## 4 Discussion  
In this research, we conducted ultra-resolution classification and mapping of land cover and vegetation types by employing machine learning techniques. Supervised classification of satellite images by employing machine learning classifiers such as Random Forests and XGBoost has been applied efficiently by previous studies for mapping of land cover and vegetation types [83,84]. The mapping suites presented in this research are built on the recent improvements of land cover and vegetation mapping through the utilization of machine learning and fusion of classification [85, 86, 87, 88] and it indicates an enhanced performance by combining the probabilities of predictions from multiple classifiers. The ultra-resolution and very high-resolution suites proposed in this research should be effective and useful for land cover and vegetation mapping in other large regions of interest as well. Figure 18: Ultra-resolution map with 12 classes produced for the Shiranuka site (**top**). The red polygon area has been zoomed in to show finer details (**bottom left**) along with the true-color composite image (**bottom right**). High-spatial-resolution images from earth-observing satellites have been effective for acquiring detailed information on land cover and vegetation types [89; 90; 91]. Previous studies have utilized high-spatial-resolution images for discriminating land cover and vegetation physiognomic types such as deciduous broad-leaved forests and evergreen broad-leaved forests [92; 93; 94; 95; 96]. Though some researchers have tried unsupervised segmentation and labeling methods such as k-means and hierarchical clustering techniques for land cover and vegetation classification and mapping [97; 98], supervised classification of land cover and vegetation types by employing machine learning classifiers has been frequently utilized in recent studies [99; 100]. Despite the free accessibility of high-resolution satellite images such as those from Sentinel-2 and Landsat 8, a number of ecological studies have utilized ultra-resolution and very high-resolution satellite images. Review of these studies indicates that ultra-resolution and very high-resolution satellite images have been adapted mainly for the detection and mapping of small patches of vegetation such as endangered plants and invasive species [101; 102; 103; 104] or for the differentiation and mapping of mixed vegetation in a heterogeneous environment [105; 106; 107]. The vegetation mixedness is a matter of the spatial resolution of the imagery. The vegetation mixed in high-resolution imagery (e.g., 30 m resolution) can be unmixed in very high-resolution or ultra-resolution imagery. Therefore, very high-resolution or ultra-resolution imagery are very suitable for the mapping of mixed vegetation in a heterogeneous environment, as in this research. The study led by [PERSON] et al. [108] for urban tree species mapping concluded that single-date WorldView-2 images did not produce satisfying classification results, with relatively low accuracy (44.7-82.5%), and found that usage of bi-temporal images could produce, on average, 10.7% higher accuracy. In a similar way, the addition of summer images to the previously used autumn-season images provided 12.8% higher accuracy for the ultra-resolution suite and 13.2% higher accuracy for the very high-resolution suite in this research across three cool temperate ecosystem sites. [PERSON] et al. [109] also explored bi-seasonal versus single-season WorldView-2 images to map three mangrove and four adjacent plant communities and found that bi-seasonal images were more effective than single-season to differentiate all communities of interest, in line with this research carried out in three mountainous ecosystems. [PERSON] et al. [37] utilized WorldView-3 images acquired in the dry and wet seasons and emphasized the usage of seasonal images for tree species discrimination in semi-deciduous forests.