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3,105 results for “vegetation”
Sevilleta LTER Vegetation Sample Catalog- Ground Samples for Chemical Analysis
Several long-term studies at the Sevilleta LTER measure net primary production (NPP) across ecosystems and treatments. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. The NPP weight data (SEV 157) is obtained by harvesting a series of covers for species observed during plot sampling. These species are always harvested from habitat comparable to the plots in which they were recorded. This data is then used to make volumetric measurements of species and build regressions correlating biomass and volume. From these calculations, seasonal biomass and seasonal and annual NPP are determined. These sampled are then vouchered for use to do analyses of inorganic and organic components such as carbon, nitrogen, and phosphorous as well as and other macro and micro nutrients and organic components such as cellulose and lignin.
Grassland Vegetation Line-Intercept Transects at the Sevilleta National Wildlife Refuge, New Mexico
In 1989, line-intercept transects were installed to evaluate temporal and spatial dynamics across vegetation transition zones. Currently, a 400m transect is sampled at a grassland site (Deep Well) which is dominated by Bouteloua eriopoda (black grama) and, near the southern end of the transect, B. gracilis (blue grama). A second grassland site (Five Points), dominated by B. eriopoda and, to the south, Larrea tridentata (creosote), is also sampled. Both sites are sampled twice a year, in May/June and September/October, and measurements are taken at a one-centimeter resolution. The biannual sampling protocol detects potential responses in both cool and warm season plants as well as pre- and post-monsoon dynamics. Several transects have been discontinued and data archived within SEV200.
Small Mammal Exclosure Study (SMES) Vegetation Data from the Chihuahuan Desert Grassland and Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (1995-2009)
This is data for vegetation canopy cover measured from each of the SMES study plots. Vegetation canopy cover was measured from each of the 36 one-meter2 quadrats twice each year. Animal consumers have important roles in ecosystems, determining plant species composition and structure, regulating rates of plant production and nutrient, and altering soil structure and chemistry. The purpose of this study is to determine whether or not the activities of small mammals regulate plant community structure, plant species diversity, and spatial vegetation patterns in Chihuahuan Desert shrublands and grasslands. The purpose of this study is to determine whether or not the activities of small mammals regulate plant community structure, plant species diversity, and spatial vegetation patterns in Chihuahuan Desert shrublands and grasslands. What role if any do indigenous small mammal consumers have in maintaining desertified landscapes in the Chihuahuan Desert? Additionally, how do the effects of small mammals interact with changing climate to affect vegetation patterns over time? This study will provide long-term experimental tests of the roles of consumers on ecosystem pattern and process across a latitudinal climate gradient. The following questions or hypotheses will be addressed. 1) Do small mammals influence patterns of plant species composition and diversity, vegetation structure, and spatial patterns of vegetation canopy cover and biomass in Chihuahuan Desert shrublands and grasslands? Are small mammals keystone species that determine plant species composition and physiognomy of Chihuahuan Desert communities? Do small mammals have a significant role in maintaining the existence of shrub islands and spatial heterogeneity of creosotebush shrub communities? 2) Do small mammals affect the taxonomic composition and spatial pattern of vegetation similarly or differently in grassland communities as compared to shrub communities? How do patterns compare between grassland and shru
Vegetation Survey on the Virginia Barrier Islands - Species by habitat, 1974
This dataset contains observations by Cheryl McCaffrey during a 1974 mapping of the vegetation on the barrier islands of the Virginia Coast Reserve (McCaffrey, CA, Dueser RD. 1990. Preliminary Vascular Flora for the Virginia Barrier Islands. Va. J. Sci.. 41:259-281. http://www.vacadsci.org/vjsArchives/V41/41-4A/p259.pdf) It also includes additional observations by Terry Cook on Hog Island in 1989. Note, because the primary purpose of this survey was mapping, species are listed if they were observed, but no extraordinary efforts were made to list all species on a particular island. Thus, an observation indicates that a species was there, but lack of an observation does not necessarily mean that the species was absent.
