Scalable Mangrove Monitoring with Limited Field Data: Integrating MREDT and DACN-M.

Mangroves play a crucial ecological and economic role but face significant threats, particularly on Hainan Island, which has the highest mangrove species diversity in China. Remote sensing and AI techniques offer potential solutions for monitoring these ecosystems, but challenges persist due to difficult access for field sampling. To address these issues, we propose a novel model combining a Mangrove Rough Extraction Decision Tree (MREDT) and a Dynamic Attention Convolutional Network (DACN-M). Initially, we used drones and field surveys to conduct multiple observations in Dongzhaigang Nature Reserve, identifying the boundaries of the mangroves. Based on these features, we constructed the MREDT model to mitigate model failure caused by light instability, simplifying transfer to other study areas without requiring annotated samples or extensive field surveys. Next, we developed the DACN-M model, which refines the rough extraction features from MREDT and incorporates contextual information for more accurate detection. Experimental results demonstrate that our proposed method effectively differentiates mangroves from other vegetation, achieving F1 Scores above 75% and IoU values greater than 60% across six study areas. In conclusion, our proposed method not only accurately identifies and monitors mangrove distribution but also offers the significant advantage of being transferable to other study areas without the need for annotated samples or field surveys. This provides a robust and scalable solution for protecting and preserving critical mangrove ecosystems and supports effective conservation efforts in various regions. [ABSTRACT FROM AUTHOR]