scholarly journals Predicting barrier island habitats and oyster and seagrass habitat suitability for various restoration measures and future conditions for Dauphin Island, Alabama

2020 ◽  
Keyword(s):  
Habitat Suitability ◽  
Barrier Island ◽  
Seagrass Habitat ◽  
Island Habitats ◽  
Restoration Measures

scholarly journals Modeling Barrier Island Habitats Using Landscape Position Information

2019 ◽  
Vol 11 (8) ◽  
pp. 976
Author(s):  
Nicholas M. Enwright ◽  
Lei Wang ◽  
Hongqing Wang ◽  
Michael J. Osland ◽  
Laura C. Feher ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Barrier Island ◽  
Barrier Islands ◽  
Machine Learning Algorithms ◽  
Landscape Position ◽  
K Nearest Neighbor ◽  
Island Habitats

Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.

Modeling benthic solar exposure (UV and visible) in dynamic coastal systems to better inform seagrass habitat suitability

2021 ◽  
pp. 151481
Author(s):  
Olivia Cronin-Golomb ◽  
Joshua P. Harringmeyer ◽  
Matthew W. Weiser ◽  
Xiaohui Zhu ◽  
Nilotpal Ghosh ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Seagrass Habitat ◽  
Solar Exposure ◽  
Coastal Systems

Using Bobcat Habitat Suitability to Prioritize Habitat Preservation on a Suburbanizing Barrier Island

2010 ◽  
Vol 74 (3) ◽  
pp. 386-394 ◽  
Author(s):  
Shane B. Roberts ◽  
James D. Jordan ◽  
Pete Bettinger ◽  
Robert J. Warren
Keyword(s):  
Habitat Suitability ◽  
Barrier Island

scholarly journals Seagrass habitat suitability model for Redang Marine Park using multibeam echosounder data: Testing different spatial resolutions and analysis window sizes

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257761
Author(s):  
Muhammad Abdul Hakim Muhamad ◽  
Rozaimi Che Hasan ◽  
Najhan Md Said ◽  
Jillian Lean-Sim Ooi
Keyword(s):  
Spatial Resolution ◽  
Habitat Suitability ◽  
Prediction Models ◽  
Seagrass Habitat ◽  
Multibeam Echosounder ◽  
Marine Park ◽  
Marine Habitats ◽  
Fringing Reefs ◽  
Analysis Window ◽  
Resolution Dataset

Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.

scholarly journals Shorebird Use of Coastal Wetland and Barrier Island Habitat in the Gulf of Mexico

2002 ◽  
Vol 2 ◽  
pp. 514-536 ◽  
Author(s):  
Kim Withers
Keyword(s):  
Gulf Of Mexico ◽  
Coastal Wetland ◽  
Gulf Coast ◽  
Barrier Island ◽  
Rio Grande ◽  
Florida Keys ◽  
Sandy Beaches ◽  
Temporary Ponds ◽  
Microhabitat Use ◽  
Island Habitats

The Gulf Coast contains some of the most important shorebird habitats in North America. This area encompasses a diverse mixture of estuarine and barrier island habitats with varying amounts of freshwater swamps and marshes, bottomland hardwood forests, and coastal prairie that has been largely altered for rice and crawfish production, temporary ponds, and river floodplain habitat. For the purposes of this review, discussion is confined to general patterns of shorebird abundance, distribution, and macro- and microhabitat use in natural coastal, estuarine, and barrier island habitats on the Gulf of Mexico Coast. The following geographic regions are considered: Northwestern Gulf (Rio Grande to Louisiana-Mississippi border), Northeastern Gulf (Mississippi to Florida Keys), and Mexico (Rio Grande to Cabo Catoche [Yucatan Strait]).Wintering and migrating shorebirds are most abundant along the Gulf Coast in Texas and Tamaulipas, particularly the Laguna Madre ecosystem. Other important areas are the Southwest Coast region of Florida and the area between Laguna Terminos and Puerto Progresso in Mexico. In general, relative abundances of shorebirds increase from north to south, and decrease south of the Tropic of Cancer (23° 27’ N). Based on bimonthly maximum counts within 5° latitudinal bands, the region between 25–30° N is used most heavily by wintering and spring migrating birds.Non-vegetated coastal wetland habitats associated with bays, inlets and lagoons, particularly tidal flats, and sandy beaches are the habitats that appear to be favored by wintering and migrating shorebirds. In general, these habitats tend to occur as habitat complexes that allow for movement between them in relation to tidal flooding of bay-shore habitats. This relationship is particularly important to Piping Plover and may be important to others.Although vegetated habitats are used by some species, they do not appear to attract large numbers of birds. This habitat is most widespread between the Texas-Louisiana border and the Florida Panhandle region, but it has not been studied extensively. Shorebird abundance and habitat use in this area need to be addressed.

