3. Multiscale Curvature Classification (MCC) Cluster Analysis

Multiscale Curvature Classification (MCC) is a geometry-based algorithm that excels at identifying ground points in environments with subtle terrain variations—making it particularly effective for wetlands. It uses the Evans and Hudak 2016 algorithm originally implemented in the mcc-lidar software. Unlike elevation-based filters, MCC analyzes the curvature of the local surface across multiple spatial scales to distinguish ground from vegetation and other elevated features.

In wetland applications, MCC offers several advantages:

• Sensitivity to microtopography: It captures gentle undulations typical of wetland terrain.

• Vegetation resilience: MCC can differentiate between flat ground and vertical structures like reeds or shrubs.

• Scale adaptability: By evaluating curvature at multiple neighborhood sizes, it balances precision and generalization.

The MCC workflow involves:

  1. Calculating surface curvature for each point using local neighborhoods of varying radii.

  2. Aggregating curvature metrics across scales to assess the likelihood of a point being ground.

  3. Classifying points based on curvature thresholds and spatial consistency.

For wetlands, it’s crucial to:

• Use fine-to-medium scale radii to capture low-relief features without over-smoothing.

• Combine MCC with intensity or return number filters to suppress water surface noise.

• Validate results using field data or high-resolution imagery, especially in vegetated zones.

Summary

##     Analysis Complete!
##     Total rasters processed: 16
##     Valid rasters plotted: 16
##     Global value range: -1.00 to 314.00

Display Final Output

The legend shows the value scale for the raster data. Here’s what it means:

Color gradient: Pink (low values) → Cyan → Blue → Black (high values) Numbers on the legend: The actual data values in each raster cell For your MCC (Cloth Simulation Filter) cluster data:

-1.00: Minimum value (possibly representing ground points or the lowest elevation/classification) 312.00: Maximum value (possibly representing the highest elevation or different feature classification) Values in between: Represent different heights, classifications, or cluster IDs depending on what your CSF processing assigned What the colors tell you:

Pink areas: Lowest values (close to -1.00) - likely ground level or lowest features Cyan/Light blue areas: Medium-low values Dark blue areas: Medium-high values Black areas: Highest values (close to 314.00) - likely elevated features or specific cluster types The legend essentially acts as a color-to-value decoder, allowing you to interpret what each color in the raster represents numerically. When all plots share the same global legend (range = -1.00 to 314.00), you can directly compare values across different rasters because the same color means the same value in all subplots.