What do the NDVI values mean ? In remote sensing, the most commonly used index is the NDVI. But how do you figure it out? ? What are the applications of NDVI for Earth scientists?
* What is NDVI? How do we figure out the NDVI?
The Normalized Difference Vegetation Index (NDVI), being one of the most widely used vegetation indices, is a useful tool for remotely assessing vegetation health and land use.
With the amount of remotely sensed images from Earth Observation (EO) satellites rising year after year, simple indices like NDVI can assist in extracting information from this vast amount of data.
Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs)
When compared to other wavelengths, healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light.
It does, however, absorb more red and blue light.
When you have negative readings, for example, it’s almost certainly water. If your NDVI number is near to +1, though, it’s likely that you’re looking at dense green foliage. When the NDVI is close to zero, however, there are no green leaves and the area may be urbanized.
There are several NDVI materials available, but it can be difficult to know where to begin.
NDVI is a measure of how plants reflect particular wavelengths of the electromagnetic spectrum, and it’s used to assess vegetation health.We need to comprehend the electromagnetic spectrum in order to understand plant health.
It’s at the heart of how NDVI works, allowing us to assess how healthy or ill a plant is depending on how it reflects energy and light.
A plant appears green to the naked eye because its chlorophyll pigment reflects green wavelengths while absorbing red radiation. Plant cell structures reflect near-infrared (NIR) wavelengths. As a result of photosynthesis, the plant develops and increases, resulting in additional cell structures. This indicates that a healthy plant absorbs red light and reflects NIR since it has a lot of chlorophyll and cell structures. A sick plant will do the polar opposite.
We may use NDVI to distinguish between healthy and unhealthy plants because of the link between light and chlorophyll.
Red light is actively absorbed by healthy plants, whereas near-infrared light is reflected.
The wavelengths of light absorbed and reflected by green plants are measured by satellite sensors in orbit. They’re a great place to get spectral signature data for NDVI study.
The NDVI index identifies and quantifies the presence of living green vegetation in visible and near-infrared light. It is a measure of the density and health of vegetation in each pixel in a satellite picture.
When was the NDVI developed?
NDVI was developed in 1973 by a research team at Texas A&M University after studying data from the first Earth observation satellites circling the Earth. Their imaging devices captured infrared light wavelengths reflected by plants on the ground.Using Landsat 1 data, the scientists tracked regional vegetation changes during the growing season. They converted wavelengths of reflected light into an NDVI that closely represented plant cover and health for each pixel of land.
Since then, NDVI has served as a global window on plant health.The index is useful in a variety of ways and for a variety of reasons. From assisting farmers with precision agriculture to allowing conservationists to analyze environmental changes, technology is advancing.
What is the NDVI formula?
The NDVI is calculated using a simple mathematical technique which turns raw satellite data into vegetation indices.
The NDVI calculation formula formula integrates information from the red and near-infrared bands into a single, typical result.
It does this by subtracting the reflectance in the red spectral band from the reflectance in the NIR range. This is then divided by the sum of the NIR and red reflectance.
The NDVI calculated value will always be between -1 and +1.
Values ranging from -1 to 0 represent dead vegetation or inorganic items such as stones, roads, and homes.
For living plants, NDVI readings vary from 0 to 1, with 1 being the healthiest and 0 being the least healthy. Every pixel in a picture, from a single leaf to a 500-acre wheat field, may be assigned a single value.
The reflectance qualities of a plant canopy vary as it transitions from winter dormancy to late-summer maturity. The NDVI can assist you in tracking this seasonal fluctuation.
It is critical to understand that the NDVI is an indication of plant health and not a method of diagnosing a specific problem. It’s more of a collection of quantitative cues about what’s going on in the field for you to arm yourself with. Drought, disease, pests, and flooding are just a few of the numerous variables that might be impacting vegetation and, as a result, NDVI readings.
Thus, NDVI analysis might point you in the right direction for additional investigation or a larger damage assessment if you already know what variables are impacting plant health.
We can build pictures that offer a measure of vegetation type, amount, and condition by converting satellite data into NDVI values.
You may give various colors to different ranges of NDVI values if you have a value for each pixel. Create a false-color map showing how NDVI fluctuates spatially this manner.
look at this image
Although there is no standard color map for NDVI mapping, one that roughly approaches reality (with high NDVI value areas looking more green) is often utilized.
We can get an immediate study of fields using NDVI, allowing farmers to optimize field output potential, reduce environmental impact, and customize precision agricultural operations.
Using NDVI in conjunction with other data streams, such as meteorological data, can provide further insight into patterns of drought, frost, or flooding that influence plants.
Do you know of any noteworthy NDVI applications?
have you seen the post about those meteorological drought indices like SPEI and PCI ?