The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data

Abstract

Abstract Several upcoming hyperspectral satellite sensor missions (e.g., the Hyperspectral Infrared Imager and the Environmental Mapping and Analysis Program) will greatly expand the opportunities for researchers to use imaging spectroscopy data for discriminating and mapping plant species and plant functional types (PFTs; defined in this study as combinations of leaf-type, leaf/plant duration and life form). Accurate knowledge of the spatial distribution of dominant plant species and PFTs is highly valuable to many scientific and management goals, including improved parameterization of ecosystem process and climate models, better invasive species distribution monitoring and forecasting, quantification of human and natural disturbance and recovery processes, and evaluations of terrestrial vegetation response to climate change. Most often, species-level discrimination has been achieved using fine spatial resolution (≤ 20 m) airborne imagery, but currently proposed spaceborne imaging spectrometers will have coarser spatial resolution (~ 30 to 60 m). In order to address the impact of coarser spatial resolutions on our ability to spectrally separate species and PFTs, we classified dominant species and PFTs in five contrasting ecosystems over a range of spatial resolutions. Study sites included a temperate broadleaf deciduous forest, a brackish tidal marsh, a mixed conifer/broadleaf montane forest, a temperate rainforest and a Mediterranean climate region encompassing grasslands, oak savanna, oak woodland and shrublands. Data were acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over each site, and spatially aggregated to 20, 40 and 60 m resolutions. Canonical Discriminant Analysis (CDA) was used to classify species and PFTs at each site and across scales with overall accuracies ranging from 61 to 96% for species and 83–100% for PFTs. The results of this study show accuracy increases at coarser resolutions (≥ 20 m) across ecosystems, supporting the use of imaging spectroscopy data at spatial resolutions up to 60 m for the purpose of discriminating among plant species and PFTs. In four of the five study sites, the best accuracies were achieved at 40 m resolution. However, at coarser resolutions, some fine-scale species variation is lost and classes that occur only in small patches cannot be mapped. We also demonstrate that spectral libraries developed from fine spatial resolution imagery can be successfully applied as training data to accurately classify coarser resolution data over multiple ecosystems.