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32 results for “Wetland Monitoring”
H2Ohio Wetland Monitoring Program Surface Water and Soil Nutrient Content from Wetlands across Ohio, USA (2021–2022).
This data package contains surface water and soil nutrient concentration datasets from wetland projects across Ohio, USA monitored by the H2Ohio Wetland Monitoring Program. Monitoring began in May 2021 and is ongoing. This data package will be updated yearly. In general, surface water samples are collected to measure concentrations of major nutrients, including inorganic nitrogen, ammonium-nitrogen, total nitrogen, dissolved reactive phosphorus, and total phosphorus. Sampling from major inflows and outflows is prioritized at flow-through wetland projects to support the calculation of nutrient filtration estimates using mass balance approaches. Surface water samples may also be collected from representative zones or hydrologic features with sufficient standing water (i.e., vernal pools, vegetated areas, interconnected smaller pond-like areas, etc.) to assess nutrient conditions and processes within the wetland system. The majority of surface water sampling (~monthly) occurs from March through December, with opportunistic sampling in January and February. Every effort is made to collect samples during hydrologic events (i.e., storms) as well as baseflow conditions. Concurrent with surface water sampling, hand-held multiparameter sensors are used to measure snapshots of physicochemical characteristics including dissolved oxygen, temperature, specific conductance, turbidity, and pH. Soil samples (0-5 cm) are collected in saturated and unsaturated zones at each identified soil "patch" determined from expert opinion, soil maps (Natural Resources Conservation Service), and/or hydrogeophysical assessment. Additionally, soil samples may be collected along major visible hydrologic or elevation gradients. Soil sampling occurs 1-3 times a year in select wetland projects.
H2Ohio Wetland Monitoring Program Vegetation Community Composition, Biomass, and Nutrient Content across Ohio, USA (2021-2022)
This data package includes vegetation community composition, percent cover, biomass, and nutrient concentration data from wetland projects across Ohio, USA, monitored by the H2Ohio Wetland Monitoring Program. Monitoring began in July 2021 and is ongoing, with annual updates planned. While data collection varies by site, most wetlands are surveyed to characterize vegetation and estimate nutrient stocks through community composition assessments and biomass sampling. Sampling points are randomly generated within and up to 5 meters outside the approximated wetland boundary. Surveys are conducted at points where wetland species (FAC–OBL) are present using a 1×1 meter quadrat to record species composition and percent cover. At approximately half of these locations, a 0.25×0.25 meter quadrat is placed outside the northeast corner to collect vegetation samples for nutrient analysis. In these smaller quadrats, community composition and percent cover are recorded prior to sampling aboveground biomass and, at approximately one third of those sites, belowground biomass. Datasets collected by the H2Ohio Wetland Monitoring Program containing surface water and soil nutrient concentration from samples collected at Ohio wetlands during the 2021–2022 sampling period can be found in an accompanying data package (edi.2087) at https://doi.org/10.6073/pasta/297bd6d93b4fcc8416e4225834db380e.
Long-term monitoring and research of the ecology of the Tres Rios constructed treatment wetland, Phoenix, Arizona, USA, ongoing since 2011
# Project Description In order to better understand the water, nutrient and treatment dynamics of aridland constructed treatment wetlands, we have developed datasets tracking primary productivity (aboveground and belowground), nutrient and water budget dynamics, soils, and aquatic metabolism at the Tres Rios wetlands, operated by the City of Phoenix Water Services Department, since July 2011. The 3-cell Tres Rios Wetlands were completed in 2010 and are associated with the 91st Avenue Wastewater Treatment Plant, the largest in Phoenix. This project is focused on the largest of the three wetlands treatment cells which was the first to be planted and became operational in Summer 2010. The wetland cells are bounded by roads (the "shoreline"), and the system we study is 42 ha in size, approximately half of which is open water and half of which is fringing vegetated marsh. Water depth is relatively consistent across the marsh (approximately 25cm) and effluent inflow to the cell varies seasonally from 95,000 to over 270,000 m3 d-1. Measurements are taken along two gradients representing the two hydraulic pathways of the system: The whole-system, from inflow to outflow, within the vegetated marsh itself. # Abstract Constructed treatment wetlands (CTW) provide cost effective and ecosystem-service based solutions to the problem of urban wastewater treatment. They are a particularly attractive option for water reuse in arid cities, where water resources are scarce, and understanding CTW function in these environments is critical to facilitating sustainable water use practices. Although CTW are well established and studied in mesic climates, how they function in and respond to hot, arid climates is comparatively not well understood. Specifically, large atmospheric water losses via evaporation and plant transpiration comprise a much larger component of the whole-system water budget than in mesic climates. Additionally, given the primary role that emergent macrophytes play in nit
Fish Restoration Program Tidal Wetland Restoration Monitoring in Upper San Francisco Estuary, 2015-ongoing
