Using ERS-1 data to measure and MAP selected conditions related to the production of methane in a wetland environment: The Nebraska Sandhills, USA

Wetlands are important as both sources and sinks for methane, a trace gas implicated in greenhouse warming. Information necessary for estimating fluxes of methane includes plant species and primary production. Measuring and monitoring the spatial and temporal variations in these parameters should facilitate modeling fluctuations in global greenhouse gas amounts. Three ERS-1 and two Landsat-TM datasets, acquired over large wetland sites in the Western Sandhills of Nebraska, were obtained during the 1995 growing season. The SAR images were despeckeled, and both ERS and TM were resampled to a 30m spatial resolution, rectified to a UTM coordinate system, and segmented to exclude uplands using a digital National Wetlands Inventory (NWI) dataset. In-situ reference data were obtained at various sites within the study area in conjunction with satellite overpasses. The ERS data were classified in an attempt to identify and map the distribution of wetlands at two levels: 1) specificity roughly equivalent to that of the NWI; and 2) at the species level, with emphasis on Typha, Scirpus, and Phragmites. The TM scenes were also classified and compared to the SAR result. Finally, a combined ERS and TM dataset was classified. Both SAR backscatter and TM reflectance were correlated to field measures of above-ground biomass. Results were considered with regard to C- and L-band (W and VH) scatterometer data acquired at close-range over experimental plots containing Typha and Phragmites. Landsat-TM seems better than ERS for generalized classification and mapping, and for emulating the NWI product. However, TM is limited with regard to identification of individual wetland species, and sparse stands of emergent, floating, and submergent macrophytes. ERS seems better than TM for detecting individual species, and sparse stands of emergent, floating, and submergent macrophytes. But, the ERS multi-temporal classification is not easily adapted for most practical mapping applications. Wind must be considered when classifying ERS in shallow lake systems. Potential exists for ERS/TM classification, but more work is needed. Biomass estimation, while encouraging, needs refinement. C- and L-band scatterometer data suggest that polarization is more important in distinguishing Typha and Phragmites than frequency.