Limitations and Potential of Satellite Imagery to Monitor Environmental Response to Coastal Flooding

Ramsey, Elijah W.; Werle, Dirk; Suzuoki, Yukihiro; Rangoonwala, Amina; Lü, Zhong

doi:10.2112/jcoastres-d-11-00052.1

Cited by 29 publications

(38 citation statements)

References 23 publications

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“…To perform the calculation, the σ 0 data in decibels (dB) were converted to amplitude and a change detection algorithm applied in PCI Geomatica® (Ramsey et al 2011b(Ramsey et al , 2012(Ramsey et al , 2013. To help alleviate change artefacts not related to flooding, global minimum change thresholds were applied to each SAR image.…”

Section: Sar Surge Extent Mappingmentioning

confidence: 99%

“…Following an approach similar to Ramsey et al (2012), NDVI was used to determine condition change in marsh greenness (i.e. condition) after the storm.…”

Section: Mapping Marsh Conditionmentioning

confidence: 99%

“…The natural variability of marsh greenness was taken into account by including both pre-and pre-to post-NDVI changes in the k-means unsupervised classification (e.g. Ramsey et al 2012) using ERDAS IMAGINE®. The pre-NDVI and change-NDVI ranges were approximately equalized before classification by multiplying the change-NDVI magnitudes by 10.…”

Section: Mapping Marsh Conditionmentioning

confidence: 99%

See 2 more Smart Citations

Radar and optical mapping of surge persistence and marsh dieback along the New Jersey Mid-Atlantic coast after Hurricane Sandy

Rangoonwala

Enwright

Ramsey

et al. 2016

International Journal of Remote Sensing

Self Cite

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Section: Sar Surge Extent Mappingmentioning

confidence: 99%

“…Following an approach similar to Ramsey et al (2012), NDVI was used to determine condition change in marsh greenness (i.e. condition) after the storm.…”

Section: Mapping Marsh Conditionmentioning

confidence: 99%

Section: Mapping Marsh Conditionmentioning

confidence: 99%

See 1 more Smart Citation

Radar and optical mapping of surge persistence and marsh dieback along the New Jersey Mid-Atlantic coast after Hurricane Sandy

Rangoonwala

Enwright

Ramsey

et al. 2016

International Journal of Remote Sensing

Self Cite

View full text Add to dashboard Cite

“…Ramsey et al (2012) presented a summary of the limitations and potentials of satellite imagery to monitor and map coastal flooding for Hurricanes Gustav and Ike (2008), demonstrating that the correspondence between ground data and ASAR-based flood mapping ranged from 86% to 96% for water levels .0.8 m.…”

Section: Microwave Remote Sensing Of Flood Extentmentioning

confidence: 99%

“…Medium-resolution imagery is used to assess the overall damage of flooding over large areas, such as coastal wetlands or entire city blocks, and can be obtained with satellite multispectral/hyperspectral or radar imagers (Gianinetto, Villa, and Lechi, 2006;Ramsey et al, 2012;Shan et al, 2009).…”

Section: Flood Damage Assessmentmentioning

confidence: 99%

Modeling of Coastal Inundation, Storm Surge, and Relative Sea-Level Rise at Naval Station Norfolk, Norfolk, Virginia, U.S.A.

Lin

Burks-Copes

2013

Journal of Coastal Research

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Restoration and resilience to sea level rise of a salt marsh affected by dieback events

et al. 2023

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The frequency of salt marsh dieback events has increased over the last 25 years with unknown consequences to the resilience of the ecosystem to accelerated sea level rise (SLR). Salt marsh ecosystems impacted by sudden vegetation dieback events were previously thought to recover naturally within a few months to years. In this study, we used a 13-year collection of remotely sensed imagery to provide evidence that approximately 14% of total marsh area has not revegetated 10 years after a dieback event in Charleston, SC. Dieback onset coincided with a severe drought in 2012, as indicated by the Palmer drought stress index. A second dieback event occurred in 2016 after a historic flood influenced by Hurricane Joaquin in 2015. Unvegetated zones reached nearly 30% of the total marsh area in 2017. We used a light detection and ranging-derived digital elevation model to determine that most affected areas were associated with lower elevation zones in the interior of the marsh. Further, restoration by grass planting was effective, with pilot-scale restored plots having greater aboveground biomass than reference sites after two years of transplanting. A positive outcome indicated that the stressors that caused the dieback are no longer present. Despite that, many affected areas have not recovered naturally, even though they are located within the typical elevation range of healthy marshes. A mechanistic modeling approach was used to assess the effects of vegetation dieback on salt marsh resilience to SLR. Predictions indicate that a highly productive restored marsh (2000 g m −2 year −1 ) would persist at a moderate SLR rate of 60 cm in 100 years, whereas a nonrestored mudflat would lose all its elevation capital after 100 years. Thus, rapid restoration of marsh dieback is critical to avoid further degradation. Also, failure to incorporate the increasing frequency and intensity of extreme climatic events that trigger irreversible marsh diebacks underestimates salt marsh vulnerability to climate change. Finally, at an elevated SLR rate of 122 cm in 100 years, which is most likely an extreme climate change scenario, even highly productive ecosystems augmented by sediment placement would not keep pace with SLR. Thus,

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Limitations and Potential of Satellite Imagery to Monitor Environmental Response to Coastal Flooding

Cited by 29 publications

References 23 publications

Radar and optical mapping of surge persistence and marsh dieback along the New Jersey Mid-Atlantic coast after Hurricane Sandy

Radar and optical mapping of surge persistence and marsh dieback along the New Jersey Mid-Atlantic coast after Hurricane Sandy

Modeling of Coastal Inundation, Storm Surge, and Relative Sea-Level Rise at Naval Station Norfolk, Norfolk, Virginia, U.S.A.

Restoration and resilience to sea level rise of a salt marsh affected by dieback events

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