The Fish Restoration Program Monitoring Team assesses the biological effectiveness of 8,000+ acres of tidal wetland restoration in the Upper San Francisco Estuary (Sacramento-San Joaquin Delta and Suisun Marsh) through a contract with the CA Department of Water Resources. The restoration is pursuant to requirements in the 2008/2009 and 2019 Biological Opinions for state and federal water project operations. Data on fish and invertebrate abundance on or near these sites was collected as baseline monitoring data and to determine the most efficient methods for monitoring wetlands. Understanding how invertebrate, fish, and phytoplankton communities change pre- to post-restoration is essential to evaluating the benefits of tidal wetlands to native fish species. Invertebrate data was collected using sweep nets, benthic cores, neuston nets, and zooplankton trawls. Fish data was collected using otter trawls, lampara nets, and beach seines. For each method, the team recorded the catch of fish, invertebrates, and other organisms, length of selected organisms, and a suite of environmental information, including: dissolved oxygen, water temperature, pH, specific conductance, turbidity, chlorophyll a, phycocyanin, and dissolved organic carbon. Additional water quality samples, processed for Chlorophyll a, Dissolved Ammonia, Dissolved Nitrite + Nitrate, Dissolved Organic Carbon, Dissolved Organic Nitrogen, Dissolved Ortho-phosphate, Pheophytin a, Total Kjeldahl Nitrogen, Total Organic Carbon, Total Phosphorus, and Total Suspended Solids, were collected at a subset of sampling sites. Data are collected before and after restoration at project and reference sites (Before-After-Control-Impact-design), as well as at adjacent water bodies.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Fish Abundance and Species Richness
These data describe the annual estimates of density of wetland fish (all species combined) and the species richness (as the number of unique species) in six main channel and six tidal creek locations at four coastal wetlands as part of the SONGS San Dieguito Wetland Restoration monitoring program to track long-term patterns in species abundance and diversity. This study began in 2012 in the San Dieguito Wetland in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA. Tijuana Estuary in San Diego County was added in 2013. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Bird Abundance and Species Richness
These data describe the annual estimates of bird density and richness (as a species density) in twenty plots at four coastal wetlands as part of the SONGS San Dieguito Wetland Restoration monitoring program to track long-term patterns in species abundance and diversity. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Bird Food Chain Support
These data describe annual estimates of the density of feeding birds at four coastal wetlands as part of the SONGS San Dieguito Wetland Restoration monitoring program to track long-term patterns in food chain support provided to birds. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Point Mugu Lagoon in Ventura County. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Invertebrate Abundance and Richness
These data contain annual estimates of the density of wetland macroinvertebrates (all species combined) and species richness (as a species density) in six main channel and six tidal creek locations at four coastal wetlands as part of the SONGS San Dieguito Wetland Restoration monitoring program to track long-term patterns in species abundance and diversity. This study began in 2012 in the San Dieguito Wetland and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Spartina Canopy
These data describe annual estimates of Spartina foliosa canopy architecture (measured as proportion of stems > 3 ft long) from four locations at two coastal wetlands as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program to track long-term patterns in Spartina size structure. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA. Beginning in 2024, Tijuana Estuary was replaced with Mugu Lagoon in Ventura County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Water Quality
These data describe annual estimates of wetland water quality, measured as the average duration of hypoxia (time dissolved oxygen concentration below 3 mg/l), collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Point Mugu Lagoon in Ventura County, CA. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Tidal Prism
These data describe estimates of tidal prism at the San Dieguito Wetland as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program to track long-term patterns of tidal prism. This study began in 2012.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Plant Reproductive Success
These data describe annual estimates reproductive success (measured by seed set) of salt marsh plants at the San Dieguito Wetland as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to track long-term patterns in reproductive success of wetland plants. Monitoring began in 2012.
Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)
<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. <em>International Journal of Applied Earth Observation and Geoinformation </em>133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> <p>Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.</p> <p>The first dataset (<strong>Dataset_1.gpkg</strong>) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>CLASS</em>": EUNIS first level habitat type, classified as following:<br> <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"<em>DATE</em>": Date associated with EUNIS habitat sample</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>TYPE</em>": Either training ("<em>train</em>") or validation ("<em>test</em>") sample</li> </ul> <p>The second dataset (<strong>Dataset_2.gpkg</strong>) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>REFERENCE</em>": Change class reference</li> <li>"<em>CCDC</em>": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"<em>PCCD</em>": Change class obtained from the Post-Classification Change Detection approach</li> </ul> <p>Supplementary layout files (<strong>Dataset_1.qml</strong> and <strong>Dataset_2.qml</strong>) support formatting of the points in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># EUNIS habitat</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. </p> <p>Habitat maps are given for the two approaches in years 1984 and 2022:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_HABITAT_1984.tif</strong> and <strong>CCDC_HABITAT_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_HABITAT_1984.tif </strong>and <strong>PCCD_HABITAT_2022.tif</strong>)</li> </ul> <p>Supplementary layout files (<strong>CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml</strong>) support formatting of raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># Change detection during the 1984-2022 period</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.</p> <p>Change detection maps are given for the two approaches:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_CHANGE_1984_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_CHANGE_1984_2022.tif</strong>)</li> </ul> <p>Supplementary layer files (<strong>CCDC_CHANGE_1984_2022.qml</strong> and<strong> PCCD_CHANGE_1984_2022.qml</strong>) support formatting of the raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># GEE repository</h3> <p>To get direct access to GEE scripts and assets, please follow those two links:</p> <p>https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin</p> <p>https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin</p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
UCSB SONGS Mitigation Monitoring: Wetland Survey - Fish Abundance
These data describe annual estimates of the density of fish in main channel and tidal creek habitats collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. Monitoring began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA. Beginning in 2024, Los Penasquitos Lagoon in San Diego County replaced Tijuana Estuary. The abundance of fish is determined using beach seine and enclosure trap sampling at six main channel and six tidal creek locations at each wetland. Both seine and enclosure trap sampling consist of several hauls through an enclosed volume of water. Sampling is conducted annually in early fall.
UCSB SONGS Mitigation Monitoring: Wetland Survey - Bird Abundance
These data describe annual estimates of bird density collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. Monitoring began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA. Los Penasquitos Lagood in San Diego County replaced Tijuana Estuary in 2024. The abundance of birds is determined from twenty plots spread across each wetland.
UCSB SONGS Mitigation Monitoring: Wetland Survey - Bird Feeding Activity
These data describe annual estimates of shorebird bird feeding activity, measured as the percentage of birds observed feeding, collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. Monitoring began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Point Mugu Lagoon in Ventura County. Bird feeding activity was estimated for twenty plots in each wetland having at least one targeted shorebird species present on the ground. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Survey - Invertebrate Abundance
These data describe the abundance of invertebrate species sampled in quadrats and sediment cores as part of the SONGS San Dieguito Wetland Restoration monitoring program to track long-term patterns in species abundance and diversity and assess compliance of the restoration project with conditions in the SONGS Coastal Development permit. This study began in 2012 in the San Dieguito Lagoon and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA. The abundance of invertebrates in quadrats and sediment cores was recorded at six main channel and six tidal creek locations at each wetland. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Survey - Spartina Size Structure
These data describe annual estimates of Spartina foliosa size structure collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. Monitoring began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA. The height of Spartina stems were recorded along four transects at each wetland. Beginning in 2024, Tijuana Estuary was replaced with Mugu Lagoon in Ventura County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Survey - Water Quality
These data describe measurements of water characteristics (temperature, salinity, and dissolved oxygen) collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. Monitoring began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA. Measurements of water depth, temperature, salinity, and dissolved oxygen were recorded daily in fifteen minute intervals at two sampling stations (primary and backup) at the San Dieguito Wetland, Carpinteria Salt Marsh, and Mugu Lagoon, and one sampling station (primary) at the Tijuana Estuary (2012-2023) and Los Penasquitos Lagoon (2024-present).
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