## 4 Discussion
The customer companies considered these practical suggestions valuable, as they did not previously understand how the fidelity and precision of their supply chain information may impact their deforestation due diligence reporting. One customer responded, \"At this stage whilst it brings important information and findings, and gives clear recommendations, these are currently known next steps for us as an organisation, albeit they are not easy next steps\". The results of the two independent accuracy assessments at tile scale and at farm scale showed over 80% overall accuracy. Table 5 shows achieved user accuracies of the forest loss detections of 76% for Guatem and 77.5% for Mato Grosso, and Table 7 shows 79% and 82% user accuracies for the two areas, respectively. Previous studies have reported user accuracies of over 90% for the GLAD alerts from Landsat in a study in Peru [29], 97.6% for the RADD alerts from Sentinel-1 for disturbance events \(\geq\)0.2 ha in the Congo basin (but leaving a large number of small-scale forest losses undetected) [11], 71.1% (but only 53.3% for Latin America) for the JJFAST change detections [30], and over 97% for previous applications of PyEO forest alerts in Mexico and Colombia [15]. Differences in the spatial resolution of the satellite images, minimum mapping unit of the forest cover loss areas, study area characteristics, and detection methods complicate a direct comparison of the user accuracies of these previous studies. For example, the GLAD alerts have a 30 m \(\times\) 30 m spatial resolution, which is an area nine times larger than the 10 m \(\times\) 10 m presented here. Despite the GLAD alerts being a global system, the only published validation known to the authors is from Peru [29]. [PERSON] et al. [15] compared the GLAD alerts with PyEO forest alerts for Mexico and Colombia and found that the GLAD alerts reported nearly four times more deforested area than PyEO for the site in Colombia, and missed a large number of small-scale forest loss areas in Mexico. The JJFAST validation [30] showed huge variations in users' accuracies between study areas. In the context of previous forest loss detection studies, the results presented here are of acceptable accuracy for the purpose of automated change detection for ongoing due diligence reporting in the view of the authors. In this study, we have presented two applications of the PyEO ForestMind reports to Guatem and Mato Grosso State in Brazil. Could its application be scaled up to largerregions or continents, or a global scale? The forest types of the two regions studied here are similar enough for the random forest model that was trained on images from Brazil to work well in Guatem. However, applying the same methodology to other forest types requires random forest models that were trained for specific forest conditions if their spectral reflectance properties are substantially different. A feasible approach might be to use a stratification of forests by ecoregion, as in [PERSON] et al. (2018), and train ecoregion-specific models. The PyEO package already allows Sentinel-2 tile-based processing and can easily be scaled up to larger areas with sufficient computational resources. Hence, it has applicability in all forested or partially forested areas of the world. Its main strengths are that the customer can define their own land cover types and land cover change transitions of interest, train their own model, and receive change detections as often as desired, from the 5-day repeat cycle of Sentinel-2 to aggregated monthly or annual reports.