# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 5 Conclusions  
This work presented a new method to detect interventions in FMZs using machine- learning algorithms and satellite images from Sentinel-2 and Sentinel-1. Combining machine- learning and open satellite data could reduce the costs associated with manual checkups of FMZs. This process is also scalable as it would only require the extraction of more satellite images over the study area. The main technique used to detect an intervention was to extract information about the vegetation in both the interior and exterior of an FMZ to detect variations. This approach required the creation of buffers outside the FMZ, in vegetation zones, which depending on the type of FMZ can be complex to generate. This information about the interior and exterior of FMZs is then used to generate new features from the input satellite spectral and radiometric images. The proposed method can serve as a baseline for future work into the detection of vegetation growth and maintenance in forest fire-sensitive areas, in this case, the FMZs.  
The results show that the usage of satellite data can be used to detect if an FMZ is properly clean and maintained, thus improving forest fire protection. As expected, due to having more information, time-series data had better results than using only a single date. Furthermore, from all the data sets tested, _DS_ALL_ had the best scores overall. Regarding the used machine- learning algorithms, ensemble methods (RF and XGBoost) prove to be robust when working with remote sensing data by having good scores across the board. In the experiments, the best scores were achieved by XGBoost when combined with the best data set, _DS_FUSION_, which uses spectral and radar data as well as some derived vegetation indices.  
Although the results presented in this work refer to road FMZs, other experiments in other types of FMZs were done, in particular for the electrical lines FMZs. Due to some uncertainty about the ground truth of this type of FMZ, the results are not shown here. Nevertheless, the preliminary work is promising, even though it cannot have the same level of certainty of results due to uncertainty in the ground truth. Apart from that, scores for the electrical lines FMZs could be affected by various factors like like regular maintenance due to the risk of fire ignition associated with these lines, making the detection of vegetation change more difficult; the size of the sections for this type of FMZ is smaller resulting in less information in comparison to the road's sections. Future work will be done to mitigate the uncertainty about the ground truth of these FMZs types, allowing the proposed algorithms to be valuable for many different types of FMZs.  
Conceptualization, [PERSON], [PERSON], [PERSON], and [PERSON]; Methodology, [PERSON]; Formal analysis, [PERSON]; Investigation, [PERSON]; Data curation, [PERSON], [PERSON]; Writing-original draft preparation, [PERSON] and [PERSON]; writing-review and editing, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; Supervision, [PERSON] and [PERSON]; Project Administration, [PERSON] and [PERSON]; Funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This work is supported by NOVA LINCS (UIDB/04516/2020) and ALGORITMI (UIDB/00319/2020) with the financial support of FCT- Fundacao para a Ciencia e a Tecnologia, through national funds; This work is also supported by the project Floresta Limpa (PCIF/MOG/0161/2019). The authors would like like to thank the city hall of Macao for its collaboration with NOVA LINCS, which provided information about FMZs that were treated in 2018. Conflicts of Interest: The authors declare no conflict of interest.