The Change in the Land Cover of Mahmudiyah City in Iraq for the Last Three Decades
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Abstract
The land cover of Mahmudiyah city, located south of the capital, Baghdad - Iraq, was studied for the period from 1986 to 2021 with five years between every two successive scenes, where Landsat scenes were used downloaded from the US Geological Survey (USGS ) website with low cloud cover for sensors TM and OLI. The land cover of the study area was classified. The total accuracy of the classification was calculated, as well as the analysis of the user accuracy and the classifier accuracy (maximum likelihood) and its impact on the overall classification accuracy. The lowest accuracy value in 2009 was (85.101% (and the highest accuracy value in 1995) was 95.654%). The constancy percentage of the class for the adopted years was calculated and compared to 1986 as a reference year to determine the changes in the land cover of the study area. It was found that there were changes in the classes influencing one the other, and the constancy percentage of the class was low due to environmental influences and human factors. The constancy percentage of the urban class was recorded at 50%, while the other classes did not exceed this rate since they suffered from the overlap of their spectral response. The low spatial resolution of the Landsat scenes (30 meters per pixel) led to recording omissions and commission, which decreased the overall classification accuracy.
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References
Abduljabbar, H.; Hatem, A.; Al-Jasim, A.; Desertification Monitoring in the South-West of Iraqi Using Fuzzy Inference System. NeuroQuantology 2020, 18, 1–11.
Barrett, E.C.; Introduction to environmental remote sensing; Fourth Edition.; Routledge: New York, 2006; ISBN ISBN 0 412 37170 7.
Mohammed, M.A.; Hatem, A.J.; Change detection of the land cover for three decades using remote sensing data and geographic information system. AIP Conference Proceedings,2020,2307, 020029.
Abduljabbar, H.M.; Satellite Images Fusion Using Modified PCA Substitution Method. Ibn AL-Haitham Journal For Pure and Applied Science2017,30,29–37.
Richards, J.A.; Richards, J.; Remote sensing digital image analysis,Springer, 1999,3.
Anderson, J. R.; A land use and land cover classification system for use with remote sensor data, US Government Printing Office, 1976,964.
Naji, T.A.; Hatem, A.J. New adaptive satellite image classification technique for al Habbinya region west of Iraq. Ibn AL-Haitham Journal For Pure and Applied Science2017, 26, 143–149.
Bukheet, Y.C.; Al-Abudi, B.Q.; Mahdi, M.S.; Land Cover Change Detection of Baghdad City Using Multi-Spectral Remote Sensing Imagery.Iraqi Journal of Science2016, 195–214.
Abbas, H.N.; Abdulameer, I. M. A.; A study of the climate change impacts on Al Shuwaija Marsh-Wasit province using satellite remote sensing. In Proceedings of the AIP Conference Proceedings,2020,2307, 020048.
Raheem, M.A.; Hatem, A. J.; Calculation of Salinity and Soil Moisture indices in south of Iraq - Using Satellite Image Data. Energy Procedia 2019, 157, 228–233.
Muhsin, I. J.; Monitoring of south Iraq marshes using classification and change detection techniques. Iraqi Journal of Physics. 2017, 15, 78–86.
Naji, T.A.; Abduljabbar, H. M.; others The seasonal effect on the water bodies in Iraqi Marshlands. Plant Archives2019, 19, 4397–4403.
Asmael, N.M.; A GIs Based Weight of Evidence for Prediction Urban Growth of Baghdad City by Using Remote Sensing Data. Al-Nahrain Journal for Engineering Sciences2015, 18, 168–178.
Congalton, R.G.; Green, K.; Assessing the accuracy of remotely sensed data. principles and practices, CRC press, 2019.
Winkler, K.; Fuchs, R.; Rounsevell, M.; Herold, M.; Global land use changes are four times greater than previously estimated. Nature communications2021, 12, 1–10.
Qassim, Z.H.; Abduljabbar, H. M.; Land cover change for Baghdad City in the period 1986 to 2019. AIP Conference Proceedings, 2020,2307,020031.
Bhatta, B.; Analysis of urban growth and sprawl from remote sensing data,1St ed.,Springer Science & Business Media: London. 2018, 14, 13-25.
Mahmoud, H.; Divigalpitiya, P.; Spatiotemporal variation analysis of urban land expansion in the establishment of new communities in Upper Egypt: A case study of New Asyut city.The Egyptian Journal of Remote Sensing and Space Science. 2019, 22, 59–66.
Merhej, S.; Administrative boundaries map; Baghdad, Iraq: Ministry of Water Resources, General Directorate of Surveying 2007.
Pradhan, B.; Spatial Modeling and Assessment of Urban Form Analysis of Urban Growth: From Sprawl to Compact Using Geospatial Data. Cham: Springer 2017.
Peacock,R.;Missouri, M.; Accuracy assessment of supervised and unsupervised classification using Landsat imagery of Little Rock, Arkansas.Master of Science thesis. 2014.
Mohammed, M.A.; Naji, T.A.; Abduljabbar, H.M.; The effect of the activation functions on the classification accuracy of satellite image by artificial neural network.Energy Procedia2019, 157, 164–170.
Lillesand,T.; Kiefer, R.W.; Chipman, J.; Remote sensing and image interpretation; seventh. John Wiley & Sons: Danvers, 2015.