Mapping Early Successional Stages of the Atlantic Subtropical Rain Forest in Southern Brazil

High-resolution image segmentation and data mining techniques were used in order to find statistical correlation between spatial, spectral, context and texture attributes, considering field data from early stages of secondary forests in the three study sites of Rain Forest in Santa Catarina State, Brazil. Field data were collected in six sampling plots with 1,600 m² each. Digital processing utilized three high resolution images (0.39 m) obtained by SAAPI sensor with three visible bands, three near infrared bands, one terrain and one surface model. Extracted data from the digital product (attributes) were used for data mining purposes, selecting the ones with relevance and dismissing those of lesser weight. It was found that both tree and regeneration layers are heterogeneous considering number of individuals (N), diameter at breast height (DBH) and basal area (AB). However, significant correlations between forest attributes and field data were found. The higher correlation of N is with the average of the band 1 (-0.64), 3 (-0.62) and IR1 (0.63) for DBH was the ratio of bands IR3 (0.56) and 2 (0.55) and the variable AB was the smallest pixel value of band 1 (-0.64) and IR3 (-0.60). All correlation coefficients were highly significant (p <0.01). These results constitute the starting point for future research on the relationship between groups of variables in order to estimate biophysical parameters

Keywords:
succession; vegetation analysis; remote sensing