Normalized Difference Vegetative Index (NDVI)

Objectives

Introduction

Assessing the type, extent, and condition of vegetation over a region is a primary goal of land use investigations. Researchers use data from Landsat and other environmental satellites to determine the number of acres of certain crop types in a region, locate vegetation that is heavily impacted by natural or man-made stresses such as pests, fire, disease, and pollution, and to delimit boundaries between such areas as wetlands or old growth forest. Such sets of data, taken over time intervals and compared, can also help us understand how vegetation changes over time. Satellite data can be used to detect vegetative change from one growing season to the next, from year to year, or from decade to decade. These types of data help us better understand the ecology of our planet and will perhaps help us understand the impact of mankind on our natural biological cycles.

A vegetative index is a value that is calculated (or derived) from sets of remotely-sensed data that is used to quantify the vegetative cover on the Earth's surface. Though many vegetative indices exist, the most widely used index is the Normalized Difference Vegetative Index (NDVI). The NDVI, like most other vegetative indices, is calculated as a ratio between measured reflectivity in the red and near infrared portions of the electromagnetic spectrum. These two spectral bands are chosen because they are most affected by the absorption of chlorophyll in leafy green vegetation and by the density of green vegetation on the surface. Also, in red and near-infrared bands, the contrast between vegetation and soil is at a maximum.

[NDVI product from NOAA AVHRR satellite data]

The NDVI is a type of product known as a transformation, which is created by transforming raw image data into an entirely new image using mathematical formulas (or algorithms) to calculate the color value of each pixel. This type of product is especially useful in multi-spectral remote sensing since transformations can be created that highlight relationships and differences in spectral intensity across multiple bands of the electromagnetic spectrum.

The NDVI transformation is computed as the ratio of the measured intensities in the red (R) and near infrared (NIR) spectral bands using the following formula:

The resulting index value is sensitive to the presence of vegetation on the Earth's land surface and can be used to address issues of vegetation type, amount, and condition. Many satellites have sensors that measure the red and near-infrared spectral bands, and many variations on the NDVI exist. The sensor that supplies one of the most widely used NDVI products is on board the National Oceanic and Atmospheric Administration (NOAA) meteorological satellites. This sensor, known as the Advanced Very High Resolution Radiometer (AVHRR), is a 5 channel radiometer with channels in the red (channel 1) and near infrared (channel 2) potion of the spectrum. The AVHRR NDVI product is created using data from these channels in the following manner:

AVHRR data is used to generate NDVI images of large portions of the Earth on a regular basis in order to provide a global set of images that portray seasonal and annual changes to vegetative cover.

The Thematic Mapper (TM and Enhanced Thematic Mapper Plus (ETM+) bands 3 and 4) provide R and NIR measurements and therefore can be used to generate NDVI data sets with the following formula:

One of the primary differences between the AVHRR and Landsat NDVI image products is the resolution. The AVHRR, despite its name, has a resolution that is much lower than the Landsat TM/ETM+ sensors. AVHRR NIR data is transmitted at a maximum resolution of 1 km, and the NDVI product is generally produced at an even further reduced resolution (usually 8 km) in favor of providing global or large scale coverage. The Landsat NDVI is produced at a resolution of 30 m, which offers far greater detail, though it is able to provide less aerial extent. Thus, the AVHRR data is more appropriate for creating frequent global NDVI products while the Landsat 7 ETM+ data is most useful for creating images with greater detail covering less area.

[NDVI image of Howard County, MD]

The Red and NIR images are obtained and used to calculate an NDVI value for each pixel. The NDVI equation produces values in the range of -1.0 to 1.0, where vegetated areas will typically have values greater than zero and negative values indicate non-vegetated surface features such as water, barren, ice, snow, or clouds. In order to maximize the range of values and provide numbers that are appropriate to display in an 8 bit image, the NDVI value must be scaled. This scaling converts a number between -1.0 and 1.0 into a pixel value that is appropriate on a gray tone display. One example of scaling an NDVI value for display is the following equation:

Thus, using this equation, a pixel with an NDVI value of 0.43 would be scaled into a gray scale value of 143. Using this technique, the NDVI computed value is scaled to the range of 0 to 200, where computed -1.0 equals 0, computed 0 equals 100, and computed 1.0 equals 200. As a result, NDVI values less than 100 now represent clouds, snow, water, and other non-vegetative surfaces, and values equal to or greater than 100 represent vegetative surfaces. The resulting scaled values can be displayed on a gray tone display or converted to a color image .

References