Woods Hole Coastal and Marine Science Center

Map showing the surficial geology from this study and Baldwin and others (2016) with equivalent onshore geology (adapted from Stone and DiGiacomo-Cohen, 2009). The areal extents over which offshore subsurface geologic units crop out at the sea floor were interpreted from seismic-reflection data.

The beach-dependent shorebirds project at the Woods Hole Coastal and Marine Science Center models current and future habitat availability for nesting shorebirds in an effort to map current and likely future habitat availability on a range of sites along the U.S. Atlantic coast. Sites include beaches with minimal human-presence, such Cedar Island shown here off of the

Map of the Martha’s Vineyard and Nantucket study sites outlined in red.

Study site map for the Massachusetts Shoreline Change Project, 2018 Update: A GIS Compilation of Shoreline Change Rates Calculated Using Digital Shoreline Analysis System Version 5.0, With Supplementary Intersects and Baselines for Massachusetts

SEABed Observation and Sampling System (SEABOSS) (center image) and the MiniSEABOSS (right) designed for rapid, inexpensive, and effective collection of seabed imagery (photographs and video) as well sediment samples from the coastal/inner-continental shelf regions.  Images from coastal Massachusetts show purple algae-encrusted cobbles and boulders covered with sea-stars (

3D Image of a multi-channel seismic (MCS) line showing gas (blue/green) migrating up through fractures in the subsurface, culminating in a 600 meter tall plume of methane gas in the water column that was captured using a Simrad EK60 split beam echo sounder.  Background bathymetry was downloaded from USGS Open-File Report 2012-1266 (

Three-dimensional model of Chimney Bluffs, New York along Lake Ontario created from low-altitude digital images collected from an unmanned aerial system (UAS).

Displaying fixed bin options (left) for rates of change for an example dataset (LRR, EPR, WLR), and scaled to data (right) (NSM).

Data visualization with (A) fixed and (B) scaled options applied. Examples of NSM and SCE are also displayed (C and D respectively). The data itself has not changed – only the scaling and statistic selected through the DSAS Data Visualization tool.

 Example of a section of DSAS summary report highlighting the use of DSAS_group to organize output statistics.

The distribution of sediment textures within the study area. The bottom-type classification is from Barnhardt and others (1998) and is based on 16 sediment classes. The classification is based on four sediment units that include gravel (G), mud (M), rock (R), and sand (S). If the texture is greater than 90 percent, it is labeled with a single letter. If the composition of