Image classification of a northern peatland complex using spectral and plant community data
Review articleOpen access

AbstractOrdination and cluster analysis are two common methods used by plant ecologists to organize species abundance data into discrete “associations”. When applied together, they offer useful information about the relationships among species and the ecological processes occurring within a community. Remote sensing provides surrogate data for characterizing the spatial distribution of ecological classes based on the assumption of characteristic reflectance of species and species associations. Currently, there exists a need to establish and clarify the link between theories and practices of classification by ecologists and remote sensing scientists. In this study, high spatial resolution Compact Airborne Spectrographic Imager (CASI) reflectance data were examined and compared to plant community data for a peatland complex in northern Manitoba, Canada. The goal of this research was to explore the relationship between classification of species cover and community data and reflectance values. Ordination and cluster analysis techniques were used in conjunction with spectral separability measures to organize clusters of community-based data that were suitable for classification of CASI reflectance data, while still maintaining their ecological significance. Results demonstrated that two-way indicator species analysis (TWINSPAN) clusters did not correspond well to spectral reflectance and gave the lowest classification results of the methods investigated. The highest classification accuracies were achieved with ecological classes defined by combining the information obtained from a suite of analysis techniques (i.e., TWINSPAN, correspondence analysis (CA), and signature separability analysis), albeit not statistically superior to the classification obtained from the signature separability analysis alone.

Request full text

References (0)

Cited By (0)

No reference data.
No citation data.
Join Copernicus Academic and get access to over 12 million papers authored by 7+ million academics.
Join for free!