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691 results for “Vegetation Data”
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>
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>
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>
Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2018-11-05 to 2018-12-31 [RAW]
<p>Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. The phenocam "hartheim2" was put into operation on November 5, 2018. There are no phenocam images before that date at this site.</p> <p>Phenocam "hartheim2" shows the view from the main tower at 8.4 m 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 in-canopy vegetation.</p>
Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2020-01-01 to 2020-12-31 [RAW]
<p>Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2020. </p> <p>Phenocam "hartheim2" shows the view from the main tower at 8.4 m 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 in-canopy vegetation.</p>
Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2021-01-01 to 2021-12-31 [RAW]
<div> <p>Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2021. </p> <p>Phenocam "hartheim2" shows the view from the main tower at 7m 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 in-canopy vegetation.</p> </div>
Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2022-01-01 to 2022-12-31 [RAW]
<div> <p>Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. </p> <p>Phenocam "hartheim2" shows the view from the main tower at at 8.4 m 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 in-canopy vegetation.</p> <p> </p> </div>
Data from: Will Current Protected Areas Harbour Refugia for Threatened Arctic Vegetation Types until 2050? A First Assessment
<p>We present predictions of Arctic vegetation for 2050 based on a combination of climate models (namely, EC-Earth3-Veg, IPSL-CM6A-LR, and MRI-ESM2-0), emission scenarios (names, SSP126 and SSP585) and tree dispersal rate scenarios (unrestricted, 20km and 5km) based on the methods of Pearson et al. (2013) and the new raster version of the Circumpolar Arctic Vegetation Map (CAVM) (Raynolds et al. 2019). We additionally present a dataset summarising total areas for each vegetation type in the CAVM and the forecasted models based on the computation of zonal histograms in ArcGIS (zonal_histogram_results.csv), for the total Arctic as well as only within protected areas, defined by the Map of Arctic Protected Areas (CAFF and PAME 2017). We also present a potential map of refugia for what we deem the realistic model (IPSL, SSP585, 20 km tree dispersal) as a raster file. Refugia were identified as regions where the vegetation remained the same between the CAVM and the predictions. Additionally, we present a map of model agreement, showing the degree to which other models agree with the vegetation classification for our refugia.</p> <p>All predictions named according to the tree dispersal rate, climate model, and emissions scenario, preceded by the term "pred". For example: "pred_unres_mri_585" represents the unrestricted tree dispersal, MRI-ESM-0 climate model, and SSP585 scenario-based prediction. The MRI-ESM-0 x SSP585 combination had gaps in data which results in a lack of predictions in some areas; this affects 3 models.</p> <p>Further details and all code associated with these datasets are found <a href="https://github.com/PlekhanovaElena/Arctic_vegetation_prediction">here</a>.</p>
Water chemistry and aquatic vegetation data from Les Cheneaux Islands, Northern Lake Huron, Michigan, USA, 2016-2018
Remote sensing approaches that could identify species of submerged aquatic vegetation (SAV) and measure their extent in lake littoral zones would greatly enhance their study and management, especially if they can provide faster or more accurate results than traditional field methods. Remote sensing with multispectral sensors can provide this capability, but SAV identification with this technology must address the challenges of light extinction in aquatic environments where chlorophyll, dissolved organic carbon, and suspended minerals can affect water clarity and the strength of the sensed light signal. Here, we present environmental data collected to support a study using an unmanned aerial system (UAS)-enabled methodology to identify the extent of the invasive SAV species Myriophyllum spicatum (Eurasian watermilfoil, or EWM) in the Les Cheneaux Islands area of northwestern Lake Huron, Michigan, USA. Data collected includes water chemistry (nitrogen, phosphorus, carbon, suspended solids, chlorophyll a), light profiles, and submerged aquatic vegetation characteristics including cover, species dominance using aquatic vegetation survey methods (AVAS), and biomass.
Submersed Aquatic Vegetation community multi-year data from the Sacramento - San Joaquin Delta in California
Since 2007, field data have been collected in the Sacramento - San Joaquin Delta in northern California for the purpose of training and validating invasive species maps derived from remote sensing imagery over the Delta. The field crew collected submersed aquatic vegetation (SAV) species location data. For each point they noted attributes such as species name(s), location, cover estimates, and patch size. In addition, a thatching rake tethered to a rope was thrown off the side of the boat and pulled back out of the water; Secchi depth was measured using a Secchi disk and depth to the SAV mat was estimated by the field crew. Points were collected in patches larger than 9 square meters (3 m x 3 m). Point locations were measured using high precision (sub-meter accuracy) Trimble DGPS units (Trimble Navigation Limited, Sunnyvale, California) with Wide Area Augmentation System (WAAS) differential correction. All data points were exported as ArcGIS shapefiles and projected to UTM Zone 10N, Datum WGS-84 however this dataset includes the Latitude and Longitude of each point in decimal degrees. The spatial and attribute data quality was checked by examining photos of the data points and confirming the identify of the documented species.
