Dynamics of Land Use and Land Cover Change Using Geospatial Techniques – A Case Study of Baghdad, Iraq

• Autorzy: Mezaal Mustafa Ridha , Mahmoud Ammar S. , Jasim Mohammed A. , Naje Ahmed Samir

Identyfikatory

DOI

10.12912/27197050/152896

• Języki publikacji: EN

Abstrakty

EN

Urban land-cover change is increasing dramatically in most emerging countries. In Iraq and in the capital city (Baghdad). Active socioeconomic progress and political stability have pushed the urban border into the countryside at the cost of natural ecosystems at ever- growing rates. Widely used classifier of Maximum Likelihood was used for classification of 2003 and 2021 Landsat images. This classifier achieved 83.20% and 99.58% overall accuracies for 2003 and 2021 scenes, respectively. This study found that the urban area decreases by 16.4% and the agriculture area decrease by 5.4% over the period. On the other hand, barren land has been expanded up to more than 7% as well as increasing in water land that should probably due to flooding (almost 15% more than 2003). To reduce the undesirable effects of land-cover changes over urban ecosystems in Baghdad and in the municipality in specific, it is suggested that Baghdad develops an urban development policy. The emphasis of policy must be the maintenance an acceptable balance among urban infrastructure development, ecological sustainability and agricultural production.

Słowa kluczowe

EN

land cover; maximum likelihood; ML; change detection

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2022
• Tom: Vol. 23, iss. 6
• Strony: 216--226
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Mezaal Mustafa Ridha

• Building & Construction Technology Engineering Department, Technical Engineering College, Northern Technical University, Mosul 41002, Iraq

• https://orcid.org/0000-0002-2523-8870

autor

Mahmoud Ammar S.

• Department of Civil Engineering, College of Engineering, University of Babylon, Babylon, Iraq

• https://orcid.org/0000-0002-6810-272X

autor

Jasim Mohammed A.

• Department of Civil Engineering, University Putra Malaysia, Jalan Universiti 1, 43400 Serdang, Selangor, Malaysia
• Directorate of Anbar Environment, Ministry of Environment, Anbar, Iraq

• https://orcid.org/0000-0003-3875-4070

autor

Naje Ahmed Samir

• Collage of Water Resource Engineering, AL-Qasim Green University, Babylon 51031, Iraq

