Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984-2011)

Wetlands are valuable ecosystems for maintaining biodiversity, but are vulnerable to climate change and land conversion. Despite their importance, wetland hydrology is poorly understood as few tools exist to monitor their hydrologic regime at a landscape scale. This is especially true when monitoring hydrologic change at scales below 30 m, the resolution of one Landsat pixel. To address this, we used spectral mixture analysis (SMA) of a time series of Landsat satellite imagery to reconstruct surface-water hydrographs for 750 wetlands in Douglas County, Washington State, USA, from 1984 to 2011. SMA estimates the fractional abundance of spectra representing physically meaningful materials, known as spectral endmembers, which comprise a mixed pixel, thus providing sub-pixel estimates of surface water extent. Endmembers for water and sage steppe were selected directly from each image scene in the Landsat time series, whereas endmembers for salt and wetland vegetation were derived from a mean spectral signature of selected dates spanning the 1984-2011 timeframe. This method worked well (R2 = 0.99) for even small wetlands (< 1800 m2) providing a wall-to-wall dataset of reconstructed surface-water hydrographs for wetlands across our study area. We have validated this method only in semi-arid regions. Further research is necessary to extend its validity to other environments. This method can be used to better understand the role of hydrology in wetland ecosystems and as a monitoring tool to identify wetlands undergoing abnormal change.