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289 results for “leaf area”
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.
RIV05 Leaf mass 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 state. We hypothesized that removal would decrease organic matter input into streams.
diFUME Leaf Area Index V0.1
<p>Description:</p> <p>The Level 2A (L2A) product by the Theia Land Data Centre of CNES (Centre national d'études spatiales) is used, which provides georeferenced and orthorectified surface reflectance (SR), water vapor content (WVC), aerosol optical thickness (AOT), cloud and geophysical masks, processed by the MAJA atmospheric processing software. The 10 m SR bands in red (SRred : 665 nm) and near-infrared (SRNIR : 842 nm) are used to compute NDVI (Normalized Difference Vegetation Index) as (SRNIR – SRred)/(SRNIR + SRred) and the product is masked for clouds, cloud shadows and snow according to the L2A product flags. NDVI is converted to Leaf Area Index (LAI) values by applying an empirical exponential formula and is then resampled from 10 m to 5 m resolution, enhancing the initial LAI values, using the vegetation fraction derived by the 1 m Land Cover product.</p> <p> </p> <p>Data specifications:</p> <p>CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected</p> <p>Spatial Extent: 392120.0,5266860.0 : 395160.0,5269840.0</p> <p>Temporal Extent: 2018 - 2020</p> <p>Units: meters</p> <p>Width: 608</p> <p>Height: 596</p> <p>Bands: 1</p> <p>Pixel Size: 5,-5</p> <p>Data type: Float32 - Thirty two bit floating point</p> <p>GDAL Driver Description: GTiff</p> <p>GDAL Driver Metadata: GeoTIFF</p>
Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach
<p>Main data used for the scientific paper entitled: "Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach".</p> <ol> <li>"Danta_dem_10cm_px.tif": orthomosaic-derived DEM</li> <li>"Danta_rgb_2.2cm_px.tif": ortophoto </li> <li>"GPS points": list of GPS samples points</li> <li>"Main data": field vegetation data and indexes used for the regressions</li> <li>"Raw PointCloud". Lidar original dataset</li> <li>"Pre-processed PointCloud": Lidar dataset after pre-processing (see paper's methods) </li> <li>"DTM_DantaGround_grid50cm_minimo": Output (TIFF); the LiDAR-derived DTM showed in the paper</li> <li>"LAI": Output (Shapefile); the LiDAR-derived LAI showed in the paper.</li> </ol> <p> </p>
Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020 (V1.2)
<p><strong>Brief Introduction:</strong></p> <p> </p> <p>The fourth generation GIMMS Leaf Area Index product (GIMMS LAI4g, version 1.2) provides spatiotemporally consistent global LAI data in half-month and 1/12° from 1982 to 2020. It is created to address two major uncertainties presented in current global long-term LAI products, i.e., (1) the effects of NOAA satellite orbital drift and AVHRR sensor degradation and (2) insufficient LAI reference data to build robust LAI model particularly before the late 1990s.</p> <p> </p> <p>The GIMMS LAI4g was generated based on biome-specific BPNN models that employed the latest PKU GIMMS NDVI product and 3.6 million high-quality global Landsat LAI samples. It was then consolidated with the Reprocess MODIS LAI to extend the temporal coverage to 2020 via a pixel-wise Random Forests fusion method.</p> <p> </p> <p>The GIMMS LAI4g exhibits overall high accuracy and low underestimation evaluated by field LAI measurements and Landsat LAI samples. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency before and after the year 2000 and a more reasonable global vegetation trend. It could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and benefit the model development in Earth and environmental sciences.