Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environments
Remote sensing can provide critical information about the health and productivity of coastal wetland ecosystems, including extent, phenology, and carbon sequestration potential. Unfortunately, periodic inundation from tides dampens the spectral signal and, in turn, causes remote sensing-based models...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X22005167 |
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| author | Caroline R. Narron Jessica L. O'Connell Deepak R. Mishra David L. Cotten Peter A. Hawman Lishen Mao |
| author_facet | Caroline R. Narron Jessica L. O'Connell Deepak R. Mishra David L. Cotten Peter A. Hawman Lishen Mao |
| author_sort | Caroline R. Narron |
| collection | DOAJ |
| description | Remote sensing can provide critical information about the health and productivity of coastal wetland ecosystems, including extent, phenology, and carbon sequestration potential. Unfortunately, periodic inundation from tides dampens the spectral signal and, in turn, causes remote sensing-based models to produce unreliable results, altering estimates of ecosystem function and services. We created the Flooding in Landsat Across Tidal Systems (FLATS) index to identify flooded pixels in Landsat 8 30-meter data and provide an inundated pixel filtering method. Novel applications of FLATS including inundation frequency and pattern detection are also demonstrated. The FLATS index was developed to identify flooding in Spartina alterniflora tidal marshes. We used ground truth inundation data from a PhenoCam and Landsat 8 pixels within the PhenoCam field of view on Sapelo Island, GA, USA to create the index. The FLATS index incorporates a normalized difference water index (NDWI) and a phenology-related variable into a generalized linear model (GLM) that predicted the presence or absence of marsh flooding. The FLATS equation for predicting flooding is 1-1e-1.6+20.0*NDWI4,6+68.6*Pheno3,4, and we found that a cutoff 0.1 was the optimized value for separating flooded and non-flooded pixel classes. FLATS identified flooded pixels with an overall accuracy of 96% and 93% across training data and novel testing data, respectively. FLATS correctly identified true flooded pixels with a sensitivity of 97% and 81%, across training and testing data, respectively. We established the need to apply FLATS when conducting vegetation time-series analysis in coastal marshes in order to reduce the per-pixel reflectance variations attributed to tidal flooding. We found that FLATS identified 12.5% of pixels as flooded in Landsat 8 tidal marsh vegetation time-series from 2013 to 2020, after traditional quality control and preprocessing steps were conducted, which could then be filtered out or modeled separately in order to conduct remotely sensed vegetation assessments. Therefore, in tidal wetlands, we recommend incorporating FLATS into Landsat 8 preprocessing prior to vegetation analysis. We also demonstrated innovative applications for the FLATS index, particularly in detecting flooding frequency and flooding patterns relevant to the broader biophysical modeling framework, including mapping marsh vulnerability due to fluctuation in inundation frequency. The FLATS index represents advancements in the understanding and application of inundation indices for coastal marshes. |
| first_indexed | 2024-12-12T01:40:50Z |
| format | Article |
| id | doaj.art-4dbd81c20dd348b38b09fe4b632a825c |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-12-12T01:40:50Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-4dbd81c20dd348b38b09fe4b632a825c2022-12-22T00:42:43ZengElsevierEcological Indicators1470-160X2022-08-01141109045Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environmentsCaroline R. Narron0Jessica L. O'Connell1Deepak R. Mishra2David L. Cotten3Peter A. Hawman4Lishen Mao5Department of Geography, University of Georgia, Athens, GA 30602-3636, United States; Corresponding author.Department of Geography, University of Georgia, Athens, GA 30602-3636, United States; Department of Marine Sciences, University of Georgia, Athens, GA 30602-3636, United States; Department of Marine Science, Marine Science Institute, University of Texas at Austin, Port Aransas, TX 78373-5015, United StatesDepartment of Geography, University of Georgia, Athens, GA 30602-3636, United StatesDepartment of Geography, University of Georgia, Athens, GA 30602-3636, United States; Oak Ridge National Laboratory, Oak Ridge, TN 37831-6420, United StatesDepartment of Geography, University of Georgia, Athens, GA 30602-3636, United StatesDepartment of Geography, University of Georgia, Athens, GA 30602-3636, United StatesRemote sensing can provide critical information about the health and productivity of coastal wetland ecosystems, including extent, phenology, and carbon sequestration potential. Unfortunately, periodic inundation from tides dampens the spectral signal and, in turn, causes remote sensing-based models to produce unreliable results, altering estimates of ecosystem function and services. We created the Flooding in Landsat Across Tidal Systems (FLATS) index to identify flooded pixels in Landsat 8 30-meter data and provide an inundated pixel filtering method. Novel applications of FLATS including inundation frequency and pattern detection are also demonstrated. The FLATS index was developed to identify flooding in Spartina alterniflora tidal marshes. We used ground truth inundation data from a PhenoCam and Landsat 8 pixels within the PhenoCam field of view on Sapelo Island, GA, USA to create the index. The FLATS index incorporates a normalized difference water index (NDWI) and a phenology-related variable into a generalized linear model (GLM) that predicted the presence or absence of marsh flooding. The FLATS equation for predicting flooding is 1-1e-1.6+20.0*NDWI4,6+68.6*Pheno3,4, and we found that a cutoff 0.1 was the optimized value for separating flooded and non-flooded pixel classes. FLATS identified flooded pixels with an overall accuracy of 96% and 93% across training data and novel testing data, respectively. FLATS correctly identified true flooded pixels with a sensitivity of 97% and 81%, across training and testing data, respectively. We established the need to apply FLATS when conducting vegetation time-series analysis in coastal marshes in order to reduce the per-pixel reflectance variations attributed to tidal flooding. We found that FLATS identified 12.5% of pixels as flooded in Landsat 8 tidal marsh vegetation time-series from 2013 to 2020, after traditional quality control and preprocessing steps were conducted, which could then be filtered out or modeled separately in order to conduct remotely sensed vegetation assessments. Therefore, in tidal wetlands, we recommend incorporating FLATS into Landsat 8 preprocessing prior to vegetation analysis. We also demonstrated innovative applications for the FLATS index, particularly in detecting flooding frequency and flooding patterns relevant to the broader biophysical modeling framework, including mapping marsh vulnerability due to fluctuation in inundation frequency. The FLATS index represents advancements in the understanding and application of inundation indices for coastal marshes.http://www.sciencedirect.com/science/article/pii/S1470160X22005167Tidal inundationSalt marshesFloodingSea level riseSpartina alternifloraCoastal wetland |
| spellingShingle | Caroline R. Narron Jessica L. O'Connell Deepak R. Mishra David L. Cotten Peter A. Hawman Lishen Mao Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environments Ecological Indicators Tidal inundation Salt marshes Flooding Sea level rise Spartina alterniflora Coastal wetland |
| title | Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environments |
| title_full | Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environments |
| title_fullStr | Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environments |
| title_full_unstemmed | Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environments |
| title_short | Flooding in Landsat across tidal systems (FLATS): An index for intermittent tidal filtering and frequency detection in salt marsh environments |
| title_sort | flooding in landsat across tidal systems flats an index for intermittent tidal filtering and frequency detection in salt marsh environments |
| topic | Tidal inundation Salt marshes Flooding Sea level rise Spartina alterniflora Coastal wetland |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X22005167 |
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