Raster Spectral & Texture Analysis

Calculate spectral indices and local texture measures from raster bands. Spectral methods combine physically meaningful bands; GLCM methods quantify spatial texture from local gray-level co-occurrence patterns.

Remote Sensing CoreEnd-User DocumentationTheory + Formula + Parameters

1. What This Feature Does

Cloud workflow: choose data → configure the scientific method → run → review the resulting layer/report. No programming is required.

2. Recommended Workflow

Choose the spectral index or GLCM texture measure. ↓ Assign the required band roles (Blue, Green, Red, Red Edge, NIR, SWIR). ↓ For GLCM, choose source band, window, quantization, distance, and direction. ↓ Run the analysis and interpret the continuous index/texture raster.

3. Theory, Methods & Equations

NDVI — Normalized Difference Vegetation Index

Classic vegetation greenness index.

(NIR - Red) / (NIR + Red)

Best used when: vegetation analysis using the required spectral bands.

GNDVI — Green Normalized Difference Vegetation Index

Vegetation index using the green band instead of red.

(NIR - Green) / (NIR + Green)

Best used when: vegetation analysis using the required spectral bands.

NDRE — Normalized Difference Red Edge Index

Red-edge vegetation index useful for higher-biomass vegetation.

(NIR - Red Edge) / (NIR + Red Edge)

Best used when: vegetation analysis using the required spectral bands.

RVI — Ratio Vegetation Index

Simple ratio vegetation index.

NIR / Red

Best used when: vegetation analysis using the required spectral bands.

DVI — Difference Vegetation Index

Difference between near infrared and red reflectance.

NIR - Red

Best used when: vegetation analysis using the required spectral bands.

SAVI — Soil Adjusted Vegetation Index

NDVI adjusted for exposed soil background.

(1 + L) × (NIR - Red) / (NIR + Red + L)

Best used when: vegetation analysis using the required spectral bands.

OSAVI — Optimized Soil Adjusted Vegetation Index

Optimized SAVI with a fixed soil adjustment of 0.16.

(NIR - Red) / (NIR + Red + 0.16)

Best used when: vegetation analysis using the required spectral bands.

MSAVI2 — Modified Soil Adjusted Vegetation Index 2

Self-adjusting soil background vegetation index.

[2×NIR + 1 - √((2×NIR + 1)² - 8×(NIR - Red))] / 2

Best used when: vegetation analysis using the required spectral bands.

EVI — Enhanced Vegetation Index

Enhanced vegetation index with atmospheric and soil-background correction.

G × (NIR - Red) / (NIR + C1×Red - C2×Blue + L)

Best used when: vegetation analysis using the required spectral bands.

EVI2 — Two-band Enhanced Vegetation Index

Two-band version of EVI that does not require blue.

2.5 × (NIR - Red) / (NIR + 2.4×Red + 1)

Best used when: vegetation analysis using the required spectral bands.

ARVI — Atmospherically Resistant Vegetation Index

Vegetation index designed to reduce atmospheric effects.

[NIR - (2×Red - Blue)] / [NIR + (2×Red - Blue)]

Best used when: vegetation analysis using the required spectral bands.

SIPI — Structure Insensitive Pigment Index

Pigment-related index less sensitive to canopy structure.

(NIR - Blue) / (NIR - Red)

Best used when: vegetation analysis using the required spectral bands.

CVI — Chlorophyll Vegetation Index

Chlorophyll-sensitive vegetation index.

NIR × Red / Green²

Best used when: chlorophyll analysis using the required spectral bands.

CI Green — Chlorophyll Index Green

Chlorophyll index using the green band.

(NIR / Green) - 1

Best used when: chlorophyll analysis using the required spectral bands.

CI Red Edge — Chlorophyll Index Red Edge

Chlorophyll index using a red-edge band.

(NIR / Red Edge) - 1

Best used when: chlorophyll analysis using the required spectral bands.

PSRI — Plant Senescence Reflectance Index

Spectral index related to leaf senescence and carotenoid/chlorophyll balance.

