Modelling and mapping topographic variations in forest soils at high resolution: A case study

This article examines the utility of a digitally derived cartographic depth-to-water (DTW) index to model and map variations in drainage, vegetation and soil type and select soil properties within a forested area (40 ha) of the Swan Hills, Alberta, Canada. This index was derived from a LiDAR (Light Detection and Ranging) derived digital elevation model (DEM), with at least 1 ground return per m2. The resulting DTW pattern was set to be zero along all DEM-derived flow channels, each with a 4 ha flow-initiation threshold. Soil type (luvisol, gleysol, mesisol), drainage type (very poor to excessive), vegetation type (hydric to xeric) and forest floor depth were determined along hillslope transects. These determinations conformed more closely to the DEM-derived log10(DTW) variations (R2 > 60%) than to the corresponding variations of the widely used topographic wetness index (TWI) (R2 < 25%). Setting log10(DTW) thresholds to represent the wet to moist to dry transitions between vegetation, drainage and soil type enabled a high-resolution mapping of these types across the study area. Also determined were soil moisture content, coarse fragment and soil particle composition (sand, silt, clay), pH, total C, N, S, P, Ca, Mg, K, Fe, Al, Mn, Zn, and available Ca, Mg, K, P and NH4, by soil layer type and depth. Most of these variables were also more correlated with log10(DTW) than with TWI, with and without soil layer depth as an additional regression variable. These variables are, therefore, subject to topographic controls to at least some extent, and can be modelled and mapped accordingly, as illustrated for soil moisture, forest floor depth and pH across the study area, from ridge tops to depressions. Further examinations revealed that the DEM-produced DTW and TWI patterns complemented one another, with DTW delineating soils in relation to local water-table influences, and with TWI delineating where the water would flow and accumulate.

Research highlights► The cartographic depth-to-water index (DTW) captured topographic variations in vegetation and soil type better than the conventionally used terrain wetness index (TWI). ► Topographic variations in soil properties were also more closely related to DTW than TWI. ► As a result, variations in soil and vegetation type, and corresponding variations in soil properties could be modeled fairly reliably across the landscape at 1 m resolution. ► Nevertheless, DTW and TWI provide a complementary means to discern and model hydrologic ally affected processes across landscapes at high resolution. ► Both DTW and TWI were derived from a LiDAR (Light Detection and Ranging)-generated digital terrain model.