Fusion of Dense Airborne LiDAR and Multispectral Sentinel-2 and Pleiades Satellite Imagery for Mapping Riparian Forest Species Biodiversity at Tree Level

Abstract

Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-source and multi-resolution satellite imagery: Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at the feature level and the decision level. At the feature level, LiDAR 2D attributes were derived and combined with multispectral imagery vegetation indices. At the decision level, LiDAR data were used for 3D tree crown delimitation, providing unique trees or groups of trees. The segmented tree crowns were used as a support for an object-based species classification at tree level. Data augmentation techniques were used to improve the training process, and classification was carried out with a random forest classifier. The workflow was entirely automated using a Python script, which allowed the assessment of four different fusion configurations. The best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a kappa of 0.66, thanks to red edge-based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages, helping to discriminate the species.

Multi-Sensor 3D Survey: Aerial and Terrestrial Data Fusion and 3D Modeling Applied to a Complex Historic Architecture at Risk

“…It is a must-have solution for outdoor and indoor surveying, as well as for surveying roofs and surroundings. This study discusses the topic of the fusion of point clouds from different sensors and methodologies [1][2][3], optimally combining LiDAR scans both from terrestrial (4.5 billion points) and Unmanned Aerial Systems (UAS, 200 million points), as well as photogrammetric point clouds (350 million points), to obtain an improved and complete 3D model of a complex historic building at risk.…”