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 km² (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.

大城市沿海地区的土地利用/土地覆盖由于快速的城市化和工业化而受到影响。印度泰米尔纳德邦沿海城市钦奈（Chennai）在过去的20年中见证了土地利用/土地覆盖的巨大变化。分类后相关变化检测方法用于识别十年来的变化。为了确保图像分类和精确的土地利用土地覆盖（LULC）制图，进行了不同的图像增强，大气校正，信息提取技术和无监督分类算法。研究表明，2005年和2016年，支持向量机（SVM）和最大似然率均具有较高的准确性，比率分别为91.50％和92.25％。研究表明，从2005年到2016年，湿地覆盖面积减少了10.05（0.98％）。²（10.13％）从2005年到2016年。使用不同的算法处理Landsat卫星图像的热红外数据，以准确估算地表温度（LST）。从各种发射率模型中，发现LST发生了微小变化，并且对结果的交叉验证表明该研究的结果是可靠的。演示了一种新的综合增强方法来提取不透水的地表。在区域尺度上，LST与不透水表面呈正相关，与植被呈负相关。因此，该研究证明了11年间LULC的变化与其与LST和印度大城市其他环境参数之间的关系。