Significance of Spectral Indexes in Accurate Vegetation Analysis

Many sensors carried aboard satellites measure red and near-infrared light waves reflected by land surfaces. Using mathematical formulas (algorithms), one can transform raw satellite data about these light waves into vegetation indices. A vegetation index is an indicator that describes the greenness i.e. the relative density and health of vegetation, for each pixel in a satellite image.  The most widely used vegetation indexes we all know is the Normalized Difference Vegetation Index (NDVI) whose values range from +1.0 to -1.0. The areas of barren rock, sand, or snow usually show very low NDVI values (for example, 0.1 or less) while sparse vegetation such as shrubs and grasslands may result in moderate NDVI values (approximately 0.2 to 0.5) and dense vegetation such as that found in temperate and tropical forests or crops at their peak growth stage shows high NDVI values (approximately 0.6 to 0.9). That said, NDVI does tend to saturate over dense vegetation and is sensitive to underlying soil color and atmosphere which becomes a limitation for this widely use method and demands possibility for other indexes to support it.

So let’s have a closer look at various other vegetation indexes which SATPALDA rely upon beside NDVI for more accurate analysis of vegetation and all sort of NDVI based analysis.

1. Soil-Adjusted Vegetation Index (SAVI)

In areas where vegetative cover is low (i.e., < 40%) and the soil surface is exposed, the reflectance of light in the red and near-infrared spectra can influence vegetation index values. This is especially problematic when comparisons are being made across different soil types that may reflect different amounts of light in the red and near infrared wavelengths (i.e., soils with different brightness values). The soil-adjusted vegetation index was developed as a modification of the Normalized Difference Vegetation Index to correct for the influence of soil brightness when vegetative cover is low.

Formula of SAVI vegetation index:

SAVI = ((NIR – Red) / (NIR + Red + L)) x (1 + L)

L is a variable. Its values range within -1 to 1, depending on the amount of green vegetation present in the area. To run the remote sensing analysis of areas with high green vegetation, L is set to zero (in which case SAVI index data will be equal to NDVI); whereas low green vegetation regions require L=1.

Application: Analysis of young or early sown crops; for arid regions with sparse vegetation and exposed soil surfaces.
 

• Modified Soil-adjusted Vegetation Index (MSAVI)

The modified soil-adjusted vegetation index (MSAVI) is soil adjusted vegetation indices that seek to address some of the limitation of NDVI when applied to areas with a high degree of exposed soil surface.  

The formula for calculating MSAVI itself is the same as the formula for calculating SAVI:

MSAVI = (NIR-RED)(1+L)/ (NIR+RED+L)

Where RED is the red band reflectance from a sensor, NIR is the near infrared band reflectance, and L is the soil brightness correction factor. The difference between SAVI and MSAVI, however, comes in how L is calculated. In SAVI, L is estimated based on how much vegetation there is while MSAVI uses the following formula to calculate L:

L= 1- (2*s*(NIR-RED)*(NIR-s*RED))/ (NIR+RED)

where s is the slope of the soil line from a plot of red versus near infrared brightness values.

MSAVI is used in the areas where indices like NDVI provide invalid data, mostly due to a small amount of vegetation, or due to a lack of chlorophyll therein. Thus, the index is used to minimize the soil background influence and to increase the dynamic range of vegetation signal.

Application: In the fields where plants have just been sown, it allow farmers to monitor the crop conditions at their earliest developmental stages and help them with early opportunities to apply fertilizers only where and when it is required. This approach allows for optimizing initial field management from the ground up, resulting in the enhancement of profit and the reduction of environmental impact.

 

• Enhanced Vegetation Index (EVI)

The enhanced vegetation index (EVI) was developed as an alternative vegetation index to address some of the limitations of the NDVI. The EVI was specifically developed to:

1. Be more sensitive to changes in areas having high biomass (a serious shortcoming of NDVI),
2. Reduce the influence of atmospheric conditions on vegetation index values, and
3. Correct for canopy background signals.

EVI tends to be more sensitive to plant canopy differences like leaf area index (LAI), canopy structure, and plant phenology and stress than does NDVI which generally responds just to the amount of chlorophyll present.

Formula of EVI:

EVI = 2.5 * ((NIR – Red) / ((NIR) + (C1 * Red) – (C2 * Blue) + L))

EVI contains coefficient C1 and C2 to correct for aerosol scattering present in the atmosphere, and L to adjust for soil and canopy background. Traditionally, for NASA’s MODIS sensor (which EVI index was developed for) C1=6, C2=7.5, and L=1. 

Application: for analyzing areas of Earth with large amounts of chlorophyll (such as rainforests), and with minimum topographic effects.

 

• Chlorophyll Index (CI)

While traditional NDVI imagery measures canopy density, the next-generation Chlorophyll Index assesses canopy quality—offering an accurate representation of plant health throughout the season. It’s a powerful resource for nutrient management, both for in-season adjustments and season-to-season planning. 

Formula of CI index:

CI green = ρNIR / ρgreen – 1 = ρ730/ρ530 – 1
CI red-edge = ρNIR/ρred_edge – 1 = ρ850/ρ730 – 1 

The red-edge band is a narrow band in the vegetation reflectance spectrum between the transitions of red to near infra-red. 

Chlorophyll Index incorporates four narrow bands in the visible/near-infrared that are particularly sensitive to differences in leaf chlorophyll content: red, green, red-edge, and near-infrared. This proprietary combination of wavelengths is closely correlated with the nitrogen content of the leaves and relative health of the crop canopy. 

Application: Identifying and addressing nutrient deficiencies in-season, Informing early yield predictions in row crops, identify plant stress and nutrients deficiencies. 

 

• Normalized Burn Ratio (NBR)

The Normalized Burn Ratio (NBR) was defined to highlight areas that have burned and to index the severity of a burn using Landsat TM imagery. The formula for the NBR is very similar to that of NDVI except that it uses near-infrared band 4 and the short-wave infrared band 7.

Formula of spectral index NBR:

NBR = (NIR – SWIR) / (NIR + SWIR)

It’s a common practice to assess burn extent and severity with the relative differenced NBR (delta Normalized Burn Ratio), that has shown the highest response to landscape changes caused by fire. It is a difference between the NBR calculated from an image of an area before the fire and NBR calculated from an image taken immediately after the burn. Additionally, there’s the NBR Thermal 1 index, which includes the Thermal band to enhance NBR and provide more accurate differentiation between the burned and unburned land.

Application: the typical use of NBR index for agriculture and forestry is detection of active fires, analysis of burn severity, and monitoring of vegetation survival after the burn. NBR fire index has become especially instrumental in the past years as extreme weather conditions (such as El Niño drought) cause significant increase in wildfires destroying forest biomass.
 

Conclusion

Different environments have their own variable and complex characteristics, which needs to be accounted when using different vegetation indexes. The most common vegetation index- NDVI, have significant limitations for detection of green vegetation as it is affected by soil and atmosphere and saturates in dense vegetation areas. There are various other indexes that can result accurate and desired result when combined with NDVI analysis.  Each of them has its specific expression of green vegetation, its own suitability for specific uses, and some limiting factors. Therefore, for practical applications, the choice of a specific vegetation index needs to be made with caution by comprehensively considering and analyzing the advantages and limitations of existing vegetation index and then combine them to be applied in a specific environment. In this way, the usage of vegetation index can be tailored to specific applications, instrumentation used, and platforms.