Forest soils are critical to forest health and productivity, and by recognising their spatial heterogeneity, we can optimise productivity and preserve our natural resources in the face of a changing climate. Electromagnetic induction (EMI) technology provides a repeatable, non-destructive, and cost-effective approach to studying soil heterogeneity in managed forests. Electromagnetic induction technology has proven its versatility in agricultural settings to map soil texture, moisture and crop productivity and geological and archaeological exploration to identify underground natural and anthropogenic structures. Yet, the application in forests has been limited, and it is essential to understand the impact of soil and environmental factors on apparent electrical conductivity (ECa) if attempting to use this EMI technology in a forested environment. The overarching aim of this thesis was to determine if EMI can be used in a forested environment, focusing on two contrasting Pinus radiata D. Don production forests to capture the spatial heterogeneity of soil properties. Furthermore, this thesis can serve as a 'toolbox' for measurement protocols and analysis for forest owners interested in low-cost, time-efficient methods of understanding microsite heterogeneity in their forest soils to guide management practices. The research addressed the three main questions: The impact of various environmental factors on apparent electrical conductivity (ECa), the ability of ECa to characterise soil texture and moisture across forested catchments, and the effectiveness of quasi-3D inversion software in capturing the spatial heterogeneity of forest soils in three dimensions. To answer the first question, measurements were taken on forest litter thickness, gravimetric water content, density, soil temperature, ambient temperature, instrument temperature, and instrument voltage. The study found no significant linear relationship between ECa and these environmental factors, indicating that a correction factor for drift in ECa caused by temperature and voltage variations was not required. The insulating effect of forest soils, the forest canopy, and the instrument's housing played a role in maintaining stability. In addition, there was no significant effect of the presence or absence of forest litter on ECa, which was most likely due to the structure and makeup of forest litter, indicating that EMI technology could predict soil properties without considering the effect of forest litter. Questions two and three aimed to evaluate the effectiveness of using apparent electrical conductivity and modelled electrical conductivity (ECm) as predictors for soil properties, including gravimetric water content (GWC), the electrical conductivity of a 1-part soil to 5-part water solution (ECe1:5), and the percentages of clay (CLAY), fine sand (FSAND), and medium sand particles (MSAND). Firstly, generalised linear mixed-effects models (GLMERs) were employed to assess the measured variables' main effects and interaction effects on ECa and ECm and the within and between site variability as a random effect. The GLMERs demonstrated that incorporating multiple predictor variables reduced unexplained variability, with specific interactions, such as GWC and ECe1:5, playing crucial roles in explaining ECm variability at particular depths. However, multicollinearity issues were observed, primarily driven by the GWC-ECe1:5 interaction. The study also discussed the findings of 3D inversion maps of ECm, which provided detailed insights into spatial distribution patterns, particularly when overlaid with topographic and soil variable data. Secondly, one-dimension and three-dimensional maps were produced and overlaid onto base maps of each catchment to identify spatial patterns within each catchment related to soil texture and moisture using standard kriging in Arc GIS Pro software and custom EM4Soil software designed to interpolate one-dimensional ECa measurements into three dimensions. Finally, the research delved into the implications of these findings for forest owners and their management practices. It emphasised that while ECa technology is a valuable tool in predicting forest soil heterogeneity, it should not be used in isolation, and soil sampling and validation remain essential. The study recommended using EMI as a time and cost-effective tool for understanding soil heterogeneity, offering repeatable and non-destructive measurements for informed land use decisions. Overlaying spatial maps with additional geospatial data was recommended to comprehensively understand soil variability within catchments.