## 3 Results
### Independent Validation of Farm-Scale Change Detection Accuracy  
In addition, a combination of Sentinel-2 imagery and monthly <5 m resolution data from Planet Norway's International Climate and Forests Initiative (NICFI) was used to validate the points. For this purpose, an image before the forest loss detection and a second image after the detection were interpreted visually for evidence of forest loss. Validation of forest change in Guatem was carried out using imagery from April 2022, and validation of forest change in Mato Grosso using imagery from August 2022. Validation of the deforestation detections was carried out at the Satellite Applications Catapult by visual inspection of the forest loss detections presented in this paper compared to the Global Forest Watch data [13]. Planet NICFI data were used for this purpose. These results are presented separately, as this was an independent validation by a team not involved in producing the forest loss dataset.  
In order to gather feedback from users on the supply chain monitoring reports, forest loss detections from the PyEO algorithm were analysed for a set of real farms from which commodities are imported by one of the ForestMind user organisations. For every user organisation, a farm-scale map was created, showing data within 400 m of its location, together with information on the extent of forest cover loss (Figure 10, Table 6). In addition, statistics about the proximity of the farm to protected areas and/or rivers were made available to the users.  
Figure 10: A farm-scale presentation of the forest cover loss data from PyEO, as included in the company reports. In this example, synthetic farm boundary polygons (dashed green areas in the centres of the 8 locations) are shown with a 100 m detection buffer (light grey). Forest loss pixels are indicated in red, shaded from dark to light to indicate the date of first forest loss detection. Results are overlayed on higher-resolution optical imagery. The real farm locations are subject to data protection and cannot be disclosed.  
In total, 263 farms were examined through the ForestMind monitoring period from January 2020 to August 2022, of which 105 were reported as 'deforestation-free', 136 were found to have only minimal deforestation (<0.1 ha), and 22 were found to have larger areas of deforestation (>0.1 ha).  
Throughout the project lifecycle, the datasets generated by ForestMind were assessed for accuracy. In the absence of regularly updated ground data for the region, a visual assessment was carried out using 3 m resolution Planet-NICFI (Norway's International Climate and Forests Initiative) data, which are openly available, as well as a comparison with the existing annual dataset of forest loss, Global Forest Loss from the University of Maryland based on Landsat imagery, available via Global Forest Watch [13].  
For each forest loss dataset, 100 points were randomly selected from two classes: those classified as having forest loss and those not classified as experiencing forest loss. The two datasets for assessment are as follows:  
* PyEO Forest Loss--this study, University of Leicester (7 February 2019-22 February 2021);
* Global Forest Loss--University of Maryland (2017-2020).  
Each dataset was visually inspected using the Planet-NICFI data, looking at a time series of imagery from before and after the date on which forest loss was detected. Statistics generated are commission (change detected when no change occurred) and omission error (change not detected when change occurred), as well as the overall accuracy percentage. In August 2022, a final accuracy review of the forest loss detections was carried out. The datasets were tested in the two different regions of Mato Grosso in Brazil, for which the random forest model had been trained, and Guatem, as a study area to which the model was transferred to test its applicability elsewhere. The State of Mato Grosso in Brazil is known for the growth of soybeans and the area of Guatema for coffee. The results are presented in Table 7.