</p> <p> </p> <p>Here we provide two versions of GIMMS LAI4g for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the Reprocess MODIS LAI (1982−2020). We strongly recommend an adequate use of the quality control (QC) layer in the product. Please refer to the Readme file for more details.</p> <p> </p> <p><strong>Major updates:</strong></p> <p>Version 1.0 (February 17, 2023):</p> <p>· The original version of the product.</p> <p> </p> <p>Version 1.1 (June 14, 2023):</p> <p>· The GIMMS LAI4g is now validated by ground LAI measurements.</p> <p>· A pixel-wise Random Forests consolidation method is used to replace the linear one.</p> <p>· Two versions of GIMMS LAI4g are now available, one solely based on AVHRR data and one consolidated with MODIS LAI.</p> <p> </p> <p>Version 1.2 (August 25, 2023):</p> <p>· The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate LAI values during 1982−1984 for all biomes, October−April for EBF, and winters for ENF, when the Landsat NDVI samples were absent or relatively scarce.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN</p> <p>Projection: Geographic</p> <p>Spatial Resolution: 1/12 degree</p> <p>Temporal Resolution: Half month</p> <p>Temporal Coverage: January 1982 to December 2020</p> <p>Image Dimension: Rows-2160; Columns-4320</p> <p>Units: m<sup>2</sup>/m<sup>2</sup></p> <p>Fill Value: 65535</p> <p>Data Type: uint16</p> <p>Valid Range: 0-7000</p> <p>Scale Factor: 0.001</p> <p>File Format: TIFF(.tif)</p> <p>File Size: ~8Mb each file</p> <p> </p> <p><strong>References:</strong></p> <p>Cao, S., Li, M., Zhu, Z., Wang, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., Chen, Y., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-68, in review, 2023.</p>
Harvest data including the shoot leaf area index, position in the canopy, and shoot and plant tissue area, count and mass for each shoot harvested at three levels in the canopy from 19 1m x 1m plots near LTER Shrub plots, Toolik Field Station, AK 2012.
Leaf and plant tissue area and mass from shoots harvested from 19 1m x 1m point frame plots near Toolik Field Station, AK during the summer of 2012. Six shoots were harvested from each plot, two from each canopy layer: upper, middle, and low. Each shoot came from a different plant, and the species selected was based on the species dominant in that canopy layer. The leaf area and mass were used to correct A/Ci and light response curves taken on each shoot [data published separately]. At the time of collection, the location relative to the point frame, height, and leaf area index (LAI) of each shoot was measured; those data are included here.
Normalized difference vegetation index and Leaf area index of tussocks from reciprocal transplant gardens at Toolik Lake, Coldfoot, and Sagwon, Alaska 2016
Normalized difference vegetation index (NDVI) and Leaf area index (LAI) data from tussocks in the reciprocal transplant gardens at Toolik Lake, Coldfoot, and Sagwon in 2016.
Alaskan Peatland Experiment: Community structure and productivity data for 2007-2010 X - Leaf Area
This dataset contains surface area measurements for all vascular species present within a plot with an abundance greater than 5%. Surface area measurements at the fen include 2008-2010, while at the bog are for 2009-2010. Within the fen site a water table manipulation has been ongiong since 2005, with control, lowered and raised water table treatment plots. Samples at the bog were collected in a plot established within the lowland black spruce permafrost plateau (permafrost), and two plots established within collapse scars embedded within the plateau. One collapse scar formed ~ 45 years ago (old collapse) and the other formed ~ 25 years ago based upon aerial photography provided by the BCEF LTER. The data provided in this data set can be sorted by site and plot.
