कृषि एवं जलवायु परिवर्तन: वनस्पति सूचकांक क्या भूमिका निभाते हैं?
Precision farming offers many valuable tools that help assess plant health yield potential and enable more efficient farm management. One such tool is vegetation indices (VIs). Choosing the proper vegetation index that suits your camera equipment, environmental conditions, crop type, and growth stage is crucial to obtaining precise results. Understanding common indices' main benefits and drawbacks is essential for selecting the best one for your business needs.
Every object that we see is a reflection of light waves on the electromagnetic spectrum. While visible wavelengths appear in different colors to us, cameras or specialized sensors from satellites, drones, or ground-based devices can detect other wavelengths of light.
Plant leaves need to absorb specific wavelengths of light to carry out photosynthesis. They can also reflect light depending on their physical condition and development. Leaf chlorophyll absorb most blue and red light and reflect higher levels of green light. That's why we see plants as green.
The reflectivity of leaves increases sharply in the red or near-infrared spectrum. Water absorbs short infrared waves, which are poorly reflected. The crown of a tree and an individual leaf reflect light differently due to the volume and orientation of the leaves and the transmission of light to the plant's lower leaves.
A vegetation index is a valuable tool that provides a single value by analyzing data from various spectral bands. Its primary function is identifying green vegetation features, enabling them to stand out amidst other objects within an image.
Depending on the transformation method and spectral bands employed, various aspects of the vegetation cover in the image can be assessed—for example, percentage of vegetation cover, chlorophyll content, leaf area index, and others. The VI’s remain reliable regardless of illumination conditions or slope effects.
Main Vegetation Indices
In healthy plants, the reflectance intensity in the near-infrared spectrum is higher, and in the red, it is lower. It is the physical basis of most vegetation indices. The choice of agriculture-relevant vegetation indices depends on how you want to use them. Each VI operates in the same optical ranges, but they differ in color. Most use near-infrared reflectivity to identify relationships with healthy vegetation growth.
- Normalized Difference Vegetation Index uses visible red and near-infrared reflectivity picked up by sensors. Thanks to this index, seasonal changes in crops can be determined. This method works best when vegetation is at its peak biomass.
- Normalized Difference Moisture Index is a vegetation index that measures plant moisture content using NIR and SWIR spectral bands. It can identify water stress, flooding, and fire risk areas, making it useful for firefighters and forestry. The NDMI helps optimize irrigation schedules in arid and semi-arid regions with limited water resources. That’s why calculating this vegetation index for agriculture is so crucial.
- Normalized Red Edge Difference Index can only be calculated if the Red edge band is available in the sensor. It is sensitive to how green the leaf is, that is, to the chlorophyll content, as well as the influence of the soil background and changes in leaf area. The lowest NDRE values usually indicate soil, while average values are assigned to unhealthy plants—accordingly, the higher the value of this VI, the healthier the plant.
- The Modified Soil Adjusted Vegetation Index is an ideal tool for analyzing plants in early plant stages. This index reduces the influence of soil on the spectrum of vegetation cover.
- Normalized Difference Water Index is extracted using a combination of near-infrared and visible green bands. This NDWI uses the SWIR (shortwave infrared) and NIR channels. This combination enables getting the best idea of the water content of plants.
Vegetation Indices in Agriculture
The indices enable reliable monitoring of crops, providing a direct and reliable assessment of plant health. Depending on which VI you use, it’s possible to obtain information about different aspects of crop development. The obtained information can be applied to optimize farm management and resource consumption.
Farmers can enhance crop yield and reduce waste by monitoring crop growth in different areas and applying different indices calculations. By doing so, they can identify the most suitable planting dates and determine the best time for harvesting. Harvesting crops at their peak ensures they are of high quality, leading to improved yields.
This technology enables the identification of field areas with low productivity and high level stress, and then make effective decisions to eliminate various problems and threats based on data from vegetation indices in remote sensing. Thus, growers can implement a widely available and environmentally sustainable approach to assessing crop health. The development of remote sensing sensors for monitoring crops in both broadband and narrowband can offer enormous opportunities to create new vegetation indices for a variety of purposes.
Climate Change & Vegetation Indices
The versatility and potential of vegetation index remote sensing are difficult to overestimate. The technology has a wide range of applications for managing agricultural systems and improving sustainability. But that's all.
The potential application of VI in climate change research and disaster management could bring many new valuable opportunities. This type of application is still in its early stages. Initial results indicate the potential for VIs to become indispensable tools in various scientific and practical fields.
Vegetation indices (VIs) face challenges in their adoption and effectiveness due to technical barriers, interpretation issues, and data quality problems. These challenges become even more complicated when new ideas of application came out. However, with technological advancements and ongoing research, VIs will become even more valuable in agriculture, forestry, and environmental applications, providing helpful information for decision-making and scientific research.
Author:
Ph.D. in Information Technologies, Senior Scientist at EOS Data Analytics
Scopus profile and Google Academy profile
Email: