Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
164
datasets available to search
ShareScore release 0.9.0
Dataset results
164 results for “Delmarva”
Soil dissolved organic matter and greenhouse gas fluxes from intact Delmarva Bay wetland soil cores during laboratory simulation of groundwater level rise
Wetlands in low-relief landscapes have dynamic terrestrial-aquatic interfaces as surface water and groundwater levels fluctuate seasonally and these variable water levels influence wetland carbon cycling. Seasonal changes in groundwater levels determine which soils are hydrologically connected to the wetland surface water-groundwater continuum and therefore which soils act as carbon sources. To quantify groundwater-mediated soil dissolved organic matter (DOM) and greenhouse gas (CO2 and CH4) fluxes, we performed a laboratory simulation of groundwater rise on intact soil cores. Soil cores were collected from four Delmarva Bay wetlands located in the low-relief landscape of the Delmarva Peninsula in the Mid-Atlantic United States. At each wetland, two cores (length = 60 cm, diameter = 10.2 cm) were collected: one from within the wetland basin and the second from the transitional zone near the edge of the wetland basin (total number of cores collected = 8). To characterize seasonal hydrologic conditions at each wetland site where intact soil cores were collected (e.g., mean water level, number of saturation events, duration of saturation), we used high frequency water level data collected in wetland center and upland groundwater monitoring wells. In the lab, cores were re-saturated with groundwater over 15 days and after cores were fully re-saturated, core water levels were maintained for an additional 25 days. Water levels in each head tank and soil core were manually recorded on sub-daily timesteps. Rhizon soil porewater samplers were installed at 8, 20, and 40 cm below the soil surface reflecting the expected depths of the O, A, and B soil horizons across the wetland sites. Source groundwater, soil porewater, and exfiltrated surface water samples were collected daily from the soil cores and analyzed for pH, ORP, and DOM concentration (dissolved organic carbon) and DOM composition (absorbance and fluorescence metrics). Discrete measurements of CO2 and CH4 fluxes were
Precipitation data for four sites on the eastern side of Delmarva Peninsula, 2001-2022
Rain gauges (Davis Instruments rain collector with HOBO Event Recorder) were installed at four sites latitudinally along the Eastern Shore, VA to measure instantaneous rainfall. The logger only records when there is a precipitation event (0.2 mm minimum). We calculated daily rain totals and provided only dates with rain. The data represents the time period of 8/9/01 on for Mill, Garg, and Mosq sites and 10/16/01 on for Wach site. The system was down between October 2008 and December 2010. It was replaced by an upgraded system in January 2011 with new Davis tipping-bucket rain gauges and data loggers, and primary responsibility for data collection and processing shifted to VCR/LTER staff. This file will be updated when additional data is downloaded from the rain gauges.
Nutrient concentrations in three small streams on the coast of the Delmarva Peninsula 2002-2009
This dataset contains dissolved nutrient measurements for three small creeks on the Atlantic side of the Delmarva Peninsula in Virginia.
Land cover for the barrier islands of the Delmarva Peninsula in Virginia, 1984-2016
We quantified state change from sand, grassland, and woody vegetation in seven undeveloped barrier islands over three time intervals because the ecological and economic value of upland barrier systems is significant, yet often overlooked. We focused on seven islands with woody land cover: Cedar, Parramore, Hog, Cobb, Wreck, Smith, and Fishermans. Landsat TM5 satellite images were obtained from the USGS Global Visualization Viewer for the following dates: September 21, 1984, September 12, 1998, August 15, 2011 and September 12, 2016. Images were chosen from available dates within the growing season and were cloud-free in order to minimize uncertainties due to heterogeneous atmospheric conditions. Barrier islands provide the first line of defense against storms for millions of people living in coastal areas. Upland vegetation (that is, grassland, shrubland, and maritime forest) has received little attention, even though this land surface is most strongly affected by development pressures. We use remote sensing analysis to assess state change on seven undeveloped Virginia barrier islands over 32 years (1984-2016) that are free from direct human influence. Our analysis highlights the spatial-temporally dynamic nature of barrier island upland land area and vegetation, with rapidly changing ecosystem states. Between 1984 and 2011, upland vegetation was dramatically reduced by 29% whereas woody vegetation cover increased 40% across all islands. Although conversions between sand, grassland, and woody vegetation were variable within each island, three major patterns of vegetative land cover change were apparent: overall loss of vegetative cover,frequent transitions between grass and woody cover, and gain in woody cover. These patterns are valuable for understanding natural evolution of barrier islands in response to sea-level rise. Evaluation of temporal dynamics in barrier upland is needed to characterize underlying processes including island resilience or chronic stress, a
Delmarva Bays Wetland Water Level Time Series (2017-2024)
This dataset contains sub-hourly water level timeseries (2017-2024) for wells monitoring (n=51) Delmarva Bay wetlands. Wells were located in three distinct landscape features, wetlands (n=23), channels/swales (n=9), and adjacent uplands (n=19). The well classification can be found in the "well_id" column (SW=wetland, CH=channel/swale, UW=upland). Its worth noting that many wells have shorter monitoring periods than the full 2017-2024 timeframe. Additionally, data gaps may exist due to equipment failure. The well metadata file contains information about GPS coordinates and select well's surveyed elevations.
