Background: Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant errors to temporal change. These errors may have major implications for the reliability of studies on long-term environmental change and deserve closer attention of vegetation ecologists. Methods: Vegetation data obtained from the resurveying of non-permanently marked plots are assessed for their potential to study environmental change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys, distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarize the challenges and limitations of resurveying non-permanently marked vegetation plots for different purposes in environmental change research. Results and conclusions: Re-sampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non-permanent plots, while the latter are further divided into quasi-permanent (with approximate relocation) and non-traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) retain a high level of experience of the observers to keep observer bias low; and (v) edit and standardize data sets before analyses.