Modeling human transmissibility via nighttime light remote sensing for Hyphantria cunea propagation pattern prediction

Abstract

Human mobility and goods transportation are essential processes that lead to pest propagation, and accurately characterizing spatial transmissibility has always been a popular research topic. Therefore, we propose a spatial modeling and quantification method based on nighttime light remote sensing to reflect human transmissibility and integrate this method with other variables to map the pest spreading risk. First, residential areas in China are extracted from annual NPP/VIIRS datasets. Then, the human transmissibility index is established based on GIS cost-distance analysis with impedance parameters related to geographical and land use features. Consequently, the propagation risk of the human-induced pest Hyphantria cuneas in China is assessed with a binary logistic model. The results show that the human transmissibility index obtained from nighttime light remote sensing images can be used to assess human accessibility in a continuous way in the field. When the Hyphantria cunea risk prediction index was adopted, the accuracy of pest range prediction improved by nearly 5% for occurrence and 3.3% overall, with a significant difference observed in a Wilcoxon signed-rank test compared with the effect of anthropogenic factors quantified considering basic geographic information and infrastructure features. In addition, all the model R2 values increased at different levels. The human transmissibility index can be used to effectively characterize the spatial pattern of human impacts and displays high accuracy in pest risk prediction. Understanding the human transmissibility mechanism of pests is important and provides a reference for achieving early warnings regarding pest spreading, performing potential host analyses for human infection caused by unknown pathogens, and modeling human ecological interference.