ndvi quastions
  • Agtech

NDVI FAQ: All You Need To Know About Index

NDVI has been one of the most commonly used vegetation indices in remote sensing since its introduction in the 1970s. With the increased availability of remotely sensed imagery from satellites and UAVs, more and more people have come to adopt NDVI in their activity beyond the scope of science.

Agriculture is now the most popular industry leveraging such advantages of satellite data as large area coverage, accuracy of results, and high acquisition frequency, which means that territory as small as a single field or as huge as an entire country can be observed from space at a certain frequency.

And yet, there’s still a lack of knowledge and lots of misbeliefs around these mysterious vegetation indices. To help users better understand how to work with NDVI in the most effective way and reap most benefits, we decided to come up with this go-to FAQ.

Let’s dive in!

1. What Is A VI, Or Vegetation Index?

You should know that vegetation’s spectral reflectance across different bands measured by the sensor serves as an indicator of the presence of plants or trees and their overall state. So, VI is a mathematical combination of two or more such spectral bands that enhances the contrast between vegetation (having high reflectance) and bare soil, manmade structures, etc. as well as quantifies plant’s characteristics, such as biomass, vigor, density, etc.
There are over a hundred indices for vegetation analysis; to learn more, check the Index Database.

2. What Is NDVI In Remote Sensing?

The Normalized Difference Vegetation Index is a simple indicator of photosynthetically active biomass or, in layman’s terms, a calculation of vegetation health. In EOSDA Crop Monitoring, a cloud-free NDVI is also available.

3. What Does NDVI Show?

Simply put, NDVI helps to differentiate vegetation from other types of land cover (artificial) and determine its overall state. It also allows to define and visualize vegetated areas on the map as well as detect abnormal changes in the growth process.

4. How To Calculate NDVI?

NDVI is calculated with the following expression: NDVI = (NIR-Red) / (NIR+Red), where NIR is near-infrared light and Red is visible red light. There’s a great number of free online GIS tools that allow you to instantly calculate NDVI.

5. If You Need To Calculate NDVI, Which Type Of Bands You Would Use?

As it follows from the NDVI formula, you need to take reflectance value in two bands: the visible red band and near-infrared band. Please note that you won’t be able to calculate NDVI by using natural color imagery or another type of band composites, even though they may contain the required bands.

6. How Does NDVI Work?

Basically, it works by mathematically comparing the amount of absorbed visible red light and the reflected near-infrared light. And here’s why.

The chlorophyll pigment in a healthy plant absorbs most of the visible red light, while the cell structure of a plant reflects most of the near-infrared light. It means that high photosynthetic activity, commonly associated with dense vegetation, will have fewer reflectance in the red band and higher reflectance in the near-infrared one. By looking at how these values compare to each other, you can reliably detect and analyze vegetation cover separately from other types of natural land cover.

how NDVI data is interpeted

7. What NDVI Value Represents Healthy Vegetation, A Positive One Or A Negative One?

As you may know, the results of the NDVI calculation range from -1 to 1. Negative values correspond to areas with water surfaces, manmade structures, rocks, clouds, snow; bare soil usually falls within 0.1- 0.2 range; and plants will always have positive values between 0.2 and 1. Healthy, dense vegetation canopy should be above 0.5, and sparse vegetation will most likely fall within 0.2 to 0.5. However, it’s only a rule of thumb and you should always take into account the season, type of plant and regional peculiarities to know exactly what NDVI values mean.

NDVI values to monitor plant health

8. How To Measure Density Of Vegetation With NDVI?

In most cases, NDVI values between 0.2 and 0.4 correspond to areas with sparse vegetation; moderate vegetation tends to vary between 0.4 and 0.6; anything above 0.6 indicates the highest possible density of green leaves.

If you’re analyzing crops with NVDI, make sure to take into account the type of planted crops and the row width as you interpret the obtained results.

The problem with NDVI as a tool to measure vegetation density is that it saturates at high amounts of green biomass. Simply put, you may end up having the same NDVI readings for low and very high vegetation density. Consider using EVI (Enhanced Vegetation Index), which is an adjusted version of NDVI that is especially accurate in areas with a dense canopy. You can learn more about EVI here. Another alternative is NDRE (Normalized Difference Red Edge), an index that is good for permanent thick crops or other dense crops.

