Search

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...

Navigation of autonomous surface vessels, less than 20 m in length, in large sea states is difficult and often precludes successful completion of the assigned mission or, in the worst case, survivability. Operation in high seas requires sensing of the local wave environment and determining a vessel trajectory that maximizes survivability based on knowledge of the vessel response functions and prediction of the incident wave field forward in time. To achieve this objective, new technologies are being developed and tested in full scale at the Marine Autonomy Research Site (MARS), located in central Lake Superior and operated by Michigan Technological University. In this initial set of experiments, a skilled human operator was used as a surrogate for an envisioned wave-adaptive autonomous control system. The test vehicle is a fully instrumented personal watercraft, operated by a U.S. Coast Guard-trained surf-boat operator in moderate sea states with Froude number (Fr) = 1.0 through a course consisting of up-wave, cross-wave, and down-wave legs. Results dramatically document that the wave-dodging maneuvers employed are designed to minimize vessel pitch (preserve propulsor and rudder control) while allowing increased vessel roll. Comparisons of the straight line with wave-dodging circuits during constant sea state conditions show that vehicle roll is at times twice greater in wave-dodging runs while vehicle pitch averages half to one third of that in the straight-line course. These data suggest that optimum paths do exist through steep, evolving incident wave fields and these optimum paths can produce significant improvements in vessel survivability.

Remote sensing can advance the work of the Circumpolar Biodiversity Monitoring Program through monitoring of satellite-derived terrestrial and marine physical and ecological variables. Standardized data facilitate an unbiased comparison across variables and environments. Using MODIS standard products of land surface temperature, percent snow covered area, NDVI, EVI, phenology, burned area, marine chlorophyll, CDOM, sea surface temperature, and marine primary productivity, significant trends were observed in almost all variables between 2000 and 2017. Analysis of seasonal data revealed significant breakpoints in temporal trends. Within the terrestrial environment, data showed significant increasing trends in land surface temperature and NDVI. In the marine environment, significant increasing trends were detected in primary productivity. Significantly earlier onset of green up date was observed in bioclimate subzones C&E and longer end of growing season in B&E. Terrestrial and marine parameters showed similar rates of change with unidirectional change in terrestrial and significant directional and magnitude shifts in marine. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s13280-019-01249-z) contains supplementary material, which is available to authorized users.

On the Keweenaw Peninsula of Lake Superior, two stamp mills (Mohawk and Wolverine) discharged 22.7 million metric tonnes (MMT) of tailings (1901–1932) into the coastal zone off the town of Gay. Migrating along the shoreline, ca. 10 MMT of the tailings dammed stream and river outlets, encroached upon wetlands, and contaminated recreational beaches. A nearly equal amount of tailings moved across bay benthic environments into critical commercial fish spawning and rearing grounds. In the middle of the bay, Buffalo Reef is important for commercial and recreational lake trout and lake whitefish production (ca. 32% of the commercial catch in Keweenaw Bay, 22% along southern Lake Superior). Aerial photographs (1938–2016) and five LiDAR and multispectral over-flights (2008–2016) emphasize: (1) the enormous amounts of tailings moving along the beach; and (2) the bathymetric complexities of an equal amount migrating underwater across the shelf. However, remote sensing studies encounter numerous specific challenges in coastal environments. Here, we utilize a combination of elevation data (LiDAR digital elevation/bathymetry models) and in situ studies to generate a series of physical, chemical, and biological geospatial maps. The maps are designed to help assess the impacts of historical mining on Buffalo Reef. Underwater, sand mixtures have complicated multispectral bottom reflectance substrate classifications. An alternative approach, in situ simple particle classification, keying off distinct sand end members: (1) allows calculation of tailings (stamp sand) percentages; (2) aids indirect and direct assays of copper concentrations; and (3) permits determinations of density effects on benthic macro-invertebrates. The geospatial mapping shows how tailings are moving onto Buffalo Reef, the copper concentrations associated with the tailings, and how both strongly influence the density of benthic communities, providing an excellent example for the International Maritime Organization on how mining may influence coastal reefs. We demonstrate that when large amounts of mine tailings are discharged into coastal environments, temporal and spatial impacts are progressive, and strongly influence resident organisms. Next steps are to utilize a combination of hi-resolution LiDAR and sonar surveys, a fish-monitoring array, and neural network analysis to characterize the geometry of cobble fields where fish are successful or unsuccessful at producing young.

