EnviNavigator is a project co-funded by the European Space Agency that makes forest analytics based on satellite monitoring a part of forest systems.
In the EnviNavigator project, forest services based on satellite monitoring that use self-learning artificial intelligence are being developed. Up-to-date information on the current state of forests, their risks, and changes in them can be produced by combining satellite data with other data sets relating to forestlands.
The objectives and needs of forest management have become more diverse. Apart from economic profit, forest owners are interested in diversity, the availability of clean water as well as ensuring the recreational and scenery values. On the other hand, global warming is already causing big problems for forest owners. Optimizing carbon storage of forests has been seen as an example of a significant measure to address the climate change.
Another significant object of development has been automating communication. When the analytics recognizes a site that requires special attention or management, the system automatically sends a notification about it. Professionals can then focus forest management work on area where the need is the gravest. At the same time, the sales process of the services from a forest professional to forest owners is streamlined as the communication is supported by the data on management need served by the analysis.
Satellite data increases knowledge on forest status. In the new generation forest management, old practices are accompanied with new ones that will assist in enhancing the correctness and accuracy of forest cover information. The shortcomings of field plot measurements can be compensated and predictions based on laser scanning can be updated with satellite data.
Deep analytics on forests as well as recognition of details and variation inaccessible with previous calculation methods can be achieved with artificial intelligence. For examples, Damages can be forecasted and limited when the artificial intelligence recognizes the changes in the health status of a forest cover directly from the satellite data.EnviNavigator AI is self-learning engine, which allows continuous improving of the results based on user’s feedback and field observations.
Forest variables that can be recognized from the satellite data include:
Felling and thinnings
Storm and insect damages
The advantages of the service:
Less field visits
Focusing work on areas with the biggest need
Automatic alerts and management suggestion notifications for forest owners
Interactive forest management process, in which it is easy for the customer to select what kind of management measures they want done
Direct connection with which the forest management work can be automatically transferred to forest professional for execution
Previous development work
The first part of the EnviNavigator project, the Feasibility Study, was co-funded by ESA in 2019. The now starting second phase (the Demonstration Project) will last until the year 2022. This project is being co-funded by the European Space Agency under ARTES 4.0 Business Applications – Space Solutions.