The normalized difference vegetation index (NDVI) is a measure of greenness. NDVI was calculated as: NDVI = (NIR - R) / (NIR + R), where NIR is the spectral reflectance in the AVHRR near-infrared channel (0.725-1.1 µm, channel 2) where light-reflectance from the plant canopy is dominant, and R is the reflectance in the red channel (0.5 to 0.68 µm, channel 1), the portion of the spectrum where chlorophyll absorbs maximally. Advanced Very High Resolution Radiometer (AVHRR) data were obtained from the USGS Global AVHRR 10-day composite data website. Glaciers and oceans were masked out using information from the Digital Chart of the World (ESRI 1993). The image is composed of 1 x 1-km pixels. The color of each pixel was determined by its reflectance at the time of maximum greenness, selected from 10-day composite images from 11 July to 30 August 1993 and 1995. These intervals cover the vegetation green-up-to-senescence period during two relatively warm years when summer-snow cover was at a minimum in the Arctic (Markon et al. 1995). Back to Alaska Arctic Tundra Vegetation Map (Raynolds et al. 2006) Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes AVHRR NDVI, Bioclimate Subzone, Elevation, False Color-Infrared CIR, Floristic Province, Lake Cover, Landscape, Substrate Chemistry, Vegetation References Markon, C. J., M. D. Fleming, and E. F. Binnian. 1995. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data. Polar Record 31:179-190.

This dataset includes vegetation cover maps, Normalized Difference Vegetation Index (NDVI) maps, snow depth and thaw depth data that were obtained as part of a biocomplexity project on the North Slope of Alaska, USA, and the Northwest Territories (NWT), Canada. In Alaska, seven sites are located along the Dalton Highway and in the Prudhoe Bay Oilfield area, forming a transect across the climate gradient of the North Slope. From South to North, the sites are Happy Valley, Sagwon (an acidic and nonacidic site), Franklin Bluffs, Deadhorse, West Dock and Howe Island. Four sites are in the NWT, forming a latitudinal gradient from South to North; the sites include Inuvik, Green Cabin, Mould Bay, and Isachsen.

The normalized difference vegetation index (NDVI) is a measure of greenness. NDVI was calculated as: NDVI = (NIR - R) / (NIR + R), where NIR is the spectral reflectance in the AVHRR near-infrared channel (0.725-1.1 µm, channel 2) where light-reflectance from the plant canopy is dominant, and R is the reflectance in the red channel (0.5 to 0.68 µm, channel 1), the portion of the spectrum where chlorophyll absorbs maximally. Trend in Arctic NDVI was calculated from a linear regression of NDVI values for all years 1982-2010. Pixels with significant trends (p < 0.05) were retained. Back to Circumpolar Arctic Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation References Bhatt, U. S., D. A. Walker, M. K. Raynolds, J. C. Comiso, H. E. Epstein, G. J. Jia, R. Gens, J. E. Pinzon, C. J. Tucker, C. E. Tweedie, and P. J. Webber. 2010. Circumpolar arctic tundra vegetation change is linked to sea ice decline. Earth Interactions 14:1-20. doi: 10.1175/2010EI1315.1171.

The Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 22-year satellite record of monthly changes in terrestrial vegetation. This data set contains three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude with data from July 1981 through December 2002. New features include reduced NDVI variations arising from calibration, view geometry, volcanic aerosols, and other effects not related to actual vegetation change. In particular, NOAA-9 descending node data from September 1994 to January 1995, volcanic stratospheric aerosol correction for 1982-1984 and 1991-1994, and improved NDVI using empirical mode decomposition/reconstruction (EMD) to minimize effects of orbital drift. Global NDVI was generated to provide inputs for computing the time series of biophysical parameters contained in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II collection. NDVI is used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface.

A Normalized Difference Vegetation Index (NDVI) was applied to the source NAIP 2018 imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness").This service is offered by the California Department of Fish and Wildlife (CDFW). For more information about CDFW map services, please visit: https://wildlife.ca.gov/Data/GIS/Map-Services

A Normalized Difference Vegetation Index (NDVI) was applied to the source NAIP 2016 imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness").This service is offered by the California Department of Fish and Wildlife (CDFW). For more information about CDFW map services, please visit: https://wildlife.ca.gov/Data/GIS/Map-Services

A Normalized Difference Vegetation Index (NDVI) was applied to the source NAIP 2012 imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness").This service is offered by the California Department of Fish and Wildlife (CDFW). For more information about CDFW map services, please visit: https://wildlife.ca.gov/Data/GIS/Map-Services

The imagery has a pixel resolution of 6 inches with a RMSE of 1.0 ft X and Y. Processing has been minimized to preserve the ability to use this data for raster analysis.The normalized difference vegetation index (NDVI) is useful for measuring the quality, density, and amount of vegetation in a particular area. It is a single band dataset that represents vegetation health, based on the difference between the red and near infrared bands. Red and orange pixels represent areas with no vegetation. Yellow pixels represent areas with low to moderate vegetation. Green pixels represent areas with high vegetation density and health. This data is provided as a web service only (no download).To view the latest imagery for any location in the state, customers should always use the "Orthoimagery_Latest" image service which can be found at https://nconemap.gov.To find specific dates the images were captured use the imagery dates app or download the data.

