Blooms of harmful cyanobacteria (cyanoHABs) were mapped for three eutrophic basins (western basin of Lake Erie, WBLE; Green Bay, Lake Michigan, GB; and Saginaw Bay, Lake Huron, SB) in the Great Lakes from 2002 to 2013 using Moderate Resolution Imaging Spectroradiometer (MODIS) ocean colour imagery. These blooms were examined in relationship to basic meteorological and environmental parameters. Annual cyanoHAB extent trends were generated using two modified remote-sensing approaches. The first approach was a modified bio-optical chlorophyll retrieval algorithm enhanced with empirical relationships to estimate water column cyanoHABs (MCH), whereas the second approach uses near-infrared (NIR) reflectance to quantify the surface scums of cyanoHABs (SSI). The development and application of the SSI are unique products in the Great Lakes and may have generic application to ecological and public health issues. Satellite-derived cyanoHAB estimates agreed well with in situ observations (89% accuracy). The annual cyanoHAB trends (MCH and SSI) for WBLE, SB, and GB were not similar for the 2002–2013 analysis period. A recent trend of increasing cyanoHABs was noted in WBLE but not in GB or SB. Moreover, extensive and persistent surface scums were observed in WBLE but not in GB or SB. Meteorological parameters were similar among the basins; however, significant differences in spring discharge of the dominant river were observed among basins. Spring discharge was a significant predictor of cyanoHAB occurrence in WBLE but not in GB and SB. Wind-induced sediment re-suspension events were common during the bloom period in WBLE but not in GB or SB and these events were highly correlated with cyanoHAB occurrence. The differences among basins in the role of riverine discharge and re-suspension suggest local factors are more important than regional factors in controlling cyanoHAB dynamics within these three basins in the Great Lakes.

Large freshwater lakes provide immense value to the surrounding populations, yet there is limited understanding of how these lakes will respond to climate change and other factors. This study uses satellite remote sensing to estimate annual, lake-wide primary production in 11 of the world’s largest lakes from 2003–2018. These lakes include the five Laurentian Great Lakes, the three African Great Lakes, Lake Baikal, and Great Bear and Great Slave Lakes. Mean annual production in these lakes ranged from under 200 mgC/m²/day to over 1100 mgC/m²/day, and the lakes were placed into one of three distinct groups (oligotrophic, mesotrophic, or eutrophic) based on their level of production. The analysis revealed only three lakes with significant production trends over the study period, with increases in Great Bear Lake (24% increase over the study period) and Great Slave Lake (27%) and a decline in Lake Tanganyika (−16%). These changes appear to be related to climate change, including increasing temperatures and solar radiation and decreasing wind speeds. This study is the first to use consistent methodology to study primary production in the world’s largest lakes, allowing for these novel between-lake comparisons and assessment of inter-annual trends.

Freshwater phytoplankton carbon fixation is an important water quality parameter that can provide information about the health of lake ecosystems as well as their impacts on regional carbon cycling dynamics. Traditional field methods for measuring and monitoring primary production are unable to capture the necessary spatial and temporal variability at the global scale due to the sheer number and diversity of the world’s lakes. Satellite remote sensing offers a potential tool to quantify freshwater lake primary production on a global scale. A new straightforward remote sensing approach was developed to estimate global freshwater carbon fixation from satellite observable lakes using a straightforward depth-integrated model (DIM). A key component of this approach is the estimation of the light utilization index, ψ, for freshwater systems. A significant negative linear model to estimate growing season ψ as a function of latitude was developed from data acquired through an exhaustive literature review. In conjunction with a previous remote sensing generated freshwater chlorophyll-a concentration data set, the DIM was used to compute growing season carbon fixation for 80,000 freshwater lakes. While these estimates are rough and could exhibit large errors for any given lake, they provide a reasonable synoptic global estimate of freshwater carbon fixation. In general, growing season areal carbon fixation was shown to decrease with increasing latitude in both northern and southern hemispheres. Carbon fixation rates in the southern hemisphere were found to be significantly higher than in the northern hemisphere, with the African continent exhibiting the highest rates. Total daily carbon fixation (areal rate × lake area) was estimated at 1.03 teragrams of carbon per day (Tg C day⁻¹) with approximately 71% occurring in the northern hemisphere. Total fixation was highest in North America, which was dominated by a very large number of Canadian Shield lakes. In general, total carbon fixation was well explained by total lake surface area, except in the far northern latitudes where lakes are more oligotrophic due to limited nutrient availability. This analysis resulted in a new freshwater carbon fixation product that provides new insights into the role freshwater lakes play in the global carbon budget.