9. How To Interpret NDVI Images?

Traditionally, NDVI results are presented as a color map, where each color corresponds to a certain range of values. There’s no standard color palette, but most software uses the “red-green” one, meaning that red-orange-yellow tints indicate bare soil or dead/sparse vegetation, and all shades of green are a sign of normal to dense vegetation cover.

If you still don’t know how to read NDVI imagery, just check the index legend, like the one we have in EOSDA LandViewer (as shown on the image in the bottom right corner). And remember that some software lets create your own index palette.

NDVI imagery from EOSDA LandViewer

10. What Are The Alternatives Of NDVI?

Actually, there is a fair amount of vegetation indices, which are based on the standard NDVI. Unlike it, they are adjusted for soil brightness, atmospheric effects, and other factors usually affecting NDVI results. They are EVI, SAVI, ARVI, GCL, SIPI, and you can learn more about them here.

11. What Does An NDVI Measure In Crops?

Simply put, NDVI measures the state and health of crops or crop vigor. This vegetation index is an indicator of greenness and has a strong correlation with green biomass, which is indicative of growth. NDVI values are also known to have a high correlation with crop yield, meaning it can be used as a tool for measuring crop productivity and predicting future yield.

12. Can NDVI Show The Phase Of Crops?

As a matter of fact, NDVI values obtained with satellite data with high temporal resolution (e.g., MODIS) have a strong correlation with crop phenological stages (emerged, maturity, harvest). However, there are certain limitations. For instance, during the early stages of crop growth, when the green leaf area is small, NDVI results are very sensitive to soil background effects. The NDVI may also saturate at later stages, when the crops reach canopy closure, and produce inaccurate results.

13. Is NDVI Value Different For Different Cultures?

It certainly is. Every crop type has a different canopy structure, growth stages, and requires specific climatic conditions for growing properly. All these factors influence crop’s reflectance properties and, as a result, produce different NDVI values across various crop types.

14. What Is A Normal Value NDVI For Corn/Wheat/Rapeseed/Soybean?

Unfortunately, there are no established norms of NDVI values for different crop types because every field is unique and readings depend on a combination of various factors (climate, soil type, agricultural management practices). We recommend taking satellite data across several seasons and generating NDVI Time Series to identify the growing patterns and normal values for your own field.

15. Can I Use NDVI For Vineyards?

It depends. NDVI has been used to assess the vine vigor, but the accuracy will depend on soil management practices. If there’s cover crop growing between vine rows, it will be hard to distinguish areas corresponding to vine NDVI from those corresponding to cover crop NDVI. If inter-rows only have bare soil in them, NDVI results tend to be more accurate.

16. How To Use NDVI In Agriculture?

When it comes to crops, there’s a bunch of applications. NDVI can be used to:

1) Measure biomass and assess the state and health of crops
2) Identify pests, diseases, fungus, or overly dried spots in the field before the damage is done
3) Observe vegetation dynamics throughout the growing season
4) Establish normal growing conditions for the crops in the specific area with NDVI Time Series
5) Estimate crop yields (never alone, only combined with other parameters used for prediction)
6) Detect areas of concern within the field faster, and spend water, crop nutrients, and pesticides more effectively
7) Monitor pasture conditions and productivity
8) Monitor drought and assist in forecasting fire-hazardous areas

EOSDA Crop Monitoring

Access high-resolution satellite images to ensure effective fields management!

17. Can NDVI Be Used To Optimize Fungicide Application?

Definitely, yes. First, you can use NDVI maps of your field to validate the results of applying various fungicides and see which one leads to healthier and more resistant crops. Second, the NDVI image can be used as a prescription map showing you the areas, where crops may have been suffering from fungal disease, so fungicide can be applied accordingly. It will cost you less than spraying the entire field.

18. How Can NDVI Assist In Eradicating Weed Infestation?

Weed management is more powerful with satellite data. The NDVI profile of weed-free crops differs noticeably from that of the weed-infested crops.

