Spatiotemporal Transformations of Leaf Area Index and Dust: Implications for Long-Term Land Degradation Assessment

Abstract. In the contemporary era, where environmental crises such as climate change, desertification, vegetation cover decline, and increasing dust storms have become major challenges for nations. Long-term analysis of ecological indicators is crucial, particularly in sensitive and semi-arid regions like the Zagros forests. This research investigates the temporal and spatial variations of Leaf Area Index (LAI) and Dust Particulate Matter (DPM) over a 25-year period (2000 to 2025) in the forests of Lordegan County, Chaharmahal va Bakhtiari Province. Landsat and MODIS satellite imagery, processed using the Google Earth Engine (GEE), were used to extract and normalize data. This study leverages the integration of Landsat and MODIS data via Google Earth Engine (GEE), enabling long-term analysis of LAI and DPM even with limited field measurements. The results demonstrate that LAI values are higher in the central and northwestern regions, ranging from 0.15 to 0.78, while DPM concentrations are greatest in the northern and western areas, varying from 0.12 to 0.65. Time series analysis and statistical modeling revealed a significant inverse relationship between LAI and DPM levels (R² = 0.8134); consequently, years with weaker vegetation cover show increased concentrations of suspended particulates in the atmosphere. These findings indicate that the reduction of vegetation cover adversely affects the intensification of dust phenomena and land degradation. The results of this study can be utilized in formulating management programs to reduce forest vulnerability, combat desertification, and improve the ecological conditions of semi-arid regions.