By Sammed Suresh Patil (Shivaji University Student)
The Infrared Percentage Vegetation Index (IPVI) is a vegetation index that measures the proportion of near-infrared (NIR) reflectance relative to total reflectance in the visible (red) and NIR bands.
The Chlorophyll Vegetation Index (CVI) is a spectral index designed to estimate chlorophyll content in vegetation. It leverages the contrast between reflectance in the near-infrared (NIR) and green or red-edge bands to minimize soil background effects while emphasizing chlorophyll absorption.
Understanding Vegetation Indices: Tools for Monitoring Plant Health
Vegetation indices are numerical indicators that use reflectance measurements from remote sensing instruments to evaluate vegetation cover, health, and biomass. These indices are essential in fields like agriculture, forestry, ecology, and environmental monitoring. By analyzing how vegetation reflects light in specific parts of the electromagnetic spectrum, vegetation indices provide an efficient, scalable method for tracking plant health and stress, drought conditions, and crop performance.
Remotesensing instruments, such as satellites (e.g., Landsat, Sentinel), drones, and handheld sensors, collect data primarily in the visible (VIS), near-infrared (NIR), and shortwave-infrared (SWIR) bands. Since healthy vegetation absorbs most visible light (especially red) and reflects strongly in the NIR region, the contrast between these bands can bemathematically expressed to indicate plant vigor.
Key Vegetation Indices
- Simple Ratio (SR) Formula:
SR=NIR/Red Description: Oneofthe earliest vegetation indices, the Simple Ratio uses the high reflectance of NIR and low reflectance of red light in healthy vegetation. A higher SR typically indicates denser and healthier vegetation. Applications:- Crop growthmonitoring- Forest biomass estimation- Early drought detection
- Infrared Percentage Vegetation Index (IPVI) Formula:
IPVI = NIR/(NIR+Red) Description: IPVI is a normalized index ranging from 0 to 1, making iteasier to interpret than SR. Itis less sensitive to differences in brightness, helping to reduce noise in varying illumination conditions. Applications:- Vegetation mapping- Soil exposure analysis- Agricultural yield estimation
- Chlorophyll Vegetation Index (CVI) Formula:
CVI =(NIR×Red)/(Green²) Description: CVI highlights chlorophyll content in plants by leveraging green band reflectance along with red and NIR. It’s sensitive to chlorophyll concentration, which correlates with plant health and nutrient status. Applications:- Chlorophyll and nutrient analysis- Crop nitrogen management- Early detection of plant stress or disease
Industrial and Practical Applications
Vegetation indices are widely used across multiple industries and research fields:
Agriculture
– Precision farming and yield prediction
– Irrigation planning and drought monitoring
– Detection of nutrient deficiencies and pest infestations
Forestry
– Estimating forest biomass and carbon stocks
– Monitoring deforestation and forest degradation
– Fire risk assessment and post-fire recovery
Environmental Science
– Land cover classification and ecosystem health
– Wetland and riparian zone monitoring- Climate change studies
Urban Planning
– Urban greenspace analysis
– Monitoring vegetation encroachment
– Green roof and vertical garden assessments
Conclusion
Vegetation indices like SR, IPVI, and CVI are critical tools for non-destructive, scalable vegetation analysis. They enable scientists, agronomists, and land managers to make informed decisions based on real-time plant health and coverage data. As remote sensing technology advances, these indices continue to evolve, offering new ways to monitor and manageEarth’s ecosystems effectively.
References
· Huete,A.R.(1988). Asoil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.
· Tucker,C.J. (1979). Redandphotographic infraredlinear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.
· Xue,J.,&Su,B.(2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017.
· Pinter,P.J., et al. (2003). Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 69(6), 647–664
Research Contributors:
By Sammed Suresh Patil (Shivaji University Student)