The low turbidity of northern Great Lakes waters allows lidar sufficient penetration to greatly aid environmental studies of coastal environments. On the Keweenaw Peninsula of Lake Superior, Big Traverse Bay provides an excellent example of mine tailings spreading from an old coastal release site. Between 1901 and 1932, two stamp mills (Mohawk and Wolverine) discharged 22.7 million metric tonnes of tailings (stamp sands) off the town of Gay. Along beaches, migrating stamp sands have dammed stream outlets, encroached upon wetlands, and contaminated recreational beaches. The tailings are now threatening benthic environments and critical commercial fish breeding grounds. Buffalo Reef is important for commercial and recreational lake trout and lake whitefish production (32% of the commercial catch in Keweenaw Bay, 22% of southern Lake Superior). Aerial photographs and five lidar over-flights emphasize: 1) the enormous amounts of stamp sands moving along the shoreline, and 2) large amounts migrating underwater across the bay towards Buffalo Reef. Differences between 2008 and 2016 lidar over-flights are used to quantify underwater stamp sand movement. For years, tailings have accumulated in an ancient riverbed cut (“trough”) just north of Buffalo Reef. Stamp sand overflow out of the “trough” is now moving into Buffalo Reef cobble fields, where fish drop eggs. Ponar sediment studies quantify % stamp sand in sand mixtures around Buffalo Reef, copper concentrations, and quantify impacts on benthic invertebrate taxa. Our study emphasizes that when large amounts of mine tailings are discharged into coastal environments, temporal and spatial impacts are progressive, threatening benthic organisms and fish.

Lake Erie has experienced dramatic changes in water quality over the past several decades requiring extensive monitoring to assess effectiveness of adaptive management strategies. Remote sensing offers a unique potential to provide synoptic monitoring at daily time scales complementing in-situ sampling activities occurring in Lake Erie. Bio-optical remote sensing algorithms require knowledge about the inherent optical properties (IOPs) of the water for parameterization to produce robust water quality products. This study reports new IOP and apparent optical property (AOP) datasets for western Lake Erie that encapsulate the May–October period for 2015 and 2016 at weekly sampling intervals. Previously reported IOP and AOP observations have been temporally limited and have not assessed statistical differences between IOPs over spatial and temporal gradients. The objective of this study is to assess trends in IOPs over variable spatial and temporal scales. Large spatio-temporal variability in IOPs was observed between 2015 and 2016 likely due to the difference in the extent and duration of mid-summer cyanobacteria blooms. Differences in the seasonal trends of the specific phytoplankton absorption coefficient between 2015 and 2016 suggest differing algal assemblages between the years. Other IOP variables, including chromophoric, dissolved organic matter (CDOM) and beam attenuation spectral slopes, suggest variability is influenced by river discharge and sediment re-suspension. The datasets presented in this study show how these IOPs and AOPs change over a season and between years, and are useful in advancing the applicability and robustness of remote sensing methods to retrieve water quality information in western Lake Erie.

Water quality issues continue to impact recreational and resource use of waterways in the Great Lakes and across smaller inland water bodies in their watersheds. With the advancements in small unmanned aerial systems (sUAS) and in small sensor production, sUAS offer flexibility to overcome several of the shortcomings of satellite and airborne systems, and complement their measurements in this environment. In this study, we deployed two different low-cost, boat launchable sUAS configurations instrumented with Ocean Optics STS hyperspectral Vis-NIR spectroradiometers capable of making measurements over an approximately 2.5 km2 area, below the cloud deck in the nearshore and open lake environment. Flights of these systems were conducted at ten locations over Lake Erie and the Maumee River. Measured spectra compared well with at-surface based Analytical Spectral Devices (ASD) Fieldspec measured spectra, and derived parameters were consistent with in-water FluoroProbe measured water quality parameters and field observations. Using flight data, we constructed transect maps of derived CI products which show the variability in algae abundance in open water. These systems provide high quality, low cost, very high spatial resolution (cm to m scale measurements) hyperspectral data in the nearshore environment, can be consistently flown at low altitude (minimizing atmospheric effects) below cloud cover and can be deployed on extremely short notice. With the continued advancement in sensor development, automated flight capabilities, and increased flight duration from vertical take off and landing (VTOL) platforms, water quality observation platforms such as these will soon be a common tool in resource manager's toolboxes.

The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015-2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 hours) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument calibration signals. A large asphalt parking lot near the Western Basin of Lake Erie was spectrally characterized and was determined to be a suitable correction target. Due to the HSI deployment aboard an aircraft, it is able to provide unique insights into water quality conditions not offered by space-based solutions. Aircraft can operate under cloud cover and flight paths can be chosen and changed on-demand, allowing for far more flexibility than space-based platforms. The HSI is also able to collect data at a high spatial resolution (~1 m), allowing for the monitoring of small water bodies, the ability to detect small patches of surface scum, and the capability to monitor the proximity of blooms to targets of interest such as water intakes. With this new rapid turnaround time, airborne data can serve as a complementary monitoring tool to existing satellite platforms, targeting critical areas and responding to bloom events on-demand.