(Red - Green) / Red Edge

Best used when: vegetation analysis using the required spectral bands.

VARI — Visible Atmospherically Resistant Index

Visible-band vegetation index.

(Green - Red) / (Green + Red - Blue)

Best used when: visible rgb analysis using the required spectral bands.

GLI — Green Leaf Index

RGB greenness index.

(2×Green - Red - Blue) / (2×Green + Red + Blue)

Best used when: visible rgb analysis using the required spectral bands.

TGI — Triangular Greenness Index

Visible-band triangular greenness approximation.

-0.5 × [190×(Red - Green) - 120×(Red - Blue)]

Best used when: visible rgb analysis using the required spectral bands.

CIVE — Color Index of Vegetation Extraction

RGB vegetation extraction index; interpretation depends on input scaling.

0.441×Red - 0.811×Green + 0.385×Blue + 18.78745

Best used when: visible rgb analysis using the required spectral bands.

ExG — Excess Green Index

Simple visible-band excess-green index.

2×Green - Red - Blue

Best used when: visible rgb analysis using the required spectral bands.

ExR — Excess Red Index

Simple visible-band excess-red index.

1.4×Red - Green

Best used when: visible rgb analysis using the required spectral bands.

NGRDI — Normalized Green Red Difference Index

Normalized RGB vegetation index.

(Green - Red) / (Green + Red)

Best used when: visible rgb analysis using the required spectral bands.

MGRVI — Modified Green Red Vegetation Index

Modified green-red index emphasizing visible vegetation contrast.

(Green² - Red²) / (Green² + Red²)

Best used when: visible rgb analysis using the required spectral bands.

RGBVI — RGB Vegetation Index

Visible RGB vegetation index.

(Green² - Red×Blue) / (Green² + Red×Blue)

Best used when: visible rgb analysis using the required spectral bands.

NDWI — Normalized Difference Water Index (McFeeters)

Water-body enhancement using green and NIR.

(Green - NIR) / (Green + NIR)

Best used when: water analysis using the required spectral bands.

MNDWI — Modified Normalized Difference Water Index

Modified water index using SWIR1.

(Green - SWIR1) / (Green + SWIR1)

Best used when: water analysis using the required spectral bands.

NDMI — Normalized Difference Moisture Index

Canopy / vegetation moisture index.

(NIR - SWIR1) / (NIR + SWIR1)

Best used when: moisture analysis using the required spectral bands.

MSI — Moisture Stress Index

Moisture stress ratio.

SWIR1 / NIR

Best used when: moisture analysis using the required spectral bands.

NBR — Normalized Burn Ratio

Burn severity and burned-area index.

(NIR - SWIR2) / (NIR + SWIR2)

Best used when: burn analysis using the required spectral bands.

NBR2 — Normalized Burn Ratio 2

SWIR-based burn and moisture sensitivity index.

(SWIR1 - SWIR2) / (SWIR1 + SWIR2)

Best used when: burn / moisture analysis using the required spectral bands.

NDBI — Normalized Difference Built-up Index

Built-up area enhancement index.

(SWIR1 - NIR) / (SWIR1 + NIR)

Best used when: built-up analysis using the required spectral bands.

BSI — Bare Soil Index

Bare soil / exposed ground index.

[(SWIR1 + Red) - (NIR + Blue)] / [(SWIR1 + Red) + (NIR + Blue)]

Best used when: soil analysis using the required spectral bands.

NDTI — Normalized Difference Tillage Index

Tillage / crop-residue index based on two SWIR bands.

(SWIR1 - SWIR2) / (SWIR1 + SWIR2)

Best used when: soil / residue analysis using the required spectral bands.

NDTI Turbidity — Normalized Difference Turbidity Index

Visible-band normalized difference turbidity index.

(Red - Green) / (Red + Green)

Best used when: water analysis using the required spectral bands.

NDSI — Normalized Difference Snow Index

Snow-cover index.

