Xue (Michelle) Li

  • Ph.D.

  • Email: lixue@msu.edu
  • Geography Building
    673 Auditorium Rd, Room 116
    East Lansing, MI 48824
  • Areas of interest: GIS and LUCC

Research Synopsis:

grad_research_liWith improving understanding of human-environment systems as well as advancing computing technology, modeling of complex system dynamics, such as Land Use and Cover Change (LUCC), is becoming increasingly better defined and sophisticated. However, a fundamental problem within these modeling processes, the relatively low quality of input data, such as land cover map, seldom receives enough attention. Even if the model accurately represents the mechanism of the system, errors and uncertainties within the input data will propagate and probably produce drastic effects on the simulation results. Due to the limitation of current technology, the quality of certain data set is difficult to improve at present. An alternative approach is to incorporate our understanding of errors and uncertainty into the process of complex system modeling, in order to acquire more knowledge on their effect and thus develop appropriate confidence intervals of the modeling result.

This research will looking at potential LUCC in Urumqi area, China, for the near-term (2021-2030). The main objectives of this research are: 1) measuring the propagation pattern of uncertainty from input data to the result, identifying confidence intervals as well as confidence regions accordingly within the study area; 2) discuss some most likely future scenarios of LUCC for Urumqi based on the acquired knowledge of uncertainty. A well defined and widely used LUCC model, dyna-CLUE, will be employed as the core approach of future LUCC simulation. An uncertainty model based on geostatistical technology will be embedded, and Monte Carlo simulation will be applied to test the effect of input data uncertainty on the result of future LUCC scenarios. The outcome of this study will improve the reliability of knowledge we draw from traditional LUCC models. Some socio-environmental adaptation recommendations will then be proposed accordingly to assist policy-making.