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403 results for “satellite data”
A monthly shortwave radiative forcing kernel for surface albedo change using CERES satellite data
We present a radiative kernel for surface albedo change founded on a novel, simplified parameterization of shortwave radiative transfer driven with inputs from the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balance and Filled (EBAF) Edition 4.0 products based on a 16-year climatology (2001-2016). Both monthly temporally-explicit and monthly climatological mean CERES albedo change kernels (CACK) are provided with their respective uncertainty layers. Octave script files for generating monthly CACK from CERES EBAF data and demonstrating the application of CACK with user-specified temporal and spatial extents are also included.
Data for 'Impact of Satellite Observations on Forecasting Sudden Stratospheric Warmings'
<p>These data were used for making plots in the manuscript entitled 'Impact of Satellite Observations on Forecasting Sudden Stratospheric Warmings'. DOI: 10.1029/2019GL086233. Data format is NetCDF.</p>
Data from: Filling knowledge gaps in a threatened shorebird flyway through satellite tracking
1. Satellite-based technologies that track individual animal movements enable the mapping of their spatial and temporal patterns of occurrence. This is particularly useful in poorly studied or remote regions where there is a need for the rapid gathering of relevant ecological knowledge to inform management actions. One such region is East Asia, where many intertidal habitats are being degraded at unprecedented rates and shorebird populations relying on these habitats show rapid declines. 2. We examine the utility of satellite tracking to accelerate the identification of coastal sites of conservation importance in the East Asian-Australasian Flyway. In 2015–2017 we used solar-powered satellite transmitters to track the migration of 32 great knots (Calidris tenuirostris), an 'Endangered' shorebird species widely distributed in the Flyway and fully dependent on intertidal habitats for foraging during the non-breeding season. 3. From the great knot tracks, a total of 92 stopping sites along the Flyway were identified. Surprisingly, 63% of these sites were not known as important shorebird sites before our study; in fact, every one of the tracked individuals used sites that were previously unrecognized. 4. Site knowledge from on-ground studies in the Flyway is most complete for the Yellow Sea and generally lacking for Southeast Asia, Southern China, and Eastern Russia. 5. Policy implications: Satellite tracking highlighted coastal habitats that are potentially important for shorebirds but lack ecological information and conservation recognition, such as those in Southern China and Southeast Asia. At the same time, the distributional data of tracked individuals can direct on-ground surveys at the lesser-known sites to collect information on bird numbers and habitat characteristics. To recognize and subsequently protect valuable coastal habitats, filling knowledge gaps by integrating bird tracking with ground-based methods should be prioritized.19-Jun-2019
Data from: Immediate and carry-over effects of insect outbreaks on vegetation growth in West Greenland assessed from cells to satellite
Aim: Tundra ecosystems are highly vulnerable to climate change and climate-growth responses of Arctic shrubs are variable and altered by microsite environmental conditions and biotic factors. With warming and drought during the growing season, insect-driven defoliation is expected to increase in frequency and severity with potential broad-scale impacts on tundra ecosystem functioning. Here we provide the first broad-scale reconstruction of spatiotemporal dynamics of past insect outbreaks by assessing their effects on shrub growth along a typical Greenlandic fjord climate gradient from the inland ice to the sea. Location: Nuuk Fjord (64°30′N/51°23′W) and adjacent areas, West Greenland. Taxa: Great brocade (Eurois occulta L.) and grey willow (Salix glauca L.). Methods: We combined dendro-anatomical and remote sensing analyses. Time series of ring width and wood-anatomical traits were obtained from chronologies of > 40 years established from 153 individuals of S. glauca collected at nine sites. We detected anomalies in satellite-based Normalized Difference Vegetation Index (NDVI) related to defoliation and reconstructed past changes in photosynthetic activity across the region. Results: We identified outbreaks as distinctive years with reduced ring width, cell-wall thickness and vessel size, without being directly related to climate but matching with years of parallel reduction in NDVI. The two subsequent years after the defoliation showed a significant increase in ring width. The reconstructed spatiotemporal dynamics of these events indicate substantial regional variation in outbreak intensity linked to the climate variability across the fjord system. Main conclusions: Our results highlight the ability of S. glauca to cope with severe insect defoliation by changing carbon investment and xylem conductivity leading to high resilience and rapid recovery after the disturbance. Our multi-proxy approach allows us to pin-point biotic drivers of narrow ring formation and to provide new broad-scale insight on the C-budget and vegetation productivity of shrub communities in a widespread arctic ecosystem
Dataset for: Sensitivity of a satellite algorithm for harmful algal blooms discrimination to the use of laboratory bio-optical data for training
<p>Two files relating to the publication by Martinez-Vicente et al. (2020).</p> <p>meris_data_karenia_alt_chla.xlsx : file containing the chlorophyll concentrations for the different areas in the MODIS images selected for training and evaluation of the algorithm.</p> <p>coefficients_for_LDA_Karenia_mikimotoi.zip: file containing the coefficients for the Linear Discriminant Analysis (LDA) resulting from the training datasets 1,2 and 3. </p> <p> </p>
Combining Satellite Remote Sensing and Climate Data in Species Distribution Models to Improve the Conservation of Iberian White Oaks (Quercus L.)
<p>The Iberian Peninsula hosts a high diversity of oak species, being a hot-spot for the conservation of European White Oaks (Quercus) due to their environmental heterogeneity and its critical role as a phylogeographic refugium. Identifying and ranking the drivers that shape the distribution of White Oaks in Iberia requires that environmental variables operating at distinct scales are considered. These include climate, but also ecosystem functioning attributes (EFAs) related to energy–matter exchanges that characterize land cover types under various environmental settings, at finer scales. Here, we used satellite-based EFAs and climate variables in species distribution models (SDMs) to assess how variables related to ecosystem functioning improve our understanding of current distributions and the identification of suitable areas for White Oak species in Iberia. We developed consensus ensemble SDMs targeting a set of thirteen oaks, including both narrow endemic and widespread taxa. Models combining EFAs and climate variables obtained a higher performance and predictive ability (true-skill statistic (TSS): 0.88, sensitivity: 99.6, specificity: 96.3), in comparison to the climate-only models (TSS: 0.86, sens.: 96.1, spec.: 90.3) and EFA-only models (TSS: 0.73, sens.: 91.2, spec.: 82.1). Overall, narrow endemic species obtained higher predictive performance using combined models (TSS: 0.96, sens.: 99.6, spec.: 96.3) in comparison to widespread oaks (TSS: 0.80, sens.: 92.6, spec.: 87.7). The Iberian White Oaks show a high dependence on precipitation and the inter-quartile range of Normalized Difference Water Index (NDWI) (i.e., seasonal water availability) which appears to be the most important EFA variable. Spatial projections of climate–EFA combined models contribute to identify the major diversity hotspots for White Oaks in Iberia, holding higher values of cumulative habitat suitability and species richness. We discuss the implications of these findings for guiding the long-term conservation of IberianWhite Oaks and provide spatially explicit geospatial information about each oak species (or set of species) relevant for developing biogeographic conservation frameworks.</p>
Data from: Alternative reproductive tactics arising from a continuous behavioral trait: callers vs. satellites in field crickets
Alternative reproductive tactics may arise when natural enemies use sexual signals to locate the signaler. In field crickets, elevated costs to male calling due to acoustically orienting parasitoid flies create opportunity for an alternative tactic, satellite behavior, where noncalling males intercept females attracted to callers. Although the caller-satellite system in crickets that risk detection by parasitoids resembles distinct behavioral phenotypes, a male's propensity to behave as caller or satellite can be a continuously variable trait over several temporal scales, and an individual may pursue alternate tactics at different times. We modeled a caller-satellite-parasitoid system as a spatially explicit interaction among male and female crickets using individual-based simulation. Males varied in their propensity to call versus behave as a satellite from one night to the next. We varied mortality, density, sex ratio, and female mating behavior, and recorded lifetime number of mates as a function of a male's probability of calling (vs. acting as a satellite) along a gradient in parasitism risk. Frequently, the optimal behavior switched abruptly from being pure caller (call every night) to pure satellite (never call) as parasitism rate increased. However, mixed strategies prevailed even with high parasitism risk under conditions of higher background mortality rate, decreasing density, increasing female-biased sex ratio, and increasing female choosiness. In natural populations, high parasitoid pressure alone would be unlikely to yield fixation of pure satellite behavior.
Data from: When and where does mortality occur in migratory birds? Direct evidence from long-term satellite tracking of raptors
1. Information about when and where animals die is important to understand population regulation. In migratory animals, mortality might occur not only during the stationary periods (e.g. breeding and wintering) but also during the migration seasons. However, the relative importance of population limiting factors during different periods of the year remains poorly understood, and previous studies mainly relied on indirect evidence. 2. Here we provide direct evidence about when and where migrants die by identifying cases of confirmed and probable deaths in three species of long-distance migratory raptors tracked by satellite telemetry. 3. We show that mortality rate was about six times higher during migration seasons than during stationary periods. However, total mortality was surprisingly similar between periods, which can be explained by the fact that risky migration periods are shorter than safer stationary periods. Nevertheless, more than half of the annual mortality occurred during migration. We also found spatiotemporal patterns in mortality: spring mortality occurred mainly in Africa in association with the crossing of the Sahara desert, while most mortality during autumn took place in Europe. 4. Our results strongly suggest that events during the migration seasons have an important impact on the population dynamics of long-distance migrants. We speculate that mortality during spring migration may account for short-term annual variation in survival and population sizes, while mortality during autumn migration may be more important for long-term population regulation (through density dependent effects).
Data from: Using satellite AIS to improve our understanding of shipping and fill gaps in ocean observation data to support marine spatial planning
1. A key stage underpinning marine spatial planning (MSP) involves mapping the spatial distribution of ecological processes and biological features, as well the social and economic interests of different user groups. One sector, merchant shipping (vessels that transport cargo or passengers), however, is often poorly represented in MSP due to a perceived lack of fine-scale spatially explicit data to support decision making processes. 2. Here, using the Republic of Congo as an example, we show how publicly accessible satellite derived Automatic Identification System (S-AIS) data can address gaps in ocean observation data for shipping at a national scale. We also demonstrate how fine-scale (0.05 km2 resolution) spatial data layers derived from S-AIS (intensity, occupancy) can be used to generate maps of vessel pressure to provide an indication of patterns of impact on the marine environment and potential for conflict with other ocean-user groups. 3. We reveal that passenger vessels, offshore service vessels, bulk carrier and cargo vessels and tankers account for 93.7% of all vessels and vessel traffic annually, and that these sectors operate in a combined area equivalent to 92% of Congo's exclusive economic zone(EEZ) – far exceeding the areas allocated for other user-groups (conservation, fisheries and petrochemicals). We also show that the shallow coastal waters and habitats of the continental shelf are subject to more persistent pressure associated with shipping; and that the potential for conflict among user groups is likely to be greater with fisheries, whose zones are subject to the highest vessel pressure scores than with conservation or petrochemical sectors. 4. Synthesis and applications. Shipping dominates ocean use, and so excluding this sector from decision making could lead to increased conflict among user groups, poor compliance and negative environmental impacts. This study demonstrates how Satellite derived Automatic Identification System data can provide a comprehensive mechanism to fill gaps in ocean observation data and visualise patterns of vessel behaviour and potential threats to better support marine spatial planning at national scales.13-Feb-2018
Data from: Effects of sea ice cover on satellite-detected primary production in the Arctic Ocean
The influence of decreasing Arctic sea ice on net primary production (NPP) in the Arctic Ocean has been considered in multiple publications but is not well constrained owing to the potentially large errors in satellite algorithms. In particular, the Arctic Ocean is rich in coloured dissolved organic matter (CDOM) that interferes in the detection of chlorophyll a concentration of the standard algorithm, which is the primary input to NPP models. We used the quasi-analytic algorithm (Lee et al. 2002 Appl. Opti. 41, 5755−5772. (doi:10.1364/AO.41.005755)) that separates absorption by phytoplankton from absorption by CDOM and detrital matter. We merged satellite data from multiple satellite sensors and created a 19 year time series (1997–2015) of NPP. During this period, both the estimated annual total and the summer monthly maximum pan-Arctic NPP increased by about 47%. Positive monthly anomalies in NPP are highly correlated with positive anomalies in open water area during the summer months. Following the earlier ice retreat, the start of the high-productivity season has become earlier, e.g. at a mean rate of −3.0 d yr−1 in the northern Barents Sea, and the length of the high-productivity period has increased from 15 days in 1998 to 62 days in 2015. While in some areas, the termination of the productive season has been extended, owing to delayed ice formation, the termination has also become earlier in other areas, likely owing to limited nutrients.
