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Selected Publication:

Iglseder, A; Immitzer, M; Dostalova, A; Kasper, A; Pfeifer, N; Bauerhansl, C; Schotl, S; Hollaus, M.
(2023): The potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes
INT J APPL EARTH OBS. 2023; 117, 103131 FullText FullText_BOKU

Abstract:
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.
Authors BOKU Wien:
Immitzer Markus

Find related publications in this database (Keywords)
Habitat Mapping
Natura 2000
Airborne Laser Scanning
Sentinel-1
Sentinel-2
Random Forest


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