Where light penetration is excellent, the combination of LiDAR (Light Detection And Ranging) and passive bottom reflectance (multispectral, hyperspectral) greatly aids environmental studies. Over a century ago, two stamp mills (Mohawk and Wolverine) released 22.7 million metric tons of copper-rich tailings into Grand Traverse Bay (Lake Superior). The tailings are crushed basalt, with low albedo and spectral signatures different from natural bedrock (Jacobsville Sandstone) and bedrock-derived quartz sands. Multiple Lidar (CHARTS and CZMIL) over-flights between 2008–2016—complemented by ground-truth (Ponar sediment sampling, ROV photography) and passive bottom reflectance studies (3-band NAIP; 13-band Sentinal-2 orbital satellite; 48 and 288-band CASI)—clarified shoreline and underwater details of tailings migrations. Underwater, the tailings are moving onto Buffalo Reef, a major breeding site important for commercial and recreational lake trout and lake whitefish production (32% of the commercial catch in Keweenaw Bay, 22% in southern Lake Superior). If nothing is done, LiDAR-assisted hydrodynamic modeling predicts 60% tailings cover of Buffalo Reef within 10 years. Bottom reflectance studies confirmed stamp sand encroachment into cobble beds in shallow (0-5m) water but had difficulties in deeper waters (>8 m). Two substrate end-members (sand particles) showed extensive mixing but were handled by CASI hyperspectral imaging. Bottom reflectance studies suggested 25-35% tailings cover of Buffalo Reef, comparable to estimates from independent counts of mixed sand particles (ca. 35% cover of Buffalo Reef by >20% stamp sand mixtures).

This study used satellite remote sensing to investigate trends in harmful algal blooms (HABs) over the last 21 years, focusing on four regions within the Laurentian Great Lakes: western Lake Erie, Green Bay, Saginaw Bay, and western Lake Superior. HABs in the water column were identified from remote sensing-derived chlorophyll concentrations, and surface HAB scums were classified based on the Normalized Difference Vegetation Index (NDVI) band ratio index. Using imagery from the Moderate Resolution Imaging Spectroradiometer sensor on the Aqua satellite (MODIS-Aqua) from 2002 to 2022, we generated daily estimates of the HAB and surface scum extents for each region, which were then averaged to generate mean annual extents. We observed a significant decline in the Saginaw Bay mean annual HAB extents over the 21-year study period. Otherwise, no significant changes were observed over this period in any region for either the HAB or surface scum mean annual extents, thus suggesting that HABs are not increasing in the Great Lakes. Despite the lack of increasing trends, the blooms are still recurring annually and causing a negative impact on the nearby communities; thus, we believe that it is crucial to continue studying Great Lakes HABs to monitor the impact of current and future abatement strategies.

Remote sensing approaches that could identify species of submerged aquatic vegetation (SAV) and measure their extent in lake littoral zones would greatly enhance SAV study and management, especially if these approaches can provide faster or more accurate results than traditional field methods. Remote sensing with multispectral sensors can provide this capability, but SAV identification with this technology must address the challenges of light extinction in aquatic environments where chlorophyll, dissolved organic carbon, and suspended minerals can affect water clarity and the strength of the sensed light signal. Here, we present an uncrewed aerial system (UAS)-enabled methodology to identify the extent of the invasive SAV species Myriophyllum spicatum (Eurasian watermilfoil, or EWM), primarily using a six-band Tetracam multispectral camera, flown over sites in the Les Cheneaux Islands area of northwestern Lake Huron, Michigan, USA. We analyzed water chemistry and light data and found our sites clustered into sites with higher and lower water clarity, although all sites had relatively high water clarity. The overall average accuracy achieved was 76.7%, with 78.7% producer’s and 77.6% user’s accuracy for the EWM. These accuracies were higher than previously reported from other studies that used remote sensing to map SAV. Our study found that two tested scale parameters did not lead to significantly different classification accuracies between sites with higher and lower water clarity. The EWM classification methodology described here should be applicable to other SAV species, especially if they have growth patterns that lead to high amounts of biomass relative to other species in the upper water column, which can be detected with the type of red-edge and infrared sensors deployed for this study.