Document Type : Research Paper

Author

• Samaneh Razavizadeh

Assistant Professor, Desert Research Division, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Terhran, Iran

Abstract

Abstract
 
Background and Objectives
Lakes and wetlands in arid and semi-arid regions play a crucial role in sustaining ecosystems, regulating local climate, controlling dust storms, and preserving biodiversity. Lake Namak, as one of the major evaporitic–saline basins of the Central Iranian Plateau, has experienced a noticeable decline in surface area and increasing instability in its water coverage over recent decades. Reduced hydrological persistence in this lake can intensify dust emissions and weaken regional ecological functions. Despite its importance, long-term analyses based on continuous satellite time series remain limited. Therefore, the objective of this study is to monitor and analyze the spatiotemporal changes in the inundated area of Lake Namak during 1995–2024 using Landsat imagery and the Google Earth Engine platform.
Materials and Methods
In this study, Landsat satellite imagery from Landsat 5, Landsat 7, and Landsat 8 acquired between 1995 and 2024 was used. To harmonize data from different sensors and minimize inconsistencies, surface reflectance products were employed, and cloud and cloud-shadow pixels were masked using standard algorithms. All processing and analyses were conducted within Google Earth Engine, enabling access to a long-term satellite archive and efficient data processing. To extract water bodies, the Normalized Difference Water Index (NDWI) was calculated for each image using the green and near-infrared bands. A threshold value was then determined empirically based on comparisons with false-color composites and visual interpretation, and binary water/non-water maps were generated. Water-covered areas were subsequently calculated at monthly, seasonal, annual, and decadal scales. Trend analysis was performed using time-series assessment and linear regression to determine the direction and magnitude of changes over time. Additionally, the non-parametric Sen’s Slope estimator was applied to obtain a more robust estimate of change rates and to reduce the influence of outliers. To evaluate the stability of the lake’s inundation regime, the Transition and Seasonality layers from the JRC Global Surface Water dataset were analyzed within Google Earth Engine.
Results
Time-series analysis of Landsat imagery revealed a decreasing and unstable trend in the extent of Lake Namak’s inundated area over the past three decades. The mean inundated area in the first decade (1995–2004) was about 154.4 km², decreasing to 48.1 km² in the second decade (2005–2014) and to approximately 41.1 km² in the third decade (2015–2024). Annually, the maximum water extent occurred in 2001 (about 383.6 km²), while the minimum was recorded in 2014 (about 1 km²), indicating strong interannual variability. Trend analysis using linear regression confirmed a significant negative trend, and the negative slope highlighted continuous decline in inundation. Similarly, Sen’s Slope analysis yielded negative annual change rates, indicating a persistent long-term reduction with higher robustness. Seasonal patterns showed that spring exhibited the highest inundation, with about 10% of the lake’s surface containing water, followed by winter with about 5.4%, while summer and autumn displayed minimal inundation. Monthly analysis showed maximum inundation in March, April, and May. The Transition layer indicated that approximately 70.4% of the lake area remained unchanged, while about 15.9% fell into the “new seasonal” class and around 13% into the “seasonal” class, reflecting the expansion of temporary water bodies. Seasonality results showed that more than 71.1% of the lake surface lacked persistent inundation, with most water-covered areas present only one to three months per year.
Conclusion
The findings indicate that Lake Namak has undergone a substantial reduction in surface area and hydrological persistence over the past three decades, with both linear regression and Sen’s Slope confirming the declining trend. The dominance of short-term and seasonal inundation reflects increasing hydrological instability. Continuation of this trend may lead to major environmental consequences, including increased salinity, intensified evaporation, and expansion of dust-source areas. The use of satellite imagery and Google Earth Engine provides an effective framework for long-term monitoring of such dynamic environments.

Keywords

• Arid regions
• Inundation variation
• NDWI index
• Remote sensing
• Surface water monitoring
• Surface water bodies