• Simosol Oy

Effective Utilisation of Data in Timberland Management


Technological development leads to more and better data about forests, but what does it mean for timberland investors? This question revolves around the availability of data, as well as utilising the data for added value.

Data Collection

Data related to forest resources is nowadays of higher quality and more readily available. For example, Sentinel satellites of the European Space Agency map every corner of the world every five days. These freely-available satellite images open up completely new possibilities for mapping large forest areas, and monitoring changes in forests anywhere in the world.

Other examples of technological developments that have lead to new types of forest data sources are laser scanning and UAVs (more commonly known as drones). A laser scanner installed in an aircraft allows mapping every tree in a forest. A laser scanner mounted on a 4x4, allows mapping each branch of each tree in a forest. As a result, data-quantity is now accompanied with data-quality. Also, the nowadays popular UAVs provide a cost-efficient tool for mapping smaller forest areas, even at single tree level.


Automatic tree identification from images taken by a UAV.

As the sensors for collecting data are developing, so is the communications technology that enables transferring the data in real time. The term Internet of Things (IoT), covers the technologies for connecting the sensors over the web and sending the data in real time. Relevant examples include automated sensors for tracking machinery in real time and monitoring supply chain performance.

Data Utilisation

Having new and better data is a good starting point, but the data needs to be transformed into valuable information. Data is nowadays a huge business, and different methods for extracting the information from the data are developing fast. Buzzwords like Artificial Intelligence and Big Data are just umbrella terms for various technologies and tools that help us to extract information from data.

Smartphone apps like Trestima or KATAM are nice examples of AI-based automatic image recognition in forestry. These user friendly apps identify the trees and estimate forest attributes from pictures captured with the smart phone. AI-based methods can also be used for solving very complex timber sourcing problems, like in this case that we did for Metsä Group in Russia.

An example of a complicated timber sourcing problem in which AI-based methods can provide invaluable help: a combined road construction and harvest plan for a large forest area.

Big Data covers various approaches for processing huge amounts of data for different sources. These technologies allow us to extract considerable value out of the data that is already available (and continuously accumulating). Real life examples from the forestry context are large-scale timber sourcing models that require huge amounts of data of forests, transportation & logistics and timber markets. An example of such a case is here.

To summarise, the technological developments provide us with more and better data, as well as tools for processing the data. The key question is how to take advantage of all this? There is no single silver bullet, but the choice of tools will make a difference. Better information enables better decisions and higher efficiency, which in the end, results in higher profitability.


©2019 by Simosol Oy

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