Volume XIX |

Estimation of changes in land surface temperature using multi-temporal Landsat data of Ghaziabad District, India

Abstract: The rapid growth in urban population in India is seen to create an essential for the development of more urban infrastructures. Land surface temperature (LST) is a significant factor in many areas like climate change, urban land use/land cover (LULC), heat balance studies and a key input for climate models. The main objective of this paper is to examine multi-temporal land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) changes of Gaziabad district in Uttar Pradesh, India using LANDSAT satellite data in GIS platform. To compute the changes and relationship between Land Surface Temperature (LST) and Land Use Land Cover (LULC), Landsat LST data for the months of September of year 2000, 2011 and 2018 were used in this study. The LST has been estimated with respect to Normalized Difference Vegetation Index (NDVI) values determined from the Red and Near Infrared bands. The Land Surface Emissivity (LSE) is retrieved directly from the Thermal Infrared bands. The present study focuses on ArcGIS Raster functions and Raster calculation using the LANDSAT in September, thermal Bands (10, 11 & 6). The output of this paper shows that the surface temperature was high in the barren and built up area whereas it is comparatively low in the thick vegetation and agriculture land. It is also recommended that in order to reduce the land surface temperature of urban areas, sustainable urban planning strategies that include increasing the vegetated areas and embracing other green initiatives such as urban forestry should be adopted.

Volume XV |

Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images

Abstract: Timely and accurate land use/land cover (LULC) information is requisite for sustainable planning and management of natural resources. Remote sensing images are major information sources and they are widely used for mapping and monitoring various land features. Images from various sensors, with different spatial resolutions, are available; however, the selection of appropriate spatial resolution is an essential task to extract desired information from images. This paper presents the conclusions of the work related to LULC classification based on multi-resolution remote sensing images. Optical data collected by three different sensors (LISS IV with 5.8 m and Landsat 8-OLI with 30 m and AWiFS with 56 m spatial resolutions respectively) in 2013 are examined against the potential to correctly classify specific LULC classes. The classifications of images are performed using Maximum Likelihood Classifier (MLC). The results indicate that the overall accuracy and kappa coefficient of LISS IV with 5.8 m are higher than that of Landsat 8-OLI with 30 m and AWiFS with 56 m images. Understanding the role of spatial resolution in LULC classification accuracy will enable the appropriate interpretation of any classified images.