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99 results for “Vegetation Index”
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2)
<p><strong>Brief Introduction:</strong></p> <p>The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.2) provides spatiotemporally consistent global NDVI data in half-month and 1/12° from 1982 to 2022. It is created to address the major uncertainties presented in current global long-term NDVI products, i.e., the effects of NOAA satellite orbital drift and AVHRR sensor degradation.</p> <p> </p> <p>The PKU GIMMS NDVI was generated based on biome-specific BPNN models that employed GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. It was then consolidated with the MODIS NDVI (MOD13C1) to extend the temporal coverage to 2022 via a pixel-wise Random Forests fusion method.</p> <p> </p> <p>The PKU GIMMS NDVI exhibits overall high accuracy evaluated by Landsat NDVI samples. Besides, it efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency with MODIS NDVI in terms of pixel value and global vegetation trend. It could potentially provide a more solid data basis for global change studies.</p> <p> </p> <p>Here we provide two versions of PKU GIMMS NDVI for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the MODIS NDVI (1982−2022). <strong>We strongly recommend an adequate use of the quality control (QC) layer in the product. </strong>Please refer to the Readme file for more details. <strong>We also recommend removing sparse vegetation by a threshold (e.g., 0.1) in trend analysis (Zhou et al., 2001; Liu et al., 2016)</strong></p> <p> </p> <p><strong>Major updates:</strong></p> <p>Version 1.0 (December 15, 2022):</p> <p>· The original version of the product.</p> <p> </p> <p>Version 1.1 (June 17, 2023):</p> <p>· A pixel-wise Random Forests consolidation method is used to replace the linear one.</p> <p>· The data files have been re-organized on a decade basis.</p> <p> </p> <p>Version 1.2 (August 17, 2023):</p> <p>· The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate NDVI values of EBF during the periods of 1982−1984 and all October to April, when the Landsat NDVI samples were 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 2022</p> <p>Image Dimension: Rows-2160; Columns-4320</p> <p>Units: unitless</p> <p>Fill Value: 65535</p> <p>Data Type: uint16</p> <p>Valid Range: 0-1000</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>Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, <a href="https://doi.org/10.5194/essd-15-4181-2023">https://doi.org/10.5194/essd-15-4181-2023</a>, 2023.</p> <p>Liu, Q., Fu, Y. H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I. A., and Piao, S.: Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology, Global Change Biology, 22, 3702–3711, <a href="https://doi.org/10.1111/gcb.13311">https://doi.org/10.1111/gcb.13311</a>, 2016.</p> <p>Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., and Myneni, R. B.: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106, 20069–20083, <a href="https://doi.org/10.1029/2000JD000115">https://doi.org/10.1029/2000JD000115</a>, 2001.</p> <p> </p>
Weekly Normalized Difference Vegetation Index (NDVI) data from Roche Moutonnee, Toolik Field Station, Imnavait, and Sag river DOT sites, in the northern foothills of the Brooks Range, Alaska, summer 2010-2014.
Weekly Normalized Difference Vegetation Index (NDVI) data from Roche Moutonnee, Toolik Lake Field Station, Imnavait Creek and Sagavanirktok River DOT sites in the northern foothills of the Brooks Range, Alaska. Located south of the Arctic LTER and Toolik Lake Field Station. Data collected from May to July 2010-2014. Methods and further data published in Ecography by Rich, et al. 2013.
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.
Annual seasonality trends of Normalized Difference Vegetation Index (NDVI), Maricopa County, Arizona, 2001-2018
Description: A dataset with 18 years (2001-2018) of consistent, spatial and temporal patterns of Normalized Difference Vegetation Index (NDVI) values in Maricopa County AZ Abstract: This dataset consists of 18 years (2001-2018) of consistent, spatial, and temporal patterns of vegetation indices, as expressed by the Normalized Difference Vegetation Index (NDVI), in Maricopa County, AZ. I download and process images, at 250m resolution, from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS uses the atmospherically-corrected reflectance in the red, near-infrared, and blue wavebands to calculate vegetation indices. In the last decades, vegetation indices have been widely used for monitoring the seasonal variation of vegetation, document vegetation structure, productivity, overall health, and land cover changes and measure vegetation productivity in desert landscapes. NDVI is also of high interest to investigate the potential distribution of plant and animal species and to investigate the effect of climate change on vegetation productivity. MODIS data were obtained from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. Spatial Extent: Maricopa County, AZ, USA.
