By Mohini Rajaram Sutar
1. What is ARVI?
- ARVI is a vegetation index developed by Kaufman and Tanré (1992).
- Designed to reduce atmospheric interference, especially from aerosols and scattering.
- Enhances accuracy in remote sensing of vegetation.
2. How it works:
When an emergency occurs, the system sends an automatic alert to family members and the Arvi support team. The support team can track the GPS location of the device every 60 seconds and arrange for an ambulance or paramedic to be sent to the location.
3. Formula
ARVI = NIR – (2. Red – Blue)
NIR + (2. Red – Blue)
Where:
NIR: Reflectance in the near-infrared band
Red: Reflectance in the red band
Blue: Reflectance in the blue band
4. Advantages
✅ Atmospheric Resistance: Less affected by aerosols and atmospheric scattering.
✅ Reliable Vegetation Monitoring: Outperforms NDVI in polluted or dusty areas.
✅ Urban Suitability: Effective in cities with high levels of air pollution.
5. Applications
🌾Agriculture: Assessing crop health in dusty or smoke-affected regions.
🏙 Environmental Monitoring: Tracking vegetation stress in urban areas.
🌍Climate Research: Studying plant response under diverse atmospheric conditions.
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2. SAVI (Soil-Adjusted Vegetation Index)
1. What is SAVI?
- SAVI is a remote sensing vegetation index.
- It reduces the influence of soil brightness in vegetation monitoring.
- Especially useful in areas with sparse vegetation or exposed soil.
2. How it works:
SAVI calculates a ratio between the red and near-infrared (NIR) reflectance values, using a soil brightness correction factor (L). The L value, typically set to 0.5 for Landsat data, helps to adjust for soil brightness variations.
3. SAVI Formula
SAVI = (NIR – R)
X (1 + L)
(NIR + R + L)
Where:
NIR= Near-Infrared reflectance
R = Red reflectance
L = Soil brightness correction factor (commonly 0.5)
4.Advantages
✅Works well in low vegetation environments.
✅Improves vegetation monitoring accuracy in semi-arid and arid zones.
✅Easy to calculate from standard satellite data.
5. Applications
🌾 Agriculture – Monitoring crops in dry or low-cover fields.
🌿 Ecology – Assessing grasslands, savannahs, and shrublands.
🏜️ Desertification – Identifying early signs of land degradation.
🌍 Land Management – Supporting conservation and land restoration planning.
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3. MSAVI (Modified Soil Adjusted Vegetation Index)
1. What is MSAVI?
MSAVI is a vegetation index used in remote sensing.
Designed to minimize soil reflectance effects in areas with sparse vegetation.
2.How it works:
MSAVI calculates a ratio between the red and near-infrared (NIR) reflectance values, incorporating an inductive L function to reduce soil effects. The L function is derived either through induction or by using the product of the Normalized Difference Vegetation Index (NDVI) and Weighted Difference Vegetation Index (WDVI).
3. MSAVI Formula
MSAVI = 2 x NIR + 1 – Ö (2 x NIR + 1)2 – 8 x (NIR – Red)
2
Where:
NIR = Near-Infrared reflectance
Red = Red band reflectance
4. Advantages
✅ Self-adjusting formula for soil correction (no need for manual L-factor like in SAVI).
✅ Performs better than NDVI in areas with low vegetation cover.
✅ Reduces the overestimation of vegetation in mixed pixels (soil + vegetation).
✅ Suitable for dryland agriculture, deserts, and semi-arid regions.
5. Applications
🌾Agriculture: Monitoring early crop growth where soil is visible.
🌿Ecology: Assessing vegetation in arid/semi-arid zones.
Forestry: Monitoring forest regrowth or deforestation.
Land Degradation: Detecting desertification and soil erosion.
Research Contributors:
- Mohini Rajaram Sutar (Shivaji University Student)
- Linkedin:
- https://www.linkedin.com/in/mohini-sutar-0996b9330/
