The patch of ground beneath your feet holds clues to stories of ancient volcanoes, vanished rivers, and the relentless sculpting of the planet over millennia.
Have you ever wondered why the landscape around you looks the way it does? Why some regions are flat and fertile while others are rugged and eroded? The answers lie in the intimate, dynamic relationship between the geomorphic surfaces—the shape of the land—and the soil that blankets it. Scientists unravel this relationship using powerful statistical tools in a quest that is crucial for everything from agriculture to conservation. This article explores how researchers act as landscape detectives, discriminating between different landforms by analyzing the soil attributes within a sandstone-basalt lithosequence.
To understand the landscape, you must first understand its foundation. Every landform is built upon a specific geologic template, primarily determined by its parent rock. This template dictates how water flows, how soil develops, and what ecosystems can thrive.
A lithosequence is a sequence of soils formed from different types of bedrock under similar climatic conditions. Studying a lithosequence allows scientists to isolate the influence of the parent rock on the resulting soil and landscape. Two rock types with strikingly different personalities are sandstone and basalt.
Sandstone, a sedimentary rock, is like a sponge. It is typically composed of quartz grains cemented together, creating a coarse, porous material. This porosity allows water to infiltrate easily, leading to well-drained soils that are often acidic and nutrient-poor.
In contrast, basalt, an igneous rock born from cooling lava, is dense and fine-grained. Rich in iron, magnesium, and calcium-rich minerals like pyroxene and plagioclase feldspar, basalt weathers into fine-textured, nutrient-rich soils 5 . As one study notes, the natural factors of parent materials and landforms "lead to high variability in soil properties," setting the stage for dramatic differences in soil composition over short distances 1 .
The concept of geomorphic surfaces refers to recognizable, spatially continuous areas of the landscape that have been shaped by a specific set of processes over time. These can be flattened surfaces, like plateaus, or lowered areas, like valleys. The recognition of these surfaces is key to understanding the long-term evolution of a region. As one research team put it, analyzing these surfaces helps scientists understand "long-term process dynamics" and the "evolutionary characteristics from climatic, morphogenetic and pedogenetic factors" 3 6 .
With countless soil samples containing measurements for dozens of attributes like texture, acidity, and organic carbon, how do researchers make sense of the data? This is where multivariate statistical analysis comes in—a powerful toolkit that acts as a compass for navigating complex datasets.
Two of the most important tools in this kit are Cluster Analysis and Principal Component Analysis (PCA).
Cluster Analysis is a classification technique that groups similar objects together. In landscape studies, it can take soil data from various locations and automatically group them into distinct clusters. For example, a study in a semi-arid region of Iran used cluster analysis on 334 soil samples and found that the data naturally fell into two clear groups: one associated with limestone parent materials and another with quaternary deposits, each with very different levels of soil organic carbon and clay 1 . This provides an objective way to classify landscapes into distinct units.
Principal Component Analysis (PCA) is a dimension-reduction technique. It takes a large number of correlated variables (e.g., clay content, sand content, pH, organic carbon) and transforms them into a smaller set of uncorrelated variables called Principal Components. These components help explain the main sources of variation in the data. In the Iranian study, PCA using remote sensing and topographical data identified five main components that explained 73.3% of the total variability in soil properties, with factors like hillslope morphology being particularly good at predicting soil organic carbon 1 .
To see these concepts in action, let's examine a pivotal study that directly tackled the discrimination of geomorphic surfaces in a sandstone-basalt lithosequence.
Researchers identified a field area where geomorphic surfaces developed on both sandstone and basalt parent materials were present and accessible.
Using tools like radar imagery and digital elevation models (DEMs), they first mapped and delineated the different geomorphic surfaces (e.g., summit, backslope, footslope) 3 .
Soil samples were systematically collected from each identified geomorphic surface and for each rock type (sandstone and basalt). This ensured a representative dataset across the entire lithosequence.
In the lab, each soil sample was analyzed for a suite of physical and chemical attributes. Key properties often included soil texture (sand, silt, clay content), soil organic carbon (SOC), pH, and cation exchange capacity (CEC).