CzechVeg-ESy: Expert system for automatic classification of vegetation plots from the Czech Republic
<p><strong>Expertní systém pro automatickou klasifikaci fytocenologických snímků z České republiky </strong><br> [popis a instrukce v češtině jsou uvedeny níže]</p> <p>***************************************************************************************************************</p> <p><strong>CzechVeg-ESy</strong> is an expert system for automatic classification of vegetation plots from the Czech Republic to the vegetation types defined in the monograph <em>Vegetation of the Czech Republic</em> (<a href="https://www.sci.muni.cz/botany/vegsci/vegetace.php?page=monograph&lang=en">Chytrý 2007-2013</a>). It is delivered in two main versions: The <strong>main version 1 (v1) </strong>is the original version used for the vegetation classification in that monograph, described in detail in its English Summary (<a href="https://www.sci.muni.cz/botany/chytry/Vegetation-Czech-Rep-Summary.pdf">Chytrý 2007</a>). Its subversions (indicated by dates) contains corrections of minor errors and species nomenclature. This version only classifies vegetation to the phytosociological associations. The <strong>main version 2 (v2)</strong> uses the same classification system as accepted in the <em>Vegetation of the Czech Republic</em>, but includes more advanced functions to provide more accurate classification. Moreover, it is hierarchical, performing classification not only to associations but also to alliances and classes (for the plots not classified at lower levels).</p> <p>Each version is delivered in two variants. The <strong>basic variant </strong>(CzechVeg-ESy-basic-v1.txt) assigns vegetation plots to associations following their formal definitions created using the Cocktail method (<a href="https://doi.org/10.2307/3236796">Bruelheide 2000</a>) modified by <a href="https://www.sci.muni.cz/botany/chytry/Koci_etal2003_JVS.pdf">Kočí et al (2003)</a>. These definitions are based on the presence of sociological species groups and the dominance of selected species. The expert system evaluates individual vegetation plots and assigns them to the associations. A classification process is considerably faster when the basic variant is used, as opposed to the full variant. The <strong>full variant</strong> (file CzechVeg-ESy-full-v1.txt) performs the same functions as the basic variant, but in addition, it can also assign the plots not classified by formal definitions based on their numerical similarity to the plots that fulfil the requirements of the formal definitions. Most of such plots can be considered as untypical from the phytosociological point of view, i.e. with poor correspondence to any of the defined vegetation types, in most cases because of the lack of ecologically specialized species. The method of similarity-based assignment is the Frequency-Positive Fidelity Index (FPFI) described in <a href="https://www.sci.muni.cz/botany/chytry/Koci_etal2003_JVS.pdf">Kočí et al. (2003)</a> and <a href="https://doi.org/10.1007/s11258-004-5798-8">Tichý (2005)</a>.</p> <p><strong>Instructions for using CzechVeg-ESy version 1</strong></p> <ol> <li>Vegetation plots have to be stored in the TURBOVEG 2 program (<a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) with the species list Czechia-Slovakia-2015 (contained in the file TurbovegSlBackup_Czechia_slovakia_2015.zip), which is largely compatible with the older species lists called Czechia-Slovakia-2012 and Central-Europe.</li> <li>Export plots from TURBOVEG 2 to a CC! file (Export / Other formats / JUICE input files) and import this file to the JUICE program (<a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>) using the species list in the file Checklist-Danihelka-et-al-2012-ver-2019-07-06.txt, which converts the plant nomenclature to correspond with the Checklist of vascular plants of the Czech Republic (<a href="http://www.preslia.cz/P123Danihelka.pdf">Danihelka et al. 2012</a>).</li> <li>Select Analysis / Expert system in the JUICE program.</li> <li>Upload the expert system file (either CzechVeg-ESy-basic-v1.txt or CzechVeg-ESy-full-v1.txt) by pressing Load ES File button.</li> <li>Modify species nomenclature by pressing the Modify Species Names button.</li> <li>If there are juvenile species in the herb layer in the plots to be analysed, delete them by pressing Delete Juveniles button.</li> <li>In some cases, narrow species concepts were changed to broader concepts, resulting in repetitions of the same names in the table. These must be merged using the Merge Same Spec. Names button. This will also merge records of the same species in different vegetation layers because the expert system assumes that each species name is contained only once in the same plot.