Public and corporate administrations are increasingly attempting to update how they manage the data that represents their territorial features. This shift is mostly related to the pressing requirement for fundamental, trustworthy information to be the cornerstone of societal decision-making. Therefore, remote sensing offers itself as a key instrument to perform monitoring successfully, in addition to strengthening the systems already in place so that it supports managers in their decisions. Remote sensing is also able to watch the earth's surface at local, regional, and global sizes. Additionally, these data have been used for science, education, and technological purposes in many nations because they are typically accessed for free. One of the most used remote sensing is Normalized Difference Vegetation Index (NDVI).
What is NDVI?
NDVI is a surface reflectance measurement which provides quantitative of biomass and vegetation growth. The NDVI analysis uses a spectrum stratum's red and near-infrared wavelengths. The NDVI value ranged from -1.0 to +1.0. High NDVI values are produced by the healthy vegetation's low red-light reflectance and high near-infrared reflectance. Although there is no absolute value for NDVI range, the following picture can be used as a guide in interpreting NDVI values:
We can generate images that provide a measure of vegetation type, quantity, and condition by converting satellite data into NDVI values. This is made feasible by GIS programs like QGIS, GRASSGIS, ArcGIS, and many more. Even though there is no universal color palette for NDVI mapping, the color that closely reflects reality is often used, like the higher NDVI value areas showing more green. The scale is referred to as "stoplight color maps" and applies a red-yellow-green color palette to NDVI-processed images. Such stoplight color maps are often considered to be more understandable, with green denoting healthy areas and red denoting risk areas or desolate areas. Alternative color gradations, like the blue-brown-green scale used by NASA, have values near zero that give shades of brown and indicate bare soil. Green is used for NDVI values near 1 that indicate live vegetation, just like the stoplight color scale. Negative values are shown as blue, which represents water.
Stoplight color maps
NDVI Uses in Agriculture
Using NDVI, certain models have been built to forecast agricultural production and biomass, control nitrogen fertilization, and regulate irrigation, among other purposes. You can utilize NDVI as a helpful technique to identify nitrogen excess, deficiency, and stress in different crops. In plants including corn, wheat, and rice, NDVI and nitrogen concentration in the leaves were found to be highly correlated. The amount of nitrogen in the leaves influences the amount of light that is absorbed and reflected since nitrogen is a key component of the chlorophyll molecule, which is linked to leaf color.
In various cropping systems, particularly maize grain production systems that are vulnerable to nitrogen (N) stress, the link between biomass, yield potential, and NDVI levels has been widely researched. Researchers discovered a strong association between ultimate yields later in the growing season and NDVI readings collected during the early vegetative growth phases. Because green leaf area represents the light energy that powers photosynthesis, which directly causes grain filling and growth, green leaf area is a highly accurate predictor of yield potential. Most techniques for predicting crop yields based on NDVI analyze datasets of NDVI and actual yield data using regression models. Plant health can be inferred from the NDVI because it is lower in unhealthy plants than in healthy ones.
The NDVI can be used to identify diseases in vast fields before they spread to other areas of the field when paired with other information, such as the leaf area index or chlorophyll content. Note that NDVI cannot be used to specifically identify a disease. On the other hand, disease-affected fields will have poor NDVI values. Instead of having to survey the entire field, the farmer can travel directly to the questionable zones after viewing the NDVI map.
NDVI map of California Sacramento Valley’s Rice Fields
Examples of NDVI Application in Businesses
Many countries' officials or governments used the Normalized Difference Vegetation Index (NDVI) as an indicator to predict and spot problems in their farmland, particularly for grains like wheat and paddy rice. The United States Department of Agriculture (USDA) expected that California's rice crop would be reduced by 38 percent this year. By looking at the satellite images of rice fields, dramatic changes between September 2021 and September 2022 are visible from space and have shown an anomaly in NDVI value as the result of drought. The NASA Harvest program is currently monitoring the southern region of Madagascar to help with their food security by analyzing the environmental factors affecting their maize and legume crops. They monitored and compared the anomalies in the region with NDVI images from the satellite. Gro predicted Australia's canola seed production would increase this year by using NDVI images they analyze and monitor in real time.
In palm oil plantations, like the project we are currently working on, NDVI is also used to get insights from the farmland. NDVI values are used to see the plant health and age in West Kalimantan, to evaluate the health of pre-replanting oil palm plants in Riau, and to monitor plant health in Malaysia. All this monitoring will be used to enable the plantation manager to determine how effectively the palms are being managed so that the palm oil plantation can have the optimum crop yield.
From the article, we know NDVI can be used widely in agriculture and has been used by many countries to improve agricultural precision. This remote sensing can also be used by your business, and we can help you with the implementation. Feel free to contact us!