Advancing barrier island habitat mapping using landscape position information

Abstract

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.

Modeling Barrier Island Habitats Using Landscape Position Information

“…The island is backed by the shallow (<4 m deep) Mississippi Sound [37] and is flanked to the east by Mobile Bay. In 2015, the length of Dauphin Island was about 25 km, and the subaerial portion of the island was estimated to be about 15.8 km 2 [28]. Situated in the northern Gulf of Mexico, Dauphin Island experiences diurnal tides with a mean tidal range of about 0.36 m (i.e., mean low water to mean high water), based on observations during the most recent North American Tidal Datum Epoch (NTDE; 1983 to 2001) from a National Oceanic and Atmospheric Administration tide gauge (station ID: 8735180) on the island.…”

“…We set our model targets to be generalized habitat classes from a geomorphology-based habitat classification scheme that was recently used for a 2015 Dauphin Island habitat map [28] (Figure 3; Table A1). The habitat generalizations we applied involved combining habitat classes that may occupy the same geomorphic setting, yet are regulated by factors we did not include in our model, such as disturbance.…”

“…The developed class was not shown since it was not explicitly modeled in this effort. Adapted from Enwright et al [28] with permission.…”

“…These islands are found along portions of every continent, except Antarctica [2], and provide numerous important ecosystem services including storm surge reduction, wave attenuation, erosion control to the mainland, habitat for fish and wildlife, carbon sequestration in marshes, vertical uncertainty within intertidal areas. Relative topography can be helpful for extracting dune habitats [21,28,33], yet elevation relative to storm water levels can also be a factor, as dunes can be eroded by high wave runup during storms [5]. Thus, elevation uncertainty analyses could also be used to evaluate the likelihood of areas depicted by a DEM being above a common extreme storm water level [28].…”

“…Relative topography can be helpful for extracting dune habitats [21,28,33], yet elevation relative to storm water levels can also be a factor, as dunes can be eroded by high wave runup during storms [5]. Thus, elevation uncertainty analyses could also be used to evaluate the likelihood of areas depicted by a DEM being above a common extreme storm water level [28]. Data-driven, machine learning algorithms are powerful tools for identifying patterns and relationships in data; however, one potential issue is that they require the assumption that the data used to train the model is representative of the phenomena being modeled.…”