Remote Sensing Analysis

Remote sensing workflows in WbW-QGIS cover multispectral and hyperspectral image analysis: spectral index computation, image enhancement, principal component analysis (PCA), segmentation, and change detection.

This chapter is aligned with the Python and R manuals while staying focused on QGIS Processing Toolbox execution patterns.

Core Concepts You Should Know First

• Spectral bands: Wavelength-specific image channels (for example blue, red, NIR, SWIR) used to separate land-cover materials.
• Spectral indices: Band combinations that highlight specific targets, such as NDVI (vegetation), NDWI (water), and NBR (burn severity).
• Spatial resolution: Pixel size affects detail and detectability of features.
• Temporal resolution: Revisit interval controls change-detection sensitivity.
• Atmospheric and cloud effects: Compare like-with-like by masking clouds and shadows and using corrected imagery where possible.
• Change detection: Compare index or class outputs across acquisition dates.
• Dimensionality reduction: PCA reduces band redundancy before segmentation or classification.

Typical Inputs

End-to-End Workflow

Step 1 - Quality Check and Harmonize Inputs

Before analysis:

• Confirm both scenes use the same CRS, grid, and pixel size.
• Confirm band order and data scale (for example 0-1 reflectance or scaled integer reflectance).
• Mask clouds/shadows using QA products or Raster Calculator conditions.

Use QGIS Raster menu tools as needed:

• Align Raster
• Warp (Reproject)
• Raster Calculator

Step 2 - Build Key Spectral Indices

Process both dates with the same settings.

Processing Toolbox -> Whitebox Workflows -> Remote Sensing:

• NDVI
• Normalized Difference Index (for NDWI, NBR, NDSI, NDBI patterns)

Recommended outputs:

• ndvi_t1.tif, ndvi_t2.tif
• ndwi_t1.tif, ndwi_t2.tif
• nbr_t1.tif, nbr_t2.tif

Example NDVI run:

Repeat for time 2.

Step 3 - Create Change Surfaces

Use QGIS Raster Calculator for differencing:

• NDVI change: ndvi_t2 - ndvi_t1
• NBR change: nbr_t2 - nbr_t1

Then classify into practical bins (loss, stable, gain) using:

• Whitebox Workflows -> Raster Analysis -> Reclass
• or Raster Calculator threshold expressions

Suggested interpretation for NDVI change:

Step 4 - Dimensionality Reduction (PCA)

Processing Toolbox -> Whitebox Workflows -> Remote Sensing -> Principal Component Analysis

Use PCA when:

• Bands are highly correlated.
• You need compact inputs for segmentation or clustering.
• You are preparing a classification feature stack.

Inspect output variance/eigenvalue diagnostics and retain only the components needed for most variance.

Step 5 - Segmentation and Classification Prep

Processing Toolbox -> Whitebox Workflows -> Remote Sensing -> Image Segmentation

Typical tuning:

• Lower threshold -> more, smaller segments
• Higher threshold -> fewer, larger segments
• Minimum segment size removes speckle

After segmentation:

• Optionally polygonize segment rasters in QGIS.
• Join zonal metrics from index layers.
• Use resulting segment features for training/validation workflows.

QGIS Python Console Equivalent

Use this pattern for reproducible batch processing in QGIS:

import processing

img_t1 = '/data/image_t1.tif'
img_t2 = '/data/image_t2.tif'

for label, img in [('t1', img_t1), ('t2', img_t2)]:
    processing.run('whitebox_workflows:ndvi', {
        'input': img,
        'nir_band': 4,
        'red_band': 3,
        'output': f'/data/ndvi_{label}.tif',
    })

processing.run('qgis:rastercalculator', {
    'EXPRESSION': '"ndvi_t2@1" - "ndvi_t1@1"',
    'LAYERS': ['/data/ndvi_t1.tif', '/data/ndvi_t2.tif'],
    'OUTPUT': '/data/ndvi_change.tif',
})

processing.run('whitebox_workflows:principal_component_analysis', {
    'input': '/data/image_t1.tif',
    'num_comp': 4,
    'output': '/data/pca_t1.tif',
})

Common Pitfalls

Validation Checklist

• Inputs are co-registered and in a common CRS.
• Band assignments match sensor metadata.
• Cloud/shadow/no-data masking applied consistently across dates.
• Index histograms are plausible for local land cover.
• Change classes were reviewed visually against source imagery.
• PCA/segmentation parameters were documented for reproducibility.