A novel framework for adapting vision language models to thermal infrared imagery has been developed, enabling more accurate species recognition and habitat context interpretation in drone-collected data. This lightweight multimodal adaptation approach addresses the representation gap between RGB-pretrained models and thermal infrared images, allowing for the effective transfer of information. By fine-tuning vision language models through multimodal projector alignment, the framework demonstrates its practical utility on a real-world dataset. The study's findings have significant implications for applications such as wildlife monitoring and environmental surveillance, where thermal imaging can provide valuable insights. The ability to accurately interpret thermal imagery can inform conservation efforts and support more effective resource management, so the development of this framework matters to practitioners seeking to leverage AI for environmental monitoring and conservation efforts1.