Since the 70 ́s remote sensing techniques have been used for surveying of natural
resources. From the 90 ́s on, the launching of new satellites carrying high-resolution
sensors has led to the implementation of new approaches in digital image processing.
Forest mapping is one of the basic tools to accurately assess forest conditions in forest
remnants, thus allowing for the establishment of strategies, which aim both to nature
conservancy as to the economic development of a real state or region. This study
evaluated the possibility of identifying and discriminating forest types in remnants of
Araucarian forests, aiming to develop a methodology for mapping the remnants of this
biome in a fast, inexpensive and accurate way. The research was developed at the
Forest Reserve EMBRAPA/EPAGRI, located in Caçador-SC. Forest types were
defined by surveying target areas on the ground. Segmentation and region-oriented
classification algorithms were tested on an Ikonos image in order to describe the forest
conditions by the time of image acquisition. Thematic accuracy was evaluated by
comparing the classification results with a reference map obtained through on-screen
visual interpretation of the same Ikonos imagery. The mapping classes were based on
the presence of species indicating the successional phases of the woody vegetation
(trees and shrubs) in canopy cover. The two-level classification scheme considered the
successional phases as well as the forest types in a more detailed manner. Thirteen
thematic classes were defined and mapped by visual interpretation. Eight of then
referred to forest types. Qualitative and quantitative analyses were performed in order
to define the best minimum area and similarity thresholds in the segmentation process.
The quantitative analysis included the development of a modified IAVAS index. This
index allowed for the comparison between different area and similarity thresholds thus
eliminating the subjectiveness of a qualitative analysis in defining the best
combinations. Among the tested threshold pairs, the best one was the 35 (similarity)
and 1200 (area). The regions generated by this pair of thresholds were submitted to a
classification process using the algorithms “Isoseg” and “Bhattacharyya”, available in
software SPRING. In the classification scheme the number of classes was reduced to
11 due to the non-discrimination of a class refering to a forest type and the grouping of
two classes refering to land use. The supervised digital classification was efficient in
determining the forest type “Predominance of Araucaria”. For the other classes the
Bhattacharyya classifier didn ́t perform well, generating low values for the overall
accuracy (51.73%) and for the kappa index (0.43).
