# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 4 Discussion  
In this paper, two types of analysis were carried out: a static analysis, using only Sentinel-2 data from one date, and a temporal analysis using the temporal patterns of the Sentinel-2 and Sentinel-1 characteristics. The FMZs along roads were analyzed using classification algorithms and temporal patterns to classify which FMZ sections that were treated. An evaluation of the different classification algorithms was also carried out, both in the temporal analysis and in the static analysis, comparing the results of these algorithms using different data sets. In addition to these classification experiments, an experiment was also carried out to try to determine the dates of the intervention of the FMZs along roads. In this section, the results of the various experiments are discussed. Different classification algorithms were used in this work: RF, SVM, XGBoost, and KNN. The algorithms that stand out are XGBoost and RF, which in almost all experiments achieved the best results. The algorithms that generally obtained the worst results were SVM and KNN. In the static analysis, SVM obtained the lowest results using the data set _DS_INDICES_. However, in the temporal analysis, the results of these algorithms improved considerably in such a way that SVM was immediately behind XGBoost using data from Sentinel-2. Nevertheless, ensemble methods, such as RF and XGBoost, methods can consistently achieve high performance and the performance per dataset does not vary by a big margin. Regarding the data, five different data sets were compared: _DS_ALL_, _DS_INDICES_, _DS_BAND_, _DS_FUSION_, and _DS_SAR_. In the static analysis, only data from Sentinel-2 was used and in the temporal analysis information from Sentinel-1 was added. In both temporal and static analysis, the _DS_ALL_ data set was always among the data sets with the highest metrics, being the best in most experiments due to having more information available for the algorithms. The _DS_INDICES_ obtained worse results in almost all experiments in comparison to the _DS_BAND_ data set, often with a big difference between the results of both. In the temporal analysis, for the RF and XGBoost algorithms, the _DS_INDICES_ obtained an F1-score close to the best results; however for the SVM and KNN the results were lower, with a difference of 22% for the best result in one of the tests (using KNN in the FMZs along roads). It can be concluded that vegetation indices by themselves are not expressive enough to model the properties of the FMZs to determine if they were treated or not. Furthermore, the SVM and KNN showed a tendency to overfit on _DS_INDICES_. The temporal analysis using the data set _DS_SAR_ (just Sentinel-1 data) generated significantly lower results. Although it achieved an average F1-Score, the Kappa reached as low as 0.25. This shows that this type of data is not reliable enough to be used for this type of application. Further research is needed to see if it is possible to leverage more performance from SAR data, either with new models or a different processing pipeline (e.g., adding textural information). SAR data is especially interesting when there is not multispectral data available, for example when there is cloud coverage over the study area. Even with the low performance of SAR, the combination of Sentinel-1 and Sentinel-2 showed slight improvements in the results, with the most significant improvement in the F1-score, increasing by almost 2%. Although the global F1-score is equal when using only Sentinel-2 data and Sentinel-2 in conjunction with Sentinel-1 data, there was a difference in the results for the treated sections class. With just data from Sentinel-2, the treated sections class had an F1-score of 0.88, when adding Sentinel-1 data this value raised to 0.90. The combination of Sentinel-1 and Sentinel-2 can increase the performance of the proposed algorithms. Even though the increase in performance was measured at 2%, the high availability of Sentinel-1 data (not affected by cloud coverage) can be beneficial.