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Nitze, Ingmar; Grosse, Guido; Jones, Benjamin M; Arp, Chistopher D; Ulrich, Mathias; Fedorov, Alexander N; Veremeeva, Alexandra (2017): Landsat-based trend analysis of lake dynamics across northern permafrost regions, supplementary material. PANGAEA, https://doi.org/10.1594/PANGAEA.876553, Supplement to: Nitze, I et al. (2017): Landsat-Based Trend Analysis of Lake Dynamics across Northern Permafrost Regions. Remote Sensing, 9(7), 640, https://doi.org/10.3390/rs9070640

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Abstract:
Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here we present, a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM,ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (-0.69%), Western Alaska (-2.82%), and Kolyma Lowland (-0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e. upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.
Coverage:
Median Latitude: 67.025000 * Median Longitude: 174.025000 * South-bound Latitude: 62.400000 * West-bound Longitude: 130.500000 * North-bound Latitude: 70.200000 * East-bound Longitude: -153.200000
Minimum Elevation: 20.0 m * Maximum Elevation: 200.0 m
Event(s):
AKS_lakes * Latitude: 66.500000 * Longitude: -159.700000 * Elevation: 20.0 m * Location: Kobuk-Selawik-Lowlands
CYA_lakes * Latitude: 62.400000 * Longitude: 130.500000 * Elevation: 200.0 m * Location: Central Yakutia
KOL_lakes * Latitude: 69.000000 * Longitude: 158.500000 * Elevation: 20.0 m * Location: Kolyma Lowland
Comment:
The data are supplementary vector data to "Landsat-based trend analysis of lake dynamics across northern permafrost regions".
The data publication contains:
1) geospatial polygon vector files (ESRI Shapefile or GeoJSON)
- Lake_change_shp.zip
- Lake_change_geojson.zip
2) geospatial point vector files (ESRI Shapefile or GeoJSON)
- Lake_change_centroid_shp.zip
- Lake_change_centroid_geojson.zip
3) geospatial raster files (GeoTIFF)
- Lake_change_rastergrid.zip
The study site abbreviations are indicated in the file name. All datasets are projected in UTM.
- Alaska North Slope (NSL); UTM Zone 5N
- Kobuk-Selawik-Lowlands (AKS); UTM Zone 4N
- Central Yakutia (CYA); UTM Zone 52N
- Kolyma Lowland (KOL); UTM Zone 57N
Vector data:
The polygon layer contains the boundaries of each detected lake object as described in the manuscript. The point layer contains the centroid of each detected lake object.
Both datasets have six different attributes; area_t0, area_t1, net-ch_ha, C-LW_ha, C-WL_ha, S-W_ha.
Attributes:
- area_T0: calculated water surface area the start of observation (1999)
- area_T1: calculated water surface area the end of observation (2014)
- net-ch_ha: net change of water surface area in ha
- C-LW_ha: gross water surface increase in ha
- C-WL_ha: gross water surface decrease in ha
- S-W_ha: stable water surface area in ha
Raster data:
The raster layer contains the net lake area change in ha within grid cells of 3 x 3 km.
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1Event labelEventNitze, Ingmar
2Latitude of eventLatitudeNitze, Ingmar
3Longitude of eventLongitudeNitze, Ingmar
4Elevation of eventElevationmNitze, Ingmar
5File sizeFile sizekByteNitze, Ingmar
6Uniform resource locator/link to fileURL fileNitze, IngmarESRI Shapefile, GeoJSON, and Rastergrid
Size:
8 data points

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