Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

By Jinru Xue, Baofeng Su. J. Sensors, 2017

2,180 citations recorded by the source corpus.

Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.

Subfields: Ecological Modeling, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/e22ecc54d016336ee3c7a8a99533b1136698727f

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Citations in the corpus

A compilation of UAV applications for precision agriculture (2020, positive): “This spectral information is employed by vegetation indices, such as NDVI which enables the generation of agroclimatic models that in turn allow the identiﬁcation and separation of a crop into individual subareas with particular characteristics.”
Precision Agriculture Techniques and Practices: From Considerations to Applications (2019, background): “Vegetation Indices (VIs) obtained from remote sensing-based canopies are effective algorithms for quantitative and qualitative evaluations of vigour, vegetation cover and growth dynamics, among other applications [26].”
Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications (2020, background): “de) on the spectral regions most useful for agricultural monitoring are suggested for a more exhaustive coverage of spectral indexes in general [31,32].”
Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS) (2021, background): “Such early detection has been challenging for other VIs aiming at ‘green-attack’ detection.”
A unified vegetation index for quantifying the terrestrial biosphere (2021, background): “A plethora of narrow-band indices has been proposed in the literature ( 1 ).”
Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, background): “Although light-weight high-definition cameras containing the NIR band are available, most of the off-the-shelf UAVs still mount only cameras working on visible bands for the derived indices [159].”
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods (2018, neutral): “(2008), Xue and Su (2017)].”
Change detection techniques for remote sensing applications: a survey (2019, background): “of vegetation cover and properties of soil (Xue and Su 2017).”
Agricultural Robotics for Field Operations (2020, background): “Further, various vegetation indexes, such as the Ratio Vegetation Index (RVI), the Normalized Difference Vegetation Index (NDVI) [60], etc., which provide useful information about plant health, photosynthetic ability and more, are used by combining wavelengths.”
County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model (2019, background): “The other indices, such as Green Leaf Area Index (GLAI) [7], Crop Water Stress Index (CWSI) [8], Normalized Difference Water Index (NDWI) [9], Green Vegetation Index (GVI), Soil-Adjusted Vegetation Index (SAVI) [10], Enhanced Vegetation Index (EVI) [11], etc.”