Spatial stratified heterogeneity analysis of field scale permafrost in Northeast China based on optimal parameters-based geographical detector.

• Published in: PLOS ONE
• Main Authors: Ying Guo, Shuai Liu, Lisha Qiu, Chengcheng Zhang, Wei Shan
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2024
• Subjects: Medicine; R; Science; Q; permafrost
• Online Access: https://doi.org/10.1371/journal.pone.0297029; https://doaj.org/article/b106e5bb082041d5ab406b1963365f7a

Affected by global warming, the permafrost in Northeast China (NEC) has been continuously degrading in recent years. Many researchers have focused on the spatial and temporal distribution characteristics of permafrost in NEC, however, few studies have delved into the field scale. In this study, based on the Optimal Parameters-based Geographical Detector (OPGD) model and Receiver Operating Characteristic (ROC) test, the spatial stratified heterogeneity of permafrost distribution and the indicating performance of environmental variables on permafrost in NEC at the field scale were analyzed. Permafrost spatial distribution data were obtained from the Engineering Geological Investigation Reports (EGIR) of six highways located in NEC and a total of 19 environmental variables related to heat transfer, vegetation, soil, topography, moisture, and ecology were selected. The H-factors (variables with the highest contribution in factor detector results and interaction detector results): slope position (γ), surface frost number (SFN), elevation (DEM), topographic diversity (TD), and annual snow cover days (ASCD) were found to be the major contributors to the distribution of permafrost at the field scale. Among them, γ has the highest contribution and is a special explanatory variable for permafrost. In most cases, interaction can improve the impact of variables, especially the interaction between H-factors. The risk of permafrost decreases with the increase of TD, RN, and SBD, and increases with the increase of SFN. The performance of SFN to indicate permafrost distribution was found to be the best among all variables (AUC = 0.7063). There is spatial heterogeneity in the distribution of permafrost on highways in different spatial locations. This study summarized the numerical and spatial location between permafrost and different environmental variables at the field scale, and many results were found to be informative for environmental studies and engineering construction in NEC.