Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environments

Narron, C. R.; O’Connell, Jessica L.; Mishra, Deepak R.; Cotten, David L.; Hawman, Peter A.; Mao, Lishen

doi:10.1016/j.ecolind.2022.109045

Cited by 19 publications

(10 citation statements)

References 35 publications

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“…In 2021, the global mean sea level experienced the highest annual average increase (3.8 inches above 1993 levels) in the entire satellite record 2 . Such increases in global mean sea level, accompanied by increasingly unpredictable regional weather patterns (extreme precipitation and storm events), are changing the flooding dynamics (frequency, magnitude, duration) in salt marshes [3][4][5] . However, research suggests these changes in flooding dynamics in salt marshes are spatially complex and dependent on nonlinear feedback between local and regional geomorphology and ecosystem production.…”

Section: Technical Notementioning

confidence: 99%

“…The result of which is a reduction in near-infrared reflectance that is dependent on inundation depth and species and can range from 7-90% compared to non-flooded conditions 7 . Spatial inundation patterns also vary across the marsh surface 5 , leading to higher uncertainty in marsh extent analysis. Areas experiencing more frequent flooding are at risk of being labeled as a loss when, in fact, they are low marsh areas experiencing more frequent and higher tidal flooding levels 8 .…”

Section: Technical Notementioning

confidence: 99%

“…To demonstrate these uncertainties, we conducted a test comparing the impact of tidal signal on loss and gain of salt marsh area estimations in Landsat pixels following the same temporal intervals presented in Campbell et al 1 (Figure 1). We measured per-pixel differences in epoch averaged normalized vegetation index (NDVI) for data with no tide filtering as used in Campbell et al 1 and with tidal filtering 5 . The data were reduced by an average of 38% per pixel for each epoch after tide filtering.…”

Section: Technical Notementioning

confidence: 99%

“…In Campbell et al 1 , the authors restricted their analysis to peak biomass months (August-September). However, some regions experience high seasonal variability in flooding frequency and magnitude, and depending on the location, these months may experience different flooding patterns and intensities 5 . Because tidal flooding magnitude and duration are highly variable across space at local 8 and regional to global scales 10 , uncertainties resulting from tide contaminated pixels will vary in global studies.…”

Section: Technical Notementioning

confidence: 99%

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Addressing Tidal Flooding Induced uncertainties in Satellite Derived Global Salt Marsh Change Studies: Impact on Blue Carbon Monitoring

Mishra¹,

Hawman²

2023

Preprint

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In the past few years, it has been established that remote sensing of wetlands change analysis studies would require careful examination of tidal flooding produced uncertainties in satellite datasets. Tide flagging and filtering mechanisms are required to reduce per-pixel variabilities and errors in change detection. We present a technical note summarizing the potential impact of a lack of tide flagging mechanism in a recent study by Campbell et al. entitled “Global hotspots of salt marsh change and carbon emissions”. Through this note, we highlight the presence of potentially large uncertainties and spatio-temporal variabilities in their wetland change analysis map products propagating to their final carbon emission estimations.

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Section: Technical Notementioning

confidence: 99%

Section: Technical Notementioning

confidence: 99%

Section: Technical Notementioning

confidence: 99%

Section: Technical Notementioning

confidence: 99%

See 2 more Smart Citations

Addressing Tidal Flooding Induced uncertainties in Satellite Derived Global Salt Marsh Change Studies: Impact on Blue Carbon Monitoring

Mishra¹,

Hawman²

2023

Preprint

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“…For example, the Tidal Marsh Inundation Index (TMII) was developed, which implements MODIS (250 m) images in the salt marsh ecosystems of Georgia and the United States Gulf Coast [129]. Similarly, a model (Flooding in Landsat Across Tidal Systems (FLATS)) has been developed to detect flooding using Landsat (30 m) images in a salt marsh ecosystem [130].…”

Section: Remote-sensing-based Subsurface Soc Prediction Model Framewo...mentioning

confidence: 99%

Remote Sensing of Surface and Subsurface Soil Organic Carbon in Tidal Wetlands: A Review and Ideas for Future Research

et al. 2022

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Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r2 = 0.68 and 0.20, respectively, p < 2.2 × 10−16 for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.

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Integrating Remote Sensing with Ground-based Observations to Quantify the Effects of an Extreme Freeze Event on Black Mangroves (Avicennia germinans) at the Landscape Scale

Martinez,

Osland,

Grace

et al. 2023

Ecosystems

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Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environments

Cited by 19 publications

References 35 publications

Addressing Tidal Flooding Induced uncertainties in Satellite Derived Global Salt Marsh Change Studies: Impact on Blue Carbon Monitoring

Addressing Tidal Flooding Induced uncertainties in Satellite Derived Global Salt Marsh Change Studies: Impact on Blue Carbon Monitoring

Remote Sensing of Surface and Subsurface Soil Organic Carbon in Tidal Wetlands: A Review and Ideas for Future Research

Integrating Remote Sensing with Ground-based Observations to Quantify the Effects of an Extreme Freeze Event on Black Mangroves (Avicennia germinans) at the Landscape Scale

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