High-resolution land cover classification for Stordalen, Abisko region, northern Sweden
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| Formato: | Dataset Open Access |
| Lenguaje: | en |
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PANGAEA
2018
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| _version_ | 1867171853813940224 |
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| author | Siewert, Matthias Benjamin |
| author_facet | Siewert, Matthias Benjamin |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | An object-based approach was used to generate a detailed land cover classification covering the Stordalen area near Abisko, northern Sweden. First, an orthophoto was combined with the DEM resampled to 1 m spatial resolution. A segmentation layer was generated by grouping pixels into homogeneous areas with a minimum region size of 130 m². From this a water mask was classified using the red band of the orthophoto and a slope layer. A land cover training set was created by combining field survey information with visual interpretation of the orthophoto and topography. The following layers were used as input for the classification algorithm: an orthophoto, elevation and slope; a SPOT5 4-band satellite image, and NDVI, SAVI and NIR/SWIR as SPOT5 derivatives. The segments were then classified using a support vector machine algorithm. Artificial surfaces were hand digitized and masked out. The individual thematic classes are described in TableA1. The quality of the classification was assessed using a set of 108 ground control points. The accuracy assessment results in a Kappa value of 0.71 and an overall accuracy of 74% for all land covers excluding water and artificial areas see TableA2. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_886298 |
| institution | PANGAEA |
| language | en |
| publishDate | 2018 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | High-resolution land cover classification for Stordalen, Abisko region, northern Sweden Siewert, Matthias Benjamin ABI; Abisko; Abisko, Lappland, northern Sweden; Changing Permafrost in the Arctic and its Global Effects in the 21st Century; MULT; Multiple investigations; PAGE21 An object-based approach was used to generate a detailed land cover classification covering the Stordalen area near Abisko, northern Sweden. First, an orthophoto was combined with the DEM resampled to 1 m spatial resolution. A segmentation layer was generated by grouping pixels into homogeneous areas with a minimum region size of 130 m². From this a water mask was classified using the red band of the orthophoto and a slope layer. A land cover training set was created by combining field survey information with visual interpretation of the orthophoto and topography. The following layers were used as input for the classification algorithm: an orthophoto, elevation and slope; a SPOT5 4-band satellite image, and NDVI, SAVI and NIR/SWIR as SPOT5 derivatives. The segments were then classified using a support vector machine algorithm. Artificial surfaces were hand digitized and masked out. The individual thematic classes are described in TableA1. The quality of the classification was assessed using a set of 108 ground control points. The accuracy assessment results in a Kappa value of 0.71 and an overall accuracy of 74% for all land covers excluding water and artificial areas see TableA2. |
| title | High-resolution land cover classification for Stordalen, Abisko region, northern Sweden |
| topic | ABI; Abisko; Abisko, Lappland, northern Sweden; Changing Permafrost in the Arctic and its Global Effects in the 21st Century; MULT; Multiple investigations; PAGE21 |
| url | https://doi.org/10.1594/PANGAEA.886298 |