A geospatial approach for assessing the relation between changing land use/land cover and environmental parameters including land surface temperature of Chennai metropolitan city, India

• Published in: Arabian journal of geosciences Vol. 14; no. 2
• Main Authors: Chanu, Changamayum Samurembi, Elango, Lakshmanan, Shankar, Ganti Ravi
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 2021; Springer Nature B.V
• Subjects: Algorithms; Atmospheric correction; Atmospheric models; Change detection; Classification; Coastal zone; Correlation; Earth and Environmental Science; Earth science; Earth Sciences; Emissivity; Environmental changes; Environmental factors; Image classification; Image enhancement; Industrialization; Information retrieval; Land cover; Land surface temperature; Land use; Landsat; Landsat satellites; Original Paper; Parameters; Remote sensing; Residential areas; Satellite imagery; Spaceborne remote sensing; Support vector machines; Surface temperature; Urbanization; India; Surface wetness; Impervious surface; Land surface temperature; Landuse change detection; NDVI and NDBI
• ISSN: 1866-7511; 1866-7538
• DOI: 10.1007/s12517-020-06409-0

Land use/land cover in coastal regions of large cities is affected due to rapid urbanization and industrialization. Chennai, a coastal city of Tamil Nadu, India, has witnessed tremendous changes in land use/land cover over the past two decades. Post-classification correlation change detection method was used to identify the changes over the decade. To ensure image classification and precise land use land cover (LULC) mapping, the different image enhancement, atmospheric correction, information extraction techniques and unsupervised classification algorithms were carried out. The study reveals that the support vector machine(SVM) and maximum likelihood gave higher accuracies with the rate of 91.50% and 92.25% for the years 2005 and 2016, respectively. The study showed that wetness land cover area has decreased by 10.05 (0.98%) from 2005 to 2016. Conversely, as a result of the expansion of new industrial, commercial, and residential areas, the built-up area has remarkably increased by 363.99 km 2 (10.13%) from 2005 to 2016. Different algorithms were used to process the thermal infrared data of Landsat satellite images to accurately estimate land surface temperature (LST). From various emissivity models, a minor shift in LST was found and the cross-validation of the results obtained indicated that the outcome of this study is reliable. A new integrated enhancement method was demonstrated to extract the impervious land surface. A positive correlation with LST to the impervious surface and a negative correlation with vegetation at the regional scale were obtained. Thus, the study demonstrated the relationship between the LULC changes over 11 years and their relationship with the LST and other environmental parameters of a large metropolitan city of India.