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781 results for “canopies”
Urban forest canopy cover, vegetation, and site characteristics, Twin Cities Metro Area, 2022 and 2023.
This data was primarily collected to assess forest quality within the Minneapolis-St. Paul (MSP) Metropolitan Area and to link above-ground and below-ground properties as part of the goals of the MSP-LTER Urban Tree Canopy research group. Here, we sampled vegetation on 48 circular plots with a 12.5 m radius distributed across 18 parks, registering the date of sampling, park and management agency names, the plot number, and geolocation (latitude, longitude, and elevation). The plots were randomly selected based on GEDI (Global Ecosystem Dynamics Investigation instrument) 2021 footprints in the MSP Metropolitan Area along accessible forested areas inside public parks, where the management agency allowed sampling. In each plot, we measured forest structure and diversity metrics, species names and abundance, DBH, height, distance from the plot center, the height where each individual canopy starts, and the relative position, exposure, and density of each canopy. We also measured understory plant structure and diversity in 4 subplots per plot, totaling 192 subplots. In these subplots, we surveyed all individual plants with heights over 20 cm, recording species names and abundance, plant basal diameter, plant height, and the total number of branches. Furthermore, we assessed the canopy openness above each subplot by calculating percent DIFN (diffuse non-interceptance) from fish eye pictures of the canopy at 1.3 meters over the subplot.
SBC LTER: Time series of quarterly NetCDF files of kelp biomass in the canopy from Landsat 5, 7 and 8, since 1984 (ongoing)
This data file represents a time series of canopy area of giant kelp, Macrocystis pyrifera, and bull kelp, Nereocystis luetkeana, and canopy biomass of giant kelp derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Landsat 9 Operational Land Imager 2 satellite imagery, along with relevant metadata. The kelp canopy is composed of the portions of fronds and stipes floating on the surface of the water. Canopy area (m) data are given for individual 30 x 30 meter pixels for all coastal areas of Baja California, Mexico, California, Oregon, and the outer coast of Washington (including offshore islands). Biomass data (wet weight, kg) are given for individual 30 x 30 meter pixels in the coastal areas extending from near Ano Nuevo, CA through the southern range limit in Baja California (including offshore islands), representing the range where giant kelp is the dominant canopy forming species. Data were derived from the three Landsat sensors listed above. Observations are made on a 16 day repeat cycle, for each instrument, but the temporal coverage is irregular because of cloud cover, instrument failure, and the mission length of each sensor (TM: 1984 – 2011, ETM+: 1999 – present, OLI: 2013 – present). Estimates of canopy area are derived from the fractional cover of kelp canopy determined from satellite surface reflectance. Estimates of kelp canopy biomass are derived from the relationship between giant kelp fractional cover determined from satellite surface reflectance and empirical measurements of giant kelp canopy biomass in long-term SBC LTER study plots obtained using SCUBA. The different Landsat sensors were calibrated to each other using simulated Landsat data derived from hyperspectral imagery. Missing data due to the ETM+ scan line corrector error were filled using a synchrony-based gap filling method. Data are organized into a single NetCDF file and contain the quarterly area and
Above- and Below-Ground Biomass and Canopy Height of Seagrass in Virginia Coastal Bays 2007-2021
This data set contains measurements of above and belowground biomass and canopy height in restored Z. marina meadows in the Virginia coastal bays. Samples were collected annually in June-July. GPS locations of sampling plots are available in the companion data set VCR11180.
Point-frame measurement of maximum canopy height for plant growth forms at the 2007 Anaktuvuk River Fire scar measured in 2019.
This file contains maximum plant heights from point frame measurements made in the southern section of the 2007 Anaktuvuk River fire scar, at a severely burned site and a nearby unburned site. Pin-vegetation contact was recorded using a 0.56 m2 frame with 41 evenly spaced sampling points. Data were collected during peak green in summer 2019. These data were used to examine the impact of post-fire changes in plant community composition and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaska moist acidic tussock tundra.
