Abstract

• Remote sensing is one of the tools which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality Landsat imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research was to classify and map land-use/land-cover of the study area using remote sensing and Geospatial Information System (GIS) techniques. Nowadays, land use and land cover (LULC) changes due to both human beings and natural environment. Consequently, LULC changes impact on water resources such as forestry, water bodies, agriculture land, wetland, urbanization, industrialization and so on. The aim of this research is To classify and map land use and land cover changes in the study area using remote sensing and GIS, and to assess the accuracy and usefulness of the classification results. ArcGIS 10.4.1 has been used to analyze the images processing and classification. LULC conditions of this area for the time periods 2009 and 2023 have been considered and downloaded from Landsat ETM and Landsat 8 OLI satellites images. Maximum likelihood method has been conducted in supervised image classification technique. The ground truth data or reference points are used to classify the image classification applying Google Earth Pro. Moreover, water bodies, Dense vegetation (forest), light Vegetation (garden land), bare soil, settlement (Built-up), and agriculture land of six LULC classes are identified in this study. Bare soil, light Vegetation and settlement are significantly changed during two periods. Further, dense vegetation (forest) area was decreased to approximately 8.02 % between 2009 and 2023. However, the water bodies of this study area were decreased slightly. LULC by agriculture land was decreased between 2009 and 2023. The study area had an overall classification accuracy of 81.03% and kappa coefficient (K) of 0.67 in 2009 and an overall classification accuracy of 87.33% and kappa coefficient (K) of 0.72 in 2023 respectability. The results of this research paper can contribute effectively about LULC change detection and help decision makers to develop plan in this study area.