High-resolution land cover classification for Stordalen, Abisko region, northern Sweden

• Main Author: Siewert, Matthias Benjamin
• Format: Dataset
• Language: English
• Published: PANGAEA 2018
• Subjects: ABI; Abisko; Lappland; northern Sweden; Changing Permafrost in the Arctic and its Global Effects in the 21st Century; MULT; Multiple investigations; PAGE21; Arctic; Stordalen; Northern Sweden; permafrost
• Online Access: https://doi.pangaea.de/10.1594/PANGAEA.886298; https://doi.org/10.1594/PANGAEA.886298

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.