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Kim, D.H., Narashiman, R., Sexton, J.O., Huang, C. and Towns-hend, J.R. (2011) Methodology to Select Phenologically Suitable Landsat Scenes for Forest Change Detection. International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, BC, 24-29 July 2011, 2613-2616.
https://doi.org/10.1109/IGARSS.2011.6049738

has been cited by the following article:

  • TITLE: Evaluation of Land Use & Land Cover Change Using Multi-Temporal Landsat Imagery: A Case Study Sulaimaniyah Governorate, Iraq

    AUTHORS: Karwan Alkaradaghi, Salahalddin S. Ali, Nadhir Al-Ansari, Jan Laue

    KEYWORDS: Settlement Expansion, Geographic Information System (GIS), Land Use Land Cover (LULC), Land Use Classification, Satellite Images, Accuracy Assessment and Change Detection

    JOURNAL NAME: Journal of Geographic Information System, Vol.10 No.3, June 19, 2018

    ABSTRACT: Land use & land cover change detection in rapid growth urbanized area have been studied by many researchers and there are many works on this topic. Commonly, settlement sprawl in area depends on many factors such as eco-nomic prosperity and population growth. Iraq is one of the countries which witnessed rapid development in the settlement area. Remote sensing and geographic information system (GIS) are analytical software technologies to evaluate this familiar worldwide phenomenon. This study illustrates settlement development in Sulaimaniyah Governorate from 2001 to 2017 using Landsat satellite imageries of different periods. All images had been classified using remote sensing software in order to proceed powerful mapping of land use classification. Maximum likelihood method is used in the accurately extracted solution information from geospatial imagery. Landsat images from the study area were categorized into four different classes. These are: forest, vegetation, soil, and settlement. Change detection analysis results illustrate that in the face of an explosive demographic shift in the settlement area where the record + 8.99 percent which is equivalent to 51.80 Km2 over a 16-year period and settlement area increasing from 3.87 percent in 2001 to 12.86 percent in 2017. Accuracy assessment model was used to evaluate (LULC) classified images. Accuracy results show an overall accuracy of 78.83% to 90.09% from 2001 to 2017 respectively while convincing results of Kappa coefficient given between substantial and almost perfect agreements. This study will help decision-makers in urban plan for future city development.