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14 results for “saltwater intrusion”
Nutrient data from the Peat Collapse-Saltwater Intrusion Field Experiment from brackish and freshwater sites within Everglades National Park, Florida (FCE LTER), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly porewater nutrients were taken at 15 cm depth from a brackish and a freshwater marsh. Porewater physicochemistry was measured 24 hours after dosing. Collection occurred from Oct 2014 - Sep 2016. The collected water was then analyzed for temperature, conductivity, salinity, pH, alkalinity, chloride, DOC, NH4, SO4, TDN, SRP, TDP, and sulfide. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. 2018. Ecological Applications 28:2092-2018.
Leaf nutrient and root biomass data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Yearly sawgrass leaf carbon, nitrogen, and phosphorus concentrations and live root biomass measurements were measured from a brackish water and freshwater marsh. All measurements were taken every other month 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosystem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Biomass data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly biomass, aboveground net primary production, and culm density measurements were measured from a brackish water and freshwater marsh. All measurements were taken every other month 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Modeled flux data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly modeled ecosystem flux measurements were calculated from a brackish water and freshwater marsh. Ecosystem flux was measured 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Flux data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park, collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly ecosystem flux measurements were taken from a brackish water and freshwater marsh. Ecosystem flux was measured 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Inventory of soil prokaryotic microbiome (via 16S based on rRNA gene amplicons) in freshwater and brackish water marshes following saltwater intrusion along Shark River Slough boundary, Everglades National Park (FCE LTER), Florida, USA, September 2018
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased episodic or chronic saltwater exposure, leading to shifts in microbial communities and related ecological services. Soil prokaryotes play a fundamental role in regulating important biogeochemical processes in coastal wetland ecosystem. Yet, it is still difficult to predict how soil prokaryotic communities respond to the saltwater exposure because of poorly understood prokaryotic sensitivity within complex wetland soil microbial communities, as well as the high heterogeneity of wetland soils and saltwater exposure. To address this, a four-year experimental simulation of saltwater intrusion in a pristine freshwater site and a previously saltwater-impacted site was conducted. The saltwater addition started in October 2014 on a monthly basis and continued through October 2018. The dataset contains amplicon sequencing date of 16S rRNA gene obtained from saltwater-exposed soils and unmanipulated native soils in both sites (collected in September 2018). The 2018 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804545 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804545). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-Habbena, W. (2023). Year-around survey and manipulation experiments reveal differential sensitivities of soil prokaryotic and fungal communities to saltwater intrusion in Florida Everglades wetlands. Science of The Total Environment, 858, 159865. https://doi.org/10.1016/j.scitotenv.2022.159865 Instead of citing this package, which is an
Figure 1 in Invasions of two estuarine gobiid species interactively induced from water diversion and saltwater intrusion
Figure 1. The East Route of South-to-North Water Transfer Project, showing the five major lakes along the route (shadow areas) as storages, the Grand Canal as conveyance, and geographic relationships of the major rivers (i.e., the Yangtze River, the Huai River, and the Yellow River) with the route. The Nansi Lake is separated into the Lower Nansi Lake and Upper Nansi Lake by the Erji Dam. The year of the first record of the two invasive species, Taenioides cirratus and Tridentiger bifasciatus, in each of these lakes was indicated to show their invasion patterns.
Everglades Saltwater Intrusion Marsh Surface Water Dissolved CO2 June 19th to 25th 2022
Dissolved CO2 (ppm) was measured in surface water at the Everglades saltwater intrusion marsh eddy covariance flux tower (US-EvM on AmeriFlux) for one week in June 2022. Minute resolution measurements were made using the CO2-LAMP (Blackstock et al., 2019).
