3.3 Raster Symbology & Display Properties

Raster data structures represent geographic phenomena as continuous gridded matrices of square cells (pixels). Unlike vector data, which relies on discrete coordinate pairs, rasters record numerical values for every cell in the grid. Displaying raster datasets effectively in ArcGIS Pro requires matching the raster's band structure and data type (continuous vs. discrete) with appropriate symbology and display enhancements.

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Single-Band vs. Multi-Band Rasters

• Single-Band Rasters: Contain a single matrix of cell values. Examples include Digital Elevation Models (DEMs), single thermal infrared bands, and slope grids. They are visualized using Stretch, Classify, or Unique Values symbology.
• Multi-Band Rasters: Contain multiple stacked spatial matrices (bands) representing different wavelengths of the electromagnetic spectrum or multi-temporal data. Examples include high-resolution aerial orthophotos (Red, Green, Blue bands) and multispectral satellite imagery like Landsat 8/9 (11 bands) or Sentinel-2 (13 bands).

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Contrast Stretch Symbology for Single-Band Rasters

When rendering continuous single-band rasters (such as elevation or bathymetry) using Stretch symbology, cell values are mapped across a smooth color ramp or grayscale range (0 to 255 for monitor display). Because raw pixel values rarely fill the full range of display brightness, ArcGIS Pro applies Contrast Stretch algorithms to expand the visual dynamic range:

Contrast Stretch Types in ArcGIS Pro:
- Minimum-Maximum: Stretches display range from absolute minimum pixel value to maximum value.
- Percent Clip: Cuts off extreme minimum and maximum tail values (e.g., 2% clip) to remove outliers.
- Standard Deviation: Stretches pixel values based on SD (e.g., 2.0 SD) from the mean value.
- Gamma Stretch: Applies non-linear power-law curve to adjust mid-tone contrast.

1. Minimum-Maximum: Maps the lowest observed pixel value to pure black (0) and the highest to pure white (255), stretching all intermediate values linearly. Extremely sensitive to statistical outliers (e.g., a single erroneous pixel value of 9999 skews the entire stretch).
2. Percent Clip: Trims a designated percentage of extreme low and high values from the histogram ends (typically a 2% clip) before stretching the remaining pixel values across the 0–255 spectrum. Eliminates outlier skewing.
3. Standard Deviation: Applies a stretch centered on the mean value, stretching pixel values within a user-specified number of standard deviations (commonly 2.0 SD). Excellent for satellite imagery with normal radiometric distributions.
4. Gamma Stretch: Applies a non-linear power-law transformation to adjust mid-tone values without shifting the extreme minimum or maximum ends. Increasing gamma (> 1.0) brightens mid-tone features; decreasing gamma (< 1.0) darkens them.

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Categorical & Discrete Raster Symbology

For rasters containing discrete integer codes or continuous ranges grouped into zones, ArcGIS Pro provides specialized renderers:

• Unique Values: Used for categorical rasters (e.g., NLCD Land Cover, soil types, zoning classes). Reads the raster's Value Attribute Table (VAT) and assigns distinct thematic colors to each integer code (e.g., Value 11 = Open Water, Value 41 = Deciduous Forest).
• Classify: Groups continuous single-band raster values into discrete numeric zones (e.g., grouping continuous DEM elevation values into 500-meter elevation zones) using statistical algorithms like Natural Breaks or Equal Interval.

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Multispectral RGB Composites

Multispectral imagery is rendered using the RGB Composite renderer in the Symbology pane. This tool maps three user-selected imagery bands directly to the monitor's Red, Green, and Blue display channels:

Common Band Combinations (Landsat 8/9 Operational Land Imager):
+-------------------------+-------------+---------------+--------------+
| Composite Type          | Red Channel | Green Channel | Blue Channel |
+-------------------------+-------------+---------------+--------------+
| Natural Color (True)    | Band 4 (R)  | Band 3 (G)    | Band 2 (B)   |
| Color Infrared (CIR)    | Band 5 (NIR)| Band 4 (R)    | Band 3 (G)   |
| Agriculture / Moisture  | Band 6(SWIR)| Band 5 (NIR)  | Band 2 (B)   |
+-------------------------+-------------+---------------+--------------+

• Natural Color (True Color - 4,3,2): Maps Visible Red to Red, Visible Green to Green, and Visible Blue to Blue. Displays terrain in colors corresponding to human visual perception.
• Color Infrared (CIR / False Color - 5,4,3): Maps Near-Infrared (NIR) to Red, Red to Green, and Green to Blue. Because healthy vegetation strongly reflects NIR energy, plant life appears vivid red, while urban areas appear cyan/gray and clear water appears dark brown or black.
• Agriculture Composite (6,5,2): Uses Short-Wave Infrared (SWIR-1), NIR, and Blue to highlight crop health, soil moisture content, and bare ground.

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Display Enhancements & Topographic Effects

On the contextual Raster Layer ribbon tab, analysts can adjust dynamic display settings:

• Raster Transparency: Adjusts layer transparency or defines specific Background Values (e.g., setting pixel value 0 or NoData to transparent to eliminate black borders around imagery).
• Dynamic Hillshade Effect: Applies real-time solar illumination across a DEM directly within the display engine. This provides 3D relief shading without requiring users to run the offline Hillshade geoprocessing tool.

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Raster Resampling Methods

Whenever a raster is displayed on screen, zoomed, rotated, or reprojected on the fly, the screen pixel grid does not line up perfectly with the raw dataset cells. ArcGIS Pro applies Resampling Methods to calculate display cell values:

1. Nearest Neighbor: Determines the output cell value using the nearest input cell center. Performs no interpolation.
   • Best Use: Categorical rasters (land cover, soil types) and discrete data, because it preserves exact original data values without creating non-existent artificial intermediate values.
2. Bilinear Interpolation: Calculates a distance-weighted average of the 4 nearest input cell centers.
   • Best Use: Continuous rasters (elevation, temperature) and aerial imagery. Produces smooth visual appearance.
3. Cubic Convolution: Performs a fitting calculation using the weighted average of the 16 nearest input cell centers via a cubic spline curve.
   • Best Use: Continuous rasters and high-resolution satellite imagery where image sharpness and smooth feature edges are required. Slightly more computationally demanding.