scholarly journals SEAGRASS HABITAT SUITABILITY MAP AT MERAMBONG SHOAL, JOHOR: A PRELIMINARY STUDY USING MULTIBEAM ECHOSOUNDER AND MAXENT MODELLING

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 463-470
Author(s):  
M. A. H. Muhamad ◽  
R. Che Hasan
Keyword(s):  
High Resolution ◽  
Habitat Suitability ◽  
Species Distribution Modelling ◽  
Seagrass Habitat ◽  
Habitat Suitability Model ◽  
Survey Area ◽  
Multibeam Echosounder ◽  
Suitability Map ◽  
Habitat Suitability Map ◽  
Northwest Region

Abstract. In recent years, there has been an increasing interest to use high-resolution multibeam dataset and Species Distribution Modelling (SDM) for seagrass habitat suitability model. This requires a specific variable derived from multibeam data and in-situ seagrass occurrence samples. The purpose of this study was (1) to derive variables from multibeam bathymetry data to be used in seagrass habitat suitability model, (2) to produce seagrass habitat suitability model using Maximum Entropy (MaxEnt), and (3) to quantify the contribution of each variable for predicting seagrass habitat suitability map. The study area was located at Merambong Shoal, covering an area of 0.04 km2, situated along Johor Strait. First, twelve (12) variables were derived from bathymetry data collected from multibeam echosounder using Benthic Terrain Modeller (BTM) tool. Secondly, all variables and seagrass occurrence samples were integrated in MaxEnt to produce seagrass habitat suitability map. The results showed that the Area Under Curve (AUC) values based on training and test data were 0.88 and 0.65, respectively. The northwest region of survey area indicated higher habitat suitability of seagrass, while the southeast region of survey area indicated lower suitability. Bathymetry mean found to be the most contributed variables among others. The spatial distribution of seagrass from modelling technique agreed with the previous studies and they are found to be distributed at depths ranging from 2.2 to 3.4 meters whilst less suitable with increasing of water depth. This study concludes that seagrass habitat suitability map with high-resolution pixel size (0.5 meter) can be produced at Merambong Shoal using acoustic data from multibeam echosounder coupled with MaxEnt and underwater video observations.

Advancing barrier island habitat mapping using landscape position information

2019 ◽  
Vol 43 (3) ◽  
pp. 425-450 ◽  
Author(s):  
Nicholas M Enwright ◽  
Lei Wang ◽  
Sinéad M Borchert ◽  
Richard H Day ◽  
Laura C Feher ◽  
...  
Keyword(s):  
Barrier Island ◽  
Water Levels ◽  
Habitat Mapping ◽  
Storm Water ◽  
Landscape Position ◽  
Visual Interpretation ◽  
Position Information ◽  
Episodic Events ◽  
Object Based ◽  
Island Habitats

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.

Investigation of Morphosedimentary Processes on a Schematic Louisiana Barrier Island Using Process-Based Numerical Modeling

2007 ◽  
Author(s):  
T. Campbell ◽  
B. de Sonneville ◽  
L. Benedet ◽  
D. J. W. Walstra ◽  
C. W. Finkl
Keyword(s):  
Numerical Modeling ◽  
Barrier Island
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