Schneider, J et al. (2009): The Lena River Delta - Land cover classification of tundra environments based on Landsat 7 ETM+ data and its application for upscaling of methane emissions. doi:10.1594/PANGAEA.759631, Supplement to:Schneider, Julia; Grosse, Guido; Wagner, Dirk (2009): Land cover classification of tundra environments in the Arctic Lena Delta based on Landsat 7 ETM+ data and its application for upscaling of methane emissions. Remote Sensing of Environment, 113(2), 380-391, doi:10.1016/j.rse.2008.10.013
The Lena River Delta, situated in Northern Siberia (72.0 - 73.8° N, 122.0 - 129.5° E), is the largest Arctic delta and covers 29,000 km**2. Since natural deltas are characterised by complex geomorphological patterns and various types of ecosystems, high spatial resolution information on the distribution and extent of the delta environments is necessary for a spatial assessment and accurate quantification of biogeochemical processes as drivers for the emission of greenhouse gases from tundra soils. In this study, the first land cover classification for the entire Lena Delta based on Landsat 7 Enhanced Thematic Mapper (ETM+) images was conducted and used for the quantification of methane emissions from the delta ecosystems on the regional scale. The applied supervised minimum distance classification was very effective with the few ancillary data that were available for training site selection. Nine land cover classes of aquatic and terrestrial ecosystems in the wetland dominated (72%) Lena Delta could be defined by this classification approach. The mean daily methane emission of the entire Lena Delta was calculated with 10.35 mg CH4/m**2/d. Taking our multi-scale approach into account we find that the methane source strength of certain tundra wetland types is lower than calculated previously on coarser scales.
Grosse, Guido; Schirrmeister, Lutz; Malthus, Timothy J (2006): Application of Landsat-7 satellite data and a DEM for the quantification of thermokarst-affected terrain types in the periglacial Lena-Anabar coastal lowland. Polar Research, 25(1), 51-67, doi:10.1111/j.1751-8369.2006.tb00150.x
Schneider, Julia (2005): Bilanzierung von Methanemissionen in Tundragebieten am Beispiel des Lena-Deltas, Nordostsibirien, auf der Basis von Fernerkundungsdaten und Geländeuntersuchungen. Diploma Thesis, Technische Universität Dresden, Germany
LenaDelta * Latitude Start: 73.900000 * Longitude Start: 123.600000 * Latitude End: 72.000000 * Longitude End: 129.500000 * Location: Lena Delta, Siberia, Russia * Campaign: RESPONSE (Remote Sensing of POlar Non-glaciated and Sensitive Environments)
The study was based on land cover classification of three almost cloud free Landsat-7 ETM+ satellite images. The acquisition dates are 27 July 2000 (path 131, rows 8 and 9) and 26 July 2001 (path 135, row 8). Both were taken approximately at the peak of the vegetation period. ERDAS Imagine software was used to carry out all image processing tasks. In addition to the ETM+ satellite imagery, we acquired and utilized numerous other ancillary data for determination of typical land cover classes and field training sites: vegetation field data, soil information, field and aerial photography.
To minimize radiometric differences between the three scenes due to different atmospheric conditions, a basic radiometric and image-based atmospheric correction according to Chavez (1996) was applied. Finally, the three scenes were projected to UTM Zone 52 with the geodetic datum WGS 1984 and a mosaic of the Lena Delta was composed. Supervised classification was carried out using the spectral Landsat bands 1-5 and 7 (VIS, NIR, SWIR) with 34 training areas for ten land cover classes. The training areas were distributed on the active floodplain and first terrace (21 sites), on the second terrace (8 sites), and the third terrace (5 sites). For the supervised classification, the minimum distance algorithm was used. After evaluation of the classes regarding their methane emission two classes were merged to the final number of nine. The accuracy assessment for our classification was based on 36 validation sites based on an image mosaic of Hexagon (synonymous with 'Keyhole-9') providing a dataset independent from the Landsat-7. Our accuracy assessment of the Landsat-7 supervised classification indicates a reasonable well overall accuracy of 77.8% (Kappa=0.74) for such a large and remote study area.