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62 results for “normalized vegetation index”
Normalized Difference Vegetation Index (NDVI):BAC: Biodiversity and Climate
Climate changes forecast for our region by GCM???s and shifts in biodiversity and composition each have the potential to alter ecosystem functioning; their interactive effects are unknown. The "BAC" experiment is designed to determine the direct and interactive effects of plant species numbers, plant community composition, temperature, and precipitation on 11 productivity, C and N dynamics, stability, and plant, microbe, and insect species abundances in CDR grassland ecosystems.
Data from: The utility of normalized difference vegetation index for predicting African buffalo forage quality
Many studies of mammalian herbivores have employed remotely sensed vegetation greenness, in the form of Normalized Difference Vegetation Index (NDVI) as a proxy for forage quality. The assumption that reflected greenness represents forage quality often goes untested, and limited data exist on the relationships between remotely sensed and traditional forage nutrient indicators. We provide the first study connecting NDVI and forage nutrient indicators within a free-ranging African herbivore ecosystem. We examined the relationships between fecal nutrient levels (nitrogen and phosphorus), forage nutrient levels, body condition, and NDVI for African buffalo (Syncerus caffer) in a South African savanna ecosystem over a 2-year period (2001 and 2002). We used an information-theoretic approach to rank models of fecal nitrogen (Nf) and phosphorus (Pf) as functions of geology, season, and NDVI in each year separately. For each year, the highest ranked models for Nf accounted for 61% and 65% of the observed variance, and these models included geology, season, and NDVI. The top-ranked model for Pf in 2001, although capturing 54% of the variability, did not include NDVI. In 2002, we could not identify a top ranking model for phosphorus (i.e., all models were within 2 AICc of each other). Body condition was most highly correlated (equation image; P ≤ 0.001) with NDVI at a 1 month time lag and with Nf at a 3 months time lag (equation image; P ≤ 0.001), but was not significantly correlated with Pf. Our findings suggest that NDVI can be used to index nitrogen content of forage and is correlated with improved body condition in African buffalo. Thus, NDVI provides a useful means to assess forage quality where crude protein is a limiting resource. We found that NDVI accounted for more than a seasonal effect, and in a system where standing biomass may be high but of low quality, understanding available nutrients is useful for management.
A long-term 250-m resolution Normalized Difference Vegetation Index (NDVI) product for 1982–2020 in Idaho
<ol> <li>We developed a novel spatio-temporal fusion method to downscale the AVHRR NDVI products to the Moderate-resolution Imaging Spectroradiometer (MODIS) resolution. The algorithm effectively combines the high spatial variability of the MODIS NDVI data and the long-term temporal information of the AVHRR NDVI data. Finally, we successfully generated a monthly global long-term (since 1982) and high-resolution (250m) NDVI database.</li> <li>Here we provide the downscaled NDVI dataset of Idaho from 1982 to 2020.</li> <li>Datasets for other regions can be easily produced by the GEE platform with the code provided in the github (https://github.com/babyfoal/downsclaed_NDVI/tree/main).</li> <li>The spatial distribution and temporal variation of this dataset have been both well validated by the simulated and real-data experiments. </li> </ol>
MUSES Normalized Difference Vegetation Index (NDVI) 16-Day 30m Geographic Grid over Beijing Since 1984
<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 NDVI product at 30 m spatial resolution and 16-day temporal resolution over Beijing. The MUSES NDVI product is provided on Geographic grid and spans from 1984 to 2021 (continuously updated). It was generated from the Landsat collection 2 surface reflectance data using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 115.416599º E – 117.508219º E, 39.441929º N – 41.059283º N</li> <li>Temporal Coverage: 1984 – 2021</li> <li>Spatial Resolution: 0.000269469º (approximately 30 m)</li> <li>Temporal Resolution: 16 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.0001</li> <li>Valid Range: 0 – 10000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p> <p> </p>
MUSES Normalized Difference Vegetation Index (NDVI) Monthly Global 0.05º Geographic Grid Since 1982
<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 NDVI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES NDVI product is provided on Geographic grid and spans from 1982 to 2015 (continuously updated). It was generated from the Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1982 – 2015</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.0001</li> <li>Valid Range: 0 – 10000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Normalized Difference Vegetation Index (NDVI) 8-Day Global 0.05º Geographic Grid Since 1982
<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 NDVI product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES NDVI product is provided on Geographic grid and spans from 1982 to 2015 (continuously updated). It was generated from the Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1982 – 2015</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.0001</li> <li>Valid Range: 0 – 10000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Data from: The utility of normalized difference vegetation index for predicting African buffalo forage quality
Open the record for dataset details and reuse information.
