## 3 Results and Discussion
### The Advantages, Disadvantages, and Potential Applications of High Resolution LULC Classification  
The high-resolution raster obtained as a result of the LULC classification provides quantitative information about the distribution of the different small-scale elements integrating an urban area. These results can be easily modified and transformed by the user within the presented methodology, reducing classification errors substantially. In that regard, a raster is a tool and source of information of easy adaptation for function of specific research purposes. This study's spatially distributed data could be used as the basis for different studies, including not only urban planning and spatial distribution but also subjects related to urban ecology, hydrology, water management, sociology, and local policy. Figure 6 shows an image employed in our study and two images derived from Sentinel-2 and Landsat 8 satellite data for the same date. These images show that true color images derived from Google Earth have a much higher spatial resolution in comparison with most common sources for LULC classification. Although high resolution Google Earth imagery can only be obtained as true color images (for example, by applying the procedure presented in this publication) at the moment, they allow one to identify, in a simple but reliable way, specific urban features in a LULC classification. A summary of the main advantages and disadvantages that we have detected for our methodology is listed in Table 4.  
Figure 6: Visualization of data obtained for the Transitional study area in 2018. Data source (**left**): Google Earth; (**middle**): Sentinel-2 satellite [41]; (**right**): LANDSAT 8 [42]. LULC classifications at high resolution are indispensable inputs to assess the urban water cycle at different spatial scales. In general, a hydrological analysis at the watershed level is based on an average classification of the land use distribution. At the urban scale, nevertheless, the processes of runoff, infiltration, and evapotranspiration are more complex, and the level of detail (spatial resolution) required for their study has increased. That involves, for instance, the need to define more detailed runoff coefficients for micro urban watersheds and the definition of the artificial urban drainage network. The results obtained from our methodology can support these endeavors. In addition, a high resolution of runoff coefficients supports the implementation of pilot projects aiming to determine the impacts of modifying the current urban stormwater management. Identifying potential patches for urban ecological improvements can also be derived from our methodology. Green spaces, considered shelters which trigger urban biodiversity, can be located and their spatial distribution also analyzed. This provides insights in order to define their connection and the potential for new spaces developed in the urban mosaic. Thus, not only parks, riparian zones, and other large green areas can be included in the analysis of interconnected networks supporting urban ecology, but also elements commonly neglected due to their smaller size. Furthermore, the spatial distribution of patches can be linked to the public or private domain. An analysis of a study site from above assists to define the level of accessibility to green areas. In that manner, ecosystem services provided by urban vegetation can be analyzed by their function in their location and the associated property rights. For example, public vegetation (e.g., street trees) not only improves air quality but also provides shade to pedestrians, and reduces the local temperature and the heat island effect. Their canopy reduces runoff directly connected to the public sewer system and can