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            Stordalen Mire landcover classifications (including 3 permafrost thaw stages: Palsa, Bog, and Fen), based on WorldView-2 satellite imagery acquired on August 8, 2014. This image is a GeoTIFF with embedded georeferencing information. FUNDING: National Aeronautics and Space Administration, Interdisciplinary Science program: From Archaea to the Atmosphere (award # NNX17AK10G) National Science Foundation, Biology Integration Institutes Program: EMERGE Biology Integration Institute (award # 2022070) United States Department of Energy Office of Biological and Environmental Research, Genomic Science Program: The IsoGenie Project (grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440) National Science Foundation, MacroSystems program (grant # EF-1241037)more » « less
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            Methane (CH4) emissions in Stordalen Mire (northern Sweden), estimated via two different approaches: "Paint by number" (field ch4_modified_prj.tif): CH4 emission across the landscape calculated via “paint-by-number” approach, using 2014 autochamber-based flux measurements (https://doi.org/10.5281/zenodo.14052690) mapped to landcover classifications (https://doi.org/10.5281/zenodo.15042233). DNDC-modeled (Modeled CH4.tif): CH4 emission across the landscape modeled via Wetland-DNDC (https://www.dndc.sr.unh.edu/), driven by remotely sensed landcover classifications (https://doi.org/10.5281/zenodo.15042233), water table depth (https://doi.org/10.5281/zenodo.15092752), climate data (provided by the Abisko Scientific Research Station), and soil parameters (defined as in Deng et al. 2014, 2017). The DNDC model was run on vegetation and water table clusters (determined by k-means clustering), and model output was spatially assigned to each map pixel. Modeled CH4 emissions account for CH4 production from DOC (Randomforest_stack_epsg32634_extent_kmeansclass10_CH4 prod from DOC.tif) and from CO2 (Randomforest_stack_epsg32634_extent_kmeansclass10_CH4 prod from CO2.tif), minus oxidation (Randomforest_stack_epsg32634_extent_kmeansclass10_CH4 oxid.tif). The model also outputs a map of CH4 isotopic composition (δ13C-CH4) of emissions (Randomforest_stack_epsg32634_extent_kmeansclass10_Delta CH4 flux.tif). The difference between these approaches is provided as a difference map (CH4diff.tif), calculated as the "paint-by-number" (PBN) emissions (field ch4_modified_prj.tif) minus the Wetland-DNDC modeled emissions (Modeled CH4.tif). These images are GeoTIFFs with embedded georeferencing information. FUNDING: National Aeronautics and Space Administration, Interdisciplinary Science program: From Archaea to the Atmosphere (award # NNX17AK10G). National Science Foundation, Biology Integration Institutes Program: EMERGE Biology Integration Institute (award # 2022070). United States Department of Energy Office of Biological and Environmental Research, Genomic Science Program: The IsoGenie Project (grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440). National Science Foundation, MacroSystems program (grant # EF-1241037). We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.more » « less
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            LiDAR data were acquired over the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH during the growing season. Data were acquired using a LiVox Avia lidar sensor on a Green Valley International LiAirV70 payload. The LiVox Avia is a triple echo 905 nm lidar sensor with a non-repetitive circular scanning pattern that can retrieve ~700,000 returns per second. The sensor payload was flown on board a DJI M300 at an altitude of ~65 m above ground level in a double grid pattern with ~32 m flight line spacing, yielding a return density across the sampling area >500 points per square meter. Returns were georeferenced to WGS84 UTM Zone 19N coordinates with heights above ellipsoid using Green Valley International’s LiGeoreference software with automatic boresight calibration. Outliers were removed, then flight line point clouds were merged. Returns were classified as ground and non-ground returns using Green Valley International’s Lidar360 software and output as LAS (v 1.4) data sets. LAS files were subsequently tiled for publication.more » « less
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            Orthorectified flight line hyperspectral cubes retiled for publication. Collectively, the tiled hyperspectral cubes cover the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH. Data were acquired using a Headwall Photonics, Inc. Nano VNIR hyperspectral line scanning imager with 273 bands from 400-1000 nm. The sensor was flown on board a DJI M600 hexacopter at an altitude of ~80 m above the forest canopy, yielding ~6 cm GSD. Flight lines were converted from raw sensor observations to upwelling radiance a using a vendor-supplied radiometric calibration file for the sensor, then converted to reflectance using a calibration tarp with known reflectance. Finally, cubes were orthorectified using a 1m DSM in Headwall’s SpectralView software, mosaicked to individual flight line cubes, then subsequently tiled for publication.more » « less
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