Vegetation Greenness

About this map

How is the behavior of vegetation growth changing over time in the Arctic? This interactive map shows average monthly anomalies in the density of green vegetation—known as greenness—in the Arctic. The measurements shown in this map are known as the Normalized Difference Vegetation Index (NDVI).

To turn sunlight into food, plants perform photosynthesis, which requires chlorophyll. Chlorophyll absorbs red and blue light and reflects green light. By measuring the absorption and reflection of red, green, and blue light, satellite sensors can measure the amount of chlorophyll on Earth’s surface, and gauge how chlorophyll changes over time. Consequently, they can measure the amount of vibrant green vegetation compared to the amount of dead or dormant vegetation, or the absence of vegetation altogether.

Each monthly map shows how greenness compares to long-term average conditions for that month over the period of 1982 to 2010. The corresponding bar graph shows how much the overall greenness for that month departs from the long-term average.

Color key and bar graph

When you select a month on the dropdown selector at the lower-right of the page, and a year on the lower-left slider, the map will show vegetation data.

• Areas with higher-than-average greenness will appear in shades of blue-green, and areas with lower-than-average greenness will appear in shades of brown.
• Areas with greenness at or near the long-term monthly average are nearly white.
• The greater the departure from average, the darker the color.

The bar graph indicates the vegetation density anomaly (departure from the long-term average) for the entire Arctic—the whole region from 60°N of the equator to the North Pole. Brown bars indicate negative anomalies (years in which greenness is less than the long-term average) and blue-green bars show positive anomalies (years in which greenness is greater than the long-term average). The bars show every year in the time series for the selected month, and the bar that correlates with the map on display is highlighted in light gray.

How to change the display

• To change the month displayed on both the map and the graph, use the month dropdown selector in the bar graph box (lower right).
• To change the year displayed, move the slider in the year box (lower left).
• To animate the time series, click the play arrow (lower left). The animation will display maps for all years in the time series.

Why NDVI and vegetation matter

The Arctic is not a perpetually frozen wasteland, and perhaps nothing proves that better than the presence of Arctic vegetation. Arctic land plants provide food and habitat for many species of Arctic wildlife. Vegetation also provides scientists with information about how the region is changing.

As in other parts of the world, Arctic vegetation is driven by seasonal changes in sunlight and surface air temperature. Arctic vegetation is also influenced by ground temperature, snow cover, and humidity. If temperatures rise, snow melts, and frozen ground thaws; these changes encourage greater vegetation growth, and longer growing seasons. Vegetation even has a relationship with sea ice. 

NDVI gives scientists a method to monitor the behavior of vegetation in remote areas such as the Arctic. Changes in NDVI provide insights into how the terrain of an ecosystem may be changing.

How greeness is measured

Satellite sensors can detect vegetation on the planet’s surface by measuring the absorption and reflection of certain wavelengths of light. Ground with plentiful plant cover generally reflects near-infrared light, and absorbs light that is visible to human eyes, especially red light.

By comparing satellite observations over time, scientists can tell whether a particular area of land is largely “greening” (increasing its vegetation density) or “browning” (decreasing its vegetation density). In the Arctic, these satellite-observed increases in greenness have been correlated with increases in shrubs and herbaceous plants. Satellite sensors can observe on a global scale, but cannot see fine details on the ground. So the actual state of vegetation in any region is often more complicated than what is shown in a satellite image. Comparing satellite imagery with ground-based fieldwork provides scientists with a clearer picture of vegetation status.

What vegetation density means

Satellite observations can identify whether a region is greening or browning. An increase in the greenness might result from a change in vegetation density, or from a change in vegetation type, such as shrubs or trees replacing smaller plants that traditionally dominated the region. An increase in brownness could mean a general decrease in plant life, or it could mean that plants “peak” earlier in the growing season before going dormant. Reductions in snow cover and increases in permafrost thaw can dramatically affect vegetation, although those relationships can be complex.

To identify the specific sources of greening or browning trends, scientists often incorporate satellite observations into larger studies that involve other data sources, including ground-based fieldwork.

More vegetation means more carbon is stored in Arctic plant life, which mitigates increases of greenhouse gases in the atmosphere. However, the effects of increasing vegetation are relatively small compared to other changes the Arctic undergoes in response to global warming, such as decreases in sea ice and snow.

What the data show

The long-term satellite record of vegetation density dates back to the early 1980s. According to the Arctic Report Card: Update for 2020, the satellite record indicates an overall greening of the Arctic, but with many exceptions. Greening has not occurred consistently across the Arctic, and anomalies can easily diverge between North America and Eurasia.

In the spring and summer months (April through September), greenness is more common later in the long-term record, and brownness is more common early in the record, but both negative and positive anomalies happen throughout the satellite record in all months.

By viewing different months and years, you can use this map to examine changes in greenness over time. Try using these maps to answer questions such as:

• In which months are greenness anomalies—either positive or negative—largest? In which months are they smallest?
• In months that display large anomalies in the bar graph, are the anomalies driven primarily by widespread above- or below-average vegetation densities across the Arctic, or driven by intense departures from average in one or two regions?
• Do the greatest positive anomalies happen in the same months over the course of the record?
• Do the biggest anomalies, either positive or negative, recur in the same geographic areas year after year? Do they persist over different months?

By comparing greenness maps with other maps in Satellite Observations of Arctic Change, you may see relationships with other changes in other parts of the Arctic ecosystem, such as surface air temperatures or frozen ground.

Data source(s)

The NDVI data shown here are from the Global Inventory Modeling and Mapping Studies (GIMMS) from the Global Land Cover Facility at the University of Maryland.  NDVI is derived from imagery acquired by the NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite instruments. The data set spans the period from 1981 to 2010.  NDVI is calculated from the amounts of reflected visible and near-infrared radiation measured by remote sensing instruments on board Earth-orbiting satellites and aircraft. Chlorophyll in plant leaves absorbs blue and red wavelengths of solar radiation. This radiation is used in photosynthesis processes. Near-infrared radiation is reflected. The greater the density of green leaves in an area, the more visible light absorbed and the more infrared light reflected, producing a high NDVI index. Areas with sparse vegetation, such as grasslands, tundra or desert, or areas with unhealthy vegetation, such as drought-stricken regions, tend to have lower values of NDVI.

Bhatt, U.S., D.A. Walker, M.K. Raynolds, J.C. Comiso, H.E. Epstein, G. 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.

Data processing steps

To create this map, NSIDC took the following steps:

• Original data is in a .25 x .25 degree latitude/longitude grid and is resampled using a nearest neighbor algorithm to a ~5 km polar stereo grid on on EPSG:3413
• Use the ‘NDVI’ variable in the dataset
• Create monthly average CSV files, i.e., for each month:
  • Mask out data south of 60 degrees North
  • Mask out data < -1 and > 1
  • Round to three decimals
  • Compute the 1982-2010 climatological mean
  • Compute anomaly by subtracting climatological mean
• Generate monthly climatology gridded datasets
  • Calculate the mean grid for each month across the years 1982-2010 (1981 not available)
• Generate anomaly images for every year/month in the full timeseries by subtracting the monthly climatological mean grid (previous step) from each month’s grid.

Quick links

Frozen ground & permafrost