Nathan P. Odgers

Generation of kth-order random toposequences

The model presented in this paper derives toposequences from a digital elevation model (DEM). It is written in ArcInfo Macro Language (AML). The toposequences are called kth-order random toposequences, because they take a random path uphill to the top of a hill and downhill to a stream or valley bottom from a randomly selected seed point, and they are located in a streamshed of order k according to a particular stream-ordering system. We define a kth-order streamshed as the area of land that drains directly to a stream segment of stream order k. The model attempts to optimise the spatial configuration of a set of derived toposequences iteratively by using simulated annealing to maximise the total sum of distances between each toposequence hilltop in the set.The user is able to select the order, k, of the derived toposequences. Toposequences are useful for determining soil sampling locations for use in collecting soil data for digital soil mapping applications. Sampling locations can be allocated according to equal elevation or equal-distance intervals along the length of the toposequence, for example.We demonstrate the use of this model for a study area in the Hunter Valley of New South Wales, Australia. Of the 64 toposequences derived, 32 were first-order random toposequences according to Strahlers stream-ordering system, and 32 were second-order random toposequences.The model that we present in this paper is an efficient method for sampling soil along soil toposequences. The soils along a toposequence are related to each other by the topography they are found in, so soil data collected by this method is useful for establishing soil-landscape rules for the preparation of digital soil maps.

Large-area spatial disaggregation of a mosaic of conventional soil maps: evaluation over Western Australia

Conventional soil maps may be the best available source for spatial soil information in data-limited areas, including individual soil properties. Spatial disaggregation of these maps, or mapping the unmapped soil components, offers potential for transforming them into spatially referenced soil class distributions. We used an automated, iterative classification tree approach to spatially disaggregate a patchwork of soil surveys covering Western Australia (2.5 × 106 km2) to produce raster surfaces of soil class occurrence. The resulting rasters capture the broad spatial patterns of dominant soils and harmonise soil class designations across most survey boundaries. More than 43 000 archived profiles were used to evaluate the accuracy of the rasters. In 20% of cases, the predicted soil class with the highest probability matched that recorded for the profile; when any of the three highest probability soil classes predicted were considered correct, the global accuracy was 40%. The accuracy increased to 71% when the rasters were reassembled to represent a higher level in the soil classification system. The predicted surfaces retained features related to the mapping intensity of the original surveys and generally had higher prediction accuracy of profile soil class where the surface geochemistry was more homogeneous. The best indicator of prediction accuracy was how common the profile soil class was in the original mapping (94% variance explained); profile observations collected during soil survey may be biased towards rare soils, making them less suitable for validation or modelling directly from point data.

Derivation of soil-attribute estimations from legacy soil maps

It is increasingly necessary to apply quantitative techniques to legacy soil polygon maps given that legacy soil maps may be the only source of soil information over large areas. Spatial disaggregation provides a means of extracting information from legacy soil maps and enables us to downscale the original information to produce new soil class maps at finer levels of detail. This is a useful outcome in its own right; however, the disaggregated soil-class coverage can also be used to make digital maps of soil properties with associated estimates of uncertainty. In this work, we take the spatially disaggregated soil-class coverage for all of Western Australia and the agricultural region of South Australia and demonstrate its application in mapping clay content at six depth intervals in the soil profile. Estimates of uncertainty are provided in the form of the 90% prediction interval. The work can be considered an example of harmonisation to a common output specification. The validation results highlighted areas in the landscape and taxonomic spaces where more knowledge of soil properties is necessary.

Soil Profile Classes

The previous chapter discussed the possibility of using pedometric techniques to make numerical classifications of soil material and soil layers. Of course it is not a step too far to use pedometric techniques to make classifications of entire profiles also. That is the subject of this chapter.

Digital Mapping of Soil Classes and Continuous Soil Properties

Soil is often described as mantling the land more or less continuously with the exception being where there is bare rock and ice (Webster and Oliver 2006). Our understanding of soil variation in any region is usually based on only a small number of observations made in the field. Across the spatial domain of the region of interest, predictions of the spatial distribution of soil properties are made at unobserved locations based on the properties of the small number of soil observations. There are two principal approaches for making predictions of soil at unobserved locations. The first approach subdivides the soil coverage into discrete spatial units within which the soils conform to the characteristics of a class in some soil classification (Heuvelink and Webster 2001). The second approach treats soils as a suite of continuous variables and attempts to describe the way these variables vary across the landscape (Heuvelink and Webster 2001). The second approach is necessarily quantitative, as it requires numerical methods for interpolation between the locations of actual soil observations.

Soil Material Classes

Soil classification is really about answering the question what makes a soil? Or, perhaps, what makes one soil different from another? To answer questions like these, soil classifiers create taxonomic rules to separate one kind of soil from another and categorise and make sense of the diverse pattern of the soil continuum. Traditionally a great deal of consideration has been given to characterising and classifying the whole soil profile in a top-down fashion. Pedometric methods allow us to answer the same questions in a bottom-up trajectory. Thus, the starting point is not the whole soil profile or even its major constituents, the soil horizons. Rather we start by classifying the actual, tangible, skeleton of soil itself: the soil material.

Some Challenges on Quantifying Soil Property Predictions Uncertainty for the GlobalSoilMap Using Legacy Data

The GlobalSoilMap project aims to create digital soil property maps in a raster format for six standard depths (0–5; 5–15; 15–30; 30–60; 60–100; 100–200 cm) and, for the first time, with estimates of uncertainty for predicted soil property maps. Data-driven methods and expert knowledge methods have been proposed, both of which present unique challenges. Initially, the majority of the predicted soil property maps will be derived from legacy soil data. The quantification of uncertainty, in particular, presents challenges due to the inherent nature of legacy data coming from different vintages (varying scales, formats, degree of completeness, differences in methods of observations, measurements, and classifications). We discuss the merits of each approach and potential practical and temporary solutions using two case studies from the USA, North America, and Llanos Orientales, Columbia, South America. Both case studies have limited data with insufficient point observations for a meaningful statistical approach for the estimation of prediction interval (PI) uncertainty. For the US case study, the available point measurements are not adequate for PI uncertainty quantification at soil map unit level and furthermore have been purposively collected to support the assignment of estimated mean, upper and lower property values to soil map units. We compared the estimated soil map unit upper and lower limits and 90 % CI from measured pedon for soil pH and found no significant differences between the two. The results suggest that the estimated upper and lower values from soil map units can be used for estimating the 90 % PI uncertainty at least initially until other independent measured point data become available. The available points in Llanos Orientales were collected for soil fertility evaluations and were independent of soil map unit polygons. However, they were surficial samples, clustered, and biased toward cultivated fields. As a result, only the 90 % CI was calculated and was found to be as wide as the range of the mean predicted soil property. These examples highlight few challenges in quantifying the 90 % PI and the need for more measured point data and flexible approaches when dealing with uncertainty quantification.