BathyBoat: An Autonomous Surface Vessel for Stand-alone Survey and Underwater Vehicle Network Supervision

Exploration of remote environments, once the domain of intrepid adventurers, can now be conducted in relative safety using unmanned vehicles. This article describes the joint University of Michigan (UMich) and Michigan Tech Research Institute’s project to design and to build a new autonomous surface vessel (ASV) for use in research, education, and resource management as well as in the commercial sector. Originally designed to assist with bathymetric surveys in the wilderness of northern Alaska, the BathyBoat has become a test-bed platform for new research in collaborative heterogeneous underwater robotic search and survey missions in ports, harbors, lakes, and rivers. The UMich Marine Hydrodynamics Laboratories are actively researching autonomous technologies such as cooperative navigation, surface vessel control, and multivehicle search and survey using the BathyBoat and the UMich Perceptual Robotics Laboratory’s Iver2 autonomous underwater vehicles. This article presents an overview of these research topics and highlights relevant real-world testing and recent missions involving the BathyBoat ASV on Alaska’s North Slope, the harbors of Illinois, and various riverine environments in Michigan.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83223/1/2010e_MTS_Journal.pd

Satellite monitoring of harmful algal blooms in the Western Basin of Lake Erie: A 20-year time-series

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 (\u3e18 mg/m3) combined with water temperature \u3e20 °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

Melt water input from the Bering Glacier watershed into the Gulf of Alaska

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

Spatial and temporal variability of inherent and apparent optical properties in western Lake Erie: Implications for water quality remote sensing

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

Light detection and ranging (LiDAR) and multispectral studies of disturbed Lake Superior coastal environments

Due to its high spatial resolution and excellent water penetration, coastal light detection and ranging (LiDAR) coupled with multispectral imaging (MSS) has great promise for resolving shoreline features in the Great Lakes. Previous investigations in Lake Superior documented a metal-rich “halo” around the Keweenaw Peninsula, related to past copper mining practices. Grand Traverse Bay on the Keweenaw Peninsula provides an excellent Great Lakes example of global mine discharges into coastal environments. For more than a century, waste rock migrating from shoreline tailings piles has moved along extensive stretches of coast, damming stream outlets, intercepting wetlands and recreational beaches, suppressing benthic invertebrate communities, and threatening critical fish breeding grounds. In the bay, the magnitude of the discarded wastes literally “reset the shoreline” and provided an intriguing field experiment in coastal erosion and spreading environmental effects. Employing a combination of historic aerial photography and LiDAR, we estimate the time course and mass of tailings eroded into the bay and the amount of copper that contributed to the metal-rich halo. We also quantify underwater tailings spread across benthic substrates by using MSS imagery on spectral reflectance differences between tailings and natural sediment types, plus a depth-correction algorithm (Lyzenga Method). We show that the coastal detail from LiDAR and MSS opens up numerous applications for ecological, ecosystem, and geological investigations

Determination of beach sand parameters using remotely sensed aircraft reflectance data

An algorithm was developed which determines the mineralogy, moisture, and grain size of beach sands based on the hemispherical reflectance in 17 discrete spectral bands. The bands chosen range between 0.40 and 2.5 [mu]m, a wavelength range practical for existing multispectral remote sensing technology. The sand spectra on which the mineralogy, moisture, and grain-size algorithm (MOGS) is based were obtained from laboratory spectrophotometric measurements. Selected spectral bands are used in a vector-length-decision framework to determine the mineralogical class of the input sand. Multiple linear regressions are then used, within a given mineralogical class, to determine the moisture and grain size of the sand. The predictive results of the MOGS algorithm are very encouraging. When tested on 70 of the sand reflectance spectra from which it was derived, the correlation of actual to predicted moisture and grain size was 96% and 88%, respectively. The MOGS algorithm has been successfully tested using aircraft multispectral scanner data collected over the Lake Michigan shoreline. The algorithm correctly identified gross mineralogy and predicted grain size to within 0.09 mm of measured values. Some difficulties were encountered in predicting high beach-sand moistures, probably due to the increasing non-Lambertian nature of sand as the moisture content of the sand increased.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24547/1/0000827.pd

Let’s Build a Picnic Table.

4 p

Spatial-temporal variability of in situ cyanobacteria vertical structure in Western Lake Erie: Implications for remote sensing observations

Remote sensing has provided expanded temporal and spatial range to the study of harmful algal blooms (cyanoHABs) in western Lake Erie, allowing for a greater understanding of bloom dynamics than is possible through in situ sampling. However, satellites are limited in their ability to specifically target cyanobacteria and can only observe the water within the first optical depth. This limits the ability of remote sensing to make conclusions about full water column cyanoHAB biomass if cyanobacteria are vertically stratified. FluoroProbe data were collected at nine stations across western Lake Erie in 2015 and 2016 and analyzed to characterize spatio-temporal variability in cyanobacteria vertical structure. Cyanobacteria were generally homogenously distributed during the growing season except under certain conditions. As water depth increased and high surface layer concentrations were observed, cyanobacteria were found to be more vertically stratified and the assumption of homogeneity was less supported. Cyanobacteria vertical distribution was related to wind speed and wave height, with increased stratification at low wind speeds (bathymetry and environmental conditions could lead to improved biomass estimates. Additionally, cyanobacteria contributions to total chlorophyll-a were shown to change throughout the season and across depth, suggesting the need for remote sensing algorithms to specifically identify cyanobacteria