Dual-season comparison of OBIA and pixel-based approaches for coastal wetland classification

Comparação de abordagens baseadas em objetos (OBIA) e em pixels em duas estações para a classificação de áreas úmidas costeiras

ABSTRACT

Mapping and classifying Coastal Wetlands is important for their conservation. The study aimed to apply Object-Based Image Analysis (OBIA) and pixel-based approaches to answer the questions: (1) which approach is more accurate for classifying Wetlands; (2) Sentinel 1A images improve the classification of Wetlands compared to Sentinel 2A; (3) dual-station sorting has greater potential for sorting Wetlands compared to single-station sorting. We used Sentinel 1 and 2 in single and double seasons (winter and summer) to classify a coastal UA in Rio Grande do Sul. The results show OBIA with greater potential, with accuracy greater than 80%. Sentinel 2 shows higher ranking importance compared to Sentinel 1. Dual season OBIA increased kappa by up to 7% compared to single season. Furthermore, the pixel-based dual season had lower kappa than the OBIA single season. We conclude that OBIA, even at a single station, has greater potential to map coastal AUs.

Keywords:
Machine learning; Coastal wetlands; São Gonçalo channel