Maps of Land Cover Degradation in Iraq Using Remote Sensingand Geographic Information Systems (RS-GIS)
DOI:
https://doi.org/10.31185/eduj.Vol64.Iss2.5541Keywords:
RS) , GIS, Composite Land Degradation , (AHP), Markov ModelAbstract
This study aimed to produce and analyze land cover degradation maps in Iraq during the period 2015–2025 using Remote Sensing (RS) and Geographic Information Systems (GIS). A Composite Land Degradation Index (CLDI) was developed based on the integration of the Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BSI), Soil Salinity Index (SSI), Desertification Severity Index (DSI), and Water Index, with the Analytical Hierarchy Process (AHP) and the Weighted Overlay technique applied to determine the relative weights of the selected indicators and generate land degradation maps. In addition, the Markov model was employed to analyze Land Use/Land Cover (LULC) changes and predict their future spatial patterns. The results revealed a significant decline in vegetation cover accompanied by an expansion of bare and degraded lands during the study period, with considerable spatial variation in degradation levels across Iraq. The integration of RS-derived indicators, GIS spatial analysis, and field survey data significantly improved the accuracy of degradation mapping and spatial classification. Furthermore, the Markov model indicated that Land Use/Land Cover changes affected 73.28% of Iraq's total area, while the Composite Land Degradation Index (IDI–DSI) showed that degraded lands accounted for approximately 45.5% of the country's area. The findings demonstrate that integrating RS and GIS techniques with the composite degradation index and the Markov model provides an effective spatial framework for monitoring land cover degradation, predicting future changes, and supporting environmental planning and the sustainable management of natural resources in Iraq
Downloads
References
1. Nachtergaele, F., Petri, M., Biancalani, R., van Lynden, G., van Velthuizen, H., & Bloise, M. (2007). Land Degradation Assessment in Drylands (LADA): Technical Report on the Use of Indicators for Assessment and Monitoring of Land Degradation. Food and Agriculture Organization of the United Nations (FAO), Rome.
1- بارود، خميس فاخر. (2019). تطبيقات الاستشعار عن بعد في برنامج نظم المعلومات الجغرافية (ArcGIS) (الطبعة الأولى). الجامعة الإسلامية، كلية الآداب.
2- صفر، زين العابدين علي. (1999). *مبادئ علم الخرائط*. عمّان، الأردن: دار الفكر للطباعة والنشر والتوزيع.
4- United Nations Convention to Combat Desertification. (1994). United Nations Convention to Combat Desertification in those countries experiencing serious drought and/or desertification, particularly in Africa. United Nations. https://www.unccd.int
1- حديد، رنا.، بشرى علي احمد. (2024). تحديد الجفاف الزراعي وشدة التصحر في محافظة ديالى، العراق باستخدام صور سينتينل-2. المجلة العراقية للعلوم، 65(5)، 2946–2957
6- Maaroof, B. F., Kareem, H. H., Al-Zubaydi, J. H., Al-Ansari, N., Aziz, M. A., AL-Quraishy, D. A. A., Al-Hasani, B., Abdellatif, M., Carnacina, I., Thannoun,
7- R. G., Al-Kubaisi, M. Sh., & Al-Maarofi, S. S. (2025). Estimating soil erosion utilizing geospatial method and revised universal soil loss equation (RUSLE) of Abu Ghraibat Watershed, Eastern Misan Governorate, Iraq. Scientific Reports, 15(1), 4442
8- Food and Agriculture Organization of the United Nations (FAO), & Intergovernmental Technical Panel on Soils (ITPS). (2015). Status of the World's Soil Resources (SWSR): Main Report. Rome: FAO
9- Jenks, G. F. (1967). The Data Model Concept in Statistical Mapping. International Yearbook of Cartography, 7, 186–190 ..
10- Rikimaru, A., Roy, P. S., & Miyatake, S. (2002). Tropical forest cover density mapping. International Society for Tropical Ecology
. 11- Burrough, P. A., McDonnell, R. A., & Lloyd, C. D. (2015). Principles of Geographical Information Systems (3rd ed.). Oxford University Press
12- Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30. https://doi.org/10.1002/hyp.3360050103
13- Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data (U.S. Geological Survey Professional Paper 964). U.S. Geological Survey
14- Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data (U.S. Geological Survey Professional Paper 964). U.S. Geological Survey .
15- Aksoy, E., Gregor, M., Schröder, C., & Löhnertz, M. (2017). Assessing and mapping land degradation using remote sensing and GIS techniques. Springer
16- Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S., Islam, T., & Saha, A. (2015). Predicting spatial and decadal land use/land cover change through cellular automata and Markov chain modeling. Egyptian Journal of Remote Sensing and Space Sciences, 18(2), 263–272
Downloads
Published
Issue
Section
License
Copyright (c) 2026 أ.م.د ضياء الدين عبد الحسين عويد، م.د اصيل جاسم محمد عواش
This work is licensed under a Creative Commons Attribution 4.0 International License.
