High resolution satellite-based water depth mapping in the Great Lakes

The capability to map water depth using satellite imagery can help fulfill bathymetric mapping needs in nearshore regions, especially where other sources such as LiDAR and sonar have not been able to reach all areas. The ability to accurately map water depth with satellite imagery lessens the need for expensive field work to derive bathymetry. Using high spatial resolution commercial satellite imagery to map depth can provide bathymetry data with accuracies better than one half meter. Several satellite depth mapping methods exist and have been tested to determine their accuracies and limitations in the Sleeping Bear Dunes National Lakeshore (SBDNL) nearshore area. Traditional algorithms required several inputs to calculate depth that may not be readily available or have acceptable accuracy. A new technique has been developed that requires less ancillary input data than existing algorithms by deriving them directly from the image being processed. Accuracies of this new technique, when compared to coastal bathymetric LiDAR, are presented for the SBDNL which primary bottom types consist of sand and submerged aquatic vegetation (SAV). The new technique was also evaluated using several different high-resolution commercial satellite sensors, including WorldView-2 and GeoEye-1

Extending Great Lakes remote sensing products to stakeholders through GLOS, Coastwatch, and custom portals

Our research team has been working closely with the Great Lakes Observing System and NOAA Great Lakes Coastwatch to make derived remote sensing products for the region more widely available. Through a combination of custom portals, standardized data sharing methods, appropriate metadata, and useful symbolization, these data have become more available over the past year. At MTRI\u27s Satellite-Derived Great Lakes Remote Sensing portal (http://www.greatlakesremotesensing.org/), MTRI Color Producing Agent Algorithm (CPA-A) products such as chlorophyll, dissolved organic carbon, and suspended minerals can be viewed for the satellite cloud-free season (usually April - October). An approval process is underway with NOAA to make these data available for display and download through the Great Lakes Coastwatch node. The new GLOS Data Portal has been working through its Data Management and Communications (DMAC) committee to make the most recent natural color, temperature, chlorophyll data available, along with recent Ranger III ship-based data and optical properties information. Through this collaboration, Great Lakes decision makers, researchers, the public, and other stakeholders have greater access to critical regional data derived from remote sensing data sources

A satellite algorithm for river plume mapping within the Great Lakes basin

A robust river sediment plume algorithm that utilizes color satellite data has been developed for the Great Lakes. The algorithm, which utilizes any ocean color satellite that has a blue, red, green and NIR band such as MODIS and the recently launched NASA NPP VIIRS satellite sensor, first generates a Total Suspended Sediment Index (TSSIGL) that is used to map the extent of the plume. The TSSIGL represents the total suspended solids (TSS) which includes both the organic and inorganic constituents of the plume. The Normalized Difference Vegetative Difference (NDVI) is calculated to generate a representation of the organic dominated concentration at the water surface. The highest TSSIGL values indicate heavy suspended sediment concentration (SSC or total suspended mineral). By comparing the TSSIGL output to the NDVI result, the composition of the plume can be ascertained (sediment dominated versus organic material). The relative concentration of the plume is obtained by examination of the index values. Given the area of the plume and its relative concentration, along with bathymetry, an estimate of the sediment load within the plume can be made. This new approach to mapping plumes in the Great Lakes is applicable to plumes in river mouths, embayment areas, and hydrodynamically complex basins such as Western Lake Erie

Mapping cladophora and other submerged aquatic vegetation in the Great Lakes using satellite imagery

Under EPA GLRI funding, the Michigan Tech team has developed and verified a remote sensing algorithm to map the extent of Cladophora and other submerged aquatic vegetation (SAV) in the nearshore zone of the Great Lakes using an index that corrects for the effect of water depth. With this algorithm, maps of SAV were generated from recent Landsat satellite imagery for all areas of the lower four Great Lakes that are shallow enough to detect the lake bottom. The area mapped varies depending on water clarity, with maximum mapping depth ranging from \u3e20 m in Lake Michigan to 7 m in Lake Erie. The maps show that 28%, 15%, 30%, and 40% of the visible bottom of Lakes Michigan, Huron, Erie and Ontario, respectively, are colonized by SAV. The total mapped area of SAV is estimated to represent between 130,000 and 260,000 metric tonnes dry weight based on published biomass density measurements. This new mapping approach was validated using field data for an overall map accuracy of 83%. The archive of Landsat imagery dating back to 1973 was also utilized to document historic changes in SAV extent and water clarity, showing increases in SAV extent in most areas following the introduction of invasive mussels. The time series analyses also captured the observed increases in water clarity in all four lakes. Overall, the effects of invasive zebra and quagga mussels on water clarity and phosphorus availability in the Lakes are enabling benthic vegetation to grow more densely and in deeper water than was previously possible, resulting in nuisance blooms even in areas without strong point sources of nutrients. These new maps will support Cladophora management efforts and help to prioritize areas for nutrient abatement programs

