Multi-source change detection with PALSAR data in the Southern of Pará state in the Brazilian Amazon

• Published in: International journal of applied earth observation and geoinformation 2020-02, Vol.84, p.101945, Article 101945
• Main Authors: Reis, Mariane Souza, Dutra, Luciano Vieira, Sant’Anna, Sidnei João Siqueira, Escada, Maria Isabel Sobral
• Format: Article
• Language: English
• Subjects: Brazilian Amazon; Multi-sensor change detection; Post-classification change detection
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2019.101945

•Use of L-band SAR and optical data for change detection in varied scenarios.•Higher amount of invalid transitions using L-band SAR data for change detection.•Using few classes leads to similar optical and L-band SAR change detection results.•Most accurate change maps are not tied to classification method.•Maps with detailed legends did not achieve accuracy values higher than 80%. Optical data is broadly used for change detection studies, despite being hindered by atmospheric conditions. Synthetic Aperture Radar (SAR) data can be useful for change detection in areas with frequent cloud coverage as SAR systems are capable of obtaining images almost independently from atmospheric conditions. This study aims to verify the difference in results of using SAR data instead of optical data for change detection purposes. Different levels of one hierarchical legend and both pixel and region-based classifiers were used. Change results were evaluated considering the use of rectangular matrices to incorporate the occurrence of impossible changes and relative comparison between change maps. Although the change maps obtained using only optical data were more accurate than those using either one or two land cover classifications based on L-band SAR data, the difference in the accuracy of change maps decreases with the use of less detailed legends. Additionally, results indicate that L-band SAR and multi-sensor approaches are adequate for deforestation identification even if post-classification results did not achieve global accuracy values superior to 0.86. The most accurate change detection results obtained in this work were not associated with the overall accuracy of land cover classifications, but with the distribution and accuracy of specific land cover classes.