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284 results for “LAI”
WSC - Leaf area index (LAI) at various points within Wibu field site, 2012-2014
Leaf area index (LAI) measurements collected at various points within the Wibu field site between 2012-2014. Measurements were collected approximately weekly from plant emergence until appr. 1 month past the onset of senescence. The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons; therefore, these are all LAI values for corn. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site and use of the LAI data.
Summary of three different Leaf Area Index (LAI) methodologies of 19 1m x 1m point frame plots sampled near the LTER Shrub plots at Toolik Field Station in AK the summer of 2012.
Summary of three methods used to estimate the Leaf Area Index (LAI) of 19 1m x 1m plots sampled with a point frame near the LTER Shrub plots at the Toolik Field Station in AK the summer of 2012. The methods used were: (1) exponential relationship between LAI and Normalized Leaf Index (NDVI) as measured above the canopy with a Unispec spectroradiometer; (2) Delta-T SunScan canopy analyzer held at 5 cm above the ground under both direct and diffuse light conditions; (3) pin-drop point frame technique. Where values have been averaged (such as for the NDVI and SunScan measurements), the standard deviation is given. Raw data are available upon request for the Unispec data; the raw SunScan data is available under the file "PF_SunScan_LAI".
Leaf area index (LAI) recorded from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains leaf area index (LAI) measurements from an nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016. LAI was recorded using a handheld plant canopy analyzer (LI-COR 2200C; LI-COR, Lincoln, NE, USA) Data spans 4 years from 2016 (when fertilization began) until 2019. Data was recorded once a year at the peak of each growing season.
Understory percent cover, plant traits, canopy LAI, PAR, temperature, and soil moisture data at multiple time points for sites in the burn chronosequence and Indian Point forest at the University of Michigan Biological Station, Pellston, MI (2022-2023)
Community ecology has sought to understand the mechanisms by which plant communities are assembled through time and space. One prominent way to address how communities are assembled is by quantifying functional traits. While there is a tremendous body of literature on functional traits, debate persists about how to account for variation in measured traits. For example, intraspecific trait variation (ITV) can be equal to or greater than interspecific trait variation and ITV has also been found to vary greatly across years. Therefore, there is a need to account for variability in functional trait measures among and within species and through time to improve our understanding of community assembly. Chronosequences are a powerful tool to address temporal changes in community dynamics, however, the inclusion of understory plants in forest chronosequence studies is still relatively uncommon. Previous chronosequence studies have been primarily performed in grasslands or in a limited subset of forest types, so further work is needed in understory plant traits across other ecosystems and climates to improve trait-based understanding of understory plant communities through time. Additionally, because plant traits change as ecosystems age, community interactions are likely to change with ecosystem age. Interactions of particular interest are herbivory, arthropod predation, and the influence of plant traits on arthropod diversity.
Leaf area index (LAI) by plant functional group in moist acidic tussock tundra, at the 2007 Anaktuvuk River fire scar measured in 2017
This file contains leaf area index (LAI) based on biomass measurements from an aboveground pluck in the southern portion of the Anaktuvuk River fire scar, and a nearby unburned site in late July 2017. Vegetation was sampled randomly at 10-m intervals along two 100 meter transects at both the burned and unburned sites. Vegetation was sampled within a 10X40 cm quadrat to the mineral layer, and plant material was sorted into new and old aboveground leaf and woody biomass by species. All samples were dried and weighed, and subsampled leaf material was scanned to determine specific leaf area (centimeterSquaredPerGram biomass) per species, which was then used to transform leaf biomass (gramPerMeterSquared) into the leaf area index for each site.