Annual seasonality trends of Enhanced Vegetation Index (EVI), Maricopa County, Arizona, 2001-2018
Description: A dataset with 18 years (2001-2018) of consistent, spatial and temporal patterns of enhanced vegetation index (EVI) values in Maricopa County AZ Abstract: This dataset consists of 18 years (2001-2018) of consistent, spatial, and temporal patterns of vegetation indices, as expressed by the enhanced vegetation index (EVI), in Maricopa County, AZ. I download and process images, at 250m resolution, from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS uses the atmospherically-corrected reflectance in the red, near-infrared, and blue wavebands to calculate vegetation indices. In the last decades, vegetation indices have been widely used for monitoring the seasonal variation of vegetation, document vegetation structure, productivity, overall health, and land cover changes and measure vegetation productivity in desert landscapes. EVI is also of high interest to investigate the potential distribution of plant and animal species and to investigate the effect of climate change on vegetation productivity. MODIS data were obtained from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. Spatial Extent: Maricopa County, AZ, USA.
Normalized difference vegetation index data for Saddle snowfence, 1994.
A snowfence was built in 1993 on the Niwot Ridge Saddle grid to determine the effects of changes in snowpack on a number of variables, one of which was vegetation greenness (measured through the normalized difference vegetation index). The study area of the snowfence was 60m x 125m. Spectral data in the red and near infra- red bands were recorded at sixty plots (points) in four rows located approximately at 10, 25, 45, and 75 meters east of the fence (the drift area). The plots were marked with thin wire stakes put into the ground and flagged (for visibility) and given aluminum tags with their plot identification code. All the plots were located in Kobresia myosuroides communities. In each row there were ten plots located in control areas just to the north and south of the area affected by the snowfence -- five to the north and five to the south -- and five plots located in the drift area of the snowfence. The individual plots were broken into groups of five according to their location (i.e., their distance from the fence (row), and location within their row (north, south, or within the snowfence area)). Within each of these groups, the plots were given a number starting with one and going to five. The plots were then given unique codes made up of plot type (c for control and sf for snowfence), location within a row (for the control plots only - n for north and s for south), the distance in meters of the row from the snowfence (10, 25, 45, or 75), and the number that uniquely identifies each plot within a group (one through five). For example, the code cn10_1 represents plot number one of the group of control plots north of the snowfence and 10 meters east of the snowfence. The location of each plot was measured as set of coordinates within the snowfence area. The southern terminus of the snowfence was used as the origin, with the y-axis running along the snowfence and the x-axis running perpendicular to the snowfence at it's southern end. The first coordinate was t
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
<p>Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at 5 time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into 2 groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.</p>
Normalized Difference Vegetation Index for Andalusia Region based on MODIS
<p><strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. </p> <p>NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation.</p> <p><br> Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Grape Wine sector, WP4- Durum wheat pasta sector)</p> <p>AoI: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_ndvi</p> <p> </p>
Normalized Difference Vegetation Index for Douro Valley based on MODIS
<p><strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. </p> <p>NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation.</p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wpe_douro_MOD09A1_ndvi</p> <p> </p>
Normalized Difference Vegetation Index (NDVI) data
<p> </p> <p>The raster data 'viCH_Landsat_NAfill.tif' represents Normalized Difference Vegetation Index (NDVI) data of Switzerland. Raster pixels have 250x250 m resolution and contain median values of the years 2013 to 2017. Data was collected by the Landsat satellite and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average.</p> <p>The raster data 'viNE_Modis2018_NAfill.tif' represents NDVI data of North Eurasia, at 1x1 km resolution, and averaged over the growing season 2018. Data was collected by the Modis sensor and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average.</p>
