Pedometric Mapping of Soil Classes: A Case Study of San Mateo de Otao, Peru

Abstract

Conventional soil maps are designed based on expert criteria, a characteristic that reduces their reproducibility and generates subjective uncertainty. Pedometric mapping uses mathematical and statistical principles, which makes it the opposite of conventional mapping. It was proposed to apply the pedometric mapping in San Mateo de Otao and find out its characteristics against the conventional one. Satellite and field data were used to extract covariables (soil-forming factors) and soil classes. The data were modeled with Naïve Bayes, global uncertainty was calculated by resubstitution, cross-validation and retention, and local uncertainty with the confusion and Shannon indices. A low uncertainty map was obtained with six identified soil classes, relief, and parent material having the most important covariates. We conclude that pedometric mapping has considerable advantages over conventional mapping and its application is possible under the context of soil survey in Peru.