scholarly journals Habitat Mapping and Change Assessment of Coastal Environments: An Examination of WorldView-2, QuickBird, and IKONOS Satellite Imagery and Airborne LiDAR for Mapping Barrier Island Habitats

2014 ◽  
Vol 3 (1) ◽  
pp. 297-325 ◽  
Author(s):  
Matthew McCarthy ◽  
Joanne Halls
Keyword(s):  
Satellite Imagery ◽  
Barrier Island ◽  
Airborne Lidar ◽  
Habitat Mapping ◽  
Coastal Environments ◽  
Change Assessment ◽  
Island Habitats

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.

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.

Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR

2018 ◽  
Vol 65 ◽  
pp. 12-23 ◽  
Author(s):  
Collins B. Kukunda ◽  
Joaquín Duque-Lazo ◽  
Eduardo González-Ferreiro ◽  
Hauke Thaden ◽  
Christoph Kleinn
Keyword(s):  
Satellite Imagery ◽  
Airborne Lidar ◽  
Ensemble Classification ◽  
Multispectral Satellite Imagery
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