A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy)
Earth Observation services guarantee continuous land cover mapping and are becoming of great interest worldwide. The Google Earth Engine Dynamic World represents a planetary example. This work aims to develop a land cover mapping service in geomorphological complex areas in the Aosta Valley in NW It...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/1/390 |
| _version_ | 1827761025657602048 |
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| author | Tommaso Orusa Duke Cammareri Enrico Borgogno Mondino |
| author_facet | Tommaso Orusa Duke Cammareri Enrico Borgogno Mondino |
| author_sort | Tommaso Orusa |
| collection | DOAJ |
| description | Earth Observation services guarantee continuous land cover mapping and are becoming of great interest worldwide. The Google Earth Engine Dynamic World represents a planetary example. This work aims to develop a land cover mapping service in geomorphological complex areas in the Aosta Valley in NW Italy, according to the newest European EAGLE legend starting in the year 2020. Sentinel-2 data were processed in the Google Earth Engine, particularly the summer yearly median composite for each band and their standard deviation with multispectral indexes, which were used to perform a k-nearest neighbor classification. To better map some classes, a minimum distance classification involving NDVI and NDRE yearly filtered and regularized stacks were computed to map the agronomical classes. Furthermore, SAR Sentinel-1 SLC data were processed in the SNAP to map urban and water surfaces to improve optical classification. Additionally, deep learning and GIS updated datasets involving urban components were adopted beginning with an aerial orthophoto. GNSS ground truth data were used to define the training and the validation sets. In order to test the effectiveness of the implemented service and its methodology, the overall accuracy was compared to other approaches. A mixed hierarchical approach represented the best solution to effectively map geomorphological complex areas to overcome the remote sensing limitations. In conclusion, this service may help in the implementation of European and local policies concerning land cover surveys both at high spatial and temporal resolutions, empowering the technological transfer in alpine realities. |
| first_indexed | 2024-03-11T10:08:06Z |
| format | Article |
| id | doaj.art-b1663ae88b494969a214eea6e14989a3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:08:06Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b1663ae88b494969a214eea6e14989a32023-11-16T14:56:23ZengMDPI AGApplied Sciences2076-34172022-12-0113139010.3390/app13010390A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy)Tommaso Orusa0Duke Cammareri1Enrico Borgogno Mondino2Department of Agricultural, Forest and Food Sciences (DISAFA), GEO4Agri DISAFA Lab, Università Degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, ItalyEarth Observation Valle d’Aosta—eoVdA, Località L’Île-Blonde, 5, 11020 Brissogne, ItalyDepartment of Agricultural, Forest and Food Sciences (DISAFA), GEO4Agri DISAFA Lab, Università Degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, ItalyEarth Observation services guarantee continuous land cover mapping and are becoming of great interest worldwide. The Google Earth Engine Dynamic World represents a planetary example. This work aims to develop a land cover mapping service in geomorphological complex areas in the Aosta Valley in NW Italy, according to the newest European EAGLE legend starting in the year 2020. Sentinel-2 data were processed in the Google Earth Engine, particularly the summer yearly median composite for each band and their standard deviation with multispectral indexes, which were used to perform a k-nearest neighbor classification. To better map some classes, a minimum distance classification involving NDVI and NDRE yearly filtered and regularized stacks were computed to map the agronomical classes. Furthermore, SAR Sentinel-1 SLC data were processed in the SNAP to map urban and water surfaces to improve optical classification. Additionally, deep learning and GIS updated datasets involving urban components were adopted beginning with an aerial orthophoto. GNSS ground truth data were used to define the training and the validation sets. In order to test the effectiveness of the implemented service and its methodology, the overall accuracy was compared to other approaches. A mixed hierarchical approach represented the best solution to effectively map geomorphological complex areas to overcome the remote sensing limitations. In conclusion, this service may help in the implementation of European and local policies concerning land cover surveys both at high spatial and temporal resolutions, empowering the technological transfer in alpine realities.https://www.mdpi.com/2076-3417/13/1/390land coverSentinel-1 SARSentinel-2deep learningGoogle Earth EngineSAGA GIS |
| spellingShingle | Tommaso Orusa Duke Cammareri Enrico Borgogno Mondino A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy) Applied Sciences land cover Sentinel-1 SAR Sentinel-2 deep learning Google Earth Engine SAGA GIS |
| title | A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy) |
| title_full | A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy) |
| title_fullStr | A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy) |
| title_full_unstemmed | A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy) |
| title_short | A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy) |
| title_sort | scalable earth observation service to map land cover in geomorphological complex areas beyond the dynamic world an application in aosta valley nw italy |
| topic | land cover Sentinel-1 SAR Sentinel-2 deep learning Google Earth Engine SAGA GIS |
| url | https://www.mdpi.com/2076-3417/13/1/390 |
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