## 4 Discussion  
### Visual Image Interpretation in a REDD+ MRV  
Visual image interpretation was adopted in this study to estimate the proportion of forest area and crown cover in seven subjectively selected sites in Ethiopia that have different forest and  
Figure 8: Standard error (\(y\)-axis) for forest area and canopy cover estimates in connection with sample size (\(x\)-axis). The results were determined through bootstrapping, except for 228 obtained from the original sample. (**a–c**) are for forest area; and (**d–f**) are for canopy cover estimates from PlanetScope, RapidEye, and Sentinel-2 images, respectively.  
vegetation types. Satellite images of PlanetScope, RapidEye, and Sentinel-2 were examined for their performance of producing estimates of forest area and canopy cover across the variety of site conditions. In addition, the study evaluated how sample size affected the uncertainty of the estimates from different image types.  
The proportion of forest cover within an area of interest can be used to estimate the area of the forest by multiplying it with the total size of the area. Such information are of paramount importance for LULC change analysis as well as the quantification of emissions and removals for REDD+ activities [23; 36]. Likewise, canopy cover information is used in several forest management applications. For instance, it serves as an important ecological indicator such as for habitat, microclimate, and light condition assessment [18], and it is used as a criterion in forest definitions [17].  
In the current study, it was possible to record data representing 390 km\({}^{2}\) per hour on average. Meanwhile, for field surveys, only moving to a field plot representing 9 km\({}^{2}\) could be difficult within the same amount of time. Thus, the efficiency of visual interpretation is the major advantage. However, a disadvantage is the amount of subjectivity that is allowed in the interpretation. In this study, the average interpretation between two interpreters was used. However, if multiple interpreters are available, the difference between them could be calculated and thus calibrated for. Hence, multiple interpreters can mitigate systematic errors occurring due to individual decisions. In addition, it is problematic that the interpreted value could also have errors when trained and experienced interpreters are carrying out the work. Experience form Tanzania showed that tree cover and the amount of woody biomass is not necessarily a good indicator of land-use or land-cover classification in field [37]. Thus, a major confine of the current study is that we did not have any ground-measured value. However, indicating that differences between the imagery exist is important. A natural assumption is that higher resolution imageries provide a more accurate interpretation.  
In this study, design-based estimates were the focus, and only the interpretations for the observation points were used to create estimates. The next step for implementing REDD+ MRV could be to include complete cover information e.g., from PlanetScope, RapidEye, or Sentinel, and combine the complete cover information with the interpretations making a wall-to-wall map that can be used in model-assisted estimation [37; 38]. This will be a natural extension of the current study, as capacity building are taking place in the REDD+ countries.