## 4 Discussion  
In recent years, land cover and land use mapping by the utilization of high-spatial resolution satellite images such as Sentinel-2 or Landsat 8 has increased worldwide. In spite of high-resolution land cover mapping research focused at a local scale [81; 82], mapping attempts at country, continental, or global scales are still limited. At the global scale, for example, [PERSON] et al. [83] produced a worldwide land cover map at 30 m resolution using Landsat images. More recently, the European Space Agency produced a worldwide land cover map including vegetation physiogonomic classes at 10 m resolution for 2020 based on Sentinel-1/2 images ([[https://esa-worldcover.org/en](https://esa-worldcover.org/en)]([https://esa-worldcover.org/en](https://esa-worldcover.org/en)), accessed on 10 April 2022). At the continental scale, [PERSON] et al. [84] produced a land cover map of Africa at 10 m resolution from multi-source remote sensing data. [PERSON] and [PERSON] [85] produced land cover maps without vegetation physiogonomic classes at 10 m resolution over Europe. However, both of these continental-scale maps do not include vegetation physiogonomic classes and include land cover classes only. At the country level, for example, [PERSON] et al. [86] produced a high-resolution land cover map of France using satellite time-series images. The countrywide Dominant Genus-Physiogonomy-Ecological (DGPE) mapping with 101 legends in this research can be considered as a paradigm shift in the field of land cover and vegetation mapping because of its unique characteristics of community-level vegetation mapping at a country scale. On the other hand, there is much research conducted in the classification of tree or shrub species [87; 88; 89; 90], forest types [91; 92; 93; 94], or crop types [95; 96; 97] using remote sensing images. However, most of this research was targeted at local scales in contrast to the country-level application of this research. Figure 3: Plant ecological communities map produced at a country scale. In recent years, there has been a surge in utility-scale installations of solar panels as a cleaner option for energy, with possible unintended environmental consequences such as land cover change, habitat fragmentation, and barriers to species movement [98,99]. In line with this, the mapping of an up-to-date distribution of solar panels at a country scale should be useful to better understand the consequences of increasing solar panel installations to nature and biodiversity conservation. In addition, classification and mapping of agricultural land types such as upland fields, paddy fields, orchards, and cultivated pasture, as conducted in the research, should provide datasets required to meet food security and sustainable development. When splitting the satellite-based raster images into prefectural boundaries using the vector polygons, the quality of boundary delineation depends on the resolution of the satellite images used. During integration of the prefecture-wise maps into the country level map, shortcomings in the delineation of a prefecture boundary pertaining to the resolution of the images was overcome by using the boundary of neighboring prefectures, except in outer coastal boundaries, and thus the seamless countrywide map was obtained. However, utilization of very-high-resolution satellite images or super-resolution of the high-resolution images through deep learning methods could produce land cover and community-level vegetation maps with precise delineation of the boundaries, improving the limitation pertaining to the high-resolution images. As far as the methodology is concerned, most of the recent research on land cover and vegetation mapping is based on a supervised classification approach by employing machine learning classifiers in the presence of ground truth data [100, 101, 102]. Moreover, application of deep learning neural networks has been advancing rapidly in recent years as a versatile technology for the mapping of land cover and vegetation types [103, 104, 105, 106].