• https://orcid.org/0000-0001-8997-4087

Bibliografia

• 1. Acito, N., Diani, M., Corsini, G. 2017. A theoretical Gaussian framework for anomalous change detection in hyperspectral images. In Image and Signal Processing for Remote Sensing XXIII. International Society for Optics and Photonics, 10427, 104270D.
• 2. Ali, I., Cawkwell, F., Dwyer, E., Barrett, B., Green, S. 2016. Satellite remote sensing of grasslands: from observation to management. Journal of Plant Ecology, 9(6), 649–671.
• 3. Bianco, S., Ciocca, G., Schettini, R. (2017). How far can you get by combining change detection algorithms?. In International conference on image analysis and processing Springer, Cham., 96–107.
• 4. Bruzzone, L., Bovolo, F. 2012. A novel framework for the design of change-detection systems for veryhigh-resolution remote sensing images. Proceedings of the IEEE, 101(3), 609–630.
• 5. Chipman, J.W. 2019. A multi-sensor approach to satellite monitoring of trends in lake area, water level, and volume. Remote Sensing, 11(2), 158.38.
• 6. Daide, F., Afgane, R., Lahrach, A., Chaouni, A.A. 2022. Beht Watershed (Morocco) Rainfall-Runoff Simulation with the HEC-HMS Hydrological Model. Ecological Engineering & Environmental ‏Technology 23.
• 7. Dibs, H. 2018. Comparison of derived Indices and unsupervised classification for AL-Razaza Lake dehydration extent using multi-temporal satellite data and remote sensing analysis. J Eng Appl Sci, 13(24), 1–8.‏
• 8. Dibs, H., Al-Janabi, A., Gomes, C. 2018. Easy To Use Remote Sensing and GIS Analysis for Landslide Risk Assessment. Journal of University of Babylon for Engineering Sciences, 26(1), 42–54.‏
• 9. Doxani, G., Vermote, E., Roger, J. C., Gascon, F., Adriaensen, S., Frantz, D., Vanhellemont, Q. 2018. Atmospheric correction inter-comparison exercise. Remote Sensing, 10(2), 352.
• 10. Gómez, C., White, J.C., Wulder, M.A. 2016. Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 55–72.
• 11. Halefom, A., Teshome, A., Sisay, E., Khare, D., Dananto, M., Singh, L., Tadesse, D. 2018. Applications of Remote Sensing and GIS in Land Use/Land Cover Change Detection: A Case Study of Woreta Zuria Watershed, Ethiopia. Applied Research Journal of Geographic Information System, 1(1), 1–9.
• 12. Jabbar, M.T., Zhou, X. 2011. Eco-environmental change detection by using remote sensing and GIS techniques: a case study Basrah province, south part of Iraq. Environmental Earth Sciences, 64(5), 1397–1407.
• 13. Khorrami, B. 2019. Monitoring the spatio-temporal trends of groundwater qualitative parameters through geostatistical tools. Sigma Journal of Engineering and Natural Sciences, 37(4), 1466–1479.‏
• 14. Kim, C. 2016. Land use classification and land use change analysis using satellite images in Lombok Island, Indonesia. Forest science and technology, 12(4), 183–191.
• 15. Mahmoud, A.S., Kalantar, B., Al-Najjar, H.A., Moayedi, H., Halin, A.A., Mansor, S. 2021. Objectoriented approach for urbanization growth by using remote sensing and gis techniques: a case study in Hilla city, Babylon Governorate, Iraq. Geospatial Technology and Smart Cities. Springer, Cham, 39–57.
• 16. Mezaal, M.R., Pradhan, B. 2018. An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data. Catena, 167, 147–159.
• 17. Mezaal, M.R., Pradhan, B., Shafri, H.Z.M., Mojaddadi, H., Yusoff, Z.M. 2017. Optimized hierarchical rule-based classification for differentiating shallow and deep-seated landslide using high-resolution LiDAR data. Global Civil Engineering Conference. Springer, Singapore 2017, 825–848.
• 18. Mimura, M., Yahara, T., Faith, D.P., Vázquez‐Domínguez, E., Colautti, R.I., Araki, H., Hollingsworth, P.M. 2017. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evolutionary applications, 10(2), 121–139.
• 19. Rahmes, M., Akbari, M., Henning, R., Pokorny, J. 2014. Robust volumetric change detection using mutual information with 3D fractals. Cyber Sensing, SPIE, ‏9097, 122–131.
• 20.Rani, K., Kaur, G. 2016. Image enhancement by adaptive filter with ant colony optimization. Int. J. Adv. Res. Ideas Innov. Technol, 2(5), 1–6.
• 21. Robertson, S., Azizpour, H., Smith, K., Hartman, J. 2018. Digital image analysis in breast pathology—from image processing techniques to artificial intelligence. Translational Research, 194, 19-35.
• 22. S Bhagat, V. 2012. Use of remote sensing techniques for robust digital change detection of land: a review. Recent Patents on Space Technology, 2(2), 123–144.‏
• 23. Samal, D.R., Gedam, S.S. 2015. Monitoring land use changes associated with urbanization: An object based image analysis approach. European Journal of Remote Sensing, 48(1), 85–99.‏
• 24. Skidmore, A. 2017. Environmental modelling with GIS and remote sensing. CRC Press.
• 25. Viana, C.M., Girão, I., Rocha, J. 2019. Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region. Remote Sensing, 11(9), 1104.
• 26. Wang, X., Liu, S., Du, P., Liang, H., Xia, J., Li, Y. 2018. Object-based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning. Remote Sensing, 10(2), 276.
• 27. Yang, J., Yin, L., Sun, C., Wang, M., Feng, S., Song, N. 2021. Sub-pixel Spectral Reduction Algorithm for Echelle Spectrometer.‏
• 28. Ziboon, A.R.T., Alwan, I.A., Khalaf, A.G. 2015. Study and analysis of desertification phenomenon in Karbala governorate by remote sensing data and GIS. Iraqi Bulletin of Geology and Mining, 11(1), 143–156.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).