Evaluation of some proximal sensing methods for mapping soils in urbanized terrain, Detroit, Michigan, USA

A study was carried out to test the accuracy and utility of field probes as proximal sensing tools for site-specific and regional soil mapping in the heavily urbanized terrain of Detroit, MI, USA. The results suggest that surface scanning in situ with a magnetic susceptibility (MS) field probe is accurate, but limited by tall grass. Better results were obtained after removal of the turf layer. The accuracy of the electrical conductivity (EC) field probe was questionable. Hence, superior results were obtained ex situ using both EC and MS lab sensors. Lab EC and MS were well suited for regional soil mapping, whereas all of the methods provided at least some useful information for site-specific applications. Tall grass, excessive artifact content, and severe compaction were the greatest limitations of proximal sensing. Tall grass interfered with surface scanners (MS; metal detector), whereas excessive artifacts and compaction restricted use of the EC surface probe. Penetrability and pH were useful for discriminating between natural and anthropogenic soils, and moisture content for proximal sensing of soil boolean AND A horizons. MS was especially useful for delineating soils impacted by fly ash. Although it is less rapid and more labor-intensive than field methods, the lab-based approach still provides a minimally invasive alternative to augering. Lab-based geophysical surveying can be carried out first, and the results used as a guide for an auger-based soil survey. An anthropogenic map index (AMI) is introduced which combines pH, penetrability, EC and MS into a single overall measure of soil quality. AMI-based maps are postulated to be a useful as a geographic representation of soil health. These results support our hypothesis that proximal sensing methods can facilitate soil mapping in urbanized terrain based on the degree of human disturbance, and differences in artifact assemblages related to land use history. (C) 2016 Elsevier B.V. All rights reserved.