research paper on vegetation indices

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• Technical Review
• Published: 31 May 2022

Optical vegetation indices for monitoring terrestrial ecosystems globally

• Yelu Zeng   ORCID: orcid.org/0000-0003-4267-1841 1 ,
• Dalei Hao   ORCID: orcid.org/0000-0001-7154-6332 2 ,
• Alfredo Huete 3 ,
• Benjamin Dechant 4 , 5 ,
• Joe Berry 6 ,
• Jing M. Chen 7 ,
• Joanna Joiner 8 ,
• Christian Frankenberg   ORCID: orcid.org/0000-0002-0546-5857 9 , 10 ,
• Ben Bond-Lamberty   ORCID: orcid.org/0000-0001-9525-4633 11 ,
• Youngryel Ryu   ORCID: orcid.org/0000-0001-6238-2479 12 ,
• Jingfeng Xiao   ORCID: orcid.org/0000-0002-0622-6903 13 ,
• Ghassem R. Asrar 14 &
• Min Chen   ORCID: orcid.org/0000-0001-6311-7124 1  

Nature Reviews Earth & Environment volume  3 ,  pages 477–493 ( 2022 ) Cite this article

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• Climate-change ecology
• Ecosystem ecology

Vegetation indices (VIs), which describe remotely sensed vegetation properties such as photosynthetic activity and canopy structure, are widely used to study vegetation dynamics across scales. However, VI-based results can vary between indices, sensors, quality control measures, compositing algorithms, and atmospheric and sun–target–sensor geometry corrections. These variations make it difficult to draw robust conclusions about ecosystem change and highlight the need for consistent VI application and verification. In this Technical Review, we summarize the history and ecological applications of VIs and the linkages and inconsistencies between them. VIs have been used since the early 1970s and have evolved rapidly with the emergence of new satellite sensors with more spectral channels, new scientific demands and advances in spectroscopy. When choosing VIs, the spectral sensitivity and features of VIs and their suitability for target application should be considered. During data analyses, steps must be taken to minimize the impact of artefacts, VI results should be verified with in situ data when possible and conclusions should be based on multiple sets of indicators. Next-generation VIs with higher signal-to-noise ratios and fewer artefacts will be possible with new satellite missions and integration with emerging vegetation metrics such as solar-induced chlorophyll fluorescence, providing opportunities for studying terrestrial ecosystems globally.

Optical vegetation indices (VIs) derived from space-borne Earth observations are widely used for monitoring terrestrial ecosystems and tracking plant biophysical, biochemical and physiological properties, vegetation dynamics and environmental stresses.

Sensor and calibration effects, quality assurance and quality control, bidirectional reflectance distribution function, atmospheric and topographic effects, and snow and soil background effects are among important uncertainty sources of VIs.

Potential artefacts must be carefully considered to avoid biased interpretations of the underlying ecological processes resulting from the improper use of VIs.

VIs based on reflectance ratios such as the normalized difference vegetation index can help reduce sensor calibration, bidirectional effects, atmospheric and topographic effects, but could be sensitive to snow and soil background and scale effects.

Mathematical analysis shows intrinsic similarity among several widely used VIs, including near-infrared reflectance of vegetation, enhanced vegetation index, two-band version of the enhanced vegetation index and difference vegetation index, whereas the ratio-based normalized difference vegetation index behaves differently.

Identifying key sensitive wavelengths for target application is the first step towards the optimal use of VIs, followed by an understanding of potential uncertainty sources in the specific ecosystem.

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Acknowledgements

Y.Z. and M.C. acknowledges support from the National Aeronautics and Space Administration (NASA) through Remote Sensing Theory and Terrestrial Ecology programmes 80NSSC21K0568 and 80NSSC21K1702. M.C. also acknowledges support by a McIntire–Stennis grant (1027576) from the National Institute of Food and Agriculture (NIFA), United States Department of Agriculture (USDA). B.D. acknowledges support by sDiv, the Synthesis Centre of iDiv (DFG FZT 118, 202548816). J.X. was supported by the National Science Foundation (NSF) (Macrosystems Biology and NEON-Enabled Science programme: DEB-2017870). Y.R. was supported by the National Research Foundation of Korea (NRF-2019R1A2C2084626). The authors thank G. Badgley for fruitful discussions on vegetation indices and P. Köhler for the TROPOMI far-red daily SIF dataset.

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Application of Remote Sensing Vegetation Indices for Forest Cover Assessments

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Forests are an indispensable foundation of life for humans. They fulfil multiple functions in a single area: they are a source of income to many; they provide wood, an environmentally compatible, renewable resource, as well as foodstuffs and many other basic commodities; they protect the soils from erosion and stabilize the water table; they stabilize the climate on a regional and global level and they offer humans numerous opportunities for recreation and relaxation. These functions have different levels of significance in the various regions of the earth. For the past 20 years, increases in the produce and income generated resulted from the increase of the agricultural area rather than that of products per unit area due to the high rate of population growth correlated with a limited area of land available for cropping and housing. Situations such as poverty and a scarcity of food have forced villagers to migrate into the forest reserves, where they subsequently destroy the forests through shifting cultivation, especially in the watershed areas.

Remote sensing using satellites can make a significant contribution to regional and global forest cover assessment. Satellite images permit the observation of large geographical areas and can be repeated at short time intervals and the costs are reasonable. The basic forest cover information that can be obtained from satellite images at different spatial resolutions relates to the area and spatial distribution of broad forest cover types, to the degree of canopy fragmentation and to the forest cover changes occurring. Recent research papers show that remotely sensed data are well correlated with forest stand parameters. Vegetation index is a spectral transformation of at least two optical bands to obtain the vegetation properties. Normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI) and soil-adjusted vegetation index (SAVI) were cited in numerous research papers and they have been widely used in the forest researches to investigate the relationship between forest parameters such as diameter at breast height (DBH), per cent crown cover, tree age class, tree height, basal area, tree volume and aboveground living biomass. Nowadays it has been possible for researcher worldwide to access the satellite data with free download, for example, Landsat 8, Landsat 9 or Sentinel-2. The use of vegetation index is necessary for understanding the forest area in a global level and the greater efficiency of sustainable forest management.

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Khunrattanasiri, W. (2022). Application of Remote Sensing Vegetation Indices for Forest Cover Assessments. In: Suratman, M.N. (eds) Concepts and Applications of Remote Sensing in Forestry . Springer, Singapore. https://doi.org/10.1007/978-981-19-4200-6_8

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Sensitivity of the enhanced vegetation index (evi) and normalized difference vegetation index (ndvi) to topographic effects: a case study in high-density cypress forest.

1. Introduction

2. methodology, 2.1. definition of evi and ndvi, 2.2. non-lambertian model for topographic effect, 3. theoretical analysis of topographic effects on evi and ndvi, 4. a case study for testing topographic effects on evi and ndvi, 4.1. study area and data processing, 4.2. results, 5. conclusions, acknowledgments, references and notes.

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Matsushita, B.; Yang, W.; Chen, J.; Onda, Y.; Qiu, G. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest. Sensors 2007 , 7 , 2636-2651. https://doi.org/10.3390/s7112636

Matsushita B, Yang W, Chen J, Onda Y, Qiu G. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest. Sensors . 2007; 7(11):2636-2651. https://doi.org/10.3390/s7112636

Matsushita, Bunkei, Wei Yang, Jin Chen, Yuyichi Onda, and Guoyu Qiu. 2007. "Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest" Sensors 7, no. 11: 2636-2651. https://doi.org/10.3390/s7112636

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