Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
160
datasets available to search
ShareScore release 0.9.0
Dataset results
160 results for “leaf area index”
Tree Canopy Leaf Area Index in CRUI Land Use Project at Harvard Forest 1997
Numerous variables related to land use disturbance and recovery processes can influence forest composition and structure. We’ve measured differences in forest communities in six sites that were formerly plowed, pastured, or continuously forested woodlots in Prospect Hill. None of the sites had noticeable canopy gap disturbance at the time of the measurements. Leaf area index (LAI) was measured with an LAI-2000 plant canopy analyzer (Li-Cor, Inc., Lincoln, NE) at all 77 edge and interior intersection points in the 30 m x 50 m permanent plot (7 columns x 11 rows) in 5 of our 6 land use sites. Under-canopy measurements were made in each site over 25-30 minutes during midday hours (11:00-2:30 EST) on overcast days near solstice (June 13, 18). The under-canopy readings were contrasted with an open-sky measurement taken in an open field near the Harvard Forest headquarters just before beginning data collection in each site. LAI averaged 3.98 and ranged from 2.28 to 5.93 across all sites. W1 had the highest site-level mean (4.62) and maximum (5.93) LAI while S2 had the lowest values (mean = 3.41, max = 4.55). The woodlot also showed the greatest spatial variation as measured by C.V., while plow #1 showed the least variation.
Leaf Area Index at Harvard Forest HEM and LPH Towers since 1998
Leaf area index (LAI) measurements are made to detect changes in forest leaf area that are important in understanding forests’ carbon dioxide (CO2) uptake and water use. Measurements of LAI in different forest types are useful understanding differences between forests in CO2 uptake and water use, while variation in LAI in the same forest type over time helps to explain interannual variation and long-term trends in carbon storage and water use. Within a single year, especially for deciduous forests, seasonal changes in LAI are very important in determining the forest’s cycle of CO2 uptake and water use. LAI measurements at Harvard Forest were begun in the old-growth hemlock stand in 1998 in order to understand and develop a predictive model for its carbon exchange, and were begun at the Little Prospect Hill site in 2002 to better understand CO2 uptake and carbon storage at this site, which were measured by the eddy-covariance method beginning in 2002. Due to a lack of personnel, multiple annual measurements of LAI to examine seasonal change in leaf area did not begin until 2007.
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.
Leaf Area Index every 15 cm of 1m x 1m chamber flux and point frame plots and sites where dataloggers monitored photosynthetically active radiation (PAR) above, within and below Salix pulchra and Betula nana canopies during the growing season at the Toolik Field Station in AK, Summer 2012.
Leaf area index (LAI) measurements were taken with the Delta-T SunScan wand every 15 cm from the ground to above the canopy under both direct and diffuse light. conditions The data includes all outputs from the SunScan wand: time of measurement, transmitted light, spread of photosynthetically active radiation (PAR) sensors, beam fraction, and zenith angle. These measurements were taken for 1m x 1m chamber flux and point frame plots sampled in tall Salix pulchra and Betula nana shrub canopies as well as sites monitored remotely by PAR sensors situated above, within, and below tall shrub canopies at the Toolik Field Station in the summer of 2012.
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.
Leaf area index for Spartina alterniflora near the GCE-LTER Flux Tower in 2018 and 2019
Leaf area index (LAI) was measured at permanent vegetation plots located in the GCE-LTER Flux Tower site for short and medium form Spartina alterniflora. LAI data were collected using a handheld ceptometer in 2018 and 2019.
Leaf area index and above ground biomass for Juncus roemerianus in the Grand Bay National Estuarine Research Reserve from 2015 to 2019
Leaf area index (LAI) and above ground biomass were measured in 16 permanent Juncus roemerianus vegetation plots located in the Grand Bay National Estuarine Research Reserve in Mississippi. Data were collected from 2015 to 2019. LAI were measured using a handheld ceptometer. Above ground biomass for each plot were collected from core samples.
Leaf Area Index on the GLBRC Biofuel Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (2009 to 2017)
Dataset AbstractThe leaf area index was measured to estimate the phenology and growth patterns of the different biofuel crops.original data source http://lter.kbs.msu.edu/datasets/225
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.
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.
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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.