A vegetation index (VI) is a spectral imaging transformation of two or more image bands designed to enhance the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations.[2][3]
6- monthly NDVI average for Australia, 1 Dec 2012 to 31 May 2013[1]
There are many VIs, with many being functionally equivalent. Many of the indices make use of the inverse relationship between red and near-infrared reflectance associated with healthy green vegetation. Since the 1960s scientists have used satellite remote sensing to monitor fluctuation in vegetation at the Earth's surface. Measurements of vegetation attributes include leaf area index (LAI), percent green cover, chlorophyll content, green biomass and absorbed photosynthetically active radiation (APAR).
VIs have been historically classified based on a range of attributes, including the number of spectral bands (2 or greater than 2); the method of calculations (ratio or orthogonal), depending on the required objective; or by their historical development (classified as first generation VIs or second generation VIs).[4] For the sake of comparison of the effectiveness of different VIs, Lyon, Yuan et al. (1998)[5] classified 7 VIs based on their computation methods (Subtraction, Division or Rational Transform). Due to advances in hyperspectral remote sensing technology, high-resolution reflectance spectrums are now available, which can be used with traditional multispectral VIs. In addition, VIs have been developed to be used specifically with hyperspectral data, such as the use of Narrow Band Vegetation Indices.
Ratio Vegetation Index (RVI): Defined as the ratio between the Red and Near Infrared lights of multispectral images [18]
Normalised Difference Vegetation Index (NDVI): The most commonly used remote sensing index [19] that calculates the ratio of the difference and sum between the Near Infrared and Red bands of multispectral images. It normally takes values between -1 and +1. It is mostly used in vegetation dynamics monitoring,[20] including biomass quantification.
Kauth-Thomas Tasseled Cap Transformation: A spectral enhancement index that transforms the spectral information of a satellite data into spectral features [21][22][23]
Infrared Index
Normalized difference water index
Perpendicular Vegetation Index
Greenness Above Bare Soil
Moisture Stress Index: A spectral index that measures the level of moisture stress in leaves [24]
Soil-Adjusted Vegetation Index (SAVI): An adjusted form of NDVI developed to minimize the effects of soil brightness on spectral vegetation indices, particularly in areas of high soil composition [26]
Modified SAVI: Mostly applied in to areas with low NDVI measures.
Atmospherically Resistant Vegetation Index
Soil and Atmospherically Resistant Vegetation Index
Enhanced Vegetation Index (EVI): Very similar to NDVI. The only difference is that it corrects atmospheric and canopy background noise, particularly in regions with high biomass
With the emergence of machine learning, certain algorithms can be used to determine vegetation indices from data. This allows to take into account all spectral bands and to discover hidden parameters that can be useful to strengthen these vegetation indices. Thus, they can be more robust against light variations, shadows or even uncalibrated images if these artifacts exist in the training data.
Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P; Gao, X.; Ferreira, L.G (2002). "Overview of the radiometric and biophysical performance of the MODIS vegetation indices". Remote Sensing of Environment. 83 (1–2): 195–213. Bibcode:2002RSEnv..83..195H. doi:10.1016/S0034-4257(02)00096-2.
Lyon, John G (1998). "A change detection experiment using vegetation indices". Photogrammetric Engineering and Remote Sensing: 143–150. CiteSeerX10.1.1.462.2056.
Gillies, R. R.; Kustas, W. P.; Humes, K. S. (1997). "A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e". International Journal of Remote Sensing. 18 (15): 3145–3166. Bibcode:1997IJRS...18.3145G. doi:10.1080/014311697217026. ISSN0143-1161.
Sandholt, Inge; Rasmussen, Kjeld; Andersen, Jens (2002). "A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status". Remote Sensing of Environment. 79 (2–3): 213–224. Bibcode:2002RSEnv..79..213S. doi:10.1016/S0034-4257(01)00274-7. ISSN0034-4257.
Kustas, W. P.; Norman, J. M. (2009). "Use of remote sensing for evapotranspiration monitoring over land surfaces". Hydrological Sciences Journal. 41 (4): 495–516. doi:10.1080/02626669609491522. ISSN0262-6667.
Kauth R. J. & G. S. Thomas (1976): The tasseled Cap - A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by LANDSAT. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data