Blooms of harmful cyanobacteria (cyanoHABs) have occurred on an annual basis in western Lake Erie for more than a decade. Previously, we developed and validated an algorithm to map the extent of the submerged and surface scum components of cyanoHABs using MODIS ocean-color satellite data. The algorithm maps submerged cyanoHABs by identifying high chlorophyll concentrations (>18 mg/m3) combined with water temperature >20 °C, while cyanoHABs surface scums are mapped using near-infrared reflectance values. Here, we adapted this algorithm for the SeaWiFS sensor to map the annual areal extents of cyanoHABs in the Western Basin of Lake Erie for the 20-year period from 1998 to 2017. The resulting classified maps were validated by comparison with historical in situ measurements, exhibiting good agreement (81% accuracy). Trends in the annual mean and maximum total submerged and surface scum extents demonstrated significant positive increases from 1998 to 2017. There was also an apparent 76% increase in year-to-year variability of mean annual extent between the 1998–2010 and 2011–2017 periods. The 1998–2017 time-series was also compared with several different river discharge nutrient loading metrics to assess the ability to predict annual cyanoHAB extents. The prediction models displayed significant relationships between spring discharge and cyanoHAB area; however, substantial variance remained unexplained due in part to the presence of very large blooms occurring in 2013 and 2015. This new multi-sensor time-series and associated statistics extend the current understanding of the extent, location, duration, and temporal patterns of cyanoHABs in western Lake Erie.

Lake Erie is a classic case of development, recovery from, and return to eutrophication, hypoxia, and harmful algal blooms. Forecast models are used annually to predict bloom intensity for the whole Western Lake Erie Basin, but do not necessarily reflect nearshore conditions or regional variations, which are important for local stakeholders. In this study we: 1) developed relationships between observed whole basin and nearshore bloom sizes, and 2) updated and extended a Bayesian seasonal bloom forecast model to provide new regional predictions. The western basin was subdivided into 5 km near-shore regions, and bloom start date, size, and intensity were quantified with MODIS-derived images of chlorophyll concentrations for July–October 2002–2016 for each subdivision and for the entire basin. While bloom severity within each subdivision is temporally and spatially unique, it increased over the study period in each subdivision. The models for the 5 km subdivisions explained between 83 and 95% of variability between regional sizes and whole bloom size for US subdivisions and 51% for the Canadian subdivision. By linking predictive basin-wide models to regional regression estimates, we are now able to better predict potential bloom impacts at scales and in specific areas that are vital to the economic well-being of the region and allow for better management responses.

The Kent State University (KSU) spectral decomposition method provides information about the spectral signals present in multispectral and hyperspectral images. Pre-processing steps that enhance signal to noise ratio (SNR) by 7.37–19.04 times, enables extraction of the environmental signals captured by the National Aeronautics and Space Administration (NASA) Glenn Research Center's, second generation, Hyperspectral imager (HSI2) into multiple, independent components. We have accomplished this by pre-processing of Level 1 HSI2 data to remove stripes from the scene, followed by a combination of spectral and spatial smoothing to further increase the SNR and remove non-Lambertian features, such as waves. On average, the residual stochastic noise removed from the HSI2 images by this method is 5.43 ± 1.42%. The method also enables removal of a spectrally coherent residual atmospheric bias of 4.28 ± 0.48%, ascribed to incomplete atmospheric correction. The total noise isolated from signal by the method is thus

Many regions of the Great Lakes now see recurring cyanobacterial harmful algal blooms (cyanoHABs), with documented repercussions for ecosystem services, public health, and ecosystem integrity. Early detection and comprehensive monitoring of cyanoHABs are fundamental to their effective management and mitigation of detrimental impacts. Satellite remote sensing has provided the means by which algal blooms in the Great Lakes can be observed with unprecedented frequency and spatial coverage. Algorithms have been developed and validated; fully automated data processing streams have been rendered operational; and stakeholders have been engaged in order to develop user-friendly end products. Such products have been integral in providing near-real-time monitoring of bloom conditions, documenting spatiotemporal trends, improving understanding of environmental drivers of blooms, and guiding nutrient management actions. In this chapter we present background information on remote sensing of algal blooms, document the current state of knowledge with a focus on Lake Erie, and discuss remote sensing products available to the Great Lakes community.