Normalized Difference Vegetation Index (NVDI) image of 2000 Landsat Enhanced Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 2000 Enhanced Landsat Thematic Mapper(ETM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is computed as follows: (NIR-RED) / (NIR+RED), where NIR (Near Infra-Red) is the ETM band 4 (0.76-0.9 micrometers) and RED is band 3 (0.78-0.82 micrometers).
NDVI (Normalized difference vegetation index) Image of 1975 Landsat MSS Image
Normalized difference vegetation index (NDVI) produced from the 1975 Landsat MSS image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
NDVI (Normalized difference vegetation index) Image of 1980 Landsat MSS Image
Normalized difference vegetation index (NDVI) produced from the 1980 Landsat MSS image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
NDVI (Normalized difference vegetation index) image of central Arizona-Phoenix from a 2005 Landsat Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 2005 Landsat Thematic Mapper (TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation. It is computed as follows: (NIR-RED) / (NIR+RED), where NIR (Near Infra-Red) is the TM band 4 (0.76-0.9 micrometers) and RED is band 3 (0.78-0.82 micrometers).
NDVI (Normalized difference vegetation index) image of central Arizona-Phoenix from a 1998 Landsat Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 1998 Landsat Thematic Mapper(TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
NDVI (Normalized difference vegetation index) image of central Arizona-Phoenix from a 1993 Landsat Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 1993 Landsat Thematic Mapper(TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
NDVI (Normalized difference vegetation index) image of central Arizona-Phoenix from a 1985 Landsat Thematic Mapper image
Normalized difference vegetation index (NDVI) produced from the 1985 Landsat Thematic Mapper(TM) image. NDVI is a means of monitoring density and vigour of green vegetation growth using the spectral reflectivity of solar radiation.
Data from: Use of normalized difference vegetation index to assess N status and predict grain yield in rice
Fine tuning N recommendations requires an understanding of crop N status and yield potential early enough in the growing season when changes to N management can influence yields. Recent studies have demonstrated the ability of Normalized Difference Vegetation Index (NDVI) to assess crop N status and predict yield in wheat (Tricticum aestivum L.) and maize (Zea mays L.); however, there has been relatively little such research on rice (Oryza sativa L.). The objectives of this study were to determine how well NDVI measured at the panicle initiation (PI) rice growth stage assesses crop N status and predicts final grain yield. Nitrogen response trials were established over a 4-yr period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. Additionally, the relationship between NDVI and crop N status was characterized across 28 on-farm plots representing a range of environmental conditions and management practices. The NDVI at PI was best correlated with total N uptake (NUP, r2 = 0.66), followed by N concentration (NCONC, r2 = 0.54), and aboveground biomass (AGB, r2 = 0.51). The utility of NDVI was greatest at lower values of crop N status, whereas at higher values, NDVI saturated. The NDVI at PI was positively correlated with final grain yield (r2 = 0.58) indicating utility for developing in-season yield predictions. While NDVI is a potentially useful tool to improve N fertilizer management and develop in-season yield predictions in rice, alternative indices that do not saturate would likely provide a basis for a better tool.
Data from: Use of normalized difference vegetation index to assess N status and predict grain yield in rice
Open the record for dataset details and reuse information.
NOAA-14 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index Daily L3 Global 0.05 Deg. CMG
The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.The NOAA-14 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N14_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N14_AVH01C1). The N14_AVH13C1 product is available in HDF4 file format.
METOP-B AVHRR Atmospherically Corrected Normalized Difference Vegetation Index Daily L3 Global 0.05 Deg. CMG
The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.The METOP-B AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name M1_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (M1_AVH01C1). The M1_AVH13C1 product is available in HDF4 file format.
NOAA-07 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index Daily L3 Global 0.05 Deg. CMG
The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.The NOAA-07 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N07_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N07_AVH01C1). The N07_AVH13C1 product is available in HDF4 file format.
NOAA-11 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index Daily L3 Global 0.05 Deg. CMG
The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B.Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product.The NOAA-11 AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name N11_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (N11_AVH01C1). The N11_AVH13C1 product is available in HDF4 file format.
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