Florida Bay Seagrass Canopy Temperature Data, Everglades National Park (FCE LTER), South Florida, USA, September 2000 - ongoing
Point measurements of hourly temperature readings at the canopy height of a seagrass bed collected during visits to TS/Ph 7a, TS/Ph8, TS/Ph9, TS/Ph10 and TS/Ph11. Graphic representation of seagrass status and trends monitoring data and other related information can be located at http://serc.fiu.edu/seagrass/!CDreport/DataHome.htm
Hubbard Brook Experimental Forest: Hourly Temperature Under Canopy by HOBO sensors at Valleywide Plots, 2013-2020
Air temperature at 1.5 m agl is measured at hourly intervals under the canopy at every fifth valleywide plot plus one more between watersheds 1 and 4. The sensors are maintained by bird crews and volunteers. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Relations of the O-horizon with canopy tree species and hydropedologic soil types, 2021
As the interface between plants and soil, the organic horizon is the foundation of forest ecosystems. Two potential predictors of O-layer properties, vegetation and mineral soil type, are difficult to separate because they typically covary. We conducted a factorial study involving four canopy tree species and two soil types with distinctly different hydrology and topographic position to parse patterns in chemistry and microbiota of the O-layer in a north-temperate deciduous forest. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Watershed 6 Temporal Canopy Leaf Chemistry, 1992 - ongoing
Overstory foliage is collected in late summer from a reference forest to the west of Watershed 6 (also referred to as Bear Brook Watershed). Concentrations of C, N, P, K, Ca, Mn, Mg, and the natural abundance of N and C isotopes (delta-15N and delta-13C) in foliage are measured. These measurements, in combination with litterfall estimates of foliar biomass, allow us to estimate the pool of nutrients in foliage. They also allow us to estimate nutrient retranslocation, using measurements of leaf litterfall chemistry. Long-term measurements continue with the aim of detecting disturbances in nutrient cycling and trends in foliar chemistry over long time scales. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
RIV04 Moss cover in streams in wooded riparian areas and areas where canopy had been cut at Konza Prairie
Our project was designed to test if woody removal in a riparian zone allowed the system to rebound to a grassland stream state. We hypothesized that removal would increase light and decrease moss biomass.
Fungal litter mat cover in Cannopy Trimming Experiment (CTE) plots responses to canopy opening, hurricanes and drought
Fungi that bind leaf litter into mats and produce white-rot via degradation of lignin and other aromatic compounds influence forest nutrient cycling and soil fertility. Over three and a half years beginning in June 2014, 6 months before the second iteration of the Canopy Trimming Experiment (CTE), we measured quarterly the extent of white-rot litter mats formed by basidiomycete fungi in the Luquillo Mountains of Puerto Rico in response to disturbances – a simulated hurricane treatment executed by canopy trimming and debris addition in December 2014 (CTE0, a mid-year drought in 2015, and two hurricanes 10 days apart in September 2017. Percent fungal litter mat cover ranged from 0.4% after hurricanes Irma and Maria to a high of 53% in forest with undisturbed canopy prior to the 2017 hurricanes, with means mostly between 10 - 45% of fungal litter mat cover in undisturbed forest. Drought decreased litter mat cover in both treatments, except in one undisturbed plot dominated by a drought-resistant fungus, Marasmius crinis-equi. Percent fungal litter mat cover sharply declined after real hurricanes and the simulated hurricane treatment (CTE). We found that solar radiation had a significant treatment effect and was strongly negatively correlated with percent litter mat cover within each of the four climatic seasons. Solar radiation was also strongly negatively correlated with relative humidity, throughfall, rain and litter wetness. However, rainfall was negatively correlated with litter mat cover, possibly due to erosion or saturation during high rainfall events. Canopy opening reduced leaf litterfall rates but did not affect litter mat cover. The main negative effect on basidiomycete fungi that bind leaf litter into mats was lower litter moisture associated with increased solar radiation from canopy opening and high leaf fall during drought. Variation in drought tolerance among basidiomycete fungal litter mat formers provided some resilience to drought. \<para\> Support f
Nutrient mineralization from green leaves in litterbags of three mesh sizes in the LUQ-LTER Canopy Trimming 2 Experiment
