## 3 Results
### Canopy Cover Estimation  
Estimates of proportions of canopy cover from the three image types for all of the study sites are shown in Figure 5 and for LULC classes in Figure 6. The largest canopy cover estimate was recorded for site B as 0.59, 0.56, and 0.34 using PlanetScope, RapidEye, and Sentinel-2 images, respectively. By contrast, site F showed the smallest estimates of all of the sites with canopy cover estimates of 0.05, 0.09, and 0.07 from PlanetScope, RapidEye, and Sentinel-2 images. Estimates from PlanetScope and RapidEye images again appeared to be comparable, with a slightly larger difference between them than those of the forest area estimates. Nevertheless, no statistically significant difference was seen in any site between the two software packages. In sites A, C, and F, all of which had less forest cover, Sentinel-2 produced quite similar estimates to the other image types. In contrast, in sites B, D, E, and G, which had large forest cover where one also could expect large canopy cover, the estimates from Sentinel-2 significantly varied (\(p<0.01\)) from the rest. This shows that the estimates from Sentinel-2 images provided smaller canopy cover values where forests tended to be denser compared to PlanetScope and RapidEye. In terms of overall performance, when taking into account the magnitude of the standard errors of canopy cover estimates of all of the sites, although the differences were not that big, PlanetScope seemed to produce a precise estimate since it on average had the smallest value, whereas RapidEye and Sentinel-2 resulted in slightly less precise estimates (Figure 7).  
Figure 4: Forest area (proportion) estimates from PlanetScope, RapidEye, and Sentinel-2 images for each study site (A–G). Error bars represent the estimated standard error of the mean.  
When analyses of estimates of tree canopy cover were carried out for each LULC class using the three image types (Figure 6) the largest proportion of canopy cover estimate was, as expected, obtained in the Forest land category, with a magnitude of 0.57 from PlanetScope, 0.59 from RapidEye, and 0.58 from the Sentinel-2 images. According to the estimates from PlanetScope, Settlements (0.10), Cropland (0.09), and Grassland (0.08) were the second, third, and fourth LULC classes in terms of their canopy coverage. Within each LULC class, the different image types resulted in estimates that were very similar to each other except for the Settlements, Wetlands, and Other land classes. However, even in these classes, the differences were not significant in the statistical sense. It seems that if a given LULC class is a homogenous one such as Forest land, Cropland, or Grassland, then any of these images could produce reasonably comparable estimates of canopy cover. Whereas on the contrary, in heterogeneous LULC scenarios, coarser resolution images tended to either overestimate or underestimate canopy cover as compared with estimates from finer resolution images. Considering overall standard errors for each image type, it was observed that PlanetScope resulted in a smaller overall variability, showing a better precision of estimates, whereas the largest variability of estimates was seen for RapidEye (Figure 7).  
Figure 5: Canopy cover (proportion) estimates from PlanetScope, RapidEye, and Sentinel-2 images for each study site (A–G). Error bars represent the estimated standard error of the mean.  
Figure 6: Canopy cover (proportion) estimates from PlanetScope, RapidEye, and Sentinel-2 images by land-use and land-cover (LULC) classes. Error bars represent the estimated standard error of the mean.