Transforming multimodal data into decision assets: A vision-language Model for ecological data using cross-modal alignment

Environmental monitoring in complex rangeland ecosystems relies on human evaluation to navigate environment shifts. Local community knowledge is important for guiding herd mobility decisions within pastoral systems that use rangeland ecosystems. Currently, human-based scouting considers factors like rangeland location, season, and plant composition to make decisions based on traditional knowledge. However, climate change impact makes rangeland monitoring more complex by reducing the predictability of vegetation availability, species composition, access, and seasonal grazing patterns. To address this, there is a demand to automate rangeland assessment to help sustainable management. We present a novel human-augmented ecological assessment framework that formalizes local community knowledge into a scalable, knowledge-aware AI system through a multimodal Mixture of Experts (MoE) architecture with bidirectional cross-attention mechanism. This model is fed by a multimodal dataset from sub-Saharan Africa rangelands collected mainly via a mobile app where stakeholders encode decision-making parameters. This architecture helps the disambiguation of visual features by grounding them in seasonal metadata, which seasonal linguistic guides the model to prioritize site specific phenological patterns over undifferentiated spectral greenness. By employing a MoE layer to manage ecological data variability, our learning model uses community knowledge to override deceptive visual cues and improves rangeland quality classification over unimodal and standard fusion baselines. These results suggest that routing data through human-centric priors can improve rangeland monitoring.