In the past century, extensive research has been done regarding the sound propagation in Arctic ice sheets. The majority of this research has focused on low-frequency propagation over long distances. Due to changing climate conditions in these environments, experimentation is warranted to determine sound propagation characteristics in, through, and under first-year, thin ice sheets, in shallow water, over short distances. In April 2016 several experiments were conducted approximately 2 km off the coast of Barrow, Alaska on shore-fast, first-year ice, approximately 1 m thick. To determine the propagation characteristics of various sound sources, frequency response functions were measured between a source location and several receiver locations at various distances from 1 m to 1 km. The primary sources used for this experiment were, an underwater speaker with various tonal outputs, an instrumented impact hammer on the ice, and a propane cannon that produced an acoustic blast wave in air. The transmission loss (TL) characteristics of the multipath propagation (air, ice, water) are investigated and reported. Data indicate that TL in frequency bands between 125 and 2000 Hz varied from approximately 3-6 dB per doubling of distance which is consistent with geometrical spreading losses, cylindrical and spherical, respectively.

Freshwater ecosystems provide numerous services for communities worldwide, including irrigation, hydropower, and municipal water; however, the services provided by inland fisheries – nourishment, employment, and recreational opportunities – are often comparatively undervalued. We provide an independent estimate of global lake harvest to improve biological and socioeconomic assessments of inland fisheries. On the basis of satellite-derived estimates of chlorophyll concentration from 80,012 globally distributed lakes, lake-specific fishing effort based on human population, and output from a Bayesian hierarchical model, we estimated that the global lake fishery harvest in the year 2011 was 8.4 million tons (mt). Our calculations excluded harvests from highly productive rivers, wetlands, and very small lakes; therefore, the true cumulative global fishery harvest from all freshwater sources likely exceeded 11 mt as reported by the Food and Agriculture Organization of the United Nations (FAO). This putative underestimate by the FAO could diminish the perceived importance of inland fisheries and perpetuate decisions that adversely affect these fisheries and millions of people.

Water clarity trends in three upper Great Lakes, Lakes Superior, Michigan, and Huron, were assessed via satellite imagery from 1998 to 2012. Light attenuation coefficients (Kd 490 ) from SeaWiFS and Aqua MODIS satellites compared favorably with in situ measurements. Significant temporal and spatial trends and differences in Kd 490 were noted within all three of the lakes. Lake-wide average Kd 490 for Lake Superior did not exhibit any changes between 1998 and 2012. Annual Kd 490 values for Lake Huron, however, showed a significant negative trend during the study period using both SeaWiFS and MODIS datasets. Similarly, annual Kd 490 values of Lake Michigan declined between 1998 and 2010. Only in the offshore waters (> 90 m depth) of northern Lake Michigan did Kd 490 increase but just after 2007. Photic depth increased significantly in both Lake Michigan (≃ 5 m), and Lake Huron (≃ 10 m) when comparing annual Kd 490 for pre- (1998–2001) and post-dreissenid mussel (2006–2010). At seasonal level, significant decreases in Kd 490 in lakes Michigan and Huron were mainly noted for the spring/fall/winter mixing periods. After these recent changes in water clarity, lake-wide photic depths in lakes Michigan and Huron superseded Lake Superior; thus, making Lake Superior no longer the clearest Great Lake. Several factors (e.g. filtering activities of quagga mussels, phosphorus abatement, climate change, etc.) are likely responsible for these large changes.

Озеро Мичиган относится к олиготрофному типу водоемов. Сильнее всего это проявляется в прибрежной зоне, где гидрологический режим вод формируется под действием различных природных и антропогенных факторов, протекающих на водосборной территории. Влияние дна достаточно сильно искажает регистрируемый спутником сигнал, препятствуя выполнению дистанционного зондирования параметров качества воды в этой части озера. Для извлечения из спутниковых снимков параметров качества воды в оптически мелких районах озера Мичиган был разработан новый биооптический алгоритм. Он основан на модели переноса излучения, гидрооптической модели озера Мичиган и многомерной оптимизации и позволяет получать оценку концентрации всех основных оптически активных веществ (ОАВ) в областях с различным типом дна: песок, ил, макрофиты (Chara или Cladophora) и известняковая галька. Тестирование алгоритма показало, что при очень низких концентрациях ОАВ (менее 0,01 в соответствующих единицах измерения) оптическое влияние дна становится незначительным при глубине около 20 метров. В воде с более высокими концентрациями взвешенного вещества (ВВ) и хлорофилла влияние дна становится неразличимым при глубине ~10 м. Тестирование чувствительности алгоритма к шуму показало, что чем меньше толщина исследуемого водного столба и чем выше альбедо дна, тем выше погрешность восстановления концентраций ОАВ. Например, в случае песчаного дна и глубины 5 м погрешность в 10 % при оценке альбедо дна приводит к ошибкам в 18, 28 и 10 % при восстановлении хлорофилла, ВВ и окрашенного растворенного органического вещества соответственно.