</li> <li>When using the full version of the expert system, a threshold similarity value for similarity-based assignment must be specified in the field in the bottom right part of the form. The higher the value, the fewer plots will be assigned, while only those plots will be assigned that have a high similarity to the association. If the threshold value is set to 0, all the plots will be assigned, but some of them may be very dissimilar to the associations which they are assigned to.</li> <li>Run expert system using the Classify Relevé button (a plot marked by a previous mouse click will be classified) or Classify [colour] Relevés button (all the plots of the selected colour will be assigned).</li> <li>If a single plot is classified, species groups present in this plot and the assignment of the plot to an association will be shown. If the plot is not classified, no association will be listed. Occasionally, a plot can be assigned to more than one association. If the full version of the expert system is used, the table will contain a list of associations ranked by decreasing similarity to the plot.</li> <li>If multiple plots are classified, association codes will appear in the header of those plots that were assigned based on the formal definitions. The legends for the codes can be found in a printed version of the <em>Vegetation of the Czech Republic</em>, in its online version (<a href="https://pladias.cz/en/vegetation/">https://pladias.cz/en/vegetation/</a>) or in the expert system txt file. Plots not assigned to any association will be marked with ? and those assigned to more than one association will be marked with +. If the full version of the expert system is used, the output will contain the most similar associations for those plots which have remained unassigned to associations or were assigned to more than one association.</li> <li>Using the formal definition, the expert system normally assigns some plots of a single vegetation stand to a certain association while other plots of the same stand remain unassigned. This means that the stand consists of patches with species composition typical of the given association and patches with a less typical species composition. If different plots from a single relatively homogeneous stand are assigned to different associations, it is appropriate to interpret the stand as transitional between these associations.</li> </ol> <p>This electronic publication of CzechVeg-ESy was supported by the Czech Science Foundation (grant no. 17-15168S).</p> <p>***************************************************************************************************************</p> <p><strong>CzechVeg-ESy</strong> je expertní systém pro automatickou klasifikaci fytocenologických snímků z České republiky do vegetačních typů definovaných v monografii <em>Vegetace České republiky </em>(<a href="https://www.sci.muni.cz/botany/vegsci/vegetace.php?lang=en&page=monograph">Chytrý 2007-2013</a>). Existují dvě hlavní verze tohoto expertního systému: <strong>Hlavní verze 1 (v1) </strong>je originální verze použitá pro klasifikaci v této národní vegetační monografii, která je podrobně popsaná v její metodické kapitole. Její dílčí verze (označené datem) obsahují opravy drobných chyb a nomenklatury druhů. Tato verze klasifikuje fytocenologické snímky pouze do fytocenologických asociací. <strong>Hlavní verze 2 (v2)</strong> používá stejný klasifikační systém, jaký byl použit ve Vegetaci České republiky, ale používá pokročilejší funkce umožňující přesnější klasifikaci. Tato verze také provádí hierarchickou klasifikaci nejen do asociací, ale také do svazů a tříd (pro snímky nezařazené do nižších jednotek).</p> <p>Každá verze má dvě varianty. <strong>Základní varianta </strong>(soubor CzechVeg-ESy-basic-v1.txt) přiřazuje fytocenologické snímky do asociací na základě jejich formálních definic vytvořených metodou Cocktail (<a href="https://doi.org/10.2307/3236796">Bruelheide 2000</a>) v úpravě podle práce <a href="https://www.sci.muni.cz/botany/chytry/Koci_etal2003_JVS.pdf">Kočí et al (2003)</a>. Tyto definice jsou založeny na prezenci sociologických skupin druhů a dominanci vybraných druhů. Expertní systém vyhodnocuje každý jednotlivý fytocenologický snímek v datovém souboru a řadí jej do asociace. Klasifikace pomocí základní varianty je výrazně rychlejší než klasifikace pomocí plné varianty. <strong>Plná varianta </strong>(soubor