Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docx
<p>Land use/land cover (LULC) mapping in fragmented landscapes, characterized by multiple and small land uses, is still a challenge. This study aims to evaluate the effectiveness of Synthetic Aperture Radar (SAR) and multispectral optical data in land cover mapping using Google Earth Engine (G...
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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| _version_ | 1852020334180433920 |
|---|---|
| author | Giovanni Romano (21373319) |
| author2 | Giovanni Francesco Ricci (21373322) Francesco Gentile (500655) |
| author2_role | author author |
| author_facet | Giovanni Romano (21373319) Giovanni Francesco Ricci (21373322) Francesco Gentile (500655) |
| author_role | author |
| dc.creator.none.fl_str_mv | Giovanni Romano (21373319) Giovanni Francesco Ricci (21373322) Francesco Gentile (500655) |
| dc.date.none.fl_str_mv | 2025-05-16T05:13:36Z |
| dc.identifier.none.fl_str_mv | 10.3389/frsen.2025.1535418.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Supplementary_file_1_Effectiveness_evaluation_of_combining_SAR_and_multiple_optical_data_on_land_cover_mapping_of_a_fragmented_landscape_in_a_cloud_computing_platform_docx/29084447 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Photogrammetry and Remote Sensing google earth engine Landsat 8 Sentinel-1 Sentinel-2 land cover classification spectral indices RF classifier |
| dc.title.none.fl_str_mv | Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docx |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Land use/land cover (LULC) mapping in fragmented landscapes, characterized by multiple and small land uses, is still a challenge. This study aims to evaluate the effectiveness of Synthetic Aperture Radar (SAR) and multispectral optical data in land cover mapping using Google Earth Engine (GEE), a cloud computing platform allowing big geospatial data analysis. The proposed approach combines multi-source satellite imagery for accurate land cover classification in a fragmented municipal territory in Southern Italy over a 5-month vegetative period. Within the GEE platform, a set of Sentinel-1, Sentinel-2, and Landsat 8 data was acquired and processed to generate a land cover map for the 2021 greenness period. A supervised pixel-based classification was performed, using a Random Forest (RF) machine learning algorithm, to classify the imagery and derived spectral indices into eight land cover classes. Classification accuracy was assessed using Overall Accuracy (OA), Producer’s and User’s accuracies (PA, UA), and F-score. McNemar’s test was applied to assess the significance of difference between classification results. The optical integrated datasets in combination with SAR data and derivate indices (NDVI, GNDVI, NDBI, VHVV) produce the most accurate LULC map among those produced (OA: 89.64%), while SAR-only datasets performed the lowest accuracy (OA: 61.30%). The classification process offers several advantages, including widespread spectral information, SAR’s ability to capture almost all-weather, day-and-night imagery, and the computation of vegetation indices in the near infrared spectrum interval, in a short revisit time. The proposed digital techniques for processing multi-temporal satellite data provide useful tools for understanding territorial and environmental dynamics, supporting decision-making in land use planning, agricultural expansion, and environmental management in fragmented landscapes.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_240dfea76ece9ebe29bced9e9014db02 |
| identifier_str_mv | 10.3389/frsen.2025.1535418.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29084447 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docxGiovanni Romano (21373319)Giovanni Francesco Ricci (21373322)Francesco Gentile (500655)Photogrammetry and Remote Sensinggoogle earth engineLandsat 8Sentinel-1Sentinel-2land cover classificationspectral indicesRF classifier<p>Land use/land cover (LULC) mapping in fragmented landscapes, characterized by multiple and small land uses, is still a challenge. This study aims to evaluate the effectiveness of Synthetic Aperture Radar (SAR) and multispectral optical data in land cover mapping using Google Earth Engine (GEE), a cloud computing platform allowing big geospatial data analysis. The proposed approach combines multi-source satellite imagery for accurate land cover classification in a fragmented municipal territory in Southern Italy over a 5-month vegetative period. Within the GEE platform, a set of Sentinel-1, Sentinel-2, and Landsat 8 data was acquired and processed to generate a land cover map for the 2021 greenness period. A supervised pixel-based classification was performed, using a Random Forest (RF) machine learning algorithm, to classify the imagery and derived spectral indices into eight land cover classes. Classification accuracy was assessed using Overall Accuracy (OA), Producer’s and User’s accuracies (PA, UA), and F-score. McNemar’s test was applied to assess the significance of difference between classification results. The optical integrated datasets in combination with SAR data and derivate indices (NDVI, GNDVI, NDBI, VHVV) produce the most accurate LULC map among those produced (OA: 89.64%), while SAR-only datasets performed the lowest accuracy (OA: 61.30%). The classification process offers several advantages, including widespread spectral information, SAR’s ability to capture almost all-weather, day-and-night imagery, and the computation of vegetation indices in the near infrared spectrum interval, in a short revisit time. The proposed digital techniques for processing multi-temporal satellite data provide useful tools for understanding territorial and environmental dynamics, supporting decision-making in land use planning, agricultural expansion, and environmental management in fragmented landscapes.</p>2025-05-16T05:13:36ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/frsen.2025.1535418.s002https://figshare.com/articles/dataset/Supplementary_file_1_Effectiveness_evaluation_of_combining_SAR_and_multiple_optical_data_on_land_cover_mapping_of_a_fragmented_landscape_in_a_cloud_computing_platform_docx/29084447CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290844472025-05-16T05:13:36Z |
| spellingShingle | Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docx Giovanni Romano (21373319) Photogrammetry and Remote Sensing google earth engine Landsat 8 Sentinel-1 Sentinel-2 land cover classification spectral indices RF classifier |
| status_str | publishedVersion |
| title | Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docx |
| title_full | Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docx |
| title_fullStr | Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docx |
| title_full_unstemmed | Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docx |
| title_short | Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docx |
| title_sort | Supplementary file 1_Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform.docx |
| topic | Photogrammetry and Remote Sensing google earth engine Landsat 8 Sentinel-1 Sentinel-2 land cover classification spectral indices RF classifier |