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.

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&ndash, 2016&mdash, 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)&mdash, 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 (&gt, 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 &gt, 20% stamp sand mixtures).

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&rsquo, s largest lakes from 2003&ndash, 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 (&minus, 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&rsquo, s largest lakes, allowing for these novel between-lake comparisons and assessment of inter-annual trends.

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.

Lake-wide phytoplankton chlorophyll a concentrations and primary production were determined for lakes Huron, Michigan, and Superior in 2010–2013. Chlorophyll a concentrations were determined using MODIS imagery with a color-producing agent algorithm and primary production with the Great Lakes Production Model using remotely sensed and empirically derived input from the Upper Great Lakes. The new chlorophyll a and primary production estimates agreed well with field measurements. Lake-wide mean chlorophyll a concentrations determined from observations in all 12 months were highest in Lake Superior (mean = 0.99 mg/m3), intermediate in Lake Michigan (mean = 0.88 mg/m3), and lowest in Lake Huron (mean = 0.77 mg/m3). In Lake Superior, a gradient in chlorophyll a concentrations was noted from the shallow zone (0–30 m, mean = 1.57 mg/m3) to the deep-water zone (> 150 m, mean = 0.94 mg/m3). However, in Lake Michigan, no differences in mean chlorophyll a concentrations were noted in shallow-, mid-, or deep-water zones (means, 0.83, 0.86, 0.90 mg/m3, respectively). Lake-wide areal integrated primary production rates in lakes Huron, Michigan, and Superior were not significantly different for the 2010–2013 period (means, 216, 259, and 228 mg C/m2/d, respectively). Also, primary production in all depth zones (shallow, mid, and deep) were similar across lakes. Annual whole-lake phytoplankton carbon fixation values for 2010–2013 ranged from 4.4 to 5.7 Tg/y for Lake Huron, 5.0–7.2 Tg/y for Lake Michigan, and 6.4–9.5 Tg/y for Lake Superior.

The availability and quality of spawning habitat may limit lake trout recovery in the Great Lakes, but comprehensive maps of current spawning habitats are unavailable at the basin scale. Current methods used to identify lake trout spawning locations are time- and labor-intensive and spatially limited. Due to the observation that some lake trout spawning sites are relatively clean of overlaying algae compared to adjacent areas not used for spawning, we hypothesized that spawning sites could be identified using satellite imagery. Satellite imagery collected just before and after the spawning season in 2013 was used to assess whether lake trout spawning habitat could be identified based on its spectral characteristics. Results indicated that Pleiades high-resolution multispectral satellite imagery can be successfully used to estimate the vegetative cover of substrates and temporal changes in vegetative coverage, and that models developed from processed imagery can be used to identify potential shallow water lake trout spawning sites based on comparison of sites where lake trout eggs were and were not observed after spawning. Satellite imagery is a new potential tool for identifying nearshore lake trout spawning habitat across broad areas of the Great Lakes.

Prediction of primary production of lentic water bodies (i.e., lakes and reservoirs) is valuable to researchers and resource managers alike, but is very rarely done at the global scale. With the development of remote sensing technologies, it is now feasible to gather large amounts of data across the world, including understudied and remote regions. To determine which factors were most important in explaining the variation of chlorophyll a (Chl- a ), an indicator of primary production in water bodies, at global and regional scales, we first developed a geospatial database of 227 water bodies and watersheds with corresponding Chl- a, nutrient, hydrogeomorphic, and climate data. Then we used a generalized additive modeling approach and developed model selection criteria to select models that most parsimoniously related Chl- a to predictor variables for all 227 water bodies and for 51 lakes in the Laurentian Great Lakes region in the data set. Our best global model contained two hydrogeomorphic variables (water body surface area and the ratio of watershed to water body surface area) and a climate variable (average temperature in the warmest model selection criteria to select models that most parsimoniously related Chl- a to predictor variables quarter) and explained ~ 30% of variation in Chl- a. Our regional model contained one hydrogeomorphic variable (flow accumulation) and the same climate variable, but explained substantially more variation (58%). Our results indicate that a regional approach to watershed modeling may be more informative to predicting Chl- a, and that nearly a third of global variability in Chl- a may be explained using hydrogeomorphic and climate variables.

