Assessing LULC dynamics in Kirkuk City, Iraq using Landsat imagery and maximum likelihood classification
2025
Jasim, Abed
A thorough examination of Land Use and Land Cover (LULC) changes is advantageous for environmental assessment and land management. The principal catalyst of this phenomena is human activity, particularly population expansion and the demand for increased urbanization. In this paper, Landsat imagery acquired from 2014 to 2022 was utilized to detect and classify alterations in land cover in Kirkuk City area. The Landsat images of the research region were analyzed employing two supervised classification techniques: the Maximum Likelihood Classification (MLC) algorithm and the Neural Network (NN) classification. Six specific signature classifications were chosen for categorization: water, bare land, soil, farm land, urban, and vegetation. The overall accuracy assessments of the MLC exceeded that of NN. The research findings revealed the substantial increase in agriculture and urban areas in Kirkuk city during the past few years. On the other hand, bare land and soil-covered regions has been reduced. These changes in land use are attributed to economic and population growth. The results of this study can significantly aid future urban planners in promoting sustainable urban development and protecting rural areas from random urbanization.
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