Northern Peatland Vegetation and the Carbon Cycle: A Remote Sensing Approach

Abstract

Rates of carbon exchange in northern peatlands are dependent on the composition, structure and spatial arrangement of vegetation. Whilst in situ observations can provide detailed information for a given location, remote sensing is the only viable means of collecting land-surface data in a spatially continuous manner across a range of spatial scales. In this paper we review and evaluate many existing and emerging remote sensing approaches used to retrieve peatland land-surface data of relevance to the carbon cycle. We review studies documented in the scientific literature that use remotely sensed data to (i) generate vegetation maps, which may be used to extrapolate field observations, calibrate and extrapolate carbon models and inform peatland management efforts; and (ii) retrieve vegetation biophysical properties, which can be used to parameterize process-based models (e.g. leaf area index (LAI)). There has been considerable progress in the development and implementation of remote sensing approaches that provide data relating to peatland carbon processes. However, there remain a number of methodological challenges, which limit the effectiveness of remote sensing data in some instances. Consequently, we propose that future research approaches focus on (i) continued development, testing and validation of approaches to overcome difficulties caused by the heterogeneous nature of peatland vegetation surfaces (e.g. mixture modeling); (ii) assessment of spatial errors and uncertainty in image classifications, (iii) synergistic use of multiple datasets, (iii) development of scaling algorithms and (iv) continued development of radiative transfer models that can be applied to heterogeneous peatland plant assemblages.