CzechVeg-ESy-full-v1.txt) zajišťuje stejné funkce jako základní varianta, ale navíc klasifikuje i snímky neklasifikované formálními definicemi, a to na základě jejich numerické podobnosti ke skupinám snímků, které vyhovují podmínkám formálních definic asociací. Většinu takových snímků lze považovat z fytocenologického hlediska za netypické porosty, tj. takové, které plně neodpovídají definovaným vegetačním typům, zpravidla kvůli absenci ekologicky specializovaných druhů. Klasifikace na základě podobnosti se počítá pomocí indexu FPFI (Frequency-Positive Fidelity Index), který definovali <a href="https://www.sci.muni.cz/botany/chytry/Koci_etal2003_JVS.pdf">Kočí et al. (2003)</a> a <a href="https://doi.org/10.1007/s11258-004-5798-8">Tichý (2005)</a>.</p> <ol> <li>Fytocenologické snímky určené k analýze musí být uloženy v databázi v programu TURBOVEG 2 (<a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) s druhovým seznamem Czechia-Slovakia-2015 (soubor TurbovegSlBackup_Czechia_slovakia_2015.zip), který je kompatibilní s druhovými seznamy Czechia-Slovakia-2012 a Central-Europe.</li> <li>Snímky se exportují z programu TURBOVEG 2 do souboru CC! (Export / Other formats / JUICE input files) a tento soubor se importuje do programu JUICE (<a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>) s použitím druhového seznamu v souboru Checklist-Danihelka-et-al-2012-ver-2019-07-06.txt, čímž se nomenklatura konvertuje do nomenklatury odpovídající Seznamu cévnatých rostlin květeny České republiky (<a href="http://www.preslia.cz/P123Danihelka.pdf">Danihelka et al. 2012</a>).</li> <li>V programu JUICE se zvolí menu Analysis / Expert system.</li> <li>Tlačítkem Load ES File se nahraje do paměti soubor s příslušným expertním systémem (buď CzechVeg-ESy-basic-v1.txt, nebo CzechVeg-ESy-full-v1.txt).</li> <li>Tlačítkem Modify Species Names se upraví nomenklatura druhů tak, aby odpovídala nomenklatuře používané expertním systémem.</li> <li>Obsahují-li snímky určené k analýze juvenilní dřeviny v bylinném patru, je potřeba je vymazat tlačítkem Delete Juveniles.</li> <li>Při převodu nomenklatury se v některých případech převedlo užší pojetí druhů na širší, čímž vznikly v tabulce druhové údaje vedené pod stejnými jmény. Ty je potřeba sloučit tlačítkem Merge Same Spec. Names. Přitom se sloučí i údaje stejného druhu v různých patrech, protože expertní systém předpokládá jen jeden výskyt stejného druhového jména v jednom snímku.</li> <li>Pokud je používána plná (Full) verze expertního systému, je potřeba v okénku vpravo dole nastavit prahovou hodnotu podobnosti pro přiřazování snímků k asociacím pomocí podobnosti. Čím vyšší hodnota, tím méně snímků se přiřadí, ale přiřadí se ty, které se dané asociaci více podobají. Při hodnotě 0 se přiřadí všechny snímky, ale některé budou dané asociaci velmi nepodobné.</li> <li>Spustí se běh expertního systému, a to buď tlačítkem Classify Relevé (bude se klasifikovat jeden snímek, na který se předtím kliklo myší) nebo Classify [colour] Relevés (budou se klasifikovat všechny snímky vybrané barvy).</li> <li>Při klasifikaci jednoho snímku se zobrazí v tabulce druhové skupiny, jejich zastoupení v daném snímku a asociace, do které byl snímek přiřazen pomocí formální definice. Pokud přiřazen nebyl, nezobrazí se žádná asociace. Snímek může být přiřazen i do více než jedné asociace. Při použití plné verze expertního systému se do tabulky vypíší asociace v pořadí klesající podobnosti ke snímku, a to u těch snímků, které nebyly přiřazeny do žádné asociace nebo byly přiřazeny do více než jedné asociace.</li> <li>Při klasifikaci více snímků se do záhlaví tabulky vepíší kódy asociací u těch snímků, které se přiřadily na základě formálních definic. Převod kódů na jména asociací lze dohledat v tištěné verzi <em>Vegetace České republiky</em>, v její online verzi (<a href="https://pladias.cz/en/vegetation/">https://pladias.cz/vegetation/</a>) nebo v textovém souboru expertního systému. U snímků, které se nepřiřadily k žádné asociaci, se v záhlaví zobrazí znak ?. U snímků přiřazených do více než jedné asociace se zobrazí znak +. Při použití plné verze expertního systému se do tabulky vypíší nejpodobnější asociace u těch snímků, které nebyly přiřazeny do žádné asociace nebo byly přiřazeny do více než jedné asociace.</li> <li>Expertní systém běžně přiřazuje pomocí formálních definic některé snímky v porostu nebo lokálně rozlišovaném rostlinném společenstvu do určité asociace a jiné do žádné asociace, což znamená, že se porost skládá z míst s druhovým složením typickým pro danou asociaci a míst s méně typickým druhovým složením. Pokud expertní systém přiřadí různé snímky z jednoho relativně homogenního porostu k různým asociacím, je vhodné porost interpretovat jako přechodný mezi těmito asociacemi.</li> </ol> <p>Tato elektronická publikace expertního systému CzechVeg-ESy byla podpořena Grantovou agenturou České republiky (grant 17-15168S).</p>
BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. </p> <p> </p> <p>Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. </p> <p> </p> <p>The file name convention for the Level 3 NetCDF files is as follows.</p> <p> </p> <p>uas_L3_yyyymmdd_hhmmss_vx.x.nc</p> <p>where</p> <p>L3 = Level 3 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>x.x = version number </p> <p>Time is the flight start time in UTC.</p> <p>Version number description is provided in the NetCDF global attributes.</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. </p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0.</p> <p><strong>December 2023 update</strong>: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
NICHE Flanders: reference values for the (a)biotic requirements of vegetation types in Flanders, Belgium
<p>This dataset contains site requirements/tolerance limits (or "reference values") for 28 vegetation types found in Flanders. It gives the lower and upper limits or the classes within which these vegetation types can occur, for 7 site factors that determine potential vegetation development. These reference values can be used to determine the potential distribution of the different vegetation types with the ecohydrological model NICHE Flanders (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>, in Dutch).</p> <p>See the Technical info (available in English and Dutch) for more information.</p>
Local plot information observed on LandKlif plots during vegetation survey 2019
<p><span>LandKlif local plot information observed on site during vegetation survey 2019, including vegetation height, slope, aspect, proximity to hedge / forest edge / water, intensity of use (only for meadows), and further information on plot habitat.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2023-01-01 to 2023-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2023. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 29.6m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosystem Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2022-01-01 to 2022-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 29.6m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2021-01-01 to 2021-12-31 [RAW]
<div> <p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2021. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 29.6m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p> <p> </p> </div>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2020-01-01 to 2020-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2020. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 29.6m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2019-01-01 to 2019-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2019. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 30m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
Survey Questionnaire from Consumers according Fruits & Vegetables shopping
<p><span>E.K.PI.ZO. in collaboration with the AGRICULTURAL UNIVERSITY OF ATHENS (GPA) and the SMART AGRO HUB S.A. company, within the framework of the FOODITY - MI4SaferFood program, conducts survey in order to capture the needs and demands of consumers when they purchase fruits and vegetables. The results of this survey help on any future activcity for design APP for consumers. The dataset includes 293 consumers answered. </span></p> <p><span>This Survey done at April 2024 , from Greeks consumers </span></p> <p> </p>
Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
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>
Data and code for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps"
<p>Data and code used for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps" by Rumpf et al., submitted December 2021 to Science</p> <p>See file ReadMe.txt for a description of the content and the original publication for further explanations.</p> <p>You are free to use these data and code for scientific purposes but are obliged to cite the above-mentioned publication.<br> For further questions, contact sabine.rumpf@unibas.ch</p>
Leaf moisture content (live-fuel moisture content) at global scale from passive microwave satellite observations of vegetation optical depth (VOD2LFMC)