The Salinity and phosphorus mesocosm experiment in freshwater sawgrass wetlands: Determining the trajectory and capacity of freshwater wetland ecosystems to recover carbon losses from saltwater intrusion (FCE LTER), Florida, USA from 2015 to 2018
In experimental wetland mesocosms located at Florida Bay Interagency Science Center, Key Largo, Florida, researchers continuously added salinity (approximately 6.9 g salt d-1) and phosphorus ( approximately 0.5 mg P d-1) to Cladium jamaicense peat monoliths from February 2015 to February 2017 and quantified changes in carbon partitioning. Several studies, focusing on the functional roles of marsh, soil, periphyton and microbe in the sawgrass-peat ecosystem, summarized detailed methodology and results (Wilson et al. 2019; Servais et al. 2019; Mazzei et al. in press). Briefly, salinity was increased (~10 ppt) and phosphorus was added (0.45 mg P d-1) to simulate four treatment effects (n = 24 plots): i) freshwater and no-added phosphorus, ii) freshwater and added phosphorus, iii) saltwater and no-added phosphorus, and iv) saltwater and added phosphorus. Upon the termination of manipulation study (early February 2017), containers holding water and peat-sawgrass cores were drained, rinsed, and refilled with only freshwater without any added nutrient and salt. Then, we experimentally restored freshwater to previous treatment and control mesocosms from February 2017 to June 2018 to examine the capacity of wetland ecosystems to recover carbon losses from saltwater intrusion. Note that FCE1226_Water_quality.csv summarizes water quality during both the manipulation and restoration study; however, all other files in the Dataset Title section only summarize results from the restoration study. Detailed methodology is provided below.
High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula
<p><strong>Abstract:</strong></p> <p>Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017.</p> <p><strong>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</strong></p> <p>This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below:</p> <p><strong>Raster value Land cover/use category</strong></p> <p>1 Forest</p> <p>2 Marsh</p> <p>3 Salt patch</p> <p>4 Built</p> <p>5 Open water</p> <p>6 Farmland</p> <p>7 Bare soil</p> <p>8 Other vegetation</p> <p> </p> <p><strong>Input Data:</strong></p> <p>These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE.</p> <p><strong>Methods:</strong></p> <p>NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel.</p> <p>NDVI = (Near-infrared – Red) / (Near-infrared + Red)</p> <p>NDWI = (Green – Near-infrared) / (Green + Near-infrared)</p> <p>SI = (256 – Blue) * (256 + Blue)</p> <p>In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel.</p> <p>EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1)</p> <p>A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands.</p> <p>Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits.</p> <p><strong>Accuracy assessment:</strong></p> <p>A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below.</p> <table> <tbody> <tr> <td> <p><strong>Delaware 2013</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.46%</p> </td> <td> <p>90.89%</p> </td> <td> <p>0.90</p> </td> <td> <p>86.37%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>84.03%</p> </td> <td> <p>80.13%</p> </td> <td> <p>0.82</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>97.02%</p> </td> <td> <p>71.18%</p> </td> <td> <p>0.82</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>94.56%</p> </td> <td> <p>95.06%</p> </td> <td> <p>0.95</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>91.01%</p> </td> <td> <p>96.63%</p> </td> <td> <p>0.94</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>83.16%</p> </td> <td> <p>86.19%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>87.70%</p> </td> <td> <p>87.30%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>82.46%</p> </td> <td> <p>84.20%</p> </td> <td> <p>0.83</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Delaware 2017</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>95.07%</p> </td> <td> <p>90.30%</p> </td> <td> <p>0.93</p> </td> <td> <p>91.37%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>88.86%</p> </td> <td> <p>92.44%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>91.82%</p> </td> <td> <p>85.59%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>87.58%</p> </td> <td> <p>93.54%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.68%</p> </td> <td> <p>90.48%</p> </td> <td> <p>0.92</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>91.61%</p> </td> <td> <p>93.61%</p> </td> <td> <p>0.93</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>95.67%</p> </td> <td> <p>87.67%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>91.16%</p> </td> <td> <p>90.24%</p> </td> <td> <p>0.91</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Maryland 2011</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.32%</p> </td> <td> <p>90.97%</p> </td> <td> <p>0.90</p> </td> <td> <p>87.20%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.14%</p> </td> <td> <p>82.08%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>96.74%</p> </td> <td> <p>78.76%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>89.02%</p> </td> <td> <p>88.50%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.74%</p> </td> <td> <p>96.10%</p> </td> <td> <p>0.94</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>84.31%</p> </td> <td> <p>89.83%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>87.53%</p> </td> <td> <p>90.05%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>86.11%</p> </td> <td> <p>80.57%</p> </td> <td> <p>0.83</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Maryland 