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3,105 results for “Vegetation =”
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2003
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2003. <em>Please <a href="https://zenodo.org/record/7923025#.ZFyiRXZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2002</strong></em>, <em>and <a href="https://zenodo.org/record/7922498#.ZFxUEBFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2004</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2003</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2001
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2001. <em>Please <a href="https://zenodo.org/record/7924809#.ZF1q-HZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2000</strong></em>, <em>and <a href="https://zenodo.org/record/7923025#.ZFyivxFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2002</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2001</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2012
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2012. <em>Please <a href="https://zenodo.org/record/7929371#.ZF44oXZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2011</strong></em>, <em>and <a href="https://zenodo.org/record/7928318#.ZF4gvhFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2013</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2012</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2013
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2013. <em>Please <a href="https://zenodo.org/record/7928610#.ZF4kEnZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2012</strong></em>, <em>and <a href="https://zenodo.org/record/7928103#.ZF4CLxFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2014</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2013</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2014
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2014. <em>Please <a href="https://zenodo.org/record/7928318#.ZF4C0nZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2013</strong></em>, <em>and <a href="https://zenodo.org/record/7927842#.ZF3l7BFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2015</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2014</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2016
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2016. <em>Please <a href="https://zenodo.org/record/7927842#.ZF3lfXZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2015</strong></em>, <em>and <a href="https://zenodo.org/record/7927492#.ZF2w6BFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2017</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2016</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2017
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2017. <em>Please <a href="https://zenodo.org/record/7927598#.ZF3PRXZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2016</strong></em>, <em>and <a href="https://zenodo.org/record/7927432#.ZF2kCBFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2019
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2019. <em>Please <a href="https://zenodo.org/record/7880335#.ZE5_M3ZByUn"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2020</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2018
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2018. <em>Please <a href="https://zenodo.org/record/7927492#.ZF2wPHZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2017</strong></em>, <em>and <a href="https://zenodo.org/record/7927277#.ZF2UYRFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2019</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Effects of long-term fixed fire regimes on African savanna vegetation biomass, vertical structure and tree stem density
<ol> <li><span>Fire plays an integral role in shaping the vegetation structure of savanna ecosystems. However, effects of fire regime characteristics, such as frequency and season of burn, on savanna vegetation structure, biomass and tree abundance across landscape types are largely unknown. </span></li> <li><span>We used high-resolution airborne Light Detection and Ranging (LiDAR) to investigate the long-term effects of fire manipulation on savanna vegetation in Kruger National Park, South Africa. We analysed the effects of fire exclusion and experimental burns every 1, 2, 3, 4 and 6 years and during different seasons on aboveground biomass (AGB), tree stem densities and vegetation vertical height profiles across a rainfall gradient and on contrasting geologies. </span></li> <li><span>Across savanna types, and especially in drier savannas, fire season was more influential for constraining AGB than was fire frequency. Plots experiencing fires during the late- and mid-dry season had 44.50% and 43.60%, respectively, lower AGB relative to unburnt plots than wet-season fires. However, in mesic savannas, fire frequency interacted with fire season to influence AGB: plots subjected to high frequency, dry season fires had 55.35% lower AGB than unburnt plots, whereas plots burnt in the wet season at lower frequencies had lower AGB (24.40% lower than unburnt plots) than plots subjected to high frequency, wet-season fires (13.74% lower AGB than unburnt plots). </span></li> <li><span>Fire regimes had variable effects on tree densities, and effects varied with savanna type. Woody vertical vegetation profiles showed the largest differences in response to dry season fires, with the greatest divergence in vegetation height classes < 5m. </span></li> <li><span><em>Synthesis and applications</em>. Understanding the influence of fire regimes on vegetation structure has important implications for the management of savanna heterogeneity, and for predicting trajectories of change in savanna vegetation as fire regimes vary with climate change. We show that the magnitude of the effect of fire on woody vegetation structure varies with savanna context. Our results suggest that heterogeneous vegetation structure can be achieved by applying fires in the dry season in mesic savannas, whereas in dry savannas, variation in fire regimes is less consequential for constraining biomass accumulation and altering vegetation structure. </span></li> </ol>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2009
