## 4 Discussion  
The technical algorithm of forest cover loss detections from the PyEO package has been described previously in the literature [14; 15]. This study presents the ForestMind extensions of PyEO, which now includes an optional verification of detected forest loss pixels with a dNDVI threshold, which reduces the commission error greatly when run in entirely automatic mode. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & \begin{tabular}{c} **Total Number of** \\ **Farms** \\ \end{tabular} & \begin{tabular}{c} **Deforestation-Free** \\ **Farms** \\ \end{tabular} & \
\begin{tabular}{c} **Farms with** \\ **Deforestation \textless{} 0.1 ha** \\ \end{tabular} &
\begin{tabular}{c} **Farms with** \\ **Deforestation \textgreater{} 0.1 ha** \\ \end{tabular} \\ \hline Jan 2020–Jan 2022 & 263 & 155 & 94 & 14 \\ (Baseline Update) & 2022–Aug 2022 & 263 & 149 & 113 & 1 \\ Jan 2020–Aug 2022 (Whole Monitoring & 263 & 105 & 136 & 22 \\ Period) & 263 & 105 & 136 & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Forest cover loss detections in the areas of the real farms for the two reporting periods (the baseline and baseline update), and summarised for the whole monitoring period. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Overall Accuracy** & \
\begin{tabular}{c} **Rate of** \\ **Commission** \\ \end{tabular} &
\begin{tabular}{c} **Rate of** \\ **Omission** \\ \end{tabular} \\ \hline PyEO forest loss— & & & \\ Soy Brazil & 83\% & 18\% & 1\% \\ PyEO forest loss— & & & \\ Coffee Guatema & 80\% & 21\% & 3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy, commission, and omission rates for forest loss vs. visual assessment of Planet NICFI datasets at farm scale of the real farm locations. The producer accuracy is 100%, rate of omission, and the user accuracy is 100%, rate of commission. Creating farm-scale company reports that clearly communicate farm forest cover loss rates for deforestation assessments to non-technical companies to provide actionable information was challenging. Feedback from the initial report indicated that the customer 'felt unclear' about the information provided and the next steps. Specific feedback included the structuring of the report to ensure that the most relevant data were easily understandable and accessible at a glance. Subsequent reports were improved by restructuring the text and providing clearer labelling, explanations, and legends. In the later versions of the report, the feedback ranged from 'felt somewhat clear' to 'felt very clear' (\(n=3\)). The customer still experienced some difficulties interpreting the data in these reports. This was primarily because of the need to learn to interpret spatial data presented in the reports. Because the due diligence requirement is a new legal instrument, companies have yet to learn how to use evidence-based due diligence systems in their supply chains. Backdrop imagery greatly supported the visualisation of the change detections, but at the scale of a coffee farm (3-12 ha), 10 m resolution Sentinel-2 imagery is not suitable as a background map. The 3 m resolution imagery from Planet was also used, yet farm boundaries were still not clearly identifiable at this resolution. The final revision of the reports used a custom background map created in Mapbox Studio, which provides very-high-resolution commercial satellite imagery with additional contours, and streets overlain from OpenStreetMap, added by the analyst to support the visualisation. These data aided in the visual interpretation, though the background imagery suffered from misalignment, and was often out of date. Whilst the accuracy of the forest loss detections allowed the production of a farm-level report based on an approximate point location for the farm without precise farm boundary data, the reports could not verify deforestation-free farm claims with high confidence. The reports communicated this limitation and suggested data collection exercises to improve the supply chain information held by importers and exporters.