# Uncovering Vegetation Changes in the Urban-Rural Interface through Semi-Automatic Methods
## 4 Conclusions  
The work carried out was satisfactory for the achievement of the desired objectives. Although the overall accuracy of the obtained model was 64.85%, relatively lower than that found in the literature consulted [25; 27; 31]. With the methodology used, it was possible to identify areas where interventions occurred in the fuel management bands. Above all, we obtained high predictive capacity to identify areas where there were no interference with vegetation, indicated by the precision of 71.43% and recall of 82.93%. This fact could be useful to find the areas where interventions must be done, optimizing the process in terms of cost and efficiency. The NDVI obtained from Sentinel-2 Level-2A images proved to be effective in identifying changes in fuel management bands. We can list two other advantages when using Sentinel-2 images for this type of analysis: (i) the high availability of images due to temporal resolution and (ii) because they are made available free of charge. This study concludes that the approach on this topic is far from being closed, but we believe that the model proposed here has the potential to improve the discussions on this topic. The knowledge applied in the development of the model proposed here can be used as a basis for new approaches and assists directly or indirectly in the search to map changes in vegetation. For future approaches, we stress that new forms and sources of data for validation can be added, and the prospect of COSsim being produced annually can minimise the effects of the time gap. To solve the problem with clouds or no data in the images, could be incorporated images from other satellites, like: Sentinel-1 or Landsat, but in this case the spatial resolution problems is not solved. Another possibility is to use satellite images with higher spatial resolution than Sentinel-2, and/or to incorporate data from other technologies, such as unmanned aerial vehicles (UAV), radar or LiDAR data, to identify details that presently go unnoticed. However, using UAV or LiDAR data is more expensive and the data collection time is longer. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12052294/s1](https://www.mdpi.com/article/10.3390/app12052294/s1)]([https://www.mdpi.com/article/10.3390/app12052294/s1](https://www.mdpi.com/article/10.3390/app12052294/s1)). Figure S1: Changes detected in Cantanhede with \(p\)-value \(\leq\) 005; Figure S2: Changes detected in Fugieiro dos Vinhos with \(p\)-value \(\leq\) 005; Figure S3: Changes detected in IIlavo with \(p\)-value \(\leq\) 005; Figure S4: Changes detected in Miranda do Corvo with \(p\)-value \(\leq\) 005; Figure S5: Changes detected in Penela with \(p\)-value \(\leq\) 005; Figure S6: Changes detected in Cantanhede with \(p\)-value \(\leq\) 00005; Figure S7: Changes detected in Figueiro dos Vinhos with \(p\)-value \(\leq\) 00005; Figure S8: Changes detected in IIlavo with \(p\)-value \(\leq\) 00005; Figure 9: Changes detected in Miranda do Corvo with \(p\)-value \(\leq\) 00005 and Figure S10: Changes detected in Penela with \(p\)-value \(\leq\) 00005. **Author Contributions:** B.B. : conceptualisation, methodology, software, validation, formal analysis, research, curatorship of data, writing--preparation of the original sketch, writing--review and editing. H.C.: resources, visualisation, supervision, project administration, writing--review and editing. J.R.: conceptualisation, methodology, validation, writing--review and editing, supervision. M.C. : conceptualisation, methodology, resources, visualisation, supervision, project administration, financing acquisition. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Portuguese Foundation for Science and Technology, I.P. (FCT), under the framework of the Project \"FORESTER--Data fusion of sensor networks and fire spread modelling for decision support in forest fire suppression\" [name of funder] grant number PCIF/SSI/0102/2017.