Characterizing and mapping coastal vegetation on a sea island in the southeastern USA

Questions : Our objectives were to classify and map coastal vegetation types on a sea island that is undergoing rapid change from storm surge intensification and sea-level rise (SLR). Study area : The Marine Corps Recruit Depot Parris Island (MCRDPI) is a sea island in South Carolina, USA, dominated by tidal salt marshes with large, inland forested areas and developed areas supporting Marine Corps training activities. The climate is humid subtropical. Methods : We established 57 permanent vegetation plots within all the major coastal vegetation types present on the island, made detailed plant community measurements, and characterized the main environmental variables associated with each vegetation type. We employed cluster analysis and non-metric multidimensional scaling ordination to delineate these types based on vegetation plot data and then cross-walked those types onto existing associations within the United States National Vegetation Classification (USNVC). Finally, supervised deep learning classification was employed on 0.5 m high-resolution RBG imagery to map the distribution of vegetation types and other land cover types. Results : We identified four marsh associations and eight forest/woodland associations. The distributions of these vegetation types were primarily related to salinity and elevation. In addition to these 12 vegetation types, we mapped 11 additional land cover types for a total of 23 distinct classes. The marsh types dominated the landscape, covering ~57% of the classified area, while forest types covered ~13%. Accuracy assessment yielded an overall accuracy of 62% and a Kappa statistic of 0.6. Conclusions : We identified 11 existing USNVC vegetation types that are widely distributed in the southeastern USA and mapped their spatial distribution. The permanent vegetation plots and vegetation map provide fundamental baseline information that can be used to monitor the effects of climate change on coastal vegetation, evaluate wildlife habitat use by species of conservation concern, and facilitate decision-making in a region that has been identified as a global hotspot of salt marsh loss. Taxonomic reference : Weakley (2022). Syntaxonomic reference : USNVC (2025). Abbreviations : DBH = diameter at breast height; MCRDPI = Marine Corps Recruit Depot Parris Island; ROI = Regions of Interest; SLR = Sea-level rise; TEC = total exchange capacity; USNVC = United States National Vegetation Classification.

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Publication Details

Journal
Vegetation Classification and Survey
Published
2025-12-23
DOI
https://doi.org/10.3897/vcs.157787
Citations
1
Primary Topic
Rangeland and Wildlife Management
Type
article
Field-Weighted Citation Impact
1.72
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article

Characterizing and mapping coastal vegetation on a sea island in the southeastern USA

Kyle A. Palmquist, Anne C. Axel, Pamela Puppo, John Holloway et al.
1 citations
Vegetation Classification and Survey
Rangeland and Wildlife Management
1.72
article

Characterizing and mapping coastal vegetation on a sea island in the southeastern USA

Kyle A. Palmquist, Anne C. Axel, Pamela Puppo, John Holloway, Peyton Debowsky, Cody Goodson, Zak H. Bartholomew
article en
1 citations

Abstract

Questions : Our objectives were to classify and map coastal vegetation types on a sea island that is undergoing rapid change from storm surge intensification and sea-level rise (SLR). Study area : The Marine Corps Recruit Depot Parris Island (MCRDPI) is a sea island in South Carolina, USA, dominated by tidal salt marshes with large, inland forested areas and developed areas supporting Marine Corps training activities. The climate is humid subtropical. Methods : We established 57 permanent vegetation plots within all the major coastal vegetation types present on the island, made detailed plant community measurements, and characterized the main environmental variables associated with each vegetation type. We employed cluster analysis and non-metric multidimensional scaling ordination to delineate these types based on vegetation plot data and then cross-walked those types onto existing associations within the United States National Vegetation Classification (USNVC). Finally, supervised deep learning classification was employed on 0.5 m high-resolution RBG imagery to map the distribution of vegetation types and other land cover types. Results : We identified four marsh associations and eight forest/woodland associations. The distributions of these vegetation types were primarily related to salinity and elevation. In addition to these 12 vegetation types, we mapped 11 additional land cover types for a total of 23 distinct classes. The marsh types dominated the landscape, covering ~57% of the classified area, while forest types covered ~13%. Accuracy assessment yielded an overall accuracy of 62% and a Kappa statistic of 0.6. Conclusions : We identified 11 existing USNVC vegetation types that are widely distributed in the southeastern USA and mapped their spatial distribution. The permanent vegetation plots and vegetation map provide fundamental baseline information that can be used to monitor the effects of climate change on coastal vegetation, evaluate wildlife habitat use by species of conservation concern, and facilitate decision-making in a region that has been identified as a global hotspot of salt marsh loss. Taxonomic reference : Weakley (2022). Syntaxonomic reference : USNVC (2025). Abbreviations : DBH = diameter at breast height; MCRDPI = Marine Corps Recruit Depot Parris Island; ROI = Regions of Interest; SLR = Sea-level rise; TEC = total exchange capacity; USNVC = United States National Vegetation Classification.

Vegetation Classification and SurveyVol. 6
Island Hospital (US), Marshall University (US)
Life below water
Openalex Percentile: Top 11%
Rangeland and Wildlife Management
1.72
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