A new method was developed, evaluated, and applied to generate a global dataset of growing-season chlorophyll-a chl concentrations in 2011 for freshwater lakes. Chl observations from freshwater lakes are valuable for estimating lake productivity as well as assessing the role that these lakes play in carbon budgets. The standard 4 km NASA OceanColor L3 chlorophyll concentration products generated from MODIS and MERIS sensor data are not sufficiently representative of global chl values because these can only resolve larger lakes, which generally have lower chl concentrations than lakes of smaller surface area. Our new methodology utilizes the 300 m-resolution MERIS full-resolution full-swath FRS global dataset as input and does not rely on the land mask used to generate standard NASA products, which masks many lakes that are otherwise resolvable in MERIS imagery. The new method produced chl concentration values for 78,938 and 1,074 lakes in the northern and southern hemispheres, respectively. The mean chl for lakes visible in the MERIS composite was 19.2 ± 19.2, the median was 13.3, and the interquartile range was 3.90–28.6 mg m−3. The accuracy of the MERIS-derived values was assessed by comparison with temporally near-coincident and globally distributed in situ measurements from the literature n = 185, RMSE = 9.39, R2 = 0.72. This represents the first global-scale dataset of satellite-derived chl estimates for medium to large lakes.

In the Laurentian Great Lakes, the prolific growth of submerged aquatic vegetation (SAV, dominated by the filamentous green alga Cladophora) is negatively impacting human and wildlife health, fisheries, and aesthetic conditions. The distribution of Cladophora and similar SAV in the Great Lakes and how that has changed over recent decades has been unknown, and the magnitude of the current problem relative to historic growth is difficult to assess given the lack of field data and monitoring. Here, we present the first comprehensive map of SAV in the nearshore zone of the lower four Great Lakes, where ‘nearshore’ indicates the zone where the bottom reflectance is sufficient to characterize bottom substrate type, using primarily the visible bands of the Landsat TM sensor. Multiple spectral bands were combined to minimize the water-depth-dependent variance in lake bottom reflectance, and pixels were classified as dense vegetation, sparse vegetation or uncolonized substrate. Changes in vegetation cover and water clarity between the mid-1970s and present were also evaluated for several focus areas across the lakes utilizing historical Landsat TM and MSS imagery. The Landsat-based bottom classification achieved an overall accuracy of 83% based on comparison to ground truth data. Lake-wide SAV cover ranged from 15% (Lake Huron) to 40% (Lake Ontario) of the mappable lake bottom based on detection depth. The total mapped area of vegetation corresponds to a conservative biomass estimate of 129 kilotonnes dry weight. The observed changes in the distribution of SAV over time indicate that increasing water clarity in the Lakes is expanding Cladophora habitat.

Cyanobacterial harmful algal blooms (CyanoHABs), mainly composed of the genus Microcystis, occur frequently throughout the Laurentian Great Lakes. We used artificial neural networks (ANNs) involving 31 hydrological and meteorological predictors to model total phytoplankton (as chlorophyll a) and Microcystis biomass from 2009 to 2011 in western Lake Erie. Continuous ANNs provided modeled-measured correspondences (and modeling efficiencies) ranging from 0.87 to 0.97 (0.75 to 0.94) and 0.71 to 0.90 (0.45 to 0.88) for training–cross-validation and test data subsets of chlorophyll a concentrations and Microcystis biovolumes, respectively. Classification ANNs correctly assigned up to 94% of instances for Microcystis presence–absence. The influences of select predictors on phytoplankton and CyanoHAB niches were visualized using biplots and three-dimensional response surfaces. These then were used to generate mathematical expressions for the relationships between modeled CyanoHAB outcomes and the direct and interactive influences of environmental factors. Based on identified conditions (∼40 to 50 μg total phosphorus (TP)·L−1, 22 to 26 °C, and prolonged wind speeds less than ∼19 km·h−1) underlying the likelihood of occurrence and accumulation of phytoplankton and Microcystis, a “target” concentration of 30 μg TP·L−1 appears appropriate for alleviating blooms. ANNs generated robust ecological niche models for Microcystis, providing a predictive framework for quantitative visualization of nonlinear CyanoHAB–environmental interactions.

