Multimodal learning for vegetation patterns classification in global arid and semi-arid regions

Vegetation patterns serve as vital indicators of desertification, particularly in arid and semi-arid regions globally. Gaining insights into their formation mechanisms and accurately predicting their types is key to combating desertification and supporting global restoration initiatives. Current research faces challenges such as a lack of comprehensive global datasets, insufficient systematic data on real-world patterns rooted in their formation characteristics, and difficulties in predicting spatially heterogeneous and irregular patterns. To address these issues, this study explores the global distribution and classification of vegetation patterns. It begins by collecting and analyzing real-world vegetation patterns from arid and semi-arid regions, incorporating precipitation and temperature data to create the multimodal Vegetation Patterns Dataset (VPD). The study further introduces the Vegetation Patterns Classification Model (VPCM), which represents patterns as graph structures and leverages graph convolutional networks to extract meaningful features. By integrating multimodal data, the model enables accurate predictions of vegetation patterns types. This interdisciplinary approach offers valuable insights for mitigating and potentially reversing desertification.