The rangelands of the Mongolian Plateau, which incorporate Mongolia and Inner Mongolia (northern China), are dynamic social-ecological systems influenced by a complex network of drivers, including climate, social institutions, market forces, and regional or national policies affecting land tenure. The sustainability and resilience of rangelands in this region depend on residents’ and policymakers’ ability to respond to changes and uncertainties regarding climate, socio-economic conditions, and land use. However, the complex nature of these systems makes it difficult to predict how changes in one aspect of the system will affect the functioning of other areas. Additionally, different historical policies and government mandates regarding resource rights and land tenure in China and Mongolia affect how resilient the two systems will be to future political or climate changes. Thus, making predictions can be difficult.
To examine potential outcomes of future scenarios, the researchers at IGRE (in collaboration with their colleagues) have developed several models that incorporate the human, climate, natural, and land-use systems in the Mongolian rangeland ecosystem to study rangeland responses to climate change and social-ecological transformation. For instance, two-panel data models (the composite and the individual) were developed to explore causal relationships between climatic variables and vegetation growth across distinct plant communities and over time. Both panel regression models confirmed that vegetation growth responses to regional climate changes were shaped by the unique characteristics of the study area and that the interactions between vegetation growth and climate were dependent on a variety of spatially and temporally varying contextual factors. In another project, we surveyed over 750 households arrayed along an ecological gradient and matched across the national border in Mongolia and the Inner Mongolia Autonomous Region, China, asking what changes in livelihoods strategies households made over the last ten years, and analyzed these choices in two broad categories of options: diversification and livestock management. We combined these data with remotely sensed information about vegetation growth and self-reported exposure to price fluctuations. Our statistical results showed that households experiencing lower ecological and economic variability, higher average levels of vegetation growth, and greater levels of material wealth, were often those that undertook more actions to improve their conditions in the face of variability. The findings have implications both for how interventions aimed at supporting ongoing choices might be targeted and for theory construction related to social adaptation.
Xie, Y., Fan, S., & Zhou, C. (2021). Examining ecosystem deterioration using a Total socioenvironmental system approach. Science of The Total Environment, 147171, https://authors.elsevier.com/a/1cyVJB8ccquVR
Z Sha, Y Bai, H Lan, X Liu, R Li, Y Xie (2020). Can more carbon be captured by grasslands? A case study of Inner Mongolia, China. Science of The Total Environment 723, 138085
T Chen, Y Xie, C Liu, Y Bai, A Zhang, L Mao, S Fan (2018). Trend analysis of relationship between primary productivity, precipitation and temperature in Inner Mongolia. ISPRS International Journal of Geo-Information 7 (6), 214.
Y Xie, Y Zhang, H Lan, L Mao, S Zeng, Y Chen (2018). Investigating long-term trends of climate change and their spatial variations caused by regional and local environments through data mining. Journal of Geographical Sciences 28 (6), 802-818
Y Xie, D Crary, Y Bai, X Cui, A Zhang (2016). Modeling grassland ecosystem responses to coupled climate and socioeconomic influences in multi-spatial-and-temporal scales. J. Environ. Inform 33, 37-46.
Mi, J., Li, J., Chen, D., Xie, Y., & Bai, Y., 2014. Predominant control of moisture on soil organic carbon mineralization across a broad range of arid and semiarid ecosystems on the Mongolia plateau. Landscape Ecology (published online 18 May 2014; DOI 10.1007/s10980-014-0040-0).
Brown, D.G., Agrawal, A., Sass, D.A., Wang, J., Hua, J., & Xie, Y., 2013. Responses to Climate and Economic Risks and Opportunities across National and Ecological Boundaries: Changing Household Strategies on the Mongolian Plateau, Environmental Research Letters, 8 (045011), doi:10.1088/1748-9326/8/4/045011, 9pp.
Li, S., Xie, Y., Brown, D.G., Bai, B., Jinhua, J., & Judd, K., 2013. Spatial variability of the adaptation of grassland vegetation to climatic change in Inner Mongolia of China. Applied Geography, 43: 1-12, DOI: 10.1016/j.apgeog.2013.05.008.