2017</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.66%</p> </td> <td> <p>88.66%</p> </td> <td> <p>0.89</p> </td> <td> <p>87.34%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.65%</p> </td> <td> <p>88.35%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>93.29%</p> </td> <td> <p>68.30%</p> </td> <td> <p>0.79</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>93.44%</p> </td> <td> <p>86.92%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.36%</p> </td> <td> <p>92.36%</p> </td> <td> <p>0.92</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>83.84%</p> </td> <td> <p>94.55%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>92.86%</p> </td> <td> <p>82.61%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>86.56%</p> </td> <td> <p>76.00%</p> </td> <td> <p>0.81</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Virginia 2012</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>84.65%</p> </td> <td> <p>91.18%</p> </td> <td> <p>0.88</p> </td> <td> <p>86.88%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.03%</p> </td> <td> <p>84.39%</p> </td> <td> <p>0.86</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>97.67%</p> </td> <td> <p>72.41%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>90.97%</p> </td> <td> <p>86.24%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>94.17%</p> </td> <td> <p>87.39%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>86.87%</p> </td> <td> <p>91.81%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>85.82%</p> </td> <td> <p>83.04%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>83.60%</p> </td> <td> <p>80.59%</p> </td> <td> <p>0.82</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Virginia 2016</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>84.65%</p> </td> <td> <p>86.00%</p> </td> <td> <p>0.85</p> </td> <td> <p>85.83%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>86.25%</p> </td> <td> <p>90.72%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>90.61%</p> </td> <td> <p>62.12%</p> </td> <td> <p>0.74</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>88.70%</p> </td> <td> <p>77.72%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.12%</p> </td> <td> <p>84.41%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>85.17%</p> </td> <td> <p>90.36%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>83.30%</p> </td> <td> <p>88.54%</p> </td> <td> <p>0.86</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>84.49%</p> </td> <td> <p>84.17%</p> </td> <td> <p>0.84</p> </td> </tr> </tbody> </table> <p>While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface.</p> <p> </p> <p><strong>Data format:</strong></p> <p>The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis.</p> <p><strong>Code:</strong> Sample code is available at <a href="https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806">https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806</a>. The user would need to upload study area boundaries and reference points in order to successfully run these codes.</p> <p><strong>Datasets for download:</strong></p> <ul> <li>Two zipped data layers for Delaware:</li> </ul> <ol> <li>DE_3counties_2013</li> <li>DE_3counties_2017</li> </ol> <p>These data layers cover 3 counties: Kent, New Castle, Sussex.</p> <ul> <li>Two zipped data layers for Maryland:</li> </ul> <ol> <li>MD_9counties_2011</li> <li>MD_9counties_2017</li> </ol> <p>These data layers cover 9 counties: Caroline, Cecil, Dorchester, Kent, Queen Anne's, Somerset, Talbot, Wicomico, Worcester.</p> <ul> <li>Two zipped data layers for Virginia:</li> </ul> <ol> <li>VA_2counties_2012</li> <li>VA_2counties_2016</li> </ol> <p>These data layers cover 2 counties: Accomack, Northampton.</p> <p>We also provided a color map (DELMARVA_ColorMap.clr) that can be used with these data files. </p> <p><strong>Data citation:</strong></p> <p>Mondal, P., Walter, M., Miller, J., Epanchin-Niell, R., Yawatkar, V., Nguyen, E., Gedan, K. and Tully, K. 2022. High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula. Available at: 10.5281/zenodo.6685695. Accessed DAY MONTH YEAR.</p>
Data from: Surface elevation trends in natural and restored coastal forested wetlands reveal vulnerability to saltwater intrusion and sea level rise
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Hydraulic and geochemical impact of occasional saltwater intrusions through a submarine spring in a karst and thermal aquifer (Balaruc peninsula near Montpellier, France)
<p>Contains geochemical and isotopic data presented in the scientific paper "Hydraulic and geochemical impact of occasional saltwater intrusions through a submarine spring in a karst and thermal aquifer (Balaruc peninsula near Montpellier, France)".</p> <p>Files contain physico-chemical parameters, major ions, rare earth elements and isotopic data (Sr, B) for karst and thermal groundwater samples collected between 2010 and 2018 in the Balaruc-les-Bains (France) area.</p>
Simulated salinity for the saltwater intrusion study in the Po Delta (Italy)
<p>This database includes the results of the SHYFEM<br> application to the Po Delta (Italy) presented in the work entitled<br> "Saltwater intrusion in a Mediterranean delta under a changing climate".<br> Model results are presented in terms of the 3D maximum salinity for the<br> present day condition (summer 2017) and the RCP8.5 scenario.<br> </p>
Evaluating the effects of land-use change and future climate change on vulnerability of coastal landscapes to saltwater intrusion
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