Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations.

We analyzed 37 satellite reflectance algorithms and 321 variants for five satellites for estimating turbidity in a freshwater inland lake in Ohio using coincident real hyperspectral aircraft imagery converted to relative reflectance and dense coincident surface observations. This study is part of an effort to develop simple proxies for turbidity and algal blooms and to evaluate their performance and portability between satellite imagers for regional operational turbidity and algal bloom monitoring. Turbidity algorithms were then applied to synthetic satellite images and compared to in situ measurements of turbidity, chlorophyll-a (Chl-a), total suspended solids (TSS) and phycocyanin as an indicator of cyanobacterial/blue green algal (BGA) abundance. Several turbidity algorithms worked well with real Compact Airborne Spectrographic Imager (CASI) and synthetic WorldView-2, Sentinel-2 and Sentinel-3/MERIS/OLCI imagery. A simple red band algorithm for MODIS imagery and a new fluorescence line height algorithm for Landsat-8 imagery had limited performance with regard to turbidity estimation. Blue-Green Algae/Phycocyanin (BGA/PC) and Chl-a algorithms were the most widely applicable algorithms for turbidity estimation because strong co-variance of turbidity, TSS, Chl-a, and BGA made them mutual proxies in this experiment.

Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations.

This study evaluated the performances of twenty-nine algorithms that use satellite-based spectral imager data to derive estimates of chlorophyll-a concentrations that, in turn, can be used as an indicator of the general status of algal cell densities and the potential for a harmful algal bloom (HAB). The performance assessment was based on making relative comparisons between two temperate inland lakes: Harsha Lake (7.99 km2) in Southwest Ohio and Taylorsville Lake (11.88 km2) in central Kentucky. Of interest was identifying algorithm-imager combinations that had high correlation with coincident chlorophyll-a surface observations for both lakes, as this suggests portability for regional HAB monitoring. The spectral data utilized to estimate surface water chlorophyll-a concentrations were derived from the airborne Compact Airborne Spectral Imager (CASI) 1500 hyperspectral imager, that was then used to derive synthetic versions of currently operational satellite-based imagers using spatial resampling and spectral binning. The synthetic data mimics the configurations of spectral imagers on current satellites in earth's orbit including, WorldView-2/3, Sentinel-2, Landsat-8, Moderate-resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS). High correlations were found between the direct measurement and the imagery-estimated chlorophyll-a concentrations at both lakes. The results determined that eleven out of the twenty-nine algorithms were considered portable, with r2 values greater than 0.5 for both lakes. Even though the two lakes are different in terms of background water quality, size and shape, with Taylorsville being generally less impaired, larger, but much narrower throughout, the results support the portability of utilizing a suite of certain algorithms across multiple sensors to detect potential algal blooms through the use of chlorophyll-a as a proxy. Furthermore, the strong performance of the Sentinel-2 algorithms is exceptionally promising, due to the recent launch of the second satellite in the constellation, which will provide higher temporal resolution for temperate inland water bodies. Additionally, scripts were written for the open-source statistical software R that automate much of the spectral data processing steps. This allows for the simultaneous consideration of numerous algorithms across multiple imagers over an expedited time frame for the near real-time monitoring required for detecting algal blooms and mitigating their adverse impacts.

Mercury concentrations in water and hybrid striped bass (Morone saxatilis × M. chrysops) muscle tissue samples collected from the Ohio River, USA.

We report on long-term aqueous mercury (Hg) measurements collected at fixed locations along the Ohio River, offer insights into patterns of water and fish tissue Hg levels, and calculate site-specific bioaccumulation factors (BAFs) along an extensive longitudinal basis. We examined the relationship between total recoverable Hg concentrations in water and fish samples collected from 12 locations on the mainstem Ohio River. Water samples were collected on a bimonthly basis from each location over a 6-year period preceding the collection of fish tissue samples. This abundance of data enabled us to calculate the long-term average aqueous Hg concentrations and approximate the lifetime aqueous Hg exposure experienced by fish, enabling the calculation of appropriate BAFs. Hybrid striped bass (HSB; Morone saxatilis × M. chrysops) were collected from the Ohio River, composited (three fish), and analyzed for Hg in muscle tissue from each location. Concentrations ranged from 0.2 to 0.4 mg/kg and 41.7% of all samples collected were higher than the US Environmental Protection Agency regulatory threshold of 0.3 mg Hg/kg wet weight. Hg levels generally increased with fish weight, length, and age. However, Hg concentration in the water was the strongest predictor of tissue concentrations. We found that both water and tissue concentrations increased with drainage area, albeit at different rates. This discrepancy in spatial patterns revealed that the bioaccumulation rate of methylmercury might not be consistent throughout the Ohio River mainstem. BAFs calculated at each location supported this finding, as values decreased with increasing drainage area. Our study serves to fill critical, previously identified data gaps and provides decision-makers with the information necessary to develop more appropriate BAF development and risk-management strategies.

Sampling effort needed to estimate condition and species richness in the Ohio river, USA.

The level of sampling effort required to characterize fish assemblage condition in a river for the purposes of bioassessment may be estimated via different approaches. However, the goal with any approach is to determine the minimum level of effort necessary to reach some specific level of confidence in the assessment. In the Ohio River, condition is estimated and reported primarily at the level of pools defined by lock and dam structures. The goal of this study was to determine the minimum level of sampling effort required to adequately characterize pools in the Ohio River for the purpose of bioassessment. We followed two approaches to estimating required sampling effort using fish assemblage data from a long-term intensive survey across a number of Ohio River pools. First, we estimated the number of samples beyond which variation in the multimetric Ohio River Fish Index (ORFIn) leveled off. Then, we determined the number of samples necessary to collect approximately 90% of the fish species observed across all samples collected within the pool. For both approaches, approximately 15 samples were adequate to reduce variation in IBI scores to acceptable levels and to capture 90% of observed species in a pool. The results of this evaluation provide a basis not only for the Ohio River Valley Water Sanitation Commission (ORSANCO) but also states and other basin commissions to develop sampling designs for bioassessment that ensure adequate sampling of all assessment units.