Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data
The present research aims at verifying whether there are significant differences between Land Use/Land Cover (LULC) classifications performed using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data—abbreviated as L8 and S2. To comprehend the degree of accurac...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Geographies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-7086/3/1/5 |
| _version_ | 1797611528622440448 |
|---|---|
| author | Giuseppe Mancino Antonio Falciano Rodolfo Console Maria Lucia Trivigno |
| author_facet | Giuseppe Mancino Antonio Falciano Rodolfo Console Maria Lucia Trivigno |
| author_sort | Giuseppe Mancino |
| collection | DOAJ |
| description | The present research aims at verifying whether there are significant differences between Land Use/Land Cover (LULC) classifications performed using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data—abbreviated as L8 and S2. To comprehend the degree of accuracy between these classifications, both L8 and S2 scenes covering the study area located in the Basilicata region (Italy) and acquired within a couple of days in August 2017 were considered. Both images were geometrically and atmospherically corrected and then resampled at 30 m. To identify the ground truth for training and validation, a LULC map and a forest map realized by the Basilicata region were used as references. Then, each point was verified through photo-interpretation using the orthophoto AGEA 2017 (spatial resolution of 20 cm) as a ground truth image and, only in doubtful cases, a direct GPS field survey. MLC and SVM supervised classifications were applied to both types of images and an error matrix was computed using the same reference points (ground truth) to evaluate the classification accuracy of different LULC classes. The contribution of S2′s red-edge bands in improving classifications was also verified. Definitively, ML classifications show better performance than SVM, and Landsat data provide higher accuracy than Sentinel-2. |
| first_indexed | 2024-03-11T06:29:01Z |
| format | Article |
| id | doaj.art-58f1b74f759549c7973038b0e5882dcd |
| institution | Directory Open Access Journal |
| issn | 2673-7086 |
| language | English |
| last_indexed | 2024-03-11T06:29:01Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geographies |
| spelling | doaj.art-58f1b74f759549c7973038b0e5882dcd2023-11-17T11:19:18ZengMDPI AGGeographies2673-70862023-01-01318210910.3390/geographies3010005Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI DataGiuseppe Mancino0Antonio Falciano1Rodolfo Console2Maria Lucia Trivigno3Centro di Geomorfologia Integrata per l’Area del Mediterraneo (CGIAM), Via F. Baracca 175, 85100 Potenza, ItalyCentro di Geomorfologia Integrata per l’Area del Mediterraneo (CGIAM), Via F. Baracca 175, 85100 Potenza, ItalyCentro di Geomorfologia Integrata per l’Area del Mediterraneo (CGIAM), Via F. Baracca 175, 85100 Potenza, ItalyCentro di Geomorfologia Integrata per l’Area del Mediterraneo (CGIAM), Via F. Baracca 175, 85100 Potenza, ItalyThe present research aims at verifying whether there are significant differences between Land Use/Land Cover (LULC) classifications performed using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data—abbreviated as L8 and S2. To comprehend the degree of accuracy between these classifications, both L8 and S2 scenes covering the study area located in the Basilicata region (Italy) and acquired within a couple of days in August 2017 were considered. Both images were geometrically and atmospherically corrected and then resampled at 30 m. To identify the ground truth for training and validation, a LULC map and a forest map realized by the Basilicata region were used as references. Then, each point was verified through photo-interpretation using the orthophoto AGEA 2017 (spatial resolution of 20 cm) as a ground truth image and, only in doubtful cases, a direct GPS field survey. MLC and SVM supervised classifications were applied to both types of images and an error matrix was computed using the same reference points (ground truth) to evaluate the classification accuracy of different LULC classes. The contribution of S2′s red-edge bands in improving classifications was also verified. Definitively, ML classifications show better performance than SVM, and Landsat data provide higher accuracy than Sentinel-2.https://www.mdpi.com/2673-7086/3/1/5Landsat 8 OLISentinel-2mappingLand Use/Land Cover classificationMaximum Likelihood ClassificationSupport Vector Machine |
| spellingShingle | Giuseppe Mancino Antonio Falciano Rodolfo Console Maria Lucia Trivigno Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data Geographies Landsat 8 OLI Sentinel-2 mapping Land Use/Land Cover classification Maximum Likelihood Classification Support Vector Machine |
| title | Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data |
| title_full | Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data |
| title_fullStr | Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data |
| title_full_unstemmed | Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data |
| title_short | Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data |
| title_sort | comparison between parametric and non parametric supervised land cover classifications of sentinel 2 msi and landsat 8 oli data |
| topic | Landsat 8 OLI Sentinel-2 mapping Land Use/Land Cover classification Maximum Likelihood Classification Support Vector Machine |
| url | https://www.mdpi.com/2673-7086/3/1/5 |
| work_keys_str_mv | AT giuseppemancino comparisonbetweenparametricandnonparametricsupervisedlandcoverclassificationsofsentinel2msiandlandsat8olidata AT antoniofalciano comparisonbetweenparametricandnonparametricsupervisedlandcoverclassificationsofsentinel2msiandlandsat8olidata AT rodolfoconsole comparisonbetweenparametricandnonparametricsupervisedlandcoverclassificationsofsentinel2msiandlandsat8olidata AT marialuciatrivigno comparisonbetweenparametricandnonparametricsupervisedlandcoverclassificationsofsentinel2msiandlandsat8olidata |