Dataset used in "Comment on "Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam" by Kim-Anh Nguyen, Yuei-An Liou, Ha-Phuong Tran, Phi-Phung Hoang and Thanh-Hung Nguyen"
<p>The Excel file provides all the data included in Tab.4 of Nguyen et al. 2020 plus reflectances extracted from the Landsat 8 OLI image acquired on 14 February 2017 and downloaded from the USGS Earth Explorer website. Observations on the number of pixels falling of water, land and mixed water/land surfaces are provided as well as water percentage cover estimated using regular spaced points. </p> <p>The dataset includes vector files (kml format) of the grids corresponding to the selected L8 pixels as well as the regularly spaced points generated within the selected pixels. These files can be imported in QGIS, Google Earth Pro and other free GIS software.</p>
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
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Alaska Statewide annual maximum Normalized Difference Vegetation Index (NDVI) values from 1982-2003 at 8km pixel size
Maximum annual NDVI values derived from twice monthly GIMMS-NDVI data, acquired for the period of 1982-2003 were downloaded fry the University of Maryland Global Land Cover Facility (http://www.landcover.org). These data were originally maximum NDVI values for each 64km2 pixel for each 15-day composite period. By selecting the maximum NDVI during each 15-day period, non-vegetation effects such as cloud or smoke contamination, and view geometry effects are reduced. The data had been calibrated by NASA scientists to correct for orbital drift and sensor degradation from a time series of five NOAA AVHRR sensors. The data were also processed by NASA scientists to correct for atmospheric effects resulting from two major volcanic eruptions: El Chichon in 1982, Mt. Pinatubo in 1991.
McMurdo Dry Valleys LTER: Microbial mat biomass and Normalized Difference Vegetation Index (NDVI) values from Lake Fryxell Basin, Antarctica, January 2018
This package contains data collected from microbial mat surveys (i.e., percent cover, ash-free dry mass (AFDM), and pigment concentrations – chlorophyll-a, scytonemin, and carotenoids) associated with satellite-derived Normalized Difference Vegetation Index (NDVI) values from the Lake Fryxell Basin of Taylor Valley, located in the McMurdo Dry Valleys of Antarctica. The purpose of this study was to quantitatively compare key microbial mat characteristics to NDVI. Data were collected at seven plot locations within the Canada Glacier Antarctic Specially Protected Area (ASPA) near Canada Stream, as well as alongside Green Creek and McKnight Creek. NDVI values were derived from a WorldView-2 multispectral satellite image taken of the Lake Fryxell Basin on January 19, 2018, while biological ground surveying and sampling were conducted during the 2nd and 4th weeks of January 2018.
Vegetation Density and Greenness Change Index (GCI), Southeast Michigan, 1990-2000-2010
<p>The vegetation density composites in 1990, 2000, and 2010 and greenness change index (GCI) between 2000 and 2010 for the Detroit-area counties of Oakland, Macomb, and Wayne.</p>
Annual maps of swidden agriculture landscape derived from MODIS vegetation index in northern Laos during 2001-2020
<p>This document (Word) is a brief introduction about the resultant maps of swidden agricultural landscape in northern Laos during 2001-2020 derived from the MODIS13Q1 Normalized Difference Vegetation Index (NDVI) time-series products using a threshold method. For more information about the dataset, one can refer to the paper entitled “Swidden agriculture landscape mapping using MODIS vegetation index time series and its spatio-temporal dynamics in northern Laos” published in Remote Sensing. The format of this dataset (swidden agriculture landscape) is raster (.tif) with an attribute value of 1. It has a spatial resolution of 250m×250m and covers eleven provinces (including Bokeo, Borikhamxay, Huaphanh, Luangnamtha, Luangprabang, Oudomxay, Phongsaly, Vientiane, Xayaboury, Xaysomboon and Xieng-khuang) and one prefecture (Vientiane, Figure 1). The geographic projection is WGS_1984_UTM_Zone_48N.</p>
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.
Seasonal Enhanced Vegetation Index (EVI) Imagery for Cimarron County, Oklahoma 2005-2020
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Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM
<p>We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables. </p>
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