Monitoring programs that can detect changes in ecosystem condition are critical to assessing the success of rehabilitation and detecting negative anthropogenic impacts from activities such as mining. However, vegetation communities vary considerably and may exhibit short-term condition changes that mask long-term trends. This paper describes the development of a virtual ecologist (VE) landscape and observation simulation model for time-series data (VELOS_t). VELOS_t can be used to quantify the relationship between vegetation temporal and spatial variability, measurement uncertainty and sampling design to evaluate the robustness of a particular monitoring strategy. The model has four components: i) a landscape model that uses synthetic data to describe vegetation condition; ii) a natural variation model; iii) an environmental impact model and iv) a sampling model to describe plot-based monitoring schemes. The VE model allows users to estimate the expected performance of a range of sampling designs a priori and thus estimate detection sensitivity. Using simulated vegetation data, the model assesses whether statistical analyses can distinguish patterns of vegetation abundance from the effects of the observation of these patterns. Furthermore, the VE approach is a useful in testing the uncertainty sources such as imprecise measurement of vegetation cover are easily modelled using the VE approach in comparison to analytical approaches. This paper introduces the virtual ecologist model and provides a simple example of its use to assess the robustness of monitoring scheme design for long-term trend analysis.