the increase in temperature of this class is only 0.24 °C due to increase in its area. This change in land-use pattern over 26 years has increased the LST by 0.94 °C. The urban-heat-island (UHI) phenomenon has also increased. The area of the ‘strongest’ heat-island phenomenon, as per UTVFI classification scheme, has increased by 20.1 km2. Positive correlation is observed between NDBI and LST’s of urban areas (r = 0.002 for 1990 and r = 0.047 for 2016) which suggests that urbanization is responsible for the rise in LST. The NCEP NOAA surface temperature model suggests that the long-term trends in the rise in maximum LST over Rajarhat is about 1 °C from January 1990 to November 2016 with 90% confidence level validating the extracted LST data from satellites. Sustainable urban planning is required to arrest the rise in LST which includes urban forestry, construction of water bodies and fountains, preserving existing aquaculture ponds and reducing construction activities.

[1]  Guido van Rossum,et al.  Python Programming Language , 2007, USENIX Annual Technical Conference.

[2]  Clement Atzberger,et al.  Satellite-based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi, India , 2009 .

[3]  Manfred Owe,et al.  On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces , 1993 .

[4]  M. Netzband,et al.  AN ASSESSMENT OF URBAN ENVIRONMENTAL ISSUES USING REMOTE SENSING AND GIS TECHNIQUES : AN INTEGRATED APPROACH . A CASE STUDY : DELHI , INDIA , 2007 .

[5]  C. Vörösmarty,et al.  Global water resources: vulnerability from climate change and population growth. , 2000, Science.

[6]  Ugur Avdan,et al.  Application of Open Source Coding Technologies in the Production of Land Surface Temperature (LST) Maps from Landsat: A PyQGIS Plugin , 2016, Remote. Sens..

[7]  D. Yuan A SIMULATION COMPARISON OF THREE MARGINAL AREA ESTIMATORS FOR IMAGE CLASSIFICATION , 1997 .

[8]  Jinqu Zhang,et al.  A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6 , 2006, Comput. Geosci..

[9]  R. Mccoy,et al.  Mapping Desert Shrub Rangeland Using Spectral Unmixing and Modeling Spectral Mixtures with TM Data , 1997 .

[10]  M. S. Moran,et al.  Variability of emissivity and surface temperature over a sparsely vegetated surface , 1994 .

[11]  Juan C. Jiménez-Muñoz,et al.  Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data , 2014, IEEE Geoscience and Remote Sensing Letters.

[12]  R. Congalton,et al.  Accuracy assessment: a user's perspective , 1986 .

[13]  L.L.F. Janssen,et al.  Accuracy assessment of satellite derived land - cover data : a review , 1994 .

[14]  Qiming Zhou,et al.  On the ground estimation of vegetation cover in Australian rangelands , 1998 .

[15]  Stephen V. Stehman,et al.  Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles , 1998 .

[16]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[17]  Lin Liu,et al.  Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong , 2011, Remote. Sens..

[18]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[19]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[20]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[21]  C. K. Singh,et al.  Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city, Delhi-India , 2013 .

[22]  A. M. Hay,et al.  The derivation of global estimates from a confusion matrix , 1988 .

[23]  Swades Pal,et al.  Detection of land use and land cover change and land surface temperature in English Bazar urban centre , 2017 .

[24]  M. S. Moran,et al.  Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output , 1992 .

[25]  Seema Jalan,et al.  SPATIO-TEMPORAL ASSESSMENT OF LAND USE/ LAND COVER DYNAMICS AND URBAN HEAT ISLAND OF JAIPUR CITY USING SATELLITE DATA , 2014 .

[26]  P. Chavez Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[27]  José A. Sobrino,et al.  Land surface temperature retrieval from LANDSAT TM 5 , 2004 .

[28]  John R. Schott,et al.  Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration , 2014, Remote. Sens..

[29]  Russell G. Congalton,et al.  AN ASSESSMENT OF REFERENCE DATA VARIABILITY USING A "VIRTUAL FIELD REFERENCE DATABASE" , 2001 .

[30]  Eva Rubio,et al.  Autonomous Measurements of Sea Surface Temperature Using In Situ Thermal Infrared Data , 2004 .

[31]  Paul C. Van Deusen,et al.  Unbiased estimates of class proportions from thematic maps , 1996 .

[32]  J. Mallick,et al.  Estimation of land surface temperature over Delhi using Landsat-7 ETM+ , 2008 .

[33]  Zhenkui Ma,et al.  Tau coefficients for accuracy assessment of classification of remote sensing data , 1995 .

[34]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[35]  R. Leemans,et al.  Comparing global vegetation maps with the Kappa statistic , 1992 .

[36]  D. Artis,et al.  Survey of emissivity variability in thermography of urban areas , 1982 .

[37]  Aakriti Grover,et al.  Analysis of Urban Heat Island (UHI) in Relation to Normalized Difference Vegetation Index (NDVI): A Comparative Study of Delhi and Mumbai , 2015 .