Living Shoreline Suitability Model for Tampa Bay: A GIS Approach

By Chris Boland

Because of the threat of shoreline erosion from strong storm action and sea level rise affecting waterfront property values, considerable attention has been focused on shoreline protection.  In the recent past, shorelines have been “stabilized with hardened structures, such as bulkheads, revetments, and concrete seawalls.  Ironically, these structures often increase the rate of coastal erosion, remove the ability of the shoreline to carry out natural processes, and provide little habitat for estuarine species.”[1]  Alternatively, government agencies responsible for resource protection have proposed more natural bank stabilization and erosion control called “living shorelines,” which NOAA defines as: “… a range of shoreline stabilization techniques along estuarine coasts, bays, sheltered coastlines, and tributaries… [that]… incorporates [natural] vegetation or other living, natural ‘soft’ elements alone or in combination with some type of harder shoreline structure (e.g. oyster reefs or rock sills) for added stability… [to] maintain continuity of the natural land-water interface and reduce erosion while providing habitat value and enhancing coastal resilience.”[2]

Figure 1

FWRI’s Center for Spatial Analysis (CSA) has taken an interest in living shorelines in the Tampa Bay region and, as a state partner in the Gulf of Mexico Alliance (GOMA), became aware of the Virginia Institute of Marine Science’s (VIMS) Living Shoreline Suitability Model (LSSM)[3] and its application in Mobile Bay, Alabama.[4]  VIMS developed the LSSM in ESRI’s ArcGIS Model Builder based on a decision tree that can assist in identifying appropriate living shoreline treatments to an area (Figure 1).  Because of the LSSM’s success in identifying locations where a living shoreline restoration project may be effective, CSA’s Kathleen O’Keife and Chris Boland received grant funding from GOMA’s Habitat Resources Team (HRT) to apply the LSSM to the Tampa Bay region.

The LSSM requires information about existing environmental conditions to correctly apply the decision tree, such as existing habitat, slope of coastal waters, environmental conditions (e.g. fetch, current speed, and sunlight shading), and potential construction barriers (e.g. nearby road or permanent structures).  The recently updated (June 2016) environmental sensitivity index (ESI) dataset, originally collected for oil spill response purposes, answered many of these required criteria and so became CSA’s base input dataset to the model.  CSA staff spent approximately four months of full-time work to manually review each of the 5,162 shoreline segments, which ranged in length from about 100 feet to about 500 feet and classified the remaining required data fields appropriately.

Once completed, the LSSM model was run based upon the derived input dataset and completed in less than an hour.  The model outputs resulted in additional fields that provide property owners and management entities with suggested Upland Best Management Practices (BMP) and Shoreline BMPs. [5]  The results are displayed in Figures 2 and 3.  Overall, the modified LSSM recommended the installation of a living shoreline to approximately 33% of the shoreline, protection from a “harder” landscape protection method to about 11% of the shoreline, and was unable to recommend a BMP to the rest (56%) Tampa Bay area’s shoreline, typically because the installation of a living shoreline would be obstructed by an existing shoreline condition.

Figure 2
Figure 3

The model results can be reviewed in CSA’s educational materials that were developed as grant deliverables.  The ArcGIS Online story map ( was developed to inform the general public of the use of living shorelines as a shoreline protection alternative, and the Web Mapping Application ( was intended to assisting managers in identifying potential preservation and mitigation areas.

[1] (National Oceanic and Atmospheric Administration, n.d.)

[2] (National Oceanic and Atmospheric Administration (NOAA), 2015)

[3] (College of William and Mary: Virginia Institute Of Marine Science: Center for Coastal Resource Management, 2018)

[4] (Woodrey, 2016)

[5] (VIMS: Center for Coastal Resource Management Program, 2015)