Analysis of visible remote sensing data research requires removing atmospheric effects by conversion from radiance to at-surface reflectance. This conversion can be achieved through theoretical radiative transfer models, which yield good results when well-constrained by field observations, although these measurements are often lacking. Additionally, radiative transfer models often perform poorly in marine or lacustrine settings or when complex air masses with variable aerosols are present. The empirical line method (ELM) measures reference targets of known reflectance in the scene. ELM methods require minimal environmental observations and are conceptually simple. However, calibration coefficients are unique to the image containing the reflectance reference. Here we compare the conversion of hyperspectral radiance observations obtained with the NASA Glenn Research Center Hyperspectral Imager to at-surface reflectance factor using two reflectance reference targets. The first target employs spherical convex mirrors, deployed on the water surface to reflect ambient direct solar and hemispherical sky irradiance to the sensor. We calculate the mirror gain using near concurrent at-sensor reflectance, integrated mirror radiance, and in situ water reflectance. The second target is the Lambertian-like blacktop surface at Maumee Bay State Park, Oregon, OH, where reflectance was measured concurrently by a downward looking, spectroradiometer on the ground, the aerial hyperspectral imager and an upward looking spectroradiometer on the aircraft. These methods allows us to produce an independently calibrated at-surface water reflectance spectrum, when atmospheric conditions are consistent. We compare the mirror and blacktop-corrected spectra to the in situ water reflectance, and find good agreement between methods. The blacktop method can be applied to all scenes, while the mirror calibration method, based on direct observation of the light illuminating the scene validates the results. The two methods are complementary and a powerful evaluation of the quality of atmospheric correction over extended areas. We decompose the resulting spectra using varimax-rotated, principal component analysis, yielding information about the underlying color producing agents that contribute to the observed reflectance factor scene, identifying several spectrally and spatially distinct mixtures of algae, cyanobacteria, illite, haematite, and goethite. These results have implications for future hyperspectral remote sensing missions, such as PACE, HyspIRI, and GeoCAPE.

Thirteen years of SeaWiFS data (1998–2010) from the early spring isothermal period (March–April) were used to determine trends of water attenuation coefficient (K dPAR ), chlorophyll a (Chl a ), Photosynthetic Available Radiation (PAR), and modeled primary production in southern Lake Michigan. Surface PAR values remained unchanged between 1998 and 2010, but there was an 18–22% drop in K dPAR during the March/April isothermal period as water clarity increased. This transparency increase was accompanied by a 41–53% decline in Chl a concentration (μg · L − 1 ) and a 42–46% decline in modeled primary production (Great Lakes Primary Production Model). These changes were most pronounced in 2001–2003 which coincided with the period of initial colonization of the quagga mussels. Statistically significant spatial differences were noted in Chl a (μg · L − 1 ) concentrations between mid-depth (z = 30–90 m deep), and offshore ( z > 90 m deep) waters. Chl a concentrations in the mid-depth region (30–90 m) decreased at a higher rate compared to offshore waters (> 90 m) likely as a result of filtration activities of quagga mussel.

The annual runoff from the melting of large glaciers and snow fields along the northern perimeter of the Gulf of Alaska is a critical component of marine physical and biological systems; yet, most of this freshwater is not measured. Here we show estimates of melt for the watershed that contains the largest and longest glacier in North America, the Bering Glacier. The procedure combines in situ observations of snow and ice melt acquired by a long-term monitoring program, multispectral satellite observations, and nearby temperature measurements. The estimated melt is 40 km3 per melt season, ± 3.0 km3, observed over the decadal period, 2002–2012. As a result of climate change, these estimates could increase to 60 km3/yr by 2050. This technique and the derived melt coefficients can be applied to estimate melt from Alaska to Washington glaciers.

There are numerous examples of past and present mine disposal into freshwater and marine coastal bays and riverine environments. Due to its high spatial resolution and extended water penetration, coastal light detection and ranging (LiDAR), coupled with multispectral scanning (MSS), has great promise for resolving disturbed shoreline features in low turbidity environments. Migrating mine tailings present serious issues for Lake Superior and coastal marine environments. Previous investigations in Lake Superior uncovered a metal-rich “halo” around the Keweenaw Peninsula, related to past copper mining practices. For over a century, waste rock migrating from shoreline tailing piles has moved along extensive stretches of coastline, compromising critical fish breeding grounds, damming stream outlets, transgressing into wetlands and along recreational beaches and suppressing benthic invertebrate communities. In Grand (Big) Traverse Bay, Buffalo Reef is an important spawning area for lake trout and whitefish threatened by drifting tailings. The movement of tailings into Buffalo Reef cobble fields may interfere with the hatching of fish eggs and fry survival, either by filling in crevices where eggs are deposited or by toxic effects on eggs, newly hatched larvae or benthic communities. Here, we show that the coastal tailing migration is not “out of sight, out of mind”, but clearly revealed by using a combination of LiDAR and MSS techniques.

