Detecting Buried Anthropogenic Structures: A Recipe Using Red and Blue Laced Multispectral Vegetation Indices

Abstract

The remote detection of buried anthropogenic features is important for environmental risk mitigation, archaeology and national security.  However, few subsurface detection studies have used geophysical methods to statistically assess the detection efficiency of broadband vegetation indices (VIs) in high-resolution multispectral satellite imagery.  In this study, we use 37 broadband VIs on eight Maxar multispectral satellite images to remotely detect stressed meadow grass and anomalous soil characteristics over an Iron Age fogou (stone-walled underground passage) in Carn Euny, Cornwall, UK.  Using Maxar’s high spatial resolution, the highest performing VIs are identified and validated using a two-tier approach.  First, we correlate the VIs with gravity and then, for a best performing image subset, we perform structural similarity index measurements (SSIMs) and 2D cross-correlations with ground penetrating radar (GPR) data.  In doing so, we numerically identify the VIs most sensitive to variations in vegetation stress from meadow grass overlying the fogou.  In summer months, the Iron Oxide index, Soil Salinity Index 7 (SI7) and the Structure Insensitive Pigment Index (SIPI) are most responsive; all of which are algebraic manifestations of the Red/Blue reflectance ratio.  By analysing their spectral profiles, gradient magnitudes, false colour composites (FCCs) and edge effects, we review the fogou’s effect on soil salinity, iron oxide concentration and chlorophyll production.  Demonstrating the broader utility of Red/Blue reflectance ratios, we then present an image processing workflow for subsurface detection which we test on two additional sites, revealing the location of an underground drainage pipe and two buried ditches.

Keywords: Remote sensing, Multispectral, subsurface, vegetation indices, Gravity, Ground Penetrating Radar, Anthropogenic