Estimation of Surface Thermal Influxes from Satellite Images of the Newly Developed Built-up Areas of South 24 Parganas District, West Bengal

The South 24 Parganas is the southernmost district of West Bengal. It is characterised by a dynamic condition involving constant adjustments between human livelihood and an extremely vulnerable natural environment. Several stages of land reclamation occurred during the pre-independence period—during successive occupations by the ‘Baroh Bhuniyas’ (the twelve landlords), the Mughals and the British. The process of development here faced several constraints linked to natural, social, cultural, infrastructure and economic factors. This study revolves around the observations that urbanisation precedes environmental stress in the Sundarban due to the drastic impacts of the built-up environment on the natural land character and subsequently the surface conditions. Concretisation in the name of regional development leads to adverse impacts on the local ecosystem. This study aims first to explore the extent of urbanisation in the Sundarbans region of South 24 Parganas District of West Bengal in the last two decades. This phenomenon is then to be related to changes in the land surface temperature (LST) and consequent geo-climatic stresses during the period in question. The paper reveals the rate of changes in land use and related surface temperature transformations within and around both the older settlements and the newly emerged urban areas.

[1]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[2]  Alan T. Murray,et al.  Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data , 2000 .

[3]  James A. Voogt,et al.  Modeling Surface Sensible Heat Flux Using Surface Radiative Temperatures in a Simple Urban Area , 2000 .

[4]  D. Nowak,et al.  Local-scale fluxes of carbon dioxide in urban environments: methodological challenges and results from Chicago. , 2002, Environmental pollution.

[5]  P. Gong,et al.  Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data , 2002 .

[6]  K. Seto,et al.  Comparing ARTMAP Neural Network with the Maximum-Likelihood Classifier for Detecting Urban Change , 2003 .

[7]  Bert Guindon,et al.  Landsat urban mapping based on a combined spectral–spatial methodology , 2004 .

[8]  M. Bauer,et al.  Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing , 2005 .

[9]  Jeffrey G. Masek,et al.  Impact of Urban Growth on Surface Climate: A Case Study in Oran, Algeria , 2009 .

[10]  B. Bhatta.  Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India , 2009 .

[11]  Fredrik Lindberg,et al.  Global to city scale urban anthropogenic heat flux: model and variability , 2011 .

[12]  Xiaojun Yang,et al.  Parameterizing Support Vector Machines for Land Cover Classification , 2011 .

[13]  Emon Bagch,et al.  Nature of the Process of Urbanisation in South 24 Parganas District of West Bengal, India: A Spatio-temporal Analysis , 2015 .

[14]  Fabio Del Frate,et al.  Urban Surface Temperature Time Series Estimation at the Local Scale by Spatial-Spectral Unmixing of Satellite Observations , 2015, Remote. Sens..

[15]  Priyakant Sinha,et al.  Urban Built-up Area Extraction and Change Detection of Adama Municipal Area using Time-Series Landsat Images , 2016 .

[16]  Rahul Dev Garg,et al.  Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data , 2017 .