Desertification is one of the major challenges in arid and semi‑arid regions of the world, exerting profound impacts on ecological sustainability, agriculture, and local livelihoods. This study aims to analyze early‑warning signals of desertification risk using time‑series remote sensing indices and field data in Mashhad County, Iran. For this purpose, the NDVI, NDSI, and TGSI indices were employed over the period 2005–2024. Satellite images, after geometric and radiometric corrections, were used alongside field observations for the required calculations. Breakpoint detection in the time series was performed using the BFAST software package, and statistical indicators (lag‑1 autocorrelation, skewness, and standard deviation) were evaluated in R. To examine the breakpoint behavior, trends were analyzed for 100 pixels, including 50 degraded and 50 non‑degraded pixels identified during field surveys. The results indicated that the main breakpoint occurred in 2013, coinciding with a sharp decline in vegetation cover and an increase in soil salinity. The NDVI, NDSI, and TGSI indices showed the strength of changes based on the Kendall’s tau test as 0.71, 0.58, and 0.53, respectively. These differences highlight the prominent role of vegetation cover and soil salinity in driving desertification in the region. The findings further revealed that NDVI and NDSI, together with lag‑1 autocorrelation, exhibited the highest efficiency in the early detection of ecosystem changes. Overall, the results underscore the importance of integrating remote sensing indices with statistical approaches to develop early‑warning systems and support sustainable natural resource management for desertification control.
Introduction
   Desertification is widely recognized as one of the most severe environmental challenges affecting arid and semi‑arid regions across the globe, with far‑reaching consequences for ecological stability, agricultural productivity, and human livelihoods. Driven by a complex interplay of climatic pressures such as declining precipitation, rising temperatures, and increased evapotranspiration and human‑induced factors including overgrazing, deforestation, groundwater depletion, and unsustainable land‑use practices, desertification has intensified in recent decades, particularly in the Middle East and Iran where fragile ecosystems are highly vulnerable to disturbance. According to global assessments, hundreds of millions of people are directly affected by land degradation, and billions more live in regions at risk, underscoring the urgent need for effective monitoring and early‑warning systems. Traditional assessment methods, which often rely on static indicators, are insufficient for capturing the dynamic and nonlinear nature of ecosystem degradation. Consequently, remote sensing time‑series analysis has emerged as a powerful tool for detecting subtle environmental changes before they evolve into irreversible degradation. Indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Salinity Index (NDSI), and the Terrain Gradient Stability Index (TGSI) provide valuable insights into vegetation dynamics, soil salinity, and land surface conditions. When combined with statistical early‑warning indicators such as lag‑1 autocorrelation, skewness, and standard deviation these datasets can reveal critical slowing down, reduced resilience, and approaching ecological thresholds. Mashhad County, located in northeastern Iran, has experienced increasing environmental stress due to prolonged droughts, declining groundwater levels, and expanding agricultural and urban activities. These pressures make the region an ideal case study for evaluating early‑warning signals of desertification using long‑term remote sensing data and advanced statistical techniques.
Material and Methods
   This study utilized a comprehensive time‑series dataset of three remote sensing indices NDVI, NDSI, and TGSI spanning the period from 2005 to 2024 to assess early‑warning signals of desertification in Mashhad County. In this study, Satellite images were used for calculations after being validated with reference data. The Breaks for Additive Seasonal and Trend (BFAST) algorithm was employed to decompose each time series into trend, seasonal, and abrupt change components, enabling the detection of structural breakpoints associated with ecosystem shifts. To evaluate early‑warning indicators of declining resilience, three statistical metrics lag‑1 autocorrelation, skewness, and standard deviation were calculated using the R statistical environment. These indicators are widely used to detect critical slowing down, a hallmark of systems approaching tipping points. A total of 100 representative pixels were selected for detailed analysis, including 50 degraded and 50 non‑degraded sites identified through extensive field surveys. This sampling strategy allowed for a robust comparison between stable and vulnerable areas. In addition to temporal analysis, spatial patterns of change were examined across the entire study area to map the distribution and intensity of degradation processes. The integration of remote sensing indices, statistical early‑warning metrics, and field‑based validation provided a comprehensive framework for assessing both temporal dynamics and spatial heterogeneity of desertification risk.
Results and Discussion
   The time‑series analysis revealed a distinct and consistent breakpoint in NDVI and NDSI during the period 2013–2014, marking a critical transition in ecosystem behavior. This abrupt change corresponded to a sharp decline in vegetation cover and a significant increase in soil salinity, indicating a shift toward reduced ecosystem productivity and heightened degradation pressure. In contrast, TGSI did not exhibit a clear structural breakpoint, suggesting that this index may be less sensitive to short‑term environmental fluctuations. Following the breakpoint, NDVI displayed a pronounced downward trend in degraded areas, while NDSI increased steadily, reflecting intensified salinization and reduced vegetation health. Non‑degraded areas, however, maintained relatively stable trends throughout the study period, highlighting the contrasting resilience levels across the landscape. Statistical early‑warning indicators further supported these findings: lag‑1 autocorrelation showed a strong and persistent increasing trend across all indices, with high positive Kendall’s tau values, indicating reduced system resilience and approaching instability. Although skewness and standard deviation also exhibited occasional increases, their sensitivity and discriminatory power were notably weaker than that of autocorrelation. Spatial analysis revealed that the western and southern regions of Mashhad experienced the most significant changes in NDVI, NDSI, and TGSI, corresponding to areas with intensive agricultural activity, groundwater depletion, and higher exposure to climatic stress. Kendall’s tau values of 0.71 for NDVI, 0.58 for NDSI, and 0.53 for TGSI underscore the dominant role of vegetation dynamics and soil salinity in driving desertification processes. These results align with previous studies demonstrating that increasing salinity, declining vegetation cover, and reduced ecological resilience are key precursors to land degradation in arid environments.
Conclusion
   This study demonstrates that integrating remote sensing time‑series indices with statistical early‑warning indicators provides a robust and effective framework for detecting emerging desertification risks in arid and semi‑arid regions. The identification of a major breakpoint in 2013–2014 highlights a critical shift toward ecological instability in Mashhad County, driven primarily by vegetation decline and increasing soil salinity. Among the indices analyzed, NDVI and NDSI exhibited the highest sensitivity to environmental changes, while lag‑1 autocorrelation emerged as the most reliable early‑warning metric, effectively capturing reduced ecosystem resilience prior to major transitions. Spatial analysis further revealed that the western and southern parts of Mashhad are the most vulnerable to degradation, emphasizing the need for targeted land management interventions in these areas. Overall, the findings underscore the importance of remote sensing‑based early‑warning systems for sustainable land management, enabling policymakers and environmental managers to implement timely mitigation strategies and prevent the progression of desertification. By providing a scientifically grounded approach to monitoring ecosystem health, this study contributes to the development of proactive management frameworks aimed at preserving ecological stability and supporting long‑term environmental sustainability in vulnerable regions.