Evaluating Adaptive Classification Methods for Mangrove Mapping with Multi-Resolution Remote Sensing Imagery

Abstract

Mangroves provide critical ecosystem services, including coastal protection, biodiversity conservation, carbon sequestration, and environmental purification. Detailed mangrove mapping is therefore essential for effective conservation, restoration, and management. With the proliferation of remotely sensed imagery across diverse spatial resolutions, comparative studies on mapping efficiency are vital for optimizing long-term, large-scale monitoring strategies. This study utilized multi-source satellite imagery, which includes Landsat-8 (15 m and 30 m), Sentinel-2 (10 m), ZiYuan-3 (ZY-3), and GaoFen-1 (GF-1) (2 m), to map mangroves in the Beibu Gulf by integrating spectral, texture, and topographic features. The optimal feature combination was determined experimentally. We comprehensively compared the identification accuracy, identification results, and area estimates derived from three distinct methods: pixel-based Random Forest (RF), object-oriented RF, and a U-Net + ResNet-34 deep learning model. Key findings include the following: (1) Feature importance varied by resolution; terrain features significantly improved accuracy for medium-resolution images (e.g., Landsat-8), increasing the F1-score by 1.91%, while texture features were critical for high-resolution images (e.g., ZY-3 and GF-1), improving the F1-score by 2.99%. (2) Deep learning achieved the highest accuracy using Sentinel-2 combined with terrain features, yielding an F1-score of 96.45%. (3) Higher-resolution imagery enabled the detection of smaller mangrove patches; deep learning produced the fewest internal gaps, whereas pixel-based RF suffered from severe “salt-and-pepper” noise. (4) While object-oriented RF using Sentinel-2 imagery produced results most consistent with the validation set (recall = 94.38%), notable area discrepancies (9.7%) persisted, primarily due to tidal dynamics. We conclude that deep learning models, particularly when integrating Sentinel-2 data with terrain features, provide superior accuracy for mangrove mapping. Nevertheless, tidal dynamics continue to pose a significant challenge for computer-aided identification approaches.