Victoria Land Cover Map: Random Forest Classification Using Sentinel-2 Imagery from April 2021 - March 2022
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| Format: | Dataset Open Access |
| Sprache: | en |
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PANGAEA
2024
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| _version_ | 1867171046283542528 |
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| author | Sabaghy, Sabah Abuzar, Mohammad Crawford, Douglas L McAllister, Andy Sheffield, Kathryn |
| author_facet | Sabaghy, Sabah Abuzar, Mohammad Crawford, Douglas L McAllister, Andy Sheffield, Kathryn |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | The land cover mapping of Victoria, Australia, for 2021/22 was conducted using Sentinel-2 satellite imagery and the random forest machine learning algorithm. This map represents the entire Victoria at a spatial resolution of 20 meters, considerably improving earlier versions that previously were developed using coarser-resolution MODIS imagery. The map follows the FAO Land Cover Classification System to maintain consistency and accuracy in defining land cover types. Sentinel-2 data from April 2020 to March 2021 have been collected to capture seasonal variation relevant to a wide range of applications, from agricultural management to environmental monitoring, including climate change modelling. The data collection was done based on the usage of Sentinel-2 Level-1C orthorectified reflectance data that were further processed with the intention of deriving temporal aggregates of both spectral bands and vegetation indices. These pre-processed data then formed the basis of training a random forest classifier, calibrated with a blend of field data and desktop-derived samples from trustworthy sources. The land cover map has been rigorously validated, achieving an overall accuracy of 86%. This dataset could serve as a base tool in policy formulation, research, and land management applications to enable informed decisions on agricultural policy-making, climate resilience initiatives, and sustainable land use practices. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_973963 |
| institution | PANGAEA |
| language | en |
| publishDate | 2024 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Victoria Land Cover Map: Random Forest Classification Using Sentinel-2 Imagery from April 2021 - March 2022 Sabaghy, Sabah Abuzar, Mohammad Crawford, Douglas L McAllister, Andy Sheffield, Kathryn Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); File content; Land cover mapping; machine learning; MultiSpectral Instrument (MSI), Sentinel, 2A; remote sensing; Sentinel -2 MSI (MultiSpectral Instrument, Level-1C); Victoria_Land_Cover The land cover mapping of Victoria, Australia, for 2021/22 was conducted using Sentinel-2 satellite imagery and the random forest machine learning algorithm. This map represents the entire Victoria at a spatial resolution of 20 meters, considerably improving earlier versions that previously were developed using coarser-resolution MODIS imagery. The map follows the FAO Land Cover Classification System to maintain consistency and accuracy in defining land cover types. Sentinel-2 data from April 2020 to March 2021 have been collected to capture seasonal variation relevant to a wide range of applications, from agricultural management to environmental monitoring, including climate change modelling. The data collection was done based on the usage of Sentinel-2 Level-1C orthorectified reflectance data that were further processed with the intention of deriving temporal aggregates of both spectral bands and vegetation indices. These pre-processed data then formed the basis of training a random forest classifier, calibrated with a blend of field data and desktop-derived samples from trustworthy sources. The land cover map has been rigorously validated, achieving an overall accuracy of 86%. This dataset could serve as a base tool in policy formulation, research, and land management applications to enable informed decisions on agricultural policy-making, climate resilience initiatives, and sustainable land use practices. |
| title | Victoria Land Cover Map: Random Forest Classification Using Sentinel-2 Imagery from April 2021 - March 2022 |
| topic | Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); File content; Land cover mapping; machine learning; MultiSpectral Instrument (MSI), Sentinel, 2A; remote sensing; Sentinel -2 MSI (MultiSpectral Instrument, Level-1C); Victoria_Land_Cover |
| url | https://doi.org/10.1594/PANGAEA.973963 |