Relationship between soil depth and terrain attributes in karst region in Southwest China

Abstract

Soil depth generally varies in peak-cluster depression regions in rather complex ways. Because conventional soil survey methods in these regions require a considerable amount of time, effort, and consequently relatively large budget, new methods are required in karst regions. This study explored the relationship between soil depth and terrain attributes abstracted from digital elevation models (DEMs) at different spatial resolutions in the Guohua Karst Ecological Experimental Area, a representative region of peak-cluster depression in Southwest China. A uniform 140 m × 140 m grid combined with representative hillslope methodology was used to select 171 sampling points where soil depth was measured. Nine primary and secondary terrain attributes, such as elevation, slope, aspect, especial catchment area, wetness index, length-slope factor, stream power index, relief degree of land surface, and distance from ridge of mountains, were computed from DEMs at different spatial resolutions. The optimal DEM spatial resolution was determined by Grey relational analysis (GRA) to reflect the correlations between soil depth and terrain attributes. GRA revealed that the 10-m spatial resolution DEM can best reflect the relationship between soil depth and terrain attributes; therefore, the terrain attributes at this resolution were used for multiple linear stepwise regression (MLSR) analysis. The result of MLSR indicated that slope, TWI, and elevation could explain about 61.4 % of the total variability in soil depth in the study area. The terrain attributes of slope, WTI and elevation can be used to evaluate soil depth in this region very well. This proposed approach may be applicable to other peak-cluster depression regions in the karst areas at a larger scale.