Three decades of variability in California's giant kelp forests from the Landsat satellites

摘要

Global, repeat satellite imagery serves as an essential tool to understand ecological patterns and processes over diverse temporal and spatial scales. Recently, the use of spaceborne imagery has become indispensable for monitoring giant kelp, a globally distributed foundation species that displays variable seasonal and interannual dynamics. In order to develop and maintain a continuous and spatially expansive time series, we describe a fully automated protocol to classify giant kelp canopy biomass across three Landsat sensors. This required correcting kelp canopy estimates to account for changes in the spectral response functions between the three sensors by simulating data using hyperspectral imagery. Combining multiple sensors also necessitated the use of an extended (15 year) time series of diver estimated kelp biomass to validate each sensor. We also describe a novel gap filling technique using known spatial scales of kelp biomass synchrony to correct for missing data due to the Enhanced Thematic Mapper Plus scan line corrector failure. These developments have led to a publicly available 34-year, seasonal time series of kelp canopy biomass across similar to 1500 km of California coastline.