Thinning at the right time in the right areas is crucial to obtaining viable forest with high timber quality. Thinning too late also increases the risk of snowfall and storm damage.
Something that many forest owners and forest managers struggle with is getting a picture of where thinning is most needed. Forests cover large areas and the need for thinning is constantly changing as forests grow. It is resource-intensive to manually monitor the state of the forest and it is difficult to get a comprehensive picture of thinning needs in large areas where you do not have access to updated forest data.
The ability to collect forest information has increased in recent years. This has led to an explosion of data in the forest industry. Data collection by satellites, aircraft, drones and ground-based devices provide good and up-to-date information about the forest. Harvest data, laser scanning and satellite images create new opportunities for smart analysis in the forest industry.
With the help of AI, it is possible to quickly identify thinning needs over large areas. Metria has developed a method where a neural network is trained to identify areas with thinning needs.
The model is trained based on a variety of datasets, including harvester data and laser scanning, and then assesses where there is the greatest likelihood of thinning needs.
The results of the analysis can be used to understand the extent of thinning needs in a specific operational area. The information also facilitates resource allocation and provides coordination benefits.
The information can also provide good support for forest management planning when the forest division is to be made. It allows field staff to allocate their time more efficiently in the field.
Read more about how geodata contributes to insights throughout the forestry process