Modeling the Trend of Soil Salinity Changes Using Vegetation Cover, Salinity Indices and Random Forest Method (Case Study: Gomishan County, Golestan Province)
Subject Areas : Optimal management of water and soil resourcesSaleh Arekhi 1 * , Somayeh Emaduddin 2 , Mohammad Bahrami 3
1 - Associate Professor, Department of Geography, Faculty of Human Sciences, Golestan University, Gorgan, Iran.
2 - Associate Professor, Department of Geography, Faculty of Human Sciences, University of Golestan, Gorgan, Iran.
3 - MSc in Environmental Hazards, Faculty of Human Sciences, Golestan University, Gorgan, Iran.
Keywords: Soil salinity, mapping, sensitivity analysis, modeling, Gomishan region, Golestan,
Abstract :
Introduction: Soil salinization is a land degradation process that leads to excessive accumulation of soluble salts in the soil. Given the lack of information regarding salinity characteristics, especially modern mapping approaches, and the importance of soil salinity as a dynamic and influential characteristic on soil quality, the present study was conducted based on spatial modeling of soil salinity with a random forest model in the study area of Gomishan County, Golestan Province, during the period 2000 to 2024.
Methods: In the present study, 150 sampling points were selected using the Latin hypercube method and 36 parameters were used as environmental data in modeling, including vegetation cover indices, soil salinity indices, and satellite images band indices (such as blue, green, red bands, etc.). Also, to prepare the desired indicators, Landsat (TM) satellite images from 2000 and Landsat 8 (OLI sensor) from 2024 were used. After preparing environmental parameters and soil salinity data, the Random Forest (RF) model was used for modeling. The results of descriptive statistics for soil surface EC show that the soil EC value in 150 soil samples varied from 0.28 to 107.46 deci-Siemens per meter (ds/m) and its standard deviation is 2.58. The most important parameters obtained from the sensitivity analysis of the random forest model in soil salinity modeling are vegetation cover indices such as MNF, PCA and band indices of satellite images. Given that the vegetation cover of the region is poor, the presence and accumulation of salt on the soil surface are easily identified by the main components of the Landsat satellite image.
Results: The results showed that there is a strong correlation between soil data and environmental variables, such that the final map predicted soil salinity with a coefficient of determination of 0.87. A comparison of the trend of soil salinity changes from 2000 to 2024 shows that the Gomishan region is moving towards salinization during this period. The maximum and minimum salinity values in 2000 were 0.93 and 54.65, respectively, and the maximum and minimum salinity values in 2024 were 1.14 and 71.87 deci-Siemens per meter (ds/m), respectively. The results of comparing the trends in soil salinity classes showed that the areas of the no-limitation, low-limitation, and high-limitation classes decreased by 848, 12,110, and 15,202 hectares (-0.63, 8.95, and -11.23 percent), respectively, and the areas of the high-limitation, very high-limitation, and severe-limitation classes increased by 3,256, 18,906, and 5,995 hectares (2.41, 13.97, and 4.43 percent), respectively.
Conclusion: The trend of salinity changes in the region is increasing towards the center and south, which is consistent with the trend of changes in the most important auxiliary variables identified, such as PCA. From the results of this study, it can be concluded that the soils of the studied region have limited salinity and also have high spatial variability in terms of the amount of soil parameters, especially salinity. This indicates that soil maps are needed to improve and refinement the soils of the Gomishan region.
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