Hubbard Brook Experimental Forest: Leaf Area Index (LAI) Watershed 1
Leaf area index (LAI) of the mature deciduous forest on WS1 at Hubbard Brook Experimental Forest is estimated on the basis of leaf litterfall collections; the raw data for litterfall are posted in the EDI data package – Fine Litterfall Data at the Hubbard Brook Experimental Forest, 1992 – present (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=49). This watershed was treated with a calcium silicate mineral (wollastonite) in 1999 to gradually replace Ca lost as a result of acid deposition. Leaf litterfall is collected in 0.097 m2 litter traps raised 1.5 m above ground level and is sorted by species. The number of leaves of each species is counted. The counts are multiplied by the average area per leaf for each species in each plot to estimate LAI. Litter traps are located randomly within each of three plots that are arranged along the elevation gradient within the deciduous forest zone.
Hubbard Brook Experimental Forest: Leaf Area Index (LAI) Bear Brook Watershed (West of Watershed 6)
Leaf area index (LAI) of the mature deciduous forest in the Bear Brook watershed (west of WS6) at Hubbard Brook Experimental Forest is estimated on the basis of leaf litterfall collections; the raw data for litterfall are posted in the EDI data package – Fine Litterfall Data at the Hubbard Brook Experimental Forest, 1992 – present (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=49). Leaf litterfall collected in 0.097 m2 litter traps is sorted by species. The number of leaves of each species is counted. The counts are multiplied by the average area per leaf for each species in each plot to estimate LAI. Litter traps are located randomly within each of four plots that are arranged along the elevation gradient within the deciduous forest zone. 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: Leaf Area Index (LAI) Throughfall Plots
Leaf area index (LAI) of the mature deciduous forest adjacent to WS6 at Hubbard Brook Experimental Forest is estimated on the basis of leaf litterfall collections; the raw data for litterfall are posted in the EDI data package – Fine Litterfall Data at the Hubbard Brook Experimental Forest, 1992 – present (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=49). These plots are designated TF, referring to throughfall chemistry collections performed at these plots many years ago (Lovett et al. 1996). Leaf litterfall is collected in 0.097 m2 litter traps raised 1.5 m above ground level and is sorted by species. The number of leaves of each species is counted. The counts are multiplied by the average area per leaf for each species in each plot to estimate LAI. Litter traps are located randomly within each of three plots that are arranged along the elevation gradient within the deciduous forest zone. 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. Gary M. Lovett, Scott S. Nolan, Charles T. Driscoll, and Timothy J. Fahey. Factors regulating throughfall flux in a New Hampshire forested landscape. Canadian Journal of Forest Research. 26(12): 2134-2144. https://doi.org/10.1139/x26-242
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Leaf Area Index (LAI), 2004
Leaf area index (LAI) is commonly used to assess forest canopies, and is calculated as the area of all leaves per unit area of ground. In September 2004, LAI was measured in all Bartlett Experimental Forest stands (C1-C9) of the MELNHE study in New Hampshire, using an LAI-2000 Plant Canopy Analyzer. Variables reported are leaf area index (LAI), standard error of LAI (SEL), diffuse non-interceptance (DIFN), mean tip angle (MTA), standard error of mean tip angle (SEM), and sample size (SMP). Additional detail on the MELNHE project, including a data table of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344. 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.