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Specific Leaf Area (SLA) for alder (Alnus crispa) and black spruce (Picea mariana) in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes specific leaf area (SLA) data for two dominant tree species in the Caribou-Poker Creeks Research Watershed: alder (Alnus crispa) and black spruce (Picea mariana). Up to 10 leaf samples were collected per species per sampling location. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Specific Leaf Area (SLA) for alder (Alnus crispa) and black spruce (Picea mariana) in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes depth-resolved soils data from September 2014 coring: soil pH, gravimetric soil moisture, roots/rocks, bulk density, humification indices as determined by FTIR, total elemental composition (carbon, nitrogen, sulfur), depth to mineral horizon, thickness of the moss layer, percent groundcover at the sampling location of several common species, and soil temperature at the time of coring. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Ecosystem-scale rainfall manipulation in a Pinon-Juniper woodland: Tree Sapwood and Leaf Area Data (2011)
Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability. Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees. Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide
Increases in vein length compensate for leaf area lost to lobing in grapevine
<p><span></span></p> <p>There is considerable variation in leaf lobing and leaf size, including among grapevines, some of the most well-studied leaves. We examined the relationship between leaf lobing and leaf size across grapevine populations which varied in extent of leaf lobing. We used homologous landmarking techniques to measure 2,632 leaves across two years in 476 unique, genetically distinct grapevines from 5 biparental crosses which vary primarily in the extent of lobing. We determined to what extent leaf area could explain variation in lobing, vein length, and vein to blade ratio. Although lobing was the primary source of variation in shape across the leaves we measured, leaf area varied only slightly as a function of lobing. Rather, leaf area increases as a function of total major vein length, total branching vein length, and decreases as a function of vein to blade ratio. These relationships are stronger for more highly lobed leaves, with the residuals for each model differing as a function of distal lobing. For a given leaf area, more highly lobed leaves have longer veins and higher vein to blade ratios, allowing them to maintain similar leaf areas despite increased lobing. These findings show how more highly lobed leaves may compensate for what would otherwise result in a reduced leaf area, allowing for increased photosynthetic capacity through similar leaf size.</p>
Text-fig. 4. Charred grass from diatomite of Saint-Bauzile. a: Overview of diatomite slab with one larger specimen of charred grass (left) and several smaller, lath-shaped charcoal fragments; SM.B 22260; scale bar = 1 cm. b: Detail of vein exhibited on split grass blade, with stomata oriented parallel to vein. c: Stomata oriented in rows and bands parallel to veins exposed on split grass blade. d: Surface of grass leaf with rectangular, elongated cells with strongly undulating margins in an intercostal area. in Evidence For Wildfires During Deposition Of The Late Miocene Diatomites Of The Konservat-Lagerstätte Lake Saint-Bauzile (Ardèche, France) - Preliminary Results
Text-fig. 4. Charred grass from diatomite of Saint-Bauzile. a: Overview of diatomite slab with one larger specimen of charred grass (left) and several smaller, lath-shaped charcoal fragments; SM.B 22260; scale bar = 1 cm. b: Detail of vein exhibited on split grass blade, with stomata oriented parallel to vein. c: Stomata oriented in rows and bands parallel to veins exposed on split grass blade. d: Surface of grass leaf with rectangular, elongated cells with strongly undulating margins in an intercostal area.
Figure 2 in Allometric equations for estimating the leaf area of Thespesia populnea by linear dimensions of leaf blades
Figure 2. Linear leaf dimensions [maximum length (L) and maximum width (W)] used to estimate the leaf area of Thespesia populnea.
Figure 1 in Allometric equations for estimating the leaf area of Thespesia populnea by linear dimensions of leaf blades
Figure 1. Geographical location of the municipality of Canguaretama, state of Rio Grande do Norte, Northeastern Brazil.
Linked collectors and determiners for: Documenting new and little known leaf-mining Nepticulidae from middle and southwestern areas of the Asian continent.
Natural history specimen data linked to collectors and determiners held within, "Documenting new and little known leaf-mining Nepticulidae from middle and southwestern areas of the Asian continent". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/8236cdde-2a32-4af1-bb69-404fec3f27c3">https://bionomia.net/dataset/8236cdde-2a32-4af1-bb69-404fec3f27c3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/8236cdde-2a32-4af1-bb69-404fec3f27c3">https://gbif.org/dataset/8236cdde-2a32-4af1-bb69-404fec3f27c3</a>. Formatted as a Frictionless Data package.