Hurricanes generate disturbances in forests that alter physicochemical characteristics of the habitat by opening the canopy and depositing fresh wood and leaves. Our objectives were to evaluate the effects of simulated hurricane driven changes to nutrient fluxes from litter to soil immediately following canopy disturbance. This study used three complete replicated blocks with two canopy treatments, control and trim+debris. Measurements were made in three 5 x 5 m subplots within 20 x 20 m plots nested in the 30 x 30 m treatment areas. Anion and cation resin membranes were inserted into the fermentation layer at the litter-soil interface and retrieved after one week. The measurement intervals were 2-4 weeks before canopy trimming, 0-1, 1-2, 2-3 and 4-5 weeks after trimming. Nutrient mineralization differed significantly between control and trim+detritus. Total N and P fluxes occurred at 4-5 weeks after canopy trimming. Litter decomposition depends primarily on the interaction among climate, litter quality and biota, so consequently any change in habitat will result in changes in these factors. Our objectives were to evaluate the effects of hurricane driven changes to forests on green litter decomposition, invertebrate communities and nutrient mineralization. This study used three complete replicated blocks with two canopy treatments, control and trim+debris. Measurements were made in three 5 x 5 m subplots within 20 x 20 m plots nested in the 30 x 30 m treatment areas. Green leaves were enclosed in litterbags of three different mesh sizes in each subplot. Litterbags were retrieved after 21, 35, 84 and 168 days; decomposer fauna was extracted and identified, mineralized nutrients were measured using ion resin membranes, and weight loss was determined. Arthropod abundance differed significantly through time. In addition, the number of arthropod taxonomic groups and nutrient mineralization differed significantly between control and trim+detritus, and nutrient mineralizat
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Solar spectral irradiance measurements above and in-canopy (SLOCS and CloudRoots Amazonia, 2022)
<p> </p> <p><strong>Shedding Light On CloudRoots</strong></p> <p>Solar spectral irradiance measurements made with the sensors produced within the Shedding Light On Cloud Shadows (SLOCS) project, deployed at the CloudRoots Amazonia 2022 campaign. </p> <p><strong>Dataset contents</strong></p> <ul> <li>Level 0 (raw): the raw data as it comes from the instruments</li> <li>Level 1 (L1): data in NetCDF format with metadata, quality control, homogenized factory calibration (counts bin-1 dt-1)</li> <li>Level 2 (L2): calibrated L1 data in W m-2 nm-1</li> <li>extras: this folder includes reference calibration spectra and data quality quicklooks</li> </ul> <p>Data is available at 1 Hz (resampled) and 10 Hz (native) resolution. 10 Hz resolution is compressed using NetCDF compression with gzip level 5 (uncompressed is 1.13 GB per date).</p> <p><strong>Data quality and uncertainty<br></strong></p> <p>Please note this dataset is in version 0.1.0, meaning you should use the dataset with caution. Not all unphysical data may have been flagged as such, and spectral calibration is an estimate based on a simple modelled spectrum. This modelled spectrum is a standard tropical atmosphere without aerosols, and is not run with observed profiles except an ERA5 estimate of total column water vapour. Please refer to 'extras' for technical validation of the spectral calibration method, and LibRadtran input/output files.</p> <p>A production (1.0) version will be released as soon data is fully validated.</p> <p>Lower-end uncertainty can be estimated by looking at the sensor to sensor spread at wavelength level during the calibration measurements. In the calibration phase, all sensors were co-located and homogenized at wavelength level. The 13:50 to 14:10 UTC time on August 7 is the reference frame for spectral calibration. </p> <p>Other sources of uncertainty are difficult to quantify due to measurements taking place in a very heteregeneous forest. These uncertainties relate primarily to the less-than-perfect placement of sensors on the towers in comparison to the reference calibration phase. </p> <p>Sensor 18 is only available in raw data or calibrated data. Precalibration (homogenizing) is not possible given its deviating spectral filter set compared to the others (sensor version 3b vs. 3a). </p> <p><strong>Technical information</strong></p> <ul> <li>The NetCDF files comply with CF1.7 where applicable.</li> <li>Metadata include sensor location (altitude relative to ground and sea level, lat, lon). </li> <li>Code for processing raw data to NetCDF available at <a href="../records/10159129">https://zenodo.org/records/10159129</a></li> <li>Calibration of raw sensor units to spectral irradiance is done using a reference clear-sky spectrum simulated with LibRadtran. Settings and output is included in "extras".</li> </ul> <p><strong>More information</strong></p> <ul> <li><a href="https://chiel.ghost.io/slocs">SLOCS project homepage</a></li> <li><a href="https://cloudroots.wur.nl/">CloudRoots project homepage</a></li> <li>2022 campaign reference paper is in preparation</li> <li>See 'related works' for the instrument reference paper </li> </ul>