LAI_TS_Val: LAI time-series validation datasets in the 1-km pixel grid at global scale from 2001 to 2011
<p>Leaf area index (LAI), which is defined as one half of the total green leaf area per unit ground surface area, is a critical structural variable for quantifying the exchange processes of energy and matter between the land surface and atmosphere, it is thus identified as a key parameter in most terrestrial ecosystem models. To acquire long-term LAI records at the global scale, several remote sensing LAI products have been generated from various satellite sensors. However, assessing the uncertainties associated with these LAI products through comparisons with independent ground-truth measurements is pivotal for an effective application of products. Many sites from global networks have collected and provided invaluable ground LAI measurements covering a wide range of biome types and spatial variabilities. These site-based LAI measurements have been obtained about 30 years (1990-now). However, the spatial scale mismatch between site and pixel observations restricts the utilization of LAI measurements for product time-series validation. This datasets were generated from site-based LAI measurements of FLUXET and Chinese Ecosystem Research Network (CERN), using the proposed GUGM (Grading and Upscaling of Ground Measurements) method to resolve the scale-mismatch issue between site and sensor observations and maximize the utility of time-series of site-based LAI measurements, which can achieve the goal of product time-series validation. This GUGM approach first ingests both high-resolution images and site-based LAI measurements to capture the spatiotemporal variability in the product pixel grid. Then, a strategy was employed to grade the spatial representativeness of LAI measurements in the product pixel grid. For those LAI measurements which cannot be directly used in the validation of products, a strategy was adopted to calculate the spatial upscaling coefficient based on site-based LAI measurements and aggregated high-resolution reference maps to derive reliable LAI time-series validation datasets. The GUGM method has been applied to the site-based LAI measurements to generate global time-series LAI validation datasets from 2001 to 2011 in the 1 km pixel grid. The datasets include 28 sites which are mainly located in North America and Asia, providing 924 validation data in total. Among these sites, 16 sites with 508 (55.0%) validation data were obtained for forest, while 11 sites with 341 (36.9%) validation data and one site with 75 (8.1%) were obtained for crops and grasses, respectively. This datasets were saved in two formats: *.xls and *.kmz and each format was zipped for 63 KB and 31 KB, respectively.</p>
CLDF dataset derived from Lai and List's "Comparison of Rgyalrongic Languages" from 2023
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lai, Yunfan and List, Johann-Mattis (2023): Lexical Data for the Historical Comparison of Rgyalrongic Languages [Dataset, Version 1.0]. Leipzig: Max Planck Institute for Evolutionary Anthropology.</p> </blockquote>
Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: LAI-Biomass data 1996
First 3-5 year resurvey of permanent monitoring plots using essentially the same protocol as the intial survey of 1992-1993 except that: 1.) standing biomass of the herbaceous groundcover was added (including for new lower salt marsh plots) using clip plots at the subplot locations; and 2.) an estimate of landcover/habitat class aggregation was conducted surrounding each plot center out to 60m in the four cardinal directions. Extends baseline data useful for estimating landscape-scale vegetative productivity, mortality, and turnover; for establishing pre-disturbance conditions in the case of later stand- or island-wide disturbance; and for assisting in the ground-truthing of landcover and habitat classification using aerial or satellite remote-sensing imagry.
LAI_area2
<p>Leaf Area Index (LAI) was derived from remote sensing data (Pleiades images)</p>
Data of LAI-L20C in Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM
<p>Data of LAI-L20C experiment in the research paper: Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM.</p> <p>The paper was submitted to JGR-Atmosphere.</p> <p>Variables are limited to those used in the paper.</p> <ul> <li>snow water equivalent (swe)</li> <li>snow cover fraction (snc)</li> <li>clear-sky downward shortwave radiation at surface (rsdscs)</li> <li>clear-sky upward shortwave radiation at surface (rsuscs)</li> <li>surface air temperature (tas)</li> </ul> <p>See the paper for the detail.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Figure 12 in Colotis lais (Butler, 1876) and Colotis euippe omphale (Godart, [1819]) use Cadaba aphylla (Thunb.) Wild LC as a host-plant at Tswalu Kalahari, Northern Cape Province, South Africa)
Figure 12 – Pupa of Colotis euippe omphale. This pupa was reared from a larva collected on 19 December 2021 on Cadaba aphylla shrub C aphy 14, north of the Dedeben Research Centre, Tswalu Kalahari, South Africa. The CRG reference number for this pupa is RFT21H79. Photo: R.F. Terblanche.
Figure 14 in Colotis lais (Butler, 1876) and Colotis euippe omphale (Godart, [1819]) use Cadaba aphylla (Thunb.) Wild LC as a host-plant at Tswalu Kalahari, Northern Cape Province, South Africa)
Figure 14 – Cadaba aphylla (CAPPARACEAE), C aphy 14, photographed on 26 September 2022, north of the Dedeben Research Centre, Tswalu Kalahari, South Africa. When conditions are favourable, many shoots with small leaves can be produced by some individuals of the species, as depicted here. No flowers were present on this plant when the photograph was taken. Photo: R.F. Terblanche.
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