#UNSWSoilScienceCentral2018_MudassarMuzzamal
Muddassar Muzzamal
Role: 
MPhil candidate
Field of Research: 
Digital Soil Mapping
Contact details:
Phone: 
+61 414 563 632
Office: 

Room 570
Biological Sciences North (D26)
UNSW, Kensington 2052

Mapping soil particle-size fractions using ALR and ILR transformations and proximally sensed ancillary data

Together the three particle size fractions (PSFs) of clay, silt, and sand are the most fundamental soil properties because of their relative abundance influences the physical, chemical and biological activities in soil. Unfortunately, determining PSFs requires a laboratory method which is time-consuming. One way to add value is to use digital soil mapping, which relies on empirical models, such as multiple linear regression (MLR), to couple ancillary data to PSFs. This approach does not account for the special requirements of compositional data. Here ancillary data was coupled, via MLR modelling, to additive log-ratio (ALR) or isometric log-ratio (ILR) transformations of the PSFs to meet these requirements. These three approaches (MLR vs. ALR-MLR and ILR-MLR) were evaluated along with the use of different ancillary data including proximally sensed gamma-ray spectrometry, electromagnetic induction and elevation data. In addition, how prediction might be improved by using ancillary data measured on transects as compared to interpolation from transects spaced far apart. Although the ALR-MLR approach did not produce significantly better results, it generated predicted soil PSFs which summed to 100 and had the advantages of interpreting the ancillary data relative to the original coordinates (i.e. clay, silt and sand). It was found that for predicting PSFs at various depths, all ancillary data was useful with elevation and gamma-ray slightly better for topsoil and elevation and EM data better for subsoil prediction. In addition, a reduced transect spacing (26 m) and sampling size (9–16) may be adopted for mapping soil PSFs and soil texture across the study field. The ALR-MLR approach can be applied elsewhere to map the spatial distribution of clay minerals.

Supervisor: Associate Professor John Triantafilis

Spatial distribution of predicted soil texture using additive log-ratio transformation with multiple linear regression (ALR-MLR) and at various depths, including; a) 0–0.3 m, b) 0.3–0.6 m, c) 0.6–0.9 m, and d) 0.9–1.2 m, respectively. Note: Soil texture classes were generated using the Australian Soil Texture Triangle.

 

PUBLICATIONS

Muzzamal, M., Huang, J., Nielson, R., Sefton, M & Triantafilis, J. (2018). Mapping soil particle-size fractions using ALR and ILR transformations and proximally sensed ancillary data. Journal of Clays and Clay Minerals.