van Geffen, Femke; Hänsch, Ronny; Kruse, Stefan; Herzschuh, Ulrike; Heim, Birgit (2024): High-resolution Sentinel-2 derived forest class maps and aggregated upscaled forest class maps from the Copernicus Global Land Service in selected regions of Western Yakutia and Chukotka, in Eastern Siberia, Russia [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.973410 (DOI registration in progress)
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Abstract:
This dataset comprises a collection of high-resolution Sentinel-2 derived forest class maps and aggregated upscaled forest class maps from the Copernicus Global Land Service (CGLC) 100 m representing forested land in selected regions of Western Yakutia and Chukotka, in Eastern Siberia, Russia. The dataset is organized into three product groups, each containing geospatial data in GeoTIFF format.
RegionalCode_classified-forest_S2_LS_10m products: These products are Sentinel-2 derived maps with a spatial resolution of 10 meters for the following locations: Lake Khamra (LK), Yakutsk (YA), Magaras (MA), Mirny (MI), Mirny-Lensk (ML), Nyurba (NY), Vilnius (VI), Suntar (SU), Suntar-West (SW) in Western Yakutia, and Bilibino (BI), in Chukotka. The preprocessed and optimized Sentinel-2 images used for the forest type classification are from our previous publication: van Geffen, Femke; Geng, Rongwei; Pflug, Bringfried; Kruse, Stefan; Pestryakova, Luidmila A; Herzschuh, Ulrike; Heim, Birgit (2021): SiDroForest: Sentinel-2 Level-2 Bottom of Atmosphere labelled image patches with seasonal information for Central Yakutia and Chukotka vegetation plots (Siberia, Russia) [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.933268. The forest class maps classify the land into four classes: evergreen needleleaf forest (class value 1), summergreen needleleaf forest (class value 2), mixed forest (class value 3), and non-forested land (class value 0). The evergreen forest class includes tree taxa such as Pine and Spruce and the summergreen forest class represents two larch species (Larix cajanderi and Larix gmelinii) common to these regions. This classification is based on the Random Forest algorithm using late summer Sentinel-2 multispectral data as detailed in the study by van Geffen et al. (submitted).
RegionalCode_aggregated-forest_100mLandCover2019_10m: These products are aggregated and upsampled maps based on the 100 m Copernicus Global Land Service 100 m 2019 (reference) data, with the resolution refined from 100 meters to 10 meters. The land cover classes have been aggregated to match the classification scheme of the Sentinel-2 derived forest type maps: open and closed evergreen forests (111, 121), open and closed summergreen forests (113, 123), and open and closed mixed forests (115, 125) have been reclassified into three forest classes (1, 2, 3) and non-forested land (0). This adaptation facilitates direct comparison between the Sentinel-2 forest type maps and the upsampled global land cover data, enabling more precise spatial analysis.
RegionalCode_unknown-forest_100mLandCover2019_10m: These products are the upsampled 10-meter resolution maps for two unknown forest classes (116 and 126) identified in the Copernicus Global Land Service 100 m 2019 (reference) dataset. These classes remain distinct from the aggregated classes allowing for further study and comparison.
This dataset is intended to support research in forest classification, land cover analysis, and ecological studies in Siberia, providing a valuable resource for understanding the complex vegetation dynamics in these remote regions. The use of both high-resolution Sentinel-2 data at a 10 m resolution and aggregated global land cover data sampled to 10 m resolution enables a comprehensive assessment of forest types across varying spatial resolutions and classifications.
Keyword(s):
Related to:
van Geffen, Femke; Heim, Birgit; Brieger, Frederic; Geng, Rongwei; Shevtsova, Iuliia; Schulte, Luise; Stuenzi, Simone Maria; Bernhardt, Nadine; Troeva, Elena I; Pestryakova, Luidmila A; Zakharov, Evgenii S; Pflug, Bringfried; Herzschuh, Ulrike; Kruse, Stefan (2022): SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches. Earth System Science Data, 14(11), 4967-4994, https://doi.org/10.5194/essd-14-4967-2022
Funding:
European Research Council (ERC), grant/award no. 772852: Glacial Legacy on the establishment of evergreen vs. summergreen boreal forests
Coverage:
Median Latitude: 62.820635 * Median Longitude: 123.690475 * South-bound Latitude: 59.900000 * West-bound Longitude: 112.800000 * North-bound Latitude: 68.494800 * East-bound Longitude: 165.200000
Event(s):
Bilibino_area * Latitude Start: 68.494800 * Longitude Start: 162.899900 * Latitude End: 68.000000 * Longitude End: 165.200000 * Method/Device: Satellite remote sensing (SAT)
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | Area/locality | Area | van Geffen, Femke | |||
| 2 | Latitude of event | Latitude | van Geffen, Femke | corner coordinates | ||
| 3 | Longitude of event | Longitude | van Geffen, Femke | corner coordinates | ||
| 4 | Latitude of event 2 | Latitude 2 | van Geffen, Femke | corner coordinates | ||
| 5 | Longitude of event 2 | Longitude 2 | van Geffen, Femke | corner coordinates | ||
| 6 | Raster graphic, GeoTIFF format | GeoTIFF | van Geffen, Femke | Satellite remote sensing (SAT) | aggregated forest product | |
| 7 | Raster graphic, GeoTIFF format | GeoTIFF | van Geffen, Femke | Satellite remote sensing (SAT) | classified forest product | |
| 8 | Raster graphic, GeoTIFF format | GeoTIFF | van Geffen, Femke | Satellite remote sensing (SAT) | unknown forest product |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Status:
Curation Level: Enhanced curation (CurationLevelC)
Size:
39 data points
Data
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