Developing a Semi-Supervised Strategy in Time Series Mapping of Wetland Covers: A Case Study of Zrebar Wetland, Iran

Abstract Wetlands, essential for Earth’s health, ecological balance, and local economies, require accurate monitoring and assessment for effective conservation. Data-driven models based on remote sensing are highly capable of monitoring the status and classification of wetlands. This study developed a semi-supervised framework for mapping wetland covers in Zrebar, Iran, using Landsat time series data from 1984 to 2022. A pixel purification technique was applied to the temporal candidate images to refine the initial training data (conventional scenario) and generate purified training data (proposed scenario). The Support Vector Machine (SVM) algorithm was utilized to classify the land cover within the wetland, and the accuracy of the two scenarios was evaluated and compared. Over the study period, the analysis of land cover changes within Zrebar Wetland revealed significant spatial and temporal changes in soil and farmland, reed, and water from 1984 to 2022. The omission error rates for the classes soil and farmland, reed, and water were decreased from 0.14, 0.14, and 0.12 for scenario 1 to 0.03, 0.05, and 0.05 for scenario 2, respectively. In addition, the commission error for these classes decreased from 0.13, 0.18, and 0.09 for scenario 1 to 0.04, 0.06, and 0.04 after applying the filtered training data in the scenario 2. Finally, the overall accuracy of the initial training data (scenario 1) and the filtered training data (scenario 2) were 0.86 and 0.94, respectively. These results underscore the effectiveness of the proposed strategy in enhancing the accuracy of land cover classification within the wetland over time, highlighting its potential for future wetland conservation efforts.