Coastal wetland ecosystems are expected to migrate landwards in response to rising seas. However, due to differences in topography and coastal urbanization, estuaries vary in their ability to accommodate migration. Low‐lying urban areas can constrain migration and lead to wetland loss (i.e. coastal squeeze), especially where existing wetlands cannot keep pace with rising seas via vertical adjustments. In many estuaries, there is a pressing need to identify landward migration corridors and better quantify the potential for landward migration and coastal squeeze. We quantified and compared the area available for landward migration of tidal saline wetlands and the area where urban development is expected to prevent migration for 39 estuaries along the wetland‐rich USA Gulf of Mexico coast. We did so under three sea level rise scenarios (0.5, 1.0, and 1.5 m by 2100). Within the region, the potential for wetland migration is highest within certain estuaries in Louisiana and southern Florida (e.g. Atchafalaya/Vermilion Bays, Mermentau River, Barataria Bay, and the North and South Ten Thousand Islands estuaries). The potential for coastal squeeze is highest in estuaries containing major metropolitan areas that extend into low‐lying lands. The Charlotte Harbor, Tampa Bay, and Crystal‐Pithlachascotee estuaries (Florida) have the highest amounts of urban land expected to constrain wetland migration. Urban barriers to migration are also high in the Galveston Bay (Texas) and Atchafalaya/Vermilion Bays (Louisiana) estuaries. Synthesis and applications. Coastal wetlands provide many ecosystem services that benefit human health and well‐being, including shoreline protection and fish and wildlife habitat. As the rate of sea level rise accelerates in response to climate change, coastal wetland resources could be lost in areas that lack space for landward migration. Migration corridors are particularly important in highly urbanized estuaries where, due to low‐lying coastal development, there is not space for wetlands to move and adapt to sea level rise. Future‐focused landscape conservation plans that incorporate the protection of wetland migration corridors can increase the adaptive capacity of these valuable ecosystems and simultaneously decrease the vulnerability of coastal human communities to the harmful effects of rising seas.

Barrier islands are dynamic ecosystems that change gradually from coastal processes, including currents and tides, and rapidly from episodic events, such as storms. These islands provide many important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism. Habitat maps, developed by scientists, provide a critical tool for monitoring changes to these dynamic ecosystems. Barrier island monitoring often requires custom habitat maps due to several factors, including island size and the classification of unique geomorphology-based habitats, such as beach, dune, and barrier flats. In this study, we reviewed barrier-island-specific habitat mapping efforts and highlighted common habitat class types, source data, and mapping approaches. We also developed a framework for mapping geomorphology-based barrier island habitats using a rule-based, geographic object-based image analysis approach, which included the use of field data, tide data, high-resolution orthophotography, and lidar data. This framework integrates several barrier island mapping advancements with regard to the use of landscape position information for automated dune extraction and the use of Monte Carlo analyses for the treatment of elevation uncertainty for elevation-dependent habitats. Specifically, we used the uncertainty analyses to refine automated dune delineation based on elevation relative to extreme storm water levels and to increase the accuracy of intertidal and supratidal/upland habitat delineation. We found that dune extraction results were enhanced when elevation relative to storm water levels and visual interpretation were also applied. This framework could also be applied to beach–dune systems found along a mainland.

While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas. The uncertainty of digital elevation models is often left unaddressed; however, in low-relief environments, such as barrier islands, centimeter differences in elevation can affect exposure to physically demanding abiotic conditions, which greatly influence ecosystem structure and function. In this study, we used airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information to delineate low-lying lands and the intertidal wetlands on Dauphin Island, a barrier island along the coast of Alabama, USA. We compared three different elevation error treatments, which included leaving error untreated and treatments that used Monte Carlo simulations to incorporate elevation vertical uncertainty using general information from lidar metadata and site-specific Real-Time Kinematic Global Position System data, respectively. To aid researchers in instances where limited information is available for error propagation, we conducted a sensitivity test to assess the effect of minor changes to error and bias. Treatment of error with site-specific observations produced the fewest omission errors, although the treatment using the lidar metadata had the most well-balanced results. The percent coverage of intertidal wetlands was increased by up to 80% when treating the vertical error of the digital elevation models. Based on the results from the sensitivity analysis, it could be reasonable to use error and positive bias values from literature for similar environments, conditions, and lidar acquisition characteristics in the event that collection of site-specific data is not feasible and information in the lidar metadata is insufficient. The methodology presented in this study should increase efficiency and enhance results for habitat mapping and analyses in dynamic, low-relief coastal environments.

For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit https://www.usgs.gov or call 1-888-ASK-USGS.For an overview of USGS information products, including maps, imagery, and publications, visit https://store.usgs.gov.Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner. AbstractBarrier islands are dynamic environments due to their position at the land-sea interface. Storms, waves, tides, currents, and relative sea-level rise are powerful forces that shape barrier island geomorphology and habitats (for example, beach, dune, marsh, and forest). Hurricane Katrina in 2005 and the Deep Water Horizon oil spill in 2010 are two major events that have affected habitats and natural resources on Dauphin Island, Alabama. The latter event prompted a collaborative effort between the U.S. Geological Survey, the U.S. Army Corps of Engineers, and the State of Alabama funded by the National Fish and Wildlife Foundation to investigate viable, sustainable restoration options that protect and restore the natural resources of Dauphin Island, Alabama.In order to understand the feasibility and sustainability of various restoration scenarios, it is important to understand current conditions on Dauphin Island. To further this understanding, a detailed 19-class habitat map for Dauphin Island was produced from 1-foot aerial infrared photography collected on December 4, 2015, and lidar data collected in January 2015. We also conducted a ground survey of habitat types, vegetation community structure, and elevations in November and December 2015. These products provide baseline data regarding the ecological and general geomorphological attributes of the area, which can be compared with observations from other dates for tracking changes over time.

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