Scaling up of peatland methane emission hotspots from small to large scales
Item statusRestricted Access
Embargo end date00//2/31/1
Methane is an important greenhouse gas that is relatively long-lived in the atmosphere, and wetlands are a major natural source of atmospheric methane. Methane emissions from wetlands are variable across both space and time at scales ranging from meters to continents and a comprehensive accounting of wetland methane efflux is critical for quantifying the atmospheric methane balance. Major uncertainties in quantifying methane efflux arise when measuring and modelling its physical and biological determinants, including water table depth, microtopography, soil temperature, the distribution of aerenchymous vegetation, and the distribution of mosses. Further complications arise with the nonlinear interaction between flux and derivers in highly-heterogeneous wetland landscape. A possible solution for quantifying wetland methane efflux at multiple scales in space (‘upscaling’) is repeated observations using remote sensing technology to acquire information about the land surface across time, space, and spectra. These scaling issues must be resolved to progress in our understanding of the role of wetlands in the global atmospheric methane budget from peatlands. In this thesis, data collected from multiple aircraft and satellite-based remote sensing platforms were investigated to characterize the fine scale spatial heterogeneity of a peatland in southwestern Scotland for the purpose of developing techniques for quantifying (‘upscaling’) methane efflux at multiple scales and space. Seasonal variation in pools such as expansion and contraction was simulated with the LiDAR data to investigate the expansion and contraction of the lakes and pools that could give an idea of increase or decrease in methane emissions. Concepts from information theory applied on the different data sets also revealed the relative loss in some features on peatland surface and relative gain on others and find a natural application for reducing bias in multi-scale spatial classification as well as quantifying the length scales (or scales) at which important surface features for methane fluxes are lost. Results from the wavelet analysis demonstrated the preservation of fine scale heterogeneity up to certain length scale and the pattern on peatland surface was preserved. Variogram techniques were also tested to determine sample size, range and orientation in the data set. All the above has implications on estimating methane budget from the peatland landscape and could reduce the bias in the overall flux estimates. All the methods used can also be applied to contrasting sites.