The collected data was then processed using multivariate techniques. Cluster Analysis was used to see if the soils naturally grouped by landscape position or parent material. PCA was employed to identify which soil attributes were most responsible for differentiating the surfaces.
The experiment yielded clear and compelling results, highlighting the powerful interplay between geology and geomorphology.
The Cluster Analysis successfully grouped soil samples into distinct clusters that corresponded strongly to specific geomorphic surfaces. This confirmed that the physical shape of the land was a major driver of soil development.
Meanwhile, the Principal Component Analysis revealed the key soil attributes that acted as the most effective "discriminators" between surfaces. For instance, the distribution of clay, iron oxides, and soil organic carbon were often identified as primary components separating the soils of the elevated basalt plateaus from the sandstone-derived slopes and valleys.
The study concluded that it is entirely possible to objectively discriminate between geomorphic surfaces based on their soil attributes. Furthermore, the underlying parent material (sandstone vs. basalt) imposed a fundamental control on the chemical and physical nature of the soil, which was then modified in predictable ways by its position on the landscape 3 .
The following tables illustrate the kind of data that underpins these findings.
| Soil Attribute | Sandstone-Derived Soil | Basalt-Derived Soil |
|---|---|---|
| Texture | Coarse-grained (Sandy) | Fine-grained (Clayey) |
| Nutrient Content | Lower (e.g., Potassium, Magnesium) | Higher 2 |
| Cation Exchange Capacity | Lower | Higher |
| pH | Often more acidic | Often more neutral |
| Density & Porosity | High porosity, well-drained | Denser, can be less permeable 5 |
| Geomorphic Setting | Key Discriminating Soil Attributes | Scientific Implication |
|---|---|---|
| Summit/Plateau (e.g., on Basalt) | High Clay, High Iron Oxides, Moderate SOC | Indicates stable surfaces with advanced chemical weathering. |
| Slope | Variable Texture, Lower SOC | Reflects active erosion and transport of materials. |
| Valley-Bottom/Alluvial Fan | High SOC, Deposited Silt & Clay 1 6 | Identifies areas of accumulation and nutrient sequestration. |
| Tool or Material | Function in Research |
|---|---|
| Digital Elevation Model (DEM) | A digital representation of topography used to extract landform measurements and map geomorphic surfaces 1 3 . |
| Cluster Analysis | A multivariate statistical method to objectively group soil samples into classes based on the similarity of their attributes 1 . |
| Principal Component Analysis (PCA) | A technique to reduce data complexity by identifying the fewest variables that explain the most variation in the dataset 1 . |
| Aqua Regia Extraction | A strong chemical mixture used to dissolve and measure the total concentration of metals in soil samples 2 . |
| Hydrometer | A standard instrument for determining soil particle size distribution (texture) 1 . |
| X-ray Fluorescence (XRF) | A rapid analytical technique used to determine the elemental composition of soil and rock materials 2 . |
The ability to discriminate geomorphic surfaces using soil data is far more than an academic exercise; it has profound practical implications for managing our environment.
Understanding the natural soil fertility and water-holding capacity of different landforms allows farmers to tailor their practices. Fertilizer can be applied more efficiently, and crops can be matched to the land units where they will thrive, boosting food security.
This research provides a blueprint for restoration. By understanding the original, natural template of the landscape—where wetlands formed, how sediments moved—we can work with the landscape's natural processes rather than against them. This is vital for restoring degraded ecosystems, from dryland wetlands to eroded hillslopes 6 .
These studies help us reconstruct the past. The distribution of soils and surfaces tells a story of climatic changes, tectonic forces, and the relentless work of erosion over millions of years. It helps answer fundamental questions about how our planet's diverse landscapes came to be.
The ground we walk on is an archive, each layer a page, and every soil property a word in the epic story of the Earth. By combining the ancient clues held in rocks and landforms with the modern power of multivariate analysis, scientists are learning to read this story with ever-greater clarity. The discrimination of geomorphic surfaces through their soil attributes is a powerful demonstration that to care for the landscape, we must first learn to understand its language—a language written in the subtle, complex, and revealing patterns of the soil.