A Normalized Differential Vegetation Index (NDVI) was applied to the source 2005 1-meter resolution color infrared (CIR) imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness"). Lack of statewide color balancing of the source CIR imagery shows inconsistencies in NDVI results between adjacent areas. No access constraints, but there are use constraints (see source product metadata).The source color infrared (CIR) imagery was acquired during NAIP 2005 flights. The imagery was purchased from the North West Group (NWG) by three state agencies (California Dept. of Fish and Game, California Dept. of Transportation, and California Dept. of Water Resources). No access constraints, but there are use constraints. CIR coverage was not available in all areas. THIS IMAGERY IS NOT A NAIP PRODUCT. Band1=NearIR, Band2=R, Band3=G.This service is offered by the California Department of Fish and Wildlife (CDFW). For more information about CDFW map services, please visit: https://wildlife.ca.gov/Data/GIS/Map-Services

These images were produced by averaging the 1-km FASIR-NDVI maps by Jing Chen to a 10' (horizontal) by 5' (vertical) pixel size in a straight latitude/longitude grid. Each pixel represents the average NDVI of the 1-km pixels that fall in each 10' by 5' pixel, where more than 50% of the 1-km pixels in the 10' by 5' area are not cloud and are not missing. If more than 50% of the 1-km pixels are missing or cloudy, a value of 0 is assigned to the 10' by 5' pixel.

This dataset is an annual time-serie of Landsat Analysis Ready Data (ARD)-derived Normalized Difference Vegetation Index (NDVI) computed from Landsat 5 Thematic Mapper (TM) and Landsat 8 Opeational Land Imager (OLI). To ensure a consistent dataset, Landsat 7 has not been used because the Scan Line Correct (SLC) failure creates gaps into the data. NDVI quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs) using this generic formula: (NIR - R) / (NIR + R) For Landsat sensors, this corresponds to the following bands: Landsat 5, NDVI = (Band 4 – Band 3) / (Band 4 + Band 3). Landsat 8, NDVI = (Band 5 – Band 4) / (Band 5 + Band 4). NDVI values ranges from -1 to +1. NDVI is used to quantify vegetation greenness and is useful in understanding vegetation density and assessing changes in plant health. Standard Deviation is also provided for each time step. Data format: GeoTiff This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch)

GCOM-C/SGLI L3 Map Normalized Difference Vegetation Index (NDVI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains.This dataset is 1 month map-projected statistics product. This dataset includes NDVI: Normalized Difference Vegetation Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.

These files were used in the analysis for "Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study" Hyam. Submitted to RIO 2020.

Extract on construction of data

NDVI data was downloaded from the United States Geological Survey (USGS) Land Satellites Data System (LSDS) Science Research and Development (LSRD) (United States Geological Survey 2018). Which produces Level 2 and Level 3 data products from the Level 1 data of instruments aboard Landsat Satellites. For this study Surface Reflectance data generated by the Landsat Surface Reflectance Code (LaSRC) from the Operational Land Imager (OLI) instrument aboard the Landsat 8 satellite was used (United States Geological Survey 2018). The Surface Reflectance NDVI (sr_ndvi) product and Level-2 Pixel Quality Assessment band (pixel_qa) were downloaded for Landsat scenes 204/21, 205/21, 206/21, 204/20, 205/20, 206/20 WRS-2 (NASA 2018) for the calendar years 2013 to 2016. These scenes cover most of Scotland and include all the major urban areas. A full list of the 333 products is given in supplemental material. Suppl. material 2

All of Scotland is over 54° North and so for many satellite images the sun is at too low an angle to give reliable surface reflectance data especially in the winter months. Scotland also has an oceanic climate so the ground is often obscured by cloud or mist. To build a detailed, contiguous NDVI map of the whole country therefore requires combining images taken on many satellite passes especially if points are to be sampled multiple times to overcome measurement errors. The images downloaded from USGS were therefore combined. A cloud free version of each NDVI image was created by setting the pixels that corresponded to cloud, snow or water in the Quality Assurance Assessment band to NA. These cloud free images were then combined into a single, mosaic stack of images to cover all of the study area and then averaged down to a single layer as a tiff image. This was done for two seasonal periods, Winter (October, November, December of 2013, 2014, 2015 and 2016 combined with January, February, March of 2014, 2015, 2016) and summer (April, May, June, July, August, September of 2014, 2015, and 2016). The resulting two images covering most of Scotland for winters and summers between 2013 and 2016 and formed the basis of subsequent analysis.

These two files are included here along with a list of the Landsat products used to produce them.

