A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing

Sha Huang, Lina Tang, Joseph P. Hupy, Yang Wang, Guofan Shao

Journal of Forestry Research ›› 2020, Vol. 32 ›› Issue (1) : 1-6.

Journal of Forestry Research ›› 2020, Vol. 32 ›› Issue (1) : 1-6. DOI: 10.1007/s11676-020-01155-1
Review Article

A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing

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Abstract

The Normalized Difference Vegetation Index (NDVI), one of the earliest remote sensing analytical products used to simplify the complexities of multi-spectral imagery, is now the most popular index used for vegetation assessment. This popularity and widespread use relate to how an NDVI can be calculated with any multispectral sensor with a visible and a near-IR band. Increasingly low costs and weights of multispectral sensors mean they can be mounted on satellite, aerial, and increasingly—Unmanned Aerial Systems (UAS). While studies have found that the NDVI is effective for expressing vegetation status and quantified vegetation attributes, its widespread use and popularity, especially in UAS applications, carry inherent risks of misuse with end users who received little to no remote sensing education. This article summarizes the progress of NDVI acquisition, highlights the areas of NDVI application, and addresses the critical problems and considerations in using NDVI. Detailed discussion mainly covers three aspects: atmospheric effect, saturation phenomenon, and sensor factors. The use of NDVI can be highly effective as long as its limitations and capabilities are understood. This consideration is particularly important to the UAS user community.

Keywords

NDVI / Atmospheric effect / Saturation phenomenon / Calibration / Multispectral / Near infrared / UAS / Drone remote sensing

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Sha Huang, Lina Tang, Joseph P. Hupy, Yang Wang, Guofan Shao. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 2020, 32(1): 1‒6 https://doi.org/10.1007/s11676-020-01155-1

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