Skip to main content

REMOTE SENSING INDICES



Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface.
They improve classification accuracy and environmental monitoring.

1. Vegetation Indices

NDVI – Normalized Difference Vegetation Index

Formula: (NIR – RED) / (NIR + RED)
Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll.
Measures: Vegetation greenness & health
Uses: Agriculture, drought monitoring, biomass estimation

EVI – Enhanced Vegetation Index

Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L)
Concept: Corrects for soil and atmospheric noise.
Measures: Vegetation vigor in dense canopies
Uses: Tropical rainforest mapping, high biomass regions

GNDVI – Green Normalized Difference Vegetation Index

Formula: (NIR – GREEN) / (NIR + GREEN)
Concept: Uses Green instead of Red
Measures: Photosynthetic activity & early plant stress
Uses: Precision agriculture, crop nitrogen assessment

SAVI – Soil Adjusted Vegetation Index

Formula: (NIR – RED) / (NIR + RED + L) × (1 + L)
Concept: Reduces soil brightness effect
Measures: Vegetation greenness in soil-exposed areas
Use: Semi-arid & sparse vegetation regions

DVI – Difference Vegetation Index

Formula: NIR – RED
Concept: Simple difference of reflectance
Measures: Vegetation amount
Use: Biomass estimation, quick vegetation change detection

NDRE – Normalized Difference Red Edge Index

Formula: (NIR – Red Edge) / (NIR + Red Edge)
Concept: Uses red-edge band sensitive to chlorophyll concentration
Use: Crop stress detection, nitrogen mapping

TVI – Transformed Vegetation Index

Formula: 0.5 × [1 + (NIR – RED)/(NIR + RED)]
Concept: Reduces saturation in dense vegetation
Use: Forest canopy monitoring

RVI – Ratio Vegetation Index

Formula: NIR / RED
Concept: Simple ratio
Use: Vegetation vigor detection in early remote sensing studies

CVI – Chlorophyll Vegetation Index

Formula: (NIR / GREEN) – 1
Concept: Estimates chlorophyll density
Use: Crop growth monitoring

VHI – Vegetation Health Index

Formula: VHI = a × NDVI + b × TCI (Temp Condition Index)
Concept: Combines vegetation + temperature
Use: Drought disaster monitoring

2. Water Indices

NDWI – Normalized Difference Water Index (McFeeters)

Formula: (GREEN – RED) / (GREEN + RED)
Concept: Water reflects more in Green than Red
Use: Water body extraction

MNDWI – Modified NDWI

Formula: (GREEN – SWIR) / (GREEN + SWIR)
Concept: Replaces RED with SWIR → SWIR strongly absorbs water
Use:

  • Urban water mapping

  • Flood detection

  • Wetland monitoring

DPSI – Difference Pond Snow Index (non-standard but used in snow/water mapping)

Formula: (NIR – BLUE)/(NIR + BLUE)
Use: Snow/ice and water differentiation

3. Snow / Ice Indices

NDSI – Normalized Difference Snow Index

Formula: (GREEN – SWIR) / (GREEN + SWIR)
Concept:

  • Snow = high reflectance in Green

  • Snow = strong absorption in SWIR
    Use: Snow area monitoring & glacier mapping

4. Soil Indices

NDSI (Soil Index Variant)

Formula: (NIR – SWIR)/(NIR + SWIR)
Measures: Soil moisture & soil brightness
Use: Soil property analysis

BSI – Bare Soil Index

Formula: (SWIR + RED) – (NIR + BLUE) / (SWIR + RED) + (NIR + BLUE)
Concept: Distinguishes bare soil from vegetation or water
Use:

  • Land degradation

  • Construction site detection

  • Soil exposure mapping

5. Urban Indices

NDBI – Normalized Difference Built-up Index

Formula: (SWIR – NIR) / (SWIR + NIR)
Concept: Built-up areas have higher SWIR reflectance than NIR
Use: Urban mapping & monitoring

UI – Urban Index

Formula: (SWIR – NIR) / (SWIR + NIR)
Concept: Similar to NDBI
Use: Urban impervious surface detection

Built-up Area Index (Variant)

Formula: (RED – SWIR)/(RED + SWIR)
Use: Urban development measurement

6. Burn & Fire Indices

NBR – Normalized Burn Ratio

Formula: (NIR – SWIR) / (NIR + SWIR)
Concept:

  • Healthy vegetation = high NIR, low SWIR

  • Burned areas = low NIR, high SWIR
    Uses:

  • Forest fire severity

  • Post-fire assessment

  • Burn scar mapping

7. Atmospheric Correction & General Indices

VARI – Visible Atmospherically Resistant Index

Formula: (GREEN – RED)/(GREEN + RED – BLUE)
Concept: Minimizes atmospheric influence in visible imagery
Uses:

  • Vegetation mapping in haze

  • UAV / drone imagery analysis

ARVI – Atmospherically Resistant Vegetation Index

Formula: NIR – (2×RED – BLUE) / NIR + (2×RED – BLUE)
Concept: Corrects atmospheric scattering using Blue band
Use: Vegetation mapping in polluted / hazy regions


IndexPrimary Application
NDVI, GNDVI, SAVI, EVIVegetation health
NDREChlorophyll stress
NDWI, MNDWIWater detection
NDSISnow/Ice mapping
NDBI, UIUrban built-up detection
BSIBare soil identification
NBRBurned area detection
VARI, ARVIAtmospheric resistance
CVIChlorophyll estimation
VHIDrought & vegetation stress
DPSIIce/snow discrimination
RVI, TVI, DVIGeneral vegetation indices


Comments

Post a Comment

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...
Post a Comment
Read more

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...
Post a Comment
Read more

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...
Post a Comment
Read more

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...
Post a Comment
Read more