Spatio-Temporal Knowledge Graph-Based Research on Agro-Meteorological Disaster Monitoring

Abstract

Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters.