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38 results for “EVI”
The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America
<p><strong>Title: </strong>The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America.</p> <p><strong>Authors:</strong> Dalagnol, Ricardo; Wagner, Fabien Hubert; Galvão, Lênio Soares; Aragão, Luiz Eduardo Oliveira e Cruz.</p> <p><strong>Contact:</strong> Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>27 Jan 2022 - MANVI v2 was released!</strong> All data were reprocessed and improved. It is advised to re-download the whole series instead of combining v1 and v2. The dataset now covers years 2000-2021.</p> <p><strong>23 May 2019 - MANVI v1 was released.</strong> It covers years 2000-2018.</p> <p> </p> <p><strong>Data:</strong> MODIS (MAIAC) EVI and NDVI indices</p> <p><strong>Scale factor</strong>: 10000</p> <p><strong>Coverage:</strong> South America land</p> <p><strong>Time period:</strong> 2000 to 2021 (starting in 2000, Julian day 64)</p> <p><strong>Spatial resolution:</strong> 1 km</p> <p><strong>Temporal resolution:</strong> 16 days</p> <p><strong>Coordinate reference system:</strong> geographic projection, datum WGS-84</p> <p><strong>Processing details:</strong></p> <ul> <li>The original MODIS (MAIAC) data were described by Lyasputin et al. 2011 (<a href="https://doi.org/10.1029/2010JD014986">https://doi.org/10.1029/2010JD014986</a>). The daily MODIS (MAIAC) surface reflectance data from collection 6, acquired from Terra and Aqua satellites, are available from the MCD19A1 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1</a>). The Bidirectional Reflectance Distribution Function (BRDF) model parameters are available from MCD19A3 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3</a>)</li> <li>The daily MCD19A1 data at 1 km spatial resolution were normalized using the BRDF parameters and Ross-Thick Li-Sparse (RTLS) model considering a fixed nadir view and a 45 deg. solar zenith angle using the parameters from the MCD19A3 product</li> <li>The daily data were aggregated into 16-day composites by the pixel’s median. The 16-day composites always start from Day Of Year (DOY) 016 and end with DOY 352. Therefore, the remaining days from 352 to 365/366 were not used. This procedure was used to facilitate inter-annual comparisons</li> <li>The tiles that cover the South America were mosaicked and re-projected from sinusoidal to geographic projection</li> <li>The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were calculated using standard formulas. The EVI parameters were: C1 = 6, C2 = 7.5, L = 1, G = 2.5</li> </ul> <p><strong>File(s) format:</strong></p> <ul> <li>Zip files for EVI and NDVI - one per year: <ul> <li>Inside them there are raster files with ".tif" format, one per 16-day window. The filename syntax is "maiac_southamerica_DATA_YYYYDOY.tif", where YYYY is the year (e.g. 2000), and the DOY is the Julian day of the last day of the composite window, i.e. YYYYDOY for January 2005 for DOY from 001 to 016 is 2005016, from DOY 017 to 032 is 2005032, etc.</li> </ul> </li> <li>Csv files with the YYYYDOY and "real" dates for the time period</li> </ul> <p><strong>Code:</strong> <a href="https://github.com/ricds/maiac_processing">https://github.com/ricds/maiac_processing</a></p> <p><strong>Acknowledgements:</strong> This work was funded by São Paulo Research Foundation – FAPESP, Brazil, grant 2015/22987-7. We thank NASA, and especially Yujie Wang and Alexei Lyapustin, for providing the freely available MODIS (MAIAC) data.</p> <p> </p> <p><strong>Dataset usage</strong>: This dataset is a product of the first author's PhD work and lots of hours of coding and patience. It is free to use, but if you use this dataset in your work, please make sure to properly cite the repository. We also welcome users to invite us for collaboration.</p> <p> </p> <p><strong>For use of this dataset please cite:</strong></p> <p>Dalagnol, Ricardo; Wagner, Fabien Hubert; Galvão, Lênio Soares; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America". (Version v2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3159487">https://doi.org/10.5281/zenodo.3159487</a></p> <p> </p> <p><strong>More information: </strong>contact Ricardo Dalagnol (ricds@hotmail.com). We also have the MODIS (MAIAC) BRDF-corrected bands 1-8, EVI, NDVI at 1 km with 16-day and monthly aggregation composites.</p>
EVI: Evidence Graph Ontology v1.0
<p>The Evidence Graph ontology (EVI v1.0) extends core concepts from the W3C Provenance Ontology PROV-O to describe evidence for correctness of findings in biomedical publications. The semantic data model in EVI is expressed using OWL2 Web Ontology Language (OWL2).</p> <p>The core PROV ontology concepts used in EVI are Entity, Activity, and Agent, with two sub-classes Person and Organization. The object properties in EVI are used to establish relations among instances, most of which are of type DigitalObject. Computations are activities performed on instances of other DigitalObjects, Software or Services.</p> <p>The latest release of EVI is v1.0 that can be accessed here at <a href="https://w3id.org/EVI">https://w3id.org/EVI.</a></p>
MODIS NDVI and EVI, 16-day time series for Europe at 1 km resolution
<p>Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS data for Europe at 1 km resolution.</p> <p>Source data:<br> - MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MOD13A1 v006): https://lpdaac.usgs.gov/products/mod13a1v006/<br> - MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MYD13A1 v006): https://lpdaac.usgs.gov/products/myd13a1v006/</p> <p><br> The MOD/MYD13A1 Version 6 product provide Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.</p> <p>For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 16 days. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data".</p> <p>File naming:<br> productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif<br> example: MOD13A1.A2020305_mosaic_1000m_16_days_NDVI.tif</p> <p>The date is Year and Day of Year.</p> <p>Values are NDVI/EVI * 10000. Example: Value 6473 = 0.6473</p> <p>Projection + EPSG code:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG: 3035)</p> <p>Spatial extent:<br> north: 72N<br> south: 30S<br> west: -52W<br> east: 49E</p> <p>Spatial resolution:<br> 1 km</p> <p>Temporal resolution:<br> 16 days</p> <p>Pixel values:<br> NDVI/EVI * 10000 (scaled to Integer; example: value 6473 = 0.6473)</p> <p>Software used:<br> GRASS GIS 8.0</p> <p>Original dataset license:<br> All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/myd13a1v006/</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Annual seasonality trends of Enhanced Vegetation Index (EVI), Maricopa County, Arizona, 2001-2018
Description: A dataset with 18 years (2001-2018) of consistent, spatial and temporal patterns of enhanced vegetation index (EVI) values in Maricopa County AZ Abstract: This dataset consists of 18 years (2001-2018) of consistent, spatial, and temporal patterns of vegetation indices, as expressed by the enhanced vegetation index (EVI), in Maricopa County, AZ. I download and process images, at 250m resolution, from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS uses the atmospherically-corrected reflectance in the red, near-infrared, and blue wavebands to calculate vegetation indices. In the last decades, vegetation indices have been widely used for monitoring the seasonal variation of vegetation, document vegetation structure, productivity, overall health, and land cover changes and measure vegetation productivity in desert landscapes. EVI is also of high interest to investigate the potential distribution of plant and animal species and to investigate the effect of climate change on vegetation productivity. MODIS data were obtained from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. Spatial Extent: Maricopa County, AZ, USA.
Avludaki merdiven, Gençlik ve Kültür Evi, Mardin
Mardin Gençlik ve Kültür Evi'nin avlusunda bulunan, mutfağa ve kütüphaneye ulaşmamızı sağlayan merdiven. Source: Objaverse 1.0 / Sketchfab
eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI
<p>Gross Primary Productivity (GPP) represents the cumulative amount of carbon dioxide (CO<sub>2</sub>) assimilated by green plants through photosynthesis at specific time intervals and spatial scales. It is the main component of the carbon exchange between the terrestrial biosphere and the atmosphere, and has a major influence on global climate and terrestrial ecosystem functioning. Over the last two decades, the continuous and reliable collection of global land surface variables by EOS-MODIS, and the parallel development of the eddy-covariance flux tower network (FLUXNET) have enabled the integration of MODIS observations with tower measurements for the calibration and validation of remote sensing models to obtain global GPP estimates. Despite the significant progress and success to date, current remote sensing GPP models based on the light use efficiency (LUE) concept share several limitations, including the difficulty in accurately predicting LUE variability and the associated use of land cover maps and look-up tables for biome specific maximum LUE, further down-regulated by coarse resolution interpolated meteorological data, which introduce significant uncertainties in the predicted GPP. To address the above limitations, here we applied a simple yet ecologically sound remote sensing GPP model based on the ecosystem light use efficiency (eLUE) concept, using the more than two decades of global MODIS Enhanced Vegetation Index (EVI) product and the publicly available FLUXDATA2015 dataset, to generate a global 5 km, 16-d GPP product (eLUE-GPP) from February 2000 to March 2024. Cross-validation with 202 flux tower sites (1494 site/year) showed favorable accuracy of eLUE-GPP (hereafter GPP<sub>eLUE</sub>) (<em>R</em><sup>2</sup> = 0.71, RMSE = 2.11 g C m<sup>-2</sup> d<sup>-1</sup>). The uncertainty associated with GPP<sub>eLUE</sub> is comparatively lower than that of the other global GPP datasets (MOD17, FluxSat, VPM, among others). We have also calculated the uncertainty analytically for each GPP estimate based on the law of error propagation, which allows quantification of the error budget in applications such as Earth system model benchmarking and atmospheric inversion. Our estimate of global total annual GPP, averaged over the period 2001-2023, was 138.46±13.92 Pg C yr<sup>-1</sup>. Furthermore, we found a significant increasing trend in global total annual GPP at a rate of 0.28±0.05 Pg C yr<sup>-1</sup> (<em>p</em> < 0.001) from 2001 to 2023, particularly in eastern Asia, northern India, Europe, eastern North America, and central South America. We expect that the eLUE-GPP product will enable a more accurate diagnostic analysis of the global carbon budget and thus contribute to climate change research.</p>
Tolerability and Satisfaction With Evie
ClinicalTrials.gov study NCT01969812. IPD Sharing: Not stated. Countries: 1. Publications: 3.
eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI
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Seasonal Enhanced Vegetation Index (EVI) Imagery for Cimarron County, Oklahoma 2005-2020
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Giriş kapısı, Gençlik ve Kültür Evi, Mardin
Mardin Gençlik ve Kültür Evi'nin ikinci giriş kapısı Source: Objaverse 1.0 / Sketchfab
The EVI data for each solar farm
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Documentos EVIS 2022
<p>La Escuela de Verano en Iniciación en Investigación para Estudiantes de Pregrado de las Ciencias de la Salud (EVIS) busca promover un mayor interés de los estudiantes del área de la salud por la ciencia y una temprana alfabetización científica. Se espera que en el mediano plazo esta iniciativa internacional permita la incorporación de estudiantes en líneas de investigación tanto dentro como fuera de sus Universidades de origen y una mayor presencia de los egresados del área de la salud en programas de postgrado científicos, dentro y fuera de Chile. El evento es coordinado por el Dr. César Rivera.</p>
Gençlik ve Kültür Evi, Mardin
Mardin Gençlik ve Kültür Evi'nin 3D modeli Source: Objaverse 1.0 / Sketchfab
Gençlik ve Kültür Evi- Çok Amaçlı Salon, Mardin
Mardin Gençlik ve Kültür Evi'nin çok amaçlı salonu Source: Objaverse 1.0 / Sketchfab
Reconstructed 250 m resolution EVI dataset in China
<p>Reconstructed 250 m spatial resolution and 16-day composite NDVI and EVI datasets in China from 2000 to 2022 were developed based on a spatio-temporal reconstruction method and MODIS MOD13Q1 data. Reference: Reconstructed NDVI and EVI datasets in China (ReVIChina) generated by a spatial-interannual reconstruction method</p>
EVIE-Study: Slow Release Insemination Versus Standard Intrauterine Insemination Study
ClinicalTrials.gov study NCT02315040. IPD Sharing: Not stated. Countries: 4. Publications: 1.
eVISualisation of Physical Activity and Pain (eVIS) for Patients With Chronic Pain
ClinicalTrials.gov study NCT05009459. IPD Sharing: YES. Countries: 1. Publications: 3.
Multivisceral Resection for Locally Advanced Gastric Cancer: A Systematic Review and Evi-dence Quality Assessment
<p>Dataset concerning the systematic review of locally advanced gastric cancer.</p>
Vegetation Index and Phenology (VIP) Phenology EVI-2 Yearly Global 0.05Deg CMG V004
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 – 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIPPHEN data product is provided globally at 0.05 degree (5600 meters (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data.
MODIS/Terra Gap-Filled, Smoothed EVI 8-Day L4 250m SIN Grid
The MODIS/Terra Gap-Filled, Smoothed NDVI 8-Day L4 250m SIN Grid product, with short-name MOD09Q1G_NDVI is calculated from MODIS surface reflectance products (MOD09), at 250-m resolution. MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in NACP, that use MODIS data as input, require gap-free data. The procedure contains two algorithm stages, one for smoothing and one for gap filling, which attempt to maximize the use of high-quality data to replace missing or poor-quality observations.
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