The potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes

• DOI: 10.1016/j.jag.2022.103131
• Publication Date: 2023-01-12T05:30:31Z
• Authors: Anna Iglseder; Markus Immitzer; Alena Dostálová; Andreas Kasper; Norbert Pfeifer; Christoph Bauerhansl; Stefan Schöttl; Markus Hollaus
• Keywords: Physical geography; Random Forest; 04 agricultural and veterinary sciences; GB3-5030; Environmental sciences; Sentinel-1; 0401 agriculture, forestry, and fisheries; Natura 2000; GE1-350; Habitat Mapping; Airborne Laser Scanning; Sentinel-2

Mapping and monitoring of habitats are requirements for protecting biodiversity. In this study, we investigated the benefit of combining airborne (laser scanning, image-based point clouds) and satellite-based (Sentinel 1 and 2) data for habitat classification. We used a two level random forest 10-fold leave-location-out cross-validation workflow to model Natura 2000 forest and grassland habitat types on a 10 m pixel scale at two study sites in Vienna, Austria. We showed that models using combined airborne and satellite-based remote sensing data perform significantly better for forests than airborne or satellite-based data alone. For frequently occurring classes, we reached class accuracies with F1-scores from 0.60 to 0.87. We identified clear difficulties of correctly assigning rare classes with model-based classification. Finally, we demonstrated the potential of the workflow to identify errors in reference data and point to the opportunities for integration in habitat mapping and monitoring.