## 3 Results
### Time-Series Analysis and Aggregation into the Analyst Report  
The analyst report consisted of a multi-layer GeoTIFF image (Figure 9) containing aggregated information extracted from the time series of hybrid change detection maps. It is updated whenever a new change map is created. The analyst report was uploaded to the Agrimetrics platform, which provided a centralised database for the ForestMind consortium. The analyst report product can be read by the QGIS software package to allow convenient viewing and analysis of the information layers (Here, QGIS version 3.28.15 was used). The reports comprise seven raster layers with information about each pixel:  
Figure 8: Hybrid change detection by random forest post-classification change detection and differential NDVI. **(a)** Tile 15 PXT, Guatema, forest loss from post-classification change detection; **(b)** Tile 15 PXT, Guatema, Sentinel-2 dNDVI image over the forest loss pixels (white squares show dilated cloud-masked areas); **(c)** Tile 15 PXT, Guatema, hybrid change detection image of confirmed forest loss based on combined random forest post-classification change detection and dNDVI thresholding; **(d)** Tile 21 LTD, Mato Grosso State, Brazil, forest loss from random forest post-classification change detection; **(e)** Mato Grosso, Sentinel-2 dNDVI; **(f)** Tile 21 LTD, Mato Grosso, hybrid change detection image of confirmed forest loss based on combined random forest post-classification change detection and dNDVI. * Layer 1 'First_Change_Date': The acquisition date of the Sentinel-2 image in which a change of interest (i.e., forest loss) was first detected. This is expressed as the number of days since 1 January 2000;
* Layer 2 'Total_Change_Detection_Count': The total number of times when a change was detected since the First_Change_Date for each pixel;
* Layer 3 'Total_NoChange_Detection_Count': The total number of times when no change was detected since the First_Change_Date for each pixel;
* Layer 4 'Total_Classification_Count': The total number of times when a land cover class was identified for each pixel, taking into account partial satellite orbit coverage and cloud cover;
* Layer 5 'Percentage_Change_Detection': The computed ratio of Layer 2 to Layer 4 expressed as a percentage. This indicates the consistency of a detected change once it has first been detected and thus the confidence that it is a permanent change rather than, for example, seasonal agricultural variation or periodic flooding;
* Layer 6 'Change_Detection_Decision': A computed binary layer that is set to 1 to indicate regions that pass a change detection threshold and so allows regions of significant change to be rapidly identified over the large spatial area of a tile. Currently, the decision criterion is that ((Layer 2 >= 5) and (Layer 5 >= 50)), i.e., that at least five land cover changes of interest were detected and that the change was present in at least 50% of the change detection images;
* Layer 7 'Change_Detection_Date_Mask': A subset of First_Change_Date only showing those areas where the change decision criteria were met. It is the product of Layer 1 and Layer 6. This allows regions where land use change is expanding over time to be more easily identified over the large spatial area of a tile. Figure 9: Aggregated analyst report image from the time series of hybrid change detection maps. (**a**) Tile 15 PXT over Guatem, Report Image Band 2 (ChangeDetectionCount); (**b**) Tile 15 PXT, Report Image Band 5 (ChangePercentage); (**c**) Tile 15 PXT, Report Image Band 7 (ChangeDecision and FirstChangeDate); (**d**) Tile 21 LTD over Mato Grosso, Report Image Band 2 (ChangeDetectionCount); (**e**) Tile 21 LTD, Report Image Band 5 (ChangePercentage); (**f**) Tile 21 LTD, Report Image Band 7 (ChangeDecision and FirstChangeDate). Darker colours represent higher values. In practice, report image Layer 7 is used to provide information on areas that require rapid attention.