Impedance imaging is a technique where stimulus currents are applied through electrodes to a body or the ground and measurements of the potential at other electrodes are collected. The data, along with any available prior information, are used to reconstruct an image of the conductivity distribution throughout the interior which provides diagnostic, cost effective information upon which decisions can be based for a broad array of geophysics, biomedical and industrial applications. The same technique is known as (biomedical) Electrical Impedance Tomography (EIT) and (geophysics) Electrical Resistivity Tomography (ERT). New geophysical applications have arisen for the automated monitoring of slope stability risks for natural landslides, transport embankments and cuttings, mine tailings dams and piles, and remote infrastructure in changing climatic environments. When impedance imaging is used in challenging scenarios, image quality can suffer unless sources of data error and instability can be addressed. This work develops computational techniques to address the issue of data set stability under adverse measurement conditions and builds practical implementations that demonstrate the effectiveness of our approach. We seek to achieve images with fewer artifacts and better detectability through improved methods for addressing boundary movement which permit the use of this technology on unstable surfaces where the positions of electrodes can change over time. Processes are developed for evaluating the correctness of an implementation and the overall validity of reconstructed images. Results demonstrated by adapting well understood strategies show improved reconstruction quality for simulated and measured geophysics data sets.