## 4 Discussion and Conclusions  
The approach followed in this study intends to support and complement national strategies for monitoring, verification and reporting of deforestation and forest degradation, especially in developing countries, where technical capacities and lack of data could be a major obstacle to monitoring changes in forest structure. This study showed that it is possible to produce, at minimum costs and with available data, maps with information about the status of forest cover and estimations of total carbon  
Figure 6: (a) Forest cover used as a reference state; (b) Forest-degraded areas according to the indicators selected. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Carbon Mg/ha** & **NPP (gC/m\({}^{2}\)/year)** \\ \hline min & 14 & 205 \\ Average & 23.28 & 519 \\ Max & 51.84 & 844 \\ St. Dev. & 7.4 & 245 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Reference values for the study area. stocks and productivity that ultimately can be used as tools for decision making concerning the volumes of Carbon involved, not only under the REDD+ strategy but also for national forest policies. The spatial information produced by the methods proposed here, depending on their final use, would require adaptation and more processing, in order to be directly useful to other fields of enquiry, for example in biological applications. Thus far, information is only mapped at the pixel level (pixel resolution of 30 by 30 m) while most ecological users would prefer to work with patch-level phenomena. Moreover, the original maps typically include single-pixel noise (\"speckle\") that is too small to either validate or interpret. Therefore, the next critical phase is one of spatial filtering, where adjacent pixels experiencing similar processes are grouped together into patches, and pixels in tiny patches are removed. The results suggest that it is possible to produce clear canopy cover and biomass estimates at high resolution over relatively large areas. Since there are several options for the estimation of canopy cover, such as empirical modelling [64], regression trees [23] and spectral mixture analysis [65], this research adopted the spectral mixture analysis approach proposed by [PERSON] et al., (2009) because it offers a standard procedure and has proven its applicability in the REDD+ context [66]. Concerning aboveground biomass, its estimation also turned out to be relatively accurate (r = 0.74, and RMSE = 29 Mg/ha), although problems with saturation above certain biomass values have been pointed out [67]. Some authors highlighted that including short wave infrared (SWIR) bands in the construction of AGB models may enhance sensitivity to the canopy water content and shadow fraction [68]. As a consequence, the capacity to predict AGB is improved. The best predictor models were precisely those that included vegetation indices based on calculations that included SWIR bands. It is important to notice that this type of approach can optimise resources used to obtain estimates of carbon with relative accuracy and a low cost, under the standards of MRV and REDD+. From estimations of AGB, it can be noted that low values of biomass correspond to areas that are very close to urban settlements and in consequence, are more prone to human disturbance and suffer some type of degradation [19]. It is important to note that the results from the estimations of AGB respond to the method used and the source of the data. From the Forest inventories that the National Forestry Commission provided, there are some inconsistencies in the data, and, as far as it can be possibly known, no quality control. In data from 2004-2007, quality control was undertaken; therefore, these data were deemed reliable. The results also showed the limitations that Vegetation indices could have in predicting biomass, due to problems of saturation. The Net Primary Productivity of the forests also proved its value in identifying and mapping forest degradation.