Geomorphometric Analysis using Whitebox Tool
Contents
Purpose
The purpose of this tutorial is to provide a clear and practical introduction to conducting a full geomorphometric analysis using WhiteboxTools within QGIS. This guide is designed for students, researchers, geoscientists, environmental analysts, and anyone working with terrain data. By following the step-by-step workflow, users will learn how to preprocess a DEM, extract primary and secondary terrain attributes, and visualize landforms using free, open-source tools. The resulting geomorphometric products can enhance understanding of the topography, structure, and processes shaping a study area, and are suitable for use in academic reports, research publications, and applied geospatial projects
Introduction
What is the geomorphometric
Geomorphometry is the science of quantitatively measuring and analyzing the physical features of the Earth’s surface. It focuses on extracting numerical parameters—such as slope, aspect, curvature, roughness, and landform indices—from Digital Elevation Models (DEMs). The goal is to describe terrain in a mathematically precise way, allowing researchers to model processes, compare landscapes objectively, and automate landform classification. In geomorphometry, every element of the terrain is expressed using measurable values, for example the slope expressed in degrees or percent rise, curvature expressed as concave or convex values, or drainage indices extracted using computational algorithms.
Advantages of Geomorphometric Analysis
Conducting a geomorphometric analysis offers major advantages for geomatics and environmental research. It transforms raw elevation data into quantifiable, reproducible metrics, enabling precise terrain interpretation that is impossible through visual observation alone. These quantitative outputs support a wide range of applications, including hydrological modeling, erosion assessment, landform classification, hazard mapping, suitability analysis, and machine-learning workflows. Because geomorphometric variables are standardized and scalable, they allow researchers and students to analyze landscapes efficiently, compare regions objectively, and produce high-quality results suitable for scientific publications or decision-making.
