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. 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
Always quote above citation when using data! You can download the citation in several formats below.
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.
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
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.
|#||Name||Short Name||Unit||Principal Investigator||Method/Device||Comment|
|1||DATE/TIME||Date/Time||Grosse, Guido||Geocode – Begin|
|2||DATE/TIME||Date/Time||Grosse, Guido||Geocode – End|
|3||File content||Content||Grosse, Guido|
|4||Uniform resource locator/link to file||URL file||Grosse, Guido||Theil-Sen regression algorithm||GeoTIFF|
|5||File size||File size||kByte||Grosse, Guido|
24 data points