## Materials and Methods
### Estimation of Forest Degradation Indicators
#### 2.4.3 Above-Ground Biomass  
Diameter at breast height (DBH), tree height (H) and species name were extracted from each sampling plot unit from the National Forest Inventory dataset and used for calculating individual tree above-ground biomass (AGB) for the species found in the study area. To estimate AGB per tree, we used allometric equations reported for the area [48]. These individual data (20,731 trees) were added on a per plot basis and used to extrapolate to a per hectare basis. Conversion of biomass/carbon from conglomerate to hectares was achieved using the \"Ratio of Means\" [49]. Later, AGB estimates were related to spectral variables obtained from satellite imagery.  
A review of a host of vegetation indices allowed for the identification of those that have been reported to estimate Above-ground Biomass accurately (Table 2). These indices were used to seek and identify those that are strongly correlated to forest biomass (AGB) measurements to generate estimates of biomass in the analysis. The calculation of the vegetation indices used reflectance values at different wavelengths so as to enhance information related to vegetation while reducing influences from environmental conditions and shade.  
Above-Ground Biomass Estimation  
Landsat Images from January 2005 and December 2010 were used as inputs to match the forest inventory data from periods 2004-2007 and 2009-2011, respectively. To estimate above-ground biomass, computed vegetation indices from Landsat imagery were resampled to a pixel size of 100 m to match the data from AGB plots at the same working scale (1 ha), for later analysis. The minimum and maximum values of the dataset were included in the calibration sample set selected, so that the domain limits of the model over which the validation process could be used, were included in the calibration. The remaining sampling points available were randomly divided into two groups consisting of 85% of the data points for model selection and 15% for validation.  
Multiple regression analysis was used for biomass estimation. We evaluated the independent variables by using the variance influence factor (VIF) as multicollinearity normally occurs between remote sensed values [58]. To diminish multicollinearity problems, we applied a principal components analysis to generate new variables to develop biomass estimation models. The predictive models were validated using the leave-one-out cross-validation procedure by calculating the corresponding cross-validated coefficient of determination (R\({}^{2}\)cv) and the root mean square error (RMSEcv).  
A random sample representing the 15% of the field plots data was not included in the construction of the models but was used for validation purposes. We compared data extracted from the AGB estimates maps against data from the field and forest inventory databases by using the RMSE and RMSE%. In addition, we calculated the Pearson correlation coefficient (r) as a measure of linear fit between predictions and observations. All data analysis carried out in this study was performed using the \"R\" software package ([[http://www.r-project.org/](http://www.r-project.org/)]([http://www.r-project.org/](http://www.r-project.org/))).