Discharge of three small creeks along the Delmarva Peninsula 2003-2009
This dataset contains flow and discharge data for three small creeks along the Atlantic Coast of the Delmarva Peninsula in Virginia. The data are not continuous, but were collected irregularly on an event-basis. It includes both base-flow and storm measurements.
Water soluble organic matter from Delmarva Bay soils
Little is known about how hydrologic processes along the terrestrial-aquatic interface in wetland dominated landscapes influence carbon dynamics, particularly regarding soil-derived dissolved organic matter (DOM) transport and transformation. To understand the role of different soil horizons as potential sources of DOM to wetland systems, we measured water soluble organic matter (WSOM) in soil horizons collected from upland to wetland transects at four Delmarva Bay wetlands. The Delmarva Bays used in this study are located on property managed by The Nature Conservancy on the Delmarva Peninsula in the eastern United States. Transects ranged from 25 – 45 m in length beginning from a monitoring well in the wetland center to an upland monitoring well. Each transect had four points (Upland, Transition, Edge, and Wetland). Soils were sampled in the late winter (January 17 and March 10) and autumn (September 21 and November 1) of 2020. Soils were sampled by horizon to a depth of approximately 50 cm at each transect point. WSOM extracted in the laboratory was analyzed for WSOM concentration, reported as Water Soluble Organic Carbon (mg WSOC / g soil). WSOM absorbance and fluorescence data were used to calculate composition metrics, providing insight to organic matter sources and chemical characteristics. WSOM fluorescence excitation-emission matrices were evaluated using the 13 component Cory and McKnight (2005) PARAFAC model. Extracted leaf litter, surface water, and groundwater samples were collected in addition to soil samples for the purpose of comparing WSOM to DOM end-members along the Delmarva Bay terrestrial-aquatic continuum. Continuous water level data, averaged to a daily time-step, was collected over the 2020 water year (October 1, 2019 to September 30, 2020) in previously established wetland and upland monitoring wells. The hydrologic conditions (e.g. mean water level, number of saturation events, duration of saturation) at each transect point were characterize
Dissolved CO2 and CH4 dynamics in Delmarva headwater wetlands, 2020-2022
This data product includes measurements of dissolved CO2 and CH4 in surface water and groundwater, along with hydrological and biogeochemical variables, across 20 headwater wetlands in the Delmarva Peninsula in the Mid-Atlantic region of the United States. Greenhouse gas samples and water chemistry samples were collected every 1-3 months over a period of 2 years. We also monitored water level at each of the wetlands with a pressure transducer.
1993 Landuse/Landcover of the Lower Delmarva Peninsula
This generalized landuse/landcover classification of the lower Delmarva Peninsula south of Snow Hill and Princess Ann, MD focuses on major habitats and vegetative communities of the Virginia Coast Reserve LTER. The classification is based on a Landsat 5 Thematic Mapper (TM) satellite image taken on July 28, 1993 at 15:02 GMT (11:02 EDT), which is within 15 minutes of observed low tide as recorded by the VCRLTER tide gauge at Redbank, VA.
Terrestrial-Marine Watershed Boundaries on the Delmarva Peninsula of Virginia 2001
This coverage contains the watershed boundaries for the coastal/marine watersheds on the eastern side of the Delmarva Peninsula in Virginia. Watersheds were delineated by Bruce P. Hayden from information (contours, elevations) on USGS 1:24,000 Quad Maps (1975). John H. Porter digitized those boundaries and closed the polygons by digitizing the 5-foot contour. VERSION 2 (2013-08-27): David L. Richardson added names for all the numbered watersheds, reprojected data to NAD83 datum, and fixed alignment issues that had crept in for northernmost watersheds (north of and including Bundick Creek).Â
Peregrine Falcon Site Occupancy along Delmarva Peninsula, VA (2006-2009)
<p>We monitored the breeding activity of peregrine falcons along the seaside of the Delmarva Peninsula (2006-2009) to determine their influence on the distribution of foraging red knots along the Virginia barrier islands. Twelve breeding territories were used during the study period. Both activity status and productivity varied across sites. Activity status was used to examine the influence of peregrines on the distribution of foraging red knots relative to peregrine eyries. This database contains the results of survey efforts (2006-2009).</p>
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>
Peregrine Falcon Site Occupancy along Delmarva Peninsula, VA (2006-2009)
Open the record for dataset details and reuse information.
