Pedometric mapping

Pedometric vs. traditional soil mapping

In traditional soil survey, spatial distribution of soil properties and soil bodies can be inferred from mental models, leading to manual delineations. Such methods can be considered subjective, and it is hence difficult or impossible to statistically assess the accuracy of such maps without additional field sampling. Traditional soil survey mapping also has limitations in a multithematic GIS, related to the fact that is often not consistently applied by different mappers, and is largely manual and difficult to automate. Most traditional soil maps are based on manual delineations of assumed soil bodies, to which soil attributes are then attached. With pedometric mapping, all outputs are based on rigorous statistical computing, and are hence reproducible.

Pedometric mapping is based largely on extensive and detailed covariate layers, such as Digital Elevation Model (DEM) derivatives, remote sensing imagery, climatic, land cover and geological GIS layers and imagery. Its evolution can be closely connected with the emergence of new technologies and global, publicly available data sources such as the SRTM DEM, MODIS, ASTER and Landsat imagery, gamma radiometrics and LiDAR imagery, and new automated mapping methods.

Pedometric vs. digital soil mapping

Pedometric analyses rely strictly on geostatistics, whereas digital soil mapping uses more traditional soil-mapping concepts not strictly pedometric in nature. Also referred to as predictive soil mapping, digital soil mapping relies on computer-assisted inference of soil properties to produce digital maps of discrete soil types. Pedometric mapping does not produce maps delineating discrete soil types.

Methods

Pedometric mapping methods differ based on the steps of soil survey data processing:

1. Sampling
2. Data screening
3. Preprocessing of soil covariates
4. Fitting of geostatistical model
5. Spatial prediction
6. Cross-validation / accuracy assessment
7. Visualization of outputs

One of the main theoretical basis for pedometric mapping is the universal model of soil variation:

${\displaystyle Z(\mathbf{s})=m(\mathbf{s})+{\epsilon }^{\prime }(\mathbf{s})+{\epsilon }^{″}}$

...where ${\displaystyle m(\mathbf{s})}$ is the deterministic part of soil variation, ${\displaystyle {\epsilon }^{\prime }(\mathbf{s})}$ is the stochastic, spatially auto-correlated part of variation, and ${\displaystyle {\epsilon }^{″}}$ is the remaining residual variation (measurement errors, short-range variability etc.) that is also possibly dependent on ${\displaystyle \mathbf{s}}$, but it is not modeled. This model was first introduced by French mathematician Georges Matheron, and has proven the Best Unbiased Linear Predictor for spatial data. One way of using this model to produce predictions or simulations is by regression-kriging (also known as universal kriging). With soil data, the model's deterministic component is often based on the soil forming factors of climate, organism, relief, parent material (lithology), and time. This conceptual model, known as the CLORPT model, was introduced to soil-landscape modelling by Hans Jenny.

A special group of pedometric mapping techniques focus on downscaling spatial information that can be area-based or continuous. Prediction of soil classes is also another subfield of pedometric mapping, where specific geostatistical methods are used to interpolate the factor-types of variables.

Pedometric mapping is also based largely on novel technologies for measuring soil properties, also referred to as digital soil mapping techniques. They include:

• Soil spectroscopy and proximal soil sensing (handheld or vehicle-driven devices)
• Remote sensing systems for soil mapping and monitoring (e.g. SMOS)
• LiDAR technology for digital elevation models
• Precision agriculture technologies