Assessment of Land Use Changes Based on the Integration of Machine Learning Method and Spectral Angle Mapper Algorithm Using Training Samples Migration: A Case Study of Anzali Wetland Basin

Given the significance of land use changes in spatial planning and the conservation of critical ecosystems such as wetlands, this study aims to analyze land use changes in the Anzali Wetland basin by integrating the Spectral Angle Mapper (SAM) algorithm with the Random Forest (RF) classifier, utilizing dynamic training samples within the Google Earth Engine (GEE). For this purpose, harmonized Sentinel-2 imagery from 2019–2023 and six spectral indices were employed to enhance classification accuracy. By collecting 500 ground points in the base year and using spectral angle difference analysis, new training samples were generated for 2021 and 2023, and classification maps were produced using the RF algorithm. The results show that over these five years, the most significant land use changes were a decrease in water bodies and an increase in wetlands and built-up areas. The modeling outcomes demonstrated an overall accuracy and kappa exceeding 87% for the study period. Additionally, the water body class exhibited the highest user and producer accuracy, exceeding 90%. The results of the relative importance of bands and indices also highlight their role in enhancing the accuracy of the generated maps. It was found that the green, blue, and red bands, along with the MNDWI, had the greatest effect on land use discrimination and the transfer of training samples. Based on the research findings, the hybrid method, incorporating dynamic sampling and automated sample generation, can effectively improve the accuracy of land use classification in wetlands. Therefore, it is a reliable and applicable method for future studies in other wetland basins.