Data from: Genetic diversity and demographic history of introduced sika deer on the Delmarva Peninsula
The introduction of non-native species can have long-term effects on native plant and animal communities. Introduced populations are occasionally not well understood and offer opportunities to evaluate changes in genetic structure through time and major population changes such as bottleneck and or founder events. Invasive species can often evolve rapidly in new and novel environments, which could be essential to their long-term success. Sika deer are native to East Asia, and their introduction and establishment to the Delmarva Peninsula, USA is poorly documented, but probably involved ≥1 founder and/or bottleneck events. We quantified neutral genetic diversity in the introduced population and compared genetic differentiation and diversity to the presumed source population from Yakushima Island, Japan, and a captive population of sika deer in Harrington, Delaware, USA. Based on data from 10 microsatellite DNA loci, we observed reduced genetic variation attributable to founder events, support for historic hybridization events, and evidence that the population did originate from Yakushima Island stocks. Estimates of population structure through Bayesian clustering and demographic history derived from Approximate Bayesian Computation (ABC), were consistent with the hypothesized founder history of the introduced population in both timing and effective population size (approximately 5 effective breeding individuals, an estimated 36 generations ago). Our ABC results further supported a single introduction into the wild happening before sika deer spread throughout the Delmarva. We conclude that free-ranging sika deer on Delmarva are descended from ca. 5 individuals introduced about 100 years ago from captive stocks of deer maintained in the United Kingdom. Free-ranging sika deer on Delmarva have lost neutral diversity due to founder and bottleneck events, yet populations have expanded in recent decades and show no evidence of abnormalities associated with inbreeding. We suggest management practices including increasing harvest areas and specifically managing sika deer outside of Maryland.
Coastal Satellite Image Segmentation (Water and Land) Labels: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)
<p>Contained here are jpegs containing coastal RGB satellite images along with a water vs. land mask. Each image is 256 pixels by 256 pixels. </p> <p>Geographic scope: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)</p> <p>Temporal range: 1984 to 2022</p> <p>Satellites: Landsat 5, 7, 8 and Sentinel-2</p> <p>All images were downloaded from Google Earth Engine using CoastSat download tools.</p> <p>The datasets are arranged into 'train', 'val', and 'test' folders. Within each of those folders are two folders 'a' and 'b'. 'a' contains the images (RGB), whereas 'b' contains the labels (land vs. water mask).</p> <p>All images were augmented with the four following augmentations: horizontal flip, vertical flip, 90 degree clockwise rotation, 90 degree counterclockwise rotation, and a horizontal+vertical flip. </p> <p>For training a new segmentation model, it is advised to not do any of these rotational or flip augmentations since they have already been performed. Instead, possibly experiment with other augmentations like introducing noise into the imagery.</p> <p>These images were used to train an image-to-image translation generative adversarial network. The code and model weights (generator and discriminator) are available at <a href="https://github.com/mlundine/Shoreline_Extraction_GAN">https://github.com/mlundine/Shoreline_Extraction_GAN</a>.</p> <p>To get to the files locally, you can download the .zip from Zenodo and then unzip the .zip file.</p>
Data from: Genetic diversity and demographic history of introduced sika deer on the Delmarva Peninsula
Open the record for dataset details and reuse information.
Figures 559-563 from: Lonsdale O (2021) Manual of North American Agromyzidae (Diptera, Schizophora), with revision of the fauna of the "Delmarva" states. ZooKeys 1051: 1-481. https://doi.org/10.3897/zookeys.1051.64603
Figures 559-563 Cerodontha (Poemyza) superciliosa (Zetterstedt), male genitalia 559 external genitalia, posterior 560 external genitalia, left lateral 561 ejaculatory apodeme 562 phallus, left lateral 563 phallus, ventral.
Figures 550-554 from: Lonsdale O (2021) Manual of North American Agromyzidae (Diptera, Schizophora), with revision of the fauna of the "Delmarva" states. ZooKeys 1051: 1-481. https://doi.org/10.3897/zookeys.1051.64603
Figures 550-554 Cerodontha (Poemyza) pygmaea (Meigen), male genitalia 550 external genitalia, anterior 551 external genitalia, left lateral 552 ejaculatory apodeme 553 phallus, left lateral 554 phallus, ventral.
Figures 541-544 from: Lonsdale O (2021) Manual of North American Agromyzidae (Diptera, Schizophora), with revision of the fauna of the "Delmarva" states. ZooKeys 1051: 1-481. https://doi.org/10.3897/zookeys.1051.64603
Figures 541-544 Cerodontha (Poemyza) incisa (Meigen), male genitalia 541 external genitalia, anterior 542 external genitalia, left lateral 543 phallus, left lateral 544 phallus, ventral.
Figures 545-549 from: Lonsdale O (2021) Manual of North American Agromyzidae (Diptera, Schizophora), with revision of the fauna of the "Delmarva" states. ZooKeys 1051: 1-481. https://doi.org/10.3897/zookeys.1051.64603
Figures 545-549 Cerodontha (Poemyza) muscina (Meigen), male genitalia 545 external genitalia, anterior 546 external genitalia, left lateral 547 ejaculatory apodeme 548 phallus, left lateral 549 phallus, ventral.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.