Data from: Modeling avian biodiversity using raw, unclassified satellite imagery
Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management.
Data from: Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment
Population-level studies of how tit species (Parus spp.) track the changing phenology of their caterpillar food source have provided a model system allowing inference into how populations can adjust to changing climates, but are often limited because they implicitly assume all individuals experience similar environments. Ecologists are increasingly using satellite-derived data to quantify aspects of animals' environments, but so far studies examining phenology have generally done so at large spatial scales. Considering the scale at which individuals experience their environment is likely to be key if we are to understand the ecological and evolutionary processes acting on reproductive phenology within populations. Here, we use time series of satellite images, with a resolution of 240 m, to quantify spatial variation in vegetation green-up for a 385-ha mixed-deciduous woodland. Using data spanning 13 years, we demonstrate that annual population-level measures of the timing of peak abundance of winter moth larvae (Operophtera brumata) and the timing of egg laying in great tits (Parus major) and blue tits (Cyanistes caeruleus) is related to satellite-derived spring vegetation phenology. We go on to show that timing of local vegetation green-up significantly explained individual differences in tit reproductive phenology within the population, and that the degree of synchrony between bird and vegetation phenology showed marked spatial variation across the woodland. Areas of high oak tree (Quercus robur) and hazel (Corylus avellana) density showed the strongest match between remote-sensed vegetation phenology and reproductive phenology in both species. Marked within-population variation in the extent to which phenology of different trophic levels match suggests that more attention should be given to small-scale processes when exploring the causes and consequences of phenological matching. We discuss how use of remotely sensed data to study within-population variation could broaden the scale and scope of studies exploring phenological synchrony between organisms and their environment.
Data from: Satellite telemetry reveals higher fishing mortality rates than previously estimated, suggesting overfishing of an apex marine predator
Overfishing is a primary cause of population declines for many shark species of conservation concern. However, means of obtaining information on fishery interactions and mortality, necessary for the development of successful conservation strategies, are often fisheries-dependent and of questionable quality for many species of commercially exploited pelagic sharks. We used satellite telemetry as a fisheries-independent tool to document fisheries interactions, and quantify fishing mortality of the highly migratory shortfin mako shark (Isurus oxyrinchus) in the western North Atlantic Ocean. Forty satellite-tagged shortfin mako sharks tracked over 3 years entered the Exclusive Economic Zones of 19 countries and were harvested in fisheries of five countries, with 30% of tagged sharks harvested. Our tagging-derived estimates of instantaneous fishing mortality rates (F = 0.19–0.56) were 10-fold higher than previous estimates from fisheries-dependent data (approx. 0.015–0.024), suggesting data used in stock assessments may considerably underestimate fishing mortality. Additionally, our estimates of F were greater than those associated with maximum sustainable yield, suggesting a state of overfishing. This information has direct application to evaluations of stock status and for effective management of populations, and thus satellite tagging studies have potential to provide more accurate estimates of fishing mortality and survival than traditional fisheries-dependent methodology.
