**2.4.3 Spectral segmentation**

The main problems in constructing predictive models related to the extraction of radiometric data using high-resolution aerial photographs of delimited forest area are: (i) human errors in the establishment of limits during the visual image interpretation process, (ii) assumption of a FRB constant value for the entire homogeneous area based on a point location that is not representative of larger forest stands (Pekkarinen, 2002).

Image segmentation can be applied to delimit homogeneous spectral features as a reference for spectral data extraction (Hall et al., 2006; Mäkelä & Pekkarinen, 2001; Pekkarinen, 2002). In this study, the segmentation algorithm RGB clustering incorporated in the software Erdas Imagine was applied. The essential parameters that control the results in this method are the RGB composition (which is used to apply the segmentation), the stretch applied to each band in the composition, and the number of bins into which each band is divided. Four different segmentations were performed, modifying the stretch (2 or 4 standard deviations) and the bins (7-6-6 and 4-3-3) using a RGB composition TM7-TM4-TM3 to model the heterogeneity of the analyzed forest (Table 2). In addition, in order to remove pixels within the immediate vicinity that were not attributable to the IFN-2 plot, the obtained image segments containing field plots were then intersected with 3 x 3 pixel windows, resulting in a mask of homogeneous pixels. Thus, the mean pixel value for each image band was then computed from the pixels that belong to the same spectral category as that of the central pixel of the NFI-2 plot (Figure 5).


Table 2. Parameters considered in the four different segmentations performed using RGB composition TM7-TM4-TM3

Fig. 5. Procedure to extract radiometric data combining the use of segmentation and 3 x 3 pixel windows with restrictions
