Fine-Scale Mapping of Coastal Plant Communities in the Northeastern USA

Correll, Maureen D.; Hantson, Wouter; Hodgman, Thomas P.; Cline, Brittany; Elphick, Chris S.; Shriver, W. Gregory; Tymkiw, Elizabeth; Olsen, Brian J.

doi:10.1007/s13157-018-1028-3

Cited by 32 publications

(24 citation statements)

References 57 publications

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“…Approximately 60% of the marsh in the study area remains unditched, comprising a present-day vegetated marsh area of 5,5060 Ha which occurs primarily in lagoonal marsh areas disconnected from the mainland and barrier islands (Figure 1). Estimates of 2015-salt marsh, open water (6,789.4 Ha) and pond area (502.2 Ha) was similar to Correll (2018) where remote sensing-derived area estimates were 8,240.3 Ha (salt marsh), 7093.5 Ha (open water) and 471.2 Ha (pond) using the same imagery. The predominant source of marsh loss (73% of total loss) was edge erosion, with 745.4 ± 80 Ha converting to open water (Figure 2).…”

Section: Resultssupporting

confidence: 66%

Minimal net loss of vegetation via interior pond dynamics in an extensive unditched salt marsh

Smith

Pellew

2020

Preprint

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Ponds in salt marshes are often interpreted as a symptom of degradation, yet ponds can also be part of a cyclical process of pool formation, expansion, tidal breaching and vegetation recovery. Pond dynamics may be altered by accelerated sea level rise, with consequences for the long-term stability of ecosystems. We test the prediction that ponds are in dynamic equilibrium across one the largest expanses of unditched salt marsh in the Northeast USA by (1) examining change in pond and marsh area between 1970 and present and (2) by tracking individual pool dynamics across an 87-year time series. We found that net pond area has remained unchanged since 1970 because the amount of marsh conversion to ponds is equivalent to the amount pond recovery to marsh. The ratio of tidally-connected ponds is increasing relative to non-tidal ponds which suggests that some rates of change may be decoupling, which may be related to a decline in the rate of pond formation. A nuanced understanding of marsh pools needs to be incorporated into marsh condition assessments and establishment of restoration priorities so that ponds are not interpreted as evidence of degradation when they are exhibiting a recovery cycle. Unditched marshes around the world are a rare resource that remains essential for advancing scientific understanding and serving as reference sites for restoration of marshes altered by past management.

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Section: Resultssupporting

confidence: 66%

Minimal net loss of vegetation via interior pond dynamics in an extensive unditched salt marsh

Smith

Pellew

2020

Preprint

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“…Additionally, our results showed confusion in some classes that could be improved with more consideration of the operational characteristics (e.g., invasive plant control and removal efforts) in the study area. Utilizing the same methods applied in this study, invasive species detection can also be explored at different phenological and physiological stages, vegetation mixes, and treatment stages [115,117,118].…”

Section: Discussionmentioning

confidence: 99%

Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets

et al. 2022

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The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.

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“…After having separated the observations into two separate classes (sand or vegetation cover) based on the NDVI index, a Random Forest (RF) classifier [58] was considered for pixel-based classification, as shown in Figure 4. RF is a suitable algorithm for coastal vegetation classification, because of its stability and ability to discriminate ground cover differences [24,32,54,59,60]. When applying the RF classifier, the classification map is accompanied by an accuracy map, which contains the indicator of the confidence degree of the classification.…”

Section: Random Forestmentioning

confidence: 99%

Classification of Atlantic Coastal Sand Dune Vegetation Using In Situ, UAV, and Airborne Hyperspectral Data

et al. 2020

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Mapping coastal dune vegetation is critical to understand dune mobility and resilience in the context of climate change, sea level rise, and increased anthropogenic pressure. However, the identification of plant species from remotely sensed data is tedious and limited to broad vegetation communities, while such environments are dominated by fragmented and small-scale landscape patterns. In June 2019, a comprehensive multi-scale survey including unmanned aerial vehicle (UAV), hyperspectral ground, and airborne data was conducted along approximately 20 km of a coastal dune system in southwest France. The objective was to generate an accurate mapping of the main sediment and plant species ground cover types in order to characterize the spatial distribution of coastal dune stability patterns. Field and UAV data were used to assess the quality of airborne data and generate a robust end-member spectral library. Next, a two-step classification approach, based on the normalized difference vegetation index and Random Forest classifier, was developed. Results show high performances with an overall accuracy of 100% and 92.5% for sand and vegetation ground cover types, respectively. Finally, a coastal dune stability index was computed across the entire study site. Different stability patterns were clearly identified along the coast, highlighting for the first time the high potential of this methodology to support coastal dune management.

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Fine-Scale Mapping of Coastal Plant Communities in the Northeastern USA

Cited by 32 publications

References 57 publications

Minimal net loss of vegetation via interior pond dynamics in an extensive unditched salt marsh

Minimal net loss of vegetation via interior pond dynamics in an extensive unditched salt marsh

Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets

Classification of Atlantic Coastal Sand Dune Vegetation Using In Situ, UAV, and Airborne Hyperspectral Data

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