## 4 Discussion
### Factors Influencing Estimates  
Proportions of forest area estimated using PlanetScope and RapidEye images were very similar across the seven sites, regardless of their differences in spatial resolution. On the other hand, estimates from Sentinel-2 images for most of the sites differed significantly from those of both PlanetScope and RapidEye images. Exactly the same pattern of similarities and differences occurred as well for canopy cover estimates. It seemed that PlanetScope and RapidEye estimates were robust and capable of producing reliable results in different biomes with different forest types and magnitudes of forest coverage. However, the performance of Sentinel-2 images seemed to be dependent on the magnitude of forest cover. It produced similar estimates to those of PlanetScope and RapidEye for areas with less forest cover, and varied as the forest cover of the area increased. Each land-cover category has its own property that needs to be considered when selecting methods, including the images to be used for land-cover classification and mapping [39]. Accordingly, the discrepancy among estimates from these image types is perhaps due to a number of factors, one of which is the differences in the resolution of the images. [PERSON] [40] investigated the effect of satellite image resolution and minimum mapping unit on the accuracy of forest cover mapping in two different sites using RapidEye, Sentinel-2, and Landsat-8, and reported a declining trend in overall accuracy as the resolution got coarser. A similar trend was reported by [PERSON] et al. [41], who compared forest cover estimates of Haiti using different satellite images.  
Another factor contributing to the differences could be missing data, which will reduce the number of observations and hence affect the quality of estimates [42]. Most of the missing data during image interpretation (NAs) were recorded for Sentinel-2 images (Table 3). Out of the total NAs in Sentinel-2, more than 70% were in sites B and D. These are the sites that are known to have denser forest cover and be accompanied by the presence of heavy cloud cover during most of the year. Therefore, its relatively coarser resolution coupled with the cloud cover might have affected the interpretation result, since it failed to sufficiently capture the existing variability within the study sites. The revisit frequency also directly influences the availability at cloudy locations. The frequent revisits of PlanetScope is highly favorable for REDD+ MRV, since 85% of the images used for the interpretations were acquired within a time period of one month, May 2017, while for RapidEye and Sentinel-2, a maximum of 34% and 40% of the images originated from one single month, respectively.  
The majority of the images used for interpretation was acquired during the dry season. One reason for this is that cloud coverage is smaller in this season compared with the rainy season. It is likely that the phenology of the trees and other vegetation influences the interpretation. A tree crown with more leaves could potentially lead to a larger interpreted crown cover compared with when the tree has less or no leaves. The design of this study did not provide data to analyze the effect of seasonality in a statistical sense.  
Satellite imagery used for the visual interpretation of forest area and canopy cover should be carefully selected. For low-density forest areas, say, somewhere below 30% canopy cover, Sentinel-2 images may be safely used, whereas for denser forest areas, higher resolution images such as PlanetScope and RapidEye should be considered. Of course, the choice may depend on the purpose of the study and available resources. To reduce costs, sampling instead of large-scale wall-to-wall observation could be used [(43; 44)]. The method used in this study is such an alternative to obtain reliable estimates at a lower cost of image procurement, especially for dense forest areas where low-resolution images have deficiencies, and the quality of information pays off.