NDVI, NDBI & NDWI Calculation Using Landsat 7, 8

Introduction:

Remote sensing data are primary sources for analysing environmental processes on a local or global scale. These data are used to find out change detection in recent decades. Remote sensing data (such as Landsat data, Sentinel data, Spot image etc.) are very useful for visualization, classification and analysis of the area. These data can be categorized based on their resolution, electromagnetic spectrum, energy source, imaging media and a number of bands. The higher the resolution of satellite data (spatial resolution, spectral resolution, radiometric resolution, temporal resolution), higher degree of accuracy will achieve during classification.

Generally, Landsat data are used for classification. Landsat data have several bands based on their wavelength (blue band, green band, red band, infrared band, thermal band, panchromatic). The panchromatic band is used to increase the resolution of data. Landsat 7 data have total of 8 bands while Landsat 8 data have 11 bands. For analysis of Normal Difference Vegetation Index (NDVI), Normal Difference Built-up Index (NDBI) and Normal Difference Water Index (NDWI), only four bands are used (Green, Red, NIR, SWIR). Landsat 7 and Landsat 8 data’s bands, wavelength & their resolution are given below.

This article is about three major land cover classes; vegetation, water bodies and build-up area using Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) & Normalized Difference Build-up Index (NDBI) respectively. The spectral reflectance curve helps to understand these indexes.

Spectral reflectance curve:

Spectral reflectance curve shows the relationship between the electromagnetic spectrum (distribution of the continuum of radiant energies plotted either as a function of wavelength or of frequency) and the associated percent reflectance for any given material. It is plotted in a chart that represents wavelengths on the horizontal axis and percent reflectance on the vertical axis (fig. 1). This curve will visualize the formula of NDVI, NDBI, and NDWI.

Fig.1 Spectral Reflectance Curve

Normalized Difference Vegetation Index (NDVI):

The Normalized Difference Vegetation Index (NDVI) is the most commonly used vegetation index for observe greenery globally. Other commonly used vegetation indices Enhanced Vegetation Index (EVI), Perpendicular Vegetation Index (PVI), Ration Vegetation Index (RVI).

In general, Healthy vegetation is good absorber of electromagnetic spectrum in visible reason. Chlorophyll contains in a greeneries highly absorbs Blue (0.4 - 0.5 µm) and Red (0.6 - 0.7 µm) spectrum and reflects Green (0.5 – 0.6 µm) spectrum. Therefore, our eye perceives healthy vegetation as green. Healthy plants having high reflectance in Near Infrared (NIR) between 0.7 to 1.3 µm (fig. 1). This is primarily due to internal structure of plant leaves. High reflectance in NIR and high absorption in Red spectrum, these two bands are used to calculate NDVI. So, following formula gives Normalized Difference Vegetation Index (NDVI). 

NDVI = (NIR – Red) / (NIR + Red)

For Landsat 7 data, NDVI = (Band 4 – Band 3) / (Band 4 + Band 3)

For Landsat 8 data, NDVI = (Band 5 – Band 4) / (Band 5 + Band 4)

The NDVI value varies from -1 to 1. Higher the value of NDVI reflects high Near Infrared (NIR), means dense greenery. Generally, we obtain following result:

• NDVI = -1 to 0 represent Water bodies
• NDVI = -0.1 to 0.1 represent Barren rocks, sand, or snow
• NDVI = 0.2 to 0.5 represent Shrubs and grasslands or senescing crops
• NDVI = 0.6 to 1.0 represent Dense vegetation or tropical rainforest

The NDVI rate can be calculated using raster calculator in ArcGIS.

Normalized Difference Built-up Index (NDBI):

There are lots of indexes for the analysis of built-up area. Normalized Difference Built-up Index (NDBI), Built-up Index (BU), Urban Index (UI), Index-based Built-up Index (IBI), Enhanced Built-up and Bareness Index (EBBI) are most common indexes for analysis the built-up areas. These different indexes having their own formula, own calculation method. The build-up areas and bare soil reflects more SWIR than NIR. Water body doesn’t reflect on Infrared spectrum. In case of greenie surface, reflection of NIR is higher than SWIR spectrum (Fig 1). For better result, you can use Built-up Index (BU). Build-up Index is the index for analysis of urban pattern using NDBI and NDVI. Built-up index is the binary image with only higher positive value indicates built-up and barren thus, allows BU to map the built-up area automatically.

