Objective  Soil organic carbon (SOC) and total nitrogen (TN) are important indicators for soil quality assessment, which are closely related to global carbon and nitrogen cycle and climate change. Topography, especially micro-topography, is a key factor driving the spatial heterogeneity of soil characteristics. This paper aims to explore the effects of micro-topography on SOC and TN, and provide a basis for unmanned aerial vehicle (UAV) data application and soil nutrient management of natural forests in Northeast China.

  Method  Micro-topography factors of 400 10 m × 10 m sample plots were extracted from UAV Lidar data in 4 1-ha stands of mixed spruce-fir-broadleaf forest using sample center method and buffer zone method. Correlation analysis and redundancy analysis were carried out to study the effects of micro-topography on SOC and TN.

  Result  In the whole study area, SOC and TN at depth of 20−40 cm were significantly positively correlated with the elevation (r = 0.26, 0.25, P < 0.01), soil TN was significantly positively correlated with the slope at depth of 0−20 cm (r = 0.18, P < 0.01), and the other correlations were not significant. The results of correlation analysis were different among varied sample plots. There was a negative correlation between SOC and the elevation in sample plot Ⅰ (0−20 cm: r = −0.37, P < 0.01; 20−40 cm: r = −0.21, P < 0.05), a negative correlation between SOC at depth of 20−40 cm and the elevation in sample plot Ⅲ and Ⅳ (r = −0.20, −0.21, P < 0.05), and a positive correlation between SOC at depth of 0−20 cm and the aspect in sample plot Ⅲ (r = 0.26, P < 0.05). Soil TN at 20−40 cm of sample plot Ⅰ was negatively correlated with elevation (r = −0.34, P < 0.01), and negatively correlated with the plane curvature of secondary terrain factor (r = −0.24, P < 0.05). In the redundancy analysis, the interpretation rate of RDA1 constraint axis reached 88.05%, and the angle between elevation and SOC at depth of 20−40 cm was small indicative of a positive correlation. The elevation and aspect had significant effects on SOC and TN.

  Conclusion  The sample center method is superior to buffer zone method due to the selection of more terrain factors and higher R² of the regression model. In the mixed spruce-fir-broadleaf forest, the elevation, slope and aspect of micro-topography have certain effects on SOC and TN in the surface horizon. Correlation analysis results vary as for the whole study area and individual sample plots, indicating a strong soil spatial heterogeneity. The correlations of SOC and TN with primary terrain factors are generally stronger than the secondary terrain factor.