Articles containing the keyword 'machine learning'

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.

• Melander, Automation Technology and Mechanical Engineering, Tampere University, FI-33014 Tampere University, Finland http://orcid.org/0000-0003-3662-5187 E-mail: lari.melander@tuni.fi
• Ritala, Automation Technology and Mechanical Engineering, Tampere University, FI-33014 Tampere University, Finland http://orcid.org/0000-0003-0721-9948 E-mail: risto.ritala@tuni.fi
• Strandström, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland E-mail: markus.strandstrom@metsateho.fi

Ungulate browsing results in important damages on the forests, affecting their structure, composition and development. In the present paper, we examine the occurrence of browsing damage in Norwegian forests, using data provided by the National Forest Inventory along several consecutive measurements (entailing the period 1995–2014). A portfolio of variables describing the stand, site and silvicultural treatments are analyzed using classification trees to retrieve combinations related to browsing damage. Our results indicate that the most vulnerable forest stands are young with densities below 1400 trees ha^–1 and dominated by birch, pine or mixed species. In addition, stand diversity and previous treatments (e.g. thinnings) increase the damage occurrence and other variables, like stand size, could play a role on forest susceptibility to browsing occurrence although the latter is based on weaker evidence. The methods and results of our study can be applied to implement management measures aiming at reducing the browsing damages of forests.

• Díaz-Yáñez, School of Forest Sciences, University of Eastern Finland, PO Box 111, 80101 Joensuu, Finland http://orcid.org/0000-0003-3829-5759 E-mail: olalla.diaz@gmail.com
• Mola-Yudego, Norwegian Institute of Bioenergy Research, P.O. Box, 115, 1431 Ås, Norway; School of Forest Sciences, University of Eastern Finland, PO Box 111, 80101 Joensuu, Finland http://orcid.org/0000-0003-0286-0170 E-mail: blas.mola@uef.fi
• González-Olabarria, Forest Sciences Centre of Catalonia (CTFC-CEMFOR), Ctra. de St. Llorenç de Morunys, km 2, 25280 Solsona, Spain http://orcid.org/0000-0002-5040-712X E-mail: jr.gonzalez@ctfc.es