Tree species classification using Sentinel-2 imagery and Bayesian inference

• Published in: International journal of applied earth observation and geoinformation 2021-08, Vol.100, p.102318, Article 102318
• Main Authors: Axelsson, Arvid, Lindberg, Eva, Reese, Heather, Olsson, Håkan
• Format: Article
• Language: English
• Subjects: Bayesian; Classification; Earth and Related Environmental Sciences; Fjärranalysteknik; Geovetenskap och miljövetenskap; Land cover; Maximum likelihood; Multi-temporal; Multi-temporal Satellite Bayesian Land cover Classification Maximum likelihood Sequential; Remote Sensing; Satellite; Sequential
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2021.102318

•Bayesian inference allows a stream of image data to be classified sequentially.•The method also allows for pixel level differences in data history.•An overall classification accuracy of 87% for four tree species was achieved.•The method achieves a high and stable classification accuracy. The increased temporal frequency of optical satellite data acquisitions provides a data stream that has the potential to improve land cover mapping, including mapping of tree species. However, for large area operational mapping, partial cloud cover and different image extents can pose challenges. Therefore, methods are needed to assimilate new images in a straightforward way without requiring a total spatial coverage for each new image. This study shows that Bayesian inference applied sequentially has the potential to solve this problem. To test Bayesian inference for tree species classification in the boreo-nemoral zone of southern Sweden, field data from the study area of Remningstorp (58°27′18.35″ N, 13°39′8.03″ E) were used. By updating class likelihood with an increasing number of combined Sentinel-2 images, a higher and more stable cross-validated overall accuracy was achieved. Based on a Mahalanobis distance, 23 images were automatically chosen from the period of 2016 to 2018 (from 142 images total). An overall accuracy of 87% (a Cohen’s kappa of 78.5%) was obtained for four tree species classes: Betula spp., Picea abies, Pinus sylvestris, and Quercus robur. This application of Bayesian inference in a boreo-nemoral forest suggests that it is a practical way to provide a high and stable classification accuracy. The method could be applied where data are not always complete for all areas. Furthermore, the method requires less reference data than if all images were used for classification simultaneously.