Spatial distribution of SOC content
Interpolation parameters were obtained based on a geostatistical semi-variance function method, and results of the parameters obtained by Kriging to get a better overall model. The SOC content in Daxing’anling Mountain is transformed from discrete point information to continuous surface information, and the spatial distribution characteristics of SOC content could then be further analyzed. Through this approach, we can use fewer sampling points to predict spatial information of soil properties in the entire Daxing’anling Mountain area, as shown in Fig. 2. Results suggest that prediction accuracy is high. It can be seen in the map of the spatial distribution that SOC content is heterogeneous, lower in the northwest and southeast. SOC content generally ranges from ~ 40–70 g/kg.
Spatial distribution of SOC content in the Daxing’anling Mountain range. Select the ordinary Kriging model and perform Kriging interpolation on the sampling point data to obtain the spatial distribution of SOC content. The figure was generated by ArcGIS 10.1.
Principal component analysis of SOC and auxiliary environmental variables
To determine the contributions of environmental auxiliary variables to SOC, correlations between SOC and environmental auxiliary variables were analyzed. Auxiliary environmental variables, their abbreviations and results are displayed in Table 1, showing a range of positive and negative correlation coefficients.
The SOC content in Daxing’an Mountain is taken as the dependent variable, and ten influential factors such as quantitative normalized difference vegetation index, integrated land use index, slope, aspect, elevation, profile curvature, plan curvature, topographic wetness index, convergence of confluence, and surface temperature are taken as independent variable, using X1 X2……X10 named. Based on ten independent variables and principal component analysis, the eigenvalues, contribution rates and cumulative contribution rates of the ten environmental auxiliary factors in this paper are obtained, and the main influencing factors of SOC content are analyzed and determined. The results are shown in Table 2.
The cumulative contribution of the first, second, third, fourth, and fifth principal components is 73.5%. The top five principal components met the requirements of the Kaiser criterion, which suggests strong explanatory power for the SOC variation for Daxing’anling Mountain.
The first principal component is NDVI, whose contribution rate is 20.4%. The second principal component is the land use comprehensive index (18.5%), indicating that the change of soil organic carbon content in Daxing’anling Mountain is related to residential land, roads, rivers, and green space. The third principal component is the slope (14.2%), the fourth principal component is the aspect (10.2%), and the fifth is the elevation (10.2%). Indicating that the topographic changes in Daxing’an Mountain range are correlated with the SOC content and will have a certain influence on it.
Evaluation of the geographically weighted regression Kriging model
Using geographically weighted regression (GWR) and multiple linear regression (MLR) models for analysis, the same auxiliary variables were selected to compare the two models. Bandwidth was set according to the modified Akaike-information criterion18 as shown in Table 3. The R2 value of the GWR model (0.47) is higher than that of the MLR model (0.30), which suggests the GWR model is better in identifying factors influencing SOC spatial distribution. Furthermore, the AICC value of the GWR model is lower than that of the MLR model, suggesting a better model fit18.
Five-fold cross-validation was used to verify and evaluate the interpolation accuracy of the geographically weighted regression kriging model (GWRK) and the regression kriging model (RK). Soil sample data were divided randomly into five parts, and then one part was designated as a verification set and was only used for evaluation of model accuracy. The remaining ones were used for spatial interpolation in model formation. The above process was carried out five times to obtain the simulated value of SOC of the data set. The average error and correlation coefficients are used to evaluate and verify the prediction accuracy of each model. Results show that the RMSE value of the GWRK model (3.5) is less than that of the RK model (3.8), suggesting the GWRK model is superior. This also suggests there are many factors to consider when studying the auxiliary variables of spatial distribution characteristics of SOC content, which requires us to consider not only the fitting of environmental auxiliary variables but also additional spatial and structural information.
Factors controlling SOC content spatial distribution
The spatial variation of SOC content, which is related to the environmental auxiliary variables, has predictable geospatial characteristics. Five key indicators (those that loaded high on the first five PCA axes) were identified: normalized vegetation difference index, integrated land use index, slope, aspect, and elevation. These five factors and results of GWRK model fitting were used to estimate the spatial distribution of SOC content and results are shown in Fig. 3. Coefficients of explanatory factors vary with location.
Explanatory variable coefficients in the GWRK model for SOC and spatial distribution of R2. Use the GWRK model to analyze the influencing factors of SOC and obtain the fitting result graph of the GWRK model. (a) NDVI, (b) Integrated land use index, (c) Slope, (d) Aspect, (e) Elevation, (f) R2. All figures were generated by ArcGIS 10.1.
The coefficient with the largest absolute value is the main controlling variable in a geographical location19. Compared with the other four environmental explanatory factors, absolute values of NDVI coefficients are highest. The influence of NDVI on the spatial distribution of SOC content decreased from the mideast to the northwest and the southeast. This suggests that the higher the vegetation coverage, the greater the control on the SOC content. The other four environmental auxiliary factors play a more secondary role.
The integrated land use index ranks second in importance to NDVI. Its influence on SOC spatial distribution is reflected in the northeast, northwest, and southeast. In the northeast part of the study area, La is positively correlated with SOC content which suggests vegetation cover will promote the accumulation of SOC. In the northwest and southeast of the study area, the integrated land use index (La) is negatively correlated with SOC.
The slope and aspect have a major influence on the spatial distribution of SOC content in the central and western areas. Some low-slope areas are disturbed by human activities. When the slope increases limiting human activities, the impact of slope on SOC is positively correlated. The sunny slope side is conducive to SOC accumulation. In the western and central areas, the elevation is positively correlated with SOC content. As the altitude increases, the vegetation coverage is higher which will promote the accumulation of SOC. In the eastern areas, the elevation is negatively correlated with SOC because of farming and other factors.
Regions with the best model fits are distributed in the eastern and central parts of the study area, whereas regions with weaker fits are in the northwest.
Source: Ecology - nature.com