According to multiple studies, NDVI images can be used for detecting late-season weed infestations in crops. Calculated from aerial or high-resolution satellite images acquired a few weeks before crop senescence, NDVI can help you differentiate between weed-free and weed-infested plants, the latter would be having a higher spectral response (= higher NDVI values). Alternatively, weed areas can be detected with post-harvesting images. NDVI images can, therefore, serve as herbicide prescription maps letting you spray only the weed patches instead of the whole field and reducing the environmental impact and costs.

To spot a weed outbreak in early-stage crops, you would need ground sensors.

19. Is NDVI An Effective Risk Management Tool For Crop Insurance?

It surely is. Insurance companies have been reaping the benefits of using satellite technology and NDVI images, in particular, to:
– quickly and accurately quantify losses caused by severe weather, overspray, drought, etc. spending far less time and human resources;
– get field insights with historical imagery dating 30-40 years back;
monitor fields of any scale (from a district to a region/state or a whole country), with real-time updates and get ready for indemnity payments early on;
– identify fields that are not eligible for payment (e.g. illegal burning).

20. How To Differentiate Between Crops And Trees?

We agree this can be tricky. Both crops and trees can have high NDVI values, which makes it challenging to distinguish between the two. The easiest way is to calculate the mean NDVI value for each of the 3 months in the past year. The vegetated areas having high NDVI values for more than 3 months in a row will most likely mean coniferous forest. Crops rarely maintain high NDVI this long.

21. How To Detect Forest Clearing/Deforestation With NDVI?

Well, you’ve got options here. You can calculate the mean NDVI value for several months since the date of alleged forest clearing and compare it with the mean NDVI of the same months a year ago. If there’s at least a 0.25 drop in NDVI, then you’ve most likely spotted a tree cut. Or you can use an automatic change detection tool: it will highlight the spots, where any land cover changes have taken place, on a pair of images taken at different dates (preferably, for the same month across different years).

Massive cutting of old-growth forests on Vancouver Island, Canada between 2017 and 2018

Massive cutting of old-growth forests on Vancouver Island, Canada between 2017 and 2018

It’s important to perform analyses with imagery having the lowest possible cloud cover to avoid false negative values. The problem with this method is that it doesn’t identify the exact reason of forest loss. Aside from clearing, the forest cover loss can be due to wildfires, hurricanes, or use of chemicals.

22. Is It Possible To Measure Forest Temperature And Identify Forest Fires With NDVI?

Not really. NDVI formula doesn’t have the bands to do this. However, NDVI has gained the reputation of drought detector, which means it can point out the areas covered with overly dry vegetation (= low NDVI values), where fire risk is obviously at its highest.

23. Сan NDVI Be Used To Measure Forage Abundance?

Actually, the answer is yes and no. Cattle farmers are known to have used NDVI to check on the presence and conditions of grass on their pastures. However, if you’re dealing with areas where forage grasses are hidden under a dense tree and/or bush canopy (e.g. tropical forests), NDVI will not be reliable as a grass abundance measurement tool.

About the author:

Vasyl Cherlinka Scientist at EOS Data Analytics

Vasyl Cherlinka has over 30 years of experience in agronomy and pedology (soil science). He is a Doctor of Biosciences with a specialization in soil science.

Dr. Cherlinka attended the engineering college in Ukraine (1989-1993), went on to deepen his expertise in agrochemistry and agronomy in the Chernivtsi National University in the specialty, “Agrochemistry and soil science”.

In 2001, he successfully defended a thesis, “Substantiation of Agroecological Conformity of Models of Soil Fertility and its Factors to the Requirements of Field Cultures” and obtained the degree of Biosciences Candidate with a special emphasis on soil science from the NSC “Institute for Soil Science and Agrochemistry Research named after O.N. Sokolovsky”.

In 2019, Dr. Cherlinka successfully defended a thesis, “Digital Elevation Models in Soil Science: Theoretical and Methodological Foundations and Practical Use” and obtained the Sc.D. in Biosciences with a specialization in soil science.

Vasyl is married, has two children (son and daughter). He has a lifelong passion for sports (he’s a candidate for Master of Sports of Ukraine in powerlifting and has even taken part in Strongman competitions).

Since 2018, Dr. Cherlinka has been advising EOSDA on problems in soil science, agronomy, and agrochemistry.

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