Detection of Invasive Black Locust (Robinia pseudoacacia) in Small Woody Features Using Spatiotemporal Compositing of Sentinel-2 Data

Abstract

Recognition of invasive species and their distribution is key for managing and protecting native species within both natural and man-made ecosystems. Small woody features (SWF) represent fragmented patches or narrow linear tree features that are of high importance in intensively utilized agricultural landscapes. Simultaneously, they frequently serve as expansion pathways for invasive species such as black locust. In this study, Sentinel-2 products, combined with spatiotemporal compositing approaches, are used to address the challenge of broad area black locust mapping at a high granularity. This is accomplished by conducting a comprehensive analysis of the classification performance of various compositing approaches and multitemporal classification settings throughout four vegetation seasons. The annual, seasonal (bi-monthly), and monthly median values of cloud-masked Sentinel-2 reflectance products are aggregated and stacked into varied time-series datasets per given year. The random forest algorithm is trained and output classification maps validated based on field-based reference datasets across Danubian lowlands (Slovakia). The main results of the study proved the usefulness of spatiotemporal compositing of Sentinel-2 products for mapping black locust in small woody features across wide area. In particular, temporally aggregated monthly composites stacked to seasonal time series datasets yielded consistently high overall accuracies ranging from 89.10% to 91.47% with balanced producer’s and user’s accuracies for each year’s annual series. We presume that a similar approach could be used for a broader scale species distribution mapping, assuming they are spectrally or phenologically distinctive, as is often the case for many invasive species.