Cross-domain adversarial learning for forage mapping and alpha-diversity assessment from UAV hyperspectral imagery in desert rangelands

Accurate assessment of forage distribution and diversity is crucial for sustainable grazing management in desert rangelands. Unmanned aerial vehicle (UAV)-based hyperspectral imagery provides a robust data source for the fine-scale monitoring of forage resources. However, the complex nature of grassland environments, combined with the high cost and time-consuming process of sampling, results in a scarcity of labeled samples, which limits the representational ability of models for downstream tasks. To address these challenges, this study proposes the domain-adversarial multi-view contrastive network (DA-MVCNet) for forage mapping and alpha ($\alpha$)-diversity assessment in desert rangelands. The network incorporates a domain adversarial strategy to mitigate discrepancies in feature distribution between the source and target domains, and employs a multi-view supervised contrastive learning module to promote feature alignment, thereby enhancing feature discrimination in the target domain. Experimental results demonstrate that DA-MVCNet achieves an overall accuracy of 92.52% in forage classification, outperforming other state-of-the-art methods, while maintaining low computational complexity with only 0.11 G FLOPs and 0.30 M parameters. Furthermore, we mapped the spatial distributions of $\alpha$-diversity indices – including species richness, the Shannon–Wiener index, the Simpson index, and Pielou’s evenness index – using a grid-based approach. Results indicate that with increasing grazing pressure, all four diversity indices significantly decreased ($P<0.05$), effectively revealing the impact of grazing disturbance on community structure. This study provides a new pathway for forage mapping and diversity assessment via UAV hyperspectral remote sensing, offering technical support for the intelligent monitoring and management of rangeland ecosystems. The code is available at https://github.com/zhang2508/DA-MVCNet.