In recent years, remote sensing and machine learning have emerged as invaluable tools supporting the understanding and monitoring of forest ecosystems to aid conservation efforts. The vast majority of forest conservation research is centred on Indigenous lands, yet it often operates at a significant remove from Indigenous communities themselves, who are the traditional custodians of the conservation priority areas. Although remote sensing and machine learning techniques have supported scalability within the field, these methods can widen the disconnect between conservation projects and Indigenous communities and weaken important links and connections to essential local knowledge. There is an urgent need for conservation to make use of state-of-the-art data science, but it is imperative that the benefits gained from including local knowledge and participation in the conservation process are not lost.
Through community co-creation, the deployment of acoustic recorders and the subsequent application of machine learning (ML) techniques this study looks to, investigate the intersection, or lack thereof, of biodiversity and forest health indicators between Indigenous Knowledge and ML analysis and develop an ethical AI in forest conservation framework.