Application of Supervised Classification Algorithms in Long-Term Survey of Surface Soil Salinity by Using Remote Sensing: A Case Study of Atabiyeh Plain, Khuzestan Province

The current study aims to find the best methods of using remote sensing and supervised classification algorithms in long-term salinity monitoring of salinity changes in the Atabieh area with an area of 5000 hectares in the west of Khuzestan province. The procedure is based on the separation of different levels of saline soils utilizing information obtained from Landsat 7 and 8 satellite images (2001 to 2015) along with salinity data taken from the study area, and salinity indices including SI1, SI2, SI3, NDSI, IPVI, and VSSI. The results show the expansion of the saline zone trend in the soils of the study area, among which, soils with EC of more than 16 dS m-1 (very saline) have the highest frequency. The area of saline soils has increased significantly over the past 15 years, with a saline land area increasing by more than 90%. The percentage of salinity class is low (S1). According to this study, the only significant index in soil salinity at a 95% confidence level is the SI3 index, which has been able to have a good estimate of the increasing changes in soils in the region. The results of the supervised classification showed that the support vector machine (with an overall accuracy of 95.78 and a kappa coefficient of 0.89) is more accurate. After the vector machine method, the methods of minimum distance, maximum likelihood, and distance of Mahalanobis have the highest accuracy, respectively. Based on salinity maps obtained in years in 2001, 2005, 2010, and 2015, it can be said that the salinity rate in the whole of the study area was progressing and at the same time the salinity area in the middle and high classes increased decreased and on the other hand, the salinity area in the high class in 2001 gradually increased and distributed in 2015 throughout the region.