SENTINEL-2 SATELLITE IMAGE (2015, AUGUST 7) FOR CHANGE DETECTION ON "MURGIA ALTA" - TIME T2 DATA
<p><strong>Time T2 data:</strong> Sentinel-2 image, 10 bands at 20 meters spatial resolution; 2015, August 7; subset of the "Murgia Alta" protected area; projected in WGS84/UTM33; coregistered on the time T1 data.</p>
Data supporting Satellite in-situ electron density observations of the mid-latitude storm enhanced density on the noon meridional plane in the F region during the 20 November 2003 magnetic storm
<p>This is the data for the submitted paper: Satellite in-situ electron density observations of the mid-latitude storm enhanced density on the noon meridional plane in the F region during the 20 November 2003 magnetic storm. The data contains five files. The file NE_TGWeimer_2003324 is the electron density (NE) data along the CHAMP orbit on Nov 20, 2003. The file TGSED4_Mlat30_2003324 is the NE, HMF2, WI_ExB, VI_ExB and VN at Mlat = 30 on the noon meridional plane (MLT = 12 hr) in the Northern hemisphere on Nov 20, 2003. The temporal resolution is 1-min. The file TGSED4_Mlat60_2003324 is the NE, HMF2, WI_ExB, VI_ExB and VN at Mlat = 60 on the noon meridional plane (MLT = 12 hr) in the Northern hemisphere on Nov 20, 2003. The temporal resolution is 1-min. The file TGSED3_Mlat30_2003324 is the NE at Mlat = 30 as a function of MLT and UT. The temporal resolution is 5-min. The file TGSED3_Mlat60_2003324 is the NE at Mlat = 60 as a function of MLT and UT. The temporal resolution is 5-min.</p>
Dataset for "Public Health Benefits from Improved Identification of Severe Air Pollution Events with Geostationary Satellite Data"
<p>Dataset for "Public Health Benefits from Improved Identification of Severe Air Pollution Events with Geostationary Satellite Data" to be published in GeoHealth doi: 10.1029/2023GH000890</p>
Data of satellite-estimated CO2 and CH4 emissions from 113 meso-eutrophic lakes of China's Yangtze and Huai River Basin
<p>This supporting information provides the data, which inlcude lake size, CO2 flux, CH4 flux, and eutrophic status such as chlorophyll a (Chl-a) concentration and trophic state index</p>
OpenET model data for assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications
<h2>Overview</h2> <p>This dataset includes daily and monthly evapotranspiration (ET) data from the remote sensing models that comprise the [OpenET](https://openetdata.org/) ensemble as described in Melton et al., 2022 (https://doi.org/10.1111/1752-1688.12956); these data were extracted at specific locations within the contiguous United States that coincide with *in situ* measurement stations, including eddy covaraiance, Bowen-ratio, and lysimeter stations. Model ET data where extracted at each site in this dataset using flux footprints as described in Volk et al., (2023) (https://doi.org/10.1016/j.agrformet.2023.109307). These model data alongside the corresponding *in situ* ET data (https://doi.org/10.1016/j.dib.2023.109274) were subsequently used in the manuscript for the OpenET Phase II Intercomparison and Accuracy Assessment (https://doi.org/10.1038/s44221-023-00181-7). </p> <h3><br>Description of the data and file structure</h3> <p>The dataset is in a compressed (zipped) archive titled "OpenET_PhaseII_model_ET_dataset", so first it needs to be downloaded and extracted. The dataset is comprised of just three files. The first file is a Microsoft Excel file "Station_metadata.xlsx" that contains information about the *in situ* ET measurement stations where the OpenET model data was extracted. This file contains information such as site ID's, coordinates, land cover information, and site principal investigator (PI) contact information. Again, the corresponding *in situ* ET data are not included in this dataset. The other two files are tab-delimited text files containing timeseries the OpenET model data themselves, namely the daily ET [mm/day] and monthly ET [mm/month] as extracted for each model and the ensemble value as used in the OpenET Phase II Intercomparison and Accuracy Assessment. </p> <h3><br>Access information and code/software</h3> <p>OpenET data that was used here was produced using operational methods that are implemented on the Google Earth Engine platform. Monthly OpenET model data can be retrieved through Google Earth Data Catalog (e.g., https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0) or through the [online data explorer](https://openetdata.org/) or using the [OpenET API](https://openetdata.org/api-info/).</p>
Yield data from field measurements and satellite imagery from Sentinel-2 for three consecutive years
<p>Data From: Kayad A, Sozzi M, Gatto S, Marinello F, Pirotti F. Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques. <em>Remote Sensing</em>. 2019; 11(23):2873. https://doi.org/10.3390/rs11232873</p> <ul> <li>Yield values as point data</li> <li>Interpolated kriging yield data</li> <li>Satellite imagery</li> </ul>
Data and code for "Ambient Formaldehyde over the United States from Ground-Based (AQS) and Satellite (OMI) Observations"
<p>This file contains data and code in the study entitled "Ambient Formaldehyde over the United States from Ground-Based (AQS) and Satellite (OMI) Observations" in the journal <em>Remote Sensing</em>. </p>
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.