Multi-source remote sensing recognition of plant communities at the reach scale of the Vistula River, Poland

Abstract

The seasonal occurrence of plant communities in rivers’ reaches has a direct impact on the flow resistance and sediment dynamics. Monitoring the occurrence of plant communities and their habitats can be of great importance to understand bio-geomorphological changes and hydrological processes and to assess human-induced changes. This work focuses on combining different remote sensors’ data acquired from an unmanned aerial vehicle (UAV) to feed Random Forest models for the recognition of plant communities in reaches of the Vistula River. Botanical surveys were carried out on more than two thousand field plots along the reaches, each being manually classified into nine different communities. Hyperspectral and RGB (Red-Green-Blue) images were collected with UAV over the botanical plots and merged with a LiDAR-based (Light Detection and Ranging) canopy height model. The modelling strategy consisted of fitting Random Forests using uncorrelated scores of principal components. A novel approach is presented to select discriminant features in the presence of high correlations after applying a ridge regularization on the inverse of the covariance matrix. We show how specific combinations of sensors’ features can impact the model’s accuracy, which reached more than 90% for dominating shrubs and trees such as Salicetum triandro-viminalis, Salicetum albo-fragilis and Chelidonio-Aceretum. On the other hand, the fitted model was not as accurate to classify plant communities such as Agropyretalia and Calamagrostietum.

Keywords

• Riparian vegetation
• Hyperspectral imagery
• Canopy height model
• Random Forest
• Feature selection