Applying multidate Sentinel-2 data for forest-type classification in complex broadleaf forest stands

Abstract

AbstractBiodiversity assessment and forest management require accurate tree species maps, which can be provided by remote sensing. Whereas the application of high-spatial resolution remote sensing data is constrained by high costs, Sentinel-2 (S2) satellites provide free imagery with appropriate spatial, spectral and temporal resolutions for mapping of various forest traits across larger spatial scales. Here we assessed the potential of multidate S2 as well as a Digital Elevation Model (DEM) in classifying tree species across a highly structured and heterogeneous broadleaf forest ecosystem in the Hyrcanian zone of northern Iran. We applied multidate S2 and DEM data as input to a variable selection using random forests algorithm for feature reduction. Ten forest types were classified using random forest algorithm and to evaluate the results we computed area-adjusted confusion matrices. Classifications based on single-date S2 data reached overall accuracies of 67–74 per cent, whereas results for multidate S2 images increased the accuracy by ~28 per cent. Joint use of DEM data along with multidate S2 images showed improvement of overall accuracy by ~3 per cent. In addition, we studied the effect of topographic correction of S2 data on classification performance. The results imply that applying topographically corrected imagery had no significant effect on the classification accuracy. Our results demonstrate the high potential of freely available multisource remotely sensed data for broadleaf tree species classification across complex broad-leaved forest landscapes.