Liu, Y. and Xie, Y. 2013. Measuring the dragging effect of natural resources on economic growth: evidence from a space-time panel filter modeling in China. Annuals of Association of American Geographers, 103(6), 1539-1551
This line of research has originated from the dissertation research, “Dynamic Urban Evolutionary Modeling” (DUEM), which situates its conceptual and technical foundations on Couclelis’s cellular automata spaces, Dendrinos’ urban selection, and contemporary techniques of complex systems and GIS. DUEM is the first operational design capable of integrating cellular automata (CA) space, model space and geographic space, and various types (or levels) of models running within these spaces.
The CA models have been extended to integrate the choice and intelligence of developers and policymakers, or so-called agent-based modeling (ABM). ABM is a type of computational model to simulate the actions and interactions of autonomous individuals in a network. ABM provides a natural description of a system. Though agents actually remain a topic of debate, they are generally regarded to be goal-driven, autonomous, and adaptive. In the context of urban and geographic studies, these agents are spatial or geographic, because they interact with scale-dependent geographic environments in given contexts and have the ability to reason spatially. ABM can integrate natural environments (the agents’ physical space) with policy-making rules (the agents’ intelligence), combine bottom-up actions with global interactions, and simulate processes of urban growth that are locally determined but moderated by a higher-level macroeconomy.
Big data and data mining are at the forefront of dynamic urban modeling. In recent years, we have applied computational data mining and computational geometry analysis tools to examine long-term trends of socio-environmental change and urban morphology.
J Yang, A Zhou, L Han, Y Li, Y Xie (2021). Monitoring urban black-odorous water by using hyperspectral data and machine learning. Environmental Pollution 269, 116166
N Jiang, A Crooks, W Wang, Y Xie (2021). Simulating Urban Shrinkage in Detroit via Agent-Based Modeling. Sustainability 13 (4), 2283
J Liang, Y Xie, Z Sha, A Zhou (2020). Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE). Computers, Environment and Urban Systems 84, 101542
Y Li, Y Xie (2018). A new urban typology model adapting data mining analytics to examine dominant trajectories of neighborhood change: a case of metro detroit. Annals of the American Association of Geographers 108 (5), 1313-1337.
Y Xie, H Gong, H Lan, S Zeng (2018). Examining shrinking city of Detroit in the context of socio-spatial inequalities. Landscape and Urban Planning 177, 350-361.
Xie. Y., Fan, S., 2014. Multi-city Sustainable Regional Urban Growth Simulation – MSRUGS: A Case Study along the Mid-Section of Silk Road of China. Stochastic Environmental Research and Risk Assessment, 28:829–841, DOI 10.1007/s00477-012-0680-z.Xie, Y., Batty, M., and Zhao, K., 2007. Simulating Emergent Urban Form Using Agent-Based Modeling: Desakota in the Suzhou-Wuxian Region in China. Annuals of Association of American Geographers, 97(3): 477-495.
IGRE has been engaged in many GIS educational projects. For instances, IGRE has played a leading role in Michigan Department of Education Marshall Plan of Talent project: GTTC – Geospatial Technologies Talent Consortium; NSF ITEST projects: GRACE: GIS Resources and Applications for Career Education and Mayor’s Youth Technology Corps – Creating Safe Communities through Information Technology Training in Homeland Security Applications; NSF Teacher Enhancement project: VISIT: Virtual Immersion in Science Inquiry for Teachers; NSF ATE project: Worksite Alliance – Community-based GIS Education; and NASA Climate Change Education project: Investigating Climate Change and Remote Sensing.
Scarlett, S., Lafreniere, D., Trepal, D., Arnold, J., & Xie, Y. (2019) Out of the Classroom and Into History: Mobile Historical GIS and Community-Engaged Teaching. The History Teacher, 53(1).
Xie, Y. (2014). Advancing STEM career and learning through civic engagement. Digital Library and Archives of the Virginia Tech University Libraries.
Xie, Y. & D Reider (2014). Integration of innovative technologies for enhancing students’ motivation for science learning and career. Journal of Science Education and Technology 23 (3), 370-380.
Xie, Y., PA Henry, D Bydlowski, J Musial (2014). Linking climate change education through the integration of a kite-borne remote sensing system. Journal of Technology and Science Education 4 (3), 120-137.