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>
Rising CO2 and warming reduce global canopy demand for nitrogen
<ul> <li>Nitrogen (N) limitation has been considered as a constraint on terrestrial carbon uptake in response to rising CO<sub>2</sub>and climate change. By extension, it has been suggested that declining carboxylation capacity (<em>V</em><sub>cmax</sub>) and leaf N content in enhanced-CO<sub>2</sub>­ experiments and satellite records signify increasing N limitation of primary production.</li> <li>We predicted <em>V</em><sub>cmax </sub>using the coordination hypothesis, and estimated changes in leaf-level photosynthetic N for 1982–2016 assuming proportionality with leaf-level <em>V</em><sub>cmax</sub> at 25˚C. Whole-canopy photosynthetic N waas derived using satellite-based leaf area index (LAI) data and an empirical extinction coefficient for <em>V</em><sub>cmax</sub>, and converted to annual N demand using estimated leaf turnover times.</li> <li>The predicted spatial pattern of <em>V</em><sub>cmax </sub>shares key features with an independent reconstruction from remotely-sensed leaf chlorophyll content. Predicted leaf photosynthetic N declined by 0.28 %/year, while observed leaf (total) N declined by 0.2–0.25 %/year. Predicted global canopy N (and N demand) declined from 1997 onwards, despite increasing LAI.</li> <li>Leaf-level responses to rising CO<sub>2</sub>, and to a lesser extent temperature, may have reduced the canopy requirement for N by more than rising LAI has increased it. This finding provides an alternative explanation for declining leaf N that does not depend on increasing N limitation.</li> </ul>
Supplementary dataset for "Rising CO2 and warming reduce global canopy demand for nitrogen"
<p>This repository contains the dataset used for “<strong>Rising CO<sub>2</sub> and warming reduce global canopy demand for nitrogen” </strong></p> <p>The deposition consists of:</p> <ol> <li>An satellite-derived leaf chlorophyll vcmax25 database (Luo<em> et al.</em>, 2019)</li> <li>Simulated <em>V<sub>cmax</sub></em> with all the factors based on the coordination hypothesis</li> <li>Simulated <em>V<sub>cmax </sub></em>with CO<sub>2</sub> fixed at 340 ppm based on the coordination hypothesis</li> <li>Simulated <em>V<sub>cmax</sub> </em>with fixed climate based on the coordination hypothesis</li> <li>Simulated turnover time.</li> <li>Simulated leaf-level <em>N</em><sub>rubisco</sub> (g m<sup>–2</sup> leaf area), canopy-level <em>N<sub>rubisco</sub></em> (g m<sup>–2</sup> ground area), annual leaf-level <em>N<sub>rubisco</sub></em>demand (g m<sup>–2</sup> leaf area year<sup>–1</sup>), and annual canopy-level of <em>N<sub>rubisco</sub></em> demand (g m<sup>–2</sup> ground area year<sup>–1</sup>) in figure 4.</li> <li>Lifespan of evergreen</li> </ol> <p>Note. </p> <ol> <li>LAI products used in the paper , such as TCDR LAI during 1982­–2016; GLASS LAI during 1982–2014; and GLOBMAP LAI during 1982–2011 are public available, the details information see Jiang <em>et al </em>(2017).</li> <li>Evergreen, deciduous and herbaceous vegetation fractions data derived from ESA CCI land cover products is publicly available, the details information see Li <em>et al </em>(2018).</li> <li>The climate force for <em>V<sub>cmax </sub></em>simulation was used CRU TS4.3 (Harris <em>et al,</em> 2020) for 1982–2016 at 0.5° resolution, which is publicly available at </li> </ol> <p><a href="https://crudata.uea.ac.uk/cru/data/hrg/">https://crudata.uea.ac.uk/cru/data/hrg/</a>.</p> <p>The data files are all in netcdf format at 0.5 resolution </p> <p>Reference:</p> <ol> <li><strong>Luo X, Croft H, Chen JM, He L, Keenan TF. 2019.</strong> Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content. <em>Global Change Biology</em> <strong>25</strong>(7): 2499-2514.</li> <li><strong>Jiang C, Ryu Y, Fang H, Myneni R, Claverie M, Zhu Z. 2017.</strong> Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. <em>Global Change Biology</em> <strong>23</strong>(10): 4133-4146.</li> <li><strong>Li W, MacBean N, Ciais P, Defourny P, Lamarche C, Bontemps S, Houghton RA, Peng S. 2018.</strong> Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015). <em>Earth Syst. Sci. Data</em> <strong>10</strong>(1): 219-234.</li> <li><strong>Harris I, Osborn TJ, Jones P, Lister D. 2020.</strong> Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. <em>Scientific Data</em> <strong>7</strong>(1): 109.</li> </ol>
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Allen Brain Atlas
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OpenNeuro
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