Mapping cladophora and other submerged aquatic vegetation in the Great Lakes using satellite imagery

The Michigan Tech team has developed and verified a remote sensing algorithm to map the extent of Cladophora and other submerged aquatic vegetation (SAV) in coastal waters using a depth-invariant bottom reflectance index. With this algorithm, maps of SAV were generated from recent Landsat satellite imagery for all optically visible areas of the lower four Great Lakes. The area mapped varies depending on water clarity, with maximum mapping depth ranging from \u3e20 m in Lake Michigan to 7 m in Lake Erie. The maps show that 28%, 15%, 30%, and 40% of the visible bottom of Lakes Michigan, Huron, Erie and Ontario, respectively, are colonized by SAV. The total mapped area of SAV is estimated to represent between 130,000 and 260,000 metric tonnes dry weight based on published biomass density measurements. This new mapping approach was validated using field data for an overall map accuracy of 83%. The archive of Landsat imagery dating back to 1973 was also utilized to document historic changes in SAV extent and water clarity, showing increases in SAV extent in most areas following the introduction of invasive mussels. A seasonal analysis of SAV extent revealed intra-annual changes of ~5% or less. The time series analyses also captured the observed increases in water clarity in all four lakes

Creating a representative Lake Erie time series of remote sensing-based water quality data sets

Remote sensing provides a method to accurately assess water quality for current and historical conditions in large lakes such as Lake Erie. Satellite sensors such as SeaWiFS and MODIS collect data that span large geographic areas and have been in operation for more than a decade, with some satellite programs that have existed since the 1970s. This remote sensing time machine allows scientist to analyze a time series of data and determine how water quality conditions have changed, particularly in light of a changing climate. Augmentation of remotely sensed data with sound in-situ measurements allows scientists to gain a deeper understanding of changes in the Great Lakes. This presentation reviews a recent collaborative study between the Michigan Tech Research Institute and the NASA Glenn Research Center that detailed satellitederived products to assess changes in Lake Erie water quality, described ancillary observations to support the time series analysis, and derived the representative set of products to characterize Lake Erie\u27s water quality and characteristics. Remote sensing analysis outputs included retrieving color-producing agent (CPA) products (chlorophyll, suspended minerals, and dissolved organic carbon concentrations), sediment plume extents, optical water parameters, and harmful algal bloom extents

Assessing impacts from historical copper mining stamps sands in the Keweenaw Peninsula through analysis of LiDAR and multispectral imagery from the US Army Corps of Engineers CHARTS System

A Michigan Tech University - US Army Corps of Engineers (USACE) collaborative team has been analyzing 2008 USACE CHARTS LiDAR and multispectral CASI imagery collected over areas of stamp sands deposited during the historical copper mining period in Michigan\u27s Keweenaw Peninsula. Our focus area has been the area near Gay, MI, where approximately 23 million metric tons of mining stamp sands were deposited between 1901 and 1932, which have been eroding into Lake Superior. We have enhanced a depth-invariant index algorithm to demonstrate how multispectral CASI imagery can be used to map underwater areas of remaining native sand vs. deposited stamp sands. We have also developed methods to analyze LiDAR waveforms to detect patterns of varying bottom types. To enhance mapping of the lake bottom, we have developed a sun-glint and surface wave removal algorithm. We have demonstrated a method of fusing CASI imagery with LiDAR data to improve classification of nearshore wetland and forested areas. Using the 2008 LiDAR data to calculate stamp sand height and volume along with historical aerial imagery, we estimate that approximately 8.6 million metric tons of stamp sands remain on-shore as of 2008 and 14 million metric tons have been deposited into the nearshore area that includes known fishery spawning grounds