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radiometric data. Analyses undertaken revealed statistically significant correlations with the three methods, but the use of 3 x 3 pixel windows and CV were indicated as the most appropriate to isolate a homogeneous group of plots that allow the most accurate regression equations (R2>0.7). In addition, despite the fact that these models were performed using half of the plots than those derived from the other two extraction methods, it was representative of the entire study area, as was shown in the validation results obtained at pixel level (RMSEr was similar in the cartographies obtained from the three methods). The visual analysis method did not yield such good results probably because of inaccuracies in the delineation of the homogeneous areas and because of the assumption of a constant value of BRF for the entire area from one precise location. The segmentation method did not improve the results either. This fact can be related to the lack of success to model effectively the spatial distribution pattern of BRF in the study area using RGB clustering. Finally, it is important to note that any of the methodologies tested to extract radiometric data successfully completed multivariate regressions models. This was because of the high autocorrelation among the well-correlated radiometric variables with FRB (wetness variables and vegetation indices). The use of these variables together will run a model affected by

Validation cartography was based at the pixel level using plots that had not been previously used in the regression equation, removing only those with higher probability of error. This approach ensured the independence of the validation. RMSEr obtained in the three cartographies (above 65%) were better or at the same level than those produced in previous experiments conducted in boreal environments to estimate forest parameters using Landsat images. This is a positive result considering the higher complexity of Mediterranean forests. The remaining errors in the estimates of FRB can be related to the factors highlighted in García-Martín et al. (2008a,b): (i) inaccuracies in the fieldwork undertaken to establish the allometric equations and statistical analyses of the equations; (ii) problems involved in relating NFI plots to satellite data, mainly related to inaccuracies in the plot field placement (the three methodologies applied in the present study in linking ground data and remotely sensed data helped to reduce this problem, but it is still possible that errors were present in the final sample data); (iii) inaccuracies related to heterogeneity of the sample, because different pine species were considered and they are distributed in different regions of the study area; and (iv) limitations related to the spectral, radiometric, and spatial resolution of the TM in highly heterogeneous environments. However, despite these limitations, the size of the scenes and ease of distribution at no cost, make Landsat TM the most suitable in terms of achieving the objective of developing a useful methodology for estimations of FRB at

Finally, in order to improve outcomes, different lines of research should be considered. Firstly, different segmentation methods other than RGB clustering should be explored to determine if they can better model the forest spatial pattern and, as a result, obtain more accurate estimation models. Concerning this, the eCognition and the Definiens Developer segmentation procedures offer the possibility of considering additional features together with spectral data, based on similarities in shape and size. Secondly, focusing only on Landsat data, it is necessary to explore the use of other statistical methods that allow highly auto-correlated dependent variables to be considered jointly. In addition, it would be useful to explore the capability of hyperespectral sensors to identify narrow spectral bands highly correlated to FRB and poorly correlated between them and to examine the capability of SAR

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