<p><strong>Related paper:</strong> <a href="https://hess.copernicus.org/preprints/hess-2022-121/">Forkel et al. (2022)</a></p> <p>The VOD2LFMC dataset contains estimates of leaf moisture content as defined as live-fuel moisture content (LFMC) derived from passive microwave satellite observation of vegetation optical depth (VOD). LFMC is defined as the fresh mass of a leaf over the dry mass and is expressed in %:</p> <p><span class="math-tex">\(LFMC = {m_{fresh}-m_{dry}\over m_{dry}}*100\%\)</span></p> <p>LFMC was estimated from the <a href="https://doi.org/10.5281/zenodo.2575599">VODCA version 1</a> dataset of Ku-band VOD using the model approach “B” as described in Forkel et al. (2022).</p> <p>The file VOD2LFMC-B_v01_2000-2017.zip contains (unzipped ~ 57 GB):</p> <ul> <li>daily global data per month netCDF files</li> <li>a README file</li> <li>Ancillary file VOD2LFMC-B_v01_support-by-obs.nc</li> </ul> <p>Grid, time and variable definitions:</p> <ul> <li> <p>Grid-name: Geographic Lat/Lon</p> </li> <li> <p>Pixel-size: 1/4 degrees</p> </li> <li> <p>Size-x: 1440</p> </li> <li> <p>Size-y: 557</p> </li> <li> <p>Time period: February 2000 – July 2017</p> </li> <li> <p>Temporal resolution: daily</p> </li> <li> <p>Variable: Live-fuel moisture content (LFMC) in %</p> </li> <li> <p>Valid-range: 0-400%</p> </li> </ul> <p> </p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2020 in China
<p>In this dataset, the MODIS vegetation index and land surface temperature products are processed into NDVI and LST monthly time series with a spatial resolution of 1 km, and the final precipitation data of GPM IMERG are downscaled, unified at a spatial resolution of 1 km. And after a standardization process, using the spatial distance model, a remote sensing drought monitoring dataset in China from 2001 to 2020 was produced based on the Temperature Vegetation Precipitation Dryness Index. For the specific construction process of this data, please refer to https://linkinghub.elsevier.com/retrieve/pii/S0034425720303278</p>
Arctic vegetation cover fractions derived from Landsat time series (1984-2020) for the greater Mackenzie Delta Region (Western Canadian Arctic)
<p>Data to the publication by Nill et al. (2022) "<em>Arctic shrub expansion revealed by Landsat-derived multitemporal<br> vegetation cover fractions in the Western Canadian Arctic"</em></p> <p>The dataset features Landsat-derived fractional cover estimates of Arctic plant functional types (shrub, evergreen trees, herbaceous, lichen) and other land cover (barren, water) in the greater Mackenzie Delta Region, Canada.<br> We utilized regression-based unmixing based on synthetic training data in order to build multitemporal Kernel Ridge Regression (KRR) models for estimating fractional cover and validated our predictions based on independent very-high-resolution imagery (please be referred to publication for details).<br> <br> <strong>Dataset information</strong><br> The fraction cover predictions ("krr-avg") are provided separately for each epoch (1984-1990, 1991-1996, ..., 2015-2020) and class/cover type. The decadal change images ("dec-cng") between 1984 and 2020 are provided separately for each class/cover type. The naming convention of the files is as follows:</p> <p>XXXX-XXXX_YYY-YYY_int16-10e3_class-Z-Z</p> <ul> <li>XXXX-XXXX = epoch, e.g. 2015-2020</li> <li>YYY-YYY = dataset ("krr-avg" = fraction cover, "dec-cng" = decadal fraction cover change)</li> <li>Z-Z = class ID and associated class name (sh = shrub, cf = coniferous, hb = herbaceous, lc = lichen, wt = water, br = barren)</li> </ul> <p>The fraction cover values are % scaled by 10,000. For instance, a value of 1234 refers to 12.34%. Further image metadata:</p> <ul> <li><strong>Datatype:</strong> Signed 16-bit integer (Int16) </li> <li><strong>Data format: </strong>GeoTiff (.tif)</li> <li><strong>No data value:</strong> -9999</li> <li><strong>Projection:</strong> EPSG:3573 with custom central meridian; WKT string: 'PROJCS["WGS 84 / North Pole LAEA Canada",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",90],PARAMETER["longitude_of_center",-135],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</li> </ul> <p><strong>Publication</strong><br> Nill, L., Grünberg, I., Ullmann, T., Gessner, M., Boike, J. & Hostert, P. (2022): Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic. Remote Sensing of Environment, 2022, 281. https://doi.org/10.1016/j.rse.2022.113228</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact Leon Nill (leon.nill@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/arctic-shrub/">here</a>.</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.