BU = NDBI - NDVI

Image classification technique (supervised classification and unsupervised classification) is lengthy and complex process. It requires compositive band & apply numbers of operation for the final result. The accuracy derived from image classification technique depends on the image analyst & method followed by analyst. However, NDBI calculation is simple and easy to derived. NDVI can be calculated by following formula.

NDBI = (SWIR – NIR) / (SWIR + NIR)

For Landsat 7 data, NDBI = (Band 5 – Band 4) / (Band 5 + Band 4)

For Landsat 8 data, NDBI = (Band 6 – Band 5) / (Band 6 + Band 5)

Also, the Normalize Difference Build-up Index value lies between -1 to +1. Negative value of NDBI represent water bodies where as higher value represent build-up areas. NDBI value for vegetation is low.

Normalized Difference Water Index (NDWI):

Normalize Difference Water Index (NDWI) is use for the water bodies analysis. The index uses Green and Near infra-red bands of remote sensing images. The NDWI can enhance water information efficiently in most cases. It is sensitive to build-up land and result in over-estimated water bodies. The NDWI products can be used in conjunction with NDVI change products to assess context of apparent change areas.

Water bodies having low reflectance. It only reflects within visible portion of the electromagnetic spectrum. Water bodies in their liquid state are generally high reflectance on Blue (0.4 - 0.5 µm) spectrum than Green (0.5 -0.6 µm) and Red (0.6 – 0.7 µm) spectrum. Clear water having greatest reflectance in the blue portion of the visible spectrum. So, water appear blue. Turbid water has higher reflectance in visible spectrum. There is no reflection in Near Infrared (NIR) and beyond. NDWI is developed by Gao(1996) to enhance the water related features of the landscapes. This index uses the near infrared (NIR) and the Short-Wave infrared (SWIR) bands. NDWI can be calculated by following formula:

NDWI = (NIR – SWIR) / (NIR + SWIR)

For Landsat 7 data, NDWI = (Band 4 – Band 5) / (Band 4 + Band 5)

For Landsat 8 data, NDWI = (Band 5 – Band 6) / (Band 5 + Band 6)

But result appear form above formula is poor in quality. The pure water neither reflects NIR nor SWIR. The formula of NDWI then modified by Xu (2005). It uses Green and SWIR band.

MNDWI = (Green – SWIR) / (Green + SWIR)

For Landsat 7 data, NDWI = (Band 2 – Band 5) / (Band 2 + Band 5)

For Landsat 8 data, NDWI = (Band 3 – Band 6) / (Band 3 + Band 6)

Similarly, Normalize Difference Water Index (NDWI) value lies between -1 to 1. Generally, water bodies NDWI value is greater than 0.5. Vegetation has much smaller values which distinguishing vegetation from water bodies easily. Build-up features having positive values lies between 0 to 0.2. 

References:

Bhatta, B. (2011). Remote sensing and GIS: Second edition. New Delhi, India: Oxford University Press.

Y. Zha, J. Gao & S. Ni (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing, 24:3, 583-594, DOI: 10.1080/01431160304987

Crippen, R.E. (1990). Calculating the vegetation index faster: Remote sensing of Enviromnment, 34, 71-73. 

Gao. "NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space." 1996. 

Chunyang He, Peijun Shi, Dingyong Xie & Yuanyuan Zhao (2010). Improving the normalized difference build-up index to map urban built-up areas using a semiautomatic segmentation approach, Remote Sensing Letters, 1:4, 213-221, DOI: 10.1080/01431161.2010.481681

Xu, H. (2007): Extraction of urban built-up land features from Landsat imagery using a thematic-oriented index combination technique. Photogrammetric Engineering & Remote Sensing. 73: 1381-1391.

Zhao, H. – Chen, X. (2005): Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. IEEE International. 3: 1666-1668.

Xu, H. (2005): A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI). Journal of Remote Sensing. 9: 589-595.

Hansen, M.C. – Loveland, T.R. (2012): A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment. 122: 66-74.

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