Current issue: 54(2)
Under compilation: 54(3)
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.
To promote the growth and survival of regenerated forests, site preparation prior to tree planting on clearcuts is necessary. This is often performed with scarifiers, either through trenching or mounding. Mounding is generally considered better in a plant survival perspective but is inefficient on obstacle-rich clearcuts. By utilising machine vision through e.g. remote sensing methods, new strategies can enable efficient mound positioning. In this paper, three realistic strategies utilizing ideal clearcut object identification through machine vision have been developed that can be used for more efficient mounding. The results show that mounding efficiency can be significantly improved with a new mound positioning strategy that employs ideal object identification, especially on obstacle-rich clearcuts.
The effects of wood ash and PK fertilization on natural regeneration and sowing of Scots pine (Pinus sylvestris L.) were studied in field experiments on nitrogen-poor (Ntot 0.87–1.26%) peat substrates. The study material was derived from three drained, nutrient-poor pine mires (64°52’ N, 25°08’ E) at Muhos, near Oulu, Finland. The experimental fields were laid out in 1985 as a split-split-plot design including the following treatments; mounding, natural regeneration and sowing and fertilization; PK (400 kg ha-1) and wood ash (5,000 kg ha-1). The seedlings were inventoried in circles in July–August 1991.
Changes in the vegetation were small and there were no statistical differences due to the fertilization treatments in the ground vegetation. PK or ash fertilization did not cause vegetation changes harmful to Scots pine regeneration on nitrogen-poor peatlands. Both sowing and fertilization significantly increased the number of pine seedlings, but not their height. Wood ash increased seedling number more than PK fertilizer. The number of seedlings varied from 7,963 (control) to 42,781 ha-1 (mounding + sowing + ash). The seedling number was adequate for successful regeneration even on non-mounded, non-fertilized naturally regenerated plots.
The number of birch seedlings varied more than that of pine (370–25,927 ha-1). Mounding especially increased the number of birches. The difference between PK fertiliser and ash was less pronounced than that for pine. In addition, to the field studies the effects of ash and PK fertilizer on the germination of Scots pine seeds was studied in a greenhouse experiment. Soaking in ash solutions strongly reduced seed germination, while the PK solution was less harmful.