**3.5 Inventory of potentially available FRB**

Previous research has shown the utility of wetness variables obtained from Landsat TM images to estimate FRB, regardless of the image date in the summer period. This is because these kinds of variables yield high and similar estimation FRB models using June, July and August scenes. In addition, statistical differences were not found in the moisture content of the four pines studied over the 3 month period (García-Martín et al., 2008a).

In relation to results in previous sections, the model selected using the extraction of 3 x 3 fixed windows was applied to the MID57 neocanal image derived from the July 2008 Landsat image in order to inventory the FRB. This model was finally selected because it had higher predictive power (R2 of 0.711 versus 0.595 on the model selected in the visual analysis method and 0.535 in the segmentation method) and allows the development of maps with the lowest estimation error. This last point shows that the limited set of plots used in the 3 x 3 fixed windows method is as representative for FRB estimation as those used in the other two methods, although they were composed of nearly double the number of plots.

Figure 10 shows the estimation cartography obtained for the entire study area. This cartography allows calculation of the total amount of FRB resource at the provincial level, which amounts to 5,449,252 tons. In addition, with the high spatial resolution (25 x 25 m), the cartography precisely reveals the richest regions and FRB distribution within them.

This makes this cartography especially suitable for determining optimal areas, taking into account other spatial variables that also determine the technical and economic feasibility in the harvest of this renewable energy resource, for example: (i) slope, which influences the possibility of using machinery and its efficiency; (ii) distance to forest tracks, which determines a portion of the transport costs; and (iii) area of forest stands, which is related to the necessary displacement during the working day. These three spatial factors and the quantity of FRB derived from remote sensing data at 25 m resolution can be integrated into a Geographical Information System (GIS) to identify areas more suitable for harvest of FRB, with attention to principles of sustainable ecological forest management (Pascual et al., 2007; García-Martín et al., 2011).

Fig. 10. FRB in the study area in July 2008