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2009. <em>Please <a href="https://zenodo.org/record/7931443#.ZF7ZEnZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2008</strong></em>, <em>and <a href="https://zenodo.org/record/7930000#.ZF5KBRFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2010</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2009</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2011
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2011. <em>Please <a href="https://zenodo.org/record/7930000#.ZF5JP3ZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2010</strong></em>, <em>and <a href="https://zenodo.org/record/7928610#.ZF4kmBFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2012</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2011</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2010
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2010. <em>Please <a href="https://zenodo.org/record/7930286#.ZF66enZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2009</strong></em>, <em>and <a href="https://zenodo.org/record/7929371#.ZF455BFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2011</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2010</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2007
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2007. <em>Please <a href="https://zenodo.org/record/7932282#.ZF-DoHZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2006</strong></em>, <em>and <a href="https://zenodo.org/record/7931443#.ZF7ZmBFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2008</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2007</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2008
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2008. <em>Please <a href="https://zenodo.org/record/7931558#.ZF861XZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2007</strong></em>, <em>and <a href="https://zenodo.org/record/7930286#.ZF668BFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2009</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2008</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
FIG. 2. — V in The Epiphytic Bryophyte Vegetation of Buxus sempervirens L. forests in the Fırtına Valley (Rize, Turkey)
FIG. 2. — V (B) and U (A) profile valleys in Fırtına Valley.
Data for: Vegetation and soil ecology of threatened Himalayan Trillium habitats in Kashmir, Himalaya
<p>Understanding the vegetation and soil ecology of natural habitats harbouring threatened species is critical in conservation planning and restoration. The present study investigated the vegetation composition and soil physico-chemical attributes of natural habitats of <em>Trillium govanianum</em> – a threatened Himalayan endemic species. We laid 120 quadrats across eight randomly selected sites where the species was growing in the Kashmir Himalaya. We collected the soil samples from these sites and determined soil physico-chemical attributes using standard methods. Across all the sites, we found a total of 57 plant species with dominance of Rosaceae and Ranunculaceae. The IVI results revealed that <em>Fragaria nubicola</em>, <em>Corydalis diphylla</em>, <em>Galium aparine</em>, and <em>Leucanthemum vulgare</em>, were the dominant species in <em>T. govanianum</em> communities. The density, abundance and IVI of 3-leaf vegetative plants was higher than 1-leaf vegetative and 3-leaf reproductive plants across all the study sites. We found that T. govanianum alone forms 23.5 % positive, 0 negative, 76.4% random co-occurrences with other associated species in its community. Our results reveal that the variations in vegetation composition among the sites was influenced by differences in soil properties. Principal component analysis revealed that several soil parameters such as organic carbon, nitrogen, potassium, and sulphur were concentrated in five sites, namely Dara, Drung, Bangus, Gulmarg, and Doodhpathri, which also showed the highest density, frequency, and abundance of <em>T. govanianum</em>. Overall, our study contributes quantitative information on the vegetation and soil ecology of <em>T. govanianum</em>-assemblages, which in turn can help in developing conservation strategies for this threatened species, and its sustainable management and habitat restoration.</p>
Widespread and complex drought effects on vegetation physiology inferred from space
<p>This repository contains the datasets used in the research article "<strong>Widespread and complex drought effects on vegetation physiology inferred from space</strong>".</p> <p>The repository contains the following folders: 1) demo_data.zip - contains all the raw datasets used to calculate vegetation physiological components. 2) figure_data.zip - contains all the processed data and additional datasets for creating main figures in the article.</p> <p>Formats: All scripts are in the programming language Python. The datasets are in the NumPy array format.</p> <p>The codes related to the research article are available in the following repository: https://doi.org/10.5281/zenodo.7971319</p>
