GEOBIA, TREE DECISION AND HIERARCHICAL CLASSIFICATION FOR MAPPING GULLY EROSION

• Autor(a) principal: Tedesco, Andrea
• Data de Publicação: 2020
• Outros Autores: Antunes, Alzir Felippe Buffara
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Ra'e Ga (Online)
• Texto Completo: https://revistas.ufpr.br/raega/article/view/74842
• topic: Geomorfologia; ALS Data; High Resolution Imagery; Multirresolution Segmentation

The gullies provoke environmental, social and financial damages. The application of corrective and preventive measures needs gullies mapping and monitoring. In this scope, this study proposes a methodology for gullies delimitation using object-oriented image analysis. For such, there were used high spatial resolution imagery and ALS data applied for two study areas, one in Uberlandia-Minas Gerais (Brazil) and another one in Queensland (Australia). The objects were generated by multiresolution segmentation. The most important attributes on the delimitation of the gullies were selected using decision tree induction algorithms, being them: spectral, altimetric and texture. Classifications by decision trees and hierarchical were carried out. The use of decision tree allowed the selection of attributes and the establishment of preliminary decision rules. However, since this procedure did not use fuzzy logic, mixtures between classes could not be evidenced in the rule base. Moreover, the classification was performed by a factor of scale only, which did not allow the identification of all the constituent features of the gully. In hierarchical classification, the procedure is performed on different scales, allowing the use of fuzzy logic to describe different degrees of membership in each class, which makes it a very attractive method for cases such as this study, where there is mixing of classes. The classification obtained with hierarchical classification it was more reliable with the field truth, by allowing the use of different scales, uncertainty insert and integration of knowledge, compared to the automatic classification by decision tree.