Large-scale extraction of check dams and silted fields on the Chinese loess plateau using ensemble learning models

Abstract

Check dams have been widely constructed in the Chinese Loess Plateau and has played an important role in controlling soil loss during last 70 years. However, the large-scale and automatic mapping of the check dams and the resulting silted fields are lacking. In this study, we present a novel methodological framework to extract silted fields and to estimate the location of the check dams at a pixel level in the Wuding River catchment by remote sensing and ensemble learning models. The random under-sampling method and 23 features were used to train and validate three ensemble learning models, namely Random Forest, Extreme Gradient Boosting and EasyEnsemble, based on a large number of samples. The established optimal model was then applied to the whole study area to map check dams and silted fields. Our results indicate that the imbalance ratio of the samples has a significant impact on the performance of the models. Validation of the results on the testing set show that the F1-score of silted fields of three models is higher than 0.75 at the pixel level. Finally, we produced a map of silted fields and check dams at 10 m-spatial resolution by the optimal model with an accuracy of ca. 90% at the object level. The proposed framework can be used for the large-scale and high-precision mapping of check dams and silted fields, which is of great significance for the monitoring and management of the dynamics of check dams and the quantitative evaluation of their eco-environmental benefits.

Keywords

• Sited field
• Ensemble learning
• Random under-sampling
• Imbalanced classification
• Chinese loess plateau