Extraction of eucalyptus trees along railway lines based on decision tree classification and identification of potential landslides: A case study along Guangxi section of the Guizhou-Guangxi railway

The unique growth characteristics of Eucalyptus can result in the depletion of soil nutrient and fertilizers, leading to land degradation and geological hazards like landslides. The Guangxi region in China has suitable climatic conditions, and numerous Eucalyptus are strategically planted along railway lines, posing the risk of potential landslides. To proactively identify potential landslides along railway lines, this study takes the Guangxi section of the Guizhou-guangxi high-speed railway as a case study. Based on Landsat 9 OLI, GF-7 imagery, and DEM data, a decision tree classification algorithm is employed to extract the planting areas of Eucalyptus within a 1 km buffer zone along the railway. Then, comprehensive analysis incorporating terrain and landforms factors is conducted to identify potential landslides hazards. The research findings show that: 1) compared to other methods, the decision tree classification algorithm conducted in this study improves the classification accuracy, with an overall average classification accuracy of 87.19% and an average Kappa coefficient of 0.80, indicating that this method can effectively extract the range of Eucalyptus in the study area; 2) A large number of eucalyptus are planted along the Guangxi section of the Guinan high-speed railway, with eucalyptus distributed in patches in hilly areas. The eucalyptus planting area within the 1 km buffer zone along the railway is approximately 14.48 km²; 3) The planting of Eucalyptus in the study area has a certain impact on the bridges, emban kments, and tunnel entrances of the railway. A total of 33 potential landslides were identified in this study, with an accuracy rate of 86.84% through field verification. The above method can effectively extract the planting range of eucalyptus along the railway line, and combined with geological factors, it can proactively identify potential landslides, enhancing the safety of railway operation.