Soil seed bank is affected by transferred soil thickness and properties in the reclaimed coal mine in the Qilian Mountains, China

Reclamation of lands abandoned after mining in mountain areas is critical to erosion control,safety from landslides,and ecological protection of mountain ecosystems.However,little is known about alpine coal mine reclamation using the soil seed bank as a potential source for revegetation.We collected samples of persistent soil seed bank for germination experiments from nine reclaimed sites with different soil cover thicknesses and from six control sites in the Qilian Mountains of China.Soil properties of each site were determined(including soil water content,soil available potassium,soil available phosphorus,soil total nitrogen,pH,soil organic matter,soil total phosphorus,and soil total potassium,and soil alkali-hydrolyzable nitrogen),and the relationships of the characteristics of the soil seed bank with soil cover thickness and soil properties were examined.The results showed that the density,number of species,and diversity of the topsoil seed bank were significantly correlated with soil cover thickness,and all increased with the increment of soil cover thickness.Soil cover thickness controlled the soil seed bank by influencing soil properties.With the increase in soil cover thickness,soil properties(e.g.,soil organic matter,soil total nitrogen,etc.)content increased while soil pH decreased.The soil seed bank had the potential to restored the pre-mining habitat at reclaimed sites with approximately 20-cm soil cover thickness.Soil properties of reclaimed sites were lower than that of natural sites.The relationship between the soil seed bank and soil cover thickness determined in this study provides a foundation for improving reclamation measures used in coal mines,as well as for the management and monitoring of reclaimed areas.

Generating soil thickness maps by means of geomorphological-empirical approach and random forest algorithm in Wanzhou County,Three Gorges Reservoir

Soil thickness,intended as depth to bedrock,is a key input parameter for many environmental models.Nevertheless,it is often difficult to obtain a reliable spatially exhaustive soil thickness map in widearea applications,and existing prediction models have been extensively applied only to test sites with shallow soil depths.This study addresses this limitation by showing the results of an application to a section of Wanzhou County(Three Gorges Reservoir Area,China),where soil thickness varies from 0 to40 m.Two different approaches were used to derive soil thickness maps:a modified version of the geomorphologically indexed soil thickness(GIST)model,purposely customized to better account for the peculiar setting of the test site,and a regression performed with a machine learning algorithm,i.e.,the random forest,combined with the geomorphological parameters of GIST(GIST-RF).Additionally,the errors of the two models were quantified,and validation with geophysical data was carried out.The results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area:the mean absolute error was 10.68 m with the root-mean-square error(RMSE)of 12.61 m,and the frequency distribution residuals showed a tendency toward underestimation.In contrast,GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 m.The derived soil thickness map could be considered a critical fundamental input parameter for further analyses.

Assessing soil thickness in a black soil watershed in northeast China using random forest and field observations

Soil thickness determines the soil productivity in the black soil region of northeast China,which is important for national food security.Existing information on the spatial variation of black soil thickness is inadequate.In this paper,we propose a model framework for spatial estimation of the black soil thickness at the watershed scale by integrating field observations,unmanned aerial vehicle variations of topography,and satellite variations of vegetation with the aid of random forest.We sampled 141 sample profiles over a watershed and identified the black soil thickness based on indices of the mollic epipedon.Topographic variables were derived from a digital elevation model and vegetation variables were derived from Landsat 8 imagery.Random forest was used to determine the relationship between black soil thickness and environmental variables.The resulting model explained 61%of the black soil thickness spatial variation,which was more than twice that of traditional interpolation methods(ordinary kriging,universal kriging and inverse distance weighting).Topographic variables contributed the most toward explaining the thickness,followed by vegetation indices.The black soil thickness over the watershed had a clear catenary soil pattern,with thickest black soil in the low depositional areas and thinnest at the higher elevations that drain into the low areas.The proposed model framework will improve estimates of soil thickness in the region of our study.