Pedometric mapping
Pedometric mapping or statistical soil mapping is a data-driven soil mapping that is based on rigorous statistical methods. The main objective of pedometric mapping is to predict values of some soil variable at unobserved locations and access the uncertainty of that estimate using statistical inference i.e. to generate maps soil properties and soil classes. Pedometric mapping is largely based on applying geostatistics in soil science and other statistical methods used in pedometrics. Although pedometric mapping can also largely be based on use of expert knowledge, it is fully data-driven and outputs are based on fitting and using various statistical models and machine learning algorithms.
In the information theory context, the objective of pedometric mapping is to describe the spatial complexity of soils (information content of soil variables over a geographical area), then represent this complexity using maps, summary measures, mathematical models and simulations.[1]
Pedometric vs traditional soil mapping
In traditional soil survey, spatial distribution of soil properties and soil bodies can be inferred using mental models which leads to manual delineations. Such methods can be considered to be subjective, and it is hence impossible or difficult to statistically assess the accuracy of such maps without additional field sampling. Traditional soil survey mapping has some limitations for use in a multithematic GIS related to the fact that is often not consistently applied by different mappers, it is largely manual and it is difficult to automate. Most of traditional soil maps in the world are based on manual delineations of assumed soil bodies, to which then soil attributes are attached.[2] [3] In the case of pedometric mapping, all outputs are based on using statistical computing and are hence reproducible.
Pedometric mapping is largely based on using extensive and detailed covariate layers such as Digital Elevation Model (DEM) derivatives, remote sensing imagery, climatic, land cover and geological GIS layers and imagery. Evolution of pedometric mapping 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.
| Expert/knowledge-driven soil mapping | Data/technology-driven (pedometric) soil mapping | |
|---|---|---|
| Target variables: | cell Soil types (soil series) | cell Analytical soil properties |
| Spatial data model: | cell Discrete (soil bodies) | cell Continuous/hybrid (quantities / probabilities) |
| Major inputs: | cell Expert knowledge / soil profile description | cell Laboratory data / proximal soil sensing |
| Important covariates: | cell Soil delineations (photo-interpretation) | cell Remote sensing images, DEM-derivatives |
| Spatial prediction model: | cell Averaging per polygon | cell Automated (geo)statistics |
| Accuracy assessment: | cell Validation of soil mapping units (kappa) | cell Cross-validation (RMSE) |
| Data representation: | cell Polygon maps + attribute tables (2D) | cell Gridded maps (2D/3D) + prediction error map |
| Major technical aspect: | cell Cartographic scale | cell Grid cell size |
| Soil sampling strategies: | cell Free survey (surveyor selects sampling) | cell Statistical (design/model-based sampling) |
Pedometric mapping methods
Pedometric mapping methods differ based on the soil survey data processing steps:
- Sampling
- Data screening
- Preprocessing of soil covariates
- Fitting of geostatistical model
- Spatial prediction
- Cross-validation / accuracy assessment
- Visualization of outputs
A special group of techniques of pedometric mapping focus on downscaling the spatial information that can be area-based or continuous. Prediction of soil classes is also another sub-field of pedometric mapping that is .
Pedometric mapping is also largely based on using novel technologies for measuring soil properties, also referred to as the digital soil mapping techniques. These include:
- Soil spectroscopy
- Soil remote sensing
- LiDAR-produced digital elevation models
References
- ^ Hengl, T. (31 March 2012). "Mapping efficiency and information content". International Journal of Applied Earth Observation and Geoinformation. doi:10.1016/j.jag.2012.02.005.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ McBratney, A.B (1 November 2003). "On digital soil mapping". Geoderma. 117 (1–2): 3–52. doi:10.1016/S0016-7061(03)00223-4.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Behrens, Thorsten (1 June 2006). "Digital soil mapping in Germany—a review". Journal of Plant Nutrition and Soil Science. 169 (3): 434–443. doi:10.1002/jpln.200521962.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help)