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PPGIS-based Stress Risk Assessment and Mapping for Crop Growing Environment

Research Scholar

Xiaodong Zhang, Geography (China)
Chunqiao Mi, Co-Researcher
Daniel Sui, Faculty Mentor


Xiaodong Zhang is a professor in the Department of Geographic Information Sciences, China Agricultural University (CAU). She also serves as the associate dean of College of Information and Electrical Engineering at CAU and is a member of the Cartography and Geographic Information System Specialty Committee, China Geographical Society. Her main research interests include GIS-based spatial analysis for agriculture extension and planning, environment evaluation, resource management and disaster monitoring. During her time at Ohio State, she is working on a project related to public participation GIS and its applications in agriculture.

What is the issue or problem addressed in your research?

The traditional statistical models for crop environmental stress risk assessment and mapping are based on historical meteorological data or disaster survey data. They have some problems: 1) the evaluation factors are not comprehensive and the dominant influence factors in different areas are difficult to discriminate, 2) the evaluated result is not real-time and difficult to be updated over time, and 3) the result based on point data cannot be used to do spatial trend analysis. Thus it cannot play a very good role in guiding agriculture production.

What methodology did you use in your research?

In our study, we are establishing a public participatory and real-time updated crop environmental stress risk assessment and mapping model based on PPGIS and multi-source data mining. The methodology include: 1) establish a stress risk assessment model based on PPGIS and using multi-source data (include public participatory data, historical meteorology data, literature data, expert experience data, and so on), 2) convert point-like environmental stress value to polygon-like value using geostatistical and spatial analysis methods with agricultural characteristic, 3) realize public participatory automatic mapping for agricultural disaster stress risk using space-property integrated clustering algorithm, PPGIS theory, and WebGIS technology. Then we can get the thematic map result of stress risk assessment with local environmental characteristics.

What are the purpose/rationale and implications of your research?

This study has important practical significance, for promoting agriculture environmental resource evaluation and management; it also has important academic significance, for extending applied research field of Geographic Information Science.