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Nitze, Ingmar; Grosse, Guido (2016): Robust trends of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks, with links to GeoTIFFs [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.854640, Supplement to: Nitze, I; Grosse, G (2016): Detection of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks. Remote Sensing of Environment, 181, 27-41, https://doi.org/10.1016/j.rse.2016.03.038

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Abstract:
Arctic permafrost landscapes are among the most vulnerable and dynamic landscapes globally, but due to their extent and remoteness most of the landscape changes remain unnoticed. In order to detect disturbances in these areas we developed an automated processing chain for the calculation and analysis of robust trends of key land surface indicators based on the full record of available Landsat TM, ETM +, and OLI data. The methodology was applied to the ~ 29,000 km**2 Lena Delta in Northeast Siberia, where robust trend parameters (slope, confidence intervals of the slope, and intercept) were calculated for Tasseled Cap Greenness, Wetness and Brightness, NDVI, and NDWI, and NDMI based on 204 Landsat scenes for the observation period between 1999 and 2014. The resulting datasets revealed regional greening trends within the Lena Delta with several localized hot-spots of change, particularly in the vicinity of the main river channels. With a 30-m spatial resolution various permafrost-thaw related processes and disturbances, such as thermokarst lake expansion and drainage, fluvial erosion, and coastal changes were detected within the Lena Delta region, many of which have not been noticed or described before. Such hotspots of permafrost change exhibit significantly different trend parameters compared to non-disturbed areas. The processed dataset, which is made freely available through the data archive PANGAEA, will be a useful resource for further process specific analysis by researchers and land managers. With the high level of automation and the use of the freely available Landsat archive data, the workflow is scalable and transferrable to other regions, which should enable the comparison of land surface changes in different permafrost affected regions and help to understand and quantify permafrost landscape dynamics.
Funding:
Seventh Framework Programme (FP7), grant/award no. 338335: Rapid Permafrost Thaw in a Warming Arctic and Impacts on the Soil Organic Carbon Pool
Coverage:
Median Latitude: 72.950000 * Median Longitude: 126.550000 * South-bound Latitude: 72.000000 * West-bound Longitude: 123.600000 * North-bound Latitude: 73.900000 * East-bound Longitude: 129.500000
Date/Time Start: 1999-06-01T00:00:00 * Date/Time End: 2014-08-31T23:59:59
Event(s):
LenaDelta * Latitude Start: 73.900000 * Longitude Start: 123.600000 * Latitude End: 72.000000 * Longitude End: 129.500000 * Location: Lena Delta, Siberia, Russia
Comment:
The robust Theil-Sen regression algorithm was used to calculate trend parameters (slope, intercept, confidence intervals) on Landsat time-series stack in the north-east Siberian Lena Delta. The trend calculation was applied to different widely used multi-spectral indices (Landsat Tasseled Cap, NDVI, NDWI, NDMI), which serve as proxies for land surface conditions. Analysis was carried over the entire Landsat archive for the peak summer season (July, August) between years 1999 and 2014. Landsat data before 1999 are not available for the study site. A more detailed description of the processing steps is presented in the accompanied publication (LINK).
The dataset contains 8 raster files in GeoTIFF format, projected in UTM zone 52N (EPSG:32652). There are three different data product types with following properties:
1. Raw Trends
Raw trend components for each multi-spectral index with 4 bands.
Band 1: slope (linear change) per decade; Band 2: Intercept (interpolated value on July 1st 2014); Band 3: lower confidence interval of slope (alpha=0.05); Band 4: upper confidence nterval of slope (alpha=0.05).
2. Number of observations
Raster file with the number of valid observations during the observation period.
3. Visual representation of Tasseled Cap slopes
A mosaicked visual representation of the trend components of tasseled cap indices (as shown in the publication) is provided as a 3-Band GeoTIFF. Please disable any visual stretch ithin the used software for correct visualization.
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
DATE/TIMEDate/TimeGrosse, GuidoGeocode – Begin
DATE/TIMEDate/TimeGrosse, GuidoGeocode – End
File contentContentGrosse, Guido
Uniform resource locator/link to fileURL fileGrosse, GuidoTheil-Sen regression algorithmGeoTIFF
File sizeFile sizekByteGrosse, Guido
Size:
24 data points

Data

Download dataset as tab-delimited text — use the following character encoding:


Date/Time
(Begin)

Date/Time
(End)

Content

URL file
(GeoTIFF)

File size [kByte]
1999-06-012014-08-31Raw Trends NDMI01_INitze_lstrends_LD_ndmi.tif1562579
1999-06-012014-08-31Raw Trends NDVI01_INitze_lstrends_LD_ndvi.tif1562579
1999-06-012014-08-31Raw Trends NDWI01_INitze_lstrends_LD_ndwi.tif1562579
1999-06-012014-08-31Raw Trends Tasseled Cap Brightness01_INitze_lstrends_LD_tcb.tif1562579
1999-06-012014-08-31Raw Trends Tasseled Cap Greenness01_INitze_lstrends_LD_tcg.tif1562579
1999-06-012014-08-31Raw Trends Tasseled Cap Wetness01_INitze_lstrends_LD_tcw.tif1562580
1999-06-012014-08-31Number of observations02_INitze_lstrends_LD_nobs.tif195391
1999-06-012014-08-31Mosaicked visual representation of the trend components of Tasseled Cap Indices03_INitze_lstrends_LD_tc-trends-rgb.tif390704