Datensatz

Zusammenfassung

Peatlands are areas with naturally accumulated thick layers of dead organic materials. While peatlands cover about 3% of the world’s land area, their carbon storage is estimated equivalent to ~30% of all soil carbon, ~75% of all atmospheric carbon, and as much carbon as all terrestrial biomass. Drained peatlands due to past human uses can emit carbon and be a key source of greenhouse gases while rewetted peatlands usually have significantly reduced CO2 emissions and can even become a carbon sink. However, quantifying the potential and limitations of reducing emissions by peatland rewetting is challenging. Carbon fluxes on peatlands are both spatially complex and temporally dynamic owing to their microtopography, changing water levels and associated vegetation status. Here we demonstrated the generation of temporally consistent ~biweekly 5-m images over 8 years (2013-2020) at visible and near infrared bands (VNIR) to track the temporal trajectories of vegetation and surface water and estimate cover-specific carbon fluxes at a rewetted peatland site in northeastern Germany (Figure 1). To ensure temporally-consistent multispectral images for the subsequent analyses of vegetation/water covers, we set up a two-stage normalization procedure that normalized the images from RapidEye (SmallSats) and PlanetScope (CubeSats) to rigorously calibrated multispectral sensors onboard large satellites (Landsat-7/8 and Sentinel-2). The two-stage normalization procedure produced two levels of image normalization that allows for downstream applications to balance between the quality and the quantity of available normalized CubeSat images in a time series. A quantitative evaluation approach using daily MODIS images as bridging benchmark data revealed that the temporal consistency in CubeSat images was comparable to that in Landsat and Sentinel-2 images, which confirmed the efficacy of the normalization procedure. The temporal information in the stack of normalized 5-m images helped us estimate the vegetation types and the changes in vegetation/surface water covers throughout the 8 years. The within-year time series of CubeSat images at the three visible and one near-infrared bands showed discernible differences among vegetation types at this peatland sites, which promises systemic mapping of vegetation compositions in peatlands using very-high-resolution CubeSat imagery time series over heterogeneous peatlands. We aggregated vegetation and surface water covers within each year into three condition categories at the peatland site, always emergent vegetation, always surface water, and alternating between vegetation and water. The estimated areas of the three condition categories closely covary with the measured water table depths at the site (Figure 2). The substantial areal expansion of always emergent vegetation at the site, that are captured by the CubeSat imagery time series, aligns well with the timing of three drought events (2016, 2018 and 2019) in this region. These surface covers and conditions at both high temporal and spatial resolutions from CubeSat images allow us to disaggregate ecosystem-scale measurements of CO2 and CH4 fluxes by the eddy covariance (EC) tower at the site into cover-specific fluxes. We attribute CO¬2 and CH4 fluxes measured by EC over 8 years to the three surface condition categories through a nonparametric approach to flux decomposition using annual maps of surface condition categories and half-hourly EC-measurement footprints. The disaggregated carbon fluxes improve our upscaled estimates of carbon emission/sequestration over rewetted peatland sites. Such spatial-temporally-resolved carbon fluxes in dynamic and heterogeneous peatlands will contribute to better informed restoration and protection of peatlands.