LUKE explains how some of the new technologies will improve our forest management.
Three of the key new technology areas are explained by LUKE’s Jori Uusitalo.
- Terrain predictions are becoming more precise: Forest machines are expensive and having them stand idle is even more costly. It is difficult to predict the conditions of forest work, because they are affected by the weather, season, soil properties, elevation differences and the developmental phases of tree species. If forest work is done at the wrong time, it causes unfortunate environmental damage. Heavy machines can easily sink into clay soil. The first step in preventing environmental damage is to understand the forest’s conditions and the changes that affect it. Big data means the collection and analysis of extremely large amounts of data. Prediction models indicate when and with what kind of equipment it is worth going to the forest with. Going into forests with fine-grained loam and silt soils, as well as peat soils, is done during the dry time in high summer or winter. During the rest of the year, the work is done on ground with high bearing capacity. When the forest’s soil type and the properties of it can be assessed, work can be offered for forest machines all year round.
- Forest machines collect data while working: Data collected by forest machines help to evaluate harvesting conditions, for example. Soon, research groups and companies doing development will not have to separately collect their data. As measurement systems and sensors develop, the data will accrue while the machines work in the forest. As measuring systems and sensors develop, systems will learn to predict conditions more precisely. The data that accumulates alongside forest work will improve and flexibly alter prediction models as conditions change. The tracks left on the ground by a forest machine tire are an indicator of how much the heavy machine sinks into the soil. The depth of the track can be measured with the help of the engine power. The tracks, combined with weather measurements, let you know when it is a good time to head to the forest. The quality of wood can be predicted by examining other trees that have been cut down in the area. The logger takes a photo of the felled tree’s stem. Counting the annual rings provides information on the growth rate of the tree. The harvester calculates the diameter and length of the trunk as the pruning and cutting proceeds. The result is called a trunk profile. The growth rate and trunk profile tell us about, for example, the wood’s density, firmness, moisture content and rottenness. The information helps us optimize what kind of products to make out of the wood.
- The forest can be managed with precision: Smart ways of measuring and analysing big data will ultimately lead to the forester getting to know the forest better. Different conditions require different procedures. Precise information on a forest and its soil helps in targeting the work. This is called precision forestry. The work can already be optimized when saplings are planted. Forestry is a chain containing many links, where each stage of work affects the costs of the next one. People may fail to notice certain details, which the prediction models are able to indicate it in good time. When you understand the soil’s properties and growth potential, you are able to take into account the total costs and total return.
This technology is being developed as part of LUKE’s EFFORTE project. Source