Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion
Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/7/1222 |
| _version_ | 1797570898172051456 |
|---|---|
| author | Luca Demarchi Wouter van de Bund Alberto Pistocchi |
| author_facet | Luca Demarchi Wouter van de Bund Alberto Pistocchi |
| author_sort | Luca Demarchi |
| collection | DOAJ |
| description | Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an important concern for understanding the macrodynamics of a river system and, if applied at large scales, it can be a powerful tool for monitoring purposes. In this study, the potentiality of GEOBIA and EL algorithms were tested for the mapping of key riverscape units along the main European river network. The Copernicus VHR Image Mosaic and the EU Digital Elevation Model (EU-DEM)—both made available through the Copernicus Land Monitoring Service—were integrated within a hierarchical object-based architecture. In a first step, the most well-known EL techniques (bagging, boosting and voting) were tested for the automatic classification of water, sediment bars, riparian vegetation and other floodplain units. Random forest was found to be the best-to-use classifier, and therefore was used in a second phase to classify the entire object-based river network. Finally, an independent validation was performed taking into consideration the polygon area within the accuracy assessment, hence improving the efficiency of the classification accuracy of the GEOBIA-derived map, both globally and by geographical zone. As a result, we automatically processed almost 2 million square kilometers at a spatial resolution of 2.5 meters, producing a riverscape-units map with a global overall accuracy of 0.915, and with per-class F1 accuracies in the range 0.79–0.97. The obtained results may allow for future studies aimed at quantitative, objective and continuous monitoring of river evolutions and fluvial geomorphological processes at the scale of Europe. |
| first_indexed | 2024-03-10T20:32:59Z |
| format | Article |
| id | doaj.art-6f2d14470c4f42e79721932e41b7eaf9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:32:59Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6f2d14470c4f42e79721932e41b7eaf92023-11-19T21:17:02ZengMDPI AGRemote Sensing2072-42922020-04-01127122210.3390/rs12071222Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data FusionLuca Demarchi0Wouter van de Bund1Alberto Pistocchi2Institute of Environmental Engineering, Department of Remote Sensing and Environmental Assessment, Warsaw University of Life Sciences, 02-787 Warsaw, PolandEuropean Commission Joint Research Centre, 21027 Ispra, ItalyEuropean Commission Joint Research Centre, 21027 Ispra, ItalyRecent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an important concern for understanding the macrodynamics of a river system and, if applied at large scales, it can be a powerful tool for monitoring purposes. In this study, the potentiality of GEOBIA and EL algorithms were tested for the mapping of key riverscape units along the main European river network. The Copernicus VHR Image Mosaic and the EU Digital Elevation Model (EU-DEM)—both made available through the Copernicus Land Monitoring Service—were integrated within a hierarchical object-based architecture. In a first step, the most well-known EL techniques (bagging, boosting and voting) were tested for the automatic classification of water, sediment bars, riparian vegetation and other floodplain units. Random forest was found to be the best-to-use classifier, and therefore was used in a second phase to classify the entire object-based river network. Finally, an independent validation was performed taking into consideration the polygon area within the accuracy assessment, hence improving the efficiency of the classification accuracy of the GEOBIA-derived map, both globally and by geographical zone. As a result, we automatically processed almost 2 million square kilometers at a spatial resolution of 2.5 meters, producing a riverscape-units map with a global overall accuracy of 0.915, and with per-class F1 accuracies in the range 0.79–0.97. The obtained results may allow for future studies aimed at quantitative, objective and continuous monitoring of river evolutions and fluvial geomorphological processes at the scale of Europe.https://www.mdpi.com/2072-4292/12/7/1222GEOBIAmachine learningrandom forestbig-datamulti-sensor analysisCopernicus |
| spellingShingle | Luca Demarchi Wouter van de Bund Alberto Pistocchi Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion Remote Sensing GEOBIA machine learning random forest big-data multi-sensor analysis Copernicus |
| title | Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion |
| title_full | Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion |
| title_fullStr | Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion |
| title_full_unstemmed | Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion |
| title_short | Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion |
| title_sort | object based ensemble learning for pan european riverscape units mapping based on copernicus vhr and eu dem data fusion |
| topic | GEOBIA machine learning random forest big-data multi-sensor analysis Copernicus |
| url | https://www.mdpi.com/2072-4292/12/7/1222 |
| work_keys_str_mv | AT lucademarchi objectbasedensemblelearningforpaneuropeanriverscapeunitsmappingbasedoncopernicusvhrandeudemdatafusion AT woutervandebund objectbasedensemblelearningforpaneuropeanriverscapeunitsmappingbasedoncopernicusvhrandeudemdatafusion AT albertopistocchi objectbasedensemblelearningforpaneuropeanriverscapeunitsmappingbasedoncopernicusvhrandeudemdatafusion |