Remote prediction of soil types

Abstract

The Dutch Ministry of Defence is interested in developing a tool or workflow that can be used to remotely predict Unified Soil Classification System (USCS) classes of any area to aid mobility-related decisions. Therefore, this study was initiated by Cohere Consultants in collaboration with NEO, a Dutch remote sensing company and Utrecht University to create such tool or workflow. This MSc thesis provides a study in how the total geological history (TGH) approach as proposed by \cite{TGHA} can be used for predicting soil USCS classes. This is done by implementing the core concept of the TGH into a decision tree model that makes use of many modules, sub-modules and supporting modules to characterise the tectonic, geologic and geomorphological setting of a soil unit. The modules are divided depending if the unit dealt with is a soil, rock or part of a mountain, whereby the soil module incorporates 7 sub-modules that characterize alluvial, lacustrine, coastal, marsh, aeolian, evaporitic and glacial/periglacial environments. The rock and mountain modules, on the other hand, attempt to determine the presence and characteristics of residual soils using a weathering grade system and a table with the weathering products of 23 common rock types. The performance of the decision tree model was tested using two pilot studies in Konna, Mali and Zamora, Spain and one validation study in 's Hertogenbosch. For each study, a map with the predicted USCS soil classes was generated for the study area. The pilot studies explored the possibility of combining the predicted USCS soil maps with topographic wetness index (TWI) and slope angle maps to make a qualitative prediction on the trafficability of the area. The pilot studies showed that the TGH-based decision tree model has potential for being expanded into a tool for aiding military mobility predictions. Next, the validation study compared the predicted USCS map for 's Hertogenbosch to 5 ground truth data points collected by the Dutch Ministry of Defence. The validation study concluded that the decision tree was in general able to distinguish between coarse grained soils and fine grained soils, however struggled with correctly predicting if a soil has high or low plasticity. Finally, the Mali pilot study was able to compare USCS soil predictions made using the decision tree model to those made by a classification of hyperspectral data (made by \cite{flipsen_2022}). Based on the comparison, there seems to be promise for future works to integrate the two methods to benefit from the detail achievable by the hyperspectral method and the qualitative soil descriptions using the TGH-based decision tree. Future recommendations include fine-tuning the decision tree model so that is is able to incorporate more detailed geologic or soil maps (currently has been trained with maps of about 1:500,000), incorporating remote sensing data to create soil units, quantifying uncertainty and possibly automatising the workflow.