## 5 Conclusions  
Maintenance of high-resolution land cover and community-level vegetation information at a country scale is a challenging task. This research demonstrated production of Dominant Genus-Physiognomy-Ecological (DGPE) maps with 101 legends for the first time at a country scale from the satellite images. This research was made possible by utilizing entire archives of the multi-spectral images collected by a constellation of two polar-orbiting Sentinel-2 mission satellites. This research applied an innovative and robust methodology by automated scripted processing of satellite data and implementation of a convolutional neural network model for the production of land cover and community-level vegetation maps effectively at a country scale. It involved paying special attention to the ground truth data, utilization of both temporally and spectrally represented satellite features, and computation with graphical processing units to make deep-learning-based production at 10 m resolution efficient at a country scale. For precise delineation of the geographical boundaries, mainly the coastlines, utilization of very-high-resolution satellite images or super-resolution of the high-resolution images through deep learning methods is recommended in the future.  
Achieving 80.2% average accuracy in terms of F1-score and 80.5% average accuracy in terms of Kappa coefficient across 48 prefectural regions shows the effectiveness of the methodology implemented in the research. This level of performance was achieved by implementing a unique prefecture-wise mapping approach by better matching the regional characteristics of the satellite data and vegetation distribution. This research highlights the importance of land cover and community-level vegetation mapping separately in small regions-of-interest and later integrating the outputs into a large scale for better performance. The promising results obtained in all prefectures in the current research confirmed the effectiveness of the DGPE classification system for large-scale classification and mapping of community-level vegetation types. The consistent accuracy in terms of F1-score across all prefectural regions obtained in the research indicates its suitability for application in other countries.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12147125/s1](https://www.mdpi.com/article/10.3390/app12147125/s1)]([https://www.mdpi.com/article/10.3390/app12147125/s1](https://www.mdpi.com/article/10.3390/app12147125/s1)), Supplementary Material S1. Photographs of some typical plant ecological communities of the study area taken during field surveys. Supplementary Material S2. Discriminative potential of the Sentinel-2 images and Spectral-indexes. Supplementary Material S3. Optimization of Deep Learning Model Parameters and Hyper-parameters. Supplementary Material S4. Cross-validation accuracies for each prefecture. Supplementary Material S5. Land cover and community-level vegetation maps produced in the research.  
This research received no external funding.  
Sentinel-2 data were available from the European Space Agency (ESA) Copernicus program. The prefectural names and boundaries used in this research were based on the Land Numerical Information Data V2.3 of MLIT. [PERSON] is appreciated for logistic support to the research. The author is thankful to the anonymous reviewers and editors of the Journal.  
The author declares no conflict of interest.