6 Spectral Indexes To Make Vegetation Analysis Complete
Did you know there is at least a hundred other spectral indexes, except NDVI (Normalized Difference Vegetation Index), that are widely used to analyze vegetation?
Every index is basically a certain combination (formula) of the sensor-measured reflectance properties (water content, chlorophyll content, pigment, etc.) at 2 or more wavelengths that reveals particular characteristics of vegetation. As sensors advance, Earth-observing satellites provide remote sensing experts with new data to fuel their research and improve existing analysis.
Let’s take a closer look at new LandViewer indexes and learn what useful insights they can add to your regular NDVI based analysis.
Every index has its limitations. NDVI is sensitive to the effects of soil and atmosphere, that’s why it’s recommended to apply additional indexes for more accurate analysis of vegetation.
What is SAVI index? The Soil Adjusted Vegetation Index was designed to minimize soil brightness influences. Its creator Huete added a soil adjustment factor L to the equation of NDVI in order to correct for soil noise effects (soil color, soil moisture, soil variability across region, etc.), which tend to impact the results.
Formula of SAVI vegetation index:
SAVI = ((NIR – Red) / (NIR + Red + L)) x (1 + L)
Key fact: 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.
When to use: for analysis of young crops; for arid regions with sparse vegetation (less than 15% of total area) and exposed soil surfaces.
As the name suggests, the Atmospherically Resistant Vegetation Index is the first vegetation index, which is notrelatively prone to atmospheric factors (such as aerosol). The formula of ARVI index invented by Kaufman and Tanré is basically NDVI corrected for atmospheric scattering effects in the red reflectance spectrum by using the measurements in blue wavelengths.
Key fact: Compared to other indexes, ARVI agriculture index is also more robust to topographic effects, which makes it a highly effective monitoring tool for tropical mountainous regions often polluted by soot coming from slash-and-burn agriculture.
When to use: for regions with high content of atmospheric aerosol (e.g. rain, fog, dust, smoke, air pollution).
EOS Crop Monitoring
Access high-resolution satellite images to ensure effective fields management!
What is EVI? The Enhanced Vegetation Index was invented by Liu and Huete to simultaneously correct NDVI results for atmospheric influences and soil background signals, especially in areas of dense canopy. The value range for EVI is -1 to 1, and for healthy vegetation it varies between 0.2 and 0.8.
Key fact: EVI contains coefficient C1 and C2 to correct for aerosol scattering present in the atmosphere, and L to adjust for soil and canopy background. Beginner GIS analysts may be confused by what values should be used and how to calculate enhanced vegetation index for different satellite data. Traditionally, for NASA’s MODIS sensor (which EVI index was developed for) C1=6, C2=7.5, and L=1. In case you’re wondering how to see Enhanced Vegetation Index using Sentinel 2 or Landsat 8 data, use the same values or simply use Crop Monitoring, which also allows to download the results.
When to use: for analyzing areas of Earth with large amounts of chlorophyll (such as rainforests), and preferably with minimum topographic effects (not mountainous regions).
In remote sensing, the Green Chlorophyll Index is used to estimate the content of leaf chlorophyll in various species of plants. The chlorophyll content reflects the physiological state of vegetation; it decreases in stressed plants and can therefore be used as a measurement of plant health.
Formula of GCI index:
GCI = (NIR) / (Green) – 1
Key fact: Better prediction of chlorophyll amount with the GCI vegetation index can be achieved by using satellite sensors that have broad NIR and green wavelengths.
When to use: for monitoring the impact of seasonality, environmental stresses, applied pesticides on plant health.
The Structure Insensitive Pigment Index is good for analysis of vegetation with the variable canopy structure. It estimates the ratio of carotenoids to chlorophyll: the increased value signals of stressed vegetation
Formula of SIPI index:
SIPI = (NIR – Blue) / (NIR – Red)
Key fact: High SIPI values (increased carotenoids and decreased chlorophyll) are often an indicator of plant disease, which is associated with loss of chlorophyll in plants.
When to use: for monitoring plant health in regions with high variability in canopy structure or leaf area index, for early detection of plant disease or other causes of stress.
What is NBR index? By definition, it is the Normalized Burn Ratio that is used to highlight burned areas following the fire. The equation of NBR vegetation index includes measurements at both NIR and SWIR wavelengths: healthy vegetation shows high reflectance in NIR spectrum, whereas the recently burned areas of vegetation reflect highly in the SWIR spectrum. 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.
To perform NBR vegetation index calculation, one needs a raster image containing the near infrared and shortwave infrared bands, that may be a satellite image collected by Landsat 7, Landsat 8, MODIS, etc. The range of values is between 1 and -1.
Formula of spectral index NBR:
NBR = (NIR – SWIR) / (NIR + SWIR)
Key fact: 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.
When to use: the typical use of NBR index for agriculture and forestry is detection of active wildfires, analysis of burn severity, and monitoring of vegetation survival after the burn.
EOSDA To Launch New Software: EOS Forest Monitoring
EOS Data Analytics launches a platform for satellite monitoring of forest stands to provide stakeholders with sustainable forest management tools. Software will help track deforestation and detect fire hazards, save resources and preserve the environment.
Forest fires cause whopping losses globally every year and often start due to human-related ignition. Therefore, caution and wildfire prevention measures are important. Satellite monitoring helps notice and address the problem on time.
Cloud-Free NDVI In Agriculture: When It Is Needed & Why
Clouds are often obstacles for optical NDVI, which negatively affect image quality and data analytics. Cloud-free NDVI helps resolve the problem as it uses radar-based imagery. This way it is possible to get accurate data even with cloud cover.
A Free Webinar: Smart Farming Technologies In Africa
EOS Data Analytics and AgroXchange host a free webinar to discuss how to accelerate the socio-economic well-being of smallholder farmers in Africa through precision farming technologies implementation.