(Green - SWIR1) / (Green + SWIR1)

Best used when: snow analysis using the required spectral bands.

BAI — Burned Area Index

Burned Area Index; fixed constants assume reflectance scaled approximately to 0–1.

1 / [(0.1 - Red)² + (0.06 - NIR)²]

Best used when: burn analysis using the required spectral bands.

IBI — Index-based Built-up Index

Composite built-up index combining NDBI, SAVI and MNDWI.

[NDBI - (SAVI + MNDWI)/2] / [NDBI + (SAVI + MNDWI)/2]

Best used when: built-up analysis using the required spectral bands.

GLCM Contrast

Local directional GLCM contrast.

Σ P(i,j) × (i - j)²

Best used when: texture — glcm analysis using the required spectral bands.

GLCM Dissimilarity

Local directional GLCM dissimilarity.

Σ P(i,j) × |i - j|

Best used when: texture — glcm analysis using the required spectral bands.

GLCM Homogeneity

Local directional GLCM homogeneity.

Σ P(i,j) / [1 + (i - j)²]

Best used when: texture — glcm analysis using the required spectral bands.

GLCM Correlation

Local directional GLCM correlation.

Σ P(i,j)(i-μi)(j-μj) / (σi σj)

Best used when: texture — glcm analysis using the required spectral bands.

4. Input Data

Input	Type	Requirement	Description
Raster Input	raster	Required	Multiband raster used for the selected spectral index or texture calculation.

5. Parameters Available in the Application

Parameter	Default	Choices / Range	How to Use It
Spectral / Texture Algorithm	—	—	Choose an algorithm first. The tester reads the selected option metadata and displays its formula, family, required bands, and description.
Blue Band	—	1 to —	1-based raster band number representing Blue.
Green Band	—	1 to —	1-based raster band number representing Green.
Red Band	—	1 to —	1-based raster band number representing Red.
Red Edge Band	—	1 to —	1-based raster band number representing Red Edge.
NIR Band	—	1 to —	1-based raster band number representing Near Infrared.
SWIR1 Band	—	1 to —	1-based raster band number representing SWIR1.
SWIR2 Band	—	1 to —	1-based raster band number representing SWIR2.
SAVI Soil Adjustment (L)	0.5	0.0 to 1.0	Shown for: savi
EVI Gain (G)	2.5	—	Shown for: evi
EVI C1	6.0	—	Shown for: evi
EVI C2	7.5	—	Shown for: evi
EVI Canopy Background (L)	1.0	—	Shown for: evi
Texture Source Band	—	1 to —	1-based raster band used to generate the GLCM texture map.
GLCM Window Size	7	3 to 31	Odd moving-window size in pixels.
GLCM Quantization Levels	32	8 to 256
GLCM Pixel Distance	1	1 to 10
GLCM Direction	0	0° — horizontal, 45°, 90° — vertical, 135°
GLCM Quantization Percentiles	[2, 98]	—	Lower/upper percentiles used before quantization to reduce outlier influence.
Output Nodata	-9999.0	—

6. Output & Interpretation

The result should be interpreted according to the selected method and the physical meaning of the input data. Preserve source units, coordinate reference information, NoData meaning, acquisition date, and preprocessing level when comparing results.

Scientific interpretation: an algorithm can produce a numerically valid result even when the input data are unsuitable. Always check masks, units, sensor characteristics, spatial resolution and reference data.

Important: Index interpretation depends on sensor wavelength placement, surface reflectance quality, scale, atmospheric conditions, and study area. Thresholds are not universally transferable between sensors or dates.

7. Best Practices

Use analysis-ready inputs and remove invalid/cloud/noise artifacts that are not part of the target phenomenon.
Choose parameters from the physical scale of the data, not only from visual appearance.
Keep categorical and continuous rasters conceptually separate when selecting interpolation/resampling methods.
Compare the result with the source image and independent reference information.
Document the settings used when results will be compared across dates, sensors or study areas.

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