About this Research Topic
Forest management under changing conditions needs new tools to find insight and forecast forest dynamics and management. Artificial intelligence (AI) could be an interesting solution for forest management. AI encompasses a wide range of techniques and frameworks dating back to the middle of the 20th century, although its use in forestry is relatively new, especially when compared to the early adoption of AI in other fields such as agriculture. Narrow AI, defined as an AI system that is specified to perform a limited task, is commonly applied in forest biometry (e.g., analysis of forest structure with 3D point cloud data) but is not so much used in other forestry domains.
Currently, adequate AI algorithms can be efficiently prototyped due to a wide range of publicly available databases, open-source libraries and the accessibility to computing platforms.
The application of AI in forest management is mainly focused on improving predictions. Due to the potential power of AI, opportunities are open to broadening the suite of applications, such as enhancing the understanding of forest processes. Thus, it is important to discuss, jointly with academia and operational sectors, the implications, limitations, and capabilities of this technology as an alternative quantitative method in forest management.
Papers focusing on system predictions, processes understanding and decision-making using AI to improve forestry are welcomed as original research, reviews of the state of the art, methods presentation, technology and code, supporting theory, applications perspective, and data report but other approaches are welcome.
Topics may include, but are by no means limited to:
- Forest inventory and forest assessment;
- Forest carbon (above and below ground) mapping;
- Information management;
- Sustainable forest management decision-making;
- Ecosystem services provision;
- Technological characterization of forest products;
- Forest stand dynamic forecasting.
Currently, adequate AI algorithms can be efficiently prototyped due to a wide range of publicly available databases, open-source libraries and the accessibility to computing platforms.
The application of AI in forest management is mainly focused on improving predictions. Due to the potential power of AI, opportunities are open to broadening the suite of applications, such as enhancing the understanding of forest processes. Thus, it is important to discuss, jointly with academia and operational sectors, the implications, limitations, and capabilities of this technology as an alternative quantitative method in forest management.
Papers focusing on system predictions, processes understanding and decision-making using AI to improve forestry are welcomed as original research, reviews of the state of the art, methods presentation, technology and code, supporting theory, applications perspective, and data report but other approaches are welcome.
Topics may include, but are by no means limited to:
- Forest inventory and forest assessment;
- Forest carbon (above and below ground) mapping;
- Information management;
- Sustainable forest management decision-making;
- Ecosystem services provision;
- Technological characterization of forest products;
- Forest stand dynamic forecasting.
Keywords: Deep learning, forest monitoring, forest management, machine learning, remote sensing, computer vision, modelling
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