Measurements from NASA's Terra and Aqua polar satellites' MODIS instrument during the vegetation period (April 1st) (October 31st) of this year. The maximum vegetation index (NDVI) for 16 days is related to the ‘greenness’ and photosynthetic activity of the surface; the NDVI change map is made from the difference of two consecutive time steps, on which the change of the NDVI over 8 days can be tracked; the NDVI anomaly map shows the difference between the current vegetation index value and the long-term average, i.e. its deviation from the average.

These operational land imager (OLI) value added data sets, maps, and associated ancillary data were compiled as part of an ongoing research aimed at quantifying the riparian vegetation greenness and water use in the lower Colorado River Delta in Mexico. In order to create trend and anomaly maps that characterize these ecosystems Vegetation Index (NDVI) time series imagery from Landsat OLI were acquired and processed over time and space along seven predefined reaches that capture different natural states and management conditions. We used Landsat OLI 30m data as an improvement upon past studies that were based on coarser remote sensing data from the NASA MODIS sensor (250 m). The OLI 30m images provide better characterization and performance over these rather narrow riparian corridors. To capture the change over time we used a simple differencing technique that compares two annual average growing season VI cycles (limited to May-October). These anomaly maps capture how the corridor vegetation health responds to both natural and anthropogenic changes. We limited this study to the full OLI record (2013-2019) since we were interested in understanding the response to Minute 319 pulse flow of 2014. The difference maps are an ideal tool for capturing how the released water impacted vegetation immediately and over long time. The Minute 319 pulse flow science team in collaboration with the University of Arizona have developed a data processing system to support this effort with focus on understanding how the riparian corridor is responding to these natural and anthropogenic stressors. All data associated with this project were acquired from the LP-DAAC and pre-processed to remove and capture issues prior to further processing (see below) which involved reprojection to a common projection, masking to only retain the area of interest, quality analysis to discard poor data, and then value addition to generate the NDVI and the difference maps. The data acquisition and analysis were performed at the University of Arizona VIP lab (vip.arizona.edu) using their large Linux cluster of computing and storage resources. A mix of off the shelf software and specialized in-house tools were used to carry the different steps and analyses.

Normalised Difference Vegetation Index (NDVI) images from Feb. 1991 have been compiled by processing and compositing AVHRR data from the Alice Springs receiving station to a nominal 1km resolution. The NDVI equation produces values in the range of -1.0 to 1.0, where increasing positive values indicate increasing green vegetation and negative values indicate nonvegetated surface features such as water, barren land, ice, and snow or clouds. Each image is compiled from AVHRR data collected over a fortnight.

The goal of the Alaska Advanced Very High Resolution Radiometer (AVHRR) project is to compile a time series data set of calibrated, georegistered daily observations and twice-monthly maximum normalized difference vegetation index (NDVI) composites for Alaska's annual growing season (April-October). This data set has applications for environmental monitoring and for assessing impacts of global climate change. An Alaska AVHRR data set is comprised of twice-monthly maximum NDVI composites of daily satellite observations. The NDVI composites contain 10 bands of information, including AVHRR channels 1-5, maximum NDVI, satellite zenith, solar zenith, and relative azimuth. The daily observations, bands 1-9, have been calibrated to reflectance, scaled to byte data, and geometrically registered to the Albers Equal-Area Conic map projection. The 10th band is a pointer to identify the date and scene ID of the source daily observation (scene) for each pixel. The compositing process required each daily overpass to be registered to a common map projection to ensure that from day to day each 1-km pixel represented the exact same ground location. The Albers Equal-Area Conic map projection provides for equal area representation, which enables easy measurement of area throughout the data. Each daily observation for the growing season was registered to a base image using image-to-image correlation. The NDVI data are calculated from the calibrated, geometrically registered daily observations. The NDVI value is the difference between near-infrared (AVHRR Channel 2) and visible (AVHRR Channel 1) reflectance values divided by total measured reflectance. A maximum NDVI compositing process was used on the daily observations. The NDVI is examined pixel by pixel for each observation during the compositing period to determine and retain the maximum value. Often when displaying data covering large areas, such as AVHRR data, it is beneficial to include an overlay of either familiar linework for reflectance or polygon data sets to derive statistical summaries of regions. All of the linework images represent lines in raster format as 1-km cells and the strata are represented as polygons registered to the AVHRR data. The linework and polygon data sets include international boundaries, Alaskan roads with the Trans-Alaska Pipeline, and a raster polygon mask of the State.

A Normalized Difference Vegetation Index (NDVI) was applied to the source NAIP 2009 imagery. NDVI=(NearIR-Red)/(NearIR+Red). The color ramp (produced by ESRI) goes from brown (less healthy vegetation) to red to green (healthier vegetation or more "greenness").

These images were produced by averaging the 1-km FASIR-NDVI maps by Jing Chen to a 10' (horizontal) by 5' (vertical) pixel size in a straight latitude/longitude grid. Each pixel represents the average NDVI of the 1-km pixels that fall in each 10' by 5' pixel, where more than 50% of the 1-km pixels in the 10' by 5' area are not cloud and are not missing. If more than 50% of the 1-km pixels are missing or cloudy, a value of 0 is assigned to the 10' by 5' pixel.