## 3 Results and Discussion
### The Advantages, Disadvantages, and Potential Applications of High Resolution LULC Classification
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline  & **Advantage** & **Disadvantage** \\ \hline Analysis at urban scale & Approach to optimal pixel resolution to identify complex and heterogeneous mosaic-patch inner-urban structures and shapes. & Accuracy between boundaries of different LULC require a higher pixel resolution not achievable by Google earth images. \\ \hline Using true color images & A simple and user-friendly method allows end-users to obtain reliable and quick results, especially in zones with limited professional experience. Results are visually obtained without requiring coding or complex algorithms. & Image size limits the application to larger scales because high computational effort may be required. Limitation to identify different LULC sharing similar colors. No multi-band assessment (e.g., Normalized Difference Vegetation Index for better distinction of vegetation) as in the case of multi-spectral imagery is possible. \\ \hline Access to data and software & Open access, direct download and free of cost images and software avoid time-consuming efforts, being this a simple way to promote a LULC analysis in areas lacking economic resources. & In certain areas, access to high-resolution images could still be limited only to commercial products or require special technology (e.g., aerial imagery). The plugin can be unstable in some versions of QGIS following unexpected crash downs. \\ \hline Post-process & Possible to extract urban objects, LULC categories or delimitate specific zones of interest (e.g., riparian areas). Quick and reliable results lead to analyse quantitatively and visually spatial and temporal aspects at different scales. & Results depend on image quality and availability, as well as, initial criteria to define ROIs and LULC categories. User effort is unavoidably required to reduce errors due to mobile or temporal objects. \\ \hline \end{tabular}
\end{table} 
Table 4: The advantages and disadvantages of open access software and true-color images for the LULC classification of urban areas when compared to other commercial high resolution data and software uses. be conceived as an element supporting runoff infiltration. This contrasts to the more disconnected trees found in the backyards of houses. An approach to such analysis using the methodology employed in this study could be based on defining and separating layers, similar to what was done in the post-editing stage. Regarding the satellite images, the access to historical imagery permits a temporal analysis of LULC changes. Thereby, tendencies and correlations of urban modifications during a specific period can be identified. External factors influencing this transformation, such as urbanization or deforestation, can be quantified using the high-resolution classification procedure. In addition, historical images can be employed to define seasonal variations of the LULC, and consequently, perennial and temporal vegetation. By doing so, significant inputs can be provided to support stakeholders, local authorities, and other actors involved in the planning and management of urban areas and decision-making processes. A limitation for realizing temporal analysis represents the presence of clouds in the images resulting from the optical (passive) satellite imagery. Images generated by active satellite imagery, such as synthetic aperture radar (SAR) satellites are not affected by clouds, and have, therefore, advantages. However, manual analysis of SAR satellite images to classify land cover is difficult [43] and high-resolution SAR images are yet not available without cost. Those examples of potential applications are linked to the methodology presented in this study. Although different methods could be available, the main advantages of the method employed in our research are the uses of free and open access software and high-resolution data. Hence, it was our aim to help overcome the limitations of some areas, especially in developing countries, regarding lack of accessibility to skills, instruments, and data.