Plot-based vegetation data for a large tract of old--growth hemlock-northern hardwood forest, Marquette Co., Michigan: 1988
In 1987-88 members of the Burton V. Barnes lab at University of Michigan conducted a landscape inventory of portions of the Huron Mountain Club lands (primarily, the self-declared 'Reserved Area') in Powell Township, northern Marquette County, MI. The data-set deposited here, collect under direction of Philip E. Stuart (then a graduate student in the lab) focuses on the ca. 1200 ha of old-growth, mesic hemlock-northern hardwood forests within the larger property. 313 plots (450 m^2) were established at nodes of an approximately 10 chain (~192 m) grid that fell within these forest types. The data-set includes canopy tree measurements, ground-layer cover estimates (for a subpplot), and a number of soil and topographic variables (measured directly and derived). A description of the study and results is published in Simpson et al. 1990. Occasional Papers of the Huron Mountain Wildlife Foundation Number 4, with associated maps.
El Yunque National Forest Vegetation Monitoring Project data, 2019-2021
This data package includes data collected as part of the El Yunque National Forest (EYNF) Vegetation Monitoring Project, the first phase of which was conducted between January 2019 and April 2021 and is now completed. EYNF is coterminous with the Luquillo Experimental Forest. The project was implemented as a collaborative endeavor between the Amigos de El Yunque Foundation, the USDA Forest Service, and the University of Puerto Rico-Río Piedras Campus. Funding was provided by the Forest Service. It includes data for 40 0.1-ha circular plots located in secondary forest within the subtropical moist and wet life zones, ranging in elevation from approximately 100-600 m asl. Plots are classified into three groups based on combinations of their historical canopy cover and post-agricultural regeneration pathways. The first group corresponds to secondary forest plots with >50% canopy cover in 1936 that have continued to recover via passive natural regeneration (>50 P plots). The second group corresponds to secondary forest plots with <50% canopy cover in 1936 that have continued to recover via passive natural regeneration (<50 P plots). The third group corresponds to secondary forest plots that also had <50% cover in 1936 and experienced a combination of both assisted and passive natural regeneration (50 A+P plots). The assisted regeneration occurred up to the early 1980s. Since the 1980s this third group of plots has only undergone exclusively passive natural restoration. Eleven plots (total area = 1.1 ha) are classified as >50 P, 21 plots (total area = 2.1 ha) are classified as <50 P, and 8 plots (total area = 0.8 ha) as <50 A+P. There are two data sets. The first represents general plot and ground cover data for the 40 plots. The second represents tree composition, structure, biomass, and ecosystem service data for 4242 trees within the 40 plots. Data were collected using i-Tree Eco methodology.
Tree regeneration after fire: Wickersham Dome long-term vegetation study, birch height data
These data represent the most recent set of observations (made in 2002 by J. Johnstone) for several long-term vegetation monitoring plots near Wickersham Dome that were set up by Les Viereck and Joan Foote following the 1971 wildfire and 1978 experimental burns. Earlier records are available in the BNZ long-term vegetation database. This dataset documents tree seedling/sapling and shrub measurements made in 2002. Lists heights of all individual paper birch (Betula papyrifera) present in a plot.
Tree regeneration after fire: Wickersham Dome long-term vegetation study, bl. spruce height data
These data represent the most recent set of observations (made in 2002 by J. Johnstone) for several long-term vegetation monitoring plots near Wickersham Dome that were set up by Les Viereck and Joan Foote following the 1971 wildfire and 1978 experimental burns. Earlier records are available in the BNZ long-term vegetation database. This dataset documents tree seedling/sapling and shrub measurements made in 2002. Lists heights of all individual black spruce (Picea mariana) present in a plot.
Tree regeneration after fire: Wickersham Dome long-term vegetation study, aspen height data
These data represent the most recent set of observations (made in 2002 by J. Johnstone) for several long-term vegetation monitoring plots near Wickersham Dome that were set up by Les Viereck and Joan Foote following the 1971 wildfire and 1978 experimental burns. Earlier records are available in the BNZ long-term vegetation database. This dataset documents tree seedling/sapling and shrub measurements made in 2002. Lists heights of all individual trembling aspen (Populus tremuloides) present in a plot.
Normalized Difference Vegetation Index (NDVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
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International Brain Laboratory public data
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OpenNeuro
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