Observations for


a) 16 day estimates of NDVI or EVI for the selected region selected for the last 12 months.

b) The 16 day estimates of NDVI and EVI for the current and five most recent years are plotted for comparison. The thick black line is the same series shown in a.

Measures of Vegetation

This tool produces maps of estimated vegetation using data from NASA's MODIS sensor.

In some semi-arid regions of eastern Africa, precipitation has been found to have a 2-3 month lagged correlation with malaria incidence. Due to the lack of station data and because of the lagged nature of precipitation yielding lagged plant growth, vegetation indicies have been used as a proxy measure to forecast malaria.

Two vegetation indices are provided: NDVI, EVI in addition to the reflectance values for the blue, red, near infrared and middle infrared channels. Each index is derived from data provided from The Moderate Resolution Imaging Spectrometer (MODIS), a key instrument aboard NASA's Terra and Aqua satellites.

The Normalized Difference Vegetation Index (NDVI) is the ratio between the difference of red and near-Infrared (NIR) divided by the sum of red and near-Infrared reflectances. The index provides some information on healthy vegetation by examining their difference in wavelength absorption and reflectance. Healthy vegetation growth, such as forests, will yield high NDVI values closer to 1 while low vegetation will yield values close to 0.2.

The Enhanced Vegetation Index is provided as a complementary index to NDVI. EVI is an 'optimized' index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences.

Using simultaneous exploitation of MIR, NIR, and Red wavelengths in a Red-Green-Blue color space, reflectance images allow for a more robust and reliable qualitative discrimination between land surfaces with sparse vegetation and those without vegetation. Land surfaces such as water bodies can also be accurately mapped. The user can follow the spatio-temporal dynamic of green vegetation and identify water bodies using the combination of MIR, NIR and Red channels.

NDVI and EVI are useful to estimate the presence of vegetation, but are subject to intrinsic commission and omission errors which lead to potential misrepresentation of land surfaces. To improve the retrieval of vegetation properties, reflectance values in the Blue, Red, near-Infrared (NIR) and Middle Infrared (MIR) channels can be used.

Images are available for western Africa, eastern Africa and southern Africa.


Ceccato, P., et al. (2007). Malaria stratification, climate, and epidemic early warning in Eritrea.. International Geoscience and Remote Sensing Symposium (IGARSS) pp. 178-180 American Journal of Tropical Medicine and Hygiene, 2007.

Dataset Documentation

Vegatation Estimates

16 day estimates on a 250m lat/lon grid
Data Source
United States Geological Survey, Land Processes Distributed Active Archive Center, Moderate Resolution Imaging Spectroradiometer (USGS LandDAAC MODIS)
Note:There is typically a 12- to 16-day delay between the end of the observation period for the latest data and the date when those data are received and displayed on this page.


Access the dataset used to create this map.


Contact help@iri.columbia.edu with any technical questions or problems with this Map Room, for example, the forecasts not displaying or updating properly.


The MODIS interface provides the ability to make graphs at a user-selected location across different resolutions of spatial averaging. The interface consists of a clickable map that allows users to generate customized time series graphs. When a desired location is clicked, 2 time series graphs will provide vegetation analyses of the past year and in comparison to the 5 most recent years.

By placing current vegetation in recent historical context, comparisons can be made to past outbreaks and useful early warning information can be developed for epidemic prone regions.