# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 1 Introduction
In [9], the Normalized Difference Vegetation Index (NDVI) was used, along with geographic information of roads and agricultural fields, to identify FMZs. In a different context, the work of [10] compared the precision of three digital surface models to estimate the elevation of the terrain in the areas that concern FMZs, in a northern part of Greece. There are several approaches regarding crop and vegetation monitoring and biomass estimation that can be used as a basis to build a robust solution for the monitoring of FMZs. Some studies in which the focus is to classify types of crops [8; 11; 12], others that classify tree species in forest areas [8], and also a more comprehensive classification approaches that focus on distinguishing different types of land cover (e.g., forest, urban, agricultural areas, etc.) [5; 13; 14]. Some studies only use data from satellite bands directly [5; 8], although in most cases vegetation indices are used as indicators of vegetation characteristics. Some works use time series to extract more information about vegetation [11; 14; 15]. The use of time series has already shown better results in the classification of vegetation and land cover than the use of data referring to only one date [14; 16]. There are also some studies in which the dimension and the characteristics of the analyzed areas are similar to those of FMZs [17; 18]. [PERSON] et al. [8] classified the vegetation in an agricultural area and a forest area using the Random Forests. In the agricultural area, 8 types of crops were classified and in the forest area 6 species of trees. Two approaches were compared: pixel-based and object-based, and it was observed that in the forest area the object-based approach obtained better results. [PERSON] et al. [17] used Sentinel-2 data and vegetation indices to analyze areas similar to the size of FMZs, with 30 by 30 m. Also using the same data type [PERSON] et al. [19] found that there is a strong correlation (\(R^{2}=0.94\)) between the NDVI and the water consumption of that plant, which may be helpful when analyzing the state of vegetation in an FMZ. Other studies attempt to estimate the production of a given crop following indicators such as Leaf Area Index (LAI) and vegetation indices [20; 21]. [PERSON] et al. [21] used Synthetic Aperture Radar (SAR) data from Sentinel-1 and multispectral data from the MODIS satellite to estimate rice production, compared to data obtained in the field, obtaining errors of less than 10%. [PERSON] et al. [22] used Sentinel-2, Sentinel-1, and elevation data, along with some vegetation indices to estimate the amount of biomass present in mangrove forests in the Philippines. The results showed a correlation between the levels of biomass with some vegetation indices and the LAI. All the information presented in these works serves as a knowledge base to establish an adequate strategy to carry out the classification of interventions and monitoring the state of vegetation in FMZs. This paper presents an automated machine- learning approach that leverages time series to detect if an FMZs was treated or not using open satellite data at a resolution of 10 by 10 m. To the best of our knowledge, this is the first work that proposes a machine- learning approach to detect if the FMZs are maintained according to the law. Machine learning combined with remote sensing can accelerate the process of monitoring these FMZs which otherwise would be time-consuming. Furthermore, the monetary costs associated with the manual labor required to monitor the FMZs could be mitigated by using open satellite data. The contributions from this paper are as follows: (1) a methodology for detecting interventions in FMZs is presented, which uses information about vegetation present inside and outside the FMZs.