Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

Abstract

Abstract:Arctic tundra ecosystems are a major source of methane (CH 4 ), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect between the measurement scales for CH 4 fluxes, which can be measured with chambers at one-meter resolution and eddy covariance towers at 100-1000 m, whereas model estimates are typically made at thẽ 100 km scale. Therefore, it is critical to upscale site level measurements to the larger scale for model comparison. As vegetation has a critical role in explaining the variability of CH 4 fluxes across the tundra landscape, we tested whether remotely-sensed maps of vegetation could be used to upscale fluxes to larger scales. The objectives of this study are to compare four different methods for mapping and two methods for upscaling plot-level CH 4 emissions to the measurements from EC towers. We show that linear discriminant analysis (LDA) provides the most accurate representation of the tundra vegetation within the EC tower footprints (classification accuracies of between 65% and 88%). The upscaled CH 4 emissions using the areal fraction of the vegetation communities showed a positive correlation (between 0.57 and 0.81) with EC tower measurements, irrespective of the mapping method. The area-weighted footprint model outperformed the simple area-weighted method, achieving a correlation of 0.88 when using the vegetation map produced with the LDA classifier. These results suggest that the high spatial heterogeneity of the tundra vegetation has a strong impact on the flux, and variation indicates the potential impact of environmental or climatic parameters on the fluxes. Nonetheless, assimilating remotely-sensed vegetation maps of tundra in a footprint model was successful in upscaling fluxes across scales.

Tundra landscape heterogeneity, not interannual variability, controls the decadal regional carbon balance in the Western Russian Arctic

“…Our findings support previous recommendations of land cover classifications using 30 m or finer resolutions (Bartsch et al, ; Davidson et al, ; Virtanen & Ek, ). Modeled net ecosystem exchange and CH 4 flux changed only slightly (≤5%) at 20 m pixel size compared to the 2.4 m resolution used as a default.…”

“…Our findings support previous recommendations of land cover classifications using 30 m or finer resolutions (Bartsch et al, 2016;Davidson et al, 2017;Virtanen & Ek, 2014). CO 2 emissions were not affected as strongly (Figure 8).…”

“…Accurately capturing the landscape heterogeneity that controls regional C balance requires relatively fine spatial resolution and an appropriate thematic resolution, meaning representation of land cover types with distinct C dynamics. The spatial resolution must be high enough to detect hot spots for C cycling, such as lakes and wetlands, within the landscape (Bartsch, Höfler, Kroisleitner, & Trofaier, ; Davidson et al, ; Virtanen & Ek, ), which can change greatly depending on the spatial resolution of classification (Muster, Heim, Abnizova, & Boike, ). Capturing functional differences among ecosystem types requires a sufficient thematic resolution in landscape classification but must be distinctly and reliably identified using classification techniques.…”

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

“…Accurate water body maps are crucial to improving methane emission estimates from models, so upscaling approaches may represent one way to transfer fine-scale information to coarse-scale maps used for models [8,38,82]. In Alaska, high-resolution imagery has been used to upscale methane fluxes based on vegetation mapping [86], and these techniques can also be applied to water and wetland maps. The CIR imagery and open water masks cover large areas at high resolution and offer a valuable airborne-scale census of surface water over a variety of landscapes.…”

Understanding spatial variability of methane fluxes in Arctic wetlands through footprint modelling

“…Budishchev et al (2014) also used a footprint model to upscale the chamber measurements to the EC scale, improving the r 2 correlation coefficient from 0.14 to 0.7, in a Russian polygonal tundra. Davidson et al (2017) show a 20%-30% improvement and an r 2 of 0.88, across several tundra sites in Alaska by using footprint modelling to upscale chamber measurements to the EC tower fluxes. One can also use footprint modelling when upscaling fluxes to estimate sensor location bias (Schmid and Lloyd 1999).…”