## 4 Conclusions and Recommendations  
The methodology to classify high-resolution true color images provided by Google Earth Pro, and the proposed methodology to improve the classification qualifies for a detailed analysis and assessment of urbanization and environmental degradation processes in space and time, as well as for land use/land cover changes occurring in any other place. The increasing availability of satellite imagery allows one to detect and analyze even temporarily and spatially, very dynamic changes. Such high-resolution LULC classifications are necessary to take informed actions in order to reduce the detrimental impact of urbanization on the environment and human well-being. The described post-processing yields significant accuracy improvements and may compensate the disadvantage of having no multi-spectral information. Although some potential misclassifications may remain, e.g., between low and high vegetation, the dynamic and drastic changes in land use/land cover driven by today's urbanization result in significant impacts on the environment. That could be shown with this study by comparing the three classification results of different times. For the study areas in Nicaragua, the results clearly identify advancing urbanization, the loss of vegetation, and threats to urban ecosystems. Hence, the lack of detailed information on the composition, structure, extent, and temporal change of urban areas in many parts of the world could be overcome with the presented classification system. This information can help to plan, design, and coordinate interactions for sustainable urban development without additional software or data costs.  
For future applications, it should be evaluated whether a classification starting with generic macro classes of built-up area, vegetation, and bare land, followed by a classification of more specific classes (e.g., high vegetation and low vegetation, and bare land and unpaved) within macro class distinctions yields better results in terms of classification accuracy. The final superimposition with the methodology presented here could still be performed. We recommend the use of images from very similar dates when realizing interannual comparisons, because vegetation status and light ratios (presence and geometry of shadows) at different times of the year may differ significantly. Furthermore, image  
Figure 8: Urbanization at both sides of a tributary of the Pochote during the last decade.  
pre-processing, e.g., increasing brightness or contrasting images and reducing the color spectrum, can reduce misclassifications and misinterpretations of the LULC classes of different images.  
Conceptualization, F.C. and J.H.; funding acquisition, J.H.; investigation, F.C., S.H., and J.H.; methodology, F.C., S.H., and J.H.; software, F.C. and S.H.; supervision, J.H.; validation, J.H.; visualization, F.C. and S.H.; writing--original draft, F.C., S.H., and J.H.; writing--review and editing, J.H.  
This research was funded by the Working Internships in Science and Engineering (WISE) program of the German Academic Exchange Service (DAAD), and by the German Federal Ministry of Education and research (BMBF), grant number 01 UU1704.  
We acknowledge support from the German Research Foundation and the Open Access Publishing Fund of Technische Universitat Darmstadt.  
The authors declare no conflict of interest.
Figure A2: Visualization of results obtained for the 'Transitional' area.