Monitoring the Area Change in the Three Gorges Reservoir Riparian Zone Based on Genetic Algorithm Optimized Machine Learning Algorithms and Sentinel-1 Data

Abstract

Riparian zones play a critical role in ecosystems. Accurately extracting the area of a riparian zone in open water is challenging due to human activities and climate change. This study used Sentinel-1 satellite data to investigate the capabilities of the support vector machine, extreme gradient boosting, and random forest methods, which were optimized by genetic algorithms for the detection of area changes in the riparian zone in the heart region of the Three Gorges Reservoir area of China. A total of 29 images were collected in 2020, and three models were created for each image, which were then transferred to other phases. The models’ performance metrics were validated using all of the images. The results indicated that the SVM method achieved the best performance with an accuracy of 0.945, an F1_Score of 0.950, and a kappa coefficient of 0.889. The optimal model was then used to monitor the area changes in the riparian zone over the study area in 2020. It was calculated that the area of the riparian zones was the smallest on 26 December and the largest on 17 June, with a maximum riparian zone of 31.97 km2. Overall, this study demonstrates that an SVM is the most stable method for detecting area changes in a riparian zone when using Sentinel-1 data compared to the RF and XGB methods. The findings are anticipated to provide a feasible plan for detecting the area dynamics in open-water riparian zones and to provide valuable information for the rational utilization of land resources and the ecological safety of the riparian zone in the Three Gorges Reservoir.

Keywords

• riparian zones
• support vector machine
• remote sensing
• extreme gradient boosting