Abstract
Mapping species composition is a focus of the wetland science community as this information will substantially enhance assessment and monitoring abilities. Hyperspectral remote sensing has been utilized as a cost-efficient approach. While hyperspectral instruments can record hundreds of contiguous narrow bands, much of the data are redundant and/or provide no increase in utility for distinguishing objects. Knowledge of the optimal bands allows users to efficiently focus on bands that provide the most information and several data reduction tools are available. The objective of this Communication was to evaluate Principal Components Analysis (PCA) for identifying optimal bands to discriminate wetland plant species. In-situ hyperspectral reflectance measurements were obtained for thirty-five species in two diverse Great Lakes wetlands. PCA was executed on a suite of categories based on botanical plant/substrate characteristics and spectral configuration schemes. Results showed that the data dependency of PCA makes it a poor, stand alone tool for selecting optimal wavelengths. PCA does not allow diagnostic comparison across sites and wavelengths identified by PCA do not necessarily represent wavelengths that indicate biophysical attributes of interest. Further, narrow bands captured by hyperspectral sensors need to be substantially re-sampled and/or smoothed in order for PCA to identify useful information.
Highlights
The conservation and restoration of wetland ecosystems are goals of many levels of legislation and the management community
Satellite remote sensing with multispectral sensors has been a useful tool in providing inventory information on wetlands types; both spatial and spectral resolutions have limited the level of detail required for comprehensive wetland assessments
In an effort to circumvent repetitive in depth presentation of the results from numerous individual subcategory experiments that contributed little to the objectives of this research, only the noteworthy data manipulations and linked Principal Components Analysis (PCA) output are presented in a sectional format
Summary
The conservation and restoration of wetland ecosystems are goals of many levels of legislation and the management community This requires assessment and the development of knowledge inventories concerning wetland extent, biological composition, and health for making management decisions and monitoring control efforts. Satellite remote sensing with multispectral sensors has been a useful tool in providing inventory information on wetlands types; both spatial and spectral resolutions have limited the level of detail required for comprehensive wetland assessments. Analytical spectroscopic systems possess capabilities to capture data at narrow spectral bandwidths continuously covering 0.4 to 2.5 μm of the spectrum This allows for small variations in plant/substrate absorptance and reflectance to be recorded [4]. With respect to hyperspectral data, PCA transforms large data sets into relatively few meaningful uncorrelated orthogonal variables/dimensions (i.e., the principal components) that represent most of the information present in the original image. The overarching goal of this Communication is to summarize results evaluating PCA as a hybrid data reduction/band selection technique for identifying optimal bands in wetland hyperspectral measurements
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