Tracking Vegetation Dynamics in Drylands with MSAVI: Insights from the South Aral Sea

Abstract Monitoring vegetation dynamics and biomass productivity in drylands is essential for assessing land degradation and guiding afforestation strategies. Despite considerable research on vegetation dynamics in drylands, no studies have specifically examined the trends in aboveground biomass (AGB) and its relationship with the Modified Soil Adjusted Vegetation Index (MSAVI) in afforested dryland areas, particularly in the context of the South Aral Seabed. This study evaluates vegetation productivity trends and AGB in afforested areas of the South Aral Seabed from 2013 to 2023 using remote sensing techniques and field measurements. MSAVI was employed to analyse long-term vegetation trends and their relationship with AGB in this exemplary dryland area. Field sampling across 24 plots revealed a strong positive correlation between MSAVI and AGB (Spearman’s ρ = 0.8238, p < 0.001), confirming the index’s suitability for biomass estimation. Trend analysis of MSAVI values indicated overall stability in land productivity; however, localized degradation hotspots, particularly in former wetland areas, highlighted ongoing environmental stress. Regression modelling revealed that using a generalized additive model (GAM) with a Gamma distribution and a log link function best captured the non-linear relationship (F = 85.8, p < 0.001) between MSAVI and AGB. Incorporating land use and land cover (LULC) as an additional predictor improved explanatory power, revealing significant associations between AGB and vegetation classes ( p < 0.001). These findings validate MSAVI as an effective tool for monitoring afforestation outcomes in arid environments and emphasize the need for adaptive land management strategies. The results have important implications for sustainable afforestation, climate action, and SDG reporting, particularly in regions where global datasets lack the spatial resolution needed for precise monitoring. Graphical Abstract This Study Integrates Satellite Earth Observation Data and field-based Biomass Measurements To Evaluate Vegetation Dynamics and Estimate Aboveground Biomass (AGB) in Afforested Drylands of the South Aral Seabed. The Data Source Consists of Landsat 8 OLI Imagery from 2013 To 2023, Processed in Google Earth Engine To Generate a time Series of the Modified Soil Adjusted Vegetation Index (MSAVI), a Spectral Index Optimized for Dryland Environments. The Analysis Involved Filtering and Extracting MSAVI Values To Detect long-term Vegetation Trends. Field Data Collected from Saxaul (Haloxylon spp.) Shrublands Were Used To Calculate AGB Using a species-specific Allometric Model. These Biomass Estimates Were Compared with Satellite-derived MSAVI Values To Explore their Relationship. The Results Confirm MSAVI’s Effectiveness as a Proxy for Biomass Estimation in Arid Ecosystems and MSAVI’s Utility for long-term Vegetation Monitoring and Highlights Localized Degradation Hotspots. This Graphical Abstract Distils the Study’s Data, Analysis, Modeling, Results, and Key Insights into a Visually Compelling Summary of an Innovative Approach To Monitoring Dryland Restoration