Internal waves in the ocean play an important role in turbulence generation due to wave-breaking processes and mixing of the ocean. Airborne radar images of internal waves and ocean eddies north of Svalbard suggested that ocean eddies could generate internal waves. Here, we test this hypothesis using data from a dedicated internal wave experiment in the Greenland Sea. Internal waves with dominant frequencies of 1–3 cycles per hour and amplitudes up to 15 m were observed using three thermistor chains suspended from a drifting array conveniently placed on the ice in a triangle with sides of several km. Analysis shows that internal waves propagated westwards with a speed of about 0.2 m/s and wavelength of 0.4–1.0 km, away from an anticyclonic ocean eddy located just east of the array. This was consistent with the remote-sensing observations of internal waves whose surface signature was imaged by an airborne radar in the western part of this eddy, and with theories that eddies and vortexes can directly generate internal waves. This case study supports our hypothesis that ocean eddies can be the direct sources of internal waves reported here for the first time and not only enhancing the local internal wave field by draining energy from the eddies, as studied previously. The present challenge is to explore the role of eddies as a new source in generating internal waves in the global ocean.

An algorithm that utilizes individual lake hydro-optical (HO) models has been developed for the Great Lakes that uses SeaWiFS, MODIS, or MERIS satellite data to estimate concentrations of chlorophyll, dissolved organic carbon, and suspended minerals. The Color Producing Agent Algorithm (CPA-A) uses a specific HO model for each lake. The HO models provide absorption functions for the Color Producing Agents (CPAs) (chlorophyll (chl), colored dissolved organic matter (as dissolved organic carbon, doc), and suspended minerals (sm)) as well as backscatter for the chlorophyll, and suspended mineral parameters. These models were generated using simultaneous optical data collected with in situ measurements of CPAs collected during research cruises in the Great Lakes using regression analysis as well as using specific absorption and backscatter coefficients at specific chl, doc, and sm concentrations. A single average HO model for the Great Lakes was found to generate insufficiently accurate concentrations for Lakes Michigan, Erie, Superior and Huron. These new individual lake retrievals were evaluated with respect to EPA in situ field observations, as well as compared to the widely used OC3 MODIS retrieval. The new algorithm retrievals provided slightly more accurate chl values for Lakes Michigan, Superior, Huron, and Ontario than those obtained using the OC3 approach as well as providing additional concentration information on doc and sm. The CPA-A chl retrieval for Lake Erie is quite robust, producing reliable chl values in the reported EPA concentration ranges. Atmospheric correction approaches were also evaluated in this study.

A new MODIS based satellite algorithm to estimate primary production (PP) has been generated and evaluated for Lake Michigan. The Great Lakes Primary Productivity Model (GLPPM) is based on previous models that required extensive in situ data but it can utilize remotely sensed observations as input for some model variables and therefore allows greater spatial resolution for primary productivity estimates. The Color Producing Agent Algorithm (CPA-A) is utilized to obtain robust chlorophyll a values and the NASA KD2M approach is used to obtain the diffuse attenuation coefficient (K d ). Only incident PAR and carbon fixation rates are additionally needed to generate the primary productivity estimate. Comparisons of the satellite derived PP estimates from single monthly images to average monthly field measurements made by NOAA/GLERL found good agreement between estimates. Satellite derived PP estimates were used to estimate a preliminary Lake Michigan annual primary production of 8.5 Tg C/year. The new algorithm can be easily adapted to work on all the Great Lakes and therefore can be used to generate time series dating back to late 1997 (launch of SeaWiFs). These time series can contribute to improved assessment of Great Lakes primary productivity changes as a result of biological events, such as Dreissenid mussel invasions, climatic change and anthropogenic forcing.

LiDAR (light detection and ranging) data can be used to create fine digital elevation and bathymetric models (DEMs). Here we examine natural coastal erosion in Grand Traverse Bay, Michigan, a part of Keweenaw Bay in Lake Superior, and discuss how a variety of geological features (submersed river bed and channels associated with the Houghton Low; Nipissing dunes) interact with long-term sediment accumulation patterns. The geological features also modify migrating tailings from a legacy mining site. The combination of LiDAR derived images and aerial photographs allowed us to reconstruct the historical movement of tailings along the coastline. A total of 22.8 million metric tonnes (Mt) of stamp sand were discharged into the coastal environment off Gay, MI. Over a span of 80 years, beaches to the southwest of Gay have progressively received 7.0 Mt (30.7%) of the mass eroded from the original pile, whereas 11.1 Mt (48.7%) have moved into the bay. The total amount accumulated along the beaches now greatly exceeds the mass remaining on the original tailings pile (3.7 Mt; 16.2%). Bathymetric differences between two LiDAR surveys (2008 and 2010) were also used to estimate the mass, and to track the movement of migrating underwater stamp sand bars. These bars are moving southwesterly towards Buffalo Reef, creating a threat to the lake trout and lake whitefish breeding ground.