The Recent Sediment Record as an Indicator of Past Soil-Erosion Dynamics. Study in Dehesa Areas in the Province of Cáceres (Spain)

The work presented is a study of the recent sediment deposits in a pilot basin in dehesa areas in the province of Cáceres (Spain) through analysis of the sediment record, radiocarbon dating and correlation with historic data to assess the factors that conditioned the deposit in these areas over time. It is a qualitative study based on the important role of sediments as recorders of history, given that sediment facies and their architecture provide one of the best records of past processes and environmental factors. For the study, sediment profile surveys were used to determine the configuration and characteristics of the infill and its chronology. The sediment model of the facies studied is associated with a context of slope water erosion that led to the infill of the watercourse areas, mainly sand and fine gravel, where alterations in the normal rate were detected due to the insertion of a thicker level of materials (soil stoniness) that was able to be dated. The sediment and chronological results obtained can be used to determine the historical events in the area that could have affected the erosion and deposit processes in the basin for the estimated period, from the late 18th to the early 19th century. During this period, pastureland that maintained the ecological balance of the dehesa, with a balanced, stable displacement of soil particles, was converted to cropland, in most cases resulting in soil with a limited profile, overuse and the consequent loss of structure and texture, making it more vulnerable to erosion. Greater remobilisation would have carried thicker material to the watercourses than the material deposited as a result of limited ploughing. This study provides data for the dehesa areas studied with regard to their hydrogeomorphological dynamics, from which past environmental impacts due to tillage can be inferred.

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

The stoniness index of forest soil describes the stone content in the upper soil layer at depths of 20–30 centimeters. This index is not available in any existing map databases, and traditional measurements for the stoniness of the soil have always necessitated laborious soil-penetration methods. Knowledge of the stone content of a forest site could be of use in a variety of forestry operations. This paper presents a novel approach to obtaining automatic measurements of soil stoniness during an excavator-based mounding operation. The excavator was equipped with only a low-cost inertial measurement unit and a satellite navigation receiver. Using the data from these sensors and manually conducted soil stoniness measurements, supervised machine learning methods were utilized to build a model that is capable of predicting the stoniness class of a given mounding location. This study compares different classifiers and feature selection methods to find the most promising solution for this learning problem. The discussion includes a proposition for a meaningful measurement resolution of the soil’s stoniness, and a practical method for evaluating the variability of the stone content of the soil. The results indicate that it is possible to predict the soil stoniness class with 70% accuracy using only the inertial and location measurements.