This tool can be used to calculate a normalized difference index (NDI) from two bands of multispectral image data. A NDI of two band images (`image1` and `image2`) takes the general form:

> NDI = (image1 - image2) / (image1 + image2 + c)

Where c is a correction factor sometimes used to avoid division by zero. It is, however, often set to 0.0. In fact, the normalized_difference_index tool will set all pixels where `image1 + image2 = 0` to 0.0 in the output image. While this is not strictly mathematically correct (0 / 0 = infinity), it is often the intended output in these cases.

NDIs generally takes the value range -1.0 to 1.0, although in practice the range of values for a particular image scene may be more restricted than this.

NDIs have two important properties that make them particularly useful for remote sensing applications. First, they emphasize certain aspects of the shape of the spectral signatures of different land covers. Secondly, they can be used to de-emphasize the effects of variable illumination within a scene. NDIs are therefore frequently used in the field of remote sensing to create vegetation indices and other indices for emphasizing various land-covers and as inputs to analytical operations like image classification. For example, the normalized difference vegetation index (NDVI), one of the most common image-derived products in remote sensing, is calculated as:

> NDVI = (NIR - RED) / (NIR + RED)

The optimal soil adjusted vegetation index (OSAVI) is:

> OSAVI = (NIR - RED) / (NIR + RED + 0.16)

The normalized difference water index (NDWI), or normalized difference moisture index (NDMI), is:

> NDWI = (NIR - SWIR) / (NIR + SWIR)

The normalized burn ratio 1 (NBR1) and normalized burn ration 2 (NBR2) are:

> NBR1 = (NIR - SWIR2) / (NIR + SWIR2)

> NBR2 = (SWIR1 - SWIR2) / (SWIR1 + SWIR2)

In addition to NDIs, Simple Ratios of image bands, are also commonly used as inputs to other remote sensing applications like image classification. Simple ratios can be calculated using the `Divide` tool. Division by zero, in this case, will result in an output NoData value.

## See Also

`Divide`

## Function Signature

`def normalized_difference_index(self, nir_image: Raster, red_image: Raster, clip_percent: float = 0.0, correction_value: float = 0.0) -> Raster: ...`

## Project Links

WbW Homepage User Manual Support WbW