# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 2 Materials and Methods
### FMZs Vector Clustering  
The FMZs have a large extension and to analyze them this paper proposes an object-based approach in which the FMZs were divided into sections with approximately the same dimension. Since the satellite images have a spatial resolution of 10 m, some FMZs can have a width of multiple pixels, making it difficult to compare the values of the pixels in the buffer with the ones inside the FMZ. Using an object-based approach simplifies this comparison by combining the value of multiple pixels into just one value, and comparing the two values (the buffer value with the FMZ value). The object-based approach has other benefits such as noise reduction of pixels that could have been affected by the reflectance of nearby structures. Another advantage of this approach is the decrease in the data set size, reducing the computation time of the machine- learning algorithms.  
The creation of sections is done after the buffers are created so each section has information about the FMZ and the outside buffer. These sections were created using K-means [33] whose cluster results will be referred to as FMZ segments. By using K-means the segments inherit some of its properties like like creating non-hierarchical and nonoverlapping clusters in a Voronoi pattern, which creates similar-sized segments along with the extent of the FMZ. One of K-means' drawbacks is that the number of clusters (K) must be known before running the algorithm.  
Figure 6: Example of the processing chain to obtain the interior of the Fuel Management Zones (FMZs) around housing. (**a**) Initial FMZ; (**b**) Concave Hull applied; (**c**) Polygon dissolveddissolved, and holes removed; (**d**) Difference between the FMZ with a 20 m buffer and the polygon that represents the interior of the initial FMZ, after removing roads from OpenStreetMaps.  
The used approach goes as follows: first, transform the pixels that correspond to the interior of the FMZ and the buffer into points (Figure 7a). The K is then selected by dividing the total number of points in an FMZs with the number of wanted pixels in a single segment. For example, in the case of FMZs along roads, with a total of 208,000 points and 40 points per cluster, 5200 segments were obtained. After testing a range of numbers, 10 to 100, of points per cluster, 40 points was the value chosen to present the results and methodology of this paper. This value is the value that better represents segments of FMZs and intersects their interior and exterior. The number of points per section cannot be very low (e.g., 10), as it generates sections that do not cover the strip and its exterior buffer, nor can be too high (e.g., 100) to reduce the possibility of a section containing treated and untreated areas. After visual inspection, this value represents a balance between the resolution of the pixels, the width of the FMZs and the created buffers. Finally, to obtain the polygon that corresponds to a section, the Convex Hull algorithm is run for each cluster generated by K-Means. The result is a set of polygons with approximately the same area, which covers the entire FMZ (Figure 7b) and keeps information on which points belong to the interior and exterior of the FMZ. Examples of the final results for each FMZ type are shown in Figure 8. Please note that the FMZ type affects the overall shape of the segments.