Land degradation is occurring at an unprecedented rate. Native biodiversity and ecosystem loss demand innovative methods for restoration amid pressures such as invasive species. This is the case for Tallgrass Prairies, one of the most endangered ecosystems in Canada; intense competition by invasive Phragmites australis (hereafter Phragmites) threatens native biodiversity and ecological processes. Long-term monitoring is needed to support Phragmites management by assessing eradication success and identifying priority areas. Such methods, however, can be laborious and unsustainable. These barriers were identified as priorities by local knowledge holders and practitioners. Drone imagery and machine learning offer a potential solution: drone-based methods generate detailed images of Phragmites in restoration sites, while machine learning models hold the potential to identify Phragmites stands and evaluate ecosystem recovery. Although these methods have proven successful, further exploration is required to (1) establish protocols and (2) improve transferability between locations and ecosystem types. The study will support these objectives and explore the effectiveness of vegetation indices derived from drone imagery and machine-learning approaches in delineating Phragmites within Tallgrass Prairies. Leveraging our knowledge co-production approach with local practitioners, this research will be integrated as an economical and efficient tool to monitor native ecosystem recovery and inform Phragmites management strategies locally. This study actively combines remote sensing technology, geospatial artificial intelligence, and practitioner knowledge of place, recognizing this confluence as irreplaceable in innovating ecosystem restoration and successful habitat recovery.