Spatial prediction of soil properties using environmental correlation

Abstract

Conventional survey methods have efficiencies in medium to low intensity survey because they use relationships between soil properties and more readily observable environmental features as a basis for mapping. However, the implicit predictive models are qualitative, complex and rarely communicated in a clear manner. The possibility of developing an explicit analogue of conventional survey practice suited to medium to low intensity surveys is considered. A key feature is the use of quantitative environmental variables from digital terrain analysis and airborne gamma radiometric remote sensing to predict the spatial distribution of soil properties. The use of these technologies for quantitative soil survey is illustrated using an example from the Bago and Maragle State Forests in southeastern Australia. A design-based, stratified, two-stage sampling scheme was adopted for the 50,000 ha area using digital geology, landform and climate as stratifying variables. The landform and climate variables were generated using a high resolution digital elevation model with a grid size of 25 m. Site and soil data were obtained from 165 sites. Regression trees and generalised linear models were then used to generate spatial predictions of soil properties using digital terrain and gamma radiometric survey data as explanatory variables. The resulting environmental correlation models generate spatial predictions with a fine grain unmatched by comparable conventional survey methods. Example models and spatial predictions are presented for soil profile depth, total phosphorus and total carbon. The models account for 42%, 78% and 54% of the variance present in the sample respectively. The role of spatial dependence, issues of scale and landscape complexity are discussed along with the capture of expert knowledge. It is suggested that environmental correlation models may form a useful trend model for various forms of kriging if spatial dependence is evident in the residuals of the model.