10 Common Vegetation Indices and Their Applications in Remote Sensing
Vegetation indices are mathematical expressions that help to quantify the health, density, and vigor of vegetation from remote sensing data.
They are widely used in various applications such as agriculture, forestry, ecology, and land management. Here are ten common vegetation indices and their applications:
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Normalized Difference Vegetation Index (NDVI): NDVI is one of the most commonly used vegetation indices. It uses the difference between the near-infrared (NIR) and red bands to assess vegetation health. NDVI has applications in agriculture, where it can be used to monitor crop growth and detect drought stress.
Enhanced Vegetation Index (EVI): EVI is an improvement over NDVI and accounts for atmospheric interference and soil background reflectance. It is particularly useful in areas with dense vegetation cover, where NDVI may saturate. EVI has applications in monitoring forest cover and tracking vegetation recovery after fires or other disturbances.
Soil Adjusted Vegetation Index (SAVI): SAVI is similar to NDVI, but it includes a soil brightness correction factor to improve its accuracy in areas with varying soil reflectance. SAVI has applications in arid and semi-arid regions, where vegetation cover is low, and soil brightness can significantly affect vegetation detection.
Transformed Vegetation Index (TVI): TVI is a variant of NDVI that enhances its sensitivity to vegetation changes. It is particularly useful in areas with low vegetation cover, where NDVI may not be sensitive enough. TVI has applications in monitoring vegetation changes in wetlands and other areas with low vegetation cover.
Green Normalized Difference Vegetation Index (GNDVI): GNDVI is similar to NDVI, but it uses the green band instead of the red band. It is particularly useful in areas with vegetation that has a strong green color. GNDVI has applications in monitoring grasslands and other areas with low vegetation density.
Modified Soil-Adjusted Vegetation Index (MSAVI): MSAVI is similar to SAVI, but it enhances the soil brightness correction factor. It is particularly useful in areas with high soil brightness, such as deserts and urban areas. MSAVI has applications in monitoring land use changes in arid regions.
Normalized Difference Infrared Index (NDII): NDII uses the difference between the NIR and short-wave infrared (SWIR) bands to detect water absorption features in vegetation. It has applications in monitoring vegetation water stress and mapping wetlands and other water-rich areas.
Chlorophyll Vegetation Index (CVI): CVI uses the ratio of the NIR and red bands to estimate chlorophyll content in vegetation. It has applications in monitoring vegetation health and detecting nutrient deficiencies in crops.
Red Edge Vegetation Index (REVI): REVI uses the difference between the NIR and red edge bands to enhance vegetation detection in areas with low vegetation cover. It has applications in monitoring vegetation changes in arid and semi-arid regions.
Photochemical Reflectance Index (PRI): PRI uses the difference between the 700nm and 670nm bands to estimate the efficiency of photosynthesis in vegetation. It has applications in monitoring vegetation stress and detecting changes in ecosystem productivity.
Here are 10 vegetation indices with their formulas and typical value ranges in remote sensing:
Normalized Difference Vegetation Index (NDVI) Formula: (NIR — RED) / (NIR + RED) Typical range: -1 to 1, with higher values indicating denser and healthier vegetation.
Enhanced Vegetation Index (EVI) Formula: 2.5 * (NIR — RED) / (NIR + 6 * RED — 7.5 * BLUE + 1) Typical range: -1 to 1, with higher values indicating denser and healthier vegetation.
Soil Adjusted Vegetation Index (SAVI) Formula: (NIR — RED) / (NIR + RED + L) * (1 + L) Typical range: -1 to 1, with higher values indicating denser and healthier vegetation. The L parameter ranges from 0 to 1 and represents the soil brightness correction factor.
Transformed Vegetation Index (TVI) Formula: 0.5 * (120 * (NIR — GREEN) — 200 * (RED — GREEN)) Typical range: 0 to 1, with higher values indicating denser and healthier vegetation.
Green Normalized Difference Vegetation Index (GNDVI) Formula: (NIR — GREEN) / (NIR + GREEN) Typical range: -1 to 1, with higher values indicating denser and healthier vegetation.
Modified Soil-Adjusted Vegetation Index (MSAVI) Formula: 0.5 * (2 * NIR + 1 — sqrt((2 * NIR + 1) ^ 2–8 * (NIR — RED))) Typical range: -1 to 1, with higher values indicating denser and healthier vegetation.
Normalized Difference Infrared Index (NDII) Formula: (NIR — SWIR) / (NIR + SWIR) Typical range: -1 to 1, with higher values indicating more water absorption and less vegetation.
Chlorophyll Vegetation Index (CVI) Formula: (NIR / RED) — 1 Typical range: 0 to infinity, with higher values indicating higher chlorophyll content in vegetation.
Red Edge Vegetation Index (REVI) Formula: (NIR / RED Edge) — 1 Typical range: 0 to infinity, with higher values indicating denser and healthier vegetation.
Photochemical Reflectance Index (PRI) Formula: (700nm — 670nm) / (700nm + 670nm) Typical range: -1 to 1, with higher values indicating more efficient photosynthesis and healthier vegetation.
Bonus: Three more vegetation indices that use the five bands: blue, green, red, red edge, and near-infrared (NIR)
Modified Chlorophyll Absorption Ratio Index (MCARI) — This index is designed to reduce soil and atmospheric effects on vegetation indices. It is calculated as [(1.2 * (NIR — Green)) — (1.5 * (Red — Green))] * ((NIR / Red) ^ 0.5).
Red Edge Normalized Difference Vegetation Index (RENDVI) — This index is designed to enhance the sensitivity to changes in vegetation structure and canopy architecture. It is calculated as (NIR — RE) / (NIR + RE), where RE is the reflectance in the red edge band.
Green Chlorophyll Index (GCI) — This index is designed to be sensitive to chlorophyll content in green leaves. It is calculated as Green / (Red * NIR).
Some codes github here.
In conclusion, vegetation indices are powerful tools for quantifying vegetation characteristics from remote sensing data. The ten indices mentioned above are just a few of the many available, and their applications are diverse and wide-ranging. Understanding the strengths and weaknesses of different vegetation indices can help researchers and practitioners make informed decisions when choosing the best index for a particular application.
Next article: Functions for 10 Vegetation Indices in Python, Matlab and R Languages
Thanks for this illuminating post. Suppose I wanted to isolate grasslands from Landsat data, would it make sense to look out for areas with moderate GNDVI values? Perhaps something between 0.1 and 0.3?