An Integrated Data Driven Approach of Multi-Index and K-Means Clustering for Spatiotemporal Monitoring and Forecasting of Desertification Risk in Iraq

Abstract

Desertification is one of the major environmental challenges threatening the ecological integrity and socio-economic stability of arid and semiarid climates worldwide. This study presents a multi-index remote sensing approach to assess and predict desertification risk across Iraq using the Google Earth Engine (GEE). For this goal, six key spectral-based indicators, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Albedo, Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), were applied through the integrated data-driven approach on the MODIS and Sentinel-2 datasets for the period 2015–2022. Annual composites were generated, and pixel-wise linear trends were calculated using least-squares regression. All trend resulting data were normalized to a common scale, and a composite Desertification Risk Index (DRI) was constructed. Future projections for 2030 and 2035 were computed based on linear extrapolation of DRI trends. Additionally, K-means clustering was applied to classify desertification risk into five categories, enabling spatial segmentation and comparative analysis. The results revealed a clear intensification of desertification in southern and central Iraq, particularly in Al-Muthanna, Dhi Qar, and Basrah, where high DRI values and class 1 clustering indicate severe ecological stress. Central governorates such as Najaf, Wasit, and Karbala are transitioning from moderate to high risk, whereas the northern regions such as Erbil and Dohuk remain relatively stable. However, early signs of vulnerability emerge in the peripheral zones between 2022 and 2035, approximately 52.7% of Iraq’s land area is projected to fall within the moderate to high desertification risk categories. The K-means clustering results for 2030 and 2035 revealed both degradation and recovery trajectories, with some regions improving from Class 3 to Class 5 and others deteriorating into Class 1

Keywords

• Desertification risk index
• Google Earth Engine
• K-Means Clustering
• Iraq

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