Analyzing Land Cover and Land Use Changes Using Remote Sensing Techniques: A Temporal Analysis of Climate Change Detection with Google Earth Engine

The detection of changes in land cover and land use (LCLU) is crucial for various geospatial applications, including urban development and environmental management. One vital aspect of LCLU research involves identifying modifications in impervious surface cover, which has significantly increased due to global economic growth and the rising urban population in many parts of the world. This investigation employs Landsat 9 OLI- 2/TIRS-2 imagery with a 30m spatial resolution to map structures in the Ziarat District of Pakistan, encompassing forests, water bodies, and barren land. It aims to detect changes in tree cover and canopy height. A time series of Landsat 9 OLI-2/TIRS-2 images were utilized to create change detection and land cover maps. The Analysis of Land Cover and Land Use (LCLU) for the Ziarat District was conducted using the GEE platform. The satellite images were classified into broad land cover classes, which include impervious surfaces, forest/tree cover, grassland/cropland, and water. The generated change detection map facilitates the identification of locations that have undergone modifications due to new constructions, offering valuable insights for the implementation of urban development policies and disaster management on a global scale.