【Objective】The Korean pine (Pinus koraiensis) is an ancient and valuable tree species with high research value. Living Korean pine trees are prone to attack by various organisms during their growth; the decay caused by wood rot fungi is one of the most extensive and serious growth hazards. The decay of living wood results in the loss of wood resources and reduces the mechanical properties of the wood. To monitor the health status of Korean pine trees and reduce the loss caused by rotting, the decay status of Korean pine was detected periodically and evaluated by the method of decay grading. 【Method】Thirty standing Korean pine trees in Wuying National Forest Park of Heilongjiang Province were tested using a resistograph and electrical resistance tomography (ERT). The cores drilling were made at the same section to obtain the resistograph readings. The field measurement data were imported into a computer and the resistance curve and resistance tomography images were obtained. After analyzing and processing the drop range of the microneedle resistance, the resistance loss of the sample wood was obtained. According to the different characteristics presented by the decayed heartwood and sapwood in the ERT images, an image processing technique based on OpenCV was used to identify the defected part of the resistance tomographic image of the decayed sample wood; further, quantitative characterization of the defected area of the heartwood and sapwood was performed. The defect area ratios of the heartwood and sapwood were calculated separately. The mass loss rate was determined using the drying method. According to the mass loss rate of each sample wood, all samples were divided into five decadent grades. Multi-category ordered-dependent-variable Logistic regression was introduced into the decay grade classification of the standing trees. The prediction model of decay classification was established by considering resistance loss, area ratio of heartwood defect, and area ratio of sapwood defect as independent variables and the decay grade as the dependent variable. The validity, goodness of fit, and prediction ability of the model were tested.【Result】The performance of the Logistic regression model was good. The comprehensive prediction accuracy of the thirty Korean pine trees was 86.67%, and the accuracy rate of each grade prediction was uneven. The prediction accuracy of high-grade decay was relatively high. 【Conclusion】The Logistic regression model could accurately predict the rank of decay in standing trees. Compared with the drying method, the evaluation efficiency of Logistic regression model was higher and the damage to the standing trees was less.