An Optimized North America MODIS Leaf Area Index (LAI) Dataset for Air Quality Modeling
<p>Air Quality Research Division, Environment and Climate Change Canada,</p> <p>4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada</p> <p>Email: Junhua.zhang@ec.gc.ca</p> <p> </p> <p>Leaf Area Index (LAI) is used in air quality models for land surface processes and for calculating biogenic emissions. MODIS LAI product provided by NASA (https://modis.gsfc.nasa.gov/data/dataprod/mod15.php) has been widely used in the air quality modeling community for such purposes. However, limitations of MODIS LAI product have been seen for some geographic areas, particularly unreasonably low LAI over the evergreen needleleaf boreal forests in the northern hemisphere during wintertime due to snow cover and low sun angle. Missing retrievals over urban areas and areas with persistent cloud cover are also seen. Considerable efforts have been made to improve the MODIS LAI product. However, some issues are still persistent, such as the very low LAI over boreal forests during wintertime. In order to solve these issues for supporting regional air quality modelling, the 8-day MODIS Collection 6 (C6) LAI product at 500m resolution (MCD15A2H) was examined for North America. Statistics were calculated by month and by land cover type defined in the “Land Cover Type 1” science data set (SDS) of the Collection 6 MODIS Land Cover (MCD12Q1) product. Comparisons with LAI calculated from the EPA’s Biogenic Emissions Landuse Database, version 4 (BELD4, https://www.epa.gov/air-emissions-modeling/biogenic-emission-sources) were also done (Zhang et al., 2020). Based on the analysis, an updated monthly LAI dataset was calculated based on 1) 17-year (2003-2019) average of MODIS summer-time peak LAI, 2) fraction of evergreen and deciduous for each pixel from BELD4, and 3) monthly profiles of LAI for evergreen and deciduous vegetation species from MODIS LAI (Zhang et al., 2021). This is the final LAI dataset for North America compiled using the 17 years of MODIS LAI product complemented by information from BELD4.</p> <p> </p> <p>REFERENCES:</p> <p>Zhang, J., M. D. Moran, P. A. Makar, and S. Kharol, 2020. Examination of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 19th CMAS Conference, 26-30 Oct., Virtual [see https://www.cmascenter.org/conference/2020/slides/ZhangJ_MODIS_LAI_CMAS_2020.pdf].</p> <p>Zhang, J., P. A. Makar, S. Kharol, M. D. Moran, and C. McLinden, 2021. Examination and Processing of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 2021 Meteorology and Climate - Modeling for Air Quality Conference, Sep 14-17, 2021, Virtual</p> <p> </p>
Data from: Specific leaf area is lower on ultramafic than on neighbouring non-ultramafic soils
<p>These are datasets in csv format for ultramafic and non-ultramafic sampling sites where specific leaf area (SLA) was collected in five climatically diverse regions: Puerto Rico (tropical wet), Costa Rica (tropical dry), South Africa (subtropical), California and Lesbos (both with Mediterranean climate). The datasets include plant species level mean SLA values, regional coordinates, regional climate data, bioclimatic variables for individual sites, and a joined dataset that includes site level coordinates and climate variables. The R script to reproduce all analyses and figures is also included.</p>
Tropical cyclones facilitate recovery of forest leaf area from dry spells in East Asia
<p>This online repository copies the source code and the download link of the input data for the research work of analyzing forest leaf area change due to the TC activities in the west pacific ocean basin. </p> <p><strong>TC Track data, mask, climate reanalysis, leaf area, ERA5 (wind speed, surface pressure data), and SPEI dataset: </strong></p> <p><a href="http://YYCdb.synology.me:5833/sharing/YizTR8HPR">http://YYCdb.synology.me:5833/sharing/YizTR8HPR</a></p> <p>password:bg-2022-115</p> <p>File size: 373G</p> <p><strong>The path for downloading the source code/script for analyzing the LAI changes:</strong></p> <p><a href="http://YYCdb.synology.me:5833/sharing/JC2AGt9Kh">http://YYCdb.synology.me:5833/sharing/JC2AGt9Kh</a></p> <p>password:bg-2022-115</p> <p>File size: 880M</p> <p><strong>Data table for all events used in this study:</strong></p> <p><a href="http://YYCdb.synology.me:5833/sharing/MqA4YFBHk">http://YYCdb.synology.me:5833/sharing/MqA4YFBHk</a></p> <p>password:bg-2022-115</p> <p>Filesize:824K</p>
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