Deforestation in the Pacific is increasingly driven by land clearing due to agriculture and developments and natural disasters such as tropical cyclones. These disturbances have accelerated the spread of invasive species, particularly across exposed and degraded landscapes, where they outcompete native vegetation. One of the most pervasive species, <em>Spathodea campanulata</em> (African tulip), along with other invasive flora , has emerged as a significant environmental stressor across many Pacific Island Countries and Territories (PICTs). Monitoring their spread remains a major challenge due to the scale and rate of invasion (Pacific Community, 2025). This study explores the use of free and open-source software (FOSS) workflows combining QField, QGIS, Digital Earth Pacific Earth observation workflows and machine learning outlined in Metherall et al., (forthcoming). Within this paper, these workflows are applied towards the practical application of mapping and monitoring the distribution of invasive tree species. As a case study, the research focuses on mapping <em>Spathodea campanulata</em> in Korotari, Vanua Levu, Fiji. Field data were collected through QField GPS-based surveys to identify confirmed locations of invasive species, which were then analysed using time-series satellite data in Digital Earth Pacific. Phenological signatures determined through seasonal patterns in vegetation were incorporated and used to train models to detect and track invasive species over time (Sultana et al., 2025). The results demonstrate promising potential for monitoring of invasive flora. These methods provide a valuable foundation for understanding spatial trends in invasion from as early as 2017, supporting more targeted ecosystem management and informing policy responses to land degradation and biodiversity loss in PICTs (Ekka et al., 2023)