Data for: Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents
<p class="MsoNormal"><strong><span>Aim: </span></strong><span>Theoretically, woody biomass turnover time (τ</span><span>) </span><span>quantified using </span><span>outflux (i.e., tree mortality)</span><span> predicts biomass dynamics better than using influx (i.e., productivity).</span> <span>This study aims at using forest inventory data to empirically test the outflux approach and generate a spatially explicit understanding of woody </span><span>τ</span><span> </span><span>in mature forests. We further compared woody τ</span><span> </span><span>estimates with dynamic global vegetation models (DGVMs) and with a data assimilation product of C stocks and fluxes - CARDAMOM.</span></p> <p class="MsoNormal"><strong><span>Location:</span></strong> <span>Continents</span></p> <p class="MsoNormal"><strong><span>Time period:</span></strong> <span>Historic from </span><span>1951 to 2018</span></p> <p class="MsoNormal"><strong><span>Major taxa studied:</span></strong> <span>Trees and Forests</span></p> <p class="MsoNormal"><strong><span>Methods: </span></strong><span>We compared the approaches of using outflux vs. influx for estimating woody <span>τ</span></span><span> and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody <span>τ</span></span><span> and generated a spatially explicit map of woody <span>τ</span></span><span> at a 0.25-degree resolution across continents using machine learning. We further examined whether six DGVMs and CARDAMOM generally captured the observational pattern of woody <span>τ</span></span><span>. </span></p> <p class="MsoNormal"><strong><span>Results: </span></strong><span>Woody <span>τ</span></span><span> quantified by the </span><span>outflux approach </span><span>better (with R<sup>2</sup> 0.4-0.5) predicted the biomass accumulation rates than the influx approach (with R<sup>2</sup> 0.1-0.4) across continents. We found large spatial variations of woody <span>τ</span></span><span> for mature forests, with highest values </span><span>in temperate forests (</span><span>98.8 ± 2.6 y</span><span>) followed by boreal forests (73</span><span>.9 ± </span><span>3</span><span>.</span><span>6</span><span> y</span><span>) and tropical forests. The map of woody <span>τ</span></span><span> extrapolated from plot data showed higher values in wetter eastern and pacific coast USA, Africa and eastern Amazon. Climate (temperature and aridity index) and vegetation structure (tree density and forest age) were the dominant drivers of woody <span>τ</span></span><span> across continents. The highest woody <span>τ</span></span><span> in temperate forests were not captured by either DGVMs or CARDAMOM. </span></p> <p class="MsoNormal"><strong><span>Main conclusions:</span></strong><span> Our study </span><span>empirically demonstrated the preference of using outflux over influx to estimate woody <span>τ</span></span><span> for predicting biomass accumulation rates. The spatially explicit map of woody </span><span>τ</span><span> and the underlying drivers provide </span><span>valuable information to improve the representation of </span><span>forest demography and </span><span>carbon turnover processes in DGVMs.</span></p>
Age specific impacts of vegetation functional traits on gastro-intestinal nematode parasite burdens in a large herbivore
<ol> <li>Gastro-intestinal nematode (GIN) parasites play an important role in the ecological dynamics of many animal populations. Recent studies suggest fine-scale spatial variation in GIN infection dynamics is important in wildlife systems, but the environmental drivers underlying this variation remain poorly understood.</li> <li>We used data from over two decades of GIN parasite egg counts, host space use, and spatial vegetation data from a long-term study of Soay sheep on St Kilda to test how spatial autocorrelation and vegetation in an individual's home range predict parasite burden across three age groups. We developed a novel approach to quantify the plant functional traits present in a home range to describe the quality of vegetation present. </li> <li>Effects of space and vegetation varied between age classes. In immature lambs, strongyle parasite faecal egg counts (FEC) were spatially structured, being highest in the north and south of our study area. Independent of host body weight and spatial autocorrelation, plant functional traits predicted parasite egg counts. Higher egg counts were associated with more digestible and preferred plant functional traits, suggesting the association could be driven by host density and habitat preference.</li> <li>In contrast, we found no evidence that parasite FEC were related to plant functional traits in the host home range in yearlings or adult sheep. Adult FEC were spatially structured, with highest burdens in the north-east of our study area, while yearling FEC showed no evidence of spatial structuring.</li> <li>Our findings support the importance of fine-scale environmental variation for wildlife disease ecology and provide new evidence that such effects may vary across demographic groups within a population. Parasite burdens in immature individuals appear more readily influenced by fine-scale spatial variation in the environment, highlighting the importance of such heterogeneity for our understanding of wildlife epidemiology and health.</li> </ol>
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.