A digital polygon layer showing waterfowl concentration and staging areas along Maryland's tidal Chesapeake Bay watershed.

This data release contains three 10-meter resolution GeoTIFFs representing 10-meter (35-foot), 30-meter (100-foot) and 90-meter (300-foot) riparian buffer zones along shorelines, rivers, streams, and other lotic (flowing) water features. The layers are binary, where the value of each cell represents the presence or absence of the buffer zone. In addition, the data release contains shapefile layers that document the extent of corrections that were made to the data to address errors in the stream network (see processing steps section for more details).. The methodology combines various fine-scale input layers, including a 1:24k stream network and Chesapeake Bay 1-meter resolution Land Use/Land Cover to approximate banks of stream channels and waterbodies to better define the riparian zone (CBP, 2023; Hopkins and others, 2020). For shorelines and large rivers, the width of the buffer zone (10, 30, 90 meters) begins at the banks, where land meets water. For finer scale (1:24k) stream features, the buffer zone includes both water and riparian land area, where the buffered width begins at the estimated top of bank. Each 10-meter resolution riparian buffer zone GeoTIFF dataset is contained in an individual .zip file (CBW_riparian_10m_24k_2024.zip, CBW_riparian_30m_24k_2024.zip, CBW_riparian_90m_24k_2024.zip). The shapefile layers that contain the data correction extents are available in the correction_layers.zip file.

U.S. Geological Survey (USGS) and Virginia Institute of Marine Science (VIMS) scientists conducted field data collection efforts during June 11th - 16th, 2020, using a combination of remote sensing technologies to map riverbank and wetland topography and vegetation at five sites in the Chesapeake Bay Region of Virginia. The five sites are located along the James, Severn, and York Rivers. The work was initiated to evaluate the utility of different remote sensing technologies in mapping river bluff and wetland topography and vegetation for change detection and sediment transport modeling. The USGS team collected Global Navigation Satellite System (GNSS), total station, and ground based lidar (GBL) data while the VIMS team collected aerial imagery using an Unmanned Aerial System (UAS). This data release contains shapefiles of the processed GNSS and total station data, point clouds in the form of lidar data exchange (las) files from the ground lidar data and aerial imagery produced via Structure from Motion (SfM).

Geologic, sediment texture, and physiographic zone maps characterize the sea floor of Vineyard and western Nantucket Sounds, Massachusetts. These maps were derived from interpretations of seismic-reflection profiles, high-resolution bathymetry, acoustic-backscatter intensity, bottom photographs, and surficial sediment samples. The interpretation of the seismic stratigraphy and mapping of glacial and Holocene marine units provided a foundation on which the surficial maps were created. This mapping is a result of a collaborative effort between the U.S. Geological Survey and the Massachusetts Office of Coastal Zone Management to characterize the surface and subsurface geologic framework offshore of Massachusetts.

Geologic, sediment texture, and physiographic zone maps characterize the sea floor of Vineyard and western Nantucket Sounds, Massachusetts. These maps were derived from interpretations of seismic-reflection profiles, high-resolution bathymetry, acoustic-backscatter intensity, bottom photographs, and surficial sediment samples. The interpretation of the seismic stratigraphy and mapping of glacial and Holocene marine units provided a foundation on which the surficial maps were created. This mapping is a result of a collaborative effort between the U.S. Geological Survey and the Massachusetts Office of Coastal Zone Management to characterize the surface and subsurface geologic framework offshore of Massachusetts.

U.S. Geological Survey (USGS) and Virginia Institute of Marine Science (VIMS) scientists conducted field data collection efforts during the week of April 8th - 14th, 2018, using a combination of remote sensing technologies to map riverbank and wetland topography and vegetation at four sites in the Chesapeake Bay Region of Virginia. The four sites are located along the James, Severn, and York Rivers. The work was initiated to evaluate the utility of different remote sensing technologies in mapping river bluff and wetland topography and vegetation for change detection and sediment transport modeling. The USGS team collected Global Navigation Satellite System (GNSS), total station, and ground based lidar (GBL) data while the VIMS team collected aerial imagery using an Unmanned Aerial System (UAS). This data release contains shapefiles of the processed GNSS and total station data, point clouds in the form of lidar data exchange (las) files from the ground lidar data and aerial imagery produced via Structure from Motion (SfM).

The Chesapeake Bay Estuary is the largest estuary in the United States and provides habitats for diverse wildlife and aquatic species, protects communities against flooding, reduces pollution to waterways, and supports local economies through commercial and recreational activities. In the Spring of 2018, the U.S. Geological Survey (USGS) Coastal National Elevation Database (CoNED) Applications Project at the USGS Earth Resources Observation and Science (EROS) Center and the Virginia Institute of Marine Science (VIMS) Center for Coastal Resources Management (CCRM) initiated collaborative work. The goal of this collaboration is to evaluate how various remote sensing technologies can be employed to model estuarine riverbank topography and measure volumetric change in riverbanks for downstream sediment transport modeling for Chesapeake Bay. Additional science interests for this USGS CoNED and VIMS CCRM collaboration include understanding the spatial extent and variation within tidal wetland boundaries, comparing microtopographic changes of protected/stabilized living shorelines versus natural shorelines, and examining riverine and estuarine land/water interface transitions between topography and bathymetry. The remote sensing technologies investigated in this collaboration include airborne lidar, ground based lidar (GBL), Structure from Motion (SfM) processing of high-resolution imagery, and Satellite Derived Bathymetry (SDB) produced from Landsat 8/9, Sentinel-2, and/or WorldView imagery. Long-term field study sites have been established by VIMS CCRM along the James, Severn, and York Rivers in the Chesapeake Bay Region, with the goal of returning to the sites biannually. The following child pages describe and contain the field data collected during these biannual efforts.

This dataset is comprised of six files related to waterbird surveys and resulting Index of Waterbird Community Integrity (IWCI) scores in 21 subestuaries of the Chesapeake Bay from 2010-2014. Two .csv files (1 data file: Prosser et al 2017_IWCI Results MS_Bird Survey Raw Data.csv, 1 definitions file: Prosser et al 2017_IWCI Results MS_Bird Survey Raw Data_Definitions.csv) contain data related to raw waterbird survey data from two seasons (summer and fall). Two .csv files (1 data file: Prosser et al 2017_IWCI Results MS_Species List.csv, 1 definitions file: Prosser et al 2017_IWCI Results MS_Species List_Definitions.csv) contain data related to 60+ Chesapeake waterbird species observed in surveys and their species attribute and IWCI scores. Two .csv files (1 data file: Prosser et al 2017_IWCI Results MS_Scores & Delineations by Site.csv, 1 definitions file: Prosser et al 2017_IWCI Results MS_Scores & Delineations by Site_Definitions.csv) contain data related to IWCI scores and shoreline/land use delineations for the 21 subestuaries of the Chesapeake Bay.

The Louisiana State Legislature created the Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) in order to conserve, restore, create and enhance Louisiana's coastal wetlands. The wetland restoration plans developed pursuant to these acts specifically require an evaluation of the effectiveness of each coastal wetlands restoration project in achieving long-term solutions to arresting coastal wetlands loss. This data set includes mosaicked aerial photographs for the Oyster Bayou Marsh Creation and Terracing (CS-59) project for 2018. This data is used as a basemap land-water classification. It also serves as a visual tool for project managers to help them identify any obvious problems or land loss within their project boundary. To better evaluate the effectiveness of restoration efforts, a land-water classification is performed on specific CWPPRA sites to help assess landscape changes.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.