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The number, size, and distribution of inland freshwater lakes present a challenge for traditional water-quality assessment due to the time, cost, and logistical constraints of field sampling and laboratory analyses. To overcome this challenge, Landsat imagery has been used as an effective tool to assess basic water-quality indicators, such as Secchi depth (SD), over a large region or to map more advanced lake attributes, such as cyanobacteria, for a single waterbody. The overarching objective of this research application was to evaluate Landsat Thematic Mapper (TM) for mapping nine water-quality metrics over a large region and to identify hot spots of potential risk. The second objective was to evaluate the addition of landscape pattern metrics to test potential improvements in mapping lake attributes and to understand drivers of lake water quality in this region. Field-level in situ water-quality measurements were collected across diverse lakes (n = 42) within the Lower Peninsula of Michigan. A multicriteria statistical approach was executed to map lake water quality that considered variable importance, model complexity, and uncertainty. Overall, band ratio radiance models performed well (R 2 = 0.65-0.81) for mapping SD, chlorophyll-a, green biovolume, total phosphorus (TP), and total nitrogen (TN) with weaker (R 2 = 0.37) ability to map total suspended solids (TSS) and cyanobacteria levels. In this application, Landsat TM and pattern metrics showed poor ability to accurately map non-purgable organic carbon (NPOC) and diatom biovolume, likely due to a combination of gaps in temporal overpass and field sampling and lack of signal sensitivity within broad spectral channels of Landsat TM. The composition and configuration of croplands, urban, and wetland patches across the landscape were found to be moderate predictors of lake water quality that can complement lake remote-sensing data. Of the 4071 lakes, over 4 ha in the Lower Peninsula, approximately two-thirds, were identified as mesotrophic (n = 2715). This application highlights how an operational tool might support lake decision-making or assessment protocols to identify hot spots of potential risk.

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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 resampled and/or smoothed in order for PCA to identify useful information.