Mapping Land Cover/Land Use for Change Derivation Using Remote Sensing and GIS Technique
Keywords:Remote Sensing, Maximum Likelihood, Supervised Classification, Land Cover
Deriving land cover information from satellite data is one of the most common applications employed to monitor, evaluate, and manage the environment. This study aims to detect the land cover/land use changes and calculate the areas of different land cover types in Baghdad, Iraq, for the period from 2015 to 2020, using Landsat 8 images. The supervised Maximum Likelihood Classification (MLC) method was applied to classify the images. Four land cover types were obtained, namely urban, vegetation, water, and barren soil. Changes in the four land cover classes during the study period were observed. The extent of the urban, vegetation, and water areas was increased by about 7.5%, 9.5%, and 1.5%, respectively, whereas the barren soil area was decreased by about 18.5%. This study shows that the MLC classifier is a very effective method to map land cover classes.