Exploring artificial intelligence for applications of drones in forest ecology and management
This paper highlights the significance of Artificial Intelligence (AI) in the realm of drone applications in forestry. Drones have revolutionized various forest operations, and their role in mapping, monitoring, and inventory procedures is explored comprehensively. Leveraging advanced imaging technologies and data processing techniques, drones enable real-time tracking of changes in forested landscapes, facilitating effective monitoring of threats such as fire outbreaks and pest infestations. They expedite forest inventory by swiftly surveying large areas, providing precise data on tree species identification, size estimation, and health assessment, thus supporting informed decision-making and sustainable forest management practices. Moreover, drones contribute to tree planting, pruning, and harvesting, while monitoring reforestation efforts in real-time. Wildlife monitoring is also enhanced, aiding in the identification of conservation concerns and informing targeted conservation strategies. Drones offer a safer and more efficient alternative in search and rescue operations within dense forests, reducing response time and improving outcomes. Additionally, drones equipped with thermal cameras enable early detection of wildfires, enabling timely response, mitigation, and preservation efforts. The integration of AI and drones holds immense potential for enhancing forestry practices and contributing to sustainable land management. In the future explainable AI (XAI) improves trust and safety by providing transparency in decision-making, aiding in liability issues, and enabling precise operations. XAI facilitates better environmental monitoring and impact analysis, contributing to efficient forest management and preservation efforts. If a drone’s AI can explain its actions, it will be easier to understand why it chose a particular path or action, which could inform safety procedures and improvements.
Top- Buchelt, Alexander
- Adrowitzer, Alexander
- Kieseberg, Peter
- Gollob, Christoph
- Nothdurft, Arne
- Eresheim, Sebastian
- Tschiatschek, Sebastian
- Stampfer, Karl
- Holzinger, Andreas
Category |
Journal Paper |
Divisions |
Data Mining and Machine Learning |
Journal or Publication Title |
Forest Ecology and Management |
ISSN |
0378-1127 |
Volume |
551 |
Date |
1 January 2024 |
Export |