6- monthly NDVI average for Australia, 1 Dec 2012 to 31 May 2013[1]

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]

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).[3] For the sake of comparison of the effectiveness of different VIs, Lyon, Yuan et al. (1998)[4] 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.

Uses

Vegetation indices have been used to:

Types of vegetation index

Multispectral Vegetation Index

NDVI through Landsat 8 applied to the urban area of Ponta Grossa, southern Brazil

Hyperspectral Vegetation Index

With the advent of hyperspectral data, vegetation index have been developed specifically for hyperspectral data.

Advanced Vegetation Indices

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.

See also

References

  1. ^ Data downloaded from "Australian Bureau of Meteorology". on 13 June 2018, mapped in R 14 June 2018
  2. ^ 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.
  3. ^ Bannari, A.; Morin, D.; Bonn, F.; Huete, A. R. (1995-08-01). "A review of vegetation indices". Remote Sensing Reviews. 13 (1–2): 95–120. doi:10.1080/02757259509532298. ISSN 0275-7257.
  4. ^ Lyon, John G (1998). "A change detection experiment using vegetation indices". Photogrammetric Engineering and Remote Sensing: 143–150. CiteSeerX 10.1.1.462.2056.
  5. ^ Eklundh, L.; Olsson, L. (2003). "Vegetation index trends for the African Sahel 1982-1999". Geophysical Research Letters. 30 (8): 1430. Bibcode:2003GeoRL..30.1430E. doi:10.1029/2002GL016772. ISSN 0094-8276. S2CID 129096989.
  6. ^ 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. ISSN 0143-1161.
  7. ^ 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. ISSN 0034-4257.
  8. ^ Peters, A.J.; Walter-Shea, E.A.; Ji, L.; Vliia, A.; Hayes, M.; Svoboda, M.D. (2002). "Drought Monitoring with NDVI-Based Standardized Vegetation Index" (PDF). Photogrammetric Engineering & Remote Sensing. 68 (1): 71–75. Retrieved 16 May 2018.
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  10. ^ Wan, Z.; Wang, P.; Li, X. (2004). "Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA". International Journal of Remote Sensing. 25 (1): 61–72. Bibcode:2004IJRS...25...61W. doi:10.1080/0143116031000115328. ISSN 0143-1161. S2CID 129234540.
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  15. ^ Lloyd, Daniel (1990). "A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery". International Journal of Remote Sensing. 11 (12): 2269–2279. Bibcode:1990IJRS...11.2269L. doi:10.1080/01431169008955174. ISSN 0143-1161.
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  17. ^ Jordan, Carl F. (July 1969). "Derivation of Leaf-Area Index from Quality of Light on the Forest Floor". Ecology. 50 (4): 663–666. doi:10.2307/1936256. ISSN 0012-9658. JSTOR 1936256.
  18. ^ Bhandari, A.K.; Kumar, A.; Singh, G.K. (2012). "Feature Extraction using Normalized Difference Vegetation Index (NDVI): A Case Study of Jabalpur City". Procedia Technology. 6: 612–621. doi:10.1016/j.protcy.2012.10.074. ISSN 2212-0173.
  19. ^ Vrieling, Anton; de Leeuw, Jan; Said, Mohammed (2013-02-22). "Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series". Remote Sensing. 5 (2): 982–1000. Bibcode:2013RemS....5..982V. doi:10.3390/rs5020982. ISSN 2072-4292.
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  24. ^ LWCI entry in the Index Database, https://www.indexdatabase.de/db/i-single.php?id=129
  25. ^ Huete, A.R (August 1988). "A soil-adjusted vegetation index (SAVI)". Remote Sensing of Environment. 25 (3): 295–309. Bibcode:1988RSEnv..25..295H. doi:10.1016/0034-4257(88)90106-X.
  26. ^ Joshi, R.C.; Ryu, D.; Sheridan, G.J.; Lane, P.N.J. (2021). "Modeling Vegetation Water Stress over the Forest from Space: Temperature Vegetation Water Stress Index (TVWSI)". Remote Sens. 13 (22:4635): 4635. doi:10.3390/rs13224635.
  27. ^ Puente, Cesar; Olague, Gustavo; Trabucchi, Mattia; Arjona-Villicaña, P. David; Soubervielle-Montalvo, Carlos (January 2019). "Synthesis of Vegetation Indices Using Genetic Programming for Soil Erosion Estimation". Remote Sensing. 11 (2): 156. doi:10.3390/rs11020156. ISSN 2072-4292.
  28. ^ Albarracín, Juan F. H.; Oliveira, Rafael S.; Hirota, Marina; dos Santos, Jefersson A.; Torres, Ricardo da S. (January 2020). "A Soft Computing Approach for Selecting and Combining Spectral Bands". Remote Sensing. 12 (14): 2267. arXiv:2011